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  • Oracle Enterprise Manager users present today at Oracle Users Forum

    - by Anand Akela
    Oracle Users Forum starts in a few minutes at Moscone West, Levels 2 & 3. There are more than hundreds of Oracle user sessions during the day. Many Oracle Oracle Enterprise Manager users are presenting today as well.  In addition, we will have a Twitter Chat today from 11:30 AM to 12:30 PM with IOUG leaders, Enterprise Manager SIG contributors and many speakers. You can participate in the chat using hash tag #em12c on Twitter.com or by going to  tweetchat.com/room/em12c      (Needs Twitter credential for participating).  Feel free to join IOUG and Enterprise team members at the User Group Pavilion on 2nd Floor, Moscone West. RSVP by going http://tweetvite.com/event/IOUG  . Don't miss the Oracle Open World welcome keynote by Larry Ellison this evening at 5 PM . Here is the complete list of Oracle Enterprise Manager sessions during the Oracle Users Forum : Time Session Title Speakers Location 8:00AM - 8:45AM UGF4569 - Oracle RAC Migration with Oracle Automatic Storage Management and Oracle Enterprise Manager 12c VINOD Emmanuel -Database Engineering, Dell, Inc. Wendy Chen - Sr. Systems Engineer, Dell, Inc. Moscone West - 2011 8:00AM - 8:45AM UGF10389 -  Monitoring Storage Systems for Oracle Enterprise Manager 12c Anand Ranganathan - Product Manager, NetApp Moscone West - 2016 9:00AM - 10:00AM UGF2571 - Make Oracle Enterprise Manager Sing and Dance with the Command-Line Interface Ray Smith - Senior Database Administrator, Portland General Electric Moscone West - 2011 10:30AM - 11:30AM UGF2850 - Optimal Support: Oracle Enterprise Manager 12c Cloud Control, My Oracle Support, and More April Sims - DBA, Southern Utah University Moscone West - 2011 12:30PM-2:00PM UGF5131 - Migrating from Oracle Enterprise Manager 10g Grid Control to 12c Cloud Control    Leighton Nelson - Database Administrator, Mercy Moscone West - 2011 2:15PM-3:15PM UGF6511 -  Database Performance Tuning: Get the Best out of Oracle Enterprise Manager 12c Cloud Control Mike Ault - Oracle Guru, TEXAS MEMORY SYSTEMS INC Tariq Farooq - CEO/Founder, BrainSurface Moscone West - 2011 3:30PM-4:30PM UGF4556 - Will It Blend? Verifying Capacity in Server and Database Consolidations Jeremiah Wilton - Database Technology, Blue Gecko / DatAvail Moscone West - 2018 3:30PM-4:30PM UGF10400 - Oracle Enterprise Manager 12c: Monitoring, Metric Extensions, and Configuration Best Practices Kellyn Pot'Vin - Sr. Technical Consultant, Enkitec Moscone West - 2011 Stay Connected: Twitter |  Face book |  You Tube |  Linked in |  Newsletter

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  • How to find and fix performance problems in ORM powered applications

    - by FransBouma
    Once in a while we get requests about how to fix performance problems with our framework. As it comes down to following the same steps and looking into the same things every single time, I decided to write a blogpost about it instead, so more people can learn from this and solve performance problems in their O/R mapper powered applications. In some parts it's focused on LLBLGen Pro but it's also usable for other O/R mapping frameworks, as the vast majority of performance problems in O/R mapper powered applications are not specific for a certain O/R mapper framework. Too often, the developer looks at the wrong part of the application, trying to fix what isn't a problem in that part, and getting frustrated that 'things are so slow with <insert your favorite framework X here>'. I'm in the O/R mapper business for a long time now (almost 10 years, full time) and as it's a small world, we O/R mapper developers know almost all tricks to pull off by now: we all know what to do to make task ABC faster and what compromises (because there are almost always compromises) to deal with if we decide to make ABC faster that way. Some O/R mapper frameworks are faster in X, others in Y, but you can be sure the difference is mainly a result of a compromise some developers are willing to deal with and others aren't. That's why the O/R mapper frameworks on the market today are different in many ways, even though they all fetch and save entities from and to a database. I'm not suggesting there's no room for improvement in today's O/R mapper frameworks, there always is, but it's not a matter of 'the slowness of the application is caused by the O/R mapper' anymore. Perhaps query generation can be optimized a bit here, row materialization can be optimized a bit there, but it's mainly coming down to milliseconds. Still worth it if you're a framework developer, but it's not much compared to the time spend inside databases and in user code: if a complete fetch takes 40ms or 50ms (from call to entity object collection), it won't make a difference for your application as that 10ms difference won't be noticed. That's why it's very important to find the real locations of the problems so developers can fix them properly and don't get frustrated because their quest to get a fast, performing application failed. Performance tuning basics and rules Finding and fixing performance problems in any application is a strict procedure with four prescribed steps: isolate, analyze, interpret and fix, in that order. It's key that you don't skip a step nor make assumptions: these steps help you find the reason of a problem which seems to be there, and how to fix it or leave it as-is. Skipping a step, or when you assume things will be bad/slow without doing analysis will lead to the path of premature optimization and won't actually solve your problems, only create new ones. The most important rule of finding and fixing performance problems in software is that you have to understand what 'performance problem' actually means. Most developers will say "when a piece of software / code is slow, you have a performance problem". But is that actually the case? If I write a Linq query which will aggregate, group and sort 5 million rows from several tables to produce a resultset of 10 rows, it might take more than a couple of milliseconds before that resultset is ready to be consumed by other logic. If I solely look at the Linq query, the code consuming the resultset of the 10 rows and then look at the time it takes to complete the whole procedure, it will appear to me to be slow: all that time taken to produce and consume 10 rows? But if you look closer, if you analyze and interpret the situation, you'll see it does a tremendous amount of work, and in that light it might even be extremely fast. With every performance problem you encounter, always do realize that what you're trying to solve is perhaps not a technical problem at all, but a perception problem. The second most important rule you have to understand is based on the old saying "Penny wise, Pound Foolish": the part which takes e.g. 5% of the total time T for a given task isn't worth optimizing if you have another part which takes a much larger part of the total time T for that same given task. Optimizing parts which are relatively insignificant for the total time taken is not going to bring you better results overall, even if you totally optimize that part away. This is the core reason why analysis of the complete set of application parts which participate in a given task is key to being successful in solving performance problems: No analysis -> no problem -> no solution. One warning up front: hunting for performance will always include making compromises. Fast software can be made maintainable, but if you want to squeeze as much performance out of your software, you will inevitably be faced with the dilemma of compromising one or more from the group {readability, maintainability, features} for the extra performance you think you'll gain. It's then up to you to decide whether it's worth it. In almost all cases it's not. The reason for this is simple: the vast majority of performance problems can be solved by implementing the proper algorithms, the ones with proven Big O-characteristics so you know the performance you'll get plus you know the algorithm will work. The time taken by the algorithm implementing code is inevitable: you already implemented the best algorithm. You might find some optimizations on the technical level but in general these are minor. Let's look at the four steps to see how they guide us through the quest to find and fix performance problems. Isolate The first thing you need to do is to isolate the areas in your application which are assumed to be slow. For example, if your application is a web application and a given page is taking several seconds or even minutes to load, it's a good candidate to check out. It's important to start with the isolate step because it allows you to focus on a single code path per area with a clear begin and end and ignore the rest. The rest of the steps are taken per identified problematic area. Keep in mind that isolation focuses on tasks in an application, not code snippets. A task is something that's started in your application by either another task or the user, or another program, and has a beginning and an end. You can see a task as a piece of functionality offered by your application.  Analyze Once you've determined the problem areas, you have to perform analysis on the code paths of each area, to see where the performance problems occur and which areas are not the problem. This is a multi-layered effort: an application which uses an O/R mapper typically consists of multiple parts: there's likely some kind of interface (web, webservice, windows etc.), a part which controls the interface and business logic, the O/R mapper part and the RDBMS, all connected with either a network or inter-process connections provided by the OS or other means. Each of these parts, including the connectivity plumbing, eat up a part of the total time it takes to complete a task, e.g. load a webpage with all orders of a given customer X. To understand which parts participate in the task / area we're investigating and how much they contribute to the total time taken to complete the task, analysis of each participating task is essential. Start with the code you wrote which starts the task, analyze the code and track the path it follows through your application. What does the code do along the way, verify whether it's correct or not. Analyze whether you have implemented the right algorithms in your code for this particular area. Remember we're looking at one area at a time, which means we're ignoring all other code paths, just the code path of the current problematic area, from begin to end and back. Don't dig in and start optimizing at the code level just yet. We're just analyzing. If your analysis reveals big architectural stupidity, it's perhaps a good idea to rethink the architecture at this point. For the rest, we're analyzing which means we collect data about what could be wrong, for each participating part of the complete application. Reviewing the code you wrote is a good tool to get deeper understanding of what is going on for a given task but ultimately it lacks precision and overview what really happens: humans aren't good code interpreters, computers are. We therefore need to utilize tools to get deeper understanding about which parts contribute how much time to the total task, triggered by which other parts and for example how many times are they called. There are two different kind of tools which are necessary: .NET profilers and O/R mapper / RDBMS profilers. .NET profiling .NET profilers (e.g. dotTrace by JetBrains or Ants by Red Gate software) show exactly which pieces of code are called, how many times they're called, and the time it took to run that piece of code, at the method level and sometimes even at the line level. The .NET profilers are essential tools for understanding whether the time taken to complete a given task / area in your application is consumed by .NET code, where exactly in your code, the path to that code, how many times that code was called by other code and thus reveals where hotspots are located: the areas where a solution can be found. Importantly, they also reveal which areas can be left alone: remember our penny wise pound foolish saying: if a profiler reveals that a group of methods are fast, or don't contribute much to the total time taken for a given task, ignore them. Even if the code in them is perhaps complex and looks like a candidate for optimization: you can work all day on that, it won't matter.  As we're focusing on a single area of the application, it's best to start profiling right before you actually activate the task/area. Most .NET profilers support this by starting the application without starting the profiling procedure just yet. You navigate to the particular part which is slow, start profiling in the profiler, in your application you perform the actions which are considered slow, and afterwards you get a snapshot in the profiler. The snapshot contains the data collected by the profiler during the slow action, so most data is produced by code in the area to investigate. This is important, because it allows you to stay focused on a single area. O/R mapper and RDBMS profiling .NET profilers give you a good insight in the .NET side of things, but not in the RDBMS side of the application. As this article is about O/R mapper powered applications, we're also looking at databases, and the software making it possible to consume the database in your application: the O/R mapper. To understand which parts of the O/R mapper and database participate how much to the total time taken for task T, we need different tools. There are two kind of tools focusing on O/R mappers and database performance profiling: O/R mapper profilers and RDBMS profilers. For O/R mapper profilers, you can look at LLBLGen Prof by hibernating rhinos or the Linq to Sql/LLBLGen Pro profiler by Huagati. Hibernating rhinos also have profilers for other O/R mappers like NHibernate (NHProf) and Entity Framework (EFProf) and work the same as LLBLGen Prof. For RDBMS profilers, you have to look whether the RDBMS vendor has a profiler. For example for SQL Server, the profiler is shipped with SQL Server, for Oracle it's build into the RDBMS, however there are also 3rd party tools. Which tool you're using isn't really important, what's important is that you get insight in which queries are executed during the task / area we're currently focused on and how long they took. Here, the O/R mapper profilers have an advantage as they collect the time it took to execute the query from the application's perspective so they also collect the time it took to transport data across the network. This is important because a query which returns a massive resultset or a resultset with large blob/clob/ntext/image fields takes more time to get transported across the network than a small resultset and a database profiler doesn't take this into account most of the time. Another tool to use in this case, which is more low level and not all O/R mappers support it (though LLBLGen Pro and NHibernate as well do) is tracing: most O/R mappers offer some form of tracing or logging system which you can use to collect the SQL generated and executed and often also other activity behind the scenes. While tracing can produce a tremendous amount of data in some cases, it also gives insight in what's going on. Interpret After we've completed the analysis step it's time to look at the data we've collected. We've done code reviews to see whether we've done anything stupid and which parts actually take place and if the proper algorithms have been implemented. We've done .NET profiling to see which parts are choke points and how much time they contribute to the total time taken to complete the task we're investigating. We've performed O/R mapper profiling and RDBMS profiling to see which queries were executed during the task, how many queries were generated and executed and how long they took to complete, including network transportation. All this data reveals two things: which parts are big contributors to the total time taken and which parts are irrelevant. Both aspects are very important. The parts which are irrelevant (i.e. don't contribute significantly to the total time taken) can be ignored from now on, we won't look at them. The parts which contribute a lot to the total time taken are important to look at. We now have to first look at the .NET profiler results, to see whether the time taken is consumed in our own code, in .NET framework code, in the O/R mapper itself or somewhere else. For example if most of the time is consumed by DbCommand.ExecuteReader, the time it took to complete the task is depending on the time the data is fetched from the database. If there was just 1 query executed, according to tracing or O/R mapper profilers / RDBMS profilers, check whether that query is optimal, uses indexes or has to deal with a lot of data. Interpret means that you follow the path from begin to end through the data collected and determine where, along the path, the most time is contributed. It also means that you have to check whether this was expected or is totally unexpected. My previous example of the 10 row resultset of a query which groups millions of rows will likely reveal that a long time is spend inside the database and almost no time is spend in the .NET code, meaning the RDBMS part contributes the most to the total time taken, the rest is compared to that time, irrelevant. Considering the vastness of the source data set, it's expected this will take some time. However, does it need tweaking? Perhaps all possible tweaks are already in place. In the interpret step you then have to decide that further action in this area is necessary or not, based on what the analysis results show: if the analysis results were unexpected and in the area where the most time is contributed to the total time taken is room for improvement, action should be taken. If not, you can only accept the situation and move on. In all cases, document your decision together with the analysis you've done. If you decide that the perceived performance problem is actually expected due to the nature of the task performed, it's essential that in the future when someone else looks at the application and starts asking questions you can answer them properly and new analysis is only necessary if situations changed. Fix After interpreting the analysis results you've concluded that some areas need adjustment. This is the fix step: you're actively correcting the performance problem with proper action targeted at the real cause. In many cases related to O/R mapper powered applications it means you'll use different features of the O/R mapper to achieve the same goal, or apply optimizations at the RDBMS level. It could also mean you apply caching inside your application (compromise memory consumption over performance) to avoid unnecessary re-querying data and re-consuming the results. After applying a change, it's key you re-do the analysis and interpretation steps: compare the results and expectations with what you had before, to see whether your actions had any effect or whether it moved the problem to a different part of the application. Don't fall into the trap to do partly analysis: do the full analysis again: .NET profiling and O/R mapper / RDBMS profiling. It might very well be that the changes you've made make one part faster but another part significantly slower, in such a way that the overall problem hasn't changed at all. Performance tuning is dealing with compromises and making choices: to use one feature over the other, to accept a higher memory footprint, to go away from the strict-OO path and execute queries directly onto the RDBMS, these are choices and compromises which will cross your path if you want to fix performance problems with respect to O/R mappers or data-access and databases in general. In most cases it's not a big issue: alternatives are often good choices too and the compromises aren't that hard to deal with. What is important is that you document why you made a choice, a compromise: which analysis data, which interpretation led you to the choice made. This is key for good maintainability in the years to come. Most common performance problems with O/R mappers Below is an incomplete list of common performance problems related to data-access / O/R mappers / RDBMS code. It will help you with fixing the hotspots you found in the interpretation step. SELECT N+1: (Lazy-loading specific). Lazy loading triggered performance bottlenecks. Consider a list of Orders bound to a grid. You have a Field mapped onto a related field in Order, Customer.CompanyName. Showing this column in the grid will make the grid fetch (indirectly) for each row the Customer row. This means you'll get for the single list not 1 query (for the orders) but 1+(the number of orders shown) queries. To solve this: use eager loading using a prefetch path to fetch the customers with the orders. SELECT N+1 is easy to spot with an O/R mapper profiler or RDBMS profiler: if you see a lot of identical queries executed at once, you have this problem. Prefetch paths using many path nodes or sorting, or limiting. Eager loading problem. Prefetch paths can help with performance, but as 1 query is fetched per node, it can be the number of data fetched in a child node is bigger than you think. Also consider that data in every node is merged on the client within the parent. This is fast, but it also can take some time if you fetch massive amounts of entities. If you keep fetches small, you can use tuning parameters like the ParameterizedPrefetchPathThreshold setting to get more optimal queries. Deep inheritance hierarchies of type Target Per Entity/Type. If you use inheritance of type Target per Entity / Type (each type in the inheritance hierarchy is mapped onto its own table/view), fetches will join subtype- and supertype tables in many cases, which can lead to a lot of performance problems if the hierarchy has many types. With this problem, keep inheritance to a minimum if possible, or switch to a hierarchy of type Target Per Hierarchy, which means all entities in the inheritance hierarchy are mapped onto the same table/view. Of course this has its own set of drawbacks, but it's a compromise you might want to take. Fetching massive amounts of data by fetching large lists of entities. LLBLGen Pro supports paging (and limiting the # of rows returned), which is often key to process through large sets of data. Use paging on the RDBMS if possible (so a query is executed which returns only the rows in the page requested). When using paging in a web application, be sure that you switch server-side paging on on the datasourcecontrol used. In this case, paging on the grid alone is not enough: this can lead to fetching a lot of data which is then loaded into the grid and paged there. Keep note that analyzing queries for paging could lead to the false assumption that paging doesn't occur, e.g. when the query contains a field of type ntext/image/clob/blob and DISTINCT can't be applied while it should have (e.g. due to a join): the datareader will do DISTINCT filtering on the client. this is a little slower but it does perform paging functionality on the data-reader so it won't fetch all rows even if the query suggests it does. Fetch massive amounts of data because blob/clob/ntext/image fields aren't excluded. LLBLGen Pro supports field exclusion for queries. You can exclude fields (also in prefetch paths) per query to avoid fetching all fields of an entity, e.g. when you don't need them for the logic consuming the resultset. Excluding fields can greatly reduce the amount of time spend on data-transport across the network. Use this optimization if you see that there's a big difference between query execution time on the RDBMS and the time reported by the .NET profiler for the ExecuteReader method call. Doing client-side aggregates/scalar calculations by consuming a lot of data. If possible, try to formulate a scalar query or group by query using the projection system or GetScalar functionality of LLBLGen Pro to do data consumption on the RDBMS server. It's far more efficient to process data on the RDBMS server than to first load it all in memory, then traverse the data in-memory to calculate a value. Using .ToList() constructs inside linq queries. It might be you use .ToList() somewhere in a Linq query which makes the query be run partially in-memory. Example: var q = from c in metaData.Customers.ToList() where c.Country=="Norway" select c; This will actually fetch all customers in-memory and do an in-memory filtering, as the linq query is defined on an IEnumerable<T>, and not on the IQueryable<T>. Linq is nice, but it can often be a bit unclear where some parts of a Linq query might run. Fetching all entities to delete into memory first. To delete a set of entities it's rather inefficient to first fetch them all into memory and then delete them one by one. It's more efficient to execute a DELETE FROM ... WHERE query on the database directly to delete the entities in one go. LLBLGen Pro supports this feature, and so do some other O/R mappers. It's not always possible to do this operation in the context of an O/R mapper however: if an O/R mapper relies on a cache, these kind of operations are likely not supported because they make it impossible to track whether an entity is actually removed from the DB and thus can be removed from the cache. Fetching all entities to update with an expression into memory first. Similar to the previous point: it is more efficient to update a set of entities directly with a single UPDATE query using an expression instead of fetching the entities into memory first and then updating the entities in a loop, and afterwards saving them. It might however be a compromise you don't want to take as it is working around the idea of having an object graph in memory which is manipulated and instead makes the code fully aware there's a RDBMS somewhere. Conclusion Performance tuning is almost always about compromises and making choices. It's also about knowing where to look and how the systems in play behave and should behave. The four steps I provided should help you stay focused on the real problem and lead you towards the solution. Knowing how to optimally use the systems participating in your own code (.NET framework, O/R mapper, RDBMS, network/services) is key for success as well as knowing what's going on inside the application you built. I hope you'll find this guide useful in tracking down performance problems and dealing with them in a useful way.  

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  • Big Data&rsquo;s Killer App&hellip;

    - by jean-pierre.dijcks
    Recently Keith spent  some time talking about the cloud on this blog and I will spare you my thoughts on the whole thing. What I do want to write down is something about the Big Data movement and what I think is the killer app for Big Data... Where is this coming from, ok, I confess... I spent 3 days in cloud land at the Cloud Connect conference in Santa Clara and it was quite a lot of fun. One of the nice things at Cloud Connect was that there was a track dedicated to Big Data, which prompted me to some extend to write this post. What is Big Data anyways? The most valuable point made in the Big Data track was that Big Data in itself is not very cool. Doing something with Big Data is what makes all of this cool and interesting to a business user! The other good insight I got was that a lot of people think Big Data means a single gigantic monolithic system holding gazillions of bytes or documents or log files. Well turns out that most people in the Big Data track are talking about a lot of collections of smaller data sets. So rather than thinking "big = monolithic" you should be thinking "big = many data sets". This is more than just theoretical, it is actually relevant when thinking about big data and how to process it. It is important because it means that the platform that stores data will most likely consist out of multiple solutions. You may be storing logs on something like HDFS, you may store your customer information in Oracle and you may store distilled clickstream information in some distilled form in MySQL. The big question you will need to solve is not what lives where, but how to get it all together and get some value out of all that data. NoSQL and MapReduce Nope, sorry, this is not the killer app... and no I'm not saying this because my business card says Oracle and I'm therefore biased. I think language is important, but as with storage I think pragmatic is better. In other words, some questions can be answered with SQL very efficiently, others can be answered with PERL or TCL others with MR. History should teach us that anyone trying to solve a problem will use any and all tools around. For example, most data warehouses (Big Data 1.0?) get a lot of data in flat files. Everyone then runs a bunch of shell scripts to massage or verify those files and then shoves those files into the database. We've even built shell script support into external tables to allow for this. I think the Big Data projects will do the same. Some people will use MapReduce, although I would argue that things like Cascading are more interesting, some people will use Java. Some data is stored on HDFS making Cascading the way to go, some data is stored in Oracle and SQL does do a good job there. As with storage and with history, be pragmatic and use what fits and neither NoSQL nor MR will be the one and only. Also, a language, while important, does in itself not deliver business value. So while cool it is not a killer app... Vertical Behavioral Analytics This is the killer app! And you are now thinking: "what does that mean?" Let's decompose that heading. First of all, analytics. I would think you had guessed by now that this is really what I'm after, and of course you are right. But not just analytics, which has a very large scope and means many things to many people. I'm not just after Business Intelligence (analytics 1.0?) or data mining (analytics 2.0?) but I'm after something more interesting that you can only do after collecting large volumes of specific data. That all important data is about behavior. What do my customers do? More importantly why do they behave like that? If you can figure that out, you can tailor web sites, stores, products etc. to that behavior and figure out how to be successful. Today's behavior that is somewhat easily tracked is web site clicks, search patterns and all of those things that a web site or web server tracks. that is where the Big Data lives and where these patters are now emerging. Other examples however are emerging, and one of the examples used at the conference was about prediction churn for a telco based on the social network its members are a part of. That social network is not about LinkedIn or Facebook, but about who calls whom. I call you a lot, you switch provider, and I might/will switch too. And that just naturally brings me to the next word, vertical. Vertical in this context means per industry, e.g. communications or retail or government or any other vertical. The reason for being more specific than just behavioral analytics is that each industry has its own data sources, has its own quirky logic and has its own demands and priorities. Of course, the methods and some of the software will be common and some will have both retail and service industry analytics in place (your corner coffee store for example). But the gist of it all is that analytics that can predict customer behavior for a specific focused group of people in a specific industry is what makes Big Data interesting. Building a Vertical Behavioral Analysis System Well, that is going to be interesting. I have not seen much going on in that space and if I had to have some criticism on the cloud connect conference it would be the lack of concrete user cases on big data. The telco example, while a step into the vertical behavioral part is not really on big data. It used a sample of data from the customers' data warehouse. One thing I do think, and this is where I think parts of the NoSQL stuff come from, is that we will be doing this analysis where the data is. Over the past 10 years we at Oracle have called this in-database analytics. I guess we were (too) early? Now the entire market is going there including companies like SAS. In-place btw does not mean "no data movement at all", what it means that you will do this on data's permanent home. For SAS that is kind of the current problem. Most of the inputs live in a data warehouse. So why move it into SAS and back? That all worked with 1 TB data warehouses, but when we are looking at 100TB to 500 TB of distilled data... Comments? As it is still early days with these systems, I'm very interested in seeing reactions and thoughts to some of these thoughts...

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  • Using the ASPxGridView DevExpress control

    - by nikolaosk
    Recently I had to implement a web application for a client of mine using ASP.Net.I used the DevExpress ASP.Net controls and I would like to present you with some hands-on examples on how to use these ASP.Net controls. In this very first post I will explore the most used ASP.Net DevExpress control, the ASPxGridView control . This is going to be a post that targets a beginner audience. ASPxGridView has great features built-in that include sorting,grouping,filtering,summaries.It uses very clever ways...(read more)

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  • Visual Studio 2010 and .NET Framework 4 IDE Enhancements –Part3

    In my previous article I explained some of the nice features related to IDE, in continuation to that I am going to explain Add Reference enhancements for developers, Windows 7 support for developers, Share Point 2010 enhancements , Office Business Application Support, Cloud Development, Document Map Margin and Visual Studio 2010 Tips

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  • The relative effort of SharePoint 2010 vs. 2007

    SharePoint 2007 was the best demo-ware ever. Its like going to the pet store and seeing a great dog that does backflips all kinds of tricks and it really is a smart dog and it does all those tricks but when you get it home you realize that what you...(read more)...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • The relative effort of SharePoint 2010 vs. 2007

    - by erobillard
    SharePoint 2007 was the best demo-ware ever. It’s like going to the pet store and seeing a great dog that does backflips all kinds of tricks – and it really is a smart dog and it does all those tricks – but when you get it home you realize that what you need is a dog that gets the paper. SharePoint 2007 can be trained, but is fundamentally a platform where Microsoft's priority was to get the infrastructure right – to make it trainable and extensible. Because it was great demo-ware it caught on like...(read more)

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  • Find a Hash Collision, Win $100

    - by Mike C
    Margarity Kerns recently published a very nice article at SQL Server Central on using hash functions to detect changes in rows during the data warehouse load ETL process. On the discussion page for the article I noticed a lot of the same old arguments against using hash functions to detect change. After having this same discussion several times over the past several months in public and private forums, I've decided to see if we can't put this argument to rest for a while. To that end I'm going to...(read more)

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  • What is Quantum Computing? Microsoft’s video explains it in simple language

    - by Gopinath
    Quantum Computing is the next promising big thing to happen in computer science and its going to revolutionize the way we solve problem using computers. To explain the concepts of Quantum Computing to common man, Microsoft released a nice video which gives brief introduction to the concepts, explains the benefits and the work being carried out by Microsoft to make this technology research a reality. Check out this embedded video and visit Microsoft’s website for more details on Quantum Computing.

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  • World Class Training For Them, an Amazon Gift Certificate For You

    - by Adam Machanic
    We have just two weeks to go before Paul Randal and Kimberly Tripp touch down in the Boston area to deliver their famous SQL Server Immersions course . This is going to be a truly fantastic SQL Server learning experience and we're hoping a few more people will join in the fun. This is where you come in: we have a few vacant seats remaining and we need your help spreading the word. Simply tell your friends and colleagues about the course and e-mail me (adam [at] bostonsqltraining [dot] com) the names...(read more)

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  • An XEvent a Day (6 of 31) – Targets Week – asynchronous_file_target

    - by Jonathan Kehayias
    Yesterday’s post, Targets Week - ring_buffer , looked at the ring_buffer Target in Extended Events and how it outputs the raw Event data in an XML document.  Today I’m going to go over the details of the other Target in Extended Events that captures raw Event data, the asynchronous_file_target. What is the asynchronous_file_target? The asynchronous_file_target holds the raw format Event data in a proprietary binary file format that persists beyond server restarts and can be provided to another...(read more)

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  • GNOME Shell tips

    <b>GHacks:</b> "Although there are many naysayers out there &#8211; who seem to either only want more of the same or who doubt the ability of any developer to release anything worth while &#8211; I trust that GNOME 3 is going to make quite a major impression."

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  • Get to Know the ‘Real’ Pyro [Humorous Team Fortress 2 Video]

    - by Asian Angel
    People sometimes wonder just what is going through Pyro’s head when he is spreading mayhem and destruction. If you are one of them, then here is your chance to see things from Pyro’s ‘unique’ point-of-view! Meet the Pyro [via Dorkly] How to Use an Xbox 360 Controller On Your Windows PC Download the Official How-To Geek Trivia App for Windows 8 How to Banish Duplicate Photos with VisiPic

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  • Team Review @ TSUG

    - by dmckinstry
    In case you haven’t heard, JB Brown is going to be presenting online at the Team System User Group this Thursday.  This month’s presentation will explain how Team Review (freely available) can be used with Team Foundation Server 2005, 2008 and even 2010! Meeting Date: Thursday, March 18th, 2010 Time: 5:00PM Pacific {Add to Calendar} {Join Meeting}

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  • Introduction to Lean Software Development and Kanban Systems – Defer Commitment and Decide As Late A

    - by Ben Griswold
    In this post, we’ll continue the series by concentrating on Principle #4: Defer Commitment and Decide As Late As Possible.   In the next part of the series, we’ll dive into Principle #5: Deliver As Fast As Possible. And I am going to be a little obnoxious about listing my Lean and Kanban references with every series post.  The references are great and they deserve this sort of attention.  

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  • SQLAuthority News – Reliving TechEd with Vinod Kumar at Bangalore User Groups

    - by pinaldave
    TechEd India 2012 was held in Bangalore last March 21 to 23, 2012. Just like every year, this event is bigger, grander and inspiring. Here is my blog post reviewing the event SQLAuthority News – #TechEdIn – TechEd India 2012 Memories and Photos. For me this is family event – I get to meet my friends who are dear as my family. I like to call User Groups as family too. Family shares life’s personal happiness and experience – the same way User Group shares professional experiences and quite often UG members become just like family member. When I learned that follower user group together building up a unique event I was pretty excited to learn who is going to be speaker for the event. BDotNet.in – Bangalore .NET Usergroup BITPro.in – Bangalore ITPro Usergroup It was indeed joy when I learned that presenter will be Vinod Kumar, who is integral part of user groups and hardcore SQL Server enthusiast. Vinod Kumar is going to present on following two sessions which are both focused on internals of the Windows and SQL Server. Understanding Windows with SysInternals Tools – This session will cover various tools from usage of Memory, x86 architecture, x64, WOW mode, Page faults, Virtual Memory mapping, OOM scenario, Perf Tool, PAL tool, Logman and more. Peeling the Onion: SQL Server Internals Demystified – This session will cover advanced disk formats, SQL Server 2012 security changes, memory changes, indirect checkPoint and more. I am very excited as this time I will get opportunity to sit in front rows (as I will be reaching there to get best possible position) and learn. I am looking forward to the event and I hope you will join us as well. Event Details: Date: Saturday, April 7, 2012 (10:30am until 1:30pm) Venue: Microsoft, Domlur, Bangalore. Event Details: https://www.facebook.com/events/139444029517882/ This session is FREE for all and everybody and anybody can walk in. Community Blog Posts Here are few of the blog post written by the community on this subject. Vinod Kumar on Reliving #TechEdIn at Blr UG Manas Dash on Reliving TechEd India 2012 with Vinod Kumar Sudeepta Ganguly on SysInternals n SQLInternals with Vinod Kumar Lohith Re Live TechEd India 2012 with Vinod Kumar  Reference: Pinal Dave (http://blog.sqlauthority.com) http://www.youtube.com/watch?v=oRw-p4mahLU Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Author Visit, T SQL, Technology, Video

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  • How many people will be with you during 24HOP?

    - by Rob Farley
    In less than a week, SQLPASS hosts another 24 Hours of PASS event, this time with an array of 24 female speakers (in honour of this month being Women’s History Month). Interestingly, the committee has had a few people ask if there are rules about how the event can be viewed, such as “How many people from any one organisation can watch it?” or “Does it matter if a few people are crowded around the same screen?” From a licensing and marketing perspective, there is value in knowing how many people are watching the event, but there are no restrictions about how the thing is viewed. In fact – if you’re planning to watch any of these events, I want to suggest an idea: Book a meeting room in your office with a projector, and watch 24HOP in there. If you’re planning to have it streaming in the background while you work, obviously this makes life a bit trickier. But if you’re planning to treat it as a training event (a 2-day conference if you like) and block out a bit of time for it (as well you should – there’s going to be some great stuff in there), then why not do it in a way that makes it so that other people can see that you’re watching it, and potentially join you. When an event like this runs, we can see how many different ‘people’ are attending each LiveMeeting session. What we can’t tell is how many actual people there are represented. Jessica Moss spoke to the Adelaide SQL Server User Group a few weeks ago via LiveMeeting, and LiveMeeting told us there were less than a dozen people attending. Really there were at least three times that number, because all the people in the room with me weren’t included. I’d love to imagine that every LiveMeeting attendee represented a crowd in a room, watching a shared screen. So there’s my challenge – don’t let your LiveMeeting session represent just you. Find a way of involving other people. At the very least, you’ll be able to discuss it with them afterwards. Now stick a comment on this post to let me know how many people are going to be joining you. :) If you’re not registered for the event yet, get yourself over to the SQLPASS site and make it happen.

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  • Up in the Air: Team Oracle Play-by-Play

    - by Aaron Lazenby
    Yesterday, I had the amazing opportunity to fly along with Sean D. Tucker and Team Oracle. Leaving from the San Carols airport, we did a 30 minute flight over the Pacific just south of the coastal town of Half Moon Bay. In that half hour, I rode through a massive 4G loop, survived a crushing hammerhead, and took control of the plane to perform a basic wing over (you can learn what the heck I'm talking about by visiting this website). I have lots of great video, but it's going to take me some time to make sense of it. For now, here's my Twitter-based play-by-play of yesterday's events. Many thanks to Sean D. Tucker and the whole crew (Ben and Ian, especially) for this great opportunity to fly with Team Oracle.Live tweets from @OracleProfitI will be spending the afternoon in a stunt plane, upside down above the San Francisco bay. http://bit.ly/cwkrkIAt the San Carlos airport. More than slightly freaked out. Shaking hands diminish texting ability. Slightly reassuring. http://yfrog.com/1qt61nj There go the doors to the photo plane... #teamoracle http://yfrog.com/58ywljSean D Tucker assures me: "The sky is a great place to be." Helpful, but I'm still nervous. #teamoracle"You get a parachute. He gets a harness." How was this decision made? #teamoracleThe plane with @radu43 has returned. I'm up next...Couldn't help myself...drank a soda before flying. Mistake? We'll see... #teamoracleAdvice of the day "If you pull with two hands, you improve the chances of the chute deploying on the first try." Lovely. #teamoracleI feel so strange. But I flew a high performance airplane. And did an aerobatics move. Wild. #teamoracle"Flying ten feet off he ground, upside-down at 250 miles per hour isn't exciting to me." Sean D. Tucker #teamoracle"What is exciting to me is flying that perfect pattern, just like I imagined it in my head." Sean D. Tucker #teamoracle"You're going to sleep well tonight. You just carried four times your body weight." #teamoracle #gforce Just watched the #teamoracle plane take off for its flight home. I'm waiting for Caltrain. #undignifiedanticlimaxEnough with the #teamoracle. Check http://blogs.oracle.com/profit for the video. Coming soon! 

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  • Un stagiaire de SAP remporte le concours du « Meilleur Développeur de France » à l'Ecole 42, Salesforce.com lui verse 10.000 Euro

    Un stagiaire de SAP remporte le concours du « Meilleur développeur de France » A l'école 42, Salesforce lui offre 10.000 eurosL'évènement a attiré du beau monde. Il faut dire que le concours du « Meilleur Développeur de France », dont la première édition a eu lieu la semaine dernière, a été particulièrement bien orchestrée par la société Going to Digital, dont un des directeurs associés est allé à bonne école en passant par une filiale de Rentabiliweb, la société de marketing numérique de l'énigmatique...

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  • SQL SERVER – DMV – sys.dm_exec_query_optimizer_info – Statistics of Optimizer

    - by pinaldave
    Incredibly, SQL Server has so much information to share with us. Every single day, I am amazed with this SQL Server technology. Sometimes I find several interesting information by just querying few of the DMV. And when I present this info in front of my client during performance tuning consultancy, they are surprised with my findings. Today, I am going to share one of the hidden gems of DMV with you, the one which I frequently use to understand what’s going on under the hood of SQL Server. SQL Server keeps the record of most of the operations of the Query Optimizer. We can learn many interesting details about the optimizer which can be utilized to improve the performance of server. SELECT * FROM sys.dm_exec_query_optimizer_info WHERE counter IN ('optimizations', 'elapsed time','final cost', 'insert stmt','delete stmt','update stmt', 'merge stmt','contains subquery','tables', 'hints','order hint','join hint', 'view reference','remote query','maximum DOP', 'maximum recursion level','indexed views loaded', 'indexed views matched','indexed views used', 'indexed views updated','dynamic cursor request', 'fast forward cursor request') All occurrence values are cumulative and are set to 0 at system restart. All values for value fields are set to NULL at system restart. I have removed a few of the internal counters from the script above, and kept only documented details. Let us check the result of the above query. As you can see, there is so much vital information that is revealed in above query. I can easily say so many things about how many times Optimizer was triggered and what the average time taken by it to optimize my queries was. Additionally, I can also determine how many times update, insert or delete statements were optimized. I was able to quickly figure out that my client was overusing the Query Hints using this dynamic management view. If you have been reading my blog, I am sure you are aware of my series related to SQL Server Views SQL SERVER – The Limitations of the Views – Eleven and more…. With this, I can take a quick look and figure out how many times Views were used in various solutions within the query. Moreover, you can easily know what fraction of the optimizations has been involved in tuning server. For example, the following query would tell me, in total optimizations, what the fraction of time View was “reference“. As this View also includes system Views and DMVs, the number is a bit higher on my machine. SELECT (SELECT CAST (occurrence AS FLOAT) FROM sys.dm_exec_query_optimizer_info WHERE counter = 'view reference') / (SELECT CAST (occurrence AS FLOAT) FROM sys.dm_exec_query_optimizer_info WHERE counter = 'optimizations') AS ViewReferencedFraction Reference : Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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