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• The Beginner’s Guide to Managing Users and Groups in Linux

  - by Zainul Franciscus

  Ubuntu Linux uses groups to help you manage users, set permissions on those users, and even monitor how much time they are spending in front of the PC. Here’s a beginner’s guide to how it all works Latest Features How-To Geek ETC The How-To Geek Holiday Gift Guide (Geeky Stuff We Like) LCD? LED? Plasma? The How-To Geek Guide to HDTV Technology The How-To Geek Guide to Learning Photoshop, Part 8: Filters Improve Digital Photography by Calibrating Your Monitor Our Favorite Tech: What We’re Thankful For at How-To Geek The How-To Geek Guide to Learning Photoshop, Part 7: Design and Typography Happy Snow Bears Theme for Chrome and Iron [Holiday] Download Full Command and Conquer: Tiberian Sun Game for Free Scorched Cometary Planet Wallpaper Quick Fix: Add the RSS Button Back to the Firefox Awesome Bar Dropbox Desktop Client 1.0.0 RC for Windows, Linux, and Mac Released Hang in There Scrat! – Ice Age Wallpaper

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• SSIS Lookup component tuning tips

  - by jamiet

  Yesterday evening I attended a London meeting of the UK SQL Server User Group at Microsoft’s offices in London Victoria. As usual it was both a fun and informative evening and in particular there seemed to be a few questions arising about tuning the SSIS Lookup component; I rattled off some comments and figured it would be prudent to drop some of them into a dedicated blog post, hence the one you are reading right now. Scene setting A popular pattern in SSIS is to use a Lookup component to determine whether a record in the pipeline already exists in the intended destination table or not and I cover this pattern in my 2006 blog post Checking if a row exists and if it does, has it changed? (note to self: must rewrite that blog post for SSIS2008). Fundamentally the SSIS lookup component (when using FullCache option) sucks some data out of a database and holds it in memory so that it can be compared to data in the pipeline. One of the big benefits of using SSIS dataflows is that they process data one buffer at a time; that means that not all of the data from your source exists in the dataflow at the same time and is why a SSIS dataflow can process data volumes that far exceed the available memory. However, that only applies to data in the pipeline; for reasons that are hopefully obvious ALL of the data in the lookup set must exist in the memory cache for the duration of the dataflow’s execution which means that any memory used by the lookup cache will not be available to be used as a pipeline buffer. Moreover, there’s an obvious correlation between the amount of data in the lookup cache and the time it takes to charge that cache; the more data you have then the longer it will take to charge and the longer you have to wait until the dataflow actually starts to do anything. For these reasons your goal is simple: ensure that the lookup cache contains as little data as possible. General tips Here is a simple tick list you can follow in order to tune your lookups: Use a SQL statement to charge your cache, don’t just pick a table from the dropdown list made available to you. (Read why in SELECT *... or select from a dropdown in an OLE DB Source component?) Only pick the columns that you need, ignore everything else Make the database columns that your cache is populated from as narrow as possible. If a column is defined as VARCHAR(20) then SSIS will allocate 20 bytes for every value in that column – that is a big waste if the actual values are significantly less than 20 characters in length. Do you need DT_WSTR typed columns or will DT_STR suffice? DT_WSTR uses twice the amount of space to hold values that can be stored using a DT_STR so if you can use DT_STR, consider doing so. Same principle goes for the numerical datatypes DT_I2/DT_I4/DT_I8. Only populate the cache with data that you KNOW you will need. In other words, think about your WHERE clause! Thinking outside the box It is tempting to build a large monolithic dataflow that does many things, one of which is a Lookup. Often though you can make better use of your available resources by, well, mixing things up a little and here are a few ideas to get your creative juices flowing: There is no rule that says everything has to happen in a single dataflow. If you have some particularly resource intensive lookups then consider putting that lookup into a dataflow all of its own and using raw files to pass the pipeline data in and out of that dataflow. Know your data. If you think, for example, that the majority of your incoming rows will match with only a small subset of your lookup data then consider chaining multiple lookup components together; the first would use a FullCache containing that data subset and the remaining data that doesn’t find a match could be passed to a second lookup that perhaps uses a NoCache lookup thus negating the need to pull all of that least-used lookup data into memory. Do you need to process all of your incoming data all at once? If you can process different partitions of your data separately then you can partition your lookup cache as well. For example, if you are using a lookup to convert a location into a [LocationId] then why not process your data one region at a time? This will mean your lookup cache only has to contain data for the location that you are currently processing and with the ability of the Lookup in SSIS2008 and beyond to charge the cache using a dynamically built SQL statement you’ll be able to achieve it using the same dataflow and simply loop over it using a ForEach loop. Taking the previous data partitioning idea further … a dataflow can contain more than one data path so why not split your data using a conditional split component and, again, charge your lookup caches with only the data that they need for that partition. Lookups have two uses: to (1) find a matching row from the lookup set and (2) put attributes from that matching row into the pipeline. Ask yourself, do you need to do these two things at the same time? After all once you have the key column(s) from your lookup set then you can use that key to get the rest of attributes further downstream, perhaps even in another dataflow. Are you using the same lookup data set multiple times? If so, consider the file caching option in SSIS 2008 and beyond. Above all, experiment and be creative with different combinations. You may be surprised at what works. Final  thoughts If you want to know more about how the Lookup component differs in SSIS2008 from SSIS2005 then I have a dedicated blog post about that at Lookup component gets a makeover. I am on a mini-crusade at the moment to get a BULK MERGE feature into the database engine, the thinking being that if the database engine can quickly merge massive amounts of data in a similar manner to how it can insert massive amounts using BULK INSERT then that’s a lot of work that wouldn’t have to be done in the SSIS pipeline. If you think that is a good idea then go and vote for BULK MERGE on Connect. If you have any other tips to share then please stick them in the comments. Hope this helps! @Jamiet Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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• Dynamic Unpivot : SSIS Nugget

  - by jamiet

  A question on the SSIS forum earlier today asked: I need to dynamically unpivot some set of columns in my source file. Every month there is one new column and its set of Values. I want to unpivot it without editing my SSIS packages that is deployed Let’s be clear about what we mean by Unpivot. It is a normalisation technique that basically converts columns into rows. By way of example it converts something like this: AccountCode Jan Feb Mar AC1 100.00 150.00 125.00 AC2 45.00 75.50 90.00 into something like this: AccountCode Month Amount AC1 Jan 100.00 AC1 Feb 150.00 AC1 Mar 125.00 AC2 Jan 45.00 AC2 Feb 75.50 AC2 Mar 90.00 The Unpivot transformation in SSIS is perfectly capable of carrying out the operation defined in this example however in the case outlined in the aforementioned forum thread the problem was a little bit different. I interpreted it to mean that the number of columns could change and in that scenario the Unpivot transformation (and indeed the SSIS dataflow in general) is rendered useless because it expects that the number of columns will not change from what is specified at design-time. There is a workaround however. Assuming all of the columns that CAN exist will appear at the end of the rows, we can (1) import all of the columns in the file as just a single column, (2) use a script component to loop over all the values in that “column” and (3) output each one as a column all of its own. Let’s go over that in a bit more detail.   I’ve prepared a data file that shows some data that we want to unpivot which shows some customers and their mythical shopping lists (it has column names in the first row): We use a Flat File Connection Manager to specify the format of our data file to SSIS: and a Flat File Source Adapter to put it into the dataflow (no need a for a screenshot of that one – its very basic). Notice that the values that we want to unpivot all exist in a column called [Groceries]. Now onto the script component where the real work goes on, although the code is pretty simple: Here I show a screenshot of this executing along with some data viewers. As you can see we have successfully pulled out all of the values into a row all of their own thus accomplishing the Dynamic Unpivot that the forum poster was after. If you want to run the demo for yourself then I have uploaded the demo package and source file up to my SkyDrive: http://cid-550f681dad532637.skydrive.live.com/self.aspx/Public/BlogShare/20100529/Dynamic%20Unpivot.zip Simply extract the two files into a folder, make sure the Connection Manager is pointing to the file, and execute! Hope this is useful. @Jamiet Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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• Analytic functions – they’re not aggregates

  - by Rob Farley

  SQL 2012 brings us a bunch of new analytic functions, together with enhancements to the OVER clause. People who have known me over the years will remember that I’m a big fan of the OVER clause and the types of things that it brings us when applied to aggregate functions, as well as the ranking functions that it enables. The OVER clause was introduced in SQL Server 2005, and remained frustratingly unchanged until SQL Server 2012. This post is going to look at a particular aspect of the analytic functions though (not the enhancements to the OVER clause). When I give presentations about the analytic functions around Australia as part of the tour of SQL Saturdays (starting in Brisbane this Thursday), and in Chicago next month, I’ll make sure it’s sufficiently well described. But for this post – I’m going to skip that and assume you get it. The analytic functions introduced in SQL 2012 seem to come in pairs – FIRST_VALUE and LAST_VALUE, LAG and LEAD, CUME_DIST and PERCENT_RANK, PERCENTILE_CONT and PERCENTILE_DISC. Perhaps frustratingly, they take slightly different forms as well. The ones I want to look at now are FIRST_VALUE and LAST_VALUE, and PERCENTILE_CONT and PERCENTILE_DISC. The reason I’m pulling this ones out is that they always produce the same result within their partitions (if you’re applying them to the whole partition). Consider the following query: SELECT     YEAR(OrderDate),     FIRST_VALUE(TotalDue)         OVER (PARTITION BY YEAR(OrderDate)               ORDER BY OrderDate, SalesOrderID               RANGE BETWEEN UNBOUNDED PRECEDING                         AND UNBOUNDED FOLLOWING),     LAST_VALUE(TotalDue)         OVER (PARTITION BY YEAR(OrderDate)               ORDER BY OrderDate, SalesOrderID               RANGE BETWEEN UNBOUNDED PRECEDING                         AND UNBOUNDED FOLLOWING),     PERCENTILE_CONT(0.95)         WITHIN GROUP (ORDER BY TotalDue)         OVER (PARTITION BY YEAR(OrderDate)),     PERCENTILE_DISC(0.95)         WITHIN GROUP (ORDER BY TotalDue)         OVER (PARTITION BY YEAR(OrderDate)) FROM Sales.SalesOrderHeader ; This is designed to get the TotalDue for the first order of the year, the last order of the year, and also the 95% percentile, using both the continuous and discrete methods (‘discrete’ means it picks the closest one from the values available – ‘continuous’ means it will happily use something between, similar to what you would do for a traditional median of four values). I’m sure you can imagine the results – a different value for each field, but within each year, all the rows the same. Notice that I’m not grouping by the year. Nor am I filtering. This query gives us a result for every row in the SalesOrderHeader table – 31465 in this case (using the original AdventureWorks that dates back to the SQL 2005 days). The RANGE BETWEEN bit in FIRST_VALUE and LAST_VALUE is needed to make sure that we’re considering all the rows available. If we don’t specify that, it assumes we only mean “RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW”, which means that LAST_VALUE ends up being the row we’re looking at. At this point you might think about other environments such as Access or Reporting Services, and remember aggregate functions like FIRST. We really should be able to do something like: SELECT     YEAR(OrderDate),     FIRST_VALUE(TotalDue)         OVER (PARTITION BY YEAR(OrderDate)               ORDER BY OrderDate, SalesOrderID               RANGE BETWEEN UNBOUNDED PRECEDING                         AND UNBOUNDED FOLLOWING) FROM Sales.SalesOrderHeader GROUP BY YEAR(OrderDate) ; But you can’t. You get that age-old error: Msg 8120, Level 16, State 1, Line 5 Column 'Sales.SalesOrderHeader.OrderDate' is invalid in the select list because it is not contained in either an aggregate function or the GROUP BY clause. Msg 8120, Level 16, State 1, Line 5 Column 'Sales.SalesOrderHeader.SalesOrderID' is invalid in the select list because it is not contained in either an aggregate function or the GROUP BY clause. Hmm. You see, FIRST_VALUE isn’t an aggregate function. None of these analytic functions are. There are too many things involved for SQL to realise that the values produced might be identical within the group. Furthermore, you can’t even surround it in a MAX. Then you get a different error, telling you that you can’t use windowed functions in the context of an aggregate. And so we end up grouping by doing a DISTINCT. SELECT DISTINCT     YEAR(OrderDate),         FIRST_VALUE(TotalDue)              OVER (PARTITION BY YEAR(OrderDate)                   ORDER BY OrderDate, SalesOrderID                   RANGE BETWEEN UNBOUNDED PRECEDING                             AND UNBOUNDED FOLLOWING),         LAST_VALUE(TotalDue)             OVER (PARTITION BY YEAR(OrderDate)                   ORDER BY OrderDate, SalesOrderID                   RANGE BETWEEN UNBOUNDED PRECEDING                             AND UNBOUNDED FOLLOWING),     PERCENTILE_CONT(0.95)          WITHIN GROUP (ORDER BY TotalDue)         OVER (PARTITION BY YEAR(OrderDate)),     PERCENTILE_DISC(0.95)         WITHIN GROUP (ORDER BY TotalDue)         OVER (PARTITION BY YEAR(OrderDate)) FROM Sales.SalesOrderHeader ; I’m sorry. It’s just the way it goes. Hopefully it’ll change the future, but for now, it’s what you’ll have to do. If we look in the execution plan, we see that it’s incredibly ugly, and actually works out the results of these analytic functions for all 31465 rows, finally performing the distinct operation to convert it into the four rows we get in the results. You might be able to achieve a better plan using things like TOP, or the kind of calculation that I used in http://sqlblog.com/blogs/rob_farley/archive/2011/08/23/t-sql-thoughts-about-the-95th-percentile.aspx (which is how PERCENTILE_CONT works), but it’s definitely convenient to use these functions, and in time, I’m sure we’ll see good improvements in the way that they are implemented. Oh, and this post should be good for fellow SQL Server MVP Nigel Sammy’s T-SQL Tuesday this month.

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• WebLogic Server Performance and Tuning: Part I - Tuning JVM

  - by Gokhan Gungor

  Each WebLogic Server instance runs in its own dedicated Java Virtual Machine (JVM) which is their runtime environment. Every Admin Server in any domain executes within a JVM. The same also applies for Managed Servers. WebLogic Server can be used for a wide variety of applications and services which uses the same runtime environment and resources. Oracle WebLogic ships with 2 different JVM, HotSpot and JRocket but you can choose which JVM you want to use. JVM is designed to optimize itself however it also provides some startup options to make small changes. There are default values for its memory and garbage collection. In real world, you will not want to stick with the default values provided by the JVM rather want to customize these values based on your applications which can produce large gains in performance by making small changes with the JVM parameters. We can tell the garbage collector how to delete garbage and we can also tell JVM how much space to allocate for each generation (of java Objects) or for heap. Remember during the garbage collection no other process is executed within the JVM or runtime, which is called STOP THE WORLD which can affect the overall throughput. Each JVM has its own memory segment called Heap Memory which is the storage for java Objects. These objects can be grouped based on their age like young generation (recently created objects) or old generation (surviving objects that have lived to some extent), etc. A java object is considered garbage when it can no longer be reached from anywhere in the running program. Each generation has its own memory segment within the heap. When this segment gets full, garbage collector deletes all the objects that are marked as garbage to create space. When the old generation space gets full, the JVM performs a major collection to remove the unused objects and reclaim their space. A major garbage collect takes a significant amount of time and can affect system performance. When we create a managed server either on the same machine or on remote machine it gets its initial startup parameters from $DOMAIN_HOME/bin/setDomainEnv.sh/cmd file. By default two parameters are set:     Xms: The initial heapsize     Xmx: The max heapsize Try to set equal initial and max heapsize. The startup time can be a little longer but for long running applications it will provide a better performance. When we set -Xms512m -Xmx1024m, the physical heap size will be 512m. This means that there are pages of memory (in the state of the 512m) that the JVM does not explicitly control. It will be controlled by OS which could be reserve for the other tasks. In this case, it is an advantage if the JVM claims the entire memory at once and try not to spend time to extend when more memory is needed. Also you can use -XX:MaxPermSize (Maximum size of the permanent generation) option for Sun JVM. You should adjust the size accordingly if your application dynamically load and unload a lot of classes in order to optimize the performance. You can set the JVM options/heap size from the following places:     Through the Admin console, in the Server start tab     In the startManagedWeblogic script for the managed servers     $DOMAIN_HOME/bin/startManagedWebLogic.sh/cmd     JAVA_OPTIONS="-Xms1024m -Xmx1024m" ${JAVA_OPTIONS}     In the setDomainEnv script for the managed servers and admin server (domain wide)     USER_MEM_ARGS="-Xms1024m -Xmx1024m" When there is free memory available in the heap but it is too fragmented and not contiguously located to store the object or when there is actually insufficient memory we can get java.lang.OutOfMemoryError. We should create Thread Dump and analyze if that is possible in case of such error. The second option we can use to produce higher throughput is to garbage collection. We can roughly divide GC algorithms into 2 categories: parallel and concurrent. Parallel GC stops the execution of all the application and performs the full GC, this generally provides better throughput but also high latency using all the CPU resources during GC. Concurrent GC on the other hand, produces low latency but also low throughput since it performs GC while application executes. The JRockit JVM provides some useful command-line parameters that to control of its GC scheme like -XgcPrio command-line parameter which takes the following options; XgcPrio:pausetime (To minimize latency, parallel GC) XgcPrio:throughput (To minimize throughput, concurrent GC ) XgcPrio:deterministic (To guarantee maximum pause time, for real time systems) Sun JVM has similar parameters (like  -XX:UseParallelGC or -XX:+UseConcMarkSweepGC) to control its GC scheme. We can add -verbosegc -XX:+PrintGCDetails to monitor indications of a problem with garbage collection. Try configuring JVM’s of all managed servers to execute in -server mode to ensure that it is optimized for a server-side production environment.

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• Cannot insert explicit value for identity column in table ‘table’ when IDENTITY_INSERT is set to OFF

  - by Derek D.

  This error occurs when trying to insert into a column containing an identity. An Identity column is not able to be inserted into without the use of a special command mentioned below. Identity columns are columns that automatically increment when a value is inserted into a row. They are commonly used [...]

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• Ask How-To Geek: Fix Annoying Arrows, Play Old-School DOS games, and Schedule Smart Computer Shutdowns

  - by Jason Fitzpatrick

  You’ve got questions and we’ve got answers. Today we highlight how to fix the oversized shortcut arrows in Windows 7, play your favorite DOS games in emulation, and schedule intelligent shutdown routines for your PC. We get tons of emails with every kind of technology and computer question under the sun. Today we’re answering some reader emails and sharing the solutions with you. Latest Features How-To Geek ETC The Complete List of iPad Tips, Tricks, and Tutorials The 50 Best Registry Hacks that Make Windows Better The How-To Geek Holiday Gift Guide (Geeky Stuff We Like) LCD? LED? Plasma? The How-To Geek Guide to HDTV Technology The How-To Geek Guide to Learning Photoshop, Part 8: Filters Improve Digital Photography by Calibrating Your Monitor The Brothers Mario – Epic Gangland Style Mario Brothers Movie Trailer [Video] Score Awesome Games on the Cheap with the Humble Indie Bundle Add a Colorful Christmas Theme to Your Windows 7 Desktop This Windows Hack Changes the Blue Screen of Death to Red Edit Images Quickly in Firefox with Pixlr Grabber Zoho Writer, Sheet, and Show Now Available in Chrome Web Store

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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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• links for 2010-04-02

  - by Bob Rhubart

  Jeff Victor: Solaris Virtualization Book Jeff Victor with an update on the status of the book, "Oracle Solaris 10 System Virtualization Essentials." (tags: sun solaris virtualization) Mitch Denny: Architecture vs. Design It's an old post but it still resonates: "In the consumer electronics business, some people are actually hired to go through a system and remove components until it stops working – they do this to remove the cost before they go into mass production. We need more of this in the software business." -- Mitch Denny (tags: architecture design development) @vambenepe: Enterprise application integration patterns for IT management: a blast from the past or from the future? "In a recent blog post, Don Ferguson (CTO at CA) describes CA Catalyst, a major architectural overall which “applies enterprise application integration patterns to the problem of integrating IT management systems”. Reading this was fascinating to me. Not because the content was some kind of revelation, but exactly for the opposite reason. Because it is so familiar." -- William Vambenepe (tags: otn oracle eai)

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• Systemupdate Nr. 10 für das FY12 – 25. Oktober 2011

  - by swalker

  Server-, Speicher- und Systemsoftware von Sun Änderungen der Preislisten: Weltweite Preisänderungen für Oracle Systemhardware und -software Weltweite Preisänderungen für Hardware und Software von Oracle Legacy-Systemen Was müssen Sie tun? Die System-Updates beinhalten Preisänderungen bei Systemkomponenten und bestehen aus zwei Dateien: Bekanntgabe der Änderungen (PDF) und Detailinformationen (XLS). Mithilfe der angegebenen ID-Nummern finden Sie im Arbeitsblatt die Teilenummern der Komponenten, die von einer Änderung betroffen sind. Weitere Informationen Besuchen Sie regelmäßig auf dem OPN-Portal die Seite für die Systempreisgestaltung. Dort finden Sie neben weiteren Informationen über diese Aktualisierungen auch die aktuelle Systempreisliste und Ressourcen. * Hinweis: Die Preisinformationen zu Oracle Systemen sind vertrauliche Informationen von Oracle und werden Ihnen gemäß der Oracle PartnerNetwork-Vereinbarung zur Verfügung gestellt.

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• links for 2010-04-08

  - by Bob Rhubart

  Rittman Mead Consulting: Realtime Data Warehouses Rittman Mead Consulting's Peter Scott with a preview of his Real Time Data Warehousing talk at Collaborate 10. (tags: oracle otn rittmanmead collaborate2010 datawarehousing) Arun Gupta: Java EE 6, GlassFish, NetBeans, Eclipse, OSGi at Über Conf: Jun 14-17, Denver "Über Conf is a conference by No Fluff Just Stuff gang and plans to blow the minds of attendees with over 100 in-depth sessions (90 minutes each) from over 40 world class speakers on the Java platform and pragmatic Agile practices targeted at developers, architects, and technical managers." Arun Gupta (tags: oracle sun javaee glassfish netbeans) Aaron Lazenby: Profit's COLLABORATE 10 Session Selections Profit Magazine editor-in-chief Aaron Lazenby shares his annual list of COLLABORATE 2010 sessions that "reflect some of the more interesting people/trends in enterprise IT." (tags: oracle otn collaborate2010)

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• Geszti Péter a HOUG konferencián

  - by Lajos Sárecz

  Közel 100%-os a HOUG konferencia programja, így a március 29. kedd délelotti plenáris eloadások is már ismertek. Dr. Magyar Gábor HOUG elnök és Reményi Csaba Oracle ügyvezeto mellett a Budapesti Corvinus Egyetemrol Dr. Bodnár Viktória tart egy érdekes eloadást "Hogyan reagálnak a vezetok a környezeti változásra?" címmel, illetve a "sztárvendég" Geszti Péter lesz, aki az innováció és kreativitás témában osztja meg velünk tudását és tapasztalatait. Hogy milyen más indokokat lehet felsorolni amellett, hogy valaki regisztráljon és részt vegyen a konferencián, arra idézném a weblapot: Miért érdemes még eljönni az idei konferenciára? Azért, * mert ez az elso HOUG konferencia az Oracle-Sun egyesülést követoen; * mert megismerheti az ügyfelek korábbi tapasztalatait, már hardware témában is, * mert bemutatkozik az ExaLogic, az alkalmazásszerver feladatokra optimalizált hardver-szoftver célrendszer; * mert a résztvevok számára nyílt oktatások, workshopok állnak rendelkezésre végig a konferencia során; * mert megismerheti az iparági legjobb gyakorlatokat az Oracle alkalmazásokra - Siebel, Hyperion, E-Business Suite, Policy Automation. * mert találkozhat a HOUG Egyesület új elnökségével, megismerheti terveiket.

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• Happy 1st Birthday to GlassFish and Java EE

  - by pieter.humphrey

  Java EE and GlassFish are officially one year old!  As with all newborns, time moves fast and it seems like just yesterday it was shiny and new.     Feel free to post any birthday wishes on the blog comments, or even better, tell us a story about your experience with Java EE6 and GlassFish in the last year and we'll work with you to get it posted on the stories blog. http://blogs.sun.com/stories/ As all parents know, it takes a village to raise a child, and we want you as part of the village!  Get involved in the project at http://glassfish.java.net .     Technorati Tags: java,java ee,development,glassfish del.icio.us Tags: java,java ee,development,glassfish

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• Global Cache CR Requested But Current Block Received

  - by Liu Maclean(???)

  ????????«MINSCN?Cache Fusion Read Consistent» ????,???????????? ??????????????????: SQL> select * from V$version; BANNER -------------------------------------------------------------------------------- Oracle Database 11g Enterprise Edition Release 11.2.0.3.0 - 64bit Production PL/SQL Release 11.2.0.3.0 - Production CORE 11.2.0.3.0 Production TNS for Linux: Version 11.2.0.3.0 - Production NLSRTL Version 11.2.0.3.0 - Production SQL> select count(*) from gv$instance; COUNT(*) ---------- 2 SQL> select * from global_name; GLOBAL_NAME -------------------------------------------------------------------------------- www.oracledatabase12g.com ?11gR2 2??RAC??????????status???XG,????Xcurrent block???INSTANCE 2?hold?,?????INSTANCE 1?????????,?????: SQL> select * from test; ID ---------- 1 2 SQL> select dbms_rowid.rowid_block_number(rowid),dbms_rowid.rowid_relative_fno(rowid) from test; DBMS_ROWID.ROWID_BLOCK_NUMBER(ROWID) DBMS_ROWID.ROWID_RELATIVE_FNO(ROWID) ------------------------------------ ------------------------------------ 89233 1 89233 1 SQL> alter system flush buffer_cache; System altered. INSTANCE 1 Session A: SQL> update test set id=id+1 where id=1; 1 row updated. INSTANCE 1 Session B: SQL> select state,cr_scn_bas from x$bh where file#=1 and dbablk=89233 and state!=0; STATE CR_SCN_BAS ---------- ---------- 1 0 3 1755287 SQL> oradebug setmypid; Statement processed. SQL> oradebug dump gc_elements 255; Statement processed. SQL> oradebug tracefile_name; /s01/orabase/diag/rdbms/vprod/VPROD1/trace/VPROD1_ora_19111.trc GLOBAL CACHE ELEMENT DUMP (address: 0xa4ff3080): id1: 0x15c91 id2: 0x1 pkey: OBJ#76896 block: (1/89233) lock: X rls: 0x0 acq: 0x0 latch: 3 flags: 0x20 fair: 0 recovery: 0 fpin: 'kdswh11: kdst_fetch' bscn: 0x0.146e20 bctx: (nil) write: 0 scan: 0x0 lcp: (nil) lnk: [NULL] lch: [0xa9f6a6f8,0xa9f6a6f8] seq: 32 hist: 58 145:0 118 66 144:0 192 352 197 48 121 113 424 180 58 LIST OF BUFFERS LINKED TO THIS GLOBAL CACHE ELEMENT: flg: 0x02000001 lflg: 0x1 state: XCURRENT tsn: 0 tsh: 2 addr: 0xa9f6a5c8 obj: 76896 cls: DATA bscn: 0x0.1ac898 BH (0xa9f6a5c8) file#: 1 rdba: 0x00415c91 (1/89233) class: 1 ba: 0xa9e56000 set: 5 pool: 3 bsz: 8192 bsi: 0 sflg: 3 pwc: 0,15 dbwrid: 0 obj: 76896 objn: 76896 tsn: 0 afn: 1 hint: f hash: [0x91f4e970,0xbae9d5b8] lru: [0x91f58848,0xa9f6a828] lru-flags: debug_dump obj-flags: object_ckpt_list ckptq: [0x9df6d1d8,0xa9f6a740] fileq: [0xa2ece670,0xbdf4ed68] objq: [0xb4964e00,0xb4964e00] objaq: [0xb4964de0,0xb4964de0] st: XCURRENT md: NULL fpin: 'kdswh11: kdst_fetch' tch: 2 le: 0xa4ff3080 flags: buffer_dirty redo_since_read LRBA: [0x19.5671.0] LSCN: [0x0.1ac898] HSCN: [0x0.1ac898] HSUB: [1] buffer tsn: 0 rdba: 0x00415c91 (1/89233) scn: 0x0000.001ac898 seq: 0x01 flg: 0x00 tail: 0xc8980601 frmt: 0x02 chkval: 0x0000 type: 0x06=trans data ??????block: (1/89233)?GLOBAL CACHE ELEMENT DUMP?LOCK????X ??XG , ??????Current Block????Instance??modify???,????????????? ????Instance 2 ????: Instance 2 Session C: SQL> update test set id=id+1 where id=2; 1 row updated. Instance 2 Session D: SQL> select state,cr_scn_bas from x$bh where file#=1 and dbablk=89233 and state!=0; STATE CR_SCN_BAS ---------- ---------- 1 0 3 1756658 SQL> oradebug setmypid; Statement processed. SQL> oradebug dump gc_elements 255; Statement processed. SQL> oradebug tracefile_name; /s01/orabase/diag/rdbms/vprod/VPROD2/trace/VPROD2_ora_13038.trc GLOBAL CACHE ELEMENT DUMP (address: 0x89fb25a0): id1: 0x15c91 id2: 0x1 pkey: OBJ#76896 block: (1/89233) lock: XG rls: 0x0 acq: 0x0 latch: 3 flags: 0x20 fair: 0 recovery: 0 fpin: 'kduwh01: kdusru' bscn: 0x0.1acdf3 bctx: (nil) write: 0 scan: 0x0 lcp: (nil) lnk: [NULL] lch: [0x96f4cf80,0x96f4cf80] seq: 61 hist: 324 21 143:0 19 16 352 329 144:6 14 7 352 197 LIST OF BUFFERS LINKED TO THIS GLOBAL CACHE ELEMENT: flg: 0x0a000001 state: XCURRENT tsn: 0 tsh: 1 addr: 0x96f4ce50 obj: 76896 cls: DATA bscn: 0x0.1acdf6 BH (0x96f4ce50) file#: 1 rdba: 0x00415c91 (1/89233) class: 1 ba: 0x96bd4000 set: 5 pool: 3 bsz: 8192 bsi: 0 sflg: 2 pwc: 0,15 dbwrid: 0 obj: 76896 objn: 76896 tsn: 0 afn: 1 hint: f hash: [0x96ee1fe8,0xbae9d5b8] lru: [0x96f4d0b0,0x96f4cdc0] obj-flags: object_ckpt_list ckptq: [0xbdf519b8,0x96f4d5a8] fileq: [0xbdf519d8,0xbdf519d8] objq: [0xb4a47b90,0xb4a47b90] objaq: [0x96f4d0e8,0xb4a47b70] st: XCURRENT md: NULL fpin: 'kduwh01: kdusru' tch: 1 le: 0x89fb25a0 flags: buffer_dirty redo_since_read remote_transfered LRBA: [0x11.9e18.0] LSCN: [0x0.1acdf6] HSCN: [0x0.1acdf6] HSUB: [1] buffer tsn: 0 rdba: 0x00415c91 (1/89233) scn: 0x0000.001acdf6 seq: 0x01 flg: 0x00 tail: 0xcdf60601 frmt: 0x02 chkval: 0x0000 type: 0x06=trans data GCS CLIENT 0x89fb2618,6 resp[(nil),0x15c91.1] pkey 76896.0 grant 2 cvt 0 mdrole 0x42 st 0x100 lst 0x20 GRANTQ rl G0 master 1 owner 2 sid 0 remote[(nil),0] hist 0x94121c601163423c history 0x3c.0x4.0xd.0xb.0x1.0xc.0x7.0x9.0x14.0x1. cflag 0x0 sender 1 flags 0x0 replay# 0 abast (nil).x0.1 dbmap (nil) disk: 0x0000.00000000 write request: 0x0000.00000000 pi scn: 0x0000.00000000 sq[(nil),(nil)] msgseq 0x1 updseq 0x0 reqids[6,0,0] infop (nil) lockseq x2b8 pkey 76896.0 hv 93 [stat 0x0, 1->1, wm 32768, RMno 0, reminc 18, dom 0] kjga st 0x4, step 0.0.0, cinc 20, rmno 6, flags 0x0 lb 0, hb 0, myb 15250, drmb 15250, apifrz 0 ?Instance 2??????block: (1/89233)? GLOBAL CACHE ELEMENT Lock Convert?lock: XG ????GC_ELEMENTS DUMP???XCUR Cache Fusion?,???????X$ VIEW,??? X$LE X$KJBR X$KJBL, ???X$ VIEW???????????????????: INSTANCE 2 Session D: SELECT * FROM x$le WHERE le_addr IN (SELECT le_addr FROM x$bh WHERE obj IN (SELECT data_object_id FROM dba_objects WHERE owner = 'SYS' AND object_name = 'TEST') AND class = 1 AND state != 3); ADDR INDX INST_ID LE_ADDR LE_ID1 LE_ID2 ---------------- ---------- ---------- ---------------- ---------- ---------- LE_RLS LE_ACQ LE_FLAGS LE_MODE LE_WRITE LE_LOCAL LE_RECOVERY ---------- ---------- ---------- ---------- ---------- ---------- ----------- LE_BLKS LE_TIME LE_KJBL ---------- ---------- ---------------- 00007F94CA14CF60 7003 2 0000000089FB25A0 89233 1 0 0 32 2 0 1 0 1 0 0000000089FB2618 PCM Resource NAME?[ID1][ID2],[BL]???, ID1?ID2 ??blockno? fileno????, ??????????GC_elements dump?? id1: 0x15c91 id2: 0×1 pkey: OBJ#76896 block: (1/89233)?? ,?  kjblname ? kjbrname ??”[0x15c91][0x1],[BL]” ??: INSTANCE 2 Session D: SQL> set linesize 80 pagesize 1400 SQL> SELECT * 2 FROM x$kjbl l 3 WHERE l.kjblname LIKE '%[0x15c91][0x1],[BL]%'; ADDR INDX INST_ID KJBLLOCKP KJBLGRANT KJBLREQUE ---------------- ---------- ---------- ---------------- --------- --------- KJBLROLE KJBLRESP KJBLNAME ---------- ---------------- ------------------------------ KJBLNAME2 KJBLQUEUE ------------------------------ ---------- KJBLLOCKST KJBLWRITING ---------------------------------------------------------------- ----------- KJBLREQWRITE KJBLOWNER KJBLMASTER KJBLBLOCKED KJBLBLOCKER KJBLSID KJBLRDOMID ------------ ---------- ---------- ----------- ----------- ---------- ---------- KJBLPKEY ---------- 00007F94CA22A288 451 2 0000000089FB2618 KJUSEREX KJUSERNL 0 00 [0x15c91][0x1],[BL][ext 0x0,0x 89233,1,BL 0 GRANTED 0 0 1 0 0 0 0 0 76896 SQL> SELECT r.* FROM x$kjbr r WHERE r.kjbrname LIKE '%[0x15c91][0x1],[BL]%'; no rows selected Instance 1 session B: SQL> SELECT r.* FROM x$kjbr r WHERE r.kjbrname LIKE '%[0x15c91][0x1],[BL]%'; ADDR INDX INST_ID KJBRRESP KJBRGRANT KJBRNCVL ---------------- ---------- ---------- ---------------- --------- --------- KJBRROLE KJBRNAME KJBRMASTER KJBRGRANTQ ---------- ------------------------------ ---------- ---------------- KJBRCVTQ KJBRWRITER KJBRSID KJBRRDOMID KJBRPKEY ---------------- ---------------- ---------- ---------- ---------- 00007F801ACA68F8 1355 1 00000000B5A62AE0 KJUSEREX KJUSERNL 0 [0x15c91][0x1],[BL][ext 0x0,0x 0 00000000B48BB330 00 00 0 0 76896 ??????Instance 1???block: (1/89233),??????Instance 2 build cr block ????Instance 1, ?????????? ????? Instance 1? Foreground Process ? Instance 2?LMS??????RAC  TRACE: Instance 2: [oracle@vrh2 ~]$ ps -ef|grep ora_lms|grep -v grep oracle 23364 1 0 Apr29 ? 00:33:15 ora_lms0_VPROD2 SQL> oradebug setospid 23364 Oracle pid: 13, Unix process pid: 23364, image: [email protected] (LMS0) SQL> oradebug event 10046 trace name context forever,level 8:10708 trace name context forever,level 103: trace[rac.*] disk high; Statement processed. SQL> oradebug tracefile_name /s01/orabase/diag/rdbms/vprod/VPROD2/trace/VPROD2_lms0_23364.trc Instance 1 session B : SQL> select state,cr_scn_bas from x$bh where file#=1 and dbablk=89233 and state!=0; STATE CR_SCN_BAS ---------- ---------- 3 1756658 3 1756661 3 1755287 Instance 1 session A : SQL> alter session set events '10046 trace name context forever,level 8:10708 trace name context forever,level 103: trace[rac.*] disk high'; Session altered. SQL> select * from test; ID ---------- 2 2 SQL> select state,cr_scn_bas from x$bh where file#=1 and dbablk=89233 and state!=0; STATE CR_SCN_BAS ---------- ---------- 3 1761520 ?x$BH?????,???????Instance 1???build??CR block,????? TRACE ??: Instance 1 foreground Process: PARSING IN CURSOR #140336527348792 len=18 dep=0 uid=0 oct=3 lid=0 tim=1335939136125254 hv=1689401402 ad='b1a4c828' sqlid='c99yw1xkb4f1u' select * from test END OF STMT PARSE #140336527348792:c=2999,e=2860,p=0,cr=0,cu=0,mis=1,r=0,dep=0,og=1,plh=1357081020,tim=1335939136125253 EXEC #140336527348792:c=0,e=40,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=1,plh=1357081020,tim=1335939136125373 WAIT #140336527348792: nam='SQL*Net message to client' ela= 6 driver id=1650815232 #bytes=1 p3=0 obj#=0 tim=1335939136125420 *** 2012-05-02 02:12:16.125 kclscrs: req=0 block=1/89233 2012-05-02 02:12:16.125574 : kjbcro[0x15c91.1 76896.0][4] *** 2012-05-02 02:12:16.125 kclscrs: req=0 typ=nowait-abort *** 2012-05-02 02:12:16.125 kclscrs: bid=1:3:1:0:f:1e:0:0:10:0:0:0:1:2:4:1:20:0:0:0:c3:49:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0:4:3:2:1:2:0:1c:0:4d:26:a3:52:0:0:0:0:c7:c:ca:62:c3:49:0:0:0:0:1:0:14:8e:47:76:1:2:dc:5:a9:fe:17:75:0:0:0:0:0:0:0:0:0:0:0:0:99:ed:0:0:0:0:0:0:10:0:0:0 2012-05-02 02:12:16.125718 : kjbcro[0x15c91.1 76896.0][4] 2012-05-02 02:12:16.125751 : GSIPC:GMBQ: buff 0xba0ee018, queue 0xbb79a7b8, pool 0x60013fa0, freeq 0, nxt 0xbb79a7b8, prv 0xbb79a7b8 2012-05-02 02:12:16.125780 : kjbsentscn[0x0.1ae0f0][to 2] 2012-05-02 02:12:16.125806 : GSIPC:SENDM: send msg 0xba0ee088 dest x20001 seq 177740 type 36 tkts xff0000 mlen x1680198 2012-05-02 02:12:16.125918 : kjbmscr(0x15c91.1)reqid=0x8(req 0xa4ff30f8)(rinst 1)hldr 2(infosz 200)(lseq x2b8) 2012-05-02 02:12:16.126959 : GSIPC:KSXPCB: msg 0xba0ee088 status 30, type 36, dest 2, rcvr 1 *** 2012-05-02 02:12:16.127 kclwcrs: wait=0 tm=1233 *** 2012-05-02 02:12:16.127 kclwcrs: got 1 blocks from ksxprcv WAIT #140336527348792: nam='gc cr block 2-way' ela= 1233 p1=1 p2=89233 p3=1 obj#=76896 tim=1335939136127199 2012-05-02 02:12:16.127272 : kjbcrcomplete[0x15c91.1 76896.0][0] 2012-05-02 02:12:16.127309 : kjbrcvdscn[0x0.1ae0f0][from 2][idx 2012-05-02 02:12:16.127329 : kjbrcvdscn[no bscn <= rscn 0x0.1ae0f0][from 2] ???? kjbcro[0x15c91.1 76896.0][4] kjbsentscn[0x0.1ae0f0][to 2] ?Instance 2??SCN=1ae0f0=1761520? block: (1/89233),???’gc cr block 2-way’ ??,?????????CR block? Instance 2 LMS TRACE 2012-05-02 02:12:15.634057 : GSIPC:RCVD: ksxp msg 0x7f16e1598588 sndr 1 seq 0.177740 type 36 tkts 0 2012-05-02 02:12:15.634094 : GSIPC:RCVD: watq msg 0x7f16e1598588 sndr 1, seq 177740, type 36, tkts 0 2012-05-02 02:12:15.634108 : GSIPC:TKT: collect msg 0x7f16e1598588 from 1 for rcvr -1, tickets 0 2012-05-02 02:12:15.634162 : kjbrcvdscn[0x0.1ae0f0][from 1][idx 2012-05-02 02:12:15.634186 : kjbrcvdscn[no bscn1, wm 32768, RMno 0, reminc 18, dom 0] kjga st 0x4, step 0.0.0, cinc 20, rmno 6, flags 0x0 lb 0, hb 0, myb 15250, drmb 15250, apifrz 0 GCS CLIENT END 2012-05-02 02:12:15.635211 : kjbdowncvt[0x15c91.1 76896.0][1][options x0] 2012-05-02 02:12:15.635230 : GSIPC:AMBUF: rcv buff 0x7f16e1c56420, pool rcvbuf, rqlen 1103 2012-05-02 02:12:15.635308 : GSIPC:GPBMSG: new bmsg 0x7f16e1c56490 mb 0x7f16e1c56420 msg 0x7f16e1c564b0 mlen 152 dest x101 flushsz -1 2012-05-02 02:12:15.635334 : kjbmslset(0x15c91.1)) seq 0x4 reqid=0x6 (shadow 0xb48bb330.xb)(rsn 2)(mas@1) 2012-05-02 02:12:15.635355 : GSIPC:SPBMSG: send bmsg 0x7f16e1c56490 blen 184 msg 0x7f16e1c564b0 mtype 33 attr|dest x30101 bsz|fsz x1ffff 2012-05-02 02:12:15.635377 : GSIPC:SNDQ: enq msg 0x7f16e1c56490, type 65521 seq 118669, inst 1, receiver 1, queued 1 *** 2012-05-02 02:12:15.635 kclccctx: cleanup copy 0x7f16e1d94798 2012-05-02 02:12:15.635479 : [kjmpmsgi:compl][type 36][msg 0x7f16e1598588][seq 177740.0][qtime 0][ptime 1257] 2012-05-02 02:12:15.635511 : GSIPC:BSEND: flushing sndq 0xb491dd28, id 1, dcx 0xbc516778, inst 1, rcvr 1 qlen 0 1 2012-05-02 02:12:15.635536 : GSIPC:BSEND: no batch1 msg 0x7f16e1c56490 type 65521 len 184 dest (1:1) 2012-05-02 02:12:15.635557 : kjbsentscn[0x0.1ae0f1][to 1] 2012-05-02 02:12:15.635578 : GSIPC:SENDM: send msg 0x7f16e1c56490 dest x10001 seq 118669 type 65521 tkts x10002 mlen xb800e8 WAIT #0: nam='gcs remote message' ela= 180 waittime=1 poll=0 event=0 obj#=0 tim=1335939135635819 2012-05-02 02:12:15.635853 : GSIPC:RCVD: ksxp msg 0x7f16e167e0b0 sndr 1 seq 0.177741 type 32 tkts 0 2012-05-02 02:12:15.635875 : GSIPC:RCVD: watq msg 0x7f16e167e0b0 sndr 1, seq 177741, type 32, tkts 0 2012-05-02 02:12:15.636012 : GSIPC:TKT: collect msg 0x7f16e167e0b0 from 1 for rcvr -1, tickets 0 2012-05-02 02:12:15.636040 : kjbrcvdscn[0x0.1ae0f1][from 1][idx 2012-05-02 02:12:15.636060 : kjbrcvdscn[no bscn <= rscn 0x0.1ae0f1][from 1] 2012-05-02 02:12:15.636082 : GSIPC:TKT: dest (1:1) rtkt not acked 1  unassigned bufs 0  tkts 0  newbufs 0 2012-05-02 02:12:15.636102 : GSIPC:TKT: remove ctx dest (1:1) 2012-05-02 02:12:15.636125 : [kjmxmpm][type 32][seq 0.177741][msg 0x7f16e167e0b0][from 1] 2012-05-02 02:12:15.636146 : kjbmpocr(0xb0.6)seq 0x1,reqid=0x23a,(client 0x9fff7b58,0x1)(from 1)(lseq xdf0) 2????LMS????????? ??gcs remote message GSIPC ????SCN=[0x0.1ae0f0] block=1/89233???,??BAST kjbmpbast(0x15c91.1),?? block=1/89233??????? ??fairness??(?11.2.0.3???_fairness_threshold=2),?current block?KCL: F156: fairness downconvert,?Xcurrent DownConvert? Scurrent: Instance 2: SQL> select state,cr_scn_bas from x$bh where file#=1 and dbablk=89233 and state!=0; STATE CR_SCN_BAS ---------- ---------- 2 0 3 1756658 ??Instance 2 LMS ?cr block??? kjbmslset(0x15c91.1)) ????SEND QUEUE GSIPC:SNDQ: enq msg 0x7f16e1c56490? ???????Instance 1???? block: (1/89233)??? ??????: Instance 2: SQL> select CURRENT_RESULTS,LIGHT_WORKS from v$cr_block_server; CURRENT_RESULTS LIGHT_WORKS --------------- ----------- 29273 437 Instance 1 session A: SQL> SQL> select * from test; ID ---------- 2 2 SQL> select state,cr_scn_bas from x$bh where file#=1 and dbablk=89233 and state!=0; STATE CR_SCN_BAS ---------- ---------- 3 1761942 3 1761932 1 0 3 1761520 Instance 2: SQL> select CURRENT_RESULTS,LIGHT_WORKS from v$cr_block_server; CURRENT_RESULTS LIGHT_WORKS --------------- ----------- 29274 437 select * from test END OF STMT PARSE #140336529675592:c=0,e=337,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=1,plh=1357081020,tim=1335939668940051 EXEC #140336529675592:c=0,e=96,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=1,plh=1357081020,tim=1335939668940204 WAIT #140336529675592: nam='SQL*Net message to client' ela= 5 driver id=1650815232 #bytes=1 p3=0 obj#=0 tim=1335939668940348 *** 2012-05-02 02:21:08.940 kclscrs: req=0 block=1/89233 2012-05-02 02:21:08.940676 : kjbcro[0x15c91.1 76896.0][5] *** 2012-05-02 02:21:08.940 kclscrs: req=0 typ=nowait-abort *** 2012-05-02 02:21:08.940 kclscrs: bid=1:3:1:0:f:21:0:0:10:0:0:0:1:2:4:1:20:0:0:0:c3:49:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0:4:3:2:1:2:0:1f:0:4d:26:a3:52:0:0:0:0:c7:c:ca:62:c3:49:0:0:0:0:1:0:17:8e:47:76:1:2:dc:5:a9:fe:17:75:0:0:0:0:0:0:0:0:0:0:0:0:99:ed:0:0:0:0:0:0:10:0:0:0 2012-05-02 02:21:08.940799 : kjbcro[0x15c91.1 76896.0][5] 2012-05-02 02:21:08.940833 : GSIPC:GMBQ: buff 0xba0ee018, queue 0xbb79a7b8, pool 0x60013fa0, freeq 0, nxt 0xbb79a7b8, prv 0xbb79a7b8 2012-05-02 02:21:08.940859 : kjbsentscn[0x0.1ae28c][to 2] 2012-05-02 02:21:08.940870 : GSIPC:SENDM: send msg 0xba0ee088 dest x20001 seq 177810 type 36 tkts xff0000 mlen x1680198 2012-05-02 02:21:08.940976 : kjbmscr(0x15c91.1)reqid=0xa(req 0xa4ff30f8)(rinst 1)hldr 2(infosz 200)(lseq x2b8) 2012-05-02 02:21:08.941314 : GSIPC:KSXPCB: msg 0xba0ee088 status 30, type 36, dest 2, rcvr 1 *** 2012-05-02 02:21:08.941 kclwcrs: wait=0 tm=707 *** 2012-05-02 02:21:08.941 kclwcrs: got 1 blocks from ksxprcv 2012-05-02 02:21:08.941818 : kjbassume[0x15c91.1][sender 2][mymode x1][myrole x0][srole x0][flgs x0][spiscn 0x0.0][swscn 0x0.0] 2012-05-02 02:21:08.941852 : kjbrcvdscn[0x0.1ae28d][from 2][idx 2012-05-02 02:21:08.941871 : kjbrcvdscn[no bscn ??????????????SCN=[0x0.1ae28c]=1761932 Version?CR block, ????receive????Xcurrent Block??SCN=1ae28d=1761933,Instance 1???Xcurrent Block???build????????SCN=1761932?CR BLOCK, ????????Current block,?????????'gc current block 2-way'? ?????????????request current block,?????kjbcro;?????Instance 2?LMS???????Current Block: Instance 2 LMS trace: 2012-05-02 02:21:08.448743 : GSIPC:RCVD: ksxp msg 0x7f16e14a4398 sndr 1 seq 0.177810 type 36 tkts 0 2012-05-02 02:21:08.448778 : GSIPC:RCVD: watq msg 0x7f16e14a4398 sndr 1, seq 177810, type 36, tkts 0 2012-05-02 02:21:08.448798 : GSIPC:TKT: collect msg 0x7f16e14a4398 from 1 for rcvr -1, tickets 0 2012-05-02 02:21:08.448816 : kjbrcvdscn[0x0.1ae28c][from 1][idx 2012-05-02 02:21:08.448834 : kjbrcvdscn[no bscn <= rscn 0x0.1ae28c][from 1] 2012-05-02 02:21:08.448857 : GSIPC:TKT: dest (1:1) rtkt not acked 2  unassigned bufs 0  tkts 0  newbufs 0 2012-05-02 02:21:08.448875 : GSIPC:TKT: remove ctx dest (1:1) 2012-05-02 02:21:08.448970 : [kjmxmpm][type 36][seq 0.177810][msg 0x7f16e14a4398][from 1] 2012-05-02 02:21:08.448993 : kjbmpbast(0x15c91.1) reqid=0x6 (req 0xa4ff30f8)(reqinst 1)(reqid 10)(flags x0) *** 2012-05-02 02:21:08.449 kclcrrf: req=48054 block=1/89233 *** 2012-05-02 02:21:08.449 kcl_compress_block: compressed: 6 free space: 7680 2012-05-02 02:21:08.449085 : kjbsentscn[0x0.1ae28d][to 1] 2012-05-02 02:21:08.449142 : kjbdeliver[to 1][0xa4ff30f8][10][current 1] 2012-05-02 02:21:08.449164 : kjbmssch(reqlock 0xa4ff30f8,10)(to 1)(bsz 344) 2012-05-02 02:21:08.449183 : GSIPC:AMBUF: rcv buff 0x7f16e18bcec8, pool rcvbuf, rqlen 1102 *** 2012-05-02 02:21:08.449 kclccctx: cleanup copy 0x7f16e1d94838 *** 2012-05-02 02:21:08.449 kcltouched: touch seconds 3271 *** 2012-05-02 02:21:08.449 kclgrantlk: req=48054 2012-05-02 02:21:08.449347 : [kjmpmsgi:compl][type 36][msg 0x7f16e14a4398][seq 177810.0][qtime 0][ptime 1119] WAIT #0: nam='gcs remote message' ela= 568 waittime=1 poll=0 event=0 obj#=0 tim=1335939668449962 2012-05-02 02:21:08.450001 : GSIPC:RCVD: ksxp msg 0x7f16e1bb22a0 sndr 1 seq 0.177811 type 32 tkts 0 2012-05-02 02:21:08.450024 : GSIPC:RCVD: watq msg 0x7f16e1bb22a0 sndr 1, seq 177811, type 32, tkts 0 2012-05-02 02:21:08.450043 : GSIPC:TKT: collect msg 0x7f16e1bb22a0 from 1 for rcvr -1, tickets 0 2012-05-02 02:21:08.450060 : kjbrcvdscn[0x0.1ae28e][from 1][idx 2012-05-02 02:21:08.450078 : kjbrcvdscn[no bscn <= rscn 0x0.1ae28e][from 1] 2012-05-02 02:21:08.450097 : GSIPC:TKT: dest (1:1) rtkt not acked 3  unassigned bufs 0  tkts 0  newbufs 0 2012-05-02 02:21:08.450116 : GSIPC:TKT: remove ctx dest (1:1) 2012-05-02 02:21:08.450136 : [kjmxmpm][type 32][seq 0.177811][msg 0x7f16e1bb22a0][from 1] 2012-05-02 02:21:08.450155 : kjbmpocr(0xb0.6)seq 0x1,reqid=0x23e,(client 0x9fff7b58,0x1)(from 1)(lseq xdf4) ???Instance 2??LMS???,???build cr block,??????Instance 1?????Current Block??????Instance 2??v$cr_block_server??????LIGHT_WORKS?????current block transfer??????,??????? CR server? Light Work Rule(Light Work Rule?8i Cr Server?????????,?Remote LMS?? build CR????????,resource holder?LMS???????block,????CR build If creating the consistent read version block involves too much work (such as reading blocks from disk), then the holder sends the block to the requestor, and the requestor completes the CR fabrication. The holder maintains a fairness counter of CR requests. After the fairness threshold is reached, the holder downgrades it to lock mode.)? ??????? CR Request ????Current Block?? ???:??????class?block,CR server??????? ??undo block?? undo header block?CR quest, LMS????Current Block, ????? ???? ??????? block cleanout? CR  Version??????? ???????? data blocks, ??????? CR quest  & CR received?(???????Light Work Rule,LMS"??"), ??Current Block??DownConvert???S lock,??LMS???????ship??current version?block? ??????? , ?????? ,???????DownConvert?????”_fairness_threshold“???200,????Xcurrent Block?????Scurrent, ????LMS?????Current Version?Data Block: SQL> show parameter fair NAME TYPE VALUE ------------------------------------ ----------- ------------------------------ _fairness_threshold integer 200 Instance 1: SQL> update test set id=id+1 where id=4; 1 row updated. Instance 2: SQL> update test set id=id+1 where id=2; 1 row updated. SQL> select state,cr_scn_bas from x$bh where file#=1 and dbablk=89233 and state!=0; STATE CR_SCN_BAS ---------- ---------- 1 0 3 1838166 ?Instance 1? ????,? ??instance 2? v$cr_block_server?? instance 1 SQL> select * from test; ID ---------- 10 3 instance 2: SQL> select state,cr_scn_bas from x$bh where file#=1 and dbablk=89233 and state!=0; STATE CR_SCN_BAS ---------- ---------- 1 0 3 1883707 8 0 SQL> select * from test; ID ---------- 10 3 SQL> select state,cr_scn_bas from x$bh where file#=1 and dbablk=89233 and state!=0; STATE CR_SCN_BAS ---------- ---------- 1 0 3 1883707 8 0 ................... SQL> / STATE CR_SCN_BAS ---------- ---------- 2 0 3 1883707 3 1883695 repeat cr request on Instance 1 SQL> / STATE CR_SCN_BAS ---------- ---------- 8 0 3 1883707 3 1883695 ??????_fairness_threshold????????,?????200 ????????CR serve??Downgrade?lock, ????data block? CR Request????Receive? Current Block?

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• Oracle Consulting North America is now live on PeopleSoft Services Procurement and PeopleSoft Resource Management

  - by Howard Shaw

  Last month, Oracle's own internal consulting group (OCS North America) went live on PeopleSoft Services Procurement and PeopleSoft Resource Management to manage all aspects of identifying, recruiting, and deploying billable subcontractors on North America Applications customer consulting projects. The primary goals were to enhance the subcontractor staffing process, improve operational and informational processes, and improve collaboration between the Oracle NA Consulting Subcontractor Program and subcontractor suppliers. Over 200 registered external suppliers access the tool, review open needs and competitively bid their resources to work on NA Applications projects. This implementation highlights the usage of Oracle’s own solutions to streamline and enhance business operations, as the PeopleSoft 9.1 applications (Services Procurement and Resource Management) were deployed using Sun hardware, Oracle Enterprise Linux, and Oracle Virtual Machines.For more information, please navigate to the following web pages: PeopleSoft Services Procurement PeopleSoft Resource Management

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• Mise à jour Systèmes n°10 de l'exercice 2012 – 25 octobre 2011

  - by swalker

  Logiciels Systèmes, Stockage et Serveurs Sun Modifications des tarifs : Oracle Révision de la tarification internationale pour les matériels et logiciels Systèmes Révision de la tarification internationale pour les matériels et logiciels hérités Oracle Que devez-vous faire ? Chaque mise à jour Systèmes inclut des modifications de la tarification Systèmes et deux fichiers : le document d'annonce (PDF) et les détails de l'annonce (XLS). Les numéros d'identification indiqués dans les documents d'annonce vous permettent de retrouver toutes les références produit concernées par l'annonce en question dans la feuille de calcul. Informations complémentaires Rendez-vous régulièrement sur la page des Tarifs Systèmes du portail OPN pour en savoir plus sur ces mises à jour et connaître les derniers tarifs et les dernières ressources applicables. * Avertissement : Les informations sur la tarification des systèmes Oracle sont des informations confidentielles Oracle, qui vous sont transmises en vertu des modalités de votre contrat Oracle PartnerNetwork.

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• A Database and LDAP Ice Breaker Video

  - by mark.wilcox

  I made another GoAnimate video - this time it's about using LDAP for database passwords. Since it's on the free site - I didn't want to violate any terms of agreement - so it doesn't mention Oracle explicitly. But if you wanted to actually do what the animation talks about with Oracle database - you need to configure the Oracle database to use Oracle Enterprise User Security. EUS requires OVD or OID and works with most popular LDAP servers including Active Directory and of course our newest Oracle Directory member - Directory Server Enterprise Edition (aka the former Sun directory). So - if you are looking for a simple way to explain why you might want to use LDAP passwords with your databases or maybe just a slight chuckle on a Friday afternoon have a look at the video: -- Posted via email from Virtual Identity Dialogue

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