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• Why We Should Learn to Stop Worrying and Love Millennials

  - by HCM-Oracle

  By Christine Mellon Much is said and written about the new generations of employees entering our workforce, as though they are a strange specimen, a mysterious life form to be “figured out,” accommodated and engaged – at a safe distance, of course.  At its worst, this talk takes a critical and disapproving tone, with baby boomer employees adamantly refusing to validate this new breed of worker, let alone determine how to help them succeed and achieve their potential.   The irony of our baby-boomer resentments and suspicions is that they belie the fact that we created the very vision that younger employees are striving to achieve.  From our frustrations with empty careers that did not fulfill us, from our opposition to “the man,” from our sharp memories of our parents’ toiling for 30 years just for the right to retire, from the simple desire not to live our lives in a state of invisibility, came the seeds of hope for something better. One characteristic of Millennial workers that grew from these seeds is the desire to experience as much as possible.  They are the “Experiential Employee”, with a passion for growing in diverse ways and expanding personal and professional horizons.  Rather than rooting themselves in a single company for a career, or even in a single career path, these employees are committed to building a broad portfolio of experiences and capabilities that will enable them to make a difference and to leave a mark of significance in the world.  How much richer is the organization that nurtures and leverages this inclination?  Our curmudgeonly ways must be surrendered and our focus redirected toward building the next generation of talent ecosystems, if we are to optimize what future generations have to offer.   Accelerating Professional Development In spite of our Boomer grumblings about Millennials’ “unrealistic” expectations, the truth is that we have a well-matched set of circumstances.  We have executives-in-waiting who want to learn quickly and a concurrent, urgent need to ramp up their development time, based on anticipated high levels of retirement in the next 10+ years.  Since we need to rapidly skill up these heirs to the corporate kingdom, isn’t it a fortunate coincidence that they are hungry to learn, develop and move fluidly throughout our organizations??  So our challenge now is to efficiently operationalize the wisdom we have acquired about effective learning and development.   We have already evolved from classroom-based models to diverse instructional methods.  The next step is to find the best approaches to help younger employees learn quickly and apply new learnings in an impactful way.   Creating temporary or even permanent functional partnerships among Millennial employees is one way to maximize outcomes.  This might take the form of 2 or more employees owning aspects of what once fell under a single role.  While one might argue this would mean duplication of resources, it could be a short term cost while employees come up to speed.  And the potential benefits would be numerous:  leveraging and validating the inherent sense of community of new generations, creating cross-functional skills with broad applicability, yielding additional perspectives and approaches to traditional work outcomes, and accelerating the performance curve for incumbents through Cooperative Learning (Johnson, D. and Johnson R., 1989, 1999).  This well-researched teaching strategy, where students support each other in the absorption and application of new information, has been shown to deliver faster, more efficient learning, and greater retention. Alternately, perhaps short term contracts with exiting retirees, or former retirees, to help facilitate the development of following generations may have merit.  Again, a short term cost, certainly.  However, the gains realized in shortening the learning curve, and strengthening engagement are substantial and lasting. Ultimately, there needs to be creative thinking applied for each organization on how to accelerate the capabilities of our future leaders in unique ways that mesh with current culture. The manner in which performance is evaluated must finally shift as well.  Employees will need to be assessed on how well they have developed key skills and capabilities vs. end-to-end mastery of functional positions they have no interest in keeping for an entire career. As we become more comfortable in placing greater and greater weight on competencies vs. tasks, we will realize increased organizational agility via this new generation of workers, which will be further enhanced by their natural flexibility and appetite for change. Revisiting Succession  For many years, organizations have failed to deliver desired succession planning outcomes.  According to CEB’s 2013 research, only 28% of current leaders were pre-identified in a succession plan. These disappointing results, along with the entrance of the experiential, Millennial employee into the workforce, may just provide the needed impetus for HR to reinvent succession processes.   We have recognized that the best professional development efforts are not always linear, and the time has come to fully adopt this philosophy in regard to succession as well.  Paths to specific organizational roles will not look the same for newer generations who seek out unique learning opportunities, without consideration of a singular career destination.  Rather than charting particular jobs as precursors for key positions, the experiences and skills behind what makes an incumbent successful must become essential in succession mapping.  And the multitude of ways in which those experiences and skills may be acquired must be factored into the process, along with the individual employee’s level of learning agility. While this may seem daunting, it is necessary and long overdue.  We have talked about the criticality of competency-based succession, however, we have not lived up to our own rhetoric.  Many Boomers have experienced the same frustration in our careers; knowing we are capable of shining in a particular role, but being denied the opportunity due to how our career history lined up, on paper, with documented job requirements.  These requirements usually emphasized past jobs/titles and specific tasks, versus capabilities, drive and willingness (let alone determination) to learn new things.  How satisfying would it be for us to leave a legacy where such narrow thinking no longer applies and potential is amplified? Realizing Diversity Another bloom from the seeds we Boomers have tried to plant over the past decades is a completely evolved view of diversity.  Millennial employees assume a diverse workforce, and are startled by anything less.  Their social tolerance, nurtured by wide and diverse networks, is unprecedented.  College graduates expect a similar landscape in the “real world” to what they experienced throughout their lives.  They appreciate and seek out divergent points of view and experiences without needing any persuasion.  The face of our U.S. workforce will likely see dramatic change as Millennials apply their fresh take on hiring and building strong teams, with an inherent sense of inclusion.  This wonderful aspect of the Millennial wave should be celebrated and strongly encouraged, as it is the fulfillment of our own aspirations. Future Perfect The Experiential Employee is operating more as a free agent than a long term player, and their commitment will essentially last as long as meaningful organizational culture and personal/professional opportunities keep their interest.  As Boomers, we have laid the foundation for this new, spirited employment attitude, and we should take pride in knowing that.  Generations to come will challenge organizations to excel in how they identify, manage and nurture talent. Let’s support and revel in the future that we’ve helped invent, rather than lament what we think has been lost.  After all, the future is always connected to the past.  And as so eloquently phrased by Antoine Lavoisier, French nobleman, chemist and politico:  “Nothing is Lost, Nothing is Created, and Everything is Transformed.” Christine has over 25 years of diverse HR experience.  She has held HR consulting and corporate roles, including CHRO positions for Echostar in Denver, a 6,000+ employee global engineering firm, and Aepona, a startup software firm, successfully acquired by Intel. Christine is a resource to Oracle clients, to assist in Human Capital Management strategy development and implementation, compensation practices, talent development initiatives, employee engagement, global HR management, and integrated HR systems and processes that support the full employee lifecycle. 

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• A star algorithm implementation problems

  - by bryan226

  I’m having some trouble implementing the A* algorithm in a 2D tile based game. The problem is basically that the algorithm gets stuck when something gets in its direct way (e.g. walls) Note that it only allows Horizontal and Vertical movement. Here's a picture as it works fine across the map without something in its direct way: (Green tile = destination, Blue = In closed list, Green = in open list) This is what happens if I try to walk 'around' a wall: I calculate costs with the F = G + H formula: G = 1 Cost per Step H = 10 Cost per Step //Count how many tiles are between current-tile & destination-tile The functions: short c_astar::GuessH(short Startx,short Starty,short Destinationx,short Destinationy) { hgeVector Start, Destination; Start.x = Startx; Start.y = Starty; Destination.x = Destinationx; Destination.y = Destinationy; short a = 0; short b = 0; if(Start.x > Destination.x) a = Start.x - Destination.x; else a = Destination.x - Start.x; if(Start.y > Destination.y) b = Start.y - Destination.y; else b = Destination.y - Start.y; return (a+b)*10; } short c_astar::GuessG(short Startx,short Starty,short Currentx,short Currenty) { hgeVector Start, Destination; Start.x = Startx; Start.y = Starty; Destination.x = Currentx; Destination.y = Currenty; short a = 0; short b = 0; if(Start.x > Destination.x) a = Start.x - Destination.x; else a = Destination.x - Start.x; if(Start.y > Destination.y) b = Start.y - Destination.y; else b = Destination.y - Start.y; return (a+b); } At the end of the loop I check which tile is the cheapest to go according to its F value: Then some quick checks are done for each tile (UP,DOWN,LEFT,RIGHT): //...CX are holding the F value of the TILE specified // Info: C0 = Center (Current) // C1 = UP // C2 = DOWN // C3 = LEFT // C4 = RIGHT //Quick checks if(((C1 < C2) && (C1 < C3) && (C1 < C4))) { Current.y -= 1; bSimilar = false; if(DEBUG) hge->System_Log("C1 < ALL"); } //.. same for C2,C3 & C4 If there are multiple tiles with the same F value: It’s actually a switch for DOWNLEFT,UPRIGHT.. etc. Here’s one of it: case UPRIGHT: { //UP Temporary = Current; Temporary.y -= 1; bTileStatus[0] = IsTileWalkable(Temporary.x,Temporary.y); if(bTileStatus[0]) { //Proceed normal we are OK & walkable Tilex.Tile = map.at(Temporary.y).at(Temporary.x); //Search in lists if(SearchInClosedList(Tilex.Tile.ID,C0)) bFoundInClosedList[0] = true; if(SearchInOpenList(Tilex.Tile.ID,C0)) bFoundInOpenList[0] = true; //RIGHT Temporary = Current; Temporary.x += 1; bTileStatus[1] = IsTileWalkable(Temporary.x,Temporary.y); if(bTileStatus[1]) { //Proceed normal we are OK & walkable Tilex.Tile = map.at(Temporary.y).at(Temporary.x); //Search in lists if(SearchInClosedList(Tilex.Tile.ID,C0)) bFoundInClosedList[1] = true; if(SearchInOpenList(Tilex.Tile.ID,C0)) bFoundInOpenList[1] = true; //************************************************* // Purpose: ClosedList behavior //************************************************* if(bFoundInClosedList[0] && !bFoundInClosedList[1]) { //UP found in ClosedList. Go RIGHT return RIGHT; } if(!bFoundInClosedList[0] && bFoundInClosedList[1]) { //RIGHT found in ClosedList. Go UP return UP; } if(bFoundInClosedList[0] && bFoundInClosedList[1]) { //Both found in ClosedList. Random value switch(hge->Random_Int(8,9)) { case 8: return UP; break; case 9: return RIGHT; break; } } //************************************************* // Purpose: OpenList behavior //************************************************* if(bFoundInOpenList[0] && !bFoundInOpenList[1]) { //UP found in OpenList. Go RIGHT return RIGHT; } if(!bFoundInOpenList[0] && bFoundInOpenList[1]) { //RIGHT found in OpenList. Go UP return UP; } if(bFoundInOpenList[0] && bFoundInOpenList[1]) { //Both found in OpenList. Random value switch(hge->Random_Int(8,9)) { case 8: return UP; break; case 9: return RIGHT; break; } } } else if(!bTileStatus[1]) { //RIGHT is not walkable OR out of range //Choose UP return UP; } } else if(!bTileStatus[0]) { //UP is not walkable OR out of range //Fast check RIGHT Temporary = Current; Temporary.x += 1; bTileStatus[1] = IsTileWalkable(Temporary.x,Temporary.y); if(bTileStatus[1]) { return RIGHT; } else return FAILED; //Failed, no valid path found! } } break; A log for the second picture: (Cut down to ten passes, because it’s just repeating itself) ----------------------------------------------------- PASS: 1 | C1: 211 | C2: 191 | C3: 211 | C4: 191 DOWN + RIGHT SIMILAR Going DOWN ----------------------------------------------------- PASS: 2 | C1: 200 | C2: 182 | C3: 202 | C4: 182 DOWN + RIGHT SIMILAR Going DOWN ----------------------------------------------------- PASS: 3 | C1: 191 | C2: 193 | C3: 193 | C4: 173 C4 < ALL Tile(12.000000,6.000000) not walkable. MAX_F_VALUE set. ----------------------------------------------------- PASS: 4 | C1: 182 | C2: 184 | C3: 182 | C4: 999 UP + LEFT SIMILAR Going UP Tile(12.000000,5.000000) not walkable. MAX_F_VALUE set. ----------------------------------------------------- PASS: 5 | C1: 191 | C2: 173 | C3: 191 | C4: 999 C2 < ALL Tile(12.000000,6.000000) not walkable. MAX_F_VALUE set. ----------------------------------------------------- PASS: 6 | C1: 182 | C2: 184 | C3: 182 | C4: 999 UP + LEFT SIMILAR Going UP Tile(12.000000,5.000000) not walkable. MAX_F_VALUE set. ----------------------------------------------------- PASS: 7 | C1: 191 | C2: 173 | C3: 191 | C4: 999 C2 < ALL Tile(12.000000,6.000000) not walkable. MAX_F_VALUE set. ----------------------------------------------------- PASS: 8 | C1: 182 | C2: 184 | C3: 182 | C4: 999 UP + LEFT SIMILAR Going LEFT ----------------------------------------------------- PASS: 9 | C1: 191 | C2: 193 | C3: 193 | C4: 173 C4 < ALL Tile(12.000000,6.000000) not walkable. MAX_F_VALUE set. ----------------------------------------------------- PASS: 10 | C1: 182 | C2: 184 | C3: 182 | C4: 999 UP + LEFT SIMILAR Going LEFT ----------------------------------------------------- Its always going after the cheapest F value, which seems to be wrong. If someone could point me to the right direction I'd be thankful. Regards, bryan226

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• When is a Seek not a Seek?

  - by Paul White

  The following script creates a single-column clustered table containing the integers from 1 to 1,000 inclusive. IF OBJECT_ID(N'tempdb..#Test', N'U') IS NOT NULL DROP TABLE #Test ; GO CREATE TABLE #Test ( id INTEGER PRIMARY KEY CLUSTERED ); ; INSERT #Test (id) SELECT V.number FROM master.dbo.spt_values AS V WHERE V.[type] = N'P' AND V.number BETWEEN 1 AND 1000 ; Let’s say we need to find the rows with values from 100 to 170, excluding any values that divide exactly by 10.  One way to write that query would be: SELECT T.id FROM #Test AS T WHERE T.id IN ( 101,102,103,104,105,106,107,108,109, 111,112,113,114,115,116,117,118,119, 121,122,123,124,125,126,127,128,129, 131,132,133,134,135,136,137,138,139, 141,142,143,144,145,146,147,148,149, 151,152,153,154,155,156,157,158,159, 161,162,163,164,165,166,167,168,169 ) ; That query produces a pretty efficient-looking query plan: Knowing that the source column is defined as an INTEGER, we could also express the query this way: SELECT T.id FROM #Test AS T WHERE T.id >= 101 AND T.id <= 169 AND T.id % 10 > 0 ; We get a similar-looking plan: If you look closely, you might notice that the line connecting the two icons is a little thinner than before.  The first query is estimated to produce 61.9167 rows – very close to the 63 rows we know the query will return.  The second query presents a tougher challenge for SQL Server because it doesn’t know how to predict the selectivity of the modulo expression (T.id % 10 > 0).  Without that last line, the second query is estimated to produce 68.1667 rows – a slight overestimate.  Adding the opaque modulo expression results in SQL Server guessing at the selectivity.  As you may know, the selectivity guess for a greater-than operation is 30%, so the final estimate is 30% of 68.1667, which comes to 20.45 rows. The second difference is that the Clustered Index Seek is costed at 99% of the estimated total for the statement.  For some reason, the final SELECT operator is assigned a small cost of 0.0000484 units; I have absolutely no idea why this is so, or what it models.  Nevertheless, we can compare the total cost for both queries: the first one comes in at 0.0033501 units, and the second at 0.0034054.  The important point is that the second query is costed very slightly higher than the first, even though it is expected to produce many fewer rows (20.45 versus 61.9167). If you run the two queries, they produce exactly the same results, and both complete so quickly that it is impossible to measure CPU usage for a single execution.  We can, however, compare the I/O statistics for a single run by running the queries with STATISTICS IO ON: Table '#Test'. Scan count 63, logical reads 126, physical reads 0. Table '#Test'. Scan count 01, logical reads 002, physical reads 0. The query with the IN list uses 126 logical reads (and has a ‘scan count’ of 63), while the second query form completes with just 2 logical reads (and a ‘scan count’ of 1).  It is no coincidence that 126 = 63 * 2, by the way.  It is almost as if the first query is doing 63 seeks, compared to one for the second query. In fact, that is exactly what it is doing.  There is no indication of this in the graphical plan, or the tool-tip that appears when you hover your mouse over the Clustered Index Seek icon.  To see the 63 seek operations, you have click on the Seek icon and look in the Properties window (press F4, or right-click and choose from the menu): The Seek Predicates list shows a total of 63 seek operations – one for each of the values from the IN list contained in the first query.  I have expanded the first seek node to show the details; it is seeking down the clustered index to find the entry with the value 101.  Each of the other 62 nodes expands similarly, and the same information is contained (even more verbosely) in the XML form of the plan. Each of the 63 seek operations starts at the root of the clustered index B-tree and navigates down to the leaf page that contains the sought key value.  Our table is just large enough to need a separate root page, so each seek incurs 2 logical reads (one for the root, and one for the leaf).  We can see the index depth using the INDEXPROPERTY function, or by using the a DMV: SELECT S.index_type_desc, S.index_depth FROM sys.dm_db_index_physical_stats ( DB_ID(N'tempdb'), OBJECT_ID(N'tempdb..#Test', N'U'), 1, 1, DEFAULT ) AS S ; Let’s look now at the Properties window when the Clustered Index Seek from the second query is selected: There is just one seek operation, which starts at the root of the index and navigates the B-tree looking for the first key that matches the Start range condition (id >= 101).  It then continues to read records at the leaf level of the index (following links between leaf-level pages if necessary) until it finds a row that does not meet the End range condition (id <= 169).  Every row that meets the seek range condition is also tested against the Residual Predicate highlighted above (id % 10 > 0), and is only returned if it matches that as well. You will not be surprised that the single seek (with a range scan and residual predicate) is much more efficient than 63 singleton seeks.  It is not 63 times more efficient (as the logical reads comparison would suggest), but it is around three times faster.  Let’s run both query forms 10,000 times and measure the elapsed time: DECLARE @i INTEGER, @n INTEGER = 10000, @s DATETIME = GETDATE() ; SET NOCOUNT ON; SET STATISTICS XML OFF; ; WHILE @n > 0 BEGIN SELECT @i = T.id FROM #Test AS T WHERE T.id IN ( 101,102,103,104,105,106,107,108,109, 111,112,113,114,115,116,117,118,119, 121,122,123,124,125,126,127,128,129, 131,132,133,134,135,136,137,138,139, 141,142,143,144,145,146,147,148,149, 151,152,153,154,155,156,157,158,159, 161,162,163,164,165,166,167,168,169 ) ; SET @n -= 1; END ; PRINT DATEDIFF(MILLISECOND, @s, GETDATE()) ; GO DECLARE @i INTEGER, @n INTEGER = 10000, @s DATETIME = GETDATE() ; SET NOCOUNT ON ; WHILE @n > 0 BEGIN SELECT @i = T.id FROM #Test AS T WHERE T.id >= 101 AND T.id <= 169 AND T.id % 10 > 0 ; SET @n -= 1; END ; PRINT DATEDIFF(MILLISECOND, @s, GETDATE()) ; On my laptop, running SQL Server 2008 build 4272 (SP2 CU2), the IN form of the query takes around 830ms and the range query about 300ms.  The main point of this post is not performance, however – it is meant as an introduction to the next few parts in this mini-series that will continue to explore scans and seeks in detail. When is a seek not a seek?  When it is 63 seeks © Paul White 2011 email: [email protected] twitter: @SQL_kiwi

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• Augmenting your Social Efforts via Data as a Service (DaaS)

  - by Mike Stiles

  The following is the 3rd in a series of posts on the value of leveraging social data across your enterprise by Oracle VP Product Development Don Springer and Oracle Cloud Data and Insight Service Sr. Director Product Management Niraj Deo. In this post, we will discuss the approach and value of integrating additional “public” data via a cloud-based Data-as-as-Service platform (or DaaS) to augment your Socially Enabled Big Data Analytics and CX Management. Let’s assume you have a functional Social-CRM platform in place. You are now successfully and continuously listening and learning from your customers and key constituents in Social Media, you are identifying relevant posts and following up with direct engagement where warranted (both 1:1, 1:community, 1:all), and you are starting to integrate signals for communication into your appropriate Customer Experience (CX) Management systems as well as insights for analysis in your business intelligence application. What is the next step? Augmenting Social Data with other Public Data for More Advanced Analytics When we say advanced analytics, we are talking about understanding causality and correlation from a wide variety, volume and velocity of data to Key Performance Indicators (KPI) to achieve and optimize business value. And in some cases, to predict future performance to make appropriate course corrections and change the outcome to your advantage while you can. The data to acquire, process and analyze this is very nuanced: It can vary across structured, semi-structured, and unstructured data It can span across content, profile, and communities of profiles data It is increasingly public, curated and user generated The key is not just getting the data, but making it value-added data and using it to help discover the insights to connect to and improve your KPIs. As we spend time working with our larger customers on advanced analytics, we have seen a need arise for more business applications to have the ability to ingest and use “quality” curated, social, transactional reference data and corresponding insights. The challenge for the enterprise has been getting this data inline into an easily accessible system and providing the contextual integration of the underlying data enriched with insights to be exported into the enterprise’s business applications. The following diagram shows the requirements for this next generation data and insights service or (DaaS): Some quick points on these requirements: Public Data, which in this context is about Common Business Entities, such as - Customers, Suppliers, Partners, Competitors (all are organizations) Contacts, Consumers, Employees (all are people) Products, Brands This data can be broadly categorized incrementally as - Base Utility data (address, industry classification) Public Master Reference data (trade style, hierarchy) Social/Web data (News, Feeds, Graph) Transactional Data generated by enterprise process, workflows etc. This Data has traits of high-volume, variety, velocity etc., and the technology needed to efficiently integrate this data for your needs includes - Change management of Public Reference Data across all categories Applied Big Data to extract statics as well as real-time insights Knowledge Diagnostics and Data Mining As you consider how to deploy this solution, many of our customers will be using an online “cloud” service that provides quality data and insights uniformly to all their necessary applications. In addition, they are requesting a service that is: Agile and Easy to Use: Applications integrated with the service can obtain data on-demand, quickly and simply Cost-effective: Pre-integrated into applications so customers don’t have to Has High Data Quality: Single point access to reference data for data quality and linkages to transactional, curated and social data Supports Data Governance: Becomes more manageable and cost-effective since control of data privacy and compliance can be enforced in a centralized place Data-as-a-Service (DaaS) Just as the cloud has transformed and now offers a better path for how an enterprise manages its IT from their infrastructure, platform, and software (IaaS, PaaS, and SaaS), the next step is data (DaaS). Over the last 3 years, we have seen the market begin to offer a cloud-based data service and gain initial traction. On one side of the DaaS continuum, we see an “appliance” type of service that provides a single, reliable source of accurate business data plus social information about accounts, leads, contacts, etc. On the other side of the continuum we see more of an online market “exchange” approach where ISVs and Data Publishers can publish and sell premium datasets within the exchange, with the exchange providing a rich set of web interfaces to improve the ease of data integration. Why the difference? It depends on the provider’s philosophy on how fast the rate of commoditization of certain data types will occur. How do you decide the best approach? Our perspective, as shown in the diagram below, is that the enterprise should develop an elastic schema to support multi-domain applicability. This allows the enterprise to take the most flexible approach to harness the speed and breadth of public data to achieve value. The key tenet of the proposed approach is that an enterprise carefully federates common utility, master reference data end points, mobility considerations and content processing, so that they are pervasively available. One way you may already be familiar with this approach is in how you do Address Verification treatments for accounts, contacts etc. If you design and revise this service in such a way that it is also easily available to social analytic needs, you could extend this to launch geo-location based social use cases (marketing, sales etc.). Our fundamental belief is that value-added data achieved through enrichment with specialized algorithms, as well as applying business “know-how” to weight-factor KPIs based on innovative combinations across an ever-increasing variety, volume and velocity of data, will be where real value is achieved. Essentially, Data-as-a-Service becomes a single entry point for the ever-increasing richness and volume of public data, with enrichment and combined capabilities to extract and integrate the right data from the right sources with the right factoring at the right time for faster decision-making and action within your core business applications. As more data becomes available (and in many cases commoditized), this value-added data processing approach will provide you with ongoing competitive advantage. Let’s look at a quick example of creating a master reference relationship that could be used as an input for a variety of your already existing business applications. In phase 1, a simple master relationship is achieved between a company (e.g. General Motors) and a variety of car brands’ social insights. The reference data allows for easy sort, export and integration into a set of CRM use cases for analytics, sales and marketing CRM. In phase 2, as you create more data relationships (e.g. competitors, contacts, other brands) to have broader and deeper references (social profiles, social meta-data) for more use cases across CRM, HCM, SRM, etc. This is just the tip of the iceberg, as the amount of master reference relationships is constrained only by your imagination and the availability of quality curated data you have to work with. DaaS is just now emerging onto the marketplace as the next step in cloud transformation. For some of you, this may be the first you have heard about it. Let us know if you have questions, or perspectives. In the meantime, we will continue to share insights as we can.Photo: Erik Araujo, stock.xchng

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• Master-slave vs. peer-to-peer archictecture: benefits and problems

  - by Ashok_Ora

  Normal 0 false false false EN-US X-NONE X-NONE Almost two decades ago, I was a member of a database development team that introduced adaptive locking. Locking, the most popular concurrency control technique in database systems, is pessimistic. Locking ensures that two or more conflicting operations on the same data item don’t “trample” on each other’s toes, resulting in data corruption. In a nutshell, here’s the issue we were trying to address. In everyday life, traffic lights serve the same purpose. They ensure that traffic flows smoothly and when everyone follows the rules, there are no accidents at intersections. As I mentioned earlier, the problem with typical locking protocols is that they are pessimistic. Regardless of whether there is another conflicting operation in the system or not, you have to hold a lock! Acquiring and releasing locks can be quite expensive, depending on how many objects the transaction touches. Every transaction has to pay this penalty. To use the earlier traffic light analogy, if you have ever waited at a red light in the middle of nowhere with no one on the road, wondering why you need to wait when there’s clearly no danger of a collision, you know what I mean. The adaptive locking scheme that we invented was able to minimize the number of locks that a transaction held, by detecting whether there were one or more transactions that needed conflicting eyou could get by without holding any lock at all. In many “well-behaved” workloads, there are few conflicts, so this optimization is a huge win. If, on the other hand, there are many concurrent, conflicting requests, the algorithm gracefully degrades to the “normal” behavior with minimal cost. We were able to reduce the number of lock requests per TPC-B transaction from 178 requests down to 2! Wow! This is a dramatic improvement in concurrency as well as transaction latency. The lesson from this exercise was that if you can identify the common scenario and optimize for that case so that only the uncommon scenarios are more expensive, you can make dramatic improvements in performance without sacrificing correctness. So how does this relate to the architecture and design of some of the modern NoSQL systems? NoSQL systems can be broadly classified as master-slave sharded, or peer-to-peer sharded systems. NoSQL systems with a peer-to-peer architecture have an interesting way of handling changes. Whenever an item is changed, the client (or an intermediary) propagates the changes synchronously or asynchronously to multiple copies (for availability) of the data. Since the change can be propagated asynchronously, during some interval in time, it will be the case that some copies have received the update, and others haven’t. What happens if someone tries to read the item during this interval? The client in a peer-to-peer system will fetch the same item from multiple copies and compare them to each other. If they’re all the same, then every copy that was queried has the same (and up-to-date) value of the data item, so all’s good. If not, then the system provides a mechanism to reconcile the discrepancy and to update stale copies. So what’s the problem with this? There are two major issues: First, IT’S HORRIBLY PESSIMISTIC because, in the common case, it is unlikely that the same data item will be updated and read from different locations at around the same time! For every read operation, you have to read from multiple copies. That’s a pretty expensive, especially if the data are stored in multiple geographically separate locations and network latencies are high. Second, if the copies are not all the same, the application has to reconcile the differences and propagate the correct value to the out-dated copies. This means that the application program has to handle discrepancies in the different versions of the data item and resolve the issue (which can further add to cost and operation latency). Resolving discrepancies is only one part of the problem. What if the same data item was updated independently on two different nodes (copies)? In that case, due to the asynchronous nature of change propagation, you might land up with different versions of the data item in different copies. In this case, the application program also has to resolve conflicts and then propagate the correct value to the copies that are out-dated or have incorrect versions. This can get really complicated. My hunch is that there are many peer-to-peer-based applications that don’t handle this correctly, and worse, don’t even know it. Imagine have 100s of millions of records in your database – how can you tell whether a particular data item is incorrect or out of date? And what price are you willing to pay for ensuring that the data can be trusted? Multiple network messages per read request? Discrepancy and conflict resolution logic in the application, and potentially, additional messages? All this overhead, when all you were trying to do was to read a data item. Wouldn’t it be simpler to avoid this problem in the first place? Master-slave architectures like the Oracle NoSQL Database handles this very elegantly. A change to a data item is always sent to the master copy. Consequently, the master copy always has the most current and authoritative version of the data item. The master is also responsible for propagating the change to the other copies (for availability and read scalability). Client drivers are aware of master copies and replicas, and client drivers are also aware of the “currency” of a replica. In other words, each NoSQL Database client knows how stale a replica is. This vastly simplifies the job of the application developer. If the application needs the most current version of the data item, the client driver will automatically route the request to the master copy. If the application is willing to tolerate some staleness of data (e.g. a version that is no more than 1 second out of date), the client can easily determine which replica (or set of replicas) can satisfy the request, and route the request to the most efficient copy. This results in a dramatic simplification in application logic and also minimizes network requests (the driver will only send the request to exactl the right replica, not many). So, back to my original point. A well designed and well architected system minimizes or eliminates unnecessary overhead and avoids pessimistic algorithms wherever possible in order to deliver a highly efficient and high performance system. If you’ve every programmed an Oracle NoSQL Database application, you’ll know the difference! /* 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-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;}

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• Different Not Automatically Implies Better

  - by Alois Kraus

  Originally posted on: http://geekswithblogs.net/akraus1/archive/2013/11/05/154556.aspxRecently I was digging deeper why some WCF hosted workflow application did consume quite a lot of memory although it did basically only load a xaml workflow. The first tool of choice is Process Explorer or even better Process Hacker (has more options and the best feature copy&paste does work). The three most important numbers of a process with regards to memory are Working Set, Private Working Set and Private Bytes. Working set is the currently consumed physical memory (parts can be shared between processes e.g. loaded dlls which are read only) Private Working Set is the physical memory needed by this process which is not shareable Private Bytes is the number of non shareable which is only visible in the current process (e.g. all new, malloc, VirtualAlloc calls do create private bytes) When you have a bigger workflow it can consume under 64 bit easily 500MB for a 1-2 MB xaml file. This does not look very scalable. Under 64 bit the issue is excessive private bytes consumption and not the managed heap. The picture is quite different for 32 bit which looks a bit strange but it seems that the hosted VB compiler is a lot less memory hungry under 32 bit. I did try to repro the issue with a medium sized xaml file (400KB) which does contain 1000 variables and 1000 if which can be represented by C# code like this: string Var1; string Var2; ... string Var1000; if (!String.IsNullOrEmpty(Var1) ) { Console.WriteLine(“Var1”); } if (!String.IsNullOrEmpty(Var2) ) { Console.WriteLine(“Var2”); } ....   Since WF is based on VB.NET expressions you are bound to the hosted VB.NET compiler which does result in (x64) 140 MB of private bytes which is ca. 140 KB for each if clause which is quite a lot if you think about the actually present functionality. But there is hope. .NET 4.5 does allow now C# expressions for WF which is a major step forward for all C# lovers. I did create some simple patcher to “cross compile” my xaml to C# expressions. Lets look at the result: C# Expressions VB Expressions x86 x86 On my home machine I have only 32 bit which gives you quite exactly half of the memory consumption under 64 bit. C# expressions are 10 times more memory hungry than VB.NET expressions! I wanted to do more with less memory but instead it did consume a magnitude more memory. That is surprising to say the least. The workflow does initialize in about the same time under x64 and x86 where the VB code does it in 2s whereas the C# version needs 18s. Also nearly ten times slower. That is a too high price to pay for any bigger sized xaml workflow to convert from VB.NET to C# expressions. If I do reduce the number of expressions to 500 then it does need 400MB which is about half of the memory. It seems that the cost per if does rise linear with the number of total expressions in a xaml workflow.  Expression Language Cost per IF Startup Time C# 1000 Ifs x64 1,5 MB 18s C# 500 Ifs x64 750 KB 9s VB 1000 Ifs x64 140 KB 2s VB 500 Ifs x64 70 KB 1s Now we can directly compare two MS implementations. It is clear that the VB.NET compiler uses the same underlying structure but it has much higher offset compared to the highly inefficient C# expression compiler. I have filed a connect bug here with a harsher wording about recent advances in memory consumption. The funniest thing is that one MS employee did give an Azure AppFabric demo around early 2011 which was so slow that he needed to investigate with xperf. He was after startup time and the call stacks with regards to VB.NET expression compilation were remarkably similar. In fact I only found this post by googling for parts of my call stacks. … “C# expressions will be coming soon to WF, and that will have different performance characteristics than VB” … What did he know Jan 2011 what I did no know until today? ;-). He knew that C# expression will come but that they will not be automatically have better footprint. It is about time to fix that. In its current state C# expressions are not usable for bigger workflows. That also explains the headline for today. You can cheat startup time by prestarting workflows so that the demo looks nice and snappy but it does hurt scalability a lot since you do need much more memory than necessary. I did find the stacks by enabling virtual allocation tracking within XPerf which is still the best tool out there. But first you need to look at your process to check where the memory is hiding: For the C# Expression compiler you do not need xperf. You can directly dump the managed heap and check with a profiler of your choice. But if the allocations are happening on the Private Data ( VirtualAlloc ) you can find it with xperf. There is a nice video on channel 9 explaining VirtualAlloc tracking it in greater detail. If your data allocations are on the Heap it does mean that the C/C++ runtime did create a heap for you where all malloc, new calls do allocate from it. You can enable heap tracing with xperf and full call stack support as well which is doable via xperf like it is shown also on channel 9. Or you can use WPRUI directly: To make “Heap Usage” it work you need to set for your executable the tracing flags (before you start it). For example devenv.exe HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Image File Execution Options\devenv.exe DWORD TracingFlags 1 Do not forget to disable it after you did complete profiling the process or it will impact the startup time quite a lot. You can with xperf attach directly to a running process and collect heap allocation information from a gone wild process. Very handy if you need to find out what a process was doing which has arrived in a funny state. “VirtualAlloc usage” does work without explicitly enabling stuff for a specific process and is always on machine wide. I had issues on my Windows 7 machines with the call stack collection and the latest Windows 8.1 Performance Toolkit. I was told that WPA from Windows 8.0 should work fine but I do not want to downgrade.

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• HTG Explains: Should You Buy Extended Warranties?

  - by Chris Hoffman

  Buy something at an electronics store and you’ll be confronted by a pushy salesperson who insists you need an extended warranty. You’ll also see extended warranties pushed hard when shopping online. But are they worth it? There’s a reason stores push extended warranties so hard. They’re almost always pure profit for the store involved. An electronics store may live on razor-thin product margins and make big profits on extended warranties and overpriced HDMI cables. You’re Already Getting Multiple Warranties First, back up. The product you’re buying already includes a warranty. In fact, you’re probably getting several different types of warranties. Store Return and Exchange: Most electronics stores allow you to return a malfunctioning product within the first 15 or 30 days and they’ll provide you with a new one. The exact period of time will vary from store to store. If you walk out of the store with a defective product and have to swap it for a new one within the first few weeks, this should be easy. Manufacturer Warranty: A device’s manufacturer — whether the device is a laptop, a television, or a graphics card — offers their own warranty period. The manufacturer warranty covers you after the store refuses to take the product back and exchange it. The length of this warranty depends on the type of product. For example, a cheap laptop may only offer a one-year manufacturer warranty, while a more expensive laptop may offer a two-year warranty. Credit Card Warranty Extension: Many credit cards offer free extended warranties on products you buy with that credit card. Credit card companies will often give you an additional year of warranty. For example, if you buy a laptop with a two year warranty and it fails in the third year, you could then contact your credit card company and they’d cover the cost of fixing or replacing it. Check your credit card’s benefits and fine print for more information. Why Extended Warranties Are Bad You’re already getting a fairly long warranty period, especially if you have a credit card that offers you a free extended warranty — these are fairly common. If the product you get is a “lemon” and has a manufacturing error, it will likely fail pretty soon — well within your warranty period. The extended warranty matters after all your other warranties are exhausted. In the case of a laptop with a two-year warranty that you purchase with a credit card giving you a one-year warranty extension, your extended warranty will kick in three years after you purchase the laptop. In that many years, your current laptop will likely feel pretty old and laptops that are as good — or better — will likely be pretty cheap. If it’s a television, better television displays will be available at a lower price point. You’ll either want to upgrade to a newer model or you’ll be able to buy a new, just-as-good product for very cheap. You’ll only have to pay out-of-pocket if your device fails after the normal warranty period — in over two or three years for typical laptops purchased with a decent credit card. Save the money you would have spent on the warranty and put it towards a future upgrade. How Much Do Extended Warranties Cost? Let’s look at an example from a typical pushy retail outlet, Best Buy. We went to Best Buy’s website and found a pretty standard $600 Samsung laptop. This laptop comes with a one-year warranty period. If purchased with a fairly common credit card, you can easily get a two-year warranty period on this laptop without spending an additional penny. (Yes, such credit cards are available with no yearly fees.) During the check-out process, Best Buy tries to sell you a Geek Squad “Accidental Protection Plan.” To get an additional year of Best Buy’s extended warranty, you’d have to pay $324.98 for a “3-Year Accidental Protection Plan”. You’d basically be paying more than half the price of your laptop for an additional year of warranty — remember, the standard warranties would cover you anyway for the first two years. If this laptop did break sometime between two and three years from now, we wouldn’t be surprised if you could purchase a comparable laptop for about $325 anyway. And, if you don’t need to replace it, you’ve saved that money. Best Buy would object that this isn’t a standard extended warranty. It’s a supercharged warranty plan that will also provide coverage if you spill something on your laptop or drop it and break it. You just have to ask yourself a question. What are the odds that you’ll drop your laptop or spill something on it? They’re probably pretty low if you’re a typical human being. Is it worth spending more than half the price of the laptop just in case you’ll make an uncommon mistake? Probably not. There may be occasional exceptions to this — some Apple users swear by Apple’s AppleCare, for example — but you should generally avoid buying these things. There’s a reason stores are so pushy about extended warranties, and it’s not because they want to help protect you. It’s because they’re making lots of profit from these plans, and they’re making so much profit because they’re not a good deal for customers. Image Credit: Philip Taylor on Flickr     

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• Javascript not working in IE but works in Firefox chrome

  - by user1290528

  So i have the following php page with a java script that gets the total of items based on their quatity, then inputs the total into a text box for each item. In ie the text boxes are being filled with $NaN. While in firefox, chrome the text boxes are filled with the correct values. Any help would be graatly appreciated. <?php echo $_SESSION['SESS_MEMBER_ID']; require_once('auth.php'); ?> <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"> <html> <head> <meta content="text/html; charset=ISO-8859-1" http-equiv="Content-Type"> <title>Breakfast Menu</title> <link href="loginmodule.css" rel="stylesheet" type="text/css"> <script type='text/javascript'> var totalarray=new Array(); var totalarray2= new Array(); var runningtotal = 0; var runningtotal2 = 0; var discount = .2; var discounttotal = 0; var discount1 = 0; runningtotal = runningtotal * 1; runningtotal2 = runningtotal2 * 1; function displayResult(price,init) { var newstring = "quantity"+init; var totstring = "total"+init; var quantity = document.getElementById(newstring).value; var quantity = parseFloat(quantity); var test = price * quantity; var test = test.toFixed(2); document.getElementById(newstring).value = quantity; document.getElementById(totstring).value = "$" + test; totalarray[init] = test; getTotal(); } function getTotal(){ runningtotal = 0; var i=0; for (i=0;i<totalarray.length;i++){ totalarray[i] = totalarray[i] *1; runningtotal = runningtotal + totalarray[i]; discounttotal = totalarray[i] * discount; discounttotal = totalarray[i] - discounttotal; This line is where IE shows its first error document.getElementById('totalcost').value="$" + runningtotal.toFixed(2); } var orderpart1 = document.getElementById('totalcost').value; var orderpart1 = orderpart1.substr(1); var orderpart1 = orderpart1 * 1; var orderpart2 = document.getElementById('totalcost2').value; var orderpart2 = orderpart2.substr(1); var orderpart2 = orderpart2 * 1; var ordertot = orderpart1 + orderpart2; document.getElementById('ordertotal').value ="$"+ ordertot.toFixed(2) } function displayResult2(price2,init2) { var newstring2 = "quantity2"+init2; var totstring2 = "total2"+init2; var quantity2 = document.getElementById(newstring2).value; var quantity2 = parseFloat(quantity2); var test2 = price2 * quantity2; var test2 = test2.toFixed(2); document.getElementById(newstring2).value = quantity2; document.getElementById(totstring2).value = "$" + test2; totalarray2[init2] = test2; getTotal2(); } function getTotal2(){ runningtotal2 = 0; var i=0; for (i=0;i<totalarray2.length;i++){ totalarray2[i] = totalarray2[i] *1; runningtotal2 = runningtotal2+ totalarray2[i]; This is where IE shows its second error document.getElementById('totalcost2').value="$" + runningtotal2.toFixed(2); }//IE Shows Second error here var orderpart1 = document.getElementById('totalcost').value; var orderpart1 = orderpart1.substr(1); var orderpart1 = orderpart1 * 1; var orderpart2 = document.getElementById('totalcost2').value; var orderpart2 = orderpart2.substr(1); var orderpart2 = orderpart2 * 1; var ordertot = orderpart1 + orderpart2; document.getElementById('ordertotal').value ="$"+ ordertot.toFixed(2); } </script> </head> <body> <?php include("newnew.php"); ?> <td style="vertical-align: top; width: 80%; height:80%;"><br> <div style="text-align: center;"> <form action="testplaceorder.php" method="post" onSubmit="return confirm('Are you sure?');"> <h4>Employee Breakfast Order Form</h4> <h1 align="left">Breakfest Foods</h1> <table border='0' cellpadding='0' cellspacing='0'> <tr> <td> <table width="100%" border="1"> <tr> <th>Item&nbsp&nbsp&nbsp&nbsp&nbsp</th> <th>Price&nbsp&nbsp&nbsp&nbsp&nbsp </th> <th>Quantity&nbsp&nbsp&nbsp&nbsp&nbsp</th> <th>Total&nbsp&nbsp&nbsp&nbsp&nbsp</th> </tr> <?php mysql_connect("localhost", "seniorproject", "farmingdale123") or die(mysql_error()); mysql_select_db("fsenior") or die(mysql_error()); $result = mysql_query("SELECT name, price,foodid FROM Food where foodtype='br'") or die(mysql_error()); $init = 0; while(list($name, $price, $brId) = mysql_fetch_row($result)) { echo "<tr> <td>$name</td> <td>\$$price</td> <td><select name='quantity$init' id='quantity$init' onchange='displayResult($price,$init)'><option>0</option><option>1</option><option>2</option><option>3</option><option>4</option><option>5</option><option>6</option><option>7</option><option>8</option><option>9</option></td> <td><input name='total$init' type='text' id='total$init' readonly='readonly' value='\$0.00'></td> </tr>" ; echo "<script type='text/javascript'>displayResult($price,$init);</script>"; $foodname = "'SESS_FOODNAME_" . $init . "'"; $foodid = "'SESS_FOODID_" . $init."'"; $_SESSION[$foodname] = $name; $_SESSION[$foodid] = $brId; $init = $init+1; } $_SESSION['SESS_INIT'] = $init; ?> <tr> <td></td> <td></td> <td>Total Cost</td> <td><input name='totalcost' type='text' id='totalcost' readonly='readonly' value='$0.00'></td> </tr> <tr><td></td><td></td><td>Discount</td><td><input name='discountvalue1' id ='discountvalue1' type='text' readonly='readonly' value='20%'></td> </tr> <tr><td></td><td></td><td>Total After Discount</td><td><input name='discounttotal1' id ='discounttotal1' type='text' readonly='readonly' value='$0.00'></td></tr> </table> <tr> <td><br></td> </tr> </table> <h1 align="left">Breakfest Drinks</h1> <table border='0' cellpadding='0' cellspacing='0'> <tr> <td> <table width="100%" border="1"> <tr> <th>Item&nbsp&nbsp&nbsp&nbsp&nbsp</th> <th>Price&nbsp&nbsp&nbsp&nbsp&nbsp </th> <th>Quantity&nbsp&nbsp&nbsp&nbsp&nbsp</th> <th>Total&nbsp&nbsp&nbsp&nbsp&nbsp</th> </tr> <?php mysql_connect("localhost", "****", "***") or die(mysql_error()); mysql_select_db("fsenior") or die(mysql_error()); $result2 = mysql_query("SELECT drinkname, price,drinkid FROM Drinks where drinktype='br'") or die(mysql_error()); $init2 = 0; while(list($name2, $price2, $brId2) = mysql_fetch_row($result2)) { echo "<tr> <td>$name2</td> <td>\$$price2</td> <td><select name='quantity2$init2' id='quantity2$init2' onchange='displayResult2($price2,$init2)'><option>0</option><option>1</option><option>2</option><option>3</option><option>4</option><option>5</option><option>6</option><option>7</option><option>8</option><option>9</option></td> <td><input name='total2$init2' type='text' id='total2$init2' readonly='readonly' value='\$0.00'></td> </tr>" ; echo "<script type='text/javascript'>displayResult2($price2,$init2);</script>"; $drinkname = "'SESS_DRINKNAME_" . $init2 . "'"; $drinkid = "'SESS_DRINKID_" . $init2."'"; $_SESSION[$drinkname] = $name2; $_SESSION[$drinkid] = $brId2; $init2 = $init2+1; } $_SESSION['SESS_INIT2'] = $init2; ?> <tr> <td></td> <td></td> <td>Total Cost</td> <td><input name='totalcost2' type='text' id='totalcost2' readonly='readonly' value='$0.00'></td> </tr> </table> <tr> <td><br></td> </tr> </table> <table border="2"> <tr><td>Total Order Cost:</td><td> <?php echo "<input name='ordertotal' type='text' id='ordertotal' readonly='readonly' value='\$0.00'></td></table>"; ?> <p align="left"><input type='submit' name='submit' value='Submit'/></p> </form> </div></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </body> </html>

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• ?12c database ????Adaptive Execution Plans ????????

  - by Liu Maclean(???)

  12c R1 ????SQL??????- Adaptive Execution Plans ????????,???????optimizer ??????(runtime)???????????????, ????????????????????? SQL???????? ????????????, ?????????????????????????????????????????????????????????????adaptive plan ????????????????????????????????????,?????subplan???????????????????? ??????, ???????? ???????????????,?????????, ?????? ???????????????”???”????, ???????????????????buffer ???????  ????????????,?????,??????????????????? ???optimizer ?????????????????????????,?????????????????????????????????????????plan???? ??12C?????????????, ???????????????????,?????? ???????????? ????????????2???: Dynamic Plans????: ???????????????????????;??????,???optimizer??????????subplans??????????????, ???????????????????,?????????????? Reoptimization????: ?Dynamic Plans????,Reoptimization??????????????????????Reoptimization??,?????????????????????????,??reoptimization????? OPTIMIZER_ADAPTIVE_REPORTING_ONLY ???? report-only????????????????TRUE,?????????report-only????,???????????????,??????????????? Dynamic Plans ??????????????,????????????????????????, ?????????????,???????????,????????????????????????????????????????? ?????????????final plan??????????????default plan, ??final plan?default plan???????,????????????? subplan ???????????????,???????????????????????? ??????,???????statistics collector ?buffer???????????statistics collector?????????????????,???????????????????????????? ?????????????????????????????????????????,??????????,?????????????? ???????????,???????buffer???? ???????????????,?????????????????????????????,??????buffer,??????final plan? ????????,???????????????????????,????????????????? ?V$SQL??????IS_RESOLVED_DYNAMIC_PLAN??????????final plan???default plan? ??????dynamic plan ???????SQL PLAN directives?????? declare cursor PLAN_DIRECTIVE_IDS is select directive_id from DBA_SQL_PLAN_DIRECTIVES; begin for z in PLAN_DIRECTIVE_IDS loop DBMS_SPD.DROP_SQL_PLAN_DIRECTIVE(z.directive_id); end loop; end; / explain plan for select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id; select * from table(dbms_xplan.display()); Plan hash value: 1255158658 www.askmaclean.com ------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 4 | 128 | 7 (0)| 00:00:01 | | 1 | NESTED LOOPS | | | | | | | 2 | NESTED LOOPS | | 4 | 128 | 7 (0)| 00:00:01 | |* 3 | TABLE ACCESS FULL | ORDER_ITEMS | 4 | 48 | 3 (0)| 00:00:01 | |* 4 | INDEX UNIQUE SCAN | PRODUCT_INFORMATION_PK | 1 | | 0 (0)| 00:00:01 | | 5 | TABLE ACCESS BY INDEX ROWID| PRODUCT_INFORMATION | 1 | 20 | 1 (0)| 00:00:01 | ------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 3 - filter("O"."UNIT_PRICE"=15 AND "QUANTITY">1) 4 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") alter session set events '10053 trace name context forever,level 1'; OR alter session set events 'trace[SQL_Plan_Directive] disk highest'; select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id; ---------------------------------------------------------------+-----------------------------------+ | Id | Operation | Name | Rows | Bytes | Cost | Time | ---------------------------------------------------------------+-----------------------------------+ | 0 | SELECT STATEMENT | | | | 7 | | | 1 | HASH JOIN | | 4 | 128 | 7 | 00:00:01 | | 2 | NESTED LOOPS | | | | | | | 3 | NESTED LOOPS | | 4 | 128 | 7 | 00:00:01 | | 4 | STATISTICS COLLECTOR | | | | | | | 5 | TABLE ACCESS FULL | ORDER_ITEMS | 4 | 48 | 3 | 00:00:01 | | 6 | INDEX UNIQUE SCAN | PRODUCT_INFORMATION_PK| 1 | | 0 | | | 7 | TABLE ACCESS BY INDEX ROWID | PRODUCT_INFORMATION | 1 | 20 | 1 | 00:00:01 | | 8 | TABLE ACCESS FULL | PRODUCT_INFORMATION | 1 | 20 | 1 | 00:00:01 | ---------------------------------------------------------------+-----------------------------------+ Predicate Information: ---------------------- 1 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") 5 - filter(("O"."UNIT_PRICE"=15 AND "QUANTITY">1)) 6 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") ===================================== SPD: BEGIN context at statement level ===================================== Stmt: ******* UNPARSED QUERY IS ******* SELECT /*+ OPT_ESTIMATE (@"SEL$1" JOIN ("P"@"SEL$1" "O"@"SEL$1") ROWS=13.000000 ) OPT_ESTIMATE (@"SEL$1" TABLE "O"@"SEL$1" ROWS=13.000000 ) */ "P"."PRODUCT_NAME" "PRODUCT_NAME" FROM "OE"."ORDER_ITEMS" "O","OE"."PRODUCT_INFORMATION" "P" WHERE "O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1 AND "P"."PRODUCT_ID"="O"."PRODUCT_ID" Objects referenced in the statement PRODUCT_INFORMATION[P] 92194, type = 1 ORDER_ITEMS[O] 92197, type = 1 Objects in the hash table Hash table Object 92197, type = 1, ownerid = 6573730143572393221: No Dynamic Sampling Directives for the object Hash table Object 92194, type = 1, ownerid = 17822962561575639002: No Dynamic Sampling Directives for the object Return code in qosdInitDirCtx: ENBLD =================================== SPD: END context at statement level =================================== ======================================= SPD: BEGIN context at query block level ======================================= Query Block SEL$1 (#0) Return code in qosdSetupDirCtx4QB: NOCTX ===================================== SPD: END context at query block level ===================================== SPD: Return code in qosdDSDirSetup: NOCTX, estType = TABLE SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92197, objtyp = 1, vecsize = 6, colvec = [4, 5, ], fid = 2896834833840853267 SPD: Inserted felem, fid=2896834833840853267, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = YES, keep = YES SPD: qosdCreateFindingSingTab retCode = CREATED, fid = 2896834833840853267 SPD: qosdCreateDirCmp retCode = CREATED, fid = 2896834833840853267 SPD: Return code in qosdDSDirSetup: NOCTX, estType = TABLE SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = JOIN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SKIP_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = JOIN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92197, objtyp = 1, vecsize = 6, colvec = [4, 5, ], fid = 2896834833840853267 SPD: Modified felem, fid=2896834833840853267, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = YES, keep = YES SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92194, objtyp = 1, vecsize = 2, colvec = [1, ], fid = 5618517328604016300 SPD: Modified felem, fid=5618517328604016300, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92194, objtyp = 1, vecsize = 2, colvec = [1, ], fid = 1142802697078608149 SPD: Modified felem, fid=1142802697078608149, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO SPD: Generating finding id: type = 1, reason = 2, objcnt = 2, obItr = 0, objid = 92194, objtyp = 1, vecsize = 0, obItr = 1, objid = 92197, objtyp = 1, vecsize = 0, fid = 1437680122701058051 SPD: Modified felem, fid=1437680122701058051, ftype = 1, freason = 2, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO select * from table(dbms_xplan.display_cursor(format=>'report')) ; ????report????adaptive plan Adaptive plan: ------------- This cursor has an adaptive plan, but adaptive plans are enabled for reporting mode only.  The plan that would be executed if adaptive plans were enabled is displayed below. ------------------------------------------------------------------------------------------ | Id  | Operation          | Name                | Rows  | Bytes | Cost (%CPU)| Time     | ------------------------------------------------------------------------------------------ |   0 | SELECT STATEMENT   |                     |       |       |     7 (100)|          | |*  1 |  HASH JOIN         |                     |     4 |   128 |     7   (0)| 00:00:01 | |*  2 |   TABLE ACCESS FULL| ORDER_ITEMS         |     4 |    48 |     3   (0)| 00:00:01 | |   3 |   TABLE ACCESS FULL| PRODUCT_INFORMATION |     1 |    20 |     1   (0)| 00:00:01 | ------------------------------------------------------------------------------------------ SQL> select SQL_ID,IS_RESOLVED_DYNAMIC_PLAN,sql_text from v$SQL WHERE SQL_TEXT like '%MALCEAN%' and sql_text not like '%like%'; SQL_ID IS -------------------------- -- SQL_TEXT -------------------------------------------------------------------------------- 6ydj1bn1bng17 Y select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id ???? explain plan for ????default plan, ??????optimizer???final plan,??V$SQL.IS_RESOLVED_DYNAMIC_PLAN???Y,????????????? DBA_SQL_PLAN_DIRECTIVES?????????????SQL PLAN DIRECTIVES, ???12c? ???MMON?????DML ???column usage??????????,????SMON??? MMON????SGA??PLAN DIRECTIVES??? ?????DBMS_SPD.flush_sql_plan_directive???? select directive_id,type,reason from DBA_SQL_PLAN_DIRECTIVES / DIRECTIVE_ID TYPE REASON ----------------------------------- -------------------------------- ----------------------------- 10321283028317893030 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE 4757086536465754886 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE 16085268038103121260 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE SQL> set pages 9999 SQL> set lines 300 SQL> col state format a5 SQL> col subobject_name format a11 SQL> col col_name format a11 SQL> col object_name format a13 SQL> select d.directive_id, o.object_type, o.object_name, o.subobject_name col_name, d.type, d.state, d.reason 2 from dba_sql_plan_directives d, dba_sql_plan_dir_objects o 3 where d.DIRECTIVE_ID=o.DIRECTIVE_ID 4 and o.object_name in ('ORDER_ITEMS') 5 order by d.directive_id; DIRECTIVE_ID OBJECT_TYPE OBJECT_NAME COL_NAME TYPE STATE REASON ------------ ------------ ------------- ----------- -------------------------------- ----- ------------------------------------- --- 1.8156E+19 COLUMN ORDER_ITEMS UNIT_PRICE DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1.8156E+19 TABLE ORDER_ITEMS DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1.8156E+19 COLUMN ORDER_ITEMS QUANTITY DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE DBA_SQL_PLAN_DIRECTIVES????? _BASE_OPT_DIRECTIVE ? _BASE_OPT_FINDING SELECT d.dir_own#, d.dir_id, d.f_id, decode(type, 1, 'DYNAMIC_SAMPLING', 'UNKNOWN'), decode(state, 1, 'NEW', 2, 'MISSING_STATS', 3, 'HAS_STATS', 4, 'CANDIDATE', 5, 'PERMANENT', 6, 'DISABLED', 'UNKNOWN'), decode(bitand(flags, 1), 1, 'YES', 'NO'), cast(d.created as timestamp), cast(d.last_modified as timestamp), -- Please see QOSD_DAYS_TO_UPDATE and QOSD_PLUS_SECONDS for more details -- about 6.5 cast(d.last_used as timestamp) - NUMTODSINTERVAL(6.5, 'day') FROM sys.opt_directive$ d ??dbms_spd??? SQL PLAN DIRECTIVES, SQL PLAN DIRECTIVES???retention ???53?: Package: DBMS_SPD This package provides subprograms for managing Sql Plan Directives(SPD). SPD are objects generated automatically by Oracle server. For example, if server detects that the single table cardinality estimated by optimizer is off from the actual number of rows returned when accessing the table, it will automatically create a directive to do dynamic sampling for the table. When any Sql statement referencing the table is compiled, optimizer will perform dynamic sampling for the table to get more accurate estimate. Notes: DBMSL_SPD is a invoker-rights package. The invoker requires ADMINISTER SQL MANAGEMENT OBJECT privilege for executing most of the subprograms of this package. Also the subprograms commit the current transaction (if any), perform the operation and commit it again. DBA view dba_sql_plan_directives shows all the directives created in the system and the view dba_sql_plan_dir_objects displays the objects that are included in the directives. -- Default value for SPD_RETENTION_WEEKS SPD_RETENTION_WEEKS_DEFAULT CONSTANT varchar2(4) := '53'; | STATE : NEW : Newly created directive. | : MISSING_STATS : The directive objects do not | have relevant stats. | : HAS_STATS : The objects have stats. | : PERMANENT : A permanent directive. Server | evaluated effectiveness and these | directives are useful. | | AUTO_DROP : YES : Directive will be dropped | automatically if not | used for SPD_RETENTION_WEEKS. | This is the default behavior. | NO : Directive will not be dropped | automatically. Procedure: flush_sql_plan_directive This procedure allows manually flushing the Sql Plan directives that are automatically recorded in SGA memory while executing sql statements. The information recorded in SGA are periodically flushed by oracle background processes. This procedure just provides a way to flush the information manually. ????”_optimizer_dynamic_plans”(enable dynamic plans)????????,???TRUE??DYNAMIC PLAN? ???FALSE???????????? ????,Dynamic Plan????????????Nested Loop?Hash Join???case ,????????Nested loop???????????HASH JOIN,?HASH JOIN????????????????? ????????subplan?????,???? pass?? ?join method???,?????STATISTICS COLLECTOR???cardinality?,???????HASH JOIN?????Nested Loop,????????????subplan?????access path; ???????Sales??????????????????,????HASH JOIN,??SUBPLAN??customers?????????;?????Nested Loop,???????cust_id?????Range Scan+Access by Rowid? Cardinality feedback Cardinality feedback????????11.2????,????????re-optimization???;  ???????????,Cardinality feedback?????????????????????????? ???????????????????,?????????????????,??????????Cardinality feedback????????????? ????????????????????????? ??????????????Cardinality feedback ??: ????????,???????????,??????????,????????????????selectivity ??? ????????????: ??????,?????????????????????????????????,??????????????????? ????????????????????????????????????????,?????????????????????????? ?????????,???????????????,?????????? ??????????Cardinality ????,??????join Cardinality ????????? Cardinality feedback???????cursor?,?Cursor???aged out????? SELECT /*+ gather_plan_statistics */ product_name FROM order_items o, product_information p WHERE o.unit_price = 15 AND quantity > 1 AND p.product_id = o.product_id Plan hash value: 1553478007 ---------------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | OMem | 1Mem | Used-Mem | ---------------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 13 |00:00:00.01 | 24 | 20 | | | | |* 1 | HASH JOIN | | 1 | 4 | 13 |00:00:00.01 | 24 | 20 | 2061K| 2061K| 429K (0)| |* 2 | TABLE ACCESS FULL| ORDER_ITEMS | 1 | 4 | 13 |00:00:00.01 | 7 | 6 | | | | | 3 | TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 1 | 288 |00:00:00.01 | 17 | 14 | | | | ---------------------------------------------------------------------------------------------------------------------------------------- SELECT /*+ gather_plan_statistics */ product_name FROM order_items o, product_information p WHERE o.unit_price = 15 AND quantity > 1 AND p.product_id = o.product_id Plan hash value: 1553478007 ------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem | ------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 13 |00:00:00.01 | 24 | | | | |* 1 | HASH JOIN | | 1 | 13 | 13 |00:00:00.01 | 24 | 2061K| 2061K| 413K (0)| |* 2 | TABLE ACCESS FULL| ORDER_ITEMS | 1 | 13 | 13 |00:00:00.01 | 7 | | | | | 3 | TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 288 | 288 |00:00:00.01 | 17 | | | | ------------------------------------------------------------------------------------------------------------------------------- Note ----- - statistics feedback used for this statement SQL> select count(*) from v$SQL where SQL_ID='cz0hg2zkvd10y'; COUNT(*) ---------- 2 SQL>select sql_ID,USE_FEEDBACK_STATS FROM V$SQL_SHARED_CURSOR where USE_FEEDBACK_STATS ='Y'; SQL_ID U ------------- - cz0hg2zkvd10y Y ????????Cardinality feedback????,???????????????????????????,????????????order_items???????? ????2??????plan hash value??(??????????),?????2????child cursor??????gather_plan_statistics???actual : A-ROWS  estimate :E-ROWS????????? Automatic Re-optimization ???dynamic plan, Re-optimization???????????????  ?  ??????????????? ????????????????????????????????  ???????????,??????????????, ???????????????????? ???????????  Re-optimization??, ????????????????????? Re-optimization????dynamic plan??????????  dynamic plan????????????????????, ???????????????????? ????,??????????join order ??????????????,?????????????join order????? ??????,????????Re-optimization, ??Re-optimization ??????????????????? ?Oracle database 12c?,join statistics?????????????????????,??????????????????????Re-optimization???????????adaptive cursor sharing????? ????????????????,???????????? ????? ???????statistics collectors ????????????????????Re-optimization??????2?????????????,???????????????? ??????????????Re-optimization?????,?????????????????????? ???v$SQL??????IS_REOPTIMIZABLE?????????????????????Re-optimization,??????????Re-optimization???,?????Re-optimization ,???????reporting????? IS_REOPTIMIZABLE VARCHAR2(1) This columns shows whether the next execution matching this child cursor will trigger a reoptimization. The values are:   Y: If the next execution will trigger a reoptimization R: If the child cursor contains reoptimization information, but will not trigger reoptimization because the cursor was compiled in reporting mode N: If the child cursor has no reoptimization information ??1: select plan_table_output from table (dbms_xplan.display_cursor('gwf99gfnm0t7g',NULL,'ALLSTATS LAST')); SQL_ID  gwf99gfnm0t7g, child number 0 ------------------------------------- SELECT /*+ SFTEST gather_plan_statistics */ o.order_id, v.product_name FROM  orders o,   ( SELECT order_id, product_name FROM order_items o, product_information p     WHERE  p.product_id = o.product_id AND list_price < 50 AND min_price < 40  ) v WHERE o.order_id = v.order_id Plan hash value: 1906736282 ------------------------------------------------------------------------------------------------------------------------------------------- | Id  | Operation             | Name                | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  |  OMem |  1Mem | Used-Mem | ------------------------------------------------------------------------------------------------------------------------------------------- |   0 | SELECT STATEMENT      |                     |      1 |        |    269 |00:00:00.02 |    1336 |     18 |       |       |          | |   1 |  NESTED LOOPS         |                     |      1 |      1 |    269 |00:00:00.02 |    1336 |     18 |       |       |          | |   2 |   MERGE JOIN CARTESIAN|                     |      1 |      4 |   9135 |00:00:00.02 |      34 |     15 |       |       |          | |*  3 |    TABLE ACCESS FULL  | PRODUCT_INFORMATION |      1 |      1 |     87 |00:00:00.01 |      33 |     14 |       |       |          | |   4 |    BUFFER SORT        |                     |     87 |    105 |   9135 |00:00:00.01 |       1 |      1 |  4096 |  4096 | 4096  (0)| |   5 |     INDEX FULL SCAN   | ORDER_PK            |      1 |    105 |    105 |00:00:00.01 |       1 |      1 |       |       |          | |*  6 |   INDEX UNIQUE SCAN   | ORDER_ITEMS_UK      |   9135 |      1 |    269 |00:00:00.01 |    1302 |      3 |       |       |          | ------------------------------------------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): ---------------------------------------------------    3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50))    6 - access("O"."ORDER_ID"="ORDER_ID" AND "P"."PRODUCT_ID"="O"."PRODUCT_ID") SQL_ID  gwf99gfnm0t7g, child number 1 ------------------------------------- SELECT /*+ SFTEST gather_plan_statistics */ o.order_id, v.product_name FROM  orders o,   ( SELECT order_id, product_name FROM order_items o, product_information p     WHERE  p.product_id = o.product_id AND list_price < 50 AND min_price < 40  ) v WHERE o.order_id = v.order_id Plan hash value: 35479787 -------------------------------------------------------------------------------------------------------------------------------------------- | Id  | Operation              | Name                | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  |  OMem |  1Mem | Used-Mem | -------------------------------------------------------------------------------------------------------------------------------------------- |   0 | SELECT STATEMENT       |                     |      1 |        |    269 |00:00:00.01 |      63 |      3 |       |       |          | |   1 |  NESTED LOOPS          |                     |      1 |    269 |    269 |00:00:00.01 |      63 |      3 |       |       |          | |*  2 |   HASH JOIN            |                     |      1 |    313 |    269 |00:00:00.01 |      42 |      3 |  1321K|  1321K| 1234K (0)| |*  3 |    TABLE ACCESS FULL   | PRODUCT_INFORMATION |      1 |     87 |     87 |00:00:00.01 |      16 |      0 |       |       |          | |   4 |    INDEX FAST FULL SCAN| ORDER_ITEMS_UK      |      1 |    665 |    665 |00:00:00.01 |      26 |      3 |       |       |          | |*  5 |   INDEX UNIQUE SCAN    | ORDER_PK            |    269 |      1 |    269 |00:00:00.01 |      21 |      0 |       |       |          | -------------------------------------------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): ---------------------------------------------------    2 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID")    3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50))    5 - access("O"."ORDER_ID"="ORDER_ID") Note -----    - statistics feedback used for this statement    SQL> select IS_REOPTIMIZABLE,child_number FROM V$SQL  A where A.SQL_ID='gwf99gfnm0t7g'; IS CHILD_NUMBER -- ------------ Y             0 N             1    1* select child_number,other_xml From v$SQL_PLAN  where SQL_ID='gwf99gfnm0t7g' and other_xml is not nul SQL> / CHILD_NUMBER OTHER_XML ------------ --------------------------------------------------------------------------------            1 <other_xml><info type="cardinality_feedback">yes</info><info type="db_version">1              2.1.0.1</info><info type="parse_schema"><![CDATA["OE"]]></info><info type="plan_              hash">35479787</info><info type="plan_hash_2">3382491761</info><outline_data><hi              nt><![CDATA[IGNORE_OPTIM_EMBEDDED_HINTS]]></hint><hint><![CDATA[OPTIMIZER_FEATUR              ES_ENABLE('12.1.0.1')]]></hint><hint><![CDATA[DB_VERSION('12.1.0.1')]]></hint><h              int><![CDATA[ALL_ROWS]]></hint><hint><![CDATA[OUTLINE_LEAF(@"SEL$F5BB74E1")]]></              hint><hint><![CDATA[MERGE(@"SEL$2")]]></hint><hint><![CDATA[OUTLINE(@"SEL$1")]]>              </hint><hint><![CDATA[OUTLINE(@"SEL$2")]]></hint><hint><![CDATA[FULL(@"SEL$F5BB7              4E1" "P"@"SEL$2")]]></hint><hint><![CDATA[INDEX_FFS(@"SEL$F5BB74E1" "O"@"SEL$2"              ("ORDER_ITEMS"."ORDER_ID" "ORDER_ITEMS"."PRODUCT_ID"))]]></hint><hint><![CDATA[I              NDEX(@"SEL$F5BB74E1" "O"@"SEL$1" ("ORDERS"."ORDER_ID"))]]></hint><hint><![CDATA[              LEADING(@"SEL$F5BB74E1" "P"@"SEL$2" "O"@"SEL$2" "O"@"SEL$1")]]></hint><hint><![C              DATA[USE_HASH(@"SEL$F5BB74E1" "O"@"SEL$2")]]></hint><hint><![CDATA[USE_NL(@"SEL$              F5BB74E1" "O"@"SEL$1")]]></hint></outline_data></other_xml>            0 <other_xml><info type="db_version">12.1.0.1</info><info type="parse_schema"><![C              DATA["OE"]]></info><info type="plan_hash">1906736282</info><info type="plan_hash              _2">2579473118</info><outline_data><hint><![CDATA[IGNORE_OPTIM_EMBEDDED_HINTS]]>              </hint><hint><![CDATA[OPTIMIZER_FEATURES_ENABLE('12.1.0.1')]]></hint><hint><![CD              ATA[DB_VERSION('12.1.0.1')]]></hint><hint><![CDATA[ALL_ROWS]]></hint><hint><![CD              ATA[OUTLINE_LEAF(@"SEL$F5BB74E1")]]></hint><hint><![CDATA[MERGE(@"SEL$2")]]></hi              nt><hint><![CDATA[OUTLINE(@"SEL$1")]]></hint><hint><![CDATA[OUTLINE(@"SEL$2")]]>              </hint><hint><![CDATA[FULL(@"SEL$F5BB74E1" "P"@"SEL$2")]]></hint><hint><![CDATA[              INDEX(@"SEL$F5BB74E1" "O"@"SEL$1" ("ORDERS"."ORDER_ID"))]]></hint><hint><![CDATA              [INDEX(@"SEL$F5BB74E1" "O"@"SEL$2" ("ORDER_ITEMS"."ORDER_ID" "ORDER_ITEMS"."PROD              UCT_ID"))]]></hint><hint><![CDATA[LEADING(@"SEL$F5BB74E1" "P"@"SEL$2" "O"@"SEL$1              " "O"@"SEL$2")]]></hint><hint><![CDATA[USE_MERGE_CARTESIAN(@"SEL$F5BB74E1" "O"@"              SEL$1")]]></hint><hint><![CDATA[USE_NL(@"SEL$F5BB74E1" "O"@"SEL$2")]]></hint></o              utline_data></other_xml> ??2: SELECT /*+gather_plan_statistics*/ * FROM customers WHERE cust_state_province='CA' AND country_id='US'; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID b74nw722wjvy3, child number 0 ------------------------------------- select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 -------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | -------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 17 | 14 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 8 | 29 |00:00:00.01 | 17 | 14 | -------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE FROM V$SQL WHERE SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%'; SQL_ID CHILD_NUMBER SQL_TEXT I ------------- ------------ ----------- - b74nw722wjvy3 0 select /*+g Y ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OWNER OBJECT_NAME COL_NAME OBJECT TYPE STATE REASON ----------------------- ----- ------------- ----------- ------ ---------------- ----- ------------------------ 1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY PROVINCE MISESTIMATE 1484026771529551585 SH CUSTOMERS TABLE DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE SELECT /*+gather_plan_statistics*/ * FROM customers WHERE cust_state_province='CA' AND country_id='US'; ELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID b74nw722wjvy3, child number 1 ------------------------------------- select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 ----------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | ----------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 17 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 29 | 29 |00:00:00.01 | 17 | ----------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) Note ----- - cardinality feedback used for this statement SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE FROM V$SQL WHERE SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%'; SQL_ID CHILD_NUMBER SQL_TEXT I ------------- ------------ ----------- - b74nw722wjvy3 0 select /*+g Y ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' b74nw722wjvy3 1 select /*+g N ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' SELECT /*+gather_plan_statistics*/ CUST_EMAIL FROM CUSTOMERS WHERE CUST_STATE_PROVINCE='MA' AND COUNTRY_ID='US'; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID 3tk6hj3nkcs2u, child number 0 ------------------------------------- Select /*+gather_plan_statistics*/ cust_email From customers Where cust_state_province='MA' And country_id='US' Plan hash value: 1683234692 ------------------------------------------------------------------------------- |Id | Operation | Name | Starts|E-Rows|A-Rows| A-Time |Buffers| ------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 2 |00:00:00.01| 16 | |*1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 2| 2 |00:00:00.01| 16 | ----------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='MA' AND "COUNTRY_ID"='US')) Note ----- - dynamic sampling used for this statement (level=2) - 1 Sql Plan Directive used for this statement EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OW OBJECT_NA COL_NAME OBJECT TYPE STATE REASON ------------------- -- --------- ---------- ------- --------------- ------------- ------------------------ 1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY PROVINCE MISESTIMATE 1484026771529551585 SH CUSTOMERS TABLE DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY MISESTIMATE

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• Use MTOM/streaming from C# calling a webservice in java exposed via jaxws

  - by raticulin

  We have this webservice created with jax-ws @WebService(name = "Mywebser", targetNamespace = "http://namespace") @MTOM(threshold = 2048) @SOAPBinding(style = SOAPBinding.Style.DOCUMENT, use = SOAPBinding.Use.LITERAL, parameterStyle = SOAPBinding.ParameterStyle.WRAPPED) public class Mywebser { @WebMethod(operationName = "doStreaming", action = "urn:doStreaming") @WebResult(name = "return") public ResultInfo doStreaming(String username, String pwd, @XmlMimeType("application/octet-stream") DataHandler data, boolean overw){ ... } } The generated client side looks like this: @WebMethod(action = "urn:doStreaming") @WebResult(targetNamespace = "") @RequestWrapper(localName = "doStreaming", targetNamespace = "http://namespace", className = "com.mypack.client.doStreaming") @ResponseWrapper(localName = "doStreamingResponse", targetNamespace = "http://namespace", className = "com.mypack.client.doStreamingResponse") public ResultInfo doStreaming( @WebParam(name = "arg0", targetNamespace = "") String arg0, @WebParam(name = "arg1", targetNamespace = "") String arg1, @WebParam(name = "arg2", targetNamespace = "") DataHandler arg2, @WebParam(name = "arg3", targetNamespace = "") boolean arg3); By using it this way it uses streaming properly (verified we can pass an argument of 80mb when the jvm had less allowed. MywebserService serv = ...; Mywebser wso = serv.getMywebserPort(new MTOMFeature()); Map<String, Object> ctxt = ((BindingProvider) wso).getRequestContext(); ctxt.put(JAXWSProperties.HTTP_CLIENT_STREAMING_CHUNK_SIZE, 8192); DataHandler dataHandler = new DataHandler(new FileDataSource("c:\\temp\\A.dat")); arcres = wso.doStreaming("a", "b", dataHandler, true); We generate a clienet for .net, with VS2008, using "Add Web Reference", we get this C# code: [System.Web.Services.Protocols.SoapDocumentMethodAttribute("urn:doStreaming",RequestNamespace="http://namespace",ResponseNamespace="http://namespace",Use=System.Web.Services.Description.SoapBindingUse.Literal,ParameterStyle=System.Web.Services.Protocols.SoapParameterStyle.Wrapped)] [return: System.Xml.Serialization.XmlElementAttribute("return",Form=System.Xml.Schema.XmlSchemaForm.Unqualified)] public ResultInfo doStreaming( [System.Xml.Serialization.XmlElementAttribute(Form=System.Xml.Schema.XmlSchemaForm.Unqualified)] string arg0, [System.Xml.Serialization.XmlElementAttribute(Form=System.Xml.Schema.XmlSchemaForm.Unqualified)] string arg1, [System.Xml.Serialization.XmlElementAttribute(Form=System.Xml.Schema.XmlSchemaForm.Unqualified,DataType="base64Binary")] byte[] arg2, [System.Xml.Serialization.XmlElementAttribute(Form=System.Xml.Schema.XmlSchemaForm.Unqualified)] bool arg3) Apparently this is not using streaming? The type base64Binary of arg2 seems not the right one? In java it's a DataHandler. By testing it with low memory on the java side we can see it is not using streaming as it fails with OOM. Does someone knows if this is possible, and if so how? Our environment: server: jdk1.6, jaxws 2.1.7 client: C# 2.0, visual studio 2008

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• AVerMedia A309-B mini-PCI to AVerMedia A317 Mini PCI card

  - by Chris

  I got an HP pavilion hdx 16 1060ED laptop (Windows Vista) with a a DVB-T tuner card Now I would like a hybrid or analog turner card in it. According to the HP data of a more expensive variant, a AVerMedia A317 Mini PCI card installed is installed. My system has a AVerMedia A309-B mini-PCI placed in the system. my questions: 1 - is it possible to replace it with a expensive one? (AVerMedia A317 Mini PCI card) and 2 - what will this cost? 3 - I can build it myself and what can I do with the old card I like to hear from you.

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• Amazon EC2 spot instances - is there a catch ?

  - by gareth_bowles

  I needed to start a new EC2 instance today and decided to try out the new spot instances, where you can reduce your instance cost by bidding on the maximum per-hour price you're prepared to pay. Since today's spot price was only 3.5c / hour, compared with 8.5c / hour for an on-demand instance, I was wondering: if I just bid a really high price, say 10c / hour, can I effectively be sure of getting a much cheaper long-running instance than an on-demand instance (since the spot instances are only charged by the current spot price) ? I suppose it's theoretically possible for the spot price to go over the on-demand price, but as far as I can tell from the data on the AWS site, the spot price has always been well below that.

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• Amazon EC2 spot instances - is there a catch ?

  - by gareth_bowles

  I needed to start a new EC2 instance today and decided to try out the new spot instances, where you can reduce your instance cost by bidding on the maximum per-hour price you're prepared to pay. Since today's spot price was only 35c / hour, compared with 85c / hour for an on-demand instance, I was wondering: if I just bid a really high price, say $1 / hour, can I effectively be sure of getting a much cheaper long-running instance than an on-demand instance (since the spot instances are only charged by the current spot price) ? I suppose it's theoretically possible for the spot price to go over the on-demand price, but as far as I can tell from the data on the AWS site, the spot price has always been well below that.

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• Started a Forum Board (with phpBB), but Now Rethinking Choice of Board App - Security

  - by nicorellius

  The main reason I even started participating on Superuser.com is because a friend ripped me a new one for using phpBB. He said, "check out StackExchange, they have their act together!" I did, and it's true. So now, after learning phpBB and implementing the board (it's still new and in its infancy), I feel slightly regretful. I would love to use the Stack Exchange tool, but the cost will eventually be the main deterrent. The attractive thing about phpBB is that it's free and open. However, I have heard that it lacks security. Has anyone had this experience, that phpBB is not secure, such that they changed board software? And, I wonder if Stack Exchange is going to introduce a cheaper option for low traffic users? Does this question belong on meta?

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• Amazon EC2 EBS volume scheduled backup/snapshots using puppet

  - by Ehrann Mehdan

  I am not a Linux admin, although I wish I was, and I have seen these questions Amazon EC2 Backup Strategy Amazon EC2 + EBS:: Regular backup plan? Simple Backup Strategy for Amazon EC2 instances / volumes? And this suggestion http://alestic.com/2009/09/ec2-consistent-snapshot I tried using command line + crontab (the command line works, but crontab for some reason, doesn't) But I'm still pretty lost, all I want is an automated, rolling backup of my amazon EC2 (EBS) data (by rolling I mean keep 3-4 weeks back, but delete old snapshots as new ones come for cost control) And as things usually go, if there is something that is hard and painful, someone creates a solution for it. My question is simple, is there a way using a tool like Puppet to do it without a painful learning curve? (or via other tools like http://ylastic.com) If yes, how?

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• Cheap dvr media server

  - by Tester101

  I would like to build a new computer to be used as a DVR and media server. I am thinking of using mythtv as the DVR software, but short of that decision I am completely open to suggestions. Requirements: Must be cheap. Must have low power consumption (since it will always be on). Should be quiet. Should be small. I'm really not sure where to start with this project, and am open to any hardware/software suggestions. Is it possible to build a small quiet and cheap system? *Keep in mind I am working on this project because I am tired of Cable rate increases, but I can't imagine living without a DVR so cost is very important and I would like the system to be sub $200.00. The system also needs to handle the new digital broadcast system. Thanks for the help,

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• 64-bit Cisco VPN client (IPsec) ?

  - by mika

  Cisco VPN client (IPsec) does not support 64bit Windows. Worse, Cisco does not even plan to release a 64-bit version, instead they say that "For x64 (64-bit) Windows support, you must utilize Cisco's next-generation Cisco AnyConnect VPN Client." Cisco VPN Client Introduction Cisco VPN Client FAQ But SSL VPN licences cost extra. For example, most new ASA firewalls come with plenty of IPSec VPN licences but only a few SSL VPN licences. What alternatives do you have for 64-bit Windows? So far, I know two: 32-bit Cisco VPN Client on a virtual machine NCP Secure Entry Client on 64-bit Windows Any other suggestions or experiences? -mika-

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• Developing online invoicing and inventory application.

  - by Rohit

  My clients are using a desktop version of my inventory solution that I developed using .NET. I want to make an online version so that data is available centrally and clients can work from any location. I searched using Google to find similar tools and found few. I want to know what type of security considerations to take while designing such an application? Some clients can't afford dedicated server cost. What if I use shared hosting only? What are the risks of shared hosting?

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• Developing online invoicing and inventory application.

  - by Rohit

  My clients are using a desktop version of my inventory solution that I developed using .NET. I want to make an online version so that data is available centrally and clients can work from any location. I searched using Google to find similar tools and found few. I want to know what type of security considerations to take while designing such an application? Some clients can't afford dedicated server cost. What if I use shared hosting only? What are the risks of shared hosting?

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• A catalogue of Cassandra log messages: What is the correct interpretation?

  - by knorv

  The following is a complete catalogue of all log messages generated by Cassandra 0.6 when stress-testing a Cassandra installation over an extended period of time: AntiEntropyService: Sending AEService tree for (,) to: [] CassandraDaemon: Binding thrift service to localhost/N.N.N.N:N CassandraDaemon: Cassandra starting up... ColumnFamilyStore: has reached its threshold; switching in a fresh Memtable at CommitLogContext(file='.../cassandra/commitlog/CommitLog-N.log', position=N) ColumnFamilyStore: Enqueuing flush of Memtable()@N CommitLog: Discarding obsolete commit log:CommitLogSegment(.../cassandra/commitlog/CommitLog-N.log) CommitLog: Log replay complete CommitLog: Replaying .../cassandra/commitlog/CommitLog-N.log, ... CommitLogSegment: Creating new commitlog segment .../cassandra/commitlog/CommitLog-N.log CompactionManager: Compacted to .../cassandra/data//-N-Data.db. N/N bytes for N keys. Time: Nms. CompactionManager: Compacting [org.apache.cassandra.io.SSTableReader(path='.../cassandra/data//-N-Data.db'), ...] DatabaseDescriptor: Auto DiskAccessMode determined to be mmap GCInspector: GC for ConcurrentMarkSweep: N ms, N reclaimed leaving N used; max is N GCInspector: GC for ParNew: N ms, N reclaimed leaving N used; max is N Memtable: Completed flushing .../cassandra/data//-N-Data.db Memtable: Writing Memtable()@N SSTable: Deleted .../cassandra/data//-N-Data.db SSTableDeletingReference: Deleted .../cassandra/data//-N-Data.db SSTableReader: Sampling index for .../cassandra/data//-N-Data.db StorageService: Starting up server gossip SystemTable: Saved ClusterName found: Test Cluster SystemTable: Saved ClusterName not found. Using Test Cluster SystemTable: Saved Token found: N SystemTable: Saved Token not found. Using N For each of the log messages listed - what is the correct interpretation of the log message?

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• Supplementary Developer Laptop

  - by David Smith

  I'm looking to buy a laptop with the following specs for a developer. The goal will be to have a development machine supplementing the devs desktop. During work hours the dev will be on a beefy desktop. For working while on the go: trains, client sites, code camps, it would be nice to have a machine which can run Visual Studio 2008 without needing to remote desktop into their primary machine. What do you think is the lowest cost laptop meeting this need? Here are the specs I have in mind: SSD drive 64GB-doesn't need to be huge, most data is stored on servers. Will need to fit Windows 7, IIS, SQL Server, and Visual Studio 2010. RAM-3GB processor =Pentium Core 2 duo Screen size = 14 inches. OS doesn't matter. It will be paved with Windows 7 Ultimate optical drive omitted would be a plus. weight and battery life aren't so important because the machine will be plugged in almost all the time.

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• Calculating skew of text OpenCV

  - by Nick

  I am trying to calculate the skew of text in an image so I can correct it for the best OCR results. Currently this is the function I am using: double compute_skew(Mat &img) { // Binarize cv::threshold(img, img, 225, 255, cv::THRESH_BINARY); // Invert colors cv::bitwise_not(img, img); cv::Mat element = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(5, 3)); cv::erode(img, img, element); std::vector<cv::Point> points; cv::Mat_<uchar>::iterator it = img.begin<uchar>(); cv::Mat_<uchar>::iterator end = img.end<uchar>(); for (; it != end; ++it) if (*it) points.push_back(it.pos()); cv::RotatedRect box = cv::minAreaRect(cv::Mat(points)); double angle = box.angle; if (angle < -45.) angle += 90.; cv::Point2f vertices[4]; box.points(vertices); for(int i = 0; i < 4; ++i) cv::line(img, vertices[i], vertices[(i + 1) % 4], cv::Scalar(255, 0, 0), 1, CV_AA); return angle; } When I look at then angle in debug I get 0.000000 However when I give it this image I get proper results of a skew of about 16 degrees: How can I properly detect the skew in the first image?

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• Synology DS210j & online backup: what do people recommend?

  - by Dean

  I've just purchased a Synology DS210j for my home network and would like to backup this NAS online. I noticed that DiskStation Manager v2.3 provides various options including Amazon S3 and rsync: Does anybody have some real usage against cost statistics for Amazon's S3 service? How is sensitive data protected on Amazon S3? Are there any rsync online backup options? If so, what do people recommend? UPDATE: I am still unable to find any decent answers to the above questions, can anybody help me out?

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• Hadoop safemode recovery - taking lot of time

  - by Algorist

  Hi, We are running our cluster on Amazon EC2. we are using cloudera scripts to setup hadoop. On the master node, we start below services. 609 $AS_HADOOP '"$HADOOP_HOME"/bin/hadoop-daemon.sh start namenode' 610 $AS_HADOOP '"$HADOOP_HOME"/bin/hadoop-daemon.sh start secondarynamenode' 611 $AS_HADOOP '"$HADOOP_HOME"/bin/hadoop-daemon.sh start jobtracker' 612 613 $AS_HADOOP '"$HADOOP_HOME"/bin/hadoop dfsadmin -safemode wait' On the slave machine, we run the below services. 625 $AS_HADOOP '"$HADOOP_HOME"/bin/hadoop-daemon.sh start datanode' 626 $AS_HADOOP '"$HADOOP_HOME"/bin/hadoop-daemon.sh start tasktracker' The main problem we are facing is, hdfs safemode recovery is taking more than an hour and this is causing delays in our job completion. Below are the main log messages. 1. domU-12-31-39-0A-34-61.compute-1.internal 10/05/05 20:44:19 INFO ipc.Client: Retrying connect to server: ec2-184-73-64-64.compute-1.amazonaws.com/10.192.11.240:8020. Already tried 21 time(s). 2. The reported blocks 283634 needs additional 322258 blocks to reach the threshold 0.9990 of total blocks 606499. Safe mode will be turned off automatically. The first message is thrown in task trackers log because, job tracker is not started. job tracker didn't start because of hdfs safemode recovery. The second message is thrown during the recovery process. Is there something I am doing wrong? How much time does normal hdfs safemode recovery takes? Will there be any speedup, by not starting task trackers till job tracker is started? Are there any known hadoop problems on amazon cluster? Thanks for your help. Regards Bala Mudiam

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• On Server Disk Storage VS SAN Storage

  - by Justin

  Hello, I am looking at buying three servers, and trying to figure out which storage solution makes the most sense in terms of performance and cost. Total budget is around: $10,000. OPTION 1: Dell servers with RAID 10 (4 Drives) each 7200RPM SAS 500GB, for a total capacity of 1TB. Each server is approx: $3000. Total storage then across all three servers is 3TB. OPTION 2: Same Dell servers with a cheap single drive no RAID for $2000 and go with a centralized SAN solution. The biggest problem is that I haven't been able to even find a SAN solution that is a reasonable price. Dell entry level storage servers are like $15,000. I am thinking just iSCSI, not fiber (too expensive). What do you guys recommend?

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