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• Why Fusion Middleware matters to Oracle Applications and Fusion Applications customers?

  - by Harish Gaur

  Did you miss this general session on Monday morning presented by Amit Zavery, VP of Oracle Fusion Middleware Product Management? There will be a recording made available shortly and in the meanwhile, here is a recap. Amit presented 5 strategies customers can leverage today to extend their applications. Figure 1: 5 Oracle Fusion Middleware strategies to extend Oracle Applications & Oracle Fusion Apps 1. Engage Everyone – Provide intuitive and social experience for application users using Oracle WebCenter 2. Extend Enterprise – Extend Oracle Applications to mobile devices using Oracle ADF Mobile 3. Orchestrate Processes – Automate key organization processes across on-premise & cloud applications using Oracle BPM Suite & Oracle SOA Suite 4. Secure the core – Provide single sign-on and self-service provisioning across multiple apps using Oracle Identity Management 5. Optimize Performance – Leverage Exalogic stack to consolidate multiple instance and improve performance of Oracle Applications Session included 3 demonstrations to illustrate these strategies. 1. First demo highlighted significance of mobile applications for unlocking existing investment in Applications such as EBS. Using a native iPhone application interacting with e-Business Suite, demo showed how expense approval can be mobile enabled with enhanced visibility using BI dashboards. 2. Second demo showed how you can extend a banking process in Siebel and Oracle Policy Automation with Oracle BPM Suite.Process starts in Siebel with a customer requesting a loan, and then jumps to OPA for loan recommendations and decision making and loan processing with approvals in handled in BPM Suite. Once approvals are completed Siebel is updated to complete the process. 3. Final demo showcased FMW components inside Fusion Applications, specifically WebCenter. Boeing, Underwriter Laboratories and Electronic Arts joined this quest and discussed 3 different approaches of leveraging Fusion Middleware stack to maximize their investment in Oracle Applications and/or Fusion Applications technology. Let’s briefly review what these customers shared during the session: 1. Extend Fusion Applications We know that Oracle Fusion Middleware is the underlying technology infrastructure for Oracle Fusion Applications. Architecturally, Oracle Fusion Apps leverages several components of Oracle Fusion Middleware from Oracle WebCenter for rich collaborative interface, Oracle SOA Suite & Oracle BPM Suite for orchestrating key underlying processes to Oracle BIEE for dash boarding and analytics. Boeing talked about how they are using Oracle BPM Suite 11g, a key component of Oracle Fusion Middleware with Oracle Fusion Apps to transform their supply chain. Tim Murnin, Director of Supply Chain talked about Boeing’s 5 year supply chain transformation journey. Boeing’s Integrated and Information Management division began with automation of critical RFQ process using Oracle BPM Suite. This 1st phase resulted in 38% reduction in labor costs for RFP. As a next step in this effort, Boeing is now creating a platform to enable electronic Order Management. Fusion Apps are playing a significant role in this phase. Boeing has gone live with Oracle Fusion Product Hub and efforts are underway with Oracle Fusion Distributed Order Orchestration (DOO). So, where does Oracle BPM Suite 11g fit in this equation? Let me explain. Business processes within Fusion Apps are designed using 2 standards: Business Process Execution Language (BPEL) and Business Process Modeling Notation (BPMN). These processes can be easily configured using declarative set of tools. Boeing leverages Oracle BPM Suite 11g (which supports BPMN 2.0) and Oracle SOA Suite (which supports BPEL) to “extend” these applications. Traditionally, customizations are done within an app using native technologies. But, instead of making process changes within Fusion Apps, Boeing has taken an approach of building “extensions” layer on top of the application. Fig 2: Boeing’s use of Oracle BPM Suite to orchestrate key supply chain processes across Fusion Apps 2. Maximize Oracle Applications investment Fusion Middleware appeals not only to Fusion Apps customers, but is also leveraged by Oracle E-Business Suite, PeopleSoft, Siebel and JD Edwards customers significantly. Using Oracle BPM Suite and Oracle SOA Suite is the recommended extension strategy for Oracle Fusion Apps and Oracle Applications Unlimited customers. Electronic Arts, E-Business Suite customer, spoke about their strategy to transform their order-to-cash process using Oracle SOA Suite, Oracle Foundation Packs and Oracle BAM. Udesh Naicker, Sr Director of IT at Elecronic Arts (EA), discussed how growth of social and digital gaming had started to put tremendous pressure on EA’s existing IT infrastructure. He discussed the challenge with millions of micro-transactions coming from several sources – Microsoft Xbox, Paypal, several service providers. EA found Order-2-Cash processes stretched to their limits. They lacked visibility into these transactions across the entire value chain. EA began by consolidating their E-Business Suite R11 instances into single E-Business Suite R12. EA needed to cater to a variety of service requirements, connectivity methods, file formats, and information latency. Their integration strategy was tactical, i.e., using file uploads, TIBCO, SQL scripts. After consolidating E-Business suite, EA standardized their integration approach with Oracle SOA Suite and Oracle AIA Foundation Pack. Oracle SOA Suite is the platform used to extend E-Business Suite R12 and standardize 60+ interfaces across several heterogeneous systems including PeopleSoft, Demantra, SF.com, Workday, and Managed EDI services spanning on-premise, hosted and cloud applications. EA believes that Oracle SOA Suite 11g based extension strategy has helped significantly in the followings ways: - It helped them keep customizations out of E-Business Suite, thereby keeping EBS R12 vanilla and upgrade safe - Developers are now proficient in technology which is also leveraged by Fusion Apps. This has helped them prepare for adoption of Fusion Apps in the future Fig 3: Using Oracle SOA Suite & Oracle e-Business Suite, Electronic Arts built new platform for order processing 3. Consolidate apps and improve scalability Exalogic is an optimal platform for customers to consolidate their application deployments and enhance performance. Underwriter Laboratories talked about their strategy to run their mission critical applications including e-Business Suite on Exalogic. Christian Anschuetz, CIO of Underwriter Laboratories (UL) shared how UL is on a growth path - $1B to $2.5B in 5 years- and planning a significant business transformation from a not-for-profit to a for-profit business. To support this growth, UL is planning to simplify its IT environment and the deployment complexity associated with ERP applications and technology it runs on. Their current applications were deployed on variety of hardware platforms and lacked comprehensive disaster recovery architecture. UL embarked on a mission to deploy E-Business Suite on Exalogic. UL’s solution is unique because it is one of the first to deploy a large number of Oracle applications and related Fusion Middleware technologies (SOA, BI, Analytical Applications AIA Foundation Pack and AIA EBS to Siebel UCM prebuilt integration) on the combined Exalogic and Exadata environment. UL is planning to move to a virtualized architecture toward the end of 2012 to securely host external facing applications like iStore Fig 4: Underwrites Labs deployed e-Business Suite on Exalogic to achieve performance gains Key takeaways are: - Fusion Middleware platform is certified with major Oracle Applications Unlimited offerings. Fusion Middleware is the underlying technological infrastructure for Fusion Apps - Customers choose Oracle Fusion Middleware to extend their applications (Apps Unlimited or Fusion Apps) to keep applications upgrade safe and prepare for Fusion Apps - Exalogic is an optimum platform to consolidate applications deployments and enhance performance TAGS: Fusion Apps, Exalogic, BPM Suite, SOA Suite, e-Business Suite Integration

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• Building KPIs to monitor your business Its not really about the Technology

  When I have discussions with people about Business Intelligence, one of the questions the inevitably come up is about building KPIs and how to accomplish that. From a technical level the concept of a KPI is very simple, almost too simple in that it is like the tip of an iceberg floating above the water. The key to that iceberg is not really the tip, but the mass of the iceberg that is hidden beneath the surface upon which the tip sits. The analogy of the iceberg is not meant to indicate that the foundation of the KPI is overly difficult or complex. The disparity in size in meant to indicate that the larger thing that needs to be defined is not the technical tip, but the underlying business definition of what the KPI means. From a technical perspective the KPI consists of primarily the following items: Actual Value This is the actual value data point that is being measured. An example would be something like the amount of sales. Target Value This is the target goal for the KPI. This is a number that can be measured against Actual Value. An example would be $10,000 in monthly sales. Target Indicator Range This is the definition of ranges that define what type of indicator the user will see comparing the Actual Value to the Target Value. Most often this is defined by stoplight, but can be any indicator that is going to show a status in a quick fashion to the user. Typically this would be something like: Red Light = Actual Value more than 5% below target; Yellow Light = Within 5% of target either direction; Green Light = More than 5% higher than Target Value Status\Trend Indicator This is an optional attribute of a KPI that is typically used to show some kind of trend. The vast majority of these indicators are used to show some type of progress against a previous period. As an example, the status indicator might be used to show how the monthly sales compare to last month. With this type of indicator there needs to be not only a definition of what the ranges are for your status indictor, but then also what value the number needs to be compared against. So now we have an idea of what data points a KPI consists of from a technical perspective lets talk a bit about tools. As you can see technically there is not a whole lot to them and the choice of technology is not as important as the definition of the KPIs, which we will get to in a minute. There are many different types of tools in the Microsoft BI stack that you can use to expose your KPI to the business. These include Performance Point, SharePoint, Excel, and SQL Reporting Services. There are pluses and minuses to each technology and the right technology is based a lot on your goals and how you want to deliver the information to the users. Additionally, there are other non-Microsoft tools that can be used to expose KPI indicators to your business users. Regardless of the technology used as your front end, the heavy lifting of KPI is in the business definition of the values and benchmarks for that KPI. The discussion about KPIs is very dependent on the history of an organization and how much they are exposed to the attributes of a KPI. Often times when discussing KPIs with a business contact who has not been exposed to KPIs the discussion tends to also be a session educating the business user about what a KPI is and what goes into the definition of a KPI. The majority of times the business user has an idea of what their actual values are and they have been tracking those numbers for some time, generally in Excel and all manually. So they will know the amount of sales last month along with sales two years ago in the same month. Where the conversation tends to get stuck is when you start discussing what the target value should be. The actual value is answering the What and How much questions. When you are talking about the Target values you are asking the question Is this number good or bad. Typically, the user will know whether or not the value is good or bad, but most of the time they are not able to quantify what is good or bad. Their response is usually something like I just know. Because they have been watching the sales quantity for years now, they can tell you that a 5% decrease in sales this month might actually be a good thing, maybe because the salespeople are all waiting until next month when the new versions come out. It can sometimes be very hard to break the business people of this habit. One of the fears generally is that the status indicator is not subjective. Thus, in the scenario above, the business user is going to be fearful that their boss, just looking at a negative red indicator, is going to haul them out to the woodshed for a bad month. But, on the flip side, if all you are displaying is the amount of sales, only a person with knowledge of last month sales and the target amount for this month would have any idea if $10,000 in sales is good or not. Here is where a key point about KPIs needs to be communicated to both the business user and any user who might be viewing the results of that KPI. The KPI is just one tool that is used to report on business performance. The KPI is meant as a quick indicator of one business statistic. It is not meant to tell the entire story. It does not answer the question Why. Its primary purpose is to objectively and quickly expose an area of the business that might warrant more review. There is always going to be the need to do further analysis on any potential negative or neutral KPI. So, hopefully, once you have convinced your business user to come up with some target numbers and ranges for status indicators, you then need to take the next step and help them answer the Why question. The main question here to ask is, Okay, you see the indicator and you need to discover why the number is what is, where do you go?. The answer is usually a combination of sources. A sales manager might have some of the following items at their disposal (Marketing report showing a decrease in the promotional discounts for the month, Pricing Report showing the reduction of prices of older models, an Inventory Report showing the discontinuation of a particular product line, or a memo showing the ending of a large affiliate partnership. The answers to the question Why are never as simple as a single indicator value. Bring able to quickly get to this information is all about designing how a user accesses the KPIs and then also how easily they can get to the additional information they need. This is where a Dashboard mentality can come in handy. For example, the business user can have a dashboard that shows their KPIs, but also has links to some of the common reports that they run regarding Sales Data. The users boss may have the same KPIs on their dashboard, but instead of links to individual reports they are going to have a link to a status report that was created by the user that pulls together all the data about the KPI in a summary format the users boss can review. So some of the key things to think about when building or evaluating KPIs for your organization: Technology should not be the driving factor KPIs are of little value without some indicator for whether a value is good, bad or neutral. KPIs only give an answer to the Is this number good\bad? question Make sure the ability to drill into the Why of a KPI is close at hand and relevant to the user who is viewing the KPI. The KPI is a key business tool when defined properly to help monitor business performance across the enterprise in an objective and consistent manner. At times it might feel like the process of defining the business aspects of a KPI can sometimes be arduous, the payoff in the end can far outweigh the costs. Some of the benefits of going through this process are a better understanding of the key metrics for an organization and the measure of those metrics and a consistent snapshot of business performance that can be utilized across the organization. And I think that these are benefits to any organization regardless of the technology or the implementation.Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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• The Business of Winning Innovation: An Exclusive Blog Series

  - by Kerrie Foy

  "The Business of Winning Innovation” is a series of articles authored by Oracle Agile PLM experts on what it takes to make innovation a successful and lucrative competitive advantage. Our customers have proven Agile PLM applications to be enormously flexible and comprehensive, so we’ve launched this article series to showcase some of the most fascinating, value-packed use cases. In this article by Keith Colonna, we kick-off the series by taking a look at the science side of innovation within the Consumer Products industry and how PLM can help companies innovate faster, cheaper, smarter. This article will review how innovation has become the lifeline for growth within consumer products companies and how certain companies are “winning” by creating a competitive advantage for themselves by taking a more enterprise-wide,systematic approach to “innovation”.   Managing the Science of Innovation within the Consumer Products Industry By: Keith Colonna, Value Chain Solution Manager, Oracle The consumer products (CP) industry is very mature and competitive. Most companies within this industry have saturated North America (NA) with their products thus maximizing their NA growth potential. Future growth is expected to come from either expansion outside of North America and/or by way of new ideas and products. Innovation plays an integral role in both of these strategies, whether you’re innovating business processes or the products themselves, and may cause several challenges for the typical CP company, Becoming more innovative is both an art and a science. Most CP companies are very good at the art of coming up with new innovative ideas, but many struggle with perfecting the science aspect that involves the best practice processes that help companies quickly turn ideas into sellable products and services. Symptoms and Causes of Business Pain Struggles associated with the science of innovation show up in a variety of ways, like: · Establishing and storing innovative product ideas and data · Funneling these ideas to the chosen few · Time to market cycle time and on-time launch rates · Success rates, or how often the best idea gets chosen · Imperfect decision making (i.e. the ability to kill projects that are not projected to be winners) · Achieving financial goals · Return on R&D investment · Communicating internally and externally as more outsource partners are added globally · Knowing your new product pipeline and project status These challenges (and others) can be consolidated into three root causes: A lack of visibility Poor data with limited access The inability to truly collaborate enterprise-wide throughout your extended value chain Choose the Right Remedy Product Lifecycle Management (PLM) solutions are uniquely designed to help companies solve these types challenges and their root causes. However, PLM solutions can vary widely in terms of configurability, functionality, time-to-value, etc. Business leaders should evaluate PLM solution in terms of their own business drivers and long-term vision to determine the right fit. Many of these solutions are point solutions that can help you cure only one or two business pains in the short term. Others have been designed to serve other industries with different needs. Then there are those solutions that demo well but are owned by companies that are either unable or unwilling to continuously improve their solution to stay abreast of the ever changing needs of the CP industry to grow through innovation. What the Right PLM Solution Should Do for You Based on more than twenty years working in the CP industry, I recommend investing in a single solution that can help you solve all of the issues associated with the science of innovation in a totally integrated fashion. By integration I mean the (1) integration of the all of the processes associated with the development, maintenance and delivery of your product data, and (2) the integration, or harmonization of this product data with other downstream sources, like ERP, product catalogues and the GS1 Global Data Synchronization Network (or GDSN, which is now a CP industry requirement for doing business with most retailers). The right PLM solution should help you: Increase Revenue. A best practice PLM solution should help a company grow its revenues by consolidating product development cycle-time and helping companies get new and improved products to market sooner. PLM should also eliminate many of the root causes for a product being returned, refused and/or reclaimed (which takes away from top-line growth) by creating an enterprise-wide, collaborative, workflow-driven environment. Reduce Costs. A strong PLM solution should help shave many unnecessary costs that companies typically take for granted. Rationalizing SKU’s, components (ingredients and packaging) and suppliers is a major opportunity at most companies that PLM should help address. A natural outcome of this rationalization is lower direct material spend and a reduction of inventory. Another cost cutting opportunity comes with PLM when it helps companies avoid certain costs associated with process inefficiencies that lead to scrap, rework, excess and obsolete inventory, poor end of life administration, higher cost of quality and regulatory and increased expediting. Mitigate Risk. Risks are the hardest to quantify but can be the most costly to a company. Food safety, recalls, line shutdowns, customer dissatisfaction and, worst of all, the potential tarnishing of your brands are a few of the debilitating risks that CP companies deal with on a daily basis. These risks are so uniquely severe that they require an enterprise PLM solution specifically designed for the CP industry that safeguards product information and processes while still allowing the art of innovation to flourish. Many CP companies have already created a winning advantage by leveraging a single, best practice PLM solution to establish an enterprise-wide, systematic approach to innovation. Oracle’s Answer for the Consumer Products Industry Oracle is dedicated to solving the growth and innovation challenges facing the CP industry. Oracle’s Agile Product Lifecycle Management for Process solution was originally developed with and for CP companies and is driven by a specialized development staff solely focused on maintaining and continuously improving the solution per the latest industry requirements. Agile PLM for Process helps CP companies handle all of the processes associated with managing the science of the innovation process, including: specification management, new product development/project and portfolio management, formulation optimization, supplier management, and quality and regulatory compliance to name a few. And as I mentioned earlier, integration is absolutely critical. Many Oracle CP customers, both with Oracle ERP systems and non-Oracle ERP systems, report benefits from Oracle’s Agile PLM for Process. In future articles we will explain in greater detail how both existing Oracle customers (like Gallo, Smuckers, Land-O-Lakes and Starbucks) and new Oracle customers (like ConAgra, Tyson, McDonalds and Heinz) have all realized the benefits of Agile PLM for Process and its integration to their ERP systems. More to Come Stay tuned for more articles in our blog series “The Business of Winning Innovation.” While we will also feature articles focused on other industries, look forward to more on how Agile PLM for Process addresses innovation challenges facing the CP industry. Additional topics include: Innovation Data Management (IDM), New Product Development (NPD), Product Quality Management (PQM), Menu Management,Private Label Management, and more! . Watch this video for more info about Agile PLM for Process

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• C# Performance Pitfall – Interop Scenarios Change the Rules

  - by Reed

  C# and .NET, overall, really do have fantastic performance in my opinion.  That being said, the performance characteristics dramatically differ from native programming, and take some relearning if you’re used to doing performance optimization in most other languages, especially C, C++, and similar.  However, there are times when revisiting tricks learned in native code play a critical role in performance optimization in C#. I recently ran across a nasty scenario that illustrated to me how dangerous following any fixed rules for optimization can be… The rules in C# when optimizing code are very different than C or C++.  Often, they’re exactly backwards.  For example, in C and C++, lifting a variable out of loops in order to avoid memory allocations often can have huge advantages.  If some function within a call graph is allocating memory dynamically, and that gets called in a loop, it can dramatically slow down a routine. This can be a tricky bottleneck to track down, even with a profiler.  Looking at the memory allocation graph is usually the key for spotting this routine, as it’s often “hidden” deep in call graph.  For example, while optimizing some of my scientific routines, I ran into a situation where I had a loop similar to: for (i=0; i<numberToProcess; ++i) { // Do some work ProcessElement(element[i]); } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This loop was at a fairly high level in the call graph, and often could take many hours to complete, depending on the input data.  As such, any performance optimization we could achieve would be greatly appreciated by our users. After a fair bit of profiling, I noticed that a couple of function calls down the call graph (inside of ProcessElement), there was some code that effectively was doing: // Allocate some data required DataStructure* data = new DataStructure(num); // Call into a subroutine that passed around and manipulated this data highly CallSubroutine(data); // Read and use some values from here double values = data->Foo; // Cleanup delete data; // ... return bar; Normally, if “DataStructure” was a simple data type, I could just allocate it on the stack.  However, it’s constructor, internally, allocated it’s own memory using new, so this wouldn’t eliminate the problem.  In this case, however, I could change the call signatures to allow the pointer to the data structure to be passed into ProcessElement and through the call graph, allowing the inner routine to reuse the same “data” memory instead of allocating.  At the highest level, my code effectively changed to something like: DataStructure* data = new DataStructure(numberToProcess); for (i=0; i<numberToProcess; ++i) { // Do some work ProcessElement(element[i], data); } delete data; Granted, this dramatically reduced the maintainability of the code, so it wasn’t something I wanted to do unless there was a significant benefit.  In this case, after profiling the new version, I found that it increased the overall performance dramatically – my main test case went from 35 minutes runtime down to 21 minutes.  This was such a significant improvement, I felt it was worth the reduction in maintainability. In C and C++, it’s generally a good idea (for performance) to: Reduce the number of memory allocations as much as possible, Use fewer, larger memory allocations instead of many smaller ones, and Allocate as high up the call stack as possible, and reuse memory I’ve seen many people try to make similar optimizations in C# code.  For good or bad, this is typically not a good idea.  The garbage collector in .NET completely changes the rules here. In C#, reallocating memory in a loop is not always a bad idea.  In this scenario, for example, I may have been much better off leaving the original code alone.  The reason for this is the garbage collector.  The GC in .NET is incredibly effective, and leaving the allocation deep inside the call stack has some huge advantages.  First and foremost, it tends to make the code more maintainable – passing around object references tends to couple the methods together more than necessary, and overall increase the complexity of the code.  This is something that should be avoided unless there is a significant reason.  Second, (unlike C and C++) memory allocation of a single object in C# is normally cheap and fast.  Finally, and most critically, there is a large advantage to having short lived objects.  If you lift a variable out of the loop and reuse the memory, its much more likely that object will get promoted to Gen1 (or worse, Gen2).  This can cause expensive compaction operations to be required, and also lead to (at least temporary) memory fragmentation as well as more costly collections later. As such, I’ve found that it’s often (though not always) faster to leave memory allocations where you’d naturally place them – deep inside of the call graph, inside of the loops.  This causes the objects to stay very short lived, which in turn increases the efficiency of the garbage collector, and can dramatically improve the overall performance of the routine as a whole. In C#, I tend to: Keep variable declarations in the tightest scope possible Declare and allocate objects at usage While this tends to cause some of the same goals (reducing unnecessary allocations, etc), the goal here is a bit different – it’s about keeping the objects rooted for as little time as possible in order to (attempt) to keep them completely in Gen0, or worst case, Gen1.  It also has the huge advantage of keeping the code very maintainable – objects are used and “released” as soon as possible, which keeps the code very clean.  It does, however, often have the side effect of causing more allocations to occur, but keeping the objects rooted for a much shorter time. Now – nowhere here am I suggesting that these rules are hard, fast rules that are always true.  That being said, my time spent optimizing over the years encourages me to naturally write code that follows the above guidelines, then profile and adjust as necessary.  In my current project, however, I ran across one of those nasty little pitfalls that’s something to keep in mind – interop changes the rules. In this case, I was dealing with an API that, internally, used some COM objects.  In this case, these COM objects were leading to native allocations (most likely C++) occurring in a loop deep in my call graph.  Even though I was writing nice, clean managed code, the normal managed code rules for performance no longer apply.  After profiling to find the bottleneck in my code, I realized that my inner loop, a innocuous looking block of C# code, was effectively causing a set of native memory allocations in every iteration.  This required going back to a “native programming” mindset for optimization.  Lifting these variables and reusing them took a 1:10 routine down to 0:20 – again, a very worthwhile improvement. Overall, the lessons here are: Always profile if you suspect a performance problem – don’t assume any rule is correct, or any code is efficient just because it looks like it should be Remember to check memory allocations when profiling, not just CPU cycles Interop scenarios often cause managed code to act very differently than “normal” managed code. Native code can be hidden very cleverly inside of managed wrappers

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• Evaluating Solutions to Manage Product Compliance? Don't Wait Much Longer

  - by Kerrie Foy

  Depending on severity, product compliance issues can cause all sorts of problems from run-away budgets to business closures. But effective policies and safeguards can create a strong foundation for innovation, productivity, market penetration and competitive advantage. If you’ve been putting off a systematic approach to product compliance, it is time to reconsider that decision, or indecision. Why now?  No matter what industry, companies face a litany of worldwide and regional regulations that require proof of product compliance and environmental friendliness for market access.  For example, Restriction of Hazardous Substances (RoHS) is a regulation that restricts the use of six dangerous materials used in the manufacture of electronic and electrical equipment.  ROHS was originally adopted by the European Union in 2003 for implementation in 2006, and it has evolved over time through various regional versions for North America, China, Japan, Korea, Norway and Turkey.  In addition, the RoHS directive allowed for material exemptions used in Medical Devices, but that exemption ends in 2014.   Additional regulations worth watching are the Battery Directive, Waste Electrical and Electronic Equipment (WEEE), and Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) directives.  Additional evolving regulations are coming from governing bodies like the Food and Drug Administration (FDA) and the International Organization for Standardization (ISO). Corporate sustainability initiatives are also gaining urgency and influencing product design. In a survey of 405 corporations in the Global 500 by Carbon Disclosure Project, co-written by PwC (CDP Global 500 Climate Change Report 2012 entitled Business Resilience in an Uncertain, Resource-Constrained World), 48% of the respondents indicated they saw potential to create new products and business services as a response to climate change. Just 21% reported a dedicated budget for the research. However, the report goes on to explain that those few companies are winning over new customers and driving additional profits by exploiting their abilities to adapt to environmental needs. The article cites Dell as an example – Dell has invested in research to develop new products designed to reduce its customers’ emissions by more than 10 million metric tons of CO2e per year. This reduction in emissions should save Dell’s customers over $1billion per year as a result! Over time we expect to see many additional companies prove that eco-design provides marketplace benefits through differentiation and direct customer value. How do you meet compliance requirements and also successfully invest in eco-friendly designs? No doubt companies struggle to answer this question. After all, the journey to get there may involve transforming business models, go-to-market strategies, supply networks, quality assurance policies and compliance processes per the rapidly evolving global and regional directives. There may be limited executive focus on the initiative, inability to quantify noncompliance, or not enough resources to justify investment. To make things even more difficult to address, compliance responsibility can be a passionate topic within an organization, making the prospect of change on an enterprise scale problematic and time-consuming. Without a single source of truth for product data and without proper processes in place, ensuring product compliance burgeons into a crushing task that is cost-prohibitive and overwhelming to an organization. With all the overhead, certain markets or demographics become simply inaccessible. Therefore, the risk to consumer goodwill and satisfaction, revenue, business continuity, and market potential is too great not to solve the compliance challenge. Companies are beginning to adapt and even thrive in today’s highly regulated and transparent environment by implementing systematic approaches to product compliance that are more than functional bandages but revenue-generating engines. Consider partnering with Oracle to help you address your compliance needs. Many of the world’s most innovative leaders and pioneers are leveraging Oracle’s Agile Product Lifecycle Management (PLM) portfolio of enterprise applications to manage the product value chain, centralize product data, automate processes, and launch more eco-friendly products to market faster.   Particularly, the Agile Product Governance & Compliance (PG&C) solution provides out-of-the-box functionality to integrate actionable regulatory information into the enterprise product record from the ideation to the disposal/recycling phase. Agile PG&C makes it possible to efficiently manage compliance per corporate green initiatives as well as regional and global directives. Options are critical, but so is ease-of-use. Anyone who’s grappled with compliance policy knows legal interpretation plays a major role in determining how an organization responds to regulation. Agile PG&C gives you the freedom to configure product compliance per your needs, while maintaining rigorous control over the product record in an easy-to-use interface that facilitates adoption efforts. It allows you to assign regulations as specifications for a part or BOM roll-up. Each specification has a threshold value that alerts you to a non-compliance issue if the threshold value is exceeded. Set however many regulations as specifications you need to make sure a product can be sold in your target countries. Another option is to implement like one of our leading consumer electronics customers and define your own “catch-all” specification to ensure compliance in all markets. You can give your suppliers secure access to enter their component data or integrate a third party’s data. With Agile PG&C you are able to design compliance earlier into your products to reduce cost and improve quality downstream when stakes are higher. Agile PG&C is a comprehensive solution that makes product compliance more reliable and efficient. Throughout product lifecycles, use the solution to support full material disclosures, efficiently manage declarations with your suppliers, feed compliance data into a corrective action if a product must be changed, and swiftly satisfy audits by showing all due diligence tracked in one solution. Given the compounding regulation and consumer focus on urgent environmental issues, now is the time to act. Implementing an enterprise, systematic approach to product compliance is a competitive investment. From the start, Agile Product Governance & Compliance enables companies to confidently design for compliance and sustainability, reduce the cost of compliance, minimize the risk of business interruption, deliver responsible products, and inspire new innovation.  Don’t wait any longer! To find out more about Agile Product Governance & Compliance download the data sheet, contact your sales representative, or call Oracle at 1-800-633-0738. Many thanks to Shane Goodwin, Senior Manager, Oracle Agile PLM Product Management, for contributions to this article. 

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• External File Upload Optimizations for Windows Azure

  - by rgillen

  [Cross posted from here: http://rob.gillenfamily.net/post/External-File-Upload-Optimizations-for-Windows-Azure.aspx] I’m wrapping up a bit of the work we’ve been doing on data movement optimizations for cloud computing and the latest set of data yielded some interesting points I thought I’d share. The work done here is not really rocket science but may, in some ways, be slightly counter-intuitive and therefore seemed worthy of posting. Summary: for those who don’t like to read detailed posts or don’t have time, the synopsis is that if you are uploading data to Azure, block your data (even down to 1MB) and upload in parallel. Set your block size based on your source file size, but if you must choose a fixed value, use 1MB. Following the above will result in significant performance gains… upwards of 10x-24x and a reduction in overall file transfer time of upwards of 90% (eg, uploading a 1GB file averaged 46.37 minutes prior to optimizations and averaged 1.86 minutes afterwards). Detail: For those of you who want more detail, or think that the claims at the end of the preceding paragraph are over-reaching, what follows is information and code supporting these claims. As the title would indicate, these tests were run from our research facility pointing to the Azure cloud (specifically US North Central as it is physically closest to us) and do not represent intra-cloud results… we have performed intra-cloud tests and the overall results are similar in notion but the data rates are significantly different as well as the tipping points for the various block sizes… this will be detailed separately). We started by building a very simple console application that would loop through a directory and upload each file to Azure storage. This application used the shipping storage client library from the 1.1 version of the azure tools. The only real variation from the client library is that we added code to collect and record the duration (in ms) and size (in bytes) for each file transferred. The code is available here. We then created a directory that had a collection of files for the following sizes: 2KB, 32KB, 64KB, 128KB, 512KB, 1MB, 5MB, 10MB, 25MB, 50MB, 100MB, 250MB, 500MB, 750MB, and 1GB (50 files for each size listed). These files contained randomly-generated binary data and do not benefit from compression (a separate discussion topic). Our file generation tool is available here. The baseline was established by running the application described above against the directory containing all of the data files. This application uploads the files in a random order so as to avoid transferring all of the files of a given size sequentially and thereby spreading the affects of periodic Internet delays across the collection of results.  We then ran some scripts to split the resulting data and generate some reports. The raw data collected for our non-optimized tests is available via the links in the Related Resources section at the bottom of this post. For each file size, we calculated the average upload time (and standard deviation) and the average transfer rate (and standard deviation). As you likely are aware, transferring data across the Internet is susceptible to many transient delays which can cause anomalies in the resulting data. It is for this reason that we randomized the order of source file processing as well as executed the tests 50x for each file size. We expect that these steps will yield a sufficiently balanced set of results. Once the baseline was collected and analyzed, we updated the test harness application with some methods to split the source file into user-defined block sizes and then to upload those blocks in parallel (using the PutBlock() method of Azure storage). The parallelization was handled by simply relying on the Parallel Extensions to .NET to provide a Parallel.For loop (see linked source for specific implementation details in Program.cs, line 173 and following… less than 100 lines total). Once all of the blocks were uploaded, we called PutBlockList() to assemble/commit the file in Azure storage. For each block transferred, the MD5 was calculated and sent ensuring that the bits that arrived matched was was intended. The timer for the blocked/parallelized transfer method wraps the entire process (source file splitting, block transfer, MD5 validation, file committal). A diagram of the process is as follows: We then tested the affects of blocking & parallelizing the transfers by running the updated application against the same source set and did a parameter sweep on the block size including 256KB, 512KB, 1MB, 2MB, and 4MB (our assumption was that anything lower than 256KB wasn’t worth the trouble and 4MB is the maximum size of a block supported by Azure). The raw data for the parallel tests is available via the links in the Related Resources section at the bottom of this post. This data was processed and then compared against the single-threaded / non-optimized transfer numbers and the results were encouraging. The Excel version of the results is available here. Two semi-obvious points need to be made prior to reviewing the data. The first is that if the block size is larger than the source file size you will end up with a “negative optimization” due to the overhead of attempting to block and parallelize. The second is that as the files get smaller, the clock-time cost of blocking and parallelizing (overhead) is more apparent and can tend towards negative optimizations. For this reason (and is supported in the raw data provided in the linked worksheet) the charts and dialog below ignore source file sizes less than 1MB. (click chart for full size image) The chart above illustrates some interesting points about the results: When the block size is smaller than the source file, performance increases but as the block size approaches and then passes the source file size, you see decreasing benefit to the point of negative gains (see the values for the 1MB file size) For some of the moderately-sized source files, small blocks (256KB) are best As the size of the source file gets larger (see values for 50MB and up), the smallest block size is not the most efficient (presumably due, at least in part, to the increased number of blocks, increased number of individual transfer requests, and reassembly/committal costs). Once you pass the 250MB source file size, the difference in rate for 1MB to 4MB blocks is more-or-less constant The 1MB block size gives the best average improvement (~16x) but the optimal approach would be to vary the block size based on the size of the source file.    (click chart for full size image) The above is another view of the same data as the prior chart just with the axis changed (x-axis represents file size and plotted data shows improvement by block size). It again highlights the fact that the 1MB block size is probably the best overall size but highlights the benefits of some of the other block sizes at different source file sizes. This last chart shows the change in total duration of the file uploads based on different block sizes for the source file sizes. Nothing really new here other than this view of the data highlights the negative affects of poorly choosing a block size for smaller files.   Summary What we have found so far is that blocking your file uploads and uploading them in parallel results in significant performance improvements. Further, utilizing extension methods and the Task Parallel Library (.NET 4.0) make short work of altering the shipping client library to provide this functionality while minimizing the amount of change to existing applications that might be using the client library for other interactions.   Related Resources Source code for upload test application Source code for random file generator ODatas feed of raw data from non-optimized transfer tests Experiment Metadata Experiment Datasets 2KB Uploads 32KB Uploads 64KB Uploads 128KB Uploads 256KB Uploads 512KB Uploads 1MB Uploads 5MB Uploads 10MB Uploads 25MB Uploads 50MB Uploads 100MB Uploads 250MB Uploads 500MB Uploads 750MB Uploads 1GB Uploads Raw Data OData feeds of raw data from blocked/parallelized transfer tests Experiment Metadata Experiment Datasets Raw Data 256KB Blocks 512KB Blocks 1MB Blocks 2MB Blocks 4MB Blocks Excel worksheet showing summarizations and comparisons

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• Problems with real-valued input deep belief networks (of RBMs)

  - by Junier

  I am trying to recreate the results reported in Reducing the dimensionality of data with neural networks of autoencoding the olivetti face dataset with an adapted version of the MNIST digits matlab code, but am having some difficulty. It seems that no matter how much tweaking I do on the number of epochs, rates, or momentum the stacked RBMs are entering the fine-tuning stage with a large amount of error and consequently fail to improve much at the fine-tuning stage. I am also experiencing a similar problem on another real-valued dataset. For the first layer I am using a RBM with a smaller learning rate (as described in the paper) and with negdata = poshidstates*vishid' + repmat(visbiases,numcases,1); I'm fairly confident I am following the instructions found in the supporting material but I cannot achieve the correct errors. Is there something I am missing? See the code I'm using for real-valued visible unit RBMs below, and for the whole deep training. The rest of the code can be found here. rbmvislinear.m: epsilonw = 0.001; % Learning rate for weights epsilonvb = 0.001; % Learning rate for biases of visible units epsilonhb = 0.001; % Learning rate for biases of hidden units weightcost = 0.0002; initialmomentum = 0.5; finalmomentum = 0.9; [numcases numdims numbatches]=size(batchdata); if restart ==1, restart=0; epoch=1; % Initializing symmetric weights and biases. vishid = 0.1*randn(numdims, numhid); hidbiases = zeros(1,numhid); visbiases = zeros(1,numdims); poshidprobs = zeros(numcases,numhid); neghidprobs = zeros(numcases,numhid); posprods = zeros(numdims,numhid); negprods = zeros(numdims,numhid); vishidinc = zeros(numdims,numhid); hidbiasinc = zeros(1,numhid); visbiasinc = zeros(1,numdims); sigmainc = zeros(1,numhid); batchposhidprobs=zeros(numcases,numhid,numbatches); end for epoch = epoch:maxepoch, fprintf(1,'epoch %d\r',epoch); errsum=0; for batch = 1:numbatches, if (mod(batch,100)==0) fprintf(1,' %d ',batch); end %%%%%%%%% START POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% data = batchdata(:,:,batch); poshidprobs = 1./(1 + exp(-data*vishid - repmat(hidbiases,numcases,1))); batchposhidprobs(:,:,batch)=poshidprobs; posprods = data' * poshidprobs; poshidact = sum(poshidprobs); posvisact = sum(data); %%%%%%%%% END OF POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% poshidstates = poshidprobs > rand(numcases,numhid); %%%%%%%%% START NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% negdata = poshidstates*vishid' + repmat(visbiases,numcases,1);% + randn(numcases,numdims) if not using mean neghidprobs = 1./(1 + exp(-negdata*vishid - repmat(hidbiases,numcases,1))); negprods = negdata'*neghidprobs; neghidact = sum(neghidprobs); negvisact = sum(negdata); %%%%%%%%% END OF NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% err= sum(sum( (data-negdata).^2 )); errsum = err + errsum; if epoch>5, momentum=finalmomentum; else momentum=initialmomentum; end; %%%%%%%%% UPDATE WEIGHTS AND BIASES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% vishidinc = momentum*vishidinc + ... epsilonw*( (posprods-negprods)/numcases - weightcost*vishid); visbiasinc = momentum*visbiasinc + (epsilonvb/numcases)*(posvisact-negvisact); hidbiasinc = momentum*hidbiasinc + (epsilonhb/numcases)*(poshidact-neghidact); vishid = vishid + vishidinc; visbiases = visbiases + visbiasinc; hidbiases = hidbiases + hidbiasinc; %%%%%%%%%%%%%%%% END OF UPDATES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% end fprintf(1, '\nepoch %4i error %f \n', epoch, errsum); end dofacedeepauto.m: clear all close all maxepoch=200; %In the Science paper we use maxepoch=50, but it works just fine. numhid=2000; numpen=1000; numpen2=500; numopen=30; fprintf(1,'Pretraining a deep autoencoder. \n'); fprintf(1,'The Science paper used 50 epochs. This uses %3i \n', maxepoch); load fdata %makeFaceData; [numcases numdims numbatches]=size(batchdata); fprintf(1,'Pretraining Layer 1 with RBM: %d-%d \n',numdims,numhid); restart=1; rbmvislinear; hidrecbiases=hidbiases; save mnistvh vishid hidrecbiases visbiases; maxepoch=50; fprintf(1,'\nPretraining Layer 2 with RBM: %d-%d \n',numhid,numpen); batchdata=batchposhidprobs; numhid=numpen; restart=1; rbm; hidpen=vishid; penrecbiases=hidbiases; hidgenbiases=visbiases; save mnisthp hidpen penrecbiases hidgenbiases; fprintf(1,'\nPretraining Layer 3 with RBM: %d-%d \n',numpen,numpen2); batchdata=batchposhidprobs; numhid=numpen2; restart=1; rbm; hidpen2=vishid; penrecbiases2=hidbiases; hidgenbiases2=visbiases; save mnisthp2 hidpen2 penrecbiases2 hidgenbiases2; fprintf(1,'\nPretraining Layer 4 with RBM: %d-%d \n',numpen2,numopen); batchdata=batchposhidprobs; numhid=numopen; restart=1; rbmhidlinear; hidtop=vishid; toprecbiases=hidbiases; topgenbiases=visbiases; save mnistpo hidtop toprecbiases topgenbiases; backpropface; Thanks for your time

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• Problems with real-valued deep belief networks (of RBMs)

  - by Junier

  I am trying to recreate the results reported in Reducing the dimensionality of data with neural networks of autoencoding the olivetti face dataset with an adapted version of the MNIST digits matlab code, but am having some difficulty. It seems that no matter how much tweaking I do on the number of epochs, rates, or momentum the stacked RBMs are entering the fine-tuning stage with a large amount of error and consequently fail to improve much at the fine-tuning stage. I am also experiencing a similar problem on another real-valued dataset. For the first layer I am using a RBM with a smaller learning rate (as described in the paper) and with negdata = poshidstates*vishid' + repmat(visbiases,numcases,1); I'm fairly confident I am following the instructions found in the supporting material but I cannot achieve the correct errors. Is there something I am missing? See the code I'm using for real-valued visible unit RBMs below, and for the whole deep training. The rest of the code can be found here. rbmvislinear.m: epsilonw = 0.001; % Learning rate for weights epsilonvb = 0.001; % Learning rate for biases of visible units epsilonhb = 0.001; % Learning rate for biases of hidden units weightcost = 0.0002; initialmomentum = 0.5; finalmomentum = 0.9; [numcases numdims numbatches]=size(batchdata); if restart ==1, restart=0; epoch=1; % Initializing symmetric weights and biases. vishid = 0.1*randn(numdims, numhid); hidbiases = zeros(1,numhid); visbiases = zeros(1,numdims); poshidprobs = zeros(numcases,numhid); neghidprobs = zeros(numcases,numhid); posprods = zeros(numdims,numhid); negprods = zeros(numdims,numhid); vishidinc = zeros(numdims,numhid); hidbiasinc = zeros(1,numhid); visbiasinc = zeros(1,numdims); sigmainc = zeros(1,numhid); batchposhidprobs=zeros(numcases,numhid,numbatches); end for epoch = epoch:maxepoch, fprintf(1,'epoch %d\r',epoch); errsum=0; for batch = 1:numbatches, if (mod(batch,100)==0) fprintf(1,' %d ',batch); end %%%%%%%%% START POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% data = batchdata(:,:,batch); poshidprobs = 1./(1 + exp(-data*vishid - repmat(hidbiases,numcases,1))); batchposhidprobs(:,:,batch)=poshidprobs; posprods = data' * poshidprobs; poshidact = sum(poshidprobs); posvisact = sum(data); %%%%%%%%% END OF POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% poshidstates = poshidprobs > rand(numcases,numhid); %%%%%%%%% START NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% negdata = poshidstates*vishid' + repmat(visbiases,numcases,1);% + randn(numcases,numdims) if not using mean neghidprobs = 1./(1 + exp(-negdata*vishid - repmat(hidbiases,numcases,1))); negprods = negdata'*neghidprobs; neghidact = sum(neghidprobs); negvisact = sum(negdata); %%%%%%%%% END OF NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% err= sum(sum( (data-negdata).^2 )); errsum = err + errsum; if epoch>5, momentum=finalmomentum; else momentum=initialmomentum; end; %%%%%%%%% UPDATE WEIGHTS AND BIASES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% vishidinc = momentum*vishidinc + ... epsilonw*( (posprods-negprods)/numcases - weightcost*vishid); visbiasinc = momentum*visbiasinc + (epsilonvb/numcases)*(posvisact-negvisact); hidbiasinc = momentum*hidbiasinc + (epsilonhb/numcases)*(poshidact-neghidact); vishid = vishid + vishidinc; visbiases = visbiases + visbiasinc; hidbiases = hidbiases + hidbiasinc; %%%%%%%%%%%%%%%% END OF UPDATES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% end fprintf(1, '\nepoch %4i error %f \n', epoch, errsum); end dofacedeepauto.m: clear all close all maxepoch=200; %In the Science paper we use maxepoch=50, but it works just fine. numhid=2000; numpen=1000; numpen2=500; numopen=30; fprintf(1,'Pretraining a deep autoencoder. \n'); fprintf(1,'The Science paper used 50 epochs. This uses %3i \n', maxepoch); load fdata %makeFaceData; [numcases numdims numbatches]=size(batchdata); fprintf(1,'Pretraining Layer 1 with RBM: %d-%d \n',numdims,numhid); restart=1; rbmvislinear; hidrecbiases=hidbiases; save mnistvh vishid hidrecbiases visbiases; maxepoch=50; fprintf(1,'\nPretraining Layer 2 with RBM: %d-%d \n',numhid,numpen); batchdata=batchposhidprobs; numhid=numpen; restart=1; rbm; hidpen=vishid; penrecbiases=hidbiases; hidgenbiases=visbiases; save mnisthp hidpen penrecbiases hidgenbiases; fprintf(1,'\nPretraining Layer 3 with RBM: %d-%d \n',numpen,numpen2); batchdata=batchposhidprobs; numhid=numpen2; restart=1; rbm; hidpen2=vishid; penrecbiases2=hidbiases; hidgenbiases2=visbiases; save mnisthp2 hidpen2 penrecbiases2 hidgenbiases2; fprintf(1,'\nPretraining Layer 4 with RBM: %d-%d \n',numpen2,numopen); batchdata=batchposhidprobs; numhid=numopen; restart=1; rbmhidlinear; hidtop=vishid; toprecbiases=hidbiases; topgenbiases=visbiases; save mnistpo hidtop toprecbiases topgenbiases; backpropface; Thanks for your time

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• Possible bug in ASP.NET MVC with form values being replaced.

  - by Dan Atkinson

  I appear to be having a problem with ASP.NET MVC in that, if I have more than one form on a page which uses the same name in each one, but as different types (radio/hidden/etc), then, when the first form posts (I choose the 'Date' radio button for instance), if the form is re-rendered (say as part of the results page), I seem to have the issue that the hidden value of the SearchType on the other forms is changed to the last radio button value (in this case, SearchType.Name). Below is an example form for reduction purposes. <% Html.BeginForm("Search", "Search", FormMethod.Post); %> <%= Html.RadioButton("SearchType", SearchType.Date, true) %> <%= Html.RadioButton("SearchType", SearchType.Name) %> <input type="submit" name="submitForm" value="Submit" /> <% Html.EndForm(); %> <% Html.BeginForm("Search", "Search", FormMethod.Post); %> <%= Html.Hidden("SearchType", SearchType.Colour) %> <input type="submit" name="submitForm" value="Submit" /> <% Html.EndForm(); %> <% Html.BeginForm("Search", "Search", FormMethod.Post); %> <%= Html.Hidden("SearchType", SearchType.Reference) %> <input type="submit" name="submitForm" value="Submit" /> <% Html.EndForm(); %> Resulting page source (this would be part of the results page) <form action="/Search/Search" method="post"> <input type="radio" name="SearchType" value="Date" /> <input type="radio" name="SearchType" value="Name" /> <input type="submit" name="submitForm" value="Submit" /> </form> <form action="/Search/Search" method="post"> <input type="hidden" name="SearchType" value="Name" /> <!-- Should be Colour --> <input type="submit" name="submitForm" value="Submit" /> </form> <form action="/Search/Search" method="post"> <input type="hidden" name="SearchType" value="Name" /> <!-- Should be Reference --> <input type="submit" name="submitForm" value="Submit" /> </form> Please can anyone else with RC1 confirm this? Maybe it's because I'm using an enum. I don't know. I should add that I can circumvent this issue by using 'manual' input () tags for the hidden fields, but if I use MVC tags (<%= Html.Hidden(...) %), .NET MVC replaces them every time. Many thanks. Update: I've seen this bug again today. It seems that this crops its head when you return a posted page and use MVC set hidden form tags with the Html helper. I've contacted Phil Haack about this, because I don't know where else to turn, and I don't believe that this should be expected behaviour as specified by David.

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• The Incremental Architect&rsquo;s Napkin - #5 - Design functions for extensibility and readability

  - by Ralf Westphal

  Originally posted on: http://geekswithblogs.net/theArchitectsNapkin/archive/2014/08/24/the-incremental-architectrsquos-napkin---5---design-functions-for.aspx The functionality of programs is entered via Entry Points. So what we´re talking about when designing software is a bunch of functions handling the requests represented by and flowing in through those Entry Points. Designing software thus consists of at least three phases: Analyzing the requirements to find the Entry Points and their signatures Designing the functionality to be executed when those Entry Points get triggered Implementing the functionality according to the design aka coding I presume, you´re familiar with phase 1 in some way. And I guess you´re proficient in implementing functionality in some programming language. But in my experience developers in general are not experienced in going through an explicit phase 2. “Designing functionality? What´s that supposed to mean?” you might already have thought. Here´s my definition: To design functionality (or functional design for short) means thinking about… well, functions. You find a solution for what´s supposed to happen when an Entry Point gets triggered in terms of functions. A conceptual solution that is, because those functions only exist in your head (or on paper) during this phase. But you may have guess that, because it´s “design” not “coding”. And here is, what functional design is not: It´s not about logic. Logic is expressions (e.g. +, -, && etc.) and control statements (e.g. if, switch, for, while etc.). Also I consider calling external APIs as logic. It´s equally basic. It´s what code needs to do in order to deliver some functionality or quality. Logic is what´s doing that needs to be done by software. Transformations are either done through expressions or API-calls. And then there is alternative control flow depending on the result of some expression. Basically it´s just jumps in Assembler, sometimes to go forward (if, switch), sometimes to go backward (for, while, do). But calling your own function is not logic. It´s not necessary to produce any outcome. Functionality is not enhanced by adding functions (subroutine calls) to your code. Nor is quality increased by adding functions. No performance gain, no higher scalability etc. through functions. Functions are not relevant to functionality. Strange, isn´t it. What they are important for is security of investment. By introducing functions into our code we can become more productive (re-use) and can increase evolvability (higher unterstandability, easier to keep code consistent). That´s no small feat, however. Evolvable code can hardly be overestimated. That´s why to me functional design is so important. It´s at the core of software development. To sum this up: Functional design is on a level of abstraction above (!) logical design or algorithmic design. Functional design is only done until you get to a point where each function is so simple you are very confident you can easily code it. Functional design an logical design (which mostly is coding, but can also be done using pseudo code or flow charts) are complementary. Software needs both. If you start coding right away you end up in a tangled mess very quickly. Then you need back out through refactoring. Functional design on the other hand is bloodless without actual code. It´s just a theory with no experiments to prove it. But how to do functional design? An example of functional design Let´s assume a program to de-duplicate strings. The user enters a number of strings separated by commas, e.g. a, b, a, c, d, b, e, c, a. And the program is supposed to clear this list of all doubles, e.g. a, b, c, d, e. There is only one Entry Point to this program: the user triggers the de-duplication by starting the program with the string list on the command line C:\>deduplicate "a, b, a, c, d, b, e, c, a" a, b, c, d, e …or by clicking on a GUI button. This leads to the Entry Point function to get called. It´s the program´s main function in case of the batch version or a button click event handler in the GUI version. That´s the physical Entry Point so to speak. It´s inevitable. What then happens is a three step process: Transform the input data from the user into a request. Call the request handler. Transform the output of the request handler into a tangible result for the user. Or to phrase it a bit more generally: Accept input. Transform input into output. Present output. This does not mean any of these steps requires a lot of effort. Maybe it´s just one line of code to accomplish it. Nevertheless it´s a distinct step in doing the processing behind an Entry Point. Call it an aspect or a responsibility - and you will realize it most likely deserves a function of its own to satisfy the Single Responsibility Principle (SRP). Interestingly the above list of steps is already functional design. There is no logic, but nevertheless the solution is described - albeit on a higher level of abstraction than you might have done yourself. But it´s still on a meta-level. The application to the domain at hand is easy, though: Accept string list from command line De-duplicate Present de-duplicated strings on standard output And this concrete list of processing steps can easily be transformed into code:static void Main(string[] args) { var input = Accept_string_list(args); var output = Deduplicate(input); Present_deduplicated_string_list(output); } Instead of a big problem there are three much smaller problems now. If you think each of those is trivial to implement, then go for it. You can stop the functional design at this point. But maybe, just maybe, you´re not so sure how to go about with the de-duplication for example. Then just implement what´s easy right now, e.g.private static string Accept_string_list(string[] args) { return args[0]; } private static void Present_deduplicated_string_list( string[] output) { var line = string.Join(", ", output); Console.WriteLine(line); } Accept_string_list() contains logic in the form of an API-call. Present_deduplicated_string_list() contains logic in the form of an expression and an API-call. And then repeat the functional design for the remaining processing step. What´s left is the domain logic: de-duplicating a list of strings. How should that be done? Without any logic at our disposal during functional design you´re left with just functions. So which functions could make up the de-duplication? Here´s a suggestion: De-duplicate Parse the input string into a true list of strings. Register each string in a dictionary/map/set. That way duplicates get cast away. Transform the data structure into a list of unique strings. Processing step 2 obviously was the core of the solution. That´s where real creativity was needed. That´s the core of the domain. But now after this refinement the implementation of each step is easy again:private static string[] Parse_string_list(string input) { return input.Split(',') .Select(s => s.Trim()) .ToArray(); } private static Dictionary<string,object> Compile_unique_strings(string[] strings) { return strings.Aggregate( new Dictionary<string, object>(), (agg, s) => { agg[s] = null; return agg; }); } private static string[] Serialize_unique_strings( Dictionary<string,object> dict) { return dict.Keys.ToArray(); } With these three additional functions Main() now looks like this:static void Main(string[] args) { var input = Accept_string_list(args); var strings = Parse_string_list(input); var dict = Compile_unique_strings(strings); var output = Serialize_unique_strings(dict); Present_deduplicated_string_list(output); } I think that´s very understandable code: just read it from top to bottom and you know how the solution to the problem works. It´s a mirror image of the initial design: Accept string list from command line Parse the input string into a true list of strings. Register each string in a dictionary/map/set. That way duplicates get cast away. Transform the data structure into a list of unique strings. Present de-duplicated strings on standard output You can even re-generate the design by just looking at the code. Code and functional design thus are always in sync - if you follow some simple rules. But about that later. And as a bonus: all the functions making up the process are small - which means easy to understand, too. So much for an initial concrete example. Now it´s time for some theory. Because there is method to this madness ;-) The above has only scratched the surface. Introducing Flow Design Functional design starts with a given function, the Entry Point. Its goal is to describe the behavior of the program when the Entry Point is triggered using a process, not an algorithm. An algorithm consists of logic, a process on the other hand consists just of steps or stages. Each processing step transforms input into output or a side effect. Also it might access resources, e.g. a printer, a database, or just memory. Processing steps thus can rely on state of some sort. This is different from Functional Programming, where functions are supposed to not be stateful and not cause side effects.[1] In its simplest form a process can be written as a bullet point list of steps, e.g. Get data from user Output result to user Transform data Parse data Map result for output Such a compilation of steps - possibly on different levels of abstraction - often is the first artifact of functional design. It can be generated by a team in an initial design brainstorming. Next comes ordering the steps. What should happen first, what next etc.? Get data from user Parse data Transform data Map result for output Output result to user That´s great for a start into functional design. It´s better than starting to code right away on a given function using TDD. Please get me right: TDD is a valuable practice. But it can be unnecessarily hard if the scope of a functionn is too large. But how do you know beforehand without investing some thinking? And how to do this thinking in a systematic fashion? My recommendation: For any given function you´re supposed to implement first do a functional design. Then, once you´re confident you know the processing steps - which are pretty small - refine and code them using TDD. You´ll see that´s much, much easier - and leads to cleaner code right away. For more information on this approach I call “Informed TDD” read my book of the same title. Thinking before coding is smart. And writing down the solution as a bunch of functions possibly is the simplest thing you can do, I´d say. It´s more according to the KISS (Keep It Simple, Stupid) principle than returning constants or other trivial stuff TDD development often is started with. So far so good. A simple ordered list of processing steps will do to start with functional design. As shown in the above example such steps can easily be translated into functions. Moving from design to coding thus is simple. However, such a list does not scale. Processing is not always that simple to be captured in a list. And then the list is just text. Again. Like code. That means the design is lacking visuality. Textual representations need more parsing by your brain than visual representations. Plus they are limited in their “dimensionality”: text just has one dimension, it´s sequential. Alternatives and parallelism are hard to encode in text. In addition the functional design using numbered lists lacks data. It´s not visible what´s the input, output, and state of the processing steps. That´s why functional design should be done using a lightweight visual notation. No tool is necessary to draw such designs. Use pen and paper; a flipchart, a whiteboard, or even a napkin is sufficient. Visualizing processes The building block of the functional design notation is a functional unit. I mostly draw it like this: Something is done, it´s clear what goes in, it´s clear what comes out, and it´s clear what the processing step requires in terms of state or hardware. Whenever input flows into a functional unit it gets processed and output is produced and/or a side effect occurs. Flowing data is the driver of something happening. That´s why I call this approach to functional design Flow Design. It´s about data flow instead of control flow. Control flow like in algorithms is of no concern to functional design. Thinking about control flow simply is too low level. Once you start with control flow you easily get bogged down by tons of details. That´s what you want to avoid during design. Design is supposed to be quick, broad brush, abstract. It should give overview. But what about all the details? As Robert C. Martin rightly said: “Programming is abot detail”. Detail is a matter of code. Once you start coding the processing steps you designed you can worry about all the detail you want. Functional design does not eliminate all the nitty gritty. It just postpones tackling them. To me that´s also an example of the SRP. Function design has the responsibility to come up with a solution to a problem posed by a single function (Entry Point). And later coding has the responsibility to implement the solution down to the last detail (i.e. statement, API-call). TDD unfortunately mixes both responsibilities. It´s just coding - and thereby trying to find detailed implementations (green phase) plus getting the design right (refactoring). To me that´s one reason why TDD has failed to deliver on its promise for many developers. Using functional units as building blocks of functional design processes can be depicted very easily. Here´s the initial process for the example problem: For each processing step draw a functional unit and label it. Choose a verb or an “action phrase” as a label, not a noun. Functional design is about activities, not state or structure. Then make the output of an upstream step the input of a downstream step. Finally think about the data that should flow between the functional units. Write the data above the arrows connecting the functional units in the direction of the data flow. Enclose the data description in brackets. That way you can clearly see if all flows have already been specified. Empty brackets mean “no data is flowing”, but nevertheless a signal is sent. A name like “list” or “strings” in brackets describes the data content. Use lower case labels for that purpose. A name starting with an upper case letter like “String” or “Customer” on the other hand signifies a data type. If you like, you also can combine descriptions with data types by separating them with a colon, e.g. (list:string) or (strings:string[]). But these are just suggestions from my practice with Flow Design. You can do it differently, if you like. Just be sure to be consistent. Flows wired-up in this manner I call one-dimensional (1D). Each functional unit just has one input and/or one output. A functional unit without an output is possible. It´s like a black hole sucking up input without producing any output. Instead it produces side effects. A functional unit without an input, though, does make much sense. When should it start to work? What´s the trigger? That´s why in the above process even the first processing step has an input. If you like, view such 1D-flows as pipelines. Data is flowing through them from left to right. But as you can see, it´s not always the same data. It get´s transformed along its passage: (args) becomes a (list) which is turned into (strings). The Principle of Mutual Oblivion A very characteristic trait of flows put together from function units is: no functional units knows another one. They are all completely independent of each other. Functional units don´t know where their input is coming from (or even when it´s gonna arrive). They just specify a range of values they can process. And they promise a certain behavior upon input arriving. Also they don´t know where their output is going. They just produce it in their own time independent of other functional units. That means at least conceptually all functional units work in parallel. Functional units don´t know their “deployment context”. They now nothing about the overall flow they are place in. They are just consuming input from some upstream, and producing output for some downstream. That makes functional units very easy to test. At least as long as they don´t depend on state or resources. I call this the Principle of Mutual Oblivion (PoMO). Functional units are oblivious of others as well as an overall context/purpose. They are just parts of a whole focused on a single responsibility. How the whole is built, how a larger goal is achieved, is of no concern to the single functional units. By building software in such a manner, functional design interestingly follows nature. Nature´s building blocks for organisms also follow the PoMO. The cells forming your body do not know each other. Take a nerve cell “controlling” a muscle cell for example:[2] The nerve cell does not know anything about muscle cells, let alone the specific muscel cell it is “attached to”. Likewise the muscle cell does not know anything about nerve cells, let a lone a specific nerve cell “attached to” it. Saying “the nerve cell is controlling the muscle cell” thus only makes sense when viewing both from the outside. “Control” is a concept of the whole, not of its parts. Control is created by wiring-up parts in a certain way. Both cells are mutually oblivious. Both just follow a contract. One produces Acetylcholine (ACh) as output, the other consumes ACh as input. Where the ACh is going, where it´s coming from neither cell cares about. Million years of evolution have led to this kind of division of labor. And million years of evolution have produced organism designs (DNA) which lead to the production of these different cell types (and many others) and also to their co-location. The result: the overall behavior of an organism. How and why this happened in nature is a mystery. For our software, though, it´s clear: functional and quality requirements needs to be fulfilled. So we as developers have to become “intelligent designers” of “software cells” which we put together to form a “software organism” which responds in satisfying ways to triggers from it´s environment. My bet is: If nature gets complex organisms working by following the PoMO, who are we to not apply this recipe for success to our much simpler “machines”? So my rule is: Wherever there is functionality to be delivered, because there is a clear Entry Point into software, design the functionality like nature would do it. Build it from mutually oblivious functional units. That´s what Flow Design is about. In that way it´s even universal, I´d say. Its notation can also be applied to biology: Never mind labeling the functional units with nouns. That´s ok in Flow Design. You´ll do that occassionally for functional units on a higher level of abstraction or when their purpose is close to hardware. Getting a cockroach to roam your bedroom takes 1,000,000 nerve cells (neurons). Getting the de-duplication program to do its job just takes 5 “software cells” (functional units). Both, though, follow the same basic principle. Translating functional units into code Moving from functional design to code is no rocket science. In fact it´s straightforward. There are two simple rules: Translate an input port to a function. Translate an output port either to a return statement in that function or to a function pointer visible to that function. The simplest translation of a functional unit is a function. That´s what you saw in the above example. Functions are mutually oblivious. That why Functional Programming likes them so much. It makes them composable. Which is the reason, nature works according to the PoMO. Let´s be clear about one thing: There is no dependency injection in nature. For all of an organism´s complexity no DI container is used. Behavior is the result of smooth cooperation between mutually oblivious building blocks. Functions will often be the adequate translation for the functional units in your designs. But not always. Take for example the case, where a processing step should not always produce an output. Maybe the purpose is to filter input. Here the functional unit consumes words and produces words. But it does not pass along every word flowing in. Some words are swallowed. Think of a spell checker. It probably should not check acronyms for correctness. There are too many of them. Or words with no more than two letters. Such words are called “stop words”. In the above picture the optionality of the output is signified by the astrisk outside the brackets. It means: Any number of (word) data items can flow from the functional unit for each input data item. It might be none or one or even more. This I call a stream of data. Such behavior cannot be translated into a function where output is generated with return. Because a function always needs to return a value. So the output port is translated into a function pointer or continuation which gets passed to the subroutine when called:[3]void filter_stop_words( string word, Action<string> onNoStopWord) { if (...check if not a stop word...) onNoStopWord(word); } If you want to be nitpicky you might call such a function pointer parameter an injection. And technically you´re right. Conceptually, though, it´s not an injection. Because the subroutine is not functionally dependent on the continuation. Firstly continuations are procedures, i.e. subroutines without a return type. Remember: Flow Design is about unidirectional data flow. Secondly the name of the formal parameter is chosen in a way as to not assume anything about downstream processing steps. onNoStopWord describes a situation (or event) within the functional unit only. Translating output ports into function pointers helps keeping functional units mutually oblivious in cases where output is optional or produced asynchronically. Either pass the function pointer to the function upon call. Or make it global by putting it on the encompassing class. Then it´s called an event. In C# that´s even an explicit feature.class Filter { public void filter_stop_words( string word) { if (...check if not a stop word...) onNoStopWord(word); } public event Action<string> onNoStopWord; } When to use a continuation and when to use an event dependens on how a functional unit is used in flows and how it´s packed together with others into classes. You´ll see examples further down the Flow Design road. Another example of 1D functional design Let´s see Flow Design once more in action using the visual notation. How about the famous word wrap kata? Robert C. Martin has posted a much cited solution including an extensive reasoning behind his TDD approach. So maybe you want to compare it to Flow Design. The function signature given is:string WordWrap(string text, int maxLineLength) {...} That´s not an Entry Point since we don´t see an application with an environment and users. Nevertheless it´s a function which is supposed to provide a certain functionality. The text passed in has to be reformatted. The input is a single line of arbitrary length consisting of words separated by spaces. The output should consist of one or more lines of a maximum length specified. If a word is longer than a the maximum line length it can be split in multiple parts each fitting in a line. Flow Design Let´s start by brainstorming the process to accomplish the feat of reformatting the text. What´s needed? Words need to be assembled into lines Words need to be extracted from the input text The resulting lines need to be assembled into the output text Words too long to fit in a line need to be split Does sound about right? I guess so. And it shows a kind of priority. Long words are a special case. So maybe there is a hint for an incremental design here. First let´s tackle “average words” (words not longer than a line). Here´s the Flow Design for this increment: The the first three bullet points turned into functional units with explicit data added. As the signature requires a text is transformed into another text. See the input of the first functional unit and the output of the last functional unit. In between no text flows, but words and lines. That´s good to see because thereby the domain is clearly represented in the design. The requirements are talking about words and lines and here they are. But note the asterisk! It´s not outside the brackets but inside. That means it´s not a stream of words or lines, but lists or sequences. For each text a sequence of words is output. For each sequence of words a sequence of lines is produced. The asterisk is used to abstract from the concrete implementation. Like with streams. Whether the list of words gets implemented as an array or an IEnumerable is not important during design. It´s an implementation detail. Does any processing step require further refinement? I don´t think so. They all look pretty “atomic” to me. And if not… I can always backtrack and refine a process step using functional design later once I´ve gained more insight into a sub-problem. Implementation The implementation is straightforward as you can imagine. The processing steps can all be translated into functions. Each can be tested easily and separately. Each has a focused responsibility. And the process flow becomes just a sequence of function calls: Easy to understand. It clearly states how word wrapping works - on a high level of abstraction. And it´s easy to evolve as you´ll see. Flow Design - Increment 2 So far only texts consisting of “average words” are wrapped correctly. Words not fitting in a line will result in lines too long. Wrapping long words is a feature of the requested functionality. Whether it´s there or not makes a difference to the user. To quickly get feedback I decided to first implement a solution without this feature. But now it´s time to add it to deliver the full scope. Fortunately Flow Design automatically leads to code following the Open Closed Principle (OCP). It´s easy to extend it - instead of changing well tested code. How´s that possible? Flow Design allows for extension of functionality by inserting functional units into the flow. That way existing functional units need not be changed. The data flow arrow between functional units is a natural extension point. No need to resort to the Strategy Pattern. No need to think ahead where extions might need to be made in the future. I just “phase in” the remaining processing step: Since neither Extract words nor Reformat know of their environment neither needs to be touched due to the “detour”. The new processing step accepts the output of the existing upstream step and produces data compatible with the existing downstream step. Implementation - Increment 2 A trivial implementation checking the assumption if this works does not do anything to split long words. The input is just passed on: Note how clean WordWrap() stays. The solution is easy to understand. A developer looking at this code sometime in the future, when a new feature needs to be build in, quickly sees how long words are dealt with. Compare this to Robert C. Martin´s solution:[4] How does this solution handle long words? Long words are not even part of the domain language present in the code. At least I need considerable time to understand the approach. Admittedly the Flow Design solution with the full implementation of long word splitting is longer than Robert C. Martin´s. At least it seems. Because his solution does not cover all the “word wrap situations” the Flow Design solution handles. Some lines would need to be added to be on par, I guess. But even then… Is a difference in LOC that important as long as it´s in the same ball park? I value understandability and openness for extension higher than saving on the last line of code. Simplicity is not just less code, it´s also clarity in design. But don´t take my word for it. Try Flow Design on larger problems and compare for yourself. What´s the easier, more straightforward way to clean code? And keep in mind: You ain´t seen all yet ;-) There´s more to Flow Design than described in this chapter. In closing I hope I was able to give you a impression of functional design that makes you hungry for more. To me it´s an inevitable step in software development. Jumping from requirements to code does not scale. And it leads to dirty code all to quickly. Some thought should be invested first. Where there is a clear Entry Point visible, it´s functionality should be designed using data flows. Because with data flows abstraction is possible. For more background on why that´s necessary read my blog article here. For now let me point out to you - if you haven´t already noticed - that Flow Design is a general purpose declarative language. It´s “programming by intention” (Shalloway et al.). Just write down how you think the solution should work on a high level of abstraction. This breaks down a large problem in smaller problems. And by following the PoMO the solutions to those smaller problems are independent of each other. So they are easy to test. Or you could even think about getting them implemented in parallel by different team members. Flow Design not only increases evolvability, but also helps becoming more productive. All team members can participate in functional design. This goes beyon collective code ownership. We´re talking collective design/architecture ownership. Because with Flow Design there is a common visual language to talk about functional design - which is the foundation for all other design activities.   PS: If you like what you read, consider getting my ebook “The Incremental Architekt´s Napkin”. It´s where I compile all the articles in this series for easier reading. I like the strictness of Function Programming - but I also find it quite hard to live by. And it certainly is not what millions of programmers are used to. Also to me it seems, the real world is full of state and side effects. So why give them such a bad image? That´s why functional design takes a more pragmatic approach. State and side effects are ok for processing steps - but be sure to follow the SRP. Don´t put too much of it into a single processing step. ? Image taken from www.physioweb.org ? My code samples are written in C#. C# sports typed function pointers called delegates. Action is such a function pointer type matching functions with signature void someName(T t). Other languages provide similar ways to work with functions as first class citizens - even Java now in version 8. I trust you find a way to map this detail of my translation to your favorite programming language. I know it works for Java, C++, Ruby, JavaScript, Python, Go. And if you´re using a Functional Programming language it´s of course a no brainer. ? Taken from his blog post “The Craftsman 62, The Dark Path”. ?

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• Going Paperless

  - by Jesse

  One year ago I came to work for a company where the entire development team is 100% “remote”; we’re spread over 3 time zones and each of us works from home. This seems to be an increasingly popular way for people to work and there are many articles and blog posts out there enumerating the advantages and disadvantages of working this way. I had read a lot about telecommuting before accepting this job and felt as if I had a pretty decent idea of what I was getting into, but I’ve encountered a few things over the past year that I did not expect. Among the most surprising by-products of working from home for me has been a dramatic reduction in the amount of paper that I use on a weekly basis. Hoarding In The Workplace Prior to my current telecommute job I worked in what most would consider pretty traditional office environments. I sat in cubicles furnished with an enormous plastic(ish) modular desks, had a mediocre (at best) PC workstation, and had ready access to a seemingly endless supply of legal pads, pens, staplers and paper clips. The ready access to paper, countless conference room meetings, and abundance of available surface area on my desk and in drawers created a perfect storm for wasting paper. I brought a pad of paper with me to every meeting I ever attended, scrawled some brief notes, and then tore that sheet off to keep next to my keyboard to follow up on any needed action items. Once my immediate need for the notes was fulfilled, that sheet would get shuffled off into a corner of my desk or filed away in a drawer “just in case”. I would guess that for all of the notes that I ever filed away, I might have actually had to dig up and refer to 2% of them (and that’s probably being very generous). That said, on those rare occasions that I did have to dig something up from old notes, it was usually pretty important and I ended up being very glad that I saved them. It was only when I would leave a job or move desks that I would finally gather all those notes together and take them to shredding bin to be disposed of. When I left my last job the amount of paper I had accumulated over my three years there was absurd, and I knew coworkers who had substance-abuse caliber paper wasting addictions that made my bad habit look like nail-biting in comparison. A Product Of My Environment I always hated using all of this paper, but simply couldn’t bring myself to stop. It would look bad if I showed up to an important conference room meeting without a pad of paper. What if someone said something profound! Plus, everyone else always brought paper with them. If you saw someone walking down the hallway with a pad of paper in hand you knew they must be on their way to a conference room meeting. Some people even had fancy looking portfolio notebook sheaths that gave their legal pads all the prestige of a briefcase. No one ever worried about running out of fresh paper because there was an endless supply, and there certainly was no shortage of places to store and file used paper. In short, the traditional office was setup for using tons and tons of paper; it’s baked into the culture there. For that reason, it didn’t take long for me to kick the paper habit once I started working from home. In my home office, desk and drawer space are at a premium. I don’t have the budget (or the tolerance) for huge modular office furniture in my spare bedroom. I also no longer have access to a bottomless pit of office supplies stock piled in cabinets and closets. If I want to use some paper, I have to go out and buy it. Finally (and most importantly), all of the meetings that I have to attend these days are “virtual”. We use instant messaging, VOIP, video conferencing, and e-mail to communicate with each other. All I need to take notes during a meeting is my computer, which I happen to be sitting right in front of all day. I don’t have any hard numbers for this, but my gut feeling is that I actually take a lot more notes now than I ever did when I worked in an office. The big difference is I don’t have to use any paper to do so. This makes it far easier to keep important information safe and organized. The Right Tool For The Job When I first started working from home I tried to find a single application that would fill the gap left by the pen and paper that I always had at my desk when I worked in an office. Well, there are no silver bullets and I’ve evolved my approach over time to try and find the best tool for the job at hand. Here’s a quick summary of how I take notes and keep everything organized. Notepad++ – This is the first application I turn to when I feel like there’s some bit of information that I need to write down and save. I use Launchy, so opening Notepad++ and creating a new file only takes a few keystrokes. If I find that the information I’m trying to get down requires a more sophisticated application I escalate as needed. The Desktop – By default, I save every file or other bit of information to the desktop. Anyone who has ever had to fix their parents computer before knows that this is a dangerous game (any file my mother has ever worked on is saved directly to the desktop and rarely moves anywhere else). I agree that storing things on the desktop isn’t a great long term approach to keeping organized, which is why I treat my desktop a bit like my e-mail inbox. I strive to keep both empty (or as close to empty as I possibly can). If something is on my desktop, it means that it’s something relevant to a task or project that I’m currently working on. About once a week I take things that I’m not longer working on and put them into my ‘Notes’ folder. The ‘Notes’ Folder – As I work on a task, I tend to accumulate multiple files associated with that task. For example, I might have a bit of SQL that I’m working on to gather data for a new report, a quick C# method that I came up with but am not yet ready to commit to source control, a bulleted list of to-do items in a .txt file, etc. If the desktop starts to get too cluttered, I create a new sub-folder in my ‘Notes’ folder. Each sub-folder’s name is the current date followed by a brief description of the task or project. Then all files related to that task or project go into that sub folder. By using the date as the first part of the folder name, these folders are automatically sorted in reverse chronological order. This means that things I worked on recently will generally be near the top of the list. Using the built-in Windows search functionality I now have a pretty quick and easy way to try and find something that I worked on a week ago or six months ago. Dropbox – Dropbox is a free service that lets you store up to 2GB of files “in the cloud” and have those files synced to all of the different computers that you use. My ‘Notes’ folder lives in Dropbox, meaning that it’s contents are constantly backed up and are always available to me regardless of which computer I’m using. They also have a pretty decent iPhone application that lets you browse and view all of the files that you have stored there. The free 2GB edition is probably enough for just storing notes, but I also pay $99/year for the 50GB storage upgrade and keep all of my music, e-books, pictures, and documents in Dropbox. It’s a fantastic service and I highly recommend it. Evernote – I use Evernote mostly to organize information that I access on a fairly regular basis. For example, my Evernote account has a running grocery shopping list, recipes that my wife and I use a lot, and contact information for people I contact infrequently enough that I don’t want to keep them in my phone. I know some people that keep nearly everything in Evernote, but there’s something about it that I find a bit clunky, so I tend to use it sparingly. Google Tasks – One of my biggest paper wasting habits was keeping a running task-list next to my computer at work. Every morning I would sit down, look at my task list, cross off what was done and add new tasks that I thought of during my morning commute. This usually resulted in having to re-copy the task list onto a fresh sheet of paper when I was done. I still keep a running task list at my desk, but I’ve started using Google Tasks instead. This is a dead-simple web-based application for quickly adding, deleting, and organizing tasks in a simple checklist style. You can quickly move tasks up and down on the list (which I use for prioritizing), and even create sub-tasks for breaking down larger tasks into smaller pieces. Balsamiq Mockups – This is a simple and lightweight tool for creating drawings of user interfaces. It’s great for sketching out a new feature, brainstorm the layout of a interface, or even draw up a quick sequence diagram. I’m terrible at drawing, so Balsamiq Mockups not only lets me create sketches that other people can actually understand, but it’s also handy because you can upload a sketch to a common location for other team members to access. I can honestly say that using these tools (and having limited resources at home) have lead me to cut my paper usage down to virtually none. If I ever were to return to a traditional office workplace (hopefully never!) I’d try to employ as many of these applications and techniques as I could to keep paper usage low. I feel far less cluttered and far better organized now.

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• Extending Oracle CEP with Predictive Analytics

  - by vikram.shukla(at)oracle.com

  Introduction: OCEP is often used as a business rules engine to execute a set of business logic rules via CQL statements, and take decisions based on the outcome of those rules. There are times where configuring rules manually is sufficient because an application needs to deal with only a small and well-defined set of static rules. However, in many situations customers don't want to pre-define such rules for two reasons. First, they are dealing with events with lots of columns and manually crafting such rules for each column or a set of columns and combinations thereof is almost impossible. Second, they are content with probabilistic outcomes and do not care about 100% precision. The former is the case when a user is dealing with data with high dimensionality, the latter when an application can live with "false" positives as they can be discarded after further inspection, say by a Human Task component in a Business Process Management software. The primary goal of this blog post is to show how this can be achieved by combining OCEP with Oracle Data Mining® and leveraging the latter's rich set of algorithms and functionality to do predictive analytics in real time on streaming events. The secondary goal of this post is also to show how OCEP can be extended to invoke any arbitrary external computation in an RDBMS from within CEP. The extensible facility is known as the JDBC cartridge. The rest of the post describes the steps required to achieve this: We use the dataset available at http://blogs.oracle.com/datamining/2010/01/fraud_and_anomaly_detection_made_simple.html to showcase the capabilities. We use it to show how transaction anomalies or fraud can be detected. Building the model: Follow the self-explanatory steps described at the above URL to build the model.  It is very simple - it uses built-in Oracle Data Mining PL/SQL packages to cleanse, normalize and build the model out of the dataset.  You can also use graphical Oracle Data Miner®  to build the models. To summarize, it involves: Specifying which algorithms to use. In this case we use Support Vector Machines as we're trying to find anomalies in highly dimensional dataset.Build model on the data in the table for the algorithms specified. For this example, the table was populated in the scott/tiger schema with appropriate privileges. Configuring the Data Source: This is the first step in building CEP application using such an integration.  Our datasource looks as follows in the server config file.  It is advisable that you use the Visualizer to add it to the running server dynamically, rather than manually edit the file.    <data-source>         <name>DataMining</name>         <data-source-params>             <jndi-names>                 <element>DataMining</element>             </jndi-names>             <global-transactions-protocol>OnePhaseCommit</global-transactions-protocol>         </data-source-params>         <connection-pool-params>             <credential-mapping-enabled></credential-mapping-enabled>             <test-table-name>SQL SELECT 1 from DUAL</test-table-name>             <initial-capacity>1</initial-capacity>             <max-capacity>15</max-capacity>             <capacity-increment>1</capacity-increment>         </connection-pool-params>         <driver-params>             <use-xa-data-source-interface>true</use-xa-data-source-interface>             <driver-name>oracle.jdbc.OracleDriver</driver-name>             <url>jdbc:oracle:thin:@localhost:1522:orcl</url>             <properties>                 <element>                     <value>scott</value>                     <name>user</name>                 </element>                 <element>                     <value>{Salted-3DES}AzFE5dDbO2g=</value>                     <name>password</name>                 </element>                                 <element>                     <name>com.bea.core.datasource.serviceName</name>                     <value>oracle11.2g</value>                 </element>                 <element>                     <name>com.bea.core.datasource.serviceVersion</name>                     <value>11.2.0</value>                 </element>                 <element>                     <name>com.bea.core.datasource.serviceObjectClass</name>                     <value>java.sql.Driver</value>                 </element>             </properties>         </driver-params>     </data-source>   Designing the EPN: The EPN is very simple in this example. We briefly describe each of the components. The adapter ("DataMiningAdapter") reads data from a .csv file and sends it to the CQL processor downstream. The event payload here is same as that of the table in the database (refer to the attached project or do a "desc table-name" from a SQL*PLUS prompt). While this is for convenience in this example, it need not be the case. One can still omit fields in the streaming events, and need not match all columns in the table on which the model was built. Better yet, it does not even need to have the same name as columns in the table, as long as you alias them in the USING clause of the mining function. (Caveat: they still need to draw values from a similar universe or domain, otherwise it constitutes incorrect usage of the model). There are two things in the CQL processor ("DataMiningProc") that make scoring possible on streaming events. 1.      User defined cartridge function Please refer to the OCEP CQL reference manual to find more details about how to define such functions. We include the function below in its entirety for illustration. <?xml version="1.0" encoding="UTF-8"?> <jdbcctxconfig:config     xmlns:jdbcctxconfig="http://www.bea.com/ns/wlevs/config/application"     xmlns:jc="http://www.oracle.com/ns/ocep/config/jdbc">        <jc:jdbc-ctx>         <name>Oracle11gR2</name>         <data-source>DataMining</data-source>               <function name="prediction2">                                 <param name="CQLMONTH" type="char"/>                      <param name="WEEKOFMONTH" type="int"/>                      <param name="DAYOFWEEK" type="char" />                      <param name="MAKE" type="char" />                      <param name="ACCIDENTAREA"   type="char" />                      <param name="DAYOFWEEKCLAIMED"  type="char" />                      <param name="MONTHCLAIMED" type="char" />                      <param name="WEEKOFMONTHCLAIMED" type="int" />                      <param name="SEX" type="char" />                      <param name="MARITALSTATUS"   type="char" />                      <param name="AGE" type="int" />                      <param name="FAULT" type="char" />                      <param name="POLICYTYPE"   type="char" />                      <param name="VEHICLECATEGORY"  type="char" />                      <param name="VEHICLEPRICE" type="char" />                      <param name="FRAUDFOUND" type="int" />                      <param name="POLICYNUMBER" type="int" />                      <param name="REPNUMBER" type="int" />                      <param name="DEDUCTIBLE"   type="int" />                      <param name="DRIVERRATING"  type="int" />                      <param name="DAYSPOLICYACCIDENT"   type="char" />                      <param name="DAYSPOLICYCLAIM" type="char" />                      <param name="PASTNUMOFCLAIMS" type="char" />                      <param name="AGEOFVEHICLES" type="char" />                      <param name="AGEOFPOLICYHOLDER" type="char" />                      <param name="POLICEREPORTFILED" type="char" />                      <param name="WITNESSPRESNT" type="char" />                      <param name="AGENTTYPE" type="char" />                      <param name="NUMOFSUPP" type="char" />                      <param name="ADDRCHGCLAIM"   type="char" />                      <param name="NUMOFCARS" type="char" />                      <param name="CQLYEAR" type="int" />                      <param name="BASEPOLICY" type="char" />                                     <return-component-type>char</return-component-type>                                                      <sql><![CDATA[             SELECT to_char(PREDICTION_PROBABILITY(CLAIMSMODEL, '0' USING *))               AS probability             FROM (SELECT  :CQLMONTH AS MONTH,                                            :WEEKOFMONTH AS WEEKOFMONTH,                          :DAYOFWEEK AS DAYOFWEEK,                           :MAKE AS MAKE,                           :ACCIDENTAREA AS ACCIDENTAREA,                           :DAYOFWEEKCLAIMED AS DAYOFWEEKCLAIMED,                           :MONTHCLAIMED AS MONTHCLAIMED,                           :WEEKOFMONTHCLAIMED,                             :SEX AS SEX,                           :MARITALSTATUS AS MARITALSTATUS,                            :AGE AS AGE,                           :FAULT AS FAULT,                           :POLICYTYPE AS POLICYTYPE,                            :VEHICLECATEGORY AS VEHICLECATEGORY,                           :VEHICLEPRICE AS VEHICLEPRICE,                           :FRAUDFOUND AS FRAUDFOUND,                           :POLICYNUMBER AS POLICYNUMBER,                           :REPNUMBER AS REPNUMBER,                           :DEDUCTIBLE AS DEDUCTIBLE,                            :DRIVERRATING AS DRIVERRATING,                           :DAYSPOLICYACCIDENT AS DAYSPOLICYACCIDENT,                            :DAYSPOLICYCLAIM AS DAYSPOLICYCLAIM,                           :PASTNUMOFCLAIMS AS PASTNUMOFCLAIMS,                           :AGEOFVEHICLES AS AGEOFVEHICLES,                           :AGEOFPOLICYHOLDER AS AGEOFPOLICYHOLDER,                           :POLICEREPORTFILED AS POLICEREPORTFILED,                           :WITNESSPRESNT AS WITNESSPRESENT,                           :AGENTTYPE AS AGENTTYPE,                           :NUMOFSUPP AS NUMOFSUPP,                           :ADDRCHGCLAIM AS ADDRCHGCLAIM,                            :NUMOFCARS AS NUMOFCARS,                           :CQLYEAR AS YEAR,                           :BASEPOLICY AS BASEPOLICY                 FROM dual)                 ]]>         </sql>        </function>     </jc:jdbc-ctx> </jdbcctxconfig:config> 2.      Invoking the function for each event. Once this function is defined, you can invoke it from CQL as follows: <?xml version="1.0" encoding="UTF-8"?> <wlevs:config xmlns:wlevs="http://www.bea.com/ns/wlevs/config/application">   <processor>     <name>DataMiningProc</name>     <rules>        <query id="q1"><![CDATA[                     ISTREAM(SELECT S.CQLMONTH,                                   S.WEEKOFMONTH,                                   S.DAYOFWEEK, S.MAKE,                                   :                                         S.BASEPOLICY,                                    C.F AS probability                                                 FROM                                 StreamDataChannel [NOW] AS S,                                 TABLE(prediction2@Oracle11gR2(S.CQLMONTH,                                      S.WEEKOFMONTH,                                      S.DAYOFWEEK,                                       S.MAKE, ...,                                      S.BASEPOLICY) AS F of char) AS C)                       ]]></query>                 </rules>               </processor>           </wlevs:config>   Finally, the last stage in the EPN prints out the probability of the event being an anomaly. One can also define a threshold in CQL to filter out events that are normal, i.e., below a certain mark as defined by the analyst or designer. Sample Runs: Now let's see how this behaves when events are streamed through CEP. We use only two events for brevity, one normal and other one not. This is one of the "normal" looking events and the probability of it being anomalous is less than 60%. Event is: eventType=DataMiningOutEvent object=q1  time=2904821976256 S.CQLMONTH=Dec, S.WEEKOFMONTH=5, S.DAYOFWEEK=Wednesday, S.MAKE=Honda, S.ACCIDENTAREA=Urban, S.DAYOFWEEKCLAIMED=Tuesday, S.MONTHCLAIMED=Jan, S.WEEKOFMONTHCLAIMED=1, S.SEX=Female, S.MARITALSTATUS=Single, S.AGE=21, S.FAULT=Policy Holder, S.POLICYTYPE=Sport - Liability, S.VEHICLECATEGORY=Sport, S.VEHICLEPRICE=more than 69000, S.FRAUDFOUND=0, S.POLICYNUMBER=1, S.REPNUMBER=12, S.DEDUCTIBLE=300, S.DRIVERRATING=1, S.DAYSPOLICYACCIDENT=more than 30, S.DAYSPOLICYCLAIM=more than 30, S.PASTNUMOFCLAIMS=none, S.AGEOFVEHICLES=3 years, S.AGEOFPOLICYHOLDER=26 to 30, S.POLICEREPORTFILED=No, S.WITNESSPRESENT=No, S.AGENTTYPE=External, S.NUMOFSUPP=none, S.ADDRCHGCLAIM=1 year, S.NUMOFCARS=3 to 4, S.CQLYEAR=1994, S.BASEPOLICY=Liability, probability=.58931702982118561 isTotalOrderGuarantee=true\nAnamoly probability: .58931702982118561 However, the following event is scored as an anomaly with a very high probability of  89%. So there is likely to be something wrong with it. A close look reveals that the value of "deductible" field (10000) is not "normal". What exactly constitutes normal here?. If you run the query on the database to find ALL distinct values for the "deductible" field, it returns the following set: {300, 400, 500, 700} Event is: eventType=DataMiningOutEvent object=q1  time=2598483773496 S.CQLMONTH=Dec, S.WEEKOFMONTH=5, S.DAYOFWEEK=Wednesday, S.MAKE=Honda, S.ACCIDENTAREA=Urban, S.DAYOFWEEKCLAIMED=Tuesday, S.MONTHCLAIMED=Jan, S.WEEKOFMONTHCLAIMED=1, S.SEX=Female, S.MARITALSTATUS=Single, S.AGE=21, S.FAULT=Policy Holder, S.POLICYTYPE=Sport - Liability, S.VEHICLECATEGORY=Sport, S.VEHICLEPRICE=more than 69000, S.FRAUDFOUND=0, S.POLICYNUMBER=1, S.REPNUMBER=12, S.DEDUCTIBLE=10000, S.DRIVERRATING=1, S.DAYSPOLICYACCIDENT=more than 30, S.DAYSPOLICYCLAIM=more than 30, S.PASTNUMOFCLAIMS=none, S.AGEOFVEHICLES=3 years, S.AGEOFPOLICYHOLDER=26 to 30, S.POLICEREPORTFILED=No, S.WITNESSPRESENT=No, S.AGENTTYPE=External, S.NUMOFSUPP=none, S.ADDRCHGCLAIM=1 year, S.NUMOFCARS=3 to 4, S.CQLYEAR=1994, S.BASEPOLICY=Liability, probability=.89171554529576691 isTotalOrderGuarantee=true\nAnamoly probability: .89171554529576691 Conclusion: By way of this example, we show: real-time scoring of events as they flow through CEP leveraging Oracle Data Mining.how CEP applications can invoke complex arbitrary external computations (function shipping) in an RDBMS.

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• E.T. Phone "Home" - Hey I've discovered a leak..!

  - by Martin Deh

  Being a member of the WebCenter ATEAM, we are often asked to performance tune a WebCenter custom portal application or a WebCenter Spaces deployment.  Most of the time, the process is pretty much the same.  For example, we often use tools like httpWatch and FireBug to monitor the application, and then perform load tests using JMeter or Selenium.  In addition, there are the fine tuning of the different performance based tuning parameters that are outlined in the documentation and by blogs that have been written by my fellow ATEAMers (click on the "performance" tag in this ATEAM blog).  While performing the load test where the outcome produces a significant reduction in the systems resources (memory), one of the causes that plays a role in memory "leakage" is due to the implementation of the navigation menu UI.  OOTB in both JDeveloper and WebCenter Spaces, there are sample (page) templates that include a "default" navigation menu.  In WebCenter Spaces, this is through the SpacesNavigationModel taskflow region, and in a custom portal (i.e. pageTemplate_globe.jspx) the menu UI is contructed using standard ADF components.  These sample menu UI's basically enable the underlying navigation model to visualize itself to some extent.  However, due to certain limitations of these sample menu implementations (i.e. deeper sub-level of navigations items, look-n-feel, .etc), many customers have developed their own custom navigation menus using a combination of HTML, CSS and JQuery.  While this is supported somewhat by the framework, it is important to know what are some of the best practices in ensuring that the navigation menu does not leak.  In addition, in this blog I will point out a leak (BUG) that is in the sample templates.  OK, E.T. the suspence is killing me, what is this leak? Note: for those who don't know, info on E.T. can be found here In both of the included templates, the example given for handling the navigation back to the "Home" page, will essentially provide a nice little memory leak every time the link is clicked. Let's take a look a simple example, which uses the default template in Spaces. The outlined section below is the "link", which is used to enable a user to navigation back quickly to the Group Space Home page. When you (mouse) hover over the link, the browser displays the target URL. From looking initially at the proposed URL, this is the intended destination.  Note: "home" in this case is the navigation model reference (id), that enables the display of the "pretty URL". Next, notice the current URL, which is displayed in the browser.  Remember, that PortalSiteHome = home.  The other highlighted item adf.ctrl-state, is very important to the framework.  This item is basically a persistent query parameter, which is used by the (ADF) framework to managing the current session and page instance.  Without this parameter present, among other things, the browser back-button navigation will fail.  In this example, the value for this parameter is currently 95K25i7dd_4.  Next, through the navigation menu item, I will click on the Page2 link. Inspecting the URL again, I can see that it reports that indeed the navigation is successful and the adf.ctrl-state is also in the URL.  For those that are wondering why the URL displays Page3.jspx, instead of Page2.jspx. Basically the (file) naming convention for pages created ar runtime in Spaces start at Page1, and then increment as you create additional pages.  The name of the actual link (i.e. Page2) is the page "title" attribute.  So the moral of the story is, unlike design time created pages, run time created pages the name of the file will 99% never match the name that appears in the link. Next, is to click on the quick link for navigating back to the Home page. Quick investigation yields that the navigation was indeed successful.  In the browser's URL there is a home (pretty URL) reference, and there is also a reference to the adf.ctrl-state parameter.  So what's the issue?  Can you remember what the value was for the adf.ctrl-state?  The current value is 3D95k25i7dd_149.  However, the previous value was 95k25i7dd_4.  Here is what happened.  Remember when (mouse) hovering over the link produced the following target URL: http://localhost:8888/webcenter/spaces/NavigationTest/home This is great for the browser as this URL will navigate to the intended targer.  However, what is missing is the adf.ctrl-state parameter.  Since this parameter was not present upon navigation "within" the framework, the ADF framework produced another adf.ctrl-state (object).  The previous adf.ctrl-state basically is orphaned while continuing to be alive in memory.  Note: the auto-creation of the adf.ctrl state does happen initially when you invoke the Spaces application  for the first time.  The following is the line of code which produced the issue: <af:goLink destination="#{boilerBean.globalLogoURIInSpace} ... Here the boilerBean is responsible for returning the "string" url, which in this case is /spaces/NavigationTest/home. Unfortunately, again what is missing is adf.ctrl-state. Note: there are more than one instance of the goLinks in the sample templates. So E.T. how can I correct this? There are 2 simple fixes.  For the goLink's destination, use the navigation model to return the actually "node" value, then use the goLinkPrettyUrl method to add the current adf.ctrl-state: <af:goLink destination="#{navigationContext.defaultNavigationModel.node['home'].goLinkPrettyUrl}"} ... />  Note: the node value is the [navigation model id]  Using a goLink does solve the main issue.  However, since the link basically does a redirect, some browsers like IE will produce a somewhat significant "flash".  In a Spaces application, this may be an annoyance to the users.  Another way to solve the leakage problem, and also remove the flash between navigations is to use a af:commandLink.  For example, here is the code example for this scenario: <af:commandLink id="pt_cl2asf" actionListener="#{navigationContext.processAction}" action="pprnav">    <f:attribute name="node" value="#{navigationContext.defaultNavigationModel.node['home']}"/> </af:commandLink> Here, the navigation node to where home is located is delivered by way of the attribute to the commandLink.  The actual navigation is performed by the processAction, which is needing the "node" value. E.T. OK, you solved the OOTB sample BUG, what about my custom navigation code? I have seen many implementations of creating a navigation menu through custom code.  In addition, there are some blog sites that also give detailed examples.  The majority of these implementations are very similar.  The code usually involves using standard HTML tags (i.e. DIVS, UL, LI, .,etc) and either CSS or JavaScript (JQuery) to produce the flyout/drop-down effect.  The navigation links in these cases are standard <a href... > tags.  Although, this type of approach is not fully accepted by the ADF community, it does work.  The important thing to note here is that the <a> tag value must use the goLinkPrettyURL method of contructing the target URL.  For example: <a href="${contextRoot}${menu.goLinkPrettyUrl}"> The main reason why this type of approach is popular is that links that are created this way (also with using af:goLinks), the pages become crawlable by search engines.  CommandLinks are currently not search friendly.  However, in the case of a Spaces instance this may be acceptable.  So in this use-case, af:commandLinks, which would replace the <a>  (or goLink) tags. The example code given of the af:commandLink above is still valid. One last important item.  If you choose to use af:commandLinks, special attention must be given to the scenario in which java script has been used to produce the flyout effect in the custom menu UI.  In many cases that I have seen, the commandLink can only be invoked once, since there is a conflict between the custom java script with the ADF frameworks own scripting to control the view.  The recommendation here, would be to use a pure CSS approach to acheive the dropdown effects. One very important thing to note.  Due to another BUG, the WebCenter environement must be patched to BP3 (patch  p14076906).  Otherwise the leak is still present using the goLinkPrettyUrl method.  Thanks E.T.!  Now I can phone home and not worry about my application running out of resources due to my custom navigation! 

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• Introducing Oracle Multitenant

  - by OracleMultitenant

  0 0 1 1142 6510 Oracle Corporation 54 15 7637 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-fareast-language:JA;} The First Database Designed for the Cloud Today Oracle announced the general availability (GA) of Oracle Database 12c, the first database designed for the Cloud. Oracle Multitenant, new with Oracle Database 12c, is a key component of this – a new architecture for consolidating databases and simplifying operations in the Cloud. With this, the inaugural post in the Multitenant blog, my goal is to start the conversation about Oracle Multitenant. We are very proud of this new architecture, which we view as a major advance for Oracle. Customers, partners and analysts who have had previews are very excited about its capabilities and its flexibility. This high level review of Oracle Multitenant will touch on our design considerations and how we re-architected our database for the cloud. I’ll briefly describe our new multitenant architecture and explain it’s key benefits. Finally I’ll mention some of the major use cases we see for Oracle Multitenant. Industry Trends We always start by talking to our customers about the pressures and challenges they’re facing and what trends they’re seeing in the industry. Some things don’t change. They face the same pressures and the same requirements as ever: Pressure to do more with less; be faster, leaner, cheaper, and deliver services 24/7. Big companies have achieved scale. Now they want to realize economies of scale. As ever, DBAs are faced with the challenges of patching and upgrading large numbers of databases, and provisioning new ones.  Requirements are familiar: Performance, scalability, reliability and high availability are non-negotiable. They need ever more security in this threatening climate. There’s no time to stop and retool with new applications. What’s new are the trends. These are the techniques to use to respond to these pressures within the constraints of the requirements. With the advent of cloud computing and availability of massively powerful servers – even engineered systems such as Exadata – our customers want to consolidate many applications into fewer larger servers. There’s a move to standardized services – even self-service. Consolidation Consolidation is not new; companies have tried various different approaches to consolidation of databases in the cloud. One approach is to partition a powerful server between several virtual machines, one per application. A downside of this is that you have the resource and management overheads of OS and RDBMS per VM – that is, per application. Another is that you have replaced physical sprawl with virtual sprawl and virtual sprawl is still expensive to manage. In the dedicated database model, we have a single physical server supporting multiple databases, one per application. So there’s a shared OS overhead, but RDBMS process and memory overhead are replicated per application. Let's think about our traditional Oracle Database architecture. Every time we create a database, be it a production database, a development or a test database, what do we do? We create a set of files, we allocate a bunch of memory for managing the data, and we kick off a series of background processes. This is replicated for every one of the databases that we create. As more and more databases are fired up, these replicated overheads quickly consume the available server resources and this limits the number of applications we can run on any given server. In Oracle Database 11g and earlier the highest degree of consolidation could be achieved by what we call schema consolidation. In this model we have one big server with one big database. Individual applications are installed in separate schemas or table-owners. Database overheads are shared between all applications, which affords maximum consolidation. The shortcomings are that application changes are often required. There is no tenant isolation. One bad apple can spoil the whole batch. New Architecture & Benefits In Oracle Database 12c, we have a new multitenant architecture, featuring pluggable databases. This delivers all the resource utilization advantages of schema consolidation with none of the downsides. There are two parts to the term “pluggable database”: "pluggable", which is new, and "database", which is familiar.  Before we get to the exciting new stuff let’s discuss what hasn’t changed. A pluggable database is a fully functional Oracle database. It’s not watered down in any way. From the perspective of an application or an end user it hasn’t changed at all. This is very important because it means that no application changes are required to adopt this new architecture. There are many thousands of applications built on Oracle databases and they are all ready to run on Oracle Multitenant. So we have these self-contained pluggable databases (PDBs), and as their name suggests, they are plugged into a multitenant container database (CDB). The CDB behaves as a single database from the operations point of view. Very much as we had with the schema consolidation model, we only have a single set of Oracle background processes and a single, shared database memory requirement. This gives us very high consolidation density, which affords maximum reduction in capital expenses (CapEx). By performing management operations at the CDB level – “managing many as one” – we can achieve great reductions in operating expenses (OpEx) as well, but we retain granular control where appropriate. Furthermore, the “pluggability” capability gives us portability and this adds a tremendous amount of agility. We can simply unplug a PDB from one CDB and plug it into another CDB, for example to move it from one SLA tier to another. I'll explore all these new capabilities in much more detail in a future posting.  Use Cases We can identify a number of use cases for Oracle Multitenant. Here are a few of the major ones. 0 0 1 113 650 Oracle Corporation 5 1 762 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-fareast-language:JA;} Development / Testing where individual engineers need rapid provisioning and recycling of private copies of a few "master test databases" Consolidation of disparate applications using fewer, more powerful servers Software as a Service deploying separate copies of identical applications to individual tenants Database as a Service typically self-service provisioning of databases on the private cloud Application Distribution from ISV / Installation by Customer Eliminating many typical installation steps (create schema, import seed data, import application code PL/SQL…) - just plug in a PDB! High volume data distribution literally via disk drives in envelopes distributed by truck! - distribution of things like GIS or MDM master databases …various others! Benefits Previous approaches to consolidation have involved a trade-off between reductions in Capital Expenses (CapEx) and Operating Expenses (OpEx), and they’ve usually come at the expense of agility. With Oracle Multitenant you can have your cake and eat it: Minimize CapEx More Applications per server Minimize OpEx Manage many as one Standardized procedures and services Rapid provisioning Maximize Agility Cloning for development and testing Portability through pluggability Scalability with RAC Ease of Adoption Applications run unchanged It’s a pure deployment choice. Neither the database backend nor the application needs to be changed. In future postings I’ll explore various aspects in more detail. However, if you feel compelled to devour everything you can about Oracle Multitenant this very minute, have no fear. Visit the Multitenant page on OTN and explore the various resources we have available there. Among these, Oracle Distinguished Product Manager Bryn Llewellyn has written an excellent, thorough, and exhaustively detailed White Paper about Oracle Multitenant, which is available here.  Follow me  I tweet @OraclePDB #OracleMultitenant

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• CodePlex Daily Summary for Tuesday, May 31, 2011

  CodePlex Daily Summary for Tuesday, May 31, 2011Popular ReleasesNearforums - ASP.NET MVC forum engine: Nearforums v6.0: Version 6.0 of Nearforums, the ASP.NET MVC Forum Engine, containing new features: Authentication using Membership Provider for SQL Server and MySql Spam prevention: Flood Control Moderation: Flag messages Content management: Pages: Create pages (about us/contact/texts) through web administration Allow nearforums to run as an IIS subapp Migrated Facebook Connect to OAuth 2.0 Visit the project Roadmap for more details.NetOffice - The easiest way to use Office in .NET: NetOffice Release 0.8b: Changes: - fix critical issue 15922(AccessViolationException) once and for all update is strongly recommended Includes: - Runtime Binaries and Source Code for .NET Framework:......v2.0, v3.0, v3.5, v4.0 - Tutorials in C# and VB.Net:..............................................................COM Proxy Management, Events, etc. - Examples in C# and VB.Net:............................................................Excel, Word, Outlook, PowerPoint, Access - COMAddin Examples in C# and VB....Facebook Graph Toolkit: Facebook Graph Toolkit 1.5.4186: Updates the API in response to Facebook's recent change of policy: All Graph Api accessing feeds or posts must provide a AccessToken.SharePoint Farm Poster: SharePoint Farm Poster: SharePoint Farm Poster is generated by a PowerShell Script. Run this script under the Farm Admin Account. After downloading, unblock the file in the Property Window. Current version is beta : v0.3.0VCC: Latest build, v2.1.40530.0: Automatic drop of latest buildServiio for Windows Home Server: Beta Release 0.5.2.0: Ready for widespread beta. Synchronized build number to Serviio version to avoid confusion.AcDown????? - Anime&Comic Downloader: AcDown????? v3.0 Beta4: ??AcDown?????????????，??????????????，????、????。?????Acfun????? ????32??64? Windows XP/Vista/7 ????????????? ??：????????Windows XP???,?????????.NET Framework 2.0???(x86)?.NET Framework 2.0???(x64)，?????"?????????"??? ??v3.0 Beta4 2011-5-31?? ???Bilibili.us????? ???? ?? ???"????" ???Bilibili.us??? ??????? ?? ??????? ?? ???????? ?? ?? ???Bilibili.us?????(??????????????????) ??????(6.cn)?????(????) ?? ?????Acfun?????????? ?????????????? ???QQ???????? ????????????Discussion...Terraria Map Generator: TerrariaMapTool 1.0.0.2 Beta: Version 1.0.0.2 Beta Release - Now has a Gui - Draws backgrounds (May still not be exact) - Hopefully fixed support on DirectX 9 machine.CodeCopy Auto Code Converter: Code Copy v0.1: Full add-in, setup project source code and setup fileEnhSim: EnhSim 2.4.5 ALPHA: 2.4.5 ALPHAThis release supports WoW patch 4.1 at level 85 To use this release, you must have the Microsoft Visual C++ 2010 Redistributable Package installed. This can be downloaded from http://www.microsoft.com/downloads/en/details.aspx?FamilyID=A7B7A05E-6DE6-4D3A-A423-37BF0912DB84 To use the GUI you must have the .NET 4.0 Framework installed. This can be downloaded from http://www.microsoft.com/downloads/en/details.aspx?FamilyID=9cfb2d51-5ff4-4491-b0e5-b386f32c0992 - Added in the T12 s...TerrariViewer: TerrariViewer v2.4.1: Added Piggy Bank editor and fixed some minor bugs.Kooboo CMS: Kooboo CMS 3.02: What is new in kooboo cms 3.02 The most important updates of this version is the Kooboo site builder, an unique and creative web design tool, design an professional website and export to Kooboo CMS. See: http://www.sitekin.com Add Version contorl on View, Layout and other elements. Add user CMS language selection, user can select a language to use on their CMS backend. Add User profile provider, you can use now stop website user information on a SQL database. Previously it stored on XML...mojoPortal: 2.3.6.6: see release notes on mojoportal.com http://www.mojoportal.com/mojoportal-2366-released Note that we have separate deployment packages for .NET 3.5 and .NET 4.0 The deployment package downloads on this page are pre-compiled and ready for production deployment, they contain no C# source code. To download the source code see the Source Code Tab I recommend getting the latest source code using TortoiseHG, you can get the source code corresponding to this release here.Terraria World Creator: Terraria World Creator: Version 1.01 Fixed a bug that would cause the application to crash. Re-named the Application.VidCoder: 0.9.0: New startup UI for one-click scanning of discs or opening a file/folder. New seek bar on the preview window to make switching previews easier (you can click anywhere on the bar). Added gradient backgrounds to the main window to visually group the sections. Added Open Video File and Open Video Folder options to the File menu. Moved preview button to be in line with the other control buttons. Fixed settings getting in a weird state if they were saved without an output folder being chos...General Media Access WebService: 0.2.0.0 Beta: Updated GMA release with sorting/ordering mechanisms. Several bug fixes.Microsoft All-In-One Code Framework - a centralized code sample library: All-In-One Code Framework 2011-05-26: Alternatively, you can install Sample Browser or Sample Browser VS extension, and download the code samples from Sample Browser. Improved and Newly Added Examples:For an up-to-date code sample index, please refer to All-In-One Code Framework Sample Catalog. NEW Samples for Dynamics Sample Description Owner CSDynamicsNAVWebServices The code sample shows syntax for calling Dynamics NAV Web Services. Lars Lohndorf-Larsen NEW Samples for WPF Sample Description Owner CSWPFDataGridCustomS...Terraria World Viewer: Version 1.1: Update May 26th Added Chest Filtering, this allows chests only containing certain items to have their symbol drawn. (Its under advanced settings tab) GUI elements (checkboxes/etc) are persistant between uses of the application Beta Worlds (i.e. Release #38) will work properly Symbols can be enabled or disabled on a per symbol basis Chest Information tab which is just a dump of the current chest information Meterorite is now visible as a bright magenta pink Application defaults to ...MVC Controls Toolkit: Mvc Controls Toolkit 1.1 RC: *Added: Compatibility with jQuery 1.6.1 Rendering of enumerables with images and/or customizable strings improved the client side tempate engine added new parameters to the template definition binding all new knockout bindings helpers have been fully implemented added a new overload for defining the client-side ViewModel The SetTme method has the option to store the theme in a permanent cookie If no CSS class is provided for the watermark of a TypedTextBox the watermark class of the current t...patterns & practices: Project Silk: Project Silk - Documentation Only Drop - May 24: To get the latest code, please see the previous drop here. Guidance Chapters Ready for Review The following chapters (provided in CHM or PDF format) are ready for community review. Our team very much appreciates your feedback and technical review. All documentation feedback should be posted in the Issue Tracker; if required, a document can be attached along with the feedback. Architecture jQuery UI Widgets Server-Side Implementation Security Unit Testing Web Applications Widget Q...New Projects#liveDB: liveDB is an in-memory database engine for Microsoft .NET providing full ACID support, lightning fast performance and offering a significant reduction of development and operational costs. liveDB is built on Live Domain Technology(TM).8 hours: 8hours Private studyABox2d: A port of Box 2d game engine doing it has an exercise to study how the game engine work.ADempiere.NET: If I have enough time and support I we will translate this into .NETAlmonaster: Almonaster is a turn-based multi-player war game. It is free for all players and comes with absolutely no warranty. The game is fully web-based and requires no downloads, Javascript, Java or ActiveX controls. ASPone API: ASPone partnerské API (aplikacní programové rozhraní) je rozhraní pro vytvorené a urcené pro partnery spolecnosti ASPone, s.r.o. Pomocí tohoto aplikacního rozhraní mužete zautomatizovat radu úkonu, které by pomocí webového rozhraní mohly být casove nárocnejší nebo vyžadují interakci cloveka. API umožnuje zautomatizovat radu úkonu souvisejících se správou domén, doménových kontaktu, webhostingu, databází, serveru a mnoha dalších. Pro zjednodušení práce s API jsou již pripraveni dva ukázkový...CodeCopy Auto Code Converter: This add-in project converts c# and vb.net codes in visual studio.drms: Data Resource Management SystemDrop Down CheckBoxList control (DropDownCheckBoxes): DropDownCheckBoxes is an ASP.NET server control directly inheriting standard ASP.NET CheckBoxList control and fully it supports parent's API (except members responsible for rendering and styling). Thus in most cases CheckBoxList control can be simply replaced with DropDownCheckBoxes with no need to change any data binding code or event handlers. In normal state the control is displayed as a select (DropDownList) control. Clicking the expand button shows a list with check boxes. When the se...Extended Registration module for Orchard CMS: This project has a dependency on the Contrib.Profile module. With this module enabled, users must fill out any parts you add to the User ContentItem in the Registration page. Ideal if you require additional information from your users.GreenWay: Car navigation softwareHost Profiles: Host Profiles is small tool to control, switch and management the hosts file of the computer. The hosts file is located in "c:\windows\system32\drivers\etc\hosts".HRM System MVVM sample code: This is the sample WPF MVVM application that i've described in my blog posts. I hope to give you a clear view of mvvm and other commonly used patterns.Mi Game Library: Ever wanted to store all the games you own into one place that you, could then later come see and search also with your own personal wish list!Micorrhiza: Micorrhiza is a client-server solution written in C# for voice- and video-communications between users in local and global networks.MPlayer.NET for Windows Forms & WPF: MPlayer.NET is a wrapper around MPlayer executable. It's developed on .NET platform and includes visual controls for both Windows Forms and WPF applications.MyGet - NuGet-as-a-Service: This project is the source for http://myget.org. MyGet offers you the possibility to create your own, private, filtered NuGet feed for use in the Visual Studio Package Manager. It can contain packages from the official NuGet feed as well as your private packages, hosted on MyGet.MZExtensions: A collection of handy C# Extension Methods.NCAds: NCadsNetSync: Universal file synchronization agent.OLE 1C7.7: OLE 1C7.7 ?????????? ??????? ??? ??????? ? 1?7.7 ????????? OLE ??????????.Pear 2.5: Pear 2.5 is a web browser which has MetroUI which is also known for WP7. Pear 2.5's graphics is totally made up with MetroUI and looks stunning when browse. This version has 3 builds - 2 alpha builds and 1 gamma delta (beta) build. It's developed in VB.NET which is the easiest.ProjectOne: ProjectOne is a Open Community Information Sharing Website regarding Realty as its primary source.russomi: russomiSopaco Server Foundation 1.x: The one earlier version of my server infrastructure(SSF, Sopaco Server Foundation 1.x, owned by ??)。 Network Layer Based On MINA， message meta in 1.x is hard coded to 6bytes message header like this struct NetworkMessageHeader { short msgId; int msgLength; } struct NetworkMTray Timer: A simple timer/stopwatch which runs fromt he system tray. I started it as a hobby learning project to understand the Win32 API. Now open sourcing it to get more inputs about the same, and at the same time it may prove helpful to othersVENSOFT DIPERCAX: Proyecto Final del Curso de Proyectos II de la Universidad Privada del NorteWindows Phone Blog Menu: A Silverlight navigation control that looks like a Windows Phone 7. The live tiles are links to websites. Use this control on your blog or website to show your love for WP7. It is a creative way to link to external sites you are interested in.

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• The Benefits of Smart Grid Business Software

  - by Sylvie MacKenzie, PMP

  Smart Grid Background What Are Smart Grids?Smart Grids use computer hardware and software, sensors, controls, and telecommunications equipment and services to: Link customers to information that helps them manage consumption and use electricity wisely. Enable customers to respond to utility notices in ways that help minimize the duration of overloads, bottlenecks, and outages. Provide utilities with information that helps them improve performance and control costs. What Is Driving Smart Grid Development? Environmental ImpactSmart Grid development is picking up speed because of the widespread interest in reducing the negative impact that energy use has on the environment. Smart Grids use technology to drive efficiencies in transmission, distribution, and consumption. As a result, utilities can serve customers’ power needs with fewer generating plants, fewer transmission and distribution assets,and lower overall generation. With the possible exception of wind farm sprawl, landscape preservation is one obvious benefit. And because most generation today results in greenhouse gas emissions, Smart Grids reduce air pollution and the potential for global climate change.Smart Grids also more easily accommodate the technical difficulties of integrating intermittent renewable resources like wind and solar into the grid, providing further greenhouse gas reductions. CostsThe ability to defer the cost of plant and grid expansion is a major benefit to both utilities and customers. Utilities do not need to use as many internal resources for traditional infrastructure project planning and management. Large T&D infrastructure expansion costs are not passed on to customers.Smart Grids will not eliminate capital expansion, of course. Transmission corridors to connect renewable generation with customers will require major near-term expenditures. Additionally, in the future, electricity to satisfy the needs of population growth and additional applications will exceed the capacity reductions available through the Smart Grid. At that point, expansion will resume—but with greater overall T&D efficiency based on demand response, load control, and many other Smart Grid technologies and business processes. Energy efficiency is a second area of Smart Grid cost saving of particular relevance to customers. The timely and detailed information Smart Grids provide encourages customers to limit waste, adopt energy-efficient building codes and standards, and invest in energy efficient appliances. Efficiency may or may not lower customer bills because customer efficiency savings may be offset by higher costs in generation fuels or carbon taxes. It is clear, however, that bills will be lower with efficiency than without it. Utility Operations Smart Grids can serve as the central focus of utility initiatives to improve business processes. Many utilities have long “wish lists” of projects and applications they would like to fund in order to improve customer service or ease staff’s burden of repetitious work, but they have difficulty cost-justifying the changes, especially in the short term. Adding Smart Grid benefits to the cost/benefit analysis frequently tips the scales in favor of the change and can also significantly reduce payback periods.Mobile workforce applications and asset management applications work together to deploy assets and then to maintain, repair, and replace them. Many additional benefits result—for instance, increased productivity and fuel savings from better routing. Similarly, customer portals that provide customers with near-real-time information can also encourage online payments, thus lowering billing costs. Utilities can and should include these cost and service improvements in the list of Smart Grid benefits. What Is Smart Grid Business Software? Smart Grid business software gathers data from a Smart Grid and uses it improve a utility’s business processes. Smart Grid business software also helps utilities provide relevant information to customers who can then use it to reduce their own consumption and improve their environmental profiles. Smart Grid Business Software Minimizes the Impact of Peak Demand Utilities must size their assets to accommodate their highest peak demand. The higher the peak rises above base demand: The more assets a utility must build that are used only for brief periods—an inefficient use of capital. The higher the utility’s risk profile rises given the uncertainties surrounding the time needed for permitting, building, and recouping costs. The higher the costs for utilities to purchase supply, because generators can charge more for contracts and spot supply during high-demand periods. Smart Grids enable a variety of programs that reduce peak demand, including: Time-of-use pricing and critical peak pricing—programs that charge customers more when they consume electricity during peak periods. Pilot projects indicate that these programs are successful in flattening peaks, thus ensuring better use of existing T&D and generation assets. Direct load control, which lets utilities reduce or eliminate electricity flow to customer equipment (such as air conditioners). Contracts govern the terms and conditions of these turn-offs. Indirect load control, which signals customers to reduce the use of on-premises equipment for contractually agreed-on time periods. Smart Grid business software enables utilities to impose penalties on customers who do not comply with their contracts. Smart Grids also help utilities manage peaks with existing assets by enabling: Real-time asset monitoring and control. In this application, advanced sensors safely enable dynamic capacity load limits, ensuring that all grid assets can be used to their maximum capacity during peak demand periods. Real-time asset monitoring and control applications also detect the location of excessive losses and pinpoint need for mitigation and asset replacements. As a result, utilities reduce outage risk and guard against excess capacity or “over-build”. Better peak demand analysis. As a result: Distribution planners can better size equipment (e.g. transformers) to avoid over-building. Operations engineers can identify and resolve bottlenecks and other inefficiencies that may cause or exacerbate peaks. As above, the result is a reduction in the tendency to over-build. Supply managers can more closely match procurement with delivery. As a result, they can fine-tune supply portfolios, reducing the tendency to over-contract for peak supply and reducing the need to resort to spot market purchases during high peaks. Smart Grids can help lower the cost of remaining peaks by: Standardizing interconnections for new distributed resources (such as electricity storage devices). Placing the interconnections where needed to support anticipated grid congestion. Smart Grid Business Software Lowers the Cost of Field Services By processing Smart Grid data through their business software, utilities can reduce such field costs as: Vegetation management. Smart Grids can pinpoint momentary interruptions and tree-caused outages. Spatial mash-up tools leverage GIS models of tree growth for targeted vegetation management. This reduces the cost of unnecessary tree trimming. Service vehicle fuel. Many utility service calls are “false alarms.” Checking meter status before dispatching crews prevents many unnecessary “truck rolls.” Similarly, crews use far less fuel when Smart Grid sensors can pinpoint a problem and mobile workforce applications can then route them directly to it. Smart Grid Business Software Ensures Regulatory Compliance Smart Grids can ensure compliance with private contracts and with regional, national, or international requirements by: Monitoring fulfillment of contract terms. Utilities can use one-hour interval meters to ensure that interruptible (“non-core”) customers actually reduce or eliminate deliveries as required. They can use the information to levy fines against contract violators. Monitoring regulations imposed on customers, such as maximum use during specific time periods. Using accurate time-stamped event history derived from intelligent devices distributed throughout the smart grid to monitor and report reliability statistics and risk compliance. Automating business processes and activities that ensure compliance with security and reliability measures (e.g. NERC-CIP 2-9). Grid Business Software Strengthens Utilities’ Connection to Customers While Reducing Customer Service Costs During outages, Smart Grid business software can: Identify outages more quickly. Software uses sensors to pinpoint outages and nested outage locations. They also permit utilities to ensure outage resolution at every meter location. Size outages more accurately, permitting utilities to dispatch crews that have the skills needed, in appropriate numbers. Provide updates on outage location and expected duration. This information helps call centers inform customers about the timing of service restoration. Smart Grids also facilitates display of outage maps for customer and public-service use. Smart Grids can significantly reduce the cost to: Connect and disconnect customers. Meters capable of remote disconnect can virtually eliminate the costs of field crews and vehicles previously required to change service from the old to the new residents of a metered property or disconnect customers for nonpayment. Resolve reports of voltage fluctuation. Smart Grids gather and report voltage and power quality data from meters and grid sensors, enabling utilities to pinpoint reported problems or resolve them before customers complain. Detect and resolve non-technical losses (e.g. theft). Smart Grids can identify illegal attempts to reconnect meters or to use electricity in supposedly vacant premises. They can also detect theft by comparing flows through delivery assets with billed consumption. Smart Grids also facilitate outreach to customers. By monitoring and analyzing consumption over time, utilities can: Identify customers with unusually high usage and contact them before they receive a bill. They can also suggest conservation techniques that might help to limit consumption. This can head off “high bill” complaints to the contact center. Note that such “high usage” or “additional charges apply because you are out of range” notices—frequently via text messaging—are already common among mobile phone providers. Help customers identify appropriate bill payment alternatives (budget billing, prepayment, etc.). Help customers find and reduce causes of over-consumption. There’s no waiting for bills in the mail before they even understand there is a problem. Utilities benefit not just through improved customer relations but also through limiting the size of bills from customers who might struggle to pay them. Where permitted, Smart Grids can open the doors to such new utility service offerings as: Monitoring properties. Landlords reduce costs of vacant properties when utilities notify them of unexpected energy or water consumption. Utilities can perform similar services for owners of vacation properties or the adult children of aging parents. Monitoring equipment. Power-use patterns can reveal a need for equipment maintenance. Smart Grids permit utilities to alert owners or managers to a need for maintenance or replacement. Facilitating home and small-business networks. Smart Grids can provide a gateway to equipment networks that automate control or let owners access equipment remotely. They also facilitate net metering, offering some utilities a path toward involvement in small-scale solar or wind generation. Prepayment plans that do not need special meters. Smart Grid Business Software Helps Customers Control Energy Costs There is no end to the ways Smart Grids help both small and large customers control energy costs. For instance: Multi-premises customers appreciate having all meters read on the same day so that they can more easily compare consumption at various sites. Customers in competitive regions can match their consumption profile (detailed via Smart Grid data) with specific offerings from competitive suppliers. Customers seeing inexplicable consumption patterns and power quality problems may investigate further. The result can be discovery of electrical problems that can be resolved through rewiring or maintenance—before more serious fires or accidents happen. Smart Grid Business Software Facilitates Use of Renewables Generation from wind and solar resources is a popular alternative to fossil fuel generation, which emits greenhouse gases. Wind and solar generation may also increase energy security in regions that currently import fossil fuel for use in generation. Utilities face many technical issues as they attempt to integrate intermittent resource generation into traditional grids, which traditionally handle only fully dispatchable generation. Smart Grid business software helps solves many of these issues by: Detecting sudden drops in production from renewables-generated electricity (wind and solar) and automatically triggering electricity storage and smart appliance response to compensate as needed. Supporting industry-standard distributed generation interconnection processes to reduce interconnection costs and avoid adding renewable supplies to locations already subject to grid congestion. Facilitating modeling and monitoring of locally generated supply from renewables and thus helping to maximize their use. Increasing the efficiency of “net metering” (through which utilities can use electricity generated by customers) by: Providing data for analysis. Integrating the production and consumption aspects of customer accounts. During non-peak periods, such techniques enable utilities to increase the percent of renewable generation in their supply mix. During peak periods, Smart Grid business software controls circuit reconfiguration to maximize available capacity. Conclusion Utility missions are changing. Yesterday, they focused on delivery of reasonably priced energy and water. Tomorrow, their missions will expand to encompass sustainable use and environmental improvement.Smart Grids are key to helping utilities achieve this expanded mission. But they come at a relatively high price. Utilities will need to invest heavily in new hardware, software, business process development, and staff training. Customer investments in home area networks and smart appliances will be large. Learning to change the energy and water consumption habits of a lifetime could ultimately prove even more formidable tasks.Smart Grid business software can ease the cost and difficulties inherent in a needed transition to a more flexible, reliable, responsive electricity grid. Justifying its implementation, however, requires a full understanding of the benefits it brings—benefits that can ultimately help customers, utilities, communities, and the world address global issues like energy security and climate change while minimizing costs and maximizing customer convenience. This white paper is available for download here. For further information about Oracle's Primavera Solutions for Utilities, please read our Utilities e-book.

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• C#/.NET Little Wonders: The Useful But Overlooked Sets

  - by James Michael Hare

  Once again we consider some of the lesser known classes and keywords of C#.  Today we will be looking at two set implementations in the System.Collections.Generic namespace: HashSet<T> and SortedSet<T>.  Even though most people think of sets as mathematical constructs, they are actually very useful classes that can be used to help make your application more performant if used appropriately. A Background From Math In mathematical terms, a set is an unordered collection of unique items.  In other words, the set {2,3,5} is identical to the set {3,5,2}.  In addition, the set {2, 2, 4, 1} would be invalid because it would have a duplicate item (2).  In addition, you can perform set arithmetic on sets such as: Intersections: The intersection of two sets is the collection of elements common to both.  Example: The intersection of {1,2,5} and {2,4,9} is the set {2}. Unions: The union of two sets is the collection of unique items present in either or both set.  Example: The union of {1,2,5} and {2,4,9} is {1,2,4,5,9}. Differences: The difference of two sets is the removal of all items from the first set that are common between the sets.  Example: The difference of {1,2,5} and {2,4,9} is {1,5}. Supersets: One set is a superset of a second set if it contains all elements that are in the second set. Example: The set {1,2,5} is a superset of {1,5}. Subsets: One set is a subset of a second set if all the elements of that set are contained in the first set. Example: The set {1,5} is a subset of {1,2,5}. If We’re Not Doing Math, Why Do We Care? Now, you may be thinking: why bother with the set classes in C# if you have no need for mathematical set manipulation?  The answer is simple: they are extremely efficient ways to determine ownership in a collection. For example, let’s say you are designing an order system that tracks the price of a particular equity, and once it reaches a certain point will trigger an order.  Now, since there’s tens of thousands of equities on the markets, you don’t want to track market data for every ticker as that would be a waste of time and processing power for symbols you don’t have orders for.  Thus, we just want to subscribe to the stock symbol for an equity order only if it is a symbol we are not already subscribed to. Every time a new order comes in, we will check the list of subscriptions to see if the new order’s stock symbol is in that list.  If it is, great, we already have that market data feed!  If not, then and only then should we subscribe to the feed for that symbol. So far so good, we have a collection of symbols and we want to see if a symbol is present in that collection and if not, add it.  This really is the essence of set processing, but for the sake of comparison, let’s say you do a list instead: 1: // class that handles are order processing service 2: public sealed class OrderProcessor 3: { 4: // contains list of all symbols we are currently subscribed to 5: private readonly List<string> _subscriptions = new List<string>(); 6:  7: ... 8: } Now whenever you are adding a new order, it would look something like: 1: public PlaceOrderResponse PlaceOrder(Order newOrder) 2: { 3: // do some validation, of course... 4:  5: // check to see if already subscribed, if not add a subscription 6: if (!_subscriptions.Contains(newOrder.Symbol)) 7: { 8: // add the symbol to the list 9: _subscriptions.Add(newOrder.Symbol); 10: 11: // do whatever magic is needed to start a subscription for the symbol 12: } 13:  14: // place the order logic! 15: } What’s wrong with this?  In short: performance!  Finding an item inside a List<T> is a linear - O(n) – operation, which is not a very performant way to find if an item exists in a collection. (I used to teach algorithms and data structures in my spare time at a local university, and when you began talking about big-O notation you could immediately begin to see eyes glossing over as if it was pure, useless theory that would not apply in the real world, but I did and still do believe it is something worth understanding well to make the best choices in computer science). Let’s think about this: a linear operation means that as the number of items increases, the time that it takes to perform the operation tends to increase in a linear fashion.  Put crudely, this means if you double the collection size, you might expect the operation to take something like the order of twice as long.  Linear operations tend to be bad for performance because they mean that to perform some operation on a collection, you must potentially “visit” every item in the collection.  Consider finding an item in a List<T>: if you want to see if the list has an item, you must potentially check every item in the list before you find it or determine it’s not found. Now, we could of course sort our list and then perform a binary search on it, but sorting is typically a linear-logarithmic complexity – O(n * log n) - and could involve temporary storage.  So performing a sort after each add would probably add more time.  As an alternative, we could use a SortedList<TKey, TValue> which sorts the list on every Add(), but this has a similar level of complexity to move the items and also requires a key and value, and in our case the key is the value. This is why sets tend to be the best choice for this type of processing: they don’t rely on separate keys and values for ordering – so they save space – and they typically don’t care about ordering – so they tend to be extremely performant.  The .NET BCL (Base Class Library) has had the HashSet<T> since .NET 3.5, but at that time it did not implement the ISet<T> interface.  As of .NET 4.0, HashSet<T> implements ISet<T> and a new set, the SortedSet<T> was added that gives you a set with ordering. HashSet<T> – For Unordered Storage of Sets When used right, HashSet<T> is a beautiful collection, you can think of it as a simplified Dictionary<T,T>.  That is, a Dictionary where the TKey and TValue refer to the same object.  This is really an oversimplification, but logically it makes sense.  I’ve actually seen people code a Dictionary<T,T> where they store the same thing in the key and the value, and that’s just inefficient because of the extra storage to hold both the key and the value. As it’s name implies, the HashSet<T> uses a hashing algorithm to find the items in the set, which means it does take up some additional space, but it has lightning fast lookups!  Compare the times below between HashSet<T> and List<T>: Operation HashSet<T> List<T> Add() O(1) O(1) at end O(n) in middle Remove() O(1) O(n) Contains() O(1) O(n)   Now, these times are amortized and represent the typical case.  In the very worst case, the operations could be linear if they involve a resizing of the collection – but this is true for both the List and HashSet so that’s a less of an issue when comparing the two. The key thing to note is that in the general case, HashSet is constant time for adds, removes, and contains!  This means that no matter how large the collection is, it takes roughly the exact same amount of time to find an item or determine if it’s not in the collection.  Compare this to the List where almost any add or remove must rearrange potentially all the elements!  And to find an item in the list (if unsorted) you must search every item in the List. So as you can see, if you want to create an unordered collection and have very fast lookup and manipulation, the HashSet is a great collection. And since HashSet<T> implements ICollection<T> and IEnumerable<T>, it supports nearly all the same basic operations as the List<T> and can use the System.Linq extension methods as well. All we have to do to switch from a List<T> to a HashSet<T>  is change our declaration.  Since List and HashSet support many of the same members, chances are we won’t need to change much else. 1: public sealed class OrderProcessor 2: { 3: private readonly HashSet<string> _subscriptions = new HashSet<string>(); 4:  5: // ... 6:  7: public PlaceOrderResponse PlaceOrder(Order newOrder) 8: { 9: // do some validation, of course... 10: 11: // check to see if already subscribed, if not add a subscription 12: if (!_subscriptions.Contains(newOrder.Symbol)) 13: { 14: // add the symbol to the list 15: _subscriptions.Add(newOrder.Symbol); 16: 17: // do whatever magic is needed to start a subscription for the symbol 18: } 19: 20: // place the order logic! 21: } 22:  23: // ... 24: } 25: Notice, we didn’t change any code other than the declaration for _subscriptions to be a HashSet<T>.  Thus, we can pick up the performance improvements in this case with minimal code changes. SortedSet<T> – Ordered Storage of Sets Just like HashSet<T> is logically similar to Dictionary<T,T>, the SortedSet<T> is logically similar to the SortedDictionary<T,T>. The SortedSet can be used when you want to do set operations on a collection, but you want to maintain that collection in sorted order.  Now, this is not necessarily mathematically relevant, but if your collection needs do include order, this is the set to use. So the SortedSet seems to be implemented as a binary tree (possibly a red-black tree) internally.  Since binary trees are dynamic structures and non-contiguous (unlike List and SortedList) this means that inserts and deletes do not involve rearranging elements, or changing the linking of the nodes.  There is some overhead in keeping the nodes in order, but it is much smaller than a contiguous storage collection like a List<T>.  Let’s compare the three: Operation HashSet<T> SortedSet<T> List<T> Add() O(1) O(log n) O(1) at end O(n) in middle Remove() O(1) O(log n) O(n) Contains() O(1) O(log n) O(n)   The MSDN documentation seems to indicate that operations on SortedSet are O(1), but this seems to be inconsistent with its implementation and seems to be a documentation error.  There’s actually a separate MSDN document (here) on SortedSet that indicates that it is, in fact, logarithmic in complexity.  Let’s put it in layman’s terms: logarithmic means you can double the collection size and typically you only add a single extra “visit” to an item in the collection.  Take that in contrast to List<T>’s linear operation where if you double the size of the collection you double the “visits” to items in the collection.  This is very good performance!  It’s still not as performant as HashSet<T> where it always just visits one item (amortized), but for the addition of sorting this is a good thing. Consider the following table, now this is just illustrative data of the relative complexities, but it’s enough to get the point: Collection Size O(1) Visits O(log n) Visits O(n) Visits 1 1 1 1 10 1 4 10 100 1 7 100 1000 1 10 1000   Notice that the logarithmic – O(log n) – visit count goes up very slowly compare to the linear – O(n) – visit count.  This is because since the list is sorted, it can do one check in the middle of the list, determine which half of the collection the data is in, and discard the other half (binary search).  So, if you need your set to be sorted, you can use the SortedSet<T> just like the HashSet<T> and gain sorting for a small performance hit, but it’s still faster than a List<T>. Unique Set Operations Now, if you do want to perform more set-like operations, both implementations of ISet<T> support the following, which play back towards the mathematical set operations described before: IntersectWith() – Performs the set intersection of two sets.  Modifies the current set so that it only contains elements also in the second set. UnionWith() – Performs a set union of two sets.  Modifies the current set so it contains all elements present both in the current set and the second set. ExceptWith() – Performs a set difference of two sets.  Modifies the current set so that it removes all elements present in the second set. IsSupersetOf() – Checks if the current set is a superset of the second set. IsSubsetOf() – Checks if the current set is a subset of the second set. For more information on the set operations themselves, see the MSDN description of ISet<T> (here). What Sets Don’t Do Don’t get me wrong, sets are not silver bullets.  You don’t really want to use a set when you want separate key to value lookups, that’s what the IDictionary implementations are best for. Also sets don’t store temporal add-order.  That is, if you are adding items to the end of a list all the time, your list is ordered in terms of when items were added to it.  This is something the sets don’t do naturally (though you could use a SortedSet with an IComparer with a DateTime but that’s overkill) but List<T> can. Also, List<T> allows indexing which is a blazingly fast way to iterate through items in the collection.  Iterating over all the items in a List<T> is generally much, much faster than iterating over a set. Summary Sets are an excellent tool for maintaining a lookup table where the item is both the key and the value.  In addition, if you have need for the mathematical set operations, the C# sets support those as well.  The HashSet<T> is the set of choice if you want the fastest possible lookups but don’t care about order.  In contrast the SortedSet<T> will give you a sorted collection at a slight reduction in performance.   Technorati Tags: C#,.Net,Little Wonders,BlackRabbitCoder,ISet,HashSet,SortedSet

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• CodePlex Daily Summary for Thursday, November 22, 2012

  CodePlex Daily Summary for Thursday, November 22, 2012Popular ReleasesStackBuilder: StackBuilder 1.0.11.0: +Added Box/Case analysis...VidCoder: 1.4.7 Beta: Added view modes to the Preview window. Now you can see the image in 1:1 or in "Corners" mode to show a close-up of cropping results. Added the ability to set a custom completion sound. Gave the encoding settings command bar a more distinctive background color and extended it to the whole width of the window. Added the preview button to the command bar. Rearranged UI in Video tab and added back the section headers. Added the "Most" choice for the advanced x264 analysis option. Updat...ServiceMon - Extensible Real-time, Service Monitoring Utility for Windows: ServiceMon Release 1.2.0.58: Auto-uploaded from build serverImapX 2: ImapX 2.0.0.6: An updated release of the ImapX 2 library, containing many bugfixes for both, the library and the sample application.WiX Toolset: WiX v3.7 RC: WiX v3.7 RC (3.7.1119.0) provides feature complete Bundle update and reference tracking plus several bug fixes. For more information see Rob's blog post about the release: http://robmensching.com/blog/posts/2012/11/20/WiX-v3.7-Release-Candidate-availablePicturethrill: Version 2.11.20.0: Fixed up Bing image provider on Windows 8Excel AddIn to reset the last worksheet cell: XSFormatCleaner.xla: Modified the commandbar code to use CommandBar IDs instead of English names.Json.NET: Json.NET 4.5 Release 11: New feature - Added ITraceWriter, MemoryTraceWriter, DiagnosticsTraceWriter New feature - Added StringEscapeHandling with options to escape HTML and non-ASCII characters New feature - Added non-generic JToken.ToObject methods New feature - Deserialize ISet<T> properties as HashSet<T> New feature - Added implicit conversions for Uri, TimeSpan, Guid New feature - Missing byte, char, Guid, TimeSpan and Uri explicit conversion operators added to JToken New feature - Special case...EntitiesToDTOs - Entity Framework DTO Generator: EntitiesToDTOs.v3.0: DTOs and Assemblers can be generated inside project folders! Choose the types you want to generate! Support for Visual Studio 2012 !!! Support for new Entity Framework EDMX (format used by VS2012) ! Support for Enum Types! Optional automatic check for updates! Added the following methods to Assemblers! IEnumerable<DTO>.ToEntities() : ICollection<Entity> IEnumerable<Entity>.ToDTOs() : ICollection<DTO> Indicate class identifier for DTOs and Assemblers! Cleaner Assemblers code....mojoPortal: 2.3.9.4: see release notes on mojoportal.com http://www.mojoportal.com/mojoportal-2394-released Note that we have separate deployment packages for .NET 3.5 and .NET 4.0, but we recommend you to use .NET 4, we will probably drop support for .NET 3.5 once .NET 4.5 is available The deployment package downloads on this page are pre-compiled and ready for production deployment, they contain no C# source code and are not intended for use in Visual Studio. To download the source code see getting the lates...DotNetNuke® Store: 03.01.07: What's New in this release? IMPORTANT: this version requires DotNetNuke 04.06.02 or higher! DO NOT REPORT BUGS HERE IN THE ISSUE TRACKER, INSTEAD USE THE DotNetNuke Store Forum! Bugs corrected: - Replaced some hard coded references to the default address provider classes by the corresponding interfaces to allow the creation of another address provider with a different name. New Features: - Added the 'pickup' delivery option at checkout. - Added the 'no delivery' option in the Store Admin ...Bundle Transformer - a modular extension for ASP.NET Web Optimization Framework: Bundle Transformer 1.6.10: Version: 1.6.10 Published: 11/18/2012 Now almost all of the Bundle Transformer's assemblies is signed (except BundleTransformer.Yui.dll); In BundleTransformer.SassAndScss the SassAndCoffee.Ruby library was replaced by my own implementation of the Sass- and SCSS-compiler (based on code of the SassAndCoffee.Ruby library version 2.0.2.0); In BundleTransformer.CoffeeScript added support of CoffeeScript version 1.4.0-3; In BundleTransformer.TypeScript added support of TypeScript version 0....ExtJS based ASP.NET 2.0 Controls: FineUI v3.2.0: +2012-11-18 v3.2.0 -?????????????????SelectedValueArray????????（◇?◆:）。 -???????????????????RecoverPropertiesFromJObject????（〓?〓、????、??、Vian_Pan）。 -????????????，?????????????，???SelectedValueArray???????（sam.chang）。 -??Alert.Show???????????（swtseaman）。 -???????????????，??Icon??IconUrl????（swtseaman）。 -?????????TimePicker（??）。 -?????????，??/res.axd?css=blue.css&v=1。 -????????，?????????????，???????。 -????MenuCheckBox（???????）。 -?RadioButton??AutoPostBack??。 -???????FCKEditor?????????...BugNET Issue Tracker: BugNET 1.2: Please read our release notes for BugNET 1.2: http://blog.bugnetproject.com/bugnet-1-2-has-been-released Please do not post questions as reviews. Questions should be posted in the Discussions tab, where they will usually get promptly responded to. If you post a question as a review, you will pollute the rating, and you won't get an answer.Paint.NET PSD Plugin: 2.2.0: Changes: Layer group visibility is now applied to all layers within the group. This greatly improves the visual fidelity of complex PSD files that have hidden layer groups. Layer group names are prefixed so that users can get an indication of the layer group hierarchy. (Paint.NET has a flat list of layers, so the hierarchy is flattened out on load.) The progress bar now reports status when saving PSD files, instead of showing an indeterminate rolling bar. Performance improvement of 1...CRM 2011 Visual Ribbon Editor: Visual Ribbon Editor (1.3.1116.7): [IMPROVED] Detailed error message descriptions for FaultException [FIX] Fixed bug in rule CrmOfflineAccessStateRule which had incorrect State attribute name [FIX] Fixed bug in rule EntityPropertyRule which was missing PropertyValue attribute [FIX] Current connection information was not displayed in status bar while refreshing list of entitiesSuper Metroid Randomizer: Super Metroid Randomizer v5: v5 -Added command line functionality for automation purposes. -Implented Krankdud's change to randomize the Etecoon's item. NOTE: this version will not accept seeds from a previous version. The seed format has changed by necessity. v4 -Started putting version numbers at the top of the form. -Added a warning when suitless Maridia is required in a parsed seed. v3 -Changed seed to only generate filename-legal characters. Using old seeds will still work exactly the same. -Files can now be saved...Caliburn Micro: WPF, Silverlight, WP7 and WinRT/Metro made easy.: Caliburn.Micro v1.4: Changes This version includes many bug fixes across all platforms, improvements to nuget support and...the biggest news of all...full support for both WinRT and WP8. Download Contents Debug and Release Assemblies Samples Readme.txt License.txt Packages Available on Nuget Caliburn.Micro – The full framework compiled into an assembly. Caliburn.Micro.Start - Includes Caliburn.Micro plus a starting bootstrapper, view model and view. Caliburn.Micro.Container – The Caliburn.Micro invers...DirectX Tool Kit: November 15, 2012: November 15, 2012 Added support for WIC2 when available on Windows 8 and Windows 7 with KB 2670838 Cleaned up warning level 4 warningsDotNetNuke® Community Edition CMS: 06.02.05: Major Highlights Updated the system so that it supports nested folders in the App_Code folder Updated the Global Error Handling so that when errors within the global.asax handler happen, they are caught and shown in a page displaying the original HTTP error code Fixed issue that stopped users from specifying Link URLs that open on a new window Security FixesFixed issue in the Member Directory module that could show members to non authenticated users Fixed issue in the Lists modul...New Projects1122case1325: It is a codeplex project1122case1327: Never be so greedy Accommodation Portal: This is a skeleton web site for holiday home owners, who wishes to rent their holiday accommodations to visitors from around the world. Analog Clock: This is project contains analog clock made in win forms.Android Socket Plus: ????Android??????。???????Android???????Socket???，?????????（?PC?Windows????????）??????Socket???，??，????????????；??????????????Socket???，??????????????Socket???。Bancosol: PFCBig Data Twitter Demo: This demo analyzes tweets in real-time, even including a dashboard. The tweets are also archived in Azure DB/Blob and Hadoop where Excel can be used for BI!Cloud For Science: This project serves as issue tracker for C4S components, and is used by participants of the C4S project. cwt: cwtDiary Application for Elementary Student: A Simple Diary Application Made for Elementary Student. Dice Dreams: Dice Dreams Dice Dreams is a dice game , the player must get 1.000.000 point to win, you start with 100.000 point. Dynamic Entity Framework Filtering: Generates linq to Entity Framework Queries by examining the EF data model, thus allowing for code reduction for queries to commonly requested entities.EazyErp: Enterprise Resource Planning System of Vf AsiaGoPlay: Tooke - add a project description here...HMyBlog: myblogHumanitarian Toolbox: This project is the publication site for bits built by the Humanitarian Toolbox ( http://humanitariantoolbox.net/).Impulse Media Player: Impulse Media Player is the ultimate media player for WindowsInspiration.Web: Description: A simple (but entertaining) ASP.NET MVC (C#) project to suggest random code names for projects. Intended audience: People who need a name (any name) to get started with their projects. Application written during Webcamp Singapore (4th Jun 2010 - 5th Jun 2010).iPictureUploader: Simple tool to upload images onto WEB and share via blogs/forums/etc lugionline: test projectMosaic Snake 3D: A clone of the popular Snake game for Windows 8. It contains a simple 3D engine based on SharpDX and is completely written in C# and Xaml.Outage Display: A simple web-based outage displayPrestaShop free Electronic Brown Shop Template - ModuleBazaar: Check out Latest and Best Featured PrestaShop Templates, Modules Magento Extensions, Opencart Extensions, Clone scripts from ModuleBazaarRackspace Cloud Files Manager: A simple Rackspace Cloud Files manager for static websitesRoll The Dice: Roll The Dice is a simple game developed by Erika Enggar Savitri and Queen Anugerah Aguslia who are currently studying Information System at Ma Chung UniversityRussian IDM: Russian IDM is free IdM solutionshootout: Comparing the speed of different languages and the constructs available within those languagessmart messaging connector: Sending fax from a PrintDocument or a file. Sending broadcast fax. Managing (Pause/Resume/Restart) current fax job. Managing configuration of the fax serverTeen Diary: Teen Diary Software - FREEWARE SOFTWARE. - SIMPLE, LIGHTWEIGHT, PORTABLE, COMPATIBLE. - made from .NET FRAMEWORK languange with XML data. Tekapo: Tekapo is a wizard style application that will help you manage your digital photos. Most digital cameras will store images as JPEG files. Information about the camera and how the camera has taken the photo is placed into the JPEG file along with the image data. One of the pieces of information stored is the date and time that the photo was taken. Tekapo uses the picture taken date to organise the photos.The Byte Kitchen's Open Sources: This project is related to The Byte Kitchen Blog (at thebytekitchen.com). It typically deals with Windows 8 apps, DirectX, and the Kinect for Windows.TheDiary: daily-self journalWCFsample: WCFsampleXNA Game Editor: This project will has familiar features like Unity Engine Editor.

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• ????ASMM

  - by Liu Maclean(???)

  ???Oracle??????????????SGA/PGA???,????10g????????????ASMM????,????????ASMM?????????Oracle??????????,?ASMM??????DBA????????????;????????ASMM???????????????DBA???:????????????DB,?????????????DBA?????????????????????????????????,ASMM??????????,???????????,??????????,??????????????????;?10g release 1?10.2??????ASMM?????????????,???????ASMM????????ASMM?????startup???????????ASMM??AMM??,????????DBA????SGA/PGA?????????”??”??”???”???,???????????DBA????chemist(???????1??2??????????????)? ?????????????????ASMM?????,?????????????…… Oracle?SGA???????9i???????????,????: Buffer Cache ????????????,??????????????? Default Pool                  ??????,???DB_CACHE_SIZE?? Keep Pool                     ??????,???DB_KEEP_CACHE_SIZE?? Non standard pool         ???????,???DB_nK_cache_size?? Recycle pool                 ???,???db_recycle_cache_size?? Shared Pool ???,???shared_pool_size?? Library cache   ?????? Row cache      ???,?????? Java Pool         java?,???Java_pool_size?? Large Pool       ??,???Large_pool_size?? Fixed SGA       ???SGA??,???Oracle???????,?????????granule? ?9i?????ASMM,???????????SGA,??????MSMM??9i???buffer cache??????????,?????????????????????????,???9i?????????????,?????????????????????????? ????SGA?????: ?????shared pool?default buffer pool????????,??????????? ?9i???????????(advisor),?????????? ??????????????? ?????????,?????? ?????,?????ORA-04031?????????? ASMM?????: ?????????? ???????????????? ???????sga_target?? ???????????,??????????? ??MSMM???????: ???? ???? ?????? ???? ??????????,??????????? ??????????????????,??????????ORA-04031??? ASMM???????????:1.??????sga_target???????2.???????,???:????(memory component),????(memory broker)???????(memory mechanism)3.????(memory advisor) ASMM????????????(Automatically set),??????:shared_pool_size?db_cache_size?java_pool_size?large_pool _size?streams_pool_size;?????????????????,???:db_keep_cache_size?db_recycle_cache_size?db_nk_cache_size?log_buffer????SGA?????,????????????????,??log_buffer?fixed sga??????????????? ??ASMM?????????sga_target??,???????ASMM??????????????????db_cache_size?java_pool_size???,?????????????????????,????????????????????(???)????????,Oracle?????????(granule,?SGA<1GB?granule???4M,?SGA>1GB?granule???16M)???????,??????????????buffer cache,??????????????????(granule)??????????????????????sga_target??,???????????????????(dism,???????)???ASMM?????????????statistics_level?????typical?ALL,?????BASIC??MMON????(Memory Monitor is a background process that gathers memory statistics (snapshots) stores this information in the AWR (automatic workload repository). MMON is also responsible for issuing alerts for metrics that exceed their thresholds)?????????????????????ASMM?????,???????????sga_target?????statistics_level?BASIC: SQL> show parameter sga NAME TYPE VALUE ------------------------------------ ----------- ------------------------------ lock_sga boolean FALSE pre_page_sga boolean FALSE sga_max_size big integer 2000M sga_target big integer 2000M SQL> show parameter sga_target NAME TYPE VALUE ------------------------------------ ----------- ------------------------------ sga_target big integer 2000M SQL> alter system set statistics_level=BASIC; alter system set statistics_level=BASIC * ERROR at line 1: ORA-02097: parameter cannot be modified because specified value is invalid ORA-00830: cannot set statistics_level to BASIC with auto-tune SGA enabled ?????server parameter file?spfile??,ASMM????shutdown??????????????(Oracle???????,????????)???spfile?,?????strings?????spfile????????????????????,?: G10R2.__db_cache_size=973078528 G10R2.__java_pool_size=16777216 G10R2.__large_pool_size=16777216 G10R2.__shared_pool_size=1006632960 G10R2.__streams_pool_size=67108864 ???spfile?????????????????,???????????”???”?????,??????????”??”?? ?ASMM?????????????? ?????(tunable):????????????????????????????buffer cache?????????,cache????????????????,?????????? IO????????????????????????????Library cache????? subheap????,?????????????????????????????????(open cursors)?????????client??????????????buffer cache???????,???????????pin??buffer???(???????) ?????(Un-tunable):???????????????????,?????????????????,?????????????????????????large pool?????? ??????(Fixed Size):???????????,??????????????????????????????????????? ????????????????(memory resize request)?????????,?????: ??????(Immediate Request):???????????ASMM????????????????????????(chunk)?,??????OUT-OF-MEMORY(ORA-04031)???,????????????????????(granule)????????????????????granule,????????????,?????????????????????????????,????granule??????????????? ??????(Deferred Request):???????????????????????????,??????????????granule???????????????MMON??????????delta. ??????(Manual Request):????????????alter system?????????????????????????????????????????????????granule,??????grow?????ORA-4033??,?????shrink?????ORA-4034??? ?ASMM????,????(Memory Broker)????????????????????????????(Deferred)??????????????????????(auto-tunable component)???????????????,???????????????MMON??????????????????????????????????,????????????????;MMON????Memory Broker?????????????????????????MMON????????????????????????????????????????(resize request system queue)?MMAN????(Memory Manager is a background process that manages the dynamic resizing of SGA memory areas as the workload increases or decreases)??????????????????? ?10gR1?Shared Pool?shrink??????????,?????????????Buffer Cache???????????granule,????Buffer Cache?granule????granule header?Metadata(???buffer header??RAC??Lock Elements)????,?????????????????????shared pool????????duration(?????)?chunk??????granule?,????????????granule??10gR2????Buffer Cache Granule????????granule header?buffer?Metadata(buffer header?LE)????,??shared pool???duration?chunk????????granule,??????buffer cache?shared pool??????????????10gr2?streams pool?????????(???????streams pool duration????) ??????????(Donor,???trace????)???,?????????granule???buffer cache,????granule????????????: ????granule???????granule header ?????chunk????granule?????????buffer header ???,???chunk??????????????????????metadata? ???2-4??,???granule???? ??????????????????,??buffer cache??granule???shared pool?,???????: MMAN??????????buffer cache???granule MMAN????granule??quiesce???(Moving 1 granule from inuse to quiesce list of DEFAULT buffer cache for an immediate req) DBWR???????quiesced???granule????buffer(dirty buffer) MMAN??shared pool????????(consume callback),granule?free?chunk???shared pool??(consume)?,????????????????????granule????shared granule??????,???????????granule???????????,??????pin??buffer??Metadata(???buffer header?LE)?????buffer cache??? ???granule???????shared pool,???granule?????shared??? ?????ASMM???????????,??????????: _enabled_shared_pool_duration:?????????10g????shared pool duration??,?????sga_target?0?????false;???10.2.0.5??cursor_space_for_time???true??????false,???10.2.0.5??cursor_space_for_time????? _memory_broker_shrink_heaps:???????0??Oracle?????shared pool?java pool,??????0,??shrink request??????????????????? _memory_management_tracing: ???????MMON?MMAN??????????(advisor)?????(Memory Broker)?????trace???;??ORA-04031????????36,???8?????????????trace,???23????Memory Broker decision???,???32???cache resize???;??????????: Level Contents 0×01 Enables statistics tracing 0×02 Enables policy tracing 0×04 Enables transfer of granules tracing 0×08 Enables startup tracing 0×10 Enables tuning tracing 0×20 Enables cache tracing ?????????_memory_management_tracing?????DUMP_TRANSFER_OPS????????????????,?????????????????trace?????????mman_trace?transfer_ops_dump? SQL> alter system set "_memory_management_tracing"=63; System altered Operation make shared pool grow and buffer cache shrink!!!.............. ???????granule?????,????default buffer pool?resize??: AUTO SGA: Request 0xdc9c2628 after pre-processing, ret=0 /* ???0xdc9c2628??????addr */ AUTO SGA: IMMEDIATE, FG request 0xdc9c2628 /* ???????????Immediate???? */ AUTO SGA: Receiver of memory is shared pool, size=16, state=3, flg=0 /* ?????????shared pool,???,????16?granule,??grow?? */ AUTO SGA: Donor of memory is DEFAULT buffer cache, size=106, state=4, flg=0 /* ???????Default buffer cache,????,????106?granule,??shrink?? */ AUTO SGA: Memory requested=3896, remaining=3896 /* ??immeidate request???????3896 bytes */ AUTO SGA: Memory received=0, minreq=3896, gransz=16777216 /* ????free?granule,??received?0,gransz?granule??? */ AUTO SGA: Request 0xdc9c2628 status is INACTIVE /* ??????????,??????inactive?? */ AUTO SGA: Init bef rsz for request 0xdc9c2628 /* ????????before-process???? */ AUTO SGA: Set rq dc9c2628 status to PENDING /* ?request??pending?? */ AUTO SGA: 0xca000000 rem=3896, rcvd=16777216, 105, 16777216, 17 /* ???????0xca000000?16M??granule */ AUTO SGA: Returning 4 from kmgs_process for request dc9c2628 AUTO SGA: Process req dc9c2628 ret 4, 1, a AUTO SGA: Resize done for pool DEFAULT, 8192 /* ???default pool?resize */ AUTO SGA: Init aft rsz for request 0xdc9c2628 AUTO SGA: Request 0xdc9c2628 after processing AUTO SGA: IMMEDIATE, FG request 0x7fff917964a0 AUTO SGA: Receiver of memory is shared pool, size=17, state=0, flg=0 AUTO SGA: Donor of memory is DEFAULT buffer cache, size=105, state=0, flg=0 AUTO SGA: Memory requested=3896, remaining=0 AUTO SGA: Memory received=16777216, minreq=3896, gransz=16777216 AUTO SGA: Request 0x7fff917964a0 status is COMPLETE /* shared pool????16M?granule */ AUTO SGA: activated granule 0xca000000 of shared pool ?????partial granule????????????trace: AUTO SGA: Request 0xdc9c2628 after pre-processing, ret=0 AUTO SGA: IMMEDIATE, FG request 0xdc9c2628 AUTO SGA: Receiver of memory is shared pool, size=82, state=3, flg=1 AUTO SGA: Donor of memory is DEFAULT buffer cache, size=36, state=4, flg=1 /* ????????shared pool,?????default buffer cache */ AUTO SGA: Memory requested=4120, remaining=4120 AUTO SGA: Memory received=0, minreq=4120, gransz=16777216 AUTO SGA: Request 0xdc9c2628 status is INACTIVE AUTO SGA: Init bef rsz for request 0xdc9c2628 AUTO SGA: Set rq dc9c2628 status to PENDING AUTO SGA: Moving granule 0x93000000 of DEFAULT buffer cache to activate list AUTO SGA: Moving 1 granule 0x8c000000 from inuse to quiesce list of DEFAULT buffer cache for an immediate req /* ???buffer cache??????0x8c000000?granule??????inuse list, ???????quiesce list? */ AUTO SGA: Returning 0 from kmgs_process for request dc9c2628 AUTO SGA: Process req dc9c2628 ret 0, 1, 20a AUTO SGA: activated granule 0x93000000 of DEFAULT buffer cache AUTO SGA: NOT_FREE for imm req for gran 0x8c000000 / * ??dbwr??0x8c000000 granule????dirty buffer */ AUTO SGA: Returning 0 from kmgs_process for request dc9c2628 AUTO SGA: Process req dc9c2628 ret 0, 1, 20a AUTO SGA: NOT_FREE for imm req for gran 0x8c000000 AUTO SGA: Returning 0 from kmgs_process for request dc9c2628 AUTO SGA: Process req dc9c2628 ret 0, 1, 20a AUTO SGA: NOT_FREE for imm req for gran 0x8c000000 AUTO SGA: Returning 0 from kmgs_process for request dc9c2628 AUTO SGA: Process req dc9c2628 ret 0, 1, 20a AUTO SGA: NOT_FREE for imm req for gran 0x8c000000 AUTO SGA: Returning 0 from kmgs_process for request dc9c2628 AUTO SGA: Process req dc9c2628 ret 0, 1, 20a AUTO SGA: NOT_FREE for imm req for gran 0x8c000000 AUTO SGA: Returning 0 from kmgs_process for request dc9c2628 AUTO SGA: Process req dc9c2628 ret 0, 1, 20a AUTO SGA: NOT_FREE for imm req for gran 0x8c000000 ......................................... AUTO SGA: Rcv shared pool consuming 8192 from 0x8c000000 in granule 0x8c000000; owner is DEFAULT buffer cache AUTO SGA: Rcv shared pool consuming 90112 from 0x8c002000 in granule 0x8c000000; owner is DEFAULT buffer cache AUTO SGA: Rcv shared pool consuming 24576 from 0x8c01a000 in granule 0x8c000000; owner is DEFAULT buffer cache AUTO SGA: Rcv shared pool consuming 65536 from 0x8c022000 in granule 0x8c000000; owner is DEFAULT buffer cache AUTO SGA: Rcv shared pool consuming 131072 from 0x8c034000 in granule 0x8c000000; owner is DEFAULT buffer cache AUTO SGA: Rcv shared pool consuming 286720 from 0x8c056000 in granule 0x8c000000; owner is DEFAULT buffer cache AUTO SGA: Rcv shared pool consuming 98304 from 0x8c09e000 in granule 0x8c000000; owner is DEFAULT buffer cache AUTO SGA: Rcv shared pool consuming 106496 from 0x8c0b8000 in granule 0x8c000000; owner is DEFAULT buffer cache ..................... /* ??shared pool????0x8c000000 granule??chunk, ??granule?owner????default buffer cache */ AUTO SGA: Imm xfer 0x8c000000 from quiesce list of DEFAULT buffer cache to partial inuse list of shared pool /* ???0x8c000000 granule?default buffer cache????????shared pool????inuse list */ AUTO SGA: Returning 4 from kmgs_process for request dc9c2628 AUTO SGA: Process req dc9c2628 ret 4, 1, 20a AUTO SGA: Init aft rsz for request 0xdc9c2628 AUTO SGA: Request 0xdc9c2628 after processing AUTO SGA: IMMEDIATE, FG request 0x7fffe9bcd0e0 AUTO SGA: Receiver of memory is shared pool, size=83, state=0, flg=1 AUTO SGA: Donor of memory is DEFAULT buffer cache, size=35, state=0, flg=1 AUTO SGA: Memory requested=4120, remaining=0 AUTO SGA: Memory received=14934016, minreq=4120, gransz=16777216 AUTO SGA: Request 0x7fffe9bcd0e0 status is COMPLETE /* ????partial transfer?? */ ?????partial transfer??????DUMP_TRANSFER_OPS????0x8c000000 partial granule???????,?: SQL> oradebug setmypid; Statement processed. SQL> oradebug dump DUMP_TRANSFER_OPS 1; Statement processed. SQL> oradebug tracefile_name; /s01/admin/G10R2/udump/g10r2_ora_21482.trc =======================trace content============================== GRANULE SIZE is 16777216 COMPONENT NAME : shared pool Number of granules in partially inuse list (listid 4) is 23 Granule addr is 0x8c000000 Granule owner is DEFAULT buffer cache /* ?0x8c000000 granule?shared pool?partially inuse list, ?????owner??default buffer cache */ Granule 0x8c000000 dump from owner perspective gptr = 0x8c000000, num buf hdrs = 1989, num buffers = 156, ghdr = 0x8cffe000 / * ?????granule?granule header????0x8cffe000, ????156?buffer block,1989?buffer header */ /* ??granule??????,??????buffer cache??shared pool chunk */ BH:0x8cf76018 BA:(nil) st:11 flg:20000 BH:0x8cf76128 BA:(nil) st:11 flg:20000 BH:0x8cf76238 BA:(nil) st:11 flg:20000 BH:0x8cf76348 BA:(nil) st:11 flg:20000 BH:0x8cf76458 BA:(nil) st:11 flg:20000 BH:0x8cf76568 BA:(nil) st:11 flg:20000 BH:0x8cf76678 BA:(nil) st:11 flg:20000 BH:0x8cf76788 BA:(nil) st:11 flg:20000 BH:0x8cf76898 BA:(nil) st:11 flg:20000 BH:0x8cf769a8 BA:(nil) st:11 flg:20000 BH:0x8cf76ab8 BA:(nil) st:11 flg:20000 BH:0x8cf76bc8 BA:(nil) st:11 flg:20000 BH:0x8cf76cd8 BA:0x8c018000 st:1 flg:622202 ............... Address 0x8cf30000 to 0x8cf74000 not in cache Address 0x8cf74000 to 0x8d000000 in cache Granule 0x8c000000 dump from receivers perspective Dumping layout Address 0x8c000000 to 0x8c018000 in sga heap(1,3) (idx=1, dur=4) Address 0x8c018000 to 0x8c01a000 not in this pool Address 0x8c01a000 to 0x8c020000 in sga heap(1,3) (idx=1, dur=4) Address 0x8c020000 to 0x8c022000 not in this pool Address 0x8c022000 to 0x8c032000 in sga heap(1,3) (idx=1, dur=4) Address 0x8c032000 to 0x8c034000 not in this pool Address 0x8c034000 to 0x8c054000 in sga heap(1,3) (idx=1, dur=4) Address 0x8c054000 to 0x8c056000 not in this pool Address 0x8c056000 to 0x8c09c000 in sga heap(1,3) (idx=1, dur=4) Address 0x8c09c000 to 0x8c09e000 not in this pool Address 0x8c09e000 to 0x8c0b6000 in sga heap(1,3) (idx=1, dur=4) Address 0x8c0b6000 to 0x8c0b8000 not in this pool Address 0x8c0b8000 to 0x8c0d2000 in sga heap(1,3) (idx=1, dur=4) ???????granule?????shared granule??????,?????????buffer block,????1?shared subpool??????durtaion?4?chunk,duration=4?execution duration;??duration?chunk???????????,??extent???quiesce list??????????????free?execution duration?????????????,??????duration???extent(??????extent????granule)??????? ?????????????ASMM?????????,????: V$SGAINFODisplays summary information about the system global area (SGA). V$SGADisplays size information about the SGA, including the sizes of different SGA components, the granule size, and free memory. V$SGASTATDisplays detailed information about the SGA. V$SGA_DYNAMIC_COMPONENTSDisplays information about the dynamic SGA components. This view summarizes information based on all completed SGA resize operations since instance startup. V$SGA_DYNAMIC_FREE_MEMORYDisplays information about the amount of SGA memory available for future dynamic SGA resize operations. V$SGA_RESIZE_OPSDisplays information about the last 400 completed SGA resize operations. V$SGA_CURRENT_RESIZE_OPSDisplays information about SGA resize operations that are currently in progress. A resize operation is an enlargement or reduction of a dynamic SGA component. V$SGA_TARGET_ADVICEDisplays information that helps you tune SGA_TARGET. ?????????shared pool duration???,?????????

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• Jquery Serialize data

  - by Richard

  So I have several text boxes, drop down menus, and radio options on this form. When the user clicks submit i want to save ALL that information so I can put it into a database. So this is all the form's inputs <div id="reg2a"> First Name: <br/><input type="text" name="fname" /> <br/> Last Name: <br/><input type="text" name="lname" /> <br/> Address: <br/><input type="text" name="address" /> <br/> City: <br/><input type="text" name="city" /> <br/> State: <br/><input type="text" name="state" /> <br/> Zip Code: <br/><input type="text" name="zip" /> <br/> Phone Number: <br/><input type="text" name="phone" /> <br/> Fax: <br/><input type="text" name="fax" /> <br/> Email: <br/><input type="text" name="email" /> <br/> Ethnicity: <i>Used only for grant reporting purposes</i> <br/><input type="text" name="ethnicity" /> <br/><br/> Instutional Information Type (select the best option) <br/> <select name="iitype"> <option value="none">None</option> <option value="uni">University</option> <option value="commorg">Community Organization</option> </select> <br/><br/> Number of sessions willing to present: <select id="vennum_select" name="vnum"> <?php for($i=0;$i<=3;$i++) { ?> <option value="<?php echo $i ?>"><?php echo $i ?></option> <?php } ?><br/> </select><br/> Number of tables requested: <select id="tabnum_select" name="tnum"> <?php for($i=1;$i<=3;$i++) { ?> <option value="<?php echo $i ?>"><?php echo $i ?></option> <?php } ?> </select><br/><br/> Awarding of a door prize during the conference will result in a reduction in the cost of your first table. <br/><br/> I am providing a door prize for delivery during the conference of $75 or more <select id="prize_select" name="pnum"> <option value="0">No</option> <option value="1">Yes</option> </select><br/> Prize name: <input type="text" name="prize_name" /><br/> Description: <input type="text" name="descr" /><br/> Value: <input type="text" name="retail" /><br/><br/> Name of Institution: <br/><input type="text" name="institution" /> <br/><br/> Type (select the best option) <br/> <select name="type"> <option value="none">None</option> <option value="k5">K-5</option> <option value="k8">K-8</option> <option value="68">6-8</option> <option value="912">9-12</option> </select> <br/><br/> Address: <br/><input type="text" name="address_sch" /> <br/> City: <br/><input type="text" name="city_sch" /> <br/> State: <br/><input type="text" name="state_sch" /> <br/> Zip Code: <br/><input type="text" name="zip_sch" /> <br/> <form name="frm2sub" id="frm2sub" action="page3.php" method="post"> <input type="submit" name="submit" value="Submit" id="submit" /> </form> </div> This is my jquery function: $("#frm2sub").submit( function() { var values = {}; values["fname"] = $("#fname").val(); }); I can do this for each one of the input boxes but I want to give all this data to the next page. So how do I put this array into $_POST? Btw, I've tried doing var data = $("#reg2a").serialize(); and var data = $(document).serialize(); Data ended up being empty. Any ideas? Thanks

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• Improving Partitioned Table Join Performance

  - by Paul White

  The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

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• A Taxonomy of Numerical Methods v1

  - by JoshReuben

  Numerical Analysis – When, What, (but not how) Once you understand the Math & know C++, Numerical Methods are basically blocks of iterative & conditional math code. I found the real trick was seeing the forest for the trees – knowing which method to use for which situation. Its pretty easy to get lost in the details – so I’ve tried to organize these methods in a way that I can quickly look this up. I’ve included links to detailed explanations and to C++ code examples. I’ve tried to classify Numerical methods in the following broad categories: Solving Systems of Linear Equations Solving Non-Linear Equations Iteratively Interpolation Curve Fitting Optimization Numerical Differentiation & Integration Solving ODEs Boundary Problems Solving EigenValue problems Enjoy – I did ! Solving Systems of Linear Equations Overview Solve sets of algebraic equations with x unknowns The set is commonly in matrix form Gauss-Jordan Elimination http://en.wikipedia.org/wiki/Gauss%E2%80%93Jordan_elimination C++: http://www.codekeep.net/snippets/623f1923-e03c-4636-8c92-c9dc7aa0d3c0.aspx Produces solution of the equations & the coefficient matrix Efficient, stable 2 steps: · Forward Elimination – matrix decomposition: reduce set to triangular form (0s below the diagonal) or row echelon form. If degenerate, then there is no solution · Backward Elimination –write the original matrix as the product of ints inverse matrix & its reduced row-echelon matrix à reduce set to row canonical form & use back-substitution to find the solution to the set Elementary ops for matrix decomposition: · Row multiplication · Row switching · Add multiples of rows to other rows Use pivoting to ensure rows are ordered for achieving triangular form LU Decomposition http://en.wikipedia.org/wiki/LU_decomposition C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-lu-decomposition-for-solving.html Represent the matrix as a product of lower & upper triangular matrices A modified version of GJ Elimination Advantage – can easily apply forward & backward elimination to solve triangular matrices Techniques: · Doolittle Method – sets the L matrix diagonal to unity · Crout Method - sets the U matrix diagonal to unity Note: both the L & U matrices share the same unity diagonal & can be stored compactly in the same matrix Gauss-Seidel Iteration http://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method C++: http://www.nr.com/forum/showthread.php?t=722 Transform the linear set of equations into a single equation & then use numerical integration (as integration formulas have Sums, it is implemented iteratively). an optimization of Gauss-Jacobi: 1.5 times faster, requires 0.25 iterations to achieve the same tolerance Solving Non-Linear Equations Iteratively find roots of polynomials – there may be 0, 1 or n solutions for an n order polynomial use iterative techniques Iterative methods · used when there are no known analytical techniques · Requires set functions to be continuous & differentiable · Requires an initial seed value – choice is critical to convergence à conduct multiple runs with different starting points & then select best result · Systematic - iterate until diminishing returns, tolerance or max iteration conditions are met · bracketing techniques will always yield convergent solutions, non-bracketing methods may fail to converge Incremental method if a nonlinear function has opposite signs at 2 ends of a small interval x1 & x2, then there is likely to be a solution in their interval – solutions are detected by evaluating a function over interval steps, for a change in sign, adjusting the step size dynamically. Limitations – can miss closely spaced solutions in large intervals, cannot detect degenerate (coinciding) solutions, limited to functions that cross the x-axis, gives false positives for singularities Fixed point method http://en.wikipedia.org/wiki/Fixed-point_iteration C++: http://books.google.co.il/books?id=weYj75E_t6MC&pg=PA79&lpg=PA79&dq=fixed+point+method++c%2B%2B&source=bl&ots=LQ-5P_taoC&sig=lENUUIYBK53tZtTwNfHLy5PEWDk&hl=en&sa=X&ei=wezDUPW1J5DptQaMsIHQCw&redir_esc=y#v=onepage&q=fixed%20point%20method%20%20c%2B%2B&f=false Algebraically rearrange a solution to isolate a variable then apply incremental method Bisection method http://en.wikipedia.org/wiki/Bisection_method C++: http://numericalcomputing.wordpress.com/category/algorithms/ Bracketed - Select an initial interval, keep bisecting it ad midpoint into sub-intervals and then apply incremental method on smaller & smaller intervals – zoom in Adv: unaffected by function gradient à reliable Disadv: slow convergence False Position Method http://en.wikipedia.org/wiki/False_position_method C++: http://www.dreamincode.net/forums/topic/126100-bisection-and-false-position-methods/ Bracketed - Select an initial interval , & use the relative value of function at interval end points to select next sub-intervals (estimate how far between the end points the solution might be & subdivide based on this) Newton-Raphson method http://en.wikipedia.org/wiki/Newton's_method C++: http://www-users.cselabs.umn.edu/classes/Summer-2012/csci1113/index.php?page=./newt3 Also known as Newton's method Convenient, efficient Not bracketed – only a single initial guess is required to start iteration – requires an analytical expression for the first derivative of the function as input. Evaluates the function & its derivative at each step. Can be extended to the Newton MutiRoot method for solving multiple roots Can be easily applied to an of n-coupled set of non-linear equations – conduct a Taylor Series expansion of a function, dropping terms of order n, rewrite as a Jacobian matrix of PDs & convert to simultaneous linear equations !!! Secant Method http://en.wikipedia.org/wiki/Secant_method C++: http://forum.vcoderz.com/showthread.php?p=205230 Unlike N-R, can estimate first derivative from an initial interval (does not require root to be bracketed) instead of inputting it Since derivative is approximated, may converge slower. Is fast in practice as it does not have to evaluate the derivative at each step. Similar implementation to False Positive method Birge-Vieta Method http://mat.iitm.ac.in/home/sryedida/public_html/caimna/transcendental/polynomial%20methods/bv%20method.html C++: http://books.google.co.il/books?id=cL1boM2uyQwC&pg=SA3-PA51&lpg=SA3-PA51&dq=Birge-Vieta+Method+c%2B%2B&source=bl&ots=QZmnDTK3rC&sig=BPNcHHbpR_DKVoZXrLi4nVXD-gg&hl=en&sa=X&ei=R-_DUK2iNIjzsgbE5ID4Dg&redir_esc=y#v=onepage&q=Birge-Vieta%20Method%20c%2B%2B&f=false combines Horner's method of polynomial evaluation (transforming into lesser degree polynomials that are more computationally efficient to process) with Newton-Raphson to provide a computational speed-up Interpolation Overview Construct new data points for as close as possible fit within range of a discrete set of known points (that were obtained via sampling, experimentation) Use Taylor Series Expansion of a function f(x) around a specific value for x Linear Interpolation http://en.wikipedia.org/wiki/Linear_interpolation C++: http://www.hamaluik.com/?p=289 Straight line between 2 points à concatenate interpolants between each pair of data points Bilinear Interpolation http://en.wikipedia.org/wiki/Bilinear_interpolation C++: http://supercomputingblog.com/graphics/coding-bilinear-interpolation/2/ Extension of the linear function for interpolating functions of 2 variables – perform linear interpolation first in 1 direction, then in another. Used in image processing – e.g. texture mapping filter. Uses 4 vertices to interpolate a value within a unit cell. Lagrange Interpolation http://en.wikipedia.org/wiki/Lagrange_polynomial C++: http://www.codecogs.com/code/maths/approximation/interpolation/lagrange.php For polynomials Requires recomputation for all terms for each distinct x value – can only be applied for small number of nodes Numerically unstable Barycentric Interpolation http://epubs.siam.org/doi/pdf/10.1137/S0036144502417715 C++: http://www.gamedev.net/topic/621445-barycentric-coordinates-c-code-check/ Rearrange the terms in the equation of the Legrange interpolation by defining weight functions that are independent of the interpolated value of x Newton Divided Difference Interpolation http://en.wikipedia.org/wiki/Newton_polynomial C++: http://jee-appy.blogspot.co.il/2011/12/newton-divided-difference-interpolation.html Hermite Divided Differences: Interpolation polynomial approximation for a given set of data points in the NR form - divided differences are used to approximately calculate the various differences. For a given set of 3 data points , fit a quadratic interpolant through the data Bracketed functions allow Newton divided differences to be calculated recursively Difference table Cubic Spline Interpolation http://en.wikipedia.org/wiki/Spline_interpolation C++: https://www.marcusbannerman.co.uk/index.php/home/latestarticles/42-articles/96-cubic-spline-class.html Spline is a piecewise polynomial Provides smoothness – for interpolations with significantly varying data Use weighted coefficients to bend the function to be smooth & its 1st & 2nd derivatives are continuous through the edge points in the interval Curve Fitting A generalization of interpolating whereby given data points may contain noise à the curve does not necessarily pass through all the points Least Squares Fit http://en.wikipedia.org/wiki/Least_squares C++: http://www.ccas.ru/mmes/educat/lab04k/02/least-squares.c Residual – difference between observed value & expected value Model function is often chosen as a linear combination of the specified functions Determines: A) The model instance in which the sum of squared residuals has the least value B) param values for which model best fits data Straight Line Fit Linear correlation between independent variable and dependent variable Linear Regression http://en.wikipedia.org/wiki/Linear_regression C++: http://www.oocities.org/david_swaim/cpp/linregc.htm Special case of statistically exact extrapolation Leverage least squares Given a basis function, the sum of the residuals is determined and the corresponding gradient equation is expressed as a set of normal linear equations in matrix form that can be solved (e.g. using LU Decomposition) Can be weighted - Drop the assumption that all errors have the same significance –-> confidence of accuracy is different for each data point. Fit the function closer to points with higher weights Polynomial Fit - use a polynomial basis function Moving Average http://en.wikipedia.org/wiki/Moving_average C++: http://www.codeproject.com/Articles/17860/A-Simple-Moving-Average-Algorithm Used for smoothing (cancel fluctuations to highlight longer-term trends & cycles), time series data analysis, signal processing filters Replace each data point with average of neighbors. Can be simple (SMA), weighted (WMA), exponential (EMA). Lags behind latest data points – extra weight can be given to more recent data points. Weights can decrease arithmetically or exponentially according to distance from point. Parameters: smoothing factor, period, weight basis Optimization Overview Given function with multiple variables, find Min (or max by minimizing –f(x)) Iterative approach Efficient, but not necessarily reliable Conditions: noisy data, constraints, non-linear models Detection via sign of first derivative - Derivative of saddle points will be 0 Local minima Bisection method Similar method for finding a root for a non-linear equation Start with an interval that contains a minimum Golden Search method http://en.wikipedia.org/wiki/Golden_section_search C++: http://www.codecogs.com/code/maths/optimization/golden.php Bisect intervals according to golden ratio 0.618.. Achieves reduction by evaluating a single function instead of 2 Newton-Raphson Method Brent method http://en.wikipedia.org/wiki/Brent's_method C++: http://people.sc.fsu.edu/~jburkardt/cpp_src/brent/brent.cpp Based on quadratic or parabolic interpolation – if the function is smooth & parabolic near to the minimum, then a parabola fitted through any 3 points should approximate the minima – fails when the 3 points are collinear , in which case the denominator is 0 Simplex Method http://en.wikipedia.org/wiki/Simplex_algorithm C++: http://www.codeguru.com/cpp/article.php/c17505/Simplex-Optimization-Algorithm-and-Implemetation-in-C-Programming.htm Find the global minima of any multi-variable function Direct search – no derivatives required At each step it maintains a non-degenerative simplex – a convex hull of n+1 vertices. Obtains the minimum for a function with n variables by evaluating the function at n-1 points, iteratively replacing the point of worst result with the point of best result, shrinking the multidimensional simplex around the best point. Point replacement involves expanding & contracting the simplex near the worst value point to determine a better replacement point Oscillation can be avoided by choosing the 2nd worst result Restart if it gets stuck Parameters: contraction & expansion factors Simulated Annealing http://en.wikipedia.org/wiki/Simulated_annealing C++: http://code.google.com/p/cppsimulatedannealing/ Analogy to heating & cooling metal to strengthen its structure Stochastic method – apply random permutation search for global minima - Avoid entrapment in local minima via hill climbing Heating schedule - Annealing schedule params: temperature, iterations at each temp, temperature delta Cooling schedule – can be linear, step-wise or exponential Differential Evolution http://en.wikipedia.org/wiki/Differential_evolution C++: http://www.amichel.com/de/doc/html/ More advanced stochastic methods analogous to biological processes: Genetic algorithms, evolution strategies Parallel direct search method against multiple discrete or continuous variables Initial population of variable vectors chosen randomly – if weighted difference vector of 2 vectors yields a lower objective function value then it replaces the comparison vector Many params: #parents, #variables, step size, crossover constant etc Convergence is slow – many more function evaluations than simulated annealing Numerical Differentiation Overview 2 approaches to finite difference methods: · A) approximate function via polynomial interpolation then differentiate · B) Taylor series approximation – additionally provides error estimate Finite Difference methods http://en.wikipedia.org/wiki/Finite_difference_method C++: http://www.wpi.edu/Pubs/ETD/Available/etd-051807-164436/unrestricted/EAMPADU.pdf Find differences between high order derivative values - Approximate differential equations by finite differences at evenly spaced data points Based on forward & backward Taylor series expansion of f(x) about x plus or minus multiples of delta h. Forward / backward difference - the sums of the series contains even derivatives and the difference of the series contains odd derivatives – coupled equations that can be solved. Provide an approximation of the derivative within a O(h^2) accuracy There is also central difference & extended central difference which has a O(h^4) accuracy Richardson Extrapolation http://en.wikipedia.org/wiki/Richardson_extrapolation C++: http://mathscoding.blogspot.co.il/2012/02/introduction-richardson-extrapolation.html A sequence acceleration method applied to finite differences Fast convergence, high accuracy O(h^4) Derivatives via Interpolation Cannot apply Finite Difference method to discrete data points at uneven intervals – so need to approximate the derivative of f(x) using the derivative of the interpolant via 3 point Lagrange Interpolation Note: the higher the order of the derivative, the lower the approximation precision Numerical Integration Estimate finite & infinite integrals of functions More accurate procedure than numerical differentiation Use when it is not possible to obtain an integral of a function analytically or when the function is not given, only the data points are Newton Cotes Methods http://en.wikipedia.org/wiki/Newton%E2%80%93Cotes_formulas C++: http://www.siafoo.net/snippet/324 For equally spaced data points Computationally easy – based on local interpolation of n rectangular strip areas that is piecewise fitted to a polynomial to get the sum total area Evaluate the integrand at n+1 evenly spaced points – approximate definite integral by Sum Weights are derived from Lagrange Basis polynomials Leverage Trapezoidal Rule for default 2nd formulas, Simpson 1/3 Rule for substituting 3 point formulas, Simpson 3/8 Rule for 4 point formulas. For 4 point formulas use Bodes Rule. Higher orders obtain more accurate results Trapezoidal Rule uses simple area, Simpsons Rule replaces the integrand f(x) with a quadratic polynomial p(x) that uses the same values as f(x) for its end points, but adds a midpoint Romberg Integration http://en.wikipedia.org/wiki/Romberg's_method C++: http://code.google.com/p/romberg-integration/downloads/detail?name=romberg.cpp&can=2&q= Combines trapezoidal rule with Richardson Extrapolation Evaluates the integrand at equally spaced points The integrand must have continuous derivatives Each R(n,m) extrapolation uses a higher order integrand polynomial replacement rule (zeroth starts with trapezoidal) à a lower triangular matrix set of equation coefficients where the bottom right term has the most accurate approximation. The process continues until the difference between 2 successive diagonal terms becomes sufficiently small. Gaussian Quadrature http://en.wikipedia.org/wiki/Gaussian_quadrature C++: http://www.alglib.net/integration/gaussianquadratures.php Data points are chosen to yield best possible accuracy – requires fewer evaluations Ability to handle singularities, functions that are difficult to evaluate The integrand can include a weighting function determined by a set of orthogonal polynomials. Points & weights are selected so that the integrand yields the exact integral if f(x) is a polynomial of degree <= 2n+1 Techniques (basically different weighting functions): · Gauss-Legendre Integration w(x)=1 · Gauss-Laguerre Integration w(x)=e^-x · Gauss-Hermite Integration w(x)=e^-x^2 · Gauss-Chebyshev Integration w(x)= 1 / Sqrt(1-x^2) Solving ODEs Use when high order differential equations cannot be solved analytically Evaluated under boundary conditions RK for systems – a high order differential equation can always be transformed into a coupled first order system of equations Euler method http://en.wikipedia.org/wiki/Euler_method C++: http://rosettacode.org/wiki/Euler_method First order Runge–Kutta method. Simple recursive method – given an initial value, calculate derivative deltas. Unstable & not very accurate (O(h) error) – not used in practice A first-order method - the local error (truncation error per step) is proportional to the square of the step size, and the global error (error at a given time) is proportional to the step size In evolving solution between data points xn & xn+1, only evaluates derivatives at beginning of interval xn à asymmetric at boundaries Higher order Runge Kutta http://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods C++: http://www.dreamincode.net/code/snippet1441.htm 2nd & 4th order RK - Introduces parameterized midpoints for more symmetric solutions à accuracy at higher computational cost Adaptive RK – RK-Fehlberg – estimate the truncation at each integration step & automatically adjust the step size to keep error within prescribed limits. At each step 2 approximations are compared – if in disagreement to a specific accuracy, the step size is reduced Boundary Value Problems Where solution of differential equations are located at 2 different values of the independent variable x à more difficult, because cannot just start at point of initial value – there may not be enough starting conditions available at the end points to produce a unique solution An n-order equation will require n boundary conditions – need to determine the missing n-1 conditions which cause the given conditions at the other boundary to be satisfied Shooting Method http://en.wikipedia.org/wiki/Shooting_method C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-shooting-method-for-solving.html Iteratively guess the missing values for one end & integrate, then inspect the discrepancy with the boundary values of the other end to adjust the estimate Given the starting boundary values u1 & u2 which contain the root u, solve u given the false position method (solving the differential equation as an initial value problem via 4th order RK), then use u to solve the differential equations. Finite Difference Method For linear & non-linear systems Higher order derivatives require more computational steps – some combinations for boundary conditions may not work though Improve the accuracy by increasing the number of mesh points Solving EigenValue Problems An eigenvalue can substitute a matrix when doing matrix multiplication à convert matrix multiplication into a polynomial EigenValue For a given set of equations in matrix form, determine what are the solution eigenvalue & eigenvectors Similar Matrices - have same eigenvalues. Use orthogonal similarity transforms to reduce a matrix to diagonal form from which eigenvalue(s) & eigenvectors can be computed iteratively Jacobi method http://en.wikipedia.org/wiki/Jacobi_method C++: http://people.sc.fsu.edu/~jburkardt/classes/acs2_2008/openmp/jacobi/jacobi.html Robust but Computationally intense – use for small matrices < 10x10 Power Iteration http://en.wikipedia.org/wiki/Power_iteration For any given real symmetric matrix, generate the largest single eigenvalue & its eigenvectors Simplest method – does not compute matrix decomposition à suitable for large, sparse matrices Inverse Iteration Variation of power iteration method – generates the smallest eigenvalue from the inverse matrix Rayleigh Method http://en.wikipedia.org/wiki/Rayleigh's_method_of_dimensional_analysis Variation of power iteration method Rayleigh Quotient Method Variation of inverse iteration method Matrix Tri-diagonalization Method Use householder algorithm to reduce an NxN symmetric matrix to a tridiagonal real symmetric matrix vua N-2 orthogonal transforms     Whats Next Outside of Numerical Methods there are lots of different types of algorithms that I’ve learned over the decades: Data Mining – (I covered this briefly in a previous post: http://geekswithblogs.net/JoshReuben/archive/2007/12/31/ssas-dm-algorithms.aspx ) Search & Sort Routing Problem Solving Logical Theorem Proving Planning Probabilistic Reasoning Machine Learning Solvers (eg MIP) Bioinformatics (Sequence Alignment, Protein Folding) Quant Finance (I read Wilmott’s books – interesting) Sooner or later, I’ll cover the above topics as well.

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• help with fixing fwts errors log

  - by jasmines

  Here is an extract of results.log: MTRR validation. Test 1 of 3: Validate the kernel MTRR IOMEM setup. FAILED [MEDIUM] MTRRIncorrectAttr: Test 1, Memory range 0xc0000000 to 0xdfffffff (PCI Bus 0000:00) has incorrect attribute Write-Combining. FAILED [MEDIUM] MTRRIncorrectAttr: Test 1, Memory range 0xfee01000 to 0xffffffff (PCI Bus 0000:00) has incorrect attribute Write-Protect. ==================================================================================================== Test 1 of 1: Kernel log error check. Kernel message: [ 0.208079] [Firmware Bug]: ACPI: BIOS _OSI(Linux) query ignored ADVICE: This is not exactly a failure mode but a warning from the kernel. The _OSI() method has implemented a match to the 'Linux' query in the DSDT and this is redundant because the ACPI driver matches onto the Windows _OSI strings by default. FAILED [HIGH] KlogACPIErrorMethodExecutionParse: Test 1, HIGH Kernel message: [ 3.512783] ACPI Error : Method parse/execution failed [\_SB_.PCI0.GFX0._DOD] (Node f7425858), AE_AML_PACKAGE_LIMIT (20110623/psparse-536) ADVICE: This is a bug picked up by the kernel, but as yet, the firmware test suite has no diagnostic advice for this particular problem. Found 1 unique errors in kernel log. ==================================================================================================== Check if system is using latest microcode. ---------------------------------------------------------------------------------------------------- Cannot read microcode file /usr/share/misc/intel-microcode.dat. Aborted test, initialisation failed. ==================================================================================================== MSR register tests. FAILED [MEDIUM] MSRCPUsInconsistent: Test 1, MSR SYSENTER_ESP (0x175) has 1 inconsistent values across 2 CPUs for (shift: 0 mask: 0xffffffffffffffff). MSR CPU 0 -> 0xf7bb9c40 vs CPU 1 -> 0xf7bc7c40 FAILED [MEDIUM] MSRCPUsInconsistent: Test 1, MSR MISC_ENABLE (0x1a0) has 1 inconsistent values across 2 CPUs for (shift: 0 mask: 0x400c51889). MSR CPU 0 -> 0x850088 vs CPU 1 -> 0x850089 ==================================================================================================== Checks firmware has set PCI Express MaxReadReq to a higher value on non-motherboard devices. ---------------------------------------------------------------------------------------------------- Test 1 of 1: Check firmware settings MaxReadReq for PCI Express devices. MaxReadReq for pci://00:00:1b.0 Audio device: Intel Corporation 82801I (ICH9 Family) HD Audio Controller (rev 03) is low (128) [Audio device]. MaxReadReq for pci://00:02:00.0 Network controller: Intel Corporation PRO/Wireless 5100 AGN [Shiloh] Network Connection is low (128) [Network controller]. FAILED [LOW] LowMaxReadReq: Test 1, 2 devices have low MaxReadReq settings. Firmware may have configured these too low. ADVICE: The MaxReadRequest size is set too low and will affect performance. It will provide excellent bus sharing at the cost of bus data transfer rates. Although not a critical issue, it may be worth considering setting the MaxReadRequest size to 256 or 512 to increase throughput on the PCI Express bus. Some drivers (for example the Brocade Fibre Channel driver) allow one to override the firmware settings. Where possible, this BIOS configuration setting is worth increasing it a little more for better performance at a small reduction of bus sharing. ==================================================================================================== PCIe ASPM check. ---------------------------------------------------------------------------------------------------- Test 1 of 2: PCIe ASPM ACPI test. PCIE ASPM is not controlled by Linux kernel. ADVICE: BIOS reports that Linux kernel should not modify ASPM settings that BIOS configured. It can be intentional because hardware vendors identified some capability bugs between the motherboard and the add-on cards. Test 2 of 2: PCIe ASPM registers test. WARNING: Test 2, RP 00h:1Ch.01h L0s not enabled. WARNING: Test 2, RP 00h:1Ch.01h L1 not enabled. WARNING: Test 2, Device 02h:00h.00h L0s not enabled. WARNING: Test 2, Device 02h:00h.00h L1 not enabled. PASSED: Test 2, PCIE aspm setting matched was matched. WARNING: Test 2, RP 00h:1Ch.05h L0s not enabled. WARNING: Test 2, RP 00h:1Ch.05h L1 not enabled. WARNING: Test 2, Device 85h:00h.00h L0s not enabled. WARNING: Test 2, Device 85h:00h.00h L1 not enabled. PASSED: Test 2, PCIE aspm setting matched was matched. ==================================================================================================== Extract and analyse Windows Management Instrumentation (WMI). Test 1 of 2: Check Windows Management Instrumentation in DSDT Found WMI Method WMAA with GUID: 5FB7F034-2C63-45E9-BE91-3D44E2C707E4, Instance 0x01 Found WMI Event, Notifier ID: 0x80, GUID: 95F24279-4D7B-4334-9387-ACCDC67EF61C, Instance 0x01 PASSED: Test 1, GUID 95F24279-4D7B-4334-9387-ACCDC67EF61C is handled by driver hp-wmi (Vendor: HP). Found WMI Event, Notifier ID: 0xa0, GUID: 2B814318-4BE8-4707-9D84-A190A859B5D0, Instance 0x01 FAILED [MEDIUM] WMIUnknownGUID: Test 1, GUID 2B814318-4BE8-4707-9D84-A190A859B5D0 is unknown to the kernel, a driver may need to be implemented for this GUID. ADVICE: A WMI driver probably needs to be written for this event. It can checked for using: wmi_has_guid("2B814318-4BE8-4707-9D84-A190A859B5D0"). One can install a notify handler using wmi_install_notify_handler("2B814318-4BE8-4707-9D84-A190A859B5D0", handler, NULL). http://lwn.net/Articles/391230 describes how to write an appropriate driver. Found WMI Object, Object ID AB, GUID: 05901221-D566-11D1-B2F0-00A0C9062910, Instance 0x01, Flags: 00 Found WMI Method WMBA with GUID: 1F4C91EB-DC5C-460B-951D-C7CB9B4B8D5E, Instance 0x01 Found WMI Object, Object ID BC, GUID: 2D114B49-2DFB-4130-B8FE-4A3C09E75133, Instance 0x7f, Flags: 00 Found WMI Object, Object ID BD, GUID: 988D08E3-68F4-4C35-AF3E-6A1B8106F83C, Instance 0x19, Flags: 00 Found WMI Object, Object ID BE, GUID: 14EA9746-CE1F-4098-A0E0-7045CB4DA745, Instance 0x01, Flags: 00 Found WMI Object, Object ID BF, GUID: 322F2028-0F84-4901-988E-015176049E2D, Instance 0x01, Flags: 00 Found WMI Object, Object ID BG, GUID: 8232DE3D-663D-4327-A8F4-E293ADB9BF05, Instance 0x01, Flags: 00 Found WMI Object, Object ID BH, GUID: 8F1F6436-9F42-42C8-BADC-0E9424F20C9A, Instance 0x00, Flags: 00 Found WMI Object, Object ID BI, GUID: 8F1F6435-9F42-42C8-BADC-0E9424F20C9A, Instance 0x00, Flags: 00 Found WMI Method WMAC with GUID: 7391A661-223A-47DB-A77A-7BE84C60822D, Instance 0x01 Found WMI Object, Object ID BJ, GUID: DF4E63B6-3BBC-4858-9737-C74F82F821F3, Instance 0x05, Flags: 00 ==================================================================================================== Disassemble DSDT to check for _OSI("Linux"). ---------------------------------------------------------------------------------------------------- Test 1 of 1: Disassemble DSDT to check for _OSI("Linux"). This is not strictly a failure mode, it just alerts one that this has been defined in the DSDT and probably should be avoided since the Linux ACPI driver matches onto the Windows _OSI strings { If (_OSI ("Linux")) { Store (0x03E8, OSYS) } If (_OSI ("Windows 2001")) { Store (0x07D1, OSYS) } If (_OSI ("Windows 2001 SP1")) { Store (0x07D1, OSYS) } If (_OSI ("Windows 2001 SP2")) { Store (0x07D2, OSYS) } If (_OSI ("Windows 2006")) { Store (0x07D6, OSYS) } If (LAnd (MPEN, LEqual (OSYS, 0x07D1))) { TRAP (0x01, 0x48) } TRAP (0x03, 0x35) } WARNING: Test 1, DSDT implements a deprecated _OSI("Linux") test. ==================================================================================================== 0 passed, 0 failed, 1 warnings, 0 aborted, 0 skipped, 0 info only. ==================================================================================================== ACPI DSDT Method Semantic Tests. ACPICA Exception AE_AML_INFINITE_LOOP during execution of method COMP Failed to install global event handler. Test 22 of 93: Check _PSR (Power Source). ACPICA Exception AE_AML_INFINITE_LOOP during execution of method COMP WARNING: Test 22, Detected an infinite loop when evaluating method '\_SB_.AC__._PSR'. ADVICE: This may occur because we are emulating the execution in this test environment and cannot handshake with the embedded controller or jump to the BIOS via SMIs. However, the fact that AML code spins forever means that lockup conditions are not being checked for in the AML bytecode. PASSED: Test 22, \_SB_.AC__._PSR correctly acquired and released locks 16 times. Test 35 of 93: Check _TMP (Thermal Zone Current Temp). ACPICA Exception AE_AML_INFINITE_LOOP during execution of method COMP WARNING: Test 35, Detected an infinite loop when evaluating method '\_TZ_.DTSZ._TMP'. ADVICE: This may occur because we are emulating the execution in this test environment and cannot handshake with the embedded controller or jump to the BIOS via SMIs. However, the fact that AML code spins forever means that lockup conditions are not being checked for in the AML bytecode. PASSED: Test 35, \_TZ_.DTSZ._TMP correctly acquired and released locks 14 times. ACPICA Exception AE_AML_INFINITE_LOOP during execution of method COMP WARNING: Test 35, Detected an infinite loop when evaluating method '\_TZ_.CPUZ._TMP'. ADVICE: This may occur because we are emulating the execution in this test environment and cannot handshake with the embedded controller or jump to the BIOS via SMIs. However, the fact that AML code spins forever means that lockup conditions are not being checked for in the AML bytecode. PASSED: Test 35, \_TZ_.CPUZ._TMP correctly acquired and released locks 10 times. ACPICA Exception AE_AML_INFINITE_LOOP during execution of method COMP WARNING: Test 35, Detected an infinite loop when evaluating method '\_TZ_.SKNZ._TMP'. ADVICE: This may occur because we are emulating the execution in this test environment and cannot handshake with the embedded controller or jump to the BIOS via SMIs. However, the fact that AML code spins forever means that lockup conditions are not being checked for in the AML bytecode. PASSED: Test 35, \_TZ_.SKNZ._TMP correctly acquired and released locks 10 times. PASSED: Test 35, _TMP correctly returned sane looking value 0x00000b4c (289.2 degrees K) PASSED: Test 35, \_TZ_.BATZ._TMP correctly acquired and released locks 9 times. PASSED: Test 35, _TMP correctly returned sane looking value 0x00000aac (273.2 degrees K) PASSED: Test 35, \_TZ_.FDTZ._TMP correctly acquired and released locks 7 times. Test 46 of 93: Check _DIS (Disable). FAILED [MEDIUM] MethodShouldReturnNothing: Test 46, \_SB_.PCI0.LPCB.SIO_.COM1._DIS returned values, but was expected to return nothing. Object returned: INTEGER: 0x00000000 ADVICE: This probably won't cause any errors, but it should be fixed as the AML code is not conforming to the expected behaviour as described in the ACPI specification. FAILED [MEDIUM] MethodShouldReturnNothing: Test 46, \_SB_.PCI0.LPCB.SIO_.LPT0._DIS returned values, but was expected to return nothing. Object returned: INTEGER: 0x00000000 ADVICE: This probably won't cause any errors, but it should be fixed as the AML code is not conforming to the expected behaviour as described in the ACPI specification. Test 61 of 93: Check _WAK (System Wake). Test _WAK(1) System Wake, State S1. ACPICA Exception AE_AML_INFINITE_LOOP during execution of method COMP WARNING: Test 61, Detected an infinite loop when evaluating method '\_WAK'. ADVICE: This may occur because we are emulating the execution in this test environment and cannot handshake with the embedded controller or jump to the BIOS via SMIs. However, the fact that AML code spins forever means that lockup conditions are not being checked for in the AML bytecode. Test _WAK(2) System Wake, State S2. ACPICA Exception AE_AML_INFINITE_LOOP during execution of method COMP WARNING: Test 61, Detected an infinite loop when evaluating method '\_WAK'. ADVICE: This may occur because we are emulating the execution in this test environment and cannot handshake with the embedded controller or jump to the BIOS via SMIs. However, the fact that AML code spins forever means that lockup conditions are not being checked for in the AML bytecode. Test _WAK(3) System Wake, State S3. ACPICA Exception AE_AML_INFINITE_LOOP during execution of method COMP WARNING: Test 61, Detected an infinite loop when evaluating method '\_WAK'. ADVICE: This may occur because we are emulating the execution in this test environment and cannot handshake with the embedded controller or jump to the BIOS via SMIs. However, the fact that AML code spins forever means that lockup conditions are not being checked for in the AML bytecode. Test _WAK(4) System Wake, State S4. ACPICA Exception AE_AML_INFINITE_LOOP during execution of method COMP WARNING: Test 61, Detected an infinite loop when evaluating method '\_WAK'. ADVICE: This may occur because we are emulating the execution in this test environment and cannot handshake with the embedded controller or jump to the BIOS via SMIs. However, the fact that AML code spins forever means that lockup conditions are not being checked for in the AML bytecode. Test _WAK(5) System Wake, State S5. ACPICA Exception AE_AML_INFINITE_LOOP during execution of method COMP WARNING: Test 61, Detected an infinite loop when evaluating method '\_WAK'. ADVICE: This may occur because we are emulating the execution in this test environment and cannot handshake with the embedded controller or jump to the BIOS via SMIs. However, the fact that AML code spins forever means that lockup conditions are not being checked for in the AML bytecode. Test 87 of 93: Check _BCL (Query List of Brightness Control Levels Supported). Package has 2 elements: 00: INTEGER: 0x00000000 01: INTEGER: 0x00000000 FAILED [MEDIUM] Method_BCLElementCount: Test 87, Method _BCL should return a package of more than 2 integers, got just 2. Test 88 of 93: Check _BCM (Set Brightness Level). ACPICA Exception AE_AML_PACKAGE_LIMIT during execution of method _BCM FAILED [CRITICAL] AEAMLPackgeLimit: Test 88, Detected error 'Package limit' when evaluating '\_SB_.PCI0.GFX0.DD02._BCM'. ==================================================================================================== ACPI table settings sanity checks. ---------------------------------------------------------------------------------------------------- Test 1 of 1: Check ACPI tables. PASSED: Test 1, Table APIC passed. Table ECDT not present to check. FAILED [MEDIUM] FADT32And64BothDefined: Test 1, FADT 32 bit FIRMWARE_CONTROL is non-zero, and X_FIRMWARE_CONTROL is also non-zero. Section 5.2.9 of the ACPI specification states that if the FIRMWARE_CONTROL is non-zero then X_FIRMWARE_CONTROL must be set to zero. ADVICE: The FADT FIRMWARE_CTRL is a 32 bit pointer that points to the physical memory address of the Firmware ACPI Control Structure (FACS). There is also an extended 64 bit version of this, the X_FIRMWARE_CTRL pointer that also can point to the FACS. Section 5.2.9 of the ACPI specification states that if the X_FIRMWARE_CTRL field contains a non zero value then the FIRMWARE_CTRL field *must* be zero. This error is also detected by the Linux kernel. If FIRMWARE_CTRL and X_FIRMWARE_CTRL are defined, then the kernel just uses the 64 bit version of the pointer. PASSED: Test 1, Table HPET passed. PASSED: Test 1, Table MCFG passed. PASSED: Test 1, Table RSDT passed. PASSED: Test 1, Table RSDP passed. Table SBST not present to check. PASSED: Test 1, Table XSDT passed. ==================================================================================================== Re-assemble DSDT and find syntax errors and warnings. ---------------------------------------------------------------------------------------------------- Test 1 of 2: Disassemble and reassemble DSDT FAILED [HIGH] AMLAssemblerError4043: Test 1, Assembler error in line 2261 Line | AML source ---------------------------------------------------------------------------------------------------- 02258| 0x00000000, // Range Minimum 02259| 0xFEDFFFFF, // Range Maximum 02260| 0x00000000, // Translation Offset 02261| 0x00000000, // Length | ^ | error 4043: Invalid combination of Length and Min/Max fixed flags 02262| ,, _Y0E, AddressRangeMemory, TypeStatic) 02263| DWordMemory (ResourceProducer, PosDecode, MinFixed, MaxFixed, Cacheable, ReadWrite, 02264| 0x00000000, // Granularity ==================================================================================================== ADVICE: (for error #4043): This occurs if the length is zero and just one of the resource MIF/MAF flags are set, or the length is non-zero and resource MIF/MAF flags are both set. These are illegal combinations and need to be fixed. See section 6.4.3.5 Address Space Resource Descriptors of version 4.0a of the ACPI specification for more details. FAILED [HIGH] AMLAssemblerError4050: Test 1, Assembler error in line 2268 Line | AML source ---------------------------------------------------------------------------------------------------- 02265| 0xFEE01000, // Range Minimum 02266| 0xFFFFFFFF, // Range Maximum 02267| 0x00000000, // Translation Offset 02268| 0x011FEFFF, // Length | ^ | error 4050: Length is not equal to fixed Min/Max window 02269| ,, , AddressRangeMemory, TypeStatic) 02270| }) 02271| Method (_CRS, 0, Serialized) ==================================================================================================== ADVICE: (for error #4050): The minimum address is greater than the maximum address. This is illegal. FAILED [HIGH] AMLAssemblerError1104: Test 1, Assembler error in line 8885 Line | AML source ---------------------------------------------------------------------------------------------------- 08882| Method (_DIS, 0, NotSerialized) 08883| { 08884| DSOD (0x02) 08885| Return (0x00) | ^ | warning level 0 1104: Reserved method should not return a value (_DIS) 08886| } 08887| 08888| Method (_SRS, 1, NotSerialized) ==================================================================================================== FAILED [HIGH] AMLAssemblerError1104: Test 1, Assembler error in line 9195 Line | AML source ---------------------------------------------------------------------------------------------------- 09192| Method (_DIS, 0, NotSerialized) 09193| { 09194| DSOD (0x01) 09195| Return (0x00) | ^ | warning level 0 1104: Reserved method should not return a value (_DIS) 09196| } 09197| 09198| Method (_SRS, 1, NotSerialized) ==================================================================================================== FAILED [HIGH] AMLAssemblerError1127: Test 1, Assembler error in line 9242 Line | AML source ---------------------------------------------------------------------------------------------------- 09239| CreateWordField (CRES, \_SB.PCI0.LPCB.SIO.LPT0._CRS._Y21._MAX, MAX2) 09240| CreateByteField (CRES, \_SB.PCI0.LPCB.SIO.LPT0._CRS._Y21._LEN, LEN2) 09241| CreateWordField (CRES, \_SB.PCI0.LPCB.SIO.LPT0._CRS._Y22._INT, IRQ0) 09242| CreateWordField (CRES, \_SB.PCI0.LPCB.SIO.LPT0._CRS._Y23._DMA, DMA0) | ^ | warning level 0 1127: ResourceTag smaller than Field (Tag: 8 bits, Field: 16 bits) 09243| If (RLPD) 09244| { 09245| Store (0x00, Local0) ==================================================================================================== FAILED [HIGH] AMLAssemblerError1128: Test 1, Assembler error in line 18682 Line | AML source ---------------------------------------------------------------------------------------------------- 18679| Store (0x01, Index (DerefOf (Index (Local0, 0x02)), 0x01)) 18680| If (And (WDPE, 0x40)) 18681| { 18682| Wait (\_SB.BEVT, 0x10) | ^ | warning level 0 1128: Result is not used, possible operator timeout will be missed 18683| } 18684| 18685| Store (BRID, Index (DerefOf (Index (Local0, 0x02)), 0x02)) ==================================================================================================== ADVICE: (for warning level 0 #1128): The operation can possibly timeout, and hence the return value indicates an timeout error. However, because the return value is not checked this very probably indicates that the code is buggy. A possible scenario is that a mutex times out and the code attempts to access data in a critical region when it should not. This will lead to undefined behaviour. This should be fixed. Table DSDT (0) reassembly: Found 2 errors, 4 warnings. Test 2 of 2: Disassemble and reassemble SSDT PASSED: Test 2, SSDT (0) reassembly, Found 0 errors, 0 warnings. FAILED [HIGH] AMLAssemblerError1104: Test 2, Assembler error in line 60 Line | AML source ---------------------------------------------------------------------------------------------------- 00057| { 00058| Store (CPDC (Arg0), Local0) 00059| GCAP (Local0) 00060| Return (Local0) | ^ | warning level 0 1104: Reserved method should not return a value (_PDC) 00061| } 00062| 00063| Method (_OSC, 4, NotSerialized) ==================================================================================================== FAILED [HIGH] AMLAssemblerError1104: Test 2, Assembler error in line 174 Line | AML source ---------------------------------------------------------------------------------------------------- 00171| { 00172| Store (\_PR.CPU0.CPDC (Arg0), Local0) 00173| GCAP (Local0) 00174| Return (Local0) | ^ | warning level 0 1104: Reserved method should not return a value (_PDC) 00175| } 00176| 00177| Method (_OSC, 4, NotSerialized) ==================================================================================================== FAILED [HIGH] AMLAssemblerError1104: Test 2, Assembler error in line 244 Line | AML source ---------------------------------------------------------------------------------------------------- 00241| { 00242| Store (\_PR.CPU0.CPDC (Arg0), Local0) 00243| GCAP (Local0) 00244| Return (Local0) | ^ | warning level 0 1104: Reserved method should not return a value (_PDC) 00245| } 00246| 00247| Method (_OSC, 4, NotSerialized) ==================================================================================================== FAILED [HIGH] AMLAssemblerError1104: Test 2, Assembler error in line 290 Line | AML source ---------------------------------------------------------------------------------------------------- 00287| { 00288| Store (\_PR.CPU0.CPDC (Arg0), Local0) 00289| GCAP (Local0) 00290| Return (Local0) | ^ | warning level 0 1104: Reserved method should not return a value (_PDC) 00291| } 00292| 00293| Method (_OSC, 4, NotSerialized) ==================================================================================================== Table SSDT (1) reassembly: Found 0 errors, 4 warnings. PASSED: Test 2, SSDT (2) reassembly, Found 0 errors, 0 warnings. PASSED: Test 2, SSDT (3) reassembly, Found 0 errors, 0 warnings. ==================================================================================================== 3 passed, 10 failed, 0 warnings, 0 aborted, 0 skipped, 0 info only. ==================================================================================================== Critical failures: 1 method test, at 1 log line: 1449: Detected error 'Package limit' when evaluating '\_SB_.PCI0.GFX0.DD02._BCM'. High failures: 11 klog test, at 1 log line: 121: HIGH Kernel message: [ 3.512783] ACPI Error: Method parse/execution failed [\_SB_.PCI0.GFX0._DOD] (Node f7425858), AE_AML_PACKAGE_LIMIT (20110623/psparse-536) syntaxcheck test, at 1 log line: 1668: Assembler error in line 2261 syntaxcheck test, at 1 log line: 1687: Assembler error in line 2268 syntaxcheck test, at 1 log line: 1703: Assembler error in line 8885 syntaxcheck test, at 1 log line: 1716: Assembler error in line 9195 syntaxcheck test, at 1 log line: 1729: Assembler error in line 9242 syntaxcheck test, at 1 log line: 1742: Assembler error in line 18682 syntaxcheck test, at 1 log line: 1766: Assembler error in line 60 syntaxcheck test, at 1 log line: 1779: Assembler error in line 174 syntaxcheck test, at 1 log line: 1792: Assembler error in line 244 syntaxcheck test, at 1 log line: 1805: Assembler error in line 290 Medium failures: 9 mtrr test, at 1 log line: 76: Memory range 0xc0000000 to 0xdfffffff (PCI Bus 0000:00) has incorrect attribute Write-Combining. mtrr test, at 1 log line: 78: Memory range 0xfee01000 to 0xffffffff (PCI Bus 0000:00) has incorrect attribute Write-Protect. msr test, at 1 log line: 165: MSR SYSENTER_ESP (0x175) has 1 inconsistent values across 2 CPUs for (shift: 0 mask: 0xffffffffffffffff). msr test, at 1 log line: 173: MSR MISC_ENABLE (0x1a0) has 1 inconsistent values across 2 CPUs for (shift: 0 mask: 0x400c51889). wmi test, at 1 log line: 528: GUID 2B814318-4BE8-4707-9D84-A190A859B5D0 is unknown to the kernel, a driver may need to be implemented for this GUID. method test, at 1 log line: 1002: \_SB_.PCI0.LPCB.SIO_.COM1._DIS returned values, but was expected to return nothing. method test, at 1 log line: 1011: \_SB_.PCI0.LPCB.SIO_.LPT0._DIS returned values, but was expected to return nothing. method test, at 1 log line: 1443: Method _BCL should return a package of more than 2 integers, got just 2. acpitables test, at 1 log line: 1643: FADT 32 bit FIRMWARE_CONTROL is non-zero, and X_FIRMWARE_CONTROL is also non-zero. Se

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• The Incremental Architect&acute;s Napkin &ndash; #3 &ndash; Make Evolvability inevitable

  - by Ralf Westphal

  Originally posted on: http://geekswithblogs.net/theArchitectsNapkin/archive/2014/06/04/the-incremental-architectacutes-napkin-ndash-3-ndash-make-evolvability-inevitable.aspxThe easier something to measure the more likely it will be produced. Deviations between what is and what should be can be readily detected. That´s what automated acceptance tests are for. That´s what sprint reviews in Scrum are for. It´s no small wonder our software looks like it looks. It has all the traits whose conformance with requirements can easily be measured. And it´s lacking traits which cannot easily be measured. Evolvability (or Changeability) is such a trait. If an operation is correct, if an operation if fast enough, that can be checked very easily. But whether Evolvability is high or low, that cannot be checked by taking a measure or two. Evolvability might correlate with certain traits, e.g. number of lines of code (LOC) per function or Cyclomatic Complexity or test coverage. But there is no threshold value signalling “evolvability too low”; also Evolvability is hardly tangible for the customer. Nevertheless Evolvability is of great importance - at least in the long run. You can get away without much of it for a short time. Eventually, though, it´s needed like any other requirement. Or even more. Because without Evolvability no other requirement can be implemented. Evolvability is the foundation on which all else is build. Such fundamental importance is in stark contrast with its immeasurability. To compensate this, Evolvability must be put at the very center of software development. It must become the hub around everything else revolves. Since we cannot measure Evolvability, though, we cannot start watching it more. Instead we need to establish practices to keep it high (enough) at all times. Chefs have known that for long. That´s why everybody in a restaurant kitchen is constantly seeing after cleanliness. Hygiene is important as is to have clean tools at standardized locations. Only then the health of the patrons can be guaranteed and production efficiency is constantly high. Still a kitchen´s level of cleanliness is easier to measure than software Evolvability. That´s why important practices like reviews, pair programming, or TDD are not enough, I guess. What we need to keep Evolvability in focus and high is… to continually evolve. Change must not be something to avoid but too embrace. To me that means the whole change cycle from requirement analysis to delivery needs to be gone through more often. Scrum´s sprints of 4, 2 even 1 week are too long. Kanban´s flow of user stories across is too unreliable; it takes as long as it takes. Instead we should fix the cycle time at 2 days max. I call that Spinning. No increment must take longer than from this morning until tomorrow evening to finish. Then it should be acceptance checked by the customer (or his/her representative, e.g. a Product Owner). For me there are several resasons for such a fixed and short cycle time for each increment: Clear expectations Absolute estimates (“This will take X days to complete.”) are near impossible in software development as explained previously. Too much unplanned research and engineering work lurk in every feature. And then pervasive interruptions of work by peers and management. However, the smaller the scope the better our absolute estimates become. That´s because we understand better what really are the requirements and what the solution should look like. But maybe more importantly the shorter the timespan the more we can control how we use our time. So much can happen over the course of a week and longer timespans. But if push comes to shove I can block out all distractions and interruptions for a day or possibly two. That´s why I believe we can give rough absolute estimates on 3 levels: Noon Tonight Tomorrow Think of a meeting with a Product Owner at 8:30 in the morning. If she asks you, how long it will take you to implement a user story or bug fix, you can say, “It´ll be fixed by noon.”, or you can say, “I can manage to implement it until tonight before I leave.”, or you can say, “You´ll get it by tomorrow night at latest.” Yes, I believe all else would be naive. If you´re not confident to get something done by tomorrow night (some 34h from now) you just cannot reliably commit to any timeframe. That means you should not promise anything, you should not even start working on the issue. So when estimating use these four categories: Noon, Tonight, Tomorrow, NoClue - with NoClue meaning the requirement needs to be broken down further so each aspect can be assigned to one of the first three categories. If you like absolute estimates, here you go. But don´t do deep estimates. Don´t estimate dozens of issues; don´t think ahead (“Issue A is a Tonight, then B will be a Tomorrow, after that it´s C as a Noon, finally D is a Tonight - that´s what I´ll do this week.”). Just estimate so Work-in-Progress (WIP) is 1 for everybody - plus a small number of buffer issues. To be blunt: Yes, this makes promises impossible as to what a team will deliver in terms of scope at a certain date in the future. But it will give a Product Owner a clear picture of what to pull for acceptance feedback tonight and tomorrow. Trust through reliability Our trade is lacking trust. Customers don´t trust software companies/departments much. Managers don´t trust developers much. I find that perfectly understandable in the light of what we´re trying to accomplish: delivering software in the face of uncertainty by means of material good production. Customers as well as managers still expect software development to be close to production of houses or cars. But that´s a fundamental misunderstanding. Software development ist development. It´s basically research. As software developers we´re constantly executing experiments to find out what really provides value to users. We don´t know what they need, we just have mediated hypothesises. That´s why we cannot reliably deliver on preposterous demands. So trust is out of the window in no time. If we switch to delivering in short cycles, though, we can regain trust. Because estimates - explicit or implicit - up to 32 hours at most can be satisfied. I´d say: reliability over scope. It´s more important to reliably deliver what was promised then to cover a lot of requirement area. So when in doubt promise less - but deliver without delay. Deliver on scope (Functionality and Quality); but also deliver on Evolvability, i.e. on inner quality according to accepted principles. Always. Trust will be the reward. Less complexity of communication will follow. More goodwill buffer will follow. So don´t wait for some Kanban board to show you, that flow can be improved by scheduling smaller stories. You don´t need to learn that the hard way. Just start with small batch sizes of three different sizes. Fast feedback What has been finished can be checked for acceptance. Why wait for a sprint of several weeks to end? Why let the mental model of the issue and its solution dissipate? If you get final feedback after one or two weeks, you hardly remember what you did and why you did it. Resoning becomes hard. But more importantly youo probably are not in the mood anymore to go back to something you deemed done a long time ago. It´s boring, it´s frustrating to open up that mental box again. Learning is harder the longer it takes from event to feedback. Effort can be wasted between event (finishing an issue) and feedback, because other work might go in the wrong direction based on false premises. Checking finished issues for acceptance is the most important task of a Product Owner. It´s even more important than planning new issues. Because as long as work started is not released (accepted) it´s potential waste. So before starting new work better make sure work already done has value. By putting the emphasis on acceptance rather than planning true pull is established. As long as planning and starting work is more important, it´s a push process. Accept a Noon issue on the same day before leaving. Accept a Tonight issue before leaving today or first thing tomorrow morning. Accept a Tomorrow issue tomorrow night before leaving or early the day after tomorrow. After acceptance the developer(s) can start working on the next issue. Flexibility As if reliability/trust and fast feedback for less waste weren´t enough economic incentive, there is flexibility. After each issue the Product Owner can change course. If on Monday morning feature slices A, B, C, D, E were important and A, B, C were scheduled for acceptance by Monday evening and Tuesday evening, the Product Owner can change her mind at any time. Maybe after A got accepted she asks for continuation with D. But maybe, just maybe, she has gotten a completely different idea by then. Maybe she wants work to continue on F. And after B it´s neither D nor E, but G. And after G it´s D. With Spinning every 32 hours at latest priorities can be changed. And nothing is lost. Because what got accepted is of value. It provides an incremental value to the customer/user. Or it provides internal value to the Product Owner as increased knowledge/decreased uncertainty. I find such reactivity over commitment economically very benefical. Why commit a team to some workload for several weeks? It´s unnecessary at beast, and inflexible and wasteful at worst. If we cannot promise delivery of a certain scope on a certain date - which is what customers/management usually want -, we can at least provide them with unpredecented flexibility in the face of high uncertainty. Where the path is not clear, cannot be clear, make small steps so you´re able to change your course at any time. Premature completion Customers/management are used to premeditating budgets. They want to know exactly how much to pay for a certain amount of requirements. That´s understandable. But it does not match with the nature of software development. We should know that by now. Maybe there´s somewhere in the world some team who can consistently deliver on scope, quality, and time, and budget. Great! Congratulations! I, however, haven´t seen such a team yet. Which does not mean it´s impossible, but I think it´s nothing I can recommend to strive for. Rather I´d say: Don´t try this at home. It might hurt you one way or the other. However, what we can do, is allow customers/management stop work on features at any moment. With spinning every 32 hours a feature can be declared as finished - even though it might not be completed according to initial definition. I think, progress over completion is an important offer software development can make. Why think in terms of completion beyond a promise for the next 32 hours? Isn´t it more important to constantly move forward? Step by step. We´re not running sprints, we´re not running marathons, not even ultra-marathons. We´re in the sport of running forever. That makes it futile to stare at the finishing line. The very concept of a burn-down chart is misleading (in most cases). Whoever can only think in terms of completed requirements shuts out the chance for saving money. The requirements for a features mostly are uncertain. So how does a Product Owner know in the first place, how much is needed. Maybe more than specified is needed - which gets uncovered step by step with each finished increment. Maybe less than specified is needed. After each 4–32 hour increment the Product Owner can do an experient (or invite users to an experiment) if a particular trait of the software system is already good enough. And if so, she can switch the attention to a different aspect. In the end, requirements A, B, C then could be finished just 70%, 80%, and 50%. What the heck? It´s good enough - for now. 33% money saved. Wouldn´t that be splendid? Isn´t that a stunning argument for any budget-sensitive customer? You can save money and still get what you need? Pull on practices So far, in addition to more trust, more flexibility, less money spent, Spinning led to “doing less” which also means less code which of course means higher Evolvability per se. Last but not least, though, I think Spinning´s short acceptance cycles have one more effect. They excert pull-power on all sorts of practices known for increasing Evolvability. If, for example, you believe high automated test coverage helps Evolvability by lowering the fear of inadverted damage to a code base, why isn´t 90% of the developer community practicing automated tests consistently? I think, the answer is simple: Because they can do without. Somehow they manage to do enough manual checks before their rare releases/acceptance checks to ensure good enough correctness - at least in the short term. The same goes for other practices like component orientation, continuous build/integration, code reviews etc. None of that is compelling, urgent, imperative. Something else always seems more important. So Evolvability principles and practices fall through the cracks most of the time - until a project hits a wall. Then everybody becomes desperate; but by then (re)gaining Evolvability has become as very, very difficult and tedious undertaking. Sometimes up to the point where the existence of a project/company is in danger. With Spinning that´s different. If you´re practicing Spinning you cannot avoid all those practices. With Spinning you very quickly realize you cannot deliver reliably even on your 32 hour promises. Spinning thus is pulling on developers to adopt principles and practices for Evolvability. They will start actively looking for ways to keep their delivery rate high. And if not, management will soon tell them to do that. Because first the Product Owner then management will notice an increasing difficulty to deliver value within 32 hours. There, finally there emerges a way to measure Evolvability: The more frequent developers tell the Product Owner there is no way to deliver anything worth of feedback until tomorrow night, the poorer Evolvability is. Don´t count the “WTF!”, count the “No way!” utterances. In closing For sustainable software development we need to put Evolvability first. Functionality and Quality must not rule software development but be implemented within a framework ensuring (enough) Evolvability. Since Evolvability cannot be measured easily, I think we need to put software development “under pressure”. Software needs to be changed more often, in smaller increments. Each increment being relevant to the customer/user in some way. That does not mean each increment is worthy of shipment. It´s sufficient to gain further insight from it. Increments primarily serve the reduction of uncertainty, not sales. Sales even needs to be decoupled from this incremental progress. No more promises to sales. No more delivery au point. Rather sales should look at a stream of accepted increments (or incremental releases) and scoup from that whatever they find valuable. Sales and marketing need to realize they should work on what´s there, not what might be possible in the future. But I digress… In my view a Spinning cycle - which is not easy to reach, which requires practice - is the core practice to compensate the immeasurability of Evolvability. From start to finish of each issue in 32 hours max - that´s the challenge we need to accept if we´re serious increasing Evolvability. Fortunately higher Evolvability is not the only outcome of Spinning. Customer/management will like the increased flexibility and “getting more bang for the buck”.

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• PTLQueue : a scalable bounded-capacity MPMC queue

  - by Dave

  Title: Fast concurrent MPMC queue -- I've used the following concurrent queue algorithm enough that it warrants a blog entry. I'll sketch out the design of a fast and scalable multiple-producer multiple-consumer (MPSC) concurrent queue called PTLQueue. The queue has bounded capacity and is implemented via a circular array. Bounded capacity can be a useful property if there's a mismatch between producer rates and consumer rates where an unbounded queue might otherwise result in excessive memory consumption by virtue of the container nodes that -- in some queue implementations -- are used to hold values. A bounded-capacity queue can provide flow control between components. Beware, however, that bounded collections can also result in resource deadlock if abused. The put() and take() operators are partial and wait for the collection to become non-full or non-empty, respectively. Put() and take() do not allocate memory, and are not vulnerable to the ABA pathologies. The PTLQueue algorithm can be implemented equally well in C/C++ and Java. Partial operators are often more convenient than total methods. In many use cases if the preconditions aren't met, there's nothing else useful the thread can do, so it may as well wait via a partial method. An exception is in the case of work-stealing queues where a thief might scan a set of queues from which it could potentially steal. Total methods return ASAP with a success-failure indication. (It's tempting to describe a queue or API as blocking or non-blocking instead of partial or total, but non-blocking is already an overloaded concurrency term. Perhaps waiting/non-waiting or patient/impatient might be better terms). It's also trivial to construct partial operators by busy-waiting via total operators, but such constructs may be less efficient than an operator explicitly and intentionally designed to wait. A PTLQueue instance contains an array of slots, where each slot has volatile Turn and MailBox fields. The array has power-of-two length allowing mod/div operations to be replaced by masking. We assume sensible padding and alignment to reduce the impact of false sharing. (On x86 I recommend 128-byte alignment and padding because of the adjacent-sector prefetch facility). Each queue also has PutCursor and TakeCursor cursor variables, each of which should be sequestered as the sole occupant of a cache line or sector. You can opt to use 64-bit integers if concerned about wrap-around aliasing in the cursor variables. Put(null) is considered illegal, but the caller or implementation can easily check for and convert null to a distinguished non-null proxy value if null happens to be a value you'd like to pass. Take() will accordingly convert the proxy value back to null. An advantage of PTLQueue is that you can use atomic fetch-and-increment for the partial methods. We initialize each slot at index I with (Turn=I, MailBox=null). Both cursors are initially 0. All shared variables are considered "volatile" and atomics such as CAS and AtomicFetchAndIncrement are presumed to have bidirectional fence semantics. Finally T is the templated type. I've sketched out a total tryTake() method below that allows the caller to poll the queue. tryPut() has an analogous construction. Zebra stripping : alternating row colors for nice-looking code listings. See also google code "prettify" : https://code.google.com/p/google-code-prettify/ Prettify is a javascript module that yields the HTML/CSS/JS equivalent of pretty-print. -- pre:nth-child(odd) { background-color:#ff0000; } pre:nth-child(even) { background-color:#0000ff; } border-left: 11px solid #ccc; margin: 1.7em 0 1.7em 0.3em; background-color:#BFB; font-size:12px; line-height:65%; " // PTLQueue : Put(v) : // producer : partial method - waits as necessary assert v != null assert Mask = 1 && (Mask & (Mask+1)) == 0 // Document invariants // doorway step // Obtain a sequence number -- ticket // As a practical concern the ticket value is temporally unique // The ticket also identifies and selects a slot auto tkt = AtomicFetchIncrement (&PutCursor, 1) slot * s = &Slots[tkt & Mask] // waiting phase : // wait for slot's generation to match the tkt value assigned to this put() invocation. // The "generation" is implicitly encoded as the upper bits in the cursor // above those used to specify the index : tkt div (Mask+1) // The generation serves as an epoch number to identify a cohort of threads // accessing disjoint slots while s-Turn != tkt : Pause assert s-MailBox == null s-MailBox = v // deposit and pass message Take() : // consumer : partial method - waits as necessary auto tkt = AtomicFetchIncrement (&TakeCursor,1) slot * s = &Slots[tkt & Mask] // 2-stage waiting : // First wait for turn for our generation // Acquire exclusive "take" access to slot's MailBox field // Then wait for the slot to become occupied while s-Turn != tkt : Pause // Concurrency in this section of code is now reduced to just 1 producer thread // vs 1 consumer thread. // For a given queue and slot, there will be most one Take() operation running // in this section. // Consumer waits for producer to arrive and make slot non-empty // Extract message; clear mailbox; advance Turn indicator // We have an obvious happens-before relation : // Put(m) happens-before corresponding Take() that returns that same "m" for T v = s-MailBox if v != null : s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 // unlock slot to admit next producer and consumer return v Pause tryTake() : // total method - returns ASAP with failure indication for auto tkt = TakeCursor slot * s = &Slots[tkt & Mask] if s-Turn != tkt : return null T v = s-MailBox // presumptive return value if v == null : return null // ratify tkt and v values and commit by advancing cursor if CAS (&TakeCursor, tkt, tkt+1) != tkt : continue s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 return v The basic idea derives from the Partitioned Ticket Lock "PTL" (US20120240126-A1) and the MultiLane Concurrent Bag (US8689237). The latter is essentially a circular ring-buffer where the elements themselves are queues or concurrent collections. You can think of the PTLQueue as a partitioned ticket lock "PTL" augmented to pass values from lock to unlock via the slots. Alternatively, you could conceptualize of PTLQueue as a degenerate MultiLane bag where each slot or "lane" consists of a simple single-word MailBox instead of a general queue. Each lane in PTLQueue also has a private Turn field which acts like the Turn (Grant) variables found in PTL. Turn enforces strict FIFO ordering and restricts concurrency on the slot mailbox field to at most one simultaneous put() and take() operation. PTL uses a single "ticket" variable and per-slot Turn (grant) fields while MultiLane has distinct PutCursor and TakeCursor cursors and abstract per-slot sub-queues. Both PTL and MultiLane advance their cursor and ticket variables with atomic fetch-and-increment. PTLQueue borrows from both PTL and MultiLane and has distinct put and take cursors and per-slot Turn fields. Instead of a per-slot queues, PTLQueue uses a simple single-word MailBox field. PutCursor and TakeCursor act like a pair of ticket locks, conferring "put" and "take" access to a given slot. PutCursor, for instance, assigns an incoming put() request to a slot and serves as a PTL "Ticket" to acquire "put" permission to that slot's MailBox field. To better explain the operation of PTLQueue we deconstruct the operation of put() and take() as follows. Put() first increments PutCursor obtaining a new unique ticket. That ticket value also identifies a slot. Put() next waits for that slot's Turn field to match that ticket value. This is tantamount to using a PTL to acquire "put" permission on the slot's MailBox field. Finally, having obtained exclusive "put" permission on the slot, put() stores the message value into the slot's MailBox. Take() similarly advances TakeCursor, identifying a slot, and then acquires and secures "take" permission on a slot by waiting for Turn. Take() then waits for the slot's MailBox to become non-empty, extracts the message, and clears MailBox. Finally, take() advances the slot's Turn field, which releases both "put" and "take" access to the slot's MailBox. Note the asymmetry : put() acquires "put" access to the slot, but take() releases that lock. At any given time, for a given slot in a PTLQueue, at most one thread has "put" access and at most one thread has "take" access. This restricts concurrency from general MPMC to 1-vs-1. We have 2 ticket locks -- one for put() and one for take() -- each with its own "ticket" variable in the form of the corresponding cursor, but they share a single "Grant" egress variable in the form of the slot's Turn variable. Advancing the PutCursor, for instance, serves two purposes. First, we obtain a unique ticket which identifies a slot. Second, incrementing the cursor is the doorway protocol step to acquire the per-slot mutual exclusion "put" lock. The cursors and operations to increment those cursors serve double-duty : slot-selection and ticket assignment for locking the slot's MailBox field. At any given time a slot MailBox field can be in one of the following states: empty with no pending operations -- neutral state; empty with one or more waiting take() operations pending -- deficit; occupied with no pending operations; occupied with one or more waiting put() operations -- surplus; empty with a pending put() or pending put() and take() operations -- transitional; or occupied with a pending take() or pending put() and take() operations -- transitional. The partial put() and take() operators can be implemented with an atomic fetch-and-increment operation, which may confer a performance advantage over a CAS-based loop. In addition we have independent PutCursor and TakeCursor cursors. Critically, a put() operation modifies PutCursor but does not access the TakeCursor and a take() operation modifies the TakeCursor cursor but does not access the PutCursor. This acts to reduce coherence traffic relative to some other queue designs. It's worth noting that slow threads or obstruction in one slot (or "lane") does not impede or obstruct operations in other slots -- this gives us some degree of obstruction isolation. PTLQueue is not lock-free, however. The implementation above is expressed with polite busy-waiting (Pause) but it's trivial to implement per-slot parking and unparking to deschedule waiting threads. It's also easy to convert the queue to a more general deque by replacing the PutCursor and TakeCursor cursors with Left/Front and Right/Back cursors that can move either direction. Specifically, to push and pop from the "left" side of the deque we would decrement and increment the Left cursor, respectively, and to push and pop from the "right" side of the deque we would increment and decrement the Right cursor, respectively. We used a variation of PTLQueue for message passing in our recent OPODIS 2013 paper. ul { list-style:none; padding-left:0; padding:0; margin:0; margin-left:0; } ul#myTagID { padding: 0px; margin: 0px; list-style:none; margin-left:0;} -- -- There's quite a bit of related literature in this area. I'll call out a few relevant references: Wilson's NYU Courant Institute UltraComputer dissertation from 1988 is classic and the canonical starting point : Operating System Data Structures for Shared-Memory MIMD Machines with Fetch-and-Add. Regarding provenance and priority, I think PTLQueue or queues effectively equivalent to PTLQueue have been independently rediscovered a number of times. See CB-Queue and BNPBV, below, for instance. But Wilson's dissertation anticipates the basic idea and seems to predate all the others. Gottlieb et al : Basic Techniques for the Efficient Coordination of Very Large Numbers of Cooperating Sequential Processors Orozco et al : CB-Queue in Toward high-throughput algorithms on many-core architectures which appeared in TACO 2012. Meneghin et al : BNPVB family in Performance evaluation of inter-thread communication mechanisms on multicore/multithreaded architecture Dmitry Vyukov : bounded MPMC queue (highly recommended) Alex Otenko : US8607249 (highly related). John Mellor-Crummey : Concurrent queues: Practical fetch-and-phi algorithms. Technical Report 229, Department of Computer Science, University of Rochester Thomasson : FIFO Distributed Bakery Algorithm (very similar to PTLQueue). Scott and Scherer : Dual Data Structures I'll propose an optimization left as an exercise for the reader. Say we wanted to reduce memory usage by eliminating inter-slot padding. Such padding is usually "dark" memory and otherwise unused and wasted. But eliminating the padding leaves us at risk of increased false sharing. Furthermore lets say it was usually the case that the PutCursor and TakeCursor were numerically close to each other. (That's true in some use cases). We might still reduce false sharing by incrementing the cursors by some value other than 1 that is not trivially small and is coprime with the number of slots. Alternatively, we might increment the cursor by one and mask as usual, resulting in a logical index. We then use that logical index value to index into a permutation table, yielding an effective index for use in the slot array. The permutation table would be constructed so that nearby logical indices would map to more distant effective indices. (Open question: what should that permutation look like? Possibly some perversion of a Gray code or De Bruijn sequence might be suitable). As an aside, say we need to busy-wait for some condition as follows : "while C == 0 : Pause". Lets say that C is usually non-zero, so we typically don't wait. But when C happens to be 0 we'll have to spin for some period, possibly brief. We can arrange for the code to be more machine-friendly with respect to the branch predictors by transforming the loop into : "if C == 0 : for { Pause; if C != 0 : break; }". Critically, we want to restructure the loop so there's one branch that controls entry and another that controls loop exit. A concern is that your compiler or JIT might be clever enough to transform this back to "while C == 0 : Pause". You can sometimes avoid this by inserting a call to a some type of very cheap "opaque" method that the compiler can't elide or reorder. On Solaris, for instance, you could use :"if C == 0 : { gethrtime(); for { Pause; if C != 0 : break; }}". It's worth noting the obvious duality between locks and queues. If you have strict FIFO lock implementation with local spinning and succession by direct handoff such as MCS or CLH,then you can usually transform that lock into a queue. Hidden commentary and annotations - invisible : * And of course there's a well-known duality between queues and locks, but I'll leave that topic for another blog post. * Compare and contrast : PTLQ vs PTL and MultiLane * Equivalent : Turn; seq; sequence; pos; position; ticket * Put = Lock; Deposit Take = identify and reserve slot; wait; extract & clear; unlock * conceptualize : Distinct PutLock and TakeLock implemented as ticket lock or PTL Distinct arrival cursors but share per-slot "Turn" variable provides exclusive role-based access to slot's mailbox field put() acquires exclusive access to a slot for purposes of "deposit" assigns slot round-robin and then acquires deposit access rights/perms to that slot take() acquires exclusive access to slot for purposes of "withdrawal" assigns slot round-robin and then acquires withdrawal access rights/perms to that slot At any given time, only one thread can have withdrawal access to a slot at any given time, only one thread can have deposit access to a slot Permissible for T1 to have deposit access and T2 to simultaneously have withdrawal access * round-robin for the purposes of; role-based; access mode; access role mailslot; mailbox; allocate/assign/identify slot rights; permission; license; access permission; * PTL/Ticket hybrid Asymmetric usage ; owner oblivious lock-unlock pairing K-exclusion add Grant cursor pass message m from lock to unlock via Slots[] array Cursor performs 2 functions : + PTL ticket + Assigns request to slot in round-robin fashion Deconstruct protocol : explication put() : allocate slot in round-robin fashion acquire PTL for "put" access store message into slot associated with PTL index take() : Acquire PTL for "take" access // doorway step seq = fetchAdd (&Grant, 1) s = &Slots[seq & Mask] // waiting phase while s-Turn != seq : pause Extract : wait for s-mailbox to be full v = s-mailbox s-mailbox = null Release PTL for both "put" and "take" access s-Turn = seq + Mask + 1 * Slot round-robin assignment and lock "doorway" protocol leverage the same cursor and FetchAdd operation on that cursor FetchAdd (&Cursor,1) + round-robin slot assignment and dispersal + PTL/ticket lock "doorway" step waiting phase is via "Turn" field in slot * PTLQueue uses 2 cursors -- put and take. Acquire "put" access to slot via PTL-like lock Acquire "take" access to slot via PTL-like lock 2 locks : put and take -- at most one thread can access slot's mailbox Both locks use same "turn" field Like multilane : 2 cursors : put and take slot is simple 1-capacity mailbox instead of queue Borrow per-slot turn/grant from PTL Provides strict FIFO Lock slot : put-vs-put take-vs-take at most one put accesses slot at any one time at most one put accesses take at any one time reduction to 1-vs-1 instead of N-vs-M concurrency Per slot locks for put/take Release put/take by advancing turn * is instrumental in ... * P-V Semaphore vs lock vs K-exclusion * See also : FastQueues-excerpt.java dice-etc/queue-mpmc-bounded-blocking-circular-xadd/ * PTLQueue is the same as PTLQB - identical * Expedient return; ASAP; prompt; immediately * Lamport's Bakery algorithm : doorway step then waiting phase Threads arriving at doorway obtain a unique ticket number Threads enter in ticket order * In the terminology of Reed and Kanodia a ticket lock corresponds to the busy-wait implementation of a semaphore using an eventcount and a sequencer It can also be thought of as an optimization of Lamport's bakery lock was designed for fault-tolerance rather than performance Instead of spinning on the release counter, processors using a bakery lock repeatedly examine the tickets of their peers --

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