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  • Conventional Parallel Inserts do Exist in Oracle 11

    - by jean-pierre.dijcks
    Had an interesting chat with Greg about said topic and searching showed the following link to discuss this topic in some detail (no reason for me to repeat this). insert /*+ noappend parallel(t1) */ into t1 select /*+ parallel(t2) */ * from t2 generates a load table conventional and does give you a parallel insert without doing a direct path insert. As this is missing from the official documentation it is probably something few people actually know existed, so kudos to Randolf Geist.

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  • Using a flash drive to speed up conventional disks (on linux)

    - by Daniel
    Hi! Is there a possibility to use a flash drive as a speed up for conventional hard disks? I got the idea to redirect all read ops to the flash drive if the data is already stored there, and to read from the conventional disks if the data is not found there (and during idle time the freshly accessed data from the conventional disk is stored on the flash disk). Is this already possible with linux standard tools?

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  • SEO and Small Conventional Businesses

    I talked with a small business owner a few weeks ago that is doing his own SEO. It consists of AdWords only because that is all he has been taught. It is costing him about 12% of his profit to keep the campaign running and he has not really seen any increase in traffic that he can identify.

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  • While porting a windows application to web, is it better to stick to conventional web technologies o

    - by Kabeer
    Hello. The web based application I am working on currently is a port from a windows application. This application is very data intensive. There are scores of modules and each of these modules have number of forms (data entry screens) and reports whereas the forms have many many fields and likewise the reports. I have been trying to identify the most suitable architecture for the presentation tier. There are many functions that are not very easily portable, for example printing (this too is very complex). For most of the others, I am planning to us "Ext JS" library which looks like capable of handling about 70% of complexity out of the box while for the remaining I would be custom coding or extending Ext JS. Having said that (sorry for being so descriptive), I wonder, if this is an Intranet application, why not port the entire application to SilverLight? While I am good at .Net, I'm somewhat alien to SilverLight. Considering I know my target audience and that the software will be used per seat license, would it be better to ride on SilverLight or is it better to stick to conventional web (XHTML, JS, CSS, etc)? Further, I have to support multiple devices in future and considering that SilverLight plug-ins for many devices are yet not out, would it be a risk?

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  • How to best config my PC. 2 small SDDs and a 1TB conventional drive

    - by Chadworthington
    I just got a new PC. It has 3 drives C Drive: 80GB SDD Drive P Drive: 120GB SDD (for core programs) D Drive: 1TB SDD (for data and other) I have an MSDN subscription and I am the type of person that loves to install tons of software to check it out. I am very worried that installation programs are forcing me to place a large amount of files on the small C drive. I fear that I will quickly run out of space on my C drive while having ample space on my TB drive. What would you do in my shoes? I didn't select this PC or set the config up. I am wishing that the 120 SSD was my C, to give me a little more room to grow. I guess there are no magic solutions here but I am just looking for your general thoughts

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  • Conventional Approaches for Passing Data to Back-End?

    - by Calvin
    Hi guys, I'm fairly new to web development, so please pardon the painfully newbie question that's about to follow. My computer science class group and I are developing a web application for class, which is built in Python (under Django) and uses jQuery on the front end. It's primarily an AJAX-ified application, and passing data from the backend to the front end is done through AJAX calls to specific URLs which return JSON. This is probably a stupid question, but what's the conventional approach for passing data in the opposite direction? We don't want to reload the page or anything, so is it an AJAX pass going the other way or something? Thanks in advance for your help!

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  • Using memory-based cache together with conventional cache

    - by Industrial
    Hi! Here's the deal. We would have taken the complete static html road to solve performance issues, but since the site will be partially dynamic, this won't work out for us. What we have thought of instead is using memcache + eAccelerator to speed up PHP and take care of caching for the most used data. Here's our two approaches that we have thought of right now: Using memcache on all<< major queries and leaving it alone to do what it does best. Usinc memcache for most commonly retrieved data, and combining with a standard harddrive-stored cache for further usage. The major advantage of only using memcache is of course the performance, but as users increases, the memory usage gets heavy. Combining the two sounds like a more natural approach to us, even though the theoretical compromize in performance. Memcached appears to have some replication features available as well, which may come handy when it's time to increase the nodes. What approach should we use? - Is it stupid to compromize and combine the two methods? Should we insted be focusing on utilizing memcache and instead focusing on upgrading the memory as the load increases with the number of users? Thanks a lot!

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  • Using memcache together with conventional cache

    - by Industrial
    Hi! Here's the deal. We would have taken the complete static html road to solve performance issues, but since the site will be partially dynamic, this won't work out for us. What we have thought of instead is using memcache + eAccelerator to speed up PHP and take care of caching for the most used data. Here's our two approaches that we have thought of right now: Using memcache on all<< major queries and leaving it alone to do what it does best. Usinc memcache for most commonly retrieved data, and combining with a standard harddrive-stored cache for further usage. The major advantage of only using memcache is of course the performance, but as users increases, the memory usage gets heavy. Combining the two sounds like a more natural approach to us, even though the theoretical compromize in performance. Memcached appears to have some replication features available as well, which may come handy when it's time to increase the nodes. What approach should we use? - Is it stupid to compromize and combine the two methods? Should we insted be focusing on utilizing memcache and instead focusing on upgrading the memory as the load increases with the number of users? Thanks a lot!

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  • The conventional location for storing my Java libraries and applications in UNIX based systems

    - by Bytecode Ninja
    I usually store the Java applications and JAR files that I download from the Web in the ~/Java folder on my computer (an OS X machine). I have been doing this since the days when I was a Windows user. However I think in UNIX based systems user local apps are conventionally stored in another directory. I have a feeling that this directory should either be /usr/local/, /usr/local/USERNAME, /opt/local, or /opt/local/USERNAME but I am not sure. Any ideas which directory can I use for this purpose? Please note that, I am talking about archive files that I download from the Web, unpack and use locally and not programs that have installation scripts or MacPorts, etc.

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  • How to make outlook.com/Office 365 use plain text and/or conventional quotes?

    - by user23122
    I am forced to use the web version of Outlook at Office 365. I really dislike how it formats e-mail as well as how it handles quoting when replying. In the desktop version of Outlook you can at least force it too display the messages in plain text and then you can manually bottom post/post interleaved. Plain text also changes how it handles quotes: " " is used rather than some braindead RTF version of format=flowed (I like format=flowed when it is implemented without bugs) although the attribution line is completely useless but in the online version I can't find a way to achieve even this. Any ideas? I guess a Greasemonkey script could do this?

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  • Changing CSS on the fly in a UIWebView on iPhone

    - by Shaggy Frog
    Let's say I'm developing an iPhone app that is a catalogue of cars. The user will choose a car from a list, and I will present a detail view for the car, which will describe things like top speed. The detail view will essentially be a UIWebView that is loading an existing HTML file. Different users will live in different parts of the world, so they will like to see the top speed for the car in whatever units are appropriate for their locale. Let's say there are two such units: SI (km/h) and conventional (mph). Let's also say the user will be able to change the display units by hitting a button on the screen; when that happens, the detail screen should switch to show the relevant units. So far, here's what I've done to try and solve this. The HTML might look something like this: <?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"> <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en-US" lang="en-US"> <head> <title>Some Car</title> <link rel="stylesheet" media="screen" type="text/css" href="persistent.css" /> <link rel="alternate stylesheet" media="screen" type="text/css" href="si.css" title="si" /> <link rel="alternate stylesheet" media="screen" type="text/css" href="conventional.css" title="conventional" /> <script type="text/javascript" src="switch.js"></script> </head> <body> <h1>Some Car</h1> <div id="si"> <h2>Top Speed: 160 km/h</h2> </div> <div id="conventional"> <h2>Top Speed: 100 mph</h2> </div> </body> The peristent stylesheet, persistent.css: #si { display:none; } #conventional { display:none; } The first alternate stylesheet, si.css: #si { display:inline; } #conventional { display:none; } And the second alternate stylesheet, conventional.css: #si { display:none; } #conventional { display:inline; } Based on a tutorial at A List Apart, my switch.js looks something like this: function disableStyleSheet(title) { var i, a; for (i = 0; (a = document.getElementsByTagName("link")[i]); i++) { if ((a.getAttribute("rel").indexOf("alt") != -1) && (a.getAttribute("title") == title)) { a.disabled = true; } } } function enableStyleSheet(title) { var i, a; for (i = 0; (a = document.getElementsByTagName("link")[i]); i++) { if ((a.getAttribute("rel").indexOf("alt") != -1) && (a.getAttribute("title") == title)) { a.disabled = false; } } } function switchToSiStyleSheet() { disableStyleSheet("conventional"); enableStyleSheet("si"); } function switchToConventionalStyleSheet() { disableStyleSheet("si"); enableStyleSheet("conventional"); } My button action handler looks something like this: - (void)notesButtonAction:(id)sender { static BOOL isUsingSi = YES; if (isUsingSi) { NSString* command = [[NSString alloc] initWithString:@"switchToSiStyleSheet();"]; [self.webView stringByEvaluatingJavaScriptFromString:command]; [command release]; } else { NSString* command = [[NSString alloc] initWithFormat:@"switchToConventionalStyleSheet();"]; [self.webView stringByEvaluatingJavaScriptFromString:command]; [command release]; } isUsingSi = !isUsingSi; } Here's the first problem. The first time the button is hit, the UIWebView doesn't change. The second time it's hit, it looks like the conventional style sheet is loaded. The third time, it switches to the SI style sheet; the fourth time, back to the conventional, and so on. So, basically, only that first button press doesn't seem to do anything. Here's the second problem. I'm not sure how to switch to the correct style sheet upon initial load of the UIWebView. I tried this: - (void)webViewDidFinishLoad:(UIWebView *)webView { NSString* command = [[NSString alloc] initWithString:@"switchToSiStyleSheet();"]; [self.webView stringByEvaluatingJavaScriptFromString:command]; [command release]; } But, like the first button hit, it doesn't seem to do anything. Can anyone help me with these two problems?

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  • Columnstore Case Study #2: Columnstore faster than SSAS Cube at DevCon Security

    - by aspiringgeek
    Preamble This is the second in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in my big deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. See also Columnstore Case Study #1: MSIT SONAR Aggregations Why Columnstore? As stated previously, If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. The Customer DevCon Security provides home & business security services & has been in business for 135 years. I met DevCon personnel while speaking to the Utah County SQL User Group on 20 February 2012. (Thanks to TJ Belt (b|@tjaybelt) & Ben Miller (b|@DBADuck) for the invitation which serendipitously coincided with the height of ski season.) The App: DevCon Security Reporting: Optimized & Ad Hoc Queries DevCon users interrogate a SQL Server 2012 Analysis Services cube via SSRS. In addition, the SQL Server 2012 relational back end is the target of ad hoc queries; this DW back end is refreshed nightly during a brief maintenance window via conventional table partition switching. SSRS, SSAS, & MDX Conventional relational structures were unable to provide adequate performance for user interaction for the SSRS reports. An SSAS solution was implemented requiring personnel to ramp up technically, including learning enough MDX to satisfy requirements. Ad Hoc Queries Even though the fact table is relatively small—only 22 million rows & 33GB—the table was a typical DW table in terms of its width: 137 columns, any of which could be the target of ad hoc interrogation. As is common in DW reporting scenarios such as this, it is often nearly to optimize for such queries using conventional indexing. DevCon DBAs & developers attended PASS 2012 & were introduced to the marvels of columnstore in a session presented by Klaus Aschenbrenner (b|@Aschenbrenner) The Details Classic vs. columnstore before-&-after metrics are impressive. Scenario Conventional Structures Columnstore ? SSRS via SSAS 10 - 12 seconds 1 second >10x Ad Hoc 5-7 minutes (300 - 420 seconds) 1 - 2 seconds >100x Here are two charts characterizing this data graphically.  The first is a linear representation of Report Duration (in seconds) for Conventional Structures vs. Columnstore Indexes.  As is so often the case when we chart such significant deltas, the linear scale doesn’t expose some the dramatically improved values corresponding to the columnstore metrics.  Just to make it fair here’s the same data represented logarithmically; yet even here the values corresponding to 1 –2 seconds aren’t visible.  The Wins Performance: Even prior to columnstore implementation, at 10 - 12 seconds canned report performance against the SSAS cube was tolerable. Yet the 1 second performance afterward is clearly better. As significant as that is, imagine the user experience re: ad hoc interrogation. The difference between several minutes vs. one or two seconds is a game changer, literally changing the way users interact with their data—no mental context switching, no wondering when the results will appear, no preoccupation with the spinning mind-numbing hurry-up-&-wait indicators.  As we’ve commonly found elsewhere, columnstore indexes here provided performance improvements of one, two, or more orders of magnitude. Simplified Infrastructure: Because in this case a nonclustered columnstore index on a conventional DW table was faster than an Analysis Services cube, the entire SSAS infrastructure was rendered superfluous & was retired. PASS Rocks: Once again, the value of attending PASS is proven out. The trip to Charlotte combined with eager & enquiring minds let directly to this success story. Find out more about the next PASS Summit here, hosted this year in Seattle on November 4 - 7, 2014. DevCon BI Team Lead Nathan Allan provided this unsolicited feedback: “What we found was pretty awesome. It has been a game changer for us in terms of the flexibility we can offer people that would like to get to the data in different ways.” Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the second in a series of reports on columnstore implementations, results from DevCon Security, a live customer production app for which performance increased by factors of from 10x to 100x for all report queries, including canned queries as well as reducing time for results for ad hoc queries from 5 - 7 minutes to 1 - 2 seconds. As a result of columnstore performance, the customer retired their SSAS infrastructure. I invite you to consider leveraging columnstore in your own environment. Let me know if you have any questions.

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  • Columnstore Case Study #2: Columnstore faster than SSAS Cube at DevCon Security

    - by aspiringgeek
    Preamble This is the second in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in my big deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. See also Columnstore Case Study #1: MSIT SONAR Aggregations Why Columnstore? As stated previously, If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. The Customer DevCon Security provides home & business security services & has been in business for 135 years. I met DevCon personnel while speaking to the Utah County SQL User Group on 20 February 2012. (Thanks to TJ Belt (b|@tjaybelt) & Ben Miller (b|@DBADuck) for the invitation which serendipitously coincided with the height of ski season.) The App: DevCon Security Reporting: Optimized & Ad Hoc Queries DevCon users interrogate a SQL Server 2012 Analysis Services cube via SSRS. In addition, the SQL Server 2012 relational back end is the target of ad hoc queries; this DW back end is refreshed nightly during a brief maintenance window via conventional table partition switching. SSRS, SSAS, & MDX Conventional relational structures were unable to provide adequate performance for user interaction for the SSRS reports. An SSAS solution was implemented requiring personnel to ramp up technically, including learning enough MDX to satisfy requirements. Ad Hoc Queries Even though the fact table is relatively small—only 22 million rows & 33GB—the table was a typical DW table in terms of its width: 137 columns, any of which could be the target of ad hoc interrogation. As is common in DW reporting scenarios such as this, it is often nearly to optimize for such queries using conventional indexing. DevCon DBAs & developers attended PASS 2012 & were introduced to the marvels of columnstore in a session presented by Klaus Aschenbrenner (b|@Aschenbrenner) The Details Classic vs. columnstore before-&-after metrics are impressive. Scenario   Conventional Structures   Columnstore   Δ SSRS via SSAS 10 - 12 seconds 1 second >10x Ad Hoc 5-7 minutes (300 - 420 seconds) 1 - 2 seconds >100x Here are two charts characterizing this data graphically.  The first is a linear representation of Report Duration (in seconds) for Conventional Structures vs. Columnstore Indexes.  As is so often the case when we chart such significant deltas, the linear scale doesn’t expose some the dramatically improved values corresponding to the columnstore metrics.  Just to make it fair here’s the same data represented logarithmically; yet even here the values corresponding to 1 –2 seconds aren’t visible.  The Wins Performance: Even prior to columnstore implementation, at 10 - 12 seconds canned report performance against the SSAS cube was tolerable. Yet the 1 second performance afterward is clearly better. As significant as that is, imagine the user experience re: ad hoc interrogation. The difference between several minutes vs. one or two seconds is a game changer, literally changing the way users interact with their data—no mental context switching, no wondering when the results will appear, no preoccupation with the spinning mind-numbing hurry-up-&-wait indicators.  As we’ve commonly found elsewhere, columnstore indexes here provided performance improvements of one, two, or more orders of magnitude. Simplified Infrastructure: Because in this case a nonclustered columnstore index on a conventional DW table was faster than an Analysis Services cube, the entire SSAS infrastructure was rendered superfluous & was retired. PASS Rocks: Once again, the value of attending PASS is proven out. The trip to Charlotte combined with eager & enquiring minds let directly to this success story. Find out more about the next PASS Summit here, hosted this year in Seattle on November 4 - 7, 2014. DevCon BI Team Lead Nathan Allan provided this unsolicited feedback: “What we found was pretty awesome. It has been a game changer for us in terms of the flexibility we can offer people that would like to get to the data in different ways.” Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the second in a series of reports on columnstore implementations, results from DevCon Security, a live customer production app for which performance increased by factors of from 10x to 100x for all report queries, including canned queries as well as reducing time for results for ad hoc queries from 5 - 7 minutes to 1 - 2 seconds. As a result of columnstore performance, the customer retired their SSAS infrastructure. I invite you to consider leveraging columnstore in your own environment. Let me know if you have any questions.

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  • How to design the scenarios in a platform game?

    - by ReyLitch
    I am developing a 3D platform game like Metroid Fusion with XNA. I have many classes for different elements as models, game screens, postprocessing and so on. Now I want to start designing the scenarios but I think that the scenarios needed in a platform game are not as conventional (by conventional I say something like this). I am very lost and not know where to start and how to structure it. Thanks in advance.

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  • How SEO Consulting Services Can Help Your Business Grow

    There are a great number of SEO Consulting Services Available in the world today. On inspecting these elements as well as necessity by nearly relevant segment of population of the world, conventional million of websites host as well as can get online inside a number of benefits from results conventional. Everywhere it proves intricate which help in novel websites to compete having remaining websites which are already running as well as are various on each most excellent of each place in Google result pages.

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  • SQL Server and Hyper-V Dynamic Memory Part 2

    - by SQLOS Team
    Part 1 of this series was an introduction and overview of Hyper-V Dynamic Memory. This part looks at SQL Server memory management and how the SQL engine responds to changing OS memory conditions.   Part 2: SQL Server Memory Management As with any Windows process, sqlserver.exe has a virtual address space (VAS) of 4GB on 32-bit and 8TB in 64-bit editions. Pages in its VAS are mapped to pages in physical memory when the memory is committed and referenced for the first time. The collection of VAS pages that have been recently referenced is known as the Working Set. How and when SQL Server allocates virtual memory and grows its working set depends on the memory model it uses. SQL Server supports three basic memory models:   1. Conventional Memory Model   The Conventional model is the default SQL Server memory model and has the following properties: - Dynamic - can grow or shrink its working set in response to load and external (operating system) memory conditions. - OS uses 4K pages – (not to be confused with SQL Server “pages” which are 8K regions of committed memory).- Pageable - Can be paged out to disk by the operating system.   2. Locked Page Model The locked page memory model is set when SQL Server is started with "Lock Pages in Memory" privilege*. It has the following characteristics: - Dynamic - can grow or shrink its working set in the same way as the Conventional model.- OS uses 4K pages - Non-Pageable – When memory is committed it is locked in memory, meaning that it will remain backed by physical memory and will not be paged out by the operating system. A common misconception is to interpret "locked" as non-dynamic. A SQL Server instance using the locked page memory model will grow and shrink (allocate memory and release memory) in response to changing workload and OS memory conditions in the same way as it does with the conventional model.   This is an important consideration when we look at Hyper-V Dynamic Memory – “locked” memory works perfectly well with “dynamic” memory.   * Note in “Denali” (Standard Edition and above), and in SQL 2008 R2 64-bit (Enterprise and above editions) the Lock Pages in Memory privilege is all that is required to set this model. In 2008 R2 64-Bit standard edition it also requires trace flag 845 to be set, in 2008 R2 32-bit editions it requires sp_configure 'awe enabled' 1.   3. Large Page Model The Large page model is set using trace flag 834 and potentially offers a small performance boost for systems that are configured with large pages. It is characterized by: - Static - memory is allocated at startup and does not change. - OS uses large (>2MB) pages - Non-Pageable The large page model is supported with Hyper-V Dynamic Memory (and Hyper-V also supports large pages), but you get no benefit from using Dynamic Memory with this model since SQL Server memory does not grow or shrink. The rest of this article will focus on the locked and conventional SQL Server memory models.   When does SQL Server grow? For “dynamic” configurations (Conventional and Locked memory models), the sqlservr.exe process grows – allocates and commits memory from the OS – in response to a workload. As much memory is allocated as is required to optimally run the query and buffer data for future queries, subject to limitations imposed by:   - SQL Server max server memory setting. If this configuration option is set, the buffer pool is not allowed to grow to more than this value. In SQL Server 2008 this value represents single page allocations, and in “Denali” it represents any size page allocations and also managed CLR procedure allocations.   - Memory signals from OS. The operating system sets a signal on memory resource notification objects to indicate whether it has memory available or whether it is low on available memory. If there is only 32MB free for every 4GB of memory a low memory signal is set, which continues until 64MB/4GB is free. If there is 96MB/4GB free the operating system sets a high memory signal. SQL Server only allocates memory when the high memory signal is set.   To summarize, for SQL Server to grow you need three conditions: a workload, max server memory setting higher than the current allocation, high memory signals from the OS.    When does SQL Server shrink caches? SQL Server as a rule does not like to return memory to the OS, but it will shrink its caches in response to memory pressure. Memory pressure can be divided into “internal” and “external”.   - External memory pressure occurs when the operating system is running low on memory and low memory signals are set. The SQL Server Resource Monitor checks for low memory signals approximately every 5 seconds and it will attempt to free memory until the signals stop.   To free memory SQL Server does the following: ·         Frees unused memory. ·         Notifies Memory Manager Clients to release memory o   Caches – Free unreferenced cache objects. o   Buffer pool - Based on oldest access times.   The freed memory is released back to the operating system. This process continues until the low memory resource notifications stop.    - Internal memory pressure occurs when the size of different caches and allocations increase but the SQL Server process needs to keep its total memory within a target value. For example if max server memory is set and certain caches are growing large, it will cause SQL to free memory for re-use internally, but not to release memory back to the OS. If you lower the value of max server memory you will generate internal memory pressure that will cause SQL to release memory back to the OS.    Memory pressure handling has not changed much since SQL 2005 and it was described in detail in a blog post by Slava Oks.   Note that SQL Server Express is an exception to the above behavior. Unlike other editions it does not assume it is the most important process running on the system but tries to be more “desktop” friendly. It will empty its working set after a period of inactivity.   How does SQL Server respond to changing OS memory?    In SQL Server 2005 support for Hot-Add memory was introduced. This feature, available in Enterprise and above editions, allows the server to make use of any extra physical memory that was added after SQL Server started. Being able to add physical memory when the system is running is limited to specialized hardware, but with the Hyper-V Dynamic Memory feature, when new memory is allocated to a guest virtual machine, it looks like hot-add physical memory to the guest. What this means is that thanks to the hot-add memory feature, SQL Server 2005 and higher can dynamically grow if more “physical” memory is granted to a guest VM by Hyper-V dynamic memory.   SQL Server checks OS memory every second and dynamically adjusts its “target” (based on available OS memory and max server memory) accordingly.   In “Denali” Standard Edition will also have sqlserver.exe support for hot-add memory when running virtualized (i.e. detecting and acting on Hyper-V Dynamic Memory allocations).   How does a SQL Server workload in a guest VM impact Hyper-V dynamic memory scheduling?   When a SQL workload causes the sqlserver.exe process to grow its working set, the Hyper-V memory scheduler will detect memory pressure in the guest VM and add memory to it. SQL Server will then detect the extra memory and grow according to workload demand. In our tests we have seen this feedback process cause a guest VM to grow quickly in response to SQL workload - we are still working on characterizing this ramp-up.    How does SQL Server respond when Hyper-V removes memory from a guest VM through ballooning?   If pressure from other VM's cause Hyper-V Dynamic Memory to take memory away from a VM through ballooning (allocating memory with a virtual device driver and returning it to the host OS), Windows Memory Manager will page out unlocked portions of memory and signal low resource notification events. When SQL Server detects these events it will shrink memory until the low memory notifications stop (see cache shrinking description above).    This raises another question. Can we make SQL Server release memory more readily and hence behave more "dynamically" without compromising performance? In certain circumstances where the application workload is predictable it may be possible to have a job which varies "max server memory" according to need, lowering it when the engine is inactive and raising it before a period of activity. This would have limited applicaability but it is something we're looking into.   What Memory Management changes are there in SQL Server “Denali”?   In SQL Server “Denali” (aka SQL11) the Memory Manager has been re-written to be more efficient. The main changes are summarized in this post. An important change with respect to Hyper-V Dynamic Memory support is that now the max server memory setting includes any size page allocations and managed CLR procedure allocations it now represents a closer approximation to total sqlserver.exe memory usage. This makes it easier to calculate a value for max server memory, which becomes important when configuring virtual machines to work well with Hyper-V Dynamic Memory Startup and Maximum RAM settings.   Another important change is no more AWE or hot-add support for 32-bit edition. This means if you're running a 32-bit edition of Denali you're limited to a 4GB address space and will not be able to take advantage of dynamically added OS memory that wasn't present when SQL Server started (though Hyper-V Dynamic Memory is still a supported configuration).   In part 3 we’ll develop some best practices for configuring and using SQL Server with Dynamic Memory. Originally posted at http://blogs.msdn.com/b/sqlosteam/

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  • Columnstore Case Study #1: MSIT SONAR Aggregations

    - by aspiringgeek
    Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in this deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. Why Columnstore? If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR.  By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows.  New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly.  Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements.  Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data.  "Conventional vs. Columnstore Metrics" document the raw data.  Note on this linear display the magnitude of the conventional index performance vs. columnstore.  The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Subsequent features in this series document performance enhancements that are even more significant. 

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  • Columnstore Case Study #1: MSIT SONAR Aggregations

    - by aspiringgeek
    Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in this deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. Why Columnstore? If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR.  By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows.  New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly.  Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements.  Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data.  "Conventional vs. Columnstore Metrics" document the raw data.  Note on this linear display the magnitude of the conventional index performance vs. columnstore.  The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Subsequent features in this series document performance enhancements that are even more significant. 

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  • How to restore your production database without needing additional storage

    - by David Atkinson
    Production databases can get very large. This in itself is to be expected, but when a copy of the database is needed the database must be restored, requiring additional and costly storage.  For example, if you want to give each developer a full copy of your production server, you’ll need n times the storage cost for your n-developer team. The same is true for any test databases that are created during the course of your project lifecycle. If you’ve read my previous blog posts, you’ll be aware that I’ve been focusing on the database continuous integration theme. In my CI setup I create a “production”-equivalent database directly from its source control representation, and use this to test my upgrade scripts. Despite this being a perfectly valid and practical thing to do as part of a CI setup, it’s not the exact equivalent to running the upgrade script on a copy of the actual production database. So why shouldn’t I instead simply restore the most recent production backup as part of my CI process? There are two reasons why this would be impractical. 1. My CI environment isn’t an exact copy of my production environment. Indeed, this would be the case in a perfect world, and it is strongly recommended as a good practice if you follow Jez Humble and David Farley’s “Continuous Delivery” teachings, but in practical terms this might not always be possible, especially where storage is concerned. It may just not be possible to restore a huge production database on the environment you’ve been allotted. 2. It’s not just about the storage requirements, it’s also the time it takes to do the restore. The whole point of continuous integration is that you are alerted as early as possible whether the build (yes, the database upgrade script counts!) is broken. If I have to run an hour-long restore each time I commit a change to source control I’m just not going to get the feedback quickly enough to react. So what’s the solution? Red Gate has a technology, SQL Virtual Restore, that is able to restore a database without using up additional storage. Although this sounds too good to be true, the explanation is quite simple (although I’m sure the technical implementation details under the hood are quite complex!) Instead of restoring the backup in the conventional sense, SQL Virtual Restore will effectively mount the backup using its HyperBac technology. It creates a data and log file, .vmdf, and .vldf, that becomes the delta between the .bak file and the virtual database. This means that both read and write operations are permitted on a virtual database as from SQL Server’s point of view it is no different from a conventional database. Instead of doubling the storage requirements upon a restore, there is no ‘duplicate’ storage requirements, other than the trivially small virtual log and data files (see illustration below). The benefit is magnified the more databases you mount to the same backup file. This technique could be used to provide a large development team a full development instance of a large production database. It is also incredibly easy to set up. Once SQL Virtual Restore is installed, you simply run a conventional RESTORE command to create the virtual database. This is what I have running as part of a nightly “release test” process triggered by my CI tool. RESTORE DATABASE WidgetProduction_Virtual FROM DISK=N'D:\VirtualDatabase\WidgetProduction.bak' WITH MOVE N'WidgetProduction' TO N'C:\WidgetWF\ProdBackup\WidgetProduction_WidgetProduction_Virtual.vmdf', MOVE N'WidgetProduction_log' TO N'C:\WidgetWF\ProdBackup\WidgetProduction_log_WidgetProduction_Virtual.vldf', NORECOVERY, STATS=1, REPLACE GO RESTORE DATABASE WidgetProduction_Virtual WITH RECOVERY   Note the only change from what you would do normally is the naming of the .vmdf and .vldf files. SQL Virtual Restore intercepts this by monitoring the extension and applies its magic, ensuring the ‘virtual’ restore happens rather than the conventional storage-heavy restore. My automated release test then applies the upgrade scripts to the virtual production database and runs some validation tests, giving me confidence that were I to run this on production for real, all would go smoothly. For illustration, here is my 8Gb production database: And its corresponding backup file: Here are the .vldf and .vmdf files, which represent the only additional used storage for the new database following the virtual restore.   The beauty of this product is its simplicity. Once it is installed, the interaction with the backup and virtual database is exactly the same as before, as the clever stuff is being done at a lower level. SQL Virtual Restore can be downloaded as a fully functional 14-day trial. Technorati Tags: SQL Server

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  • How to restore your production database without needing additional storage

    - by David Atkinson
    Production databases can get very large. This in itself is to be expected, but when a copy of the database is needed the database must be restored, requiring additional and costly storage.  For example, if you want to give each developer a full copy of your production server, you'll need n times the storage cost for your n-developer team. The same is true for any test databases that are created during the course of your project lifecycle. If you've read my previous blog posts, you'll be aware that I've been focusing on the database continuous integration theme. In my CI setup I create a "production"-equivalent database directly from its source control representation, and use this to test my upgrade scripts. Despite this being a perfectly valid and practical thing to do as part of a CI setup, it's not the exact equivalent to running the upgrade script on a copy of the actual production database. So why shouldn't I instead simply restore the most recent production backup as part of my CI process? There are two reasons why this would be impractical. 1. My CI environment isn't an exact copy of my production environment. Indeed, this would be the case in a perfect world, and it is strongly recommended as a good practice if you follow Jez Humble and David Farley's "Continuous Delivery" teachings, but in practical terms this might not always be possible, especially where storage is concerned. It may just not be possible to restore a huge production database on the environment you've been allotted. 2. It's not just about the storage requirements, it's also the time it takes to do the restore. The whole point of continuous integration is that you are alerted as early as possible whether the build (yes, the database upgrade script counts!) is broken. If I have to run an hour-long restore each time I commit a change to source control I'm just not going to get the feedback quickly enough to react. So what's the solution? Red Gate has a technology, SQL Virtual Restore, that is able to restore a database without using up additional storage. Although this sounds too good to be true, the explanation is quite simple (although I'm sure the technical implementation details under the hood are quite complex!) Instead of restoring the backup in the conventional sense, SQL Virtual Restore will effectively mount the backup using its HyperBac technology. It creates a data and log file, .vmdf, and .vldf, that becomes the delta between the .bak file and the virtual database. This means that both read and write operations are permitted on a virtual database as from SQL Server's point of view it is no different from a conventional database. Instead of doubling the storage requirements upon a restore, there is no 'duplicate' storage requirements, other than the trivially small virtual log and data files (see illustration below). The benefit is magnified the more databases you mount to the same backup file. This technique could be used to provide a large development team a full development instance of a large production database. It is also incredibly easy to set up. Once SQL Virtual Restore is installed, you simply run a conventional RESTORE command to create the virtual database. This is what I have running as part of a nightly "release test" process triggered by my CI tool. RESTORE DATABASE WidgetProduction_virtual FROM DISK=N'C:\WidgetWF\ProdBackup\WidgetProduction.bak' WITH MOVE N'WidgetProduction' TO N'C:\WidgetWF\ProdBackup\WidgetProduction_WidgetProduction_Virtual.vmdf', MOVE N'WidgetProduction_log' TO N'C:\WidgetWF\ProdBackup\WidgetProduction_log_WidgetProduction_Virtual.vldf', NORECOVERY, STATS=1, REPLACE GO RESTORE DATABASE mydatabase WITH RECOVERY   Note the only change from what you would do normally is the naming of the .vmdf and .vldf files. SQL Virtual Restore intercepts this by monitoring the extension and applies its magic, ensuring the 'virtual' restore happens rather than the conventional storage-heavy restore. My automated release test then applies the upgrade scripts to the virtual production database and runs some validation tests, giving me confidence that were I to run this on production for real, all would go smoothly. For illustration, here is my 8Gb production database: And its corresponding backup file: Here are the .vldf and .vmdf files, which represent the only additional used storage for the new database following the virtual restore.   The beauty of this product is its simplicity. Once it is installed, the interaction with the backup and virtual database is exactly the same as before, as the clever stuff is being done at a lower level. SQL Virtual Restore can be downloaded as a fully functional 14-day trial. Technorati Tags: SQL Server

    Read the article

  • How to restore your production database without needing additional storage

    - by David Atkinson
    Production databases can get very large. This in itself is to be expected, but when a copy of the database is needed the database must be restored, requiring additional and costly storage.  For example, if you want to give each developer a full copy of your production server, you'll need n times the storage cost for your n-developer team. The same is true for any test databases that are created during the course of your project lifecycle. If you've read my previous blog posts, you'll be aware that I've been focusing on the database continuous integration theme. In my CI setup I create a "production"-equivalent database directly from its source control representation, and use this to test my upgrade scripts. Despite this being a perfectly valid and practical thing to do as part of a CI setup, it's not the exact equivalent to running the upgrade script on a copy of the actual production database. So why shouldn't I instead simply restore the most recent production backup as part of my CI process? There are two reasons why this would be impractical. 1. My CI environment isn't an exact copy of my production environment. Indeed, this would be the case in a perfect world, and it is strongly recommended as a good practice if you follow Jez Humble and David Farley's "Continuous Delivery" teachings, but in practical terms this might not always be possible, especially where storage is concerned. It may just not be possible to restore a huge production database on the environment you've been allotted. 2. It's not just about the storage requirements, it's also the time it takes to do the restore. The whole point of continuous integration is that you are alerted as early as possible whether the build (yes, the database upgrade script counts!) is broken. If I have to run an hour-long restore each time I commit a change to source control I'm just not going to get the feedback quickly enough to react. So what's the solution? Red Gate has a technology, SQL Virtual Restore, that is able to restore a database without using up additional storage. Although this sounds too good to be true, the explanation is quite simple (although I'm sure the technical implementation details under the hood are quite complex!) Instead of restoring the backup in the conventional sense, SQL Virtual Restore will effectively mount the backup using its HyperBac technology. It creates a data and log file, .vmdf, and .vldf, that becomes the delta between the .bak file and the virtual database. This means that both read and write operations are permitted on a virtual database as from SQL Server's point of view it is no different from a conventional database. Instead of doubling the storage requirements upon a restore, there is no 'duplicate' storage requirements, other than the trivially small virtual log and data files (see illustration below). The benefit is magnified the more databases you mount to the same backup file. This technique could be used to provide a large development team a full development instance of a large production database. It is also incredibly easy to set up. Once SQL Virtual Restore is installed, you simply run a conventional RESTORE command to create the virtual database. This is what I have running as part of a nightly "release test" process triggered by my CI tool. RESTORE DATABASE WidgetProduction_virtual FROM DISK=N'C:\WidgetWF\ProdBackup\WidgetProduction.bak' WITH MOVE N'WidgetProduction' TO N'C:\WidgetWF\ProdBackup\WidgetProduction_WidgetProduction_Virtual.vmdf', MOVE N'WidgetProduction_log' TO N'C:\WidgetWF\ProdBackup\WidgetProduction_log_WidgetProduction_Virtual.vldf', NORECOVERY, STATS=1, REPLACE GO RESTORE DATABASE mydatabase WITH RECOVERY   Note the only change from what you would do normally is the naming of the .vmdf and .vldf files. SQL Virtual Restore intercepts this by monitoring the extension and applies its magic, ensuring the 'virtual' restore happens rather than the conventional storage-heavy restore. My automated release test then applies the upgrade scripts to the virtual production database and runs some validation tests, giving me confidence that were I to run this on production for real, all would go smoothly. For illustration, here is my 8Gb production database: And its corresponding backup file: Here are the .vldf and .vmdf files, which represent the only additional used storage for the new database following the virtual restore.   The beauty of this product is its simplicity. Once it is installed, the interaction with the backup and virtual database is exactly the same as before, as the clever stuff is being done at a lower level. SQL Virtual Restore can be downloaded as a fully functional 14-day trial. Technorati Tags: SQL Server

    Read the article

  • Nest reinvents smoke detectors. Introduces smart and talking smoke detector that keeps quite when you wave

    - by Gopinath
    Nest, the leading smart thermostat maker has introduced a smart home device today- Nest Protect, a smart, talking smoke & carbon monoxide detector that can quite when you wave your hand. Less annoyances and more intelligence Smoke detectors are around for hundreds of years and playing a major role in providing safety from fire accidents at home. But the technology of these devices is stale and there is no major innovation for the past several years. With the introduction of Nest Protect, the landscape of smoke detectors is all set to change just like how Nest thermostat redefined the industry two years ago. Nest Protect is internet enabled and equipped with motion- and smoke-detection sensors so that when it starts beeping you can silence it by waving hand instead of doing circus feats to turn off the alarm. Everyone who cooks in a home equipped with smoke detector would know how annoying it is to turn off sensitive smoke detectors that goes off control quite often. Apart from addressing the annoyances of regular smoke detector, Nest Protect has talking capabilities. It can alert users with clear & actionable instructions when it detects a danger. Instead of harsh beeps it actually speak to you so you know what is happening. It will tell you what smoke it has detected and in which room it is detected. Multiple Nest Protects installed in a home can communicate with each other. Lets say that there is a smoke in bed room, the Nest Protect installed in bed room shares this information to all Nest Protects installed in the home and your kitchen device can alert you that there is a smoke in bed room. There is an App for that The internet enabled Nest Protect has an app to view its status and various alerts. When the Protect is running on low battery it alerts you to replace them soon. If there is a smoke at home and you are away, you will get message alerts. The app works on all major smartphones as well as tablets. Auto shuts down gas furnaces/heaters on smoke Apart from forming a network with other Nest Protect devices installed at home, they can also communicate with Nest Thermostat if it is installed. When carbon monoxide is detected it can shut off your gas furnace automatically. Also with the help of motion detectors it improves Nest Thermostat’s auto-away functionality. It looks elegant and costs a lot more than a regular smoke detector Just like Nest Thermostat, Nest Protect is elegant and adorable. You just fall in love with it the moment you see it. It’s another master piece from the designer of Apple’s iPod. All is good with the Nest Protect, except the price!! It costs whooping $129, which is almost 4 times more expensive than the best selling conventional thermostats available at $30. A single bed room apartment would require at least 3 detectors and it costs around $390 to install Nest Protects compared to 90$ required for conventional smoke detectors. Though Nest Thermostat is an expensive one compared to conventional thermostats, it offered great savings through its intelligent auto-away feature. Users were able to able to see returns on their investments. If Nest Protect also can provide good return on investment the it will be very successful.

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  • links for 2011-02-28

    - by Bob Rhubart
    Apache Tuscany : SCA Java 2.x Releases (tags: ping.fm) Richard Veryard on Architecture: Modernism and Enterprise Architecture "Underlying conventional enterprise architecture theory and practice are some implicit assumptions that could be loosely characterized as modernist. Several people are offering more or less radical departures from conventional enterprise architecture..." - Richard Veryard (tags: ping.fm entarch) Java / Oracle SOA blog: Building an asynchronous web service with OSB "A few weeks ago I made a blogpost over how you can build an asynchronous web service with JAX-WS. In this blogpost I will do the same in the Oracle Service Bus." - Oracle ACE Edwin Biemond (tags: oracle otn oracleace servicebus esb osb webservices soa) Enterprise Software Development with Java: GlassFish 3.1 arrived! Yes sir, we do cluster now! "GlassFish 3.1 is finally there. As promised by Oracle back in March last year! And it is an exciting release. It brings back all the clustering and high availability support we were missing since 2.x into the Java EE 6 world." - Oracle ACE Director Markus Eisele (tags: oracle otn oracleace glassfish)

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