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  • Active Directory Time Synchronisation - Time-Service Event ID 50

    - by George
    I have an Active Directory domain with two DCs. The first DC in the forest/domain is Server 2012, the second is 2008 R2. The first DC holds the PDC Emulator role. I sporadically receive a warning from the Time-Service source, event ID 50: The time service detected a time difference of greater than %1 milliseconds for %2 seconds. The time difference might be caused by synchronization with low-accuracy time sources or by suboptimal network conditions. The time service is no longer synchronized and cannot provide the time to other clients or update the system clock. When a valid time stamp is received from a time service provider, the time service will correct itself. Time sync in the domain is configured with the second DC to synchronise using the /syncfromflags:DOMHIER flag. The first DC is configured to sync time using a /syncfromflags:MANUAL /reliable:YES, from a peerlist consisting of a number of UK based stratum 2 servers, such as ntp2d.mcc.ac.uk. I'm confused why I receive this event warning. It implies that my PDC emulator cannot synchronise time with a supposedly reliable external time source, and it quotes a time difference of 5 seconds for 900 seconds. It's worth also mentioning that I used to use a UK pool from ntp.org but I would receive the warning much more often. Since updating to a number of UK based academic time servers, it seems to be more reliable. Can someone with more experience shed some light on this - perhaps it is purely transient? Should I disregard the warning? Is my configuration sound? EDIT: I should add that the DCs are virtual, and installed on two separate VMware ESXi/vSphere physical hosts. I can also confirm that as per MDMarra's comment and best practice, VMware timesync is disabled, since: c:\Program Files\VMware\VMware Tools\VMwareToolboxCmd.exe timesync status returns Disabled. EDIT 2 Some strange new issue has cropped up. I've noticed a pattern. Originally, the event ID 50 warnings would occur at about 1230pm each day. This is interesting since our veeam backup happens at 12 midday. Since I made the changes discussed here, I now receive an event ID 51 instead of 50. The new warning says that: The time sample received from peer server.ac.uk differs from the local time by -40 seconds (Or approximately 40 seconds). This has happened two days in a row. Now I'm even more confused. Obviously the time never updates until I manually intervene. The issue seems to be related to virtualisation and veeam. Something may be occuring when veeam is backing up the PDCe. Any suggestions? UPDATE & SUMMARY msemack's excellent list of resources below (the accepted answer) provided enough information to correctly configure the time service in the domain. This should be the first port of call for any future people looking to verify their configuration. The final "40 second jump" issue I have resolved (there are no more warnings) through adjusting the VMware time sync settings as noted in the veeam knowledge base article here: http://www.veeam.com/kb1202 In any case, should any future reader use ESXi, veeam or not, the resources here are an excellent source of information on the time sync topic and msemack's answer is particularly invaluable.

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  • Migrate Domain from Server 2008 R2 to Small Business Server 2011

    - by josecortesp
    I'm looking for some advice here, rather than the big how to do it I'm looking for what do to I have this home server, quad core and 4 GB of ram (I really can't afford more right now). With a Windows Serve 2008 R2 With ActiveDirectory and a Hyper-V-Virtual machine with SharePoint, TFS and a couple of more thigs. I have a least 10 remote users, all of them joined a Hamachi VPN (working great by the way). But I want to migrate that to a Small Business Server 2011 Standard. I tried to make a VM to join the domain and then promote that VM, back up it and then format the physical server, boot up the VM, Promote the Phisical and then erase the VM, but I can't do that because of SBS requiring a least 4 GB of ram to install (so I can't give all the 4 GB of physical ram to a VM). I was thinking in using a laptop (All the clients are laptop) as a temporal server, join the domain, promote it, then format the server and install SBS on the server and do all again. I really need some advice. Thanks in advance. BTW, I know that the software I'm using is kindda expensive, and I can't afford more hardware. I have access to MS downloads by a University partnership so I have all this software for free.

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  • Upgraded Ubuntu, all drives in one zpool marked unavailable

    - by Matt Sieker
    I just upgraded Ubuntu 14.04, and I had two ZFS pools on the server. There was some minor issue with me fighting with the ZFS driver and the kernel version, but that's worked out now. One pool came online, and mounted fine. The other didn't. The main difference between the tool is one was just a pool of disks (video/music storage), and the other was a raidz set (documents, etc) I've already attempted exporting and re-importing the pool, to no avail, attempting to import gets me this: root@kyou:/home/matt# zpool import -fFX -d /dev/disk/by-id/ pool: storage id: 15855792916570596778 state: UNAVAIL status: One or more devices contains corrupted data. action: The pool cannot be imported due to damaged devices or data. see: http://zfsonlinux.org/msg/ZFS-8000-5E config: storage UNAVAIL insufficient replicas raidz1-0 UNAVAIL insufficient replicas ata-SAMSUNG_HD103SJ_S246J90B134910 UNAVAIL ata-WDC_WD10EARS-00Y5B1_WD-WMAV51422523 UNAVAIL ata-WDC_WD10EARS-00Y5B1_WD-WMAV51535969 UNAVAIL The symlinks for those in /dev/disk/by-id also exist: root@kyou:/home/matt# ls -l /dev/disk/by-id/ata-SAMSUNG_HD103SJ_S246J90B134910* /dev/disk/by-id/ata-WDC_WD10EARS-00Y5B1_WD-WMAV51* lrwxrwxrwx 1 root root 9 May 27 19:31 /dev/disk/by-id/ata-SAMSUNG_HD103SJ_S246J90B134910 -> ../../sdb lrwxrwxrwx 1 root root 10 May 27 19:15 /dev/disk/by-id/ata-SAMSUNG_HD103SJ_S246J90B134910-part1 -> ../../sdb1 lrwxrwxrwx 1 root root 10 May 27 19:15 /dev/disk/by-id/ata-SAMSUNG_HD103SJ_S246J90B134910-part9 -> ../../sdb9 lrwxrwxrwx 1 root root 9 May 27 19:15 /dev/disk/by-id/ata-WDC_WD10EARS-00Y5B1_WD-WMAV51422523 -> ../../sdd lrwxrwxrwx 1 root root 10 May 27 19:15 /dev/disk/by-id/ata-WDC_WD10EARS-00Y5B1_WD-WMAV51422523-part1 -> ../../sdd1 lrwxrwxrwx 1 root root 10 May 27 19:15 /dev/disk/by-id/ata-WDC_WD10EARS-00Y5B1_WD-WMAV51422523-part9 -> ../../sdd9 lrwxrwxrwx 1 root root 9 May 27 19:15 /dev/disk/by-id/ata-WDC_WD10EARS-00Y5B1_WD-WMAV51535969 -> ../../sde lrwxrwxrwx 1 root root 10 May 27 19:15 /dev/disk/by-id/ata-WDC_WD10EARS-00Y5B1_WD-WMAV51535969-part1 -> ../../sde1 lrwxrwxrwx 1 root root 10 May 27 19:15 /dev/disk/by-id/ata-WDC_WD10EARS-00Y5B1_WD-WMAV51535969-part9 -> ../../sde9 Inspecting the various /dev/sd* devices listed, they appear to be the correct ones (The 3 1TB drives that were in a raidz array). I've run zdb -l on each drive, dumping it to a file, and running a diff. The only difference on the 3 are the guid fields (Which I assume is expected). All 3 labels on each one are basically identical, and are as follows: version: 5000 name: 'storage' state: 0 txg: 4 pool_guid: 15855792916570596778 hostname: 'kyou' top_guid: 1683909657511667860 guid: 8815283814047599968 vdev_children: 1 vdev_tree: type: 'raidz' id: 0 guid: 1683909657511667860 nparity: 1 metaslab_array: 33 metaslab_shift: 34 ashift: 9 asize: 3000569954304 is_log: 0 create_txg: 4 children[0]: type: 'disk' id: 0 guid: 8815283814047599968 path: '/dev/disk/by-id/ata-SAMSUNG_HD103SJ_S246J90B134910-part1' whole_disk: 1 create_txg: 4 children[1]: type: 'disk' id: 1 guid: 18036424618735999728 path: '/dev/disk/by-id/ata-WDC_WD10EARS-00Y5B1_WD-WMAV51422523-part1' whole_disk: 1 create_txg: 4 children[2]: type: 'disk' id: 2 guid: 10307555127976192266 path: '/dev/disk/by-id/ata-WDC_WD10EARS-00Y5B1_WD-WMAV51535969-part1' whole_disk: 1 create_txg: 4 features_for_read: Stupidly, I do not have a recent backup of this pool. However, the pool was fine before reboot, and Linux sees the disks fine (I have smartctl running now to double check) So, in summary: I upgraded Ubuntu, and lost access to one of my two zpools. The difference between the pools is the one that came up was JBOD, the other was zraid. All drives in the unmountable zpool are marked UNAVAIL, with no notes for corrupted data The pools were both created with disks referenced from /dev/disk/by-id/. Symlinks from /dev/disk/by-id to the various /dev/sd devices seems to be correct zdb can read the labels from the drives. Pool has already been attempted to be exported/imported, and isn't able to import again. Is there some sort of black magic I can invoke via zpool/zfs to bring these disks back into a reasonable array? Can I run zpool create zraid ... without losing my data? Is my data gone anyhow?

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  • MySQL not releasing temp file descriptors

    - by Wakaru44
    Since a few days ago, we’ve been experiencing some serious problems with our MySQL installation: MySQL keeps opening temporal files (normal behaviour) but these files are never released. The consequence is that, eventually, the disk space is exhausted and we have to restart the service and clean up /tmp manually. Using lsof, we see something like this: mysqld 16866 mysql 5u REG 8,3 0 692 /tmp/ibyWJylQ (deleted) mysqld 16866 mysql 6u REG 8,3 0 707 /tmp/ibf5adsT (deleted) mysqld 16866 mysql 7u REG 8,3 0 728 /tmp/ibGjPRyW (deleted) mysqld 16866 mysql 8u REG 8,3 0 5678 /tmp/ibMQDLMZ (deleted) mysqld 16866 mysql 13u REG 8,3 0 5679 /tmp/ibQAnM42 (deleted) Maybe it's not related, but when we shutdown the server, the files are finally freed, and we can see the following warnings in the MySQL log: 121029 7:44:27 [Warning] /usr/local/mysql/bin/mysqld: Forcing close of thread 1333 user: 'xxx' 121029 7:44:27 [Warning] /usr/local/mysql/bin/mysqld: Forcing close of thread 1156 user: 'yyy' 121029 7:44:27 [Warning] /usr/local/mysql/bin/mysqld: Forcing close of thread 1151 user: 'zzz' where 'xxx', 'yyy' and 'zzz' are distinct mysql users (and the only 3 users with active connections to the database). We have a few theories: There is a problem in the OS, that keeps file handlers open. Could it be possible that the OS "delete" operation blocks the threads until shutdown? This may explain the warning at shutdown and the fact that files are finally deleted when the process dies. Until now, data sets were so small that temp files were relatively small and there was enough time to release the file handles without exhausting disk space. We are using Mysql 5.5 on a RHEL 6.2 with the default kernel.

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  • Adobe Flash not working in 12.04

    - by catnthehat
    I cannot get Adobe Flash working in either Firefox or Chrome. I have tried the Flash-Aid plug in for Firefox but it has not made any difference. As far as I can see, Flash installs without error and Firefox thinks it can run Flash but (for example) YouTube just shows a blank square where the movie should be me. Chrome reports "missing plugin". about:plugins in Firefox reports: Shockwave Flash File: libflashplayer.so Version: Shockwave Flash 11.2 r202 MIME Type Description Suffixes application/x-shockwave-flash Shockwave Flash swf application/futuresplash FutureSplash Player spl

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  • SSIS packages incompatibilities between SSIS 2008 and SSIS 2008 R2

    - by Marco Russo (SQLBI)
    When you install SQL 2008 R2 workstation components you get a newer version of BIDS (BI Developer Studio, included in the workstation components) that replaces BIDS 2008 version (BIDS 2005 still live side-by-side). Everything would be good if you can use the newer version to edit any 2008 AND 2008R2 project. SSIS editor doesn't offer a way to set the "compatibility level" of the package, becuase it is almost all unchanged. However, if a package has an ADO.NET Destination Adapter, there is a difference...(read more)

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  • Setting up Mercurial server in IIS7 using a ISAPI module

    - by mhawley
    I'm using Twitter. Follow me @matthawley Previously, Jeremy Skinner posted a very thorough guide on setting up Mercurial in IIS. The difference between his guide, and what I'll be walking you through, is how Mercurial is hosted in IIS. Where he shows you using a CGI script that executes python.exe, my guide will show you how to use isapi-wsgi to host Mercurial. (read more…)

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  • Optimizing the MySQL Query Cache

    MySQL's query cache is an impressive piece of engineering if sometimes misunderstood. Keeping it optimized and used efficiently can make a big difference in the overall throughput of your application, so it's worth taking a look under the hood, understanding it, and then keeping it tuned optimally.

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  • Comparison of Architecture presentation patterns MVP(SC),MVP(PV),PM,MVVM and MVC

    This article will compare four important architecture presentation patterns i.e. MVP(SC),MVP(PV),PM,MVVM and MVC. Many developers are confused around what is the difference between these patterns and when should we use what. This article will first kick start with a background and explain different...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Comparison of Architecture presentation patterns MVP(SC),MVP(PV),PM,MVVM and MVC

    This article will compare four important architecture presentation patterns i.e. MVP(SC),MVP(PV),PM,MVVM and MVC. Many developers are confused around what is the difference between these patterns and when should we use what. This article will first kick start with a background and explain different...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Preserving Permalinks

    - by Daniel Moth
    One of the things that gets me on a rant is websites that break permalinks. If you have posted something somewhere and there is a public URL pointing to it, that URL should never ever return a 404. You are breaking all websites that ever linked to you and you are breaking all search engine links to your content (that others will try and follow). It is a pet peeve of mine. So when I had to move my blog, obviously I would preserve the root URL (www.danielmoth.com/Blog/), but I also wanted to preserve every URL my blog has generated over the years. To be clear, our focus here is on the URL formatting, not the content migration which I'll talk about in my next post. In this post, I'll describe my solution first and then what it solves. 1. The IIS7 Rewrite Module and web.config There are a few ways you can map an old URL to a new one (so when requests to the old URL come in, they get redirected to the new one). The new blog engine I use (dasBlog) has built-in functionality to do that (Scott refers to it here). Instead, the way I chose to address the issue was to use the IIS7 rewrite module. The IIS7 rewrite module allows redirecting URLs based on pattern matching, regular expressions and, of course, hardcoded full URLs for things that don't fall into any pattern. You can configure it visually from IIS Manager using a handy dialog that allows testing patterns against input URLs. Here is what mine looked like after configuring a few rules: To learn more about this technology check out this video, the reference page and this overview blog post; all 3 pages have a collection of related resources at the bottom worth checking out too. All the visual configuration ends up in a web.config file at the root folder of your website. If you are on a shared hosting service, probably the only way you can use the Rewrite Module is by directly editing the web.config file. Next, I'll describe the URLs I had to map and how that manifested itself in the web.config file. What I did was create the rules locally using the GUI, and then took the generated web.config file and uploaded it to my live site. You can view my web.config here. 2. Monthly Archives Observe the difference between the way the two blog engines generate this type of URL Blogger: /Blog/2004_07_01_mothblog_archive.html dasBlog: /Blog/default,month,2004-07.aspx In my web.config file, the rule that deals with this is the one named "monthlyarchive_redirect". 3. Categories Observe the difference between the way the two blog engines generate this type of URL Blogger: /Blog/labels/Personal.html dasBlog: /Blog/CategoryView,category,Personal.aspx In my web.config file the rule that deals with this is the one named "category_redirect". 4. Posts Observe the difference between the way the two blog engines generate this type of URL Blogger: /Blog/2004/07/hello-world.html dasBlog: /Blog/Hello-World.aspx In my web.config file the rule that deals with this is the one named "post_redirect". Note: The decision is taken to use dasBlog URLs that do not include the date info (see the description of my Appearance settings). If we included the date info then it would have to include the day part, which blogger did not generate. This makes it impossible to redirect correctly and to have a single permalink for blog posts moving forward. An implication of this decision, is that no two blog posts can have the same title. The tool I will describe in my next post (inelegantly) deals with duplicates, but not with triplicates or higher. 5. Unhandled by a generic rule Unfortunately, the two blog engines use different rules for generating URLs for blog posts. Most of the time the conversion is as simple as the example of the previous section where a post titled "Hello World" generates a URL with the words separated by a hyphen. Some times that is not the case, for example: /Blog/2006/05/medc-wrap-up.html /Blog/MEDC-Wrapup.aspx or /Blog/2005/01/best-of-moth-2004.html /Blog/Best-Of-The-Moth-2004.aspx or /Blog/2004/11/more-windows-mobile-2005-details.html /Blog/More-Windows-Mobile-2005-Details-Emerge.aspx In short, blogger does not add words to the title beyond ~39 characters, it drops some words from the title generation (e.g. a, an, on, the), and it preserve hyphens that appear in the title. For this reason, we need to detect these and explicitly list them for redirects (no regular expression can help here because the full set of rules is not listed anywhere). In my web.config file the rule that deals with this is the one named "Redirect rule1 for FullRedirects" combined with the rewriteMap named "StaticRedirects". Note: The tool I describe in my next post will detect all the URLs that need to be explicitly redirected and will list them in a file ready for you to copy them to your web.config rewriteMap. 6. C# code doing the same as the web.config I wrote some naive code that does the same thing as the web.config: given a string it will return a new string converted according to the 3 rules above. It does not take into account the 4th case where an explicit hard-coded conversion is needed (the tool I present in the next post does take that into account). static string REGEX_post_redirect = "[0-9]{4}/[0-9]{2}/([0-9a-z-]+).html"; static string REGEX_category_redirect = "labels/([_0-9a-z-% ]+).html"; static string REGEX_monthlyarchive_redirect = "([0-9]{4})_([0-9]{2})_[0-9]{2}_mothblog_archive.html"; static string Redirect(string oldUrl) { GroupCollection g; if (RunRegExOnIt(oldUrl, REGEX_post_redirect, 2, out g)) return string.Concat(g[1].Value, ".aspx"); if (RunRegExOnIt(oldUrl, REGEX_category_redirect, 2, out g)) return string.Concat("CategoryView,category,", g[1].Value, ".aspx"); if (RunRegExOnIt(oldUrl, REGEX_monthlyarchive_redirect, 3, out g)) return string.Concat("default,month,", g[1].Value, "-", g[2], ".aspx"); return string.Empty; } static bool RunRegExOnIt(string toRegEx, string pattern, int groupCount, out GroupCollection g) { if (pattern.Length == 0) { g = null; return false; } g = new Regex(pattern, RegexOptions.IgnoreCase | RegexOptions.Compiled).Match(toRegEx).Groups; return (g.Count == groupCount); } Comments about this post welcome at the original blog.

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  • SSIS Design Pattern: Loading Variable-Length Rows

    - by andyleonard
    Introduction I encounter flat file sources with variable-length rows on occassion. Here, I supply one SSIS Design Pattern for loading them. What's a Variable-Length Row Flat File? Great question - let's start with a definition. A variable-length row flat file is a text source of some flavor - comma-separated values (CSV), tab-delimited file (TDF), or even fixed-length, positional-, or ordinal-based (where the location of the data on the row defines its field). The major difference between a "normal"...(read more)

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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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  • Advanced TSQL Tuning: Why Internals Knowledge Matters

    - by Paul White
    There is much more to query tuning than reducing logical reads and adding covering nonclustered indexes.  Query tuning is not complete as soon as the query returns results quickly in the development or test environments.  In production, your query will compete for memory, CPU, locks, I/O and other resources on the server.  Today’s entry looks at some tuning considerations that are often overlooked, and shows how deep internals knowledge can help you write better TSQL. As always, we’ll need some example data.  In fact, we are going to use three tables today, each of which is structured like this: Each table has 50,000 rows made up of an INTEGER id column and a padding column containing 3,999 characters in every row.  The only difference between the three tables is in the type of the padding column: the first table uses CHAR(3999), the second uses VARCHAR(MAX), and the third uses the deprecated TEXT type.  A script to create a database with the three tables and load the sample data follows: USE master; GO IF DB_ID('SortTest') IS NOT NULL DROP DATABASE SortTest; GO CREATE DATABASE SortTest COLLATE LATIN1_GENERAL_BIN; GO ALTER DATABASE SortTest MODIFY FILE ( NAME = 'SortTest', SIZE = 3GB, MAXSIZE = 3GB ); GO ALTER DATABASE SortTest MODIFY FILE ( NAME = 'SortTest_log', SIZE = 256MB, MAXSIZE = 1GB, FILEGROWTH = 128MB ); GO ALTER DATABASE SortTest SET ALLOW_SNAPSHOT_ISOLATION OFF ; ALTER DATABASE SortTest SET AUTO_CLOSE OFF ; ALTER DATABASE SortTest SET AUTO_CREATE_STATISTICS ON ; ALTER DATABASE SortTest SET AUTO_SHRINK OFF ; ALTER DATABASE SortTest SET AUTO_UPDATE_STATISTICS ON ; ALTER DATABASE SortTest SET AUTO_UPDATE_STATISTICS_ASYNC ON ; ALTER DATABASE SortTest SET PARAMETERIZATION SIMPLE ; ALTER DATABASE SortTest SET READ_COMMITTED_SNAPSHOT OFF ; ALTER DATABASE SortTest SET MULTI_USER ; ALTER DATABASE SortTest SET RECOVERY SIMPLE ; USE SortTest; GO CREATE TABLE dbo.TestCHAR ( id INTEGER IDENTITY (1,1) NOT NULL, padding CHAR(3999) NOT NULL,   CONSTRAINT [PK dbo.TestCHAR (id)] PRIMARY KEY CLUSTERED (id), ) ; CREATE TABLE dbo.TestMAX ( id INTEGER IDENTITY (1,1) NOT NULL, padding VARCHAR(MAX) NOT NULL,   CONSTRAINT [PK dbo.TestMAX (id)] PRIMARY KEY CLUSTERED (id), ) ; CREATE TABLE dbo.TestTEXT ( id INTEGER IDENTITY (1,1) NOT NULL, padding TEXT NOT NULL,   CONSTRAINT [PK dbo.TestTEXT (id)] PRIMARY KEY CLUSTERED (id), ) ; -- ============= -- Load TestCHAR (about 3s) -- ============= INSERT INTO dbo.TestCHAR WITH (TABLOCKX) ( padding ) SELECT padding = REPLICATE(CHAR(65 + (Data.n % 26)), 3999) FROM ( SELECT TOP (50000) n = ROW_NUMBER() OVER (ORDER BY (SELECT 0)) - 1 FROM master.sys.columns C1, master.sys.columns C2, master.sys.columns C3 ORDER BY n ASC ) AS Data ORDER BY Data.n ASC ; -- ============ -- Load TestMAX (about 3s) -- ============ INSERT INTO dbo.TestMAX WITH (TABLOCKX) ( padding ) SELECT CONVERT(VARCHAR(MAX), padding) FROM dbo.TestCHAR ORDER BY id ; -- ============= -- Load TestTEXT (about 5s) -- ============= INSERT INTO dbo.TestTEXT WITH (TABLOCKX) ( padding ) SELECT CONVERT(TEXT, padding) FROM dbo.TestCHAR ORDER BY id ; -- ========== -- Space used -- ========== -- EXECUTE sys.sp_spaceused @objname = 'dbo.TestCHAR'; EXECUTE sys.sp_spaceused @objname = 'dbo.TestMAX'; EXECUTE sys.sp_spaceused @objname = 'dbo.TestTEXT'; ; CHECKPOINT ; That takes around 15 seconds to run, and shows the space allocated to each table in its output: To illustrate the points I want to make today, the example task we are going to set ourselves is to return a random set of 150 rows from each table.  The basic shape of the test query is the same for each of the three test tables: SELECT TOP (150) T.id, T.padding FROM dbo.Test AS T ORDER BY NEWID() OPTION (MAXDOP 1) ; Test 1 – CHAR(3999) Running the template query shown above using the TestCHAR table as the target, we find that the query takes around 5 seconds to return its results.  This seems slow, considering that the table only has 50,000 rows.  Working on the assumption that generating a GUID for each row is a CPU-intensive operation, we might try enabling parallelism to see if that speeds up the response time.  Running the query again (but without the MAXDOP 1 hint) on a machine with eight logical processors, the query now takes 10 seconds to execute – twice as long as when run serially. Rather than attempting further guesses at the cause of the slowness, let’s go back to serial execution and add some monitoring.  The script below monitors STATISTICS IO output and the amount of tempdb used by the test query.  We will also run a Profiler trace to capture any warnings generated during query execution. DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TC.id, TC.padding FROM dbo.TestCHAR AS TC ORDER BY NEWID() OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; Let’s take a closer look at the statistics and query plan generated from this: Following the flow of the data from right to left, we see the expected 50,000 rows emerging from the Clustered Index Scan, with a total estimated size of around 191MB.  The Compute Scalar adds a column containing a random GUID (generated from the NEWID() function call) for each row.  With this extra column in place, the size of the data arriving at the Sort operator is estimated to be 192MB. Sort is a blocking operator – it has to examine all of the rows on its input before it can produce its first row of output (the last row received might sort first).  This characteristic means that Sort requires a memory grant – memory allocated for the query’s use by SQL Server just before execution starts.  In this case, the Sort is the only memory-consuming operator in the plan, so it has access to the full 243MB (248,696KB) of memory reserved by SQL Server for this query execution. Notice that the memory grant is significantly larger than the expected size of the data to be sorted.  SQL Server uses a number of techniques to speed up sorting, some of which sacrifice size for comparison speed.  Sorts typically require a very large number of comparisons, so this is usually a very effective optimization.  One of the drawbacks is that it is not possible to exactly predict the sort space needed, as it depends on the data itself.  SQL Server takes an educated guess based on data types, sizes, and the number of rows expected, but the algorithm is not perfect. In spite of the large memory grant, the Profiler trace shows a Sort Warning event (indicating that the sort ran out of memory), and the tempdb usage monitor shows that 195MB of tempdb space was used – all of that for system use.  The 195MB represents physical write activity on tempdb, because SQL Server strictly enforces memory grants – a query cannot ‘cheat’ and effectively gain extra memory by spilling to tempdb pages that reside in memory.  Anyway, the key point here is that it takes a while to write 195MB to disk, and this is the main reason that the query takes 5 seconds overall. If you are wondering why using parallelism made the problem worse, consider that eight threads of execution result in eight concurrent partial sorts, each receiving one eighth of the memory grant.  The eight sorts all spilled to tempdb, resulting in inefficiencies as the spilled sorts competed for disk resources.  More importantly, there are specific problems at the point where the eight partial results are combined, but I’ll cover that in a future post. CHAR(3999) Performance Summary: 5 seconds elapsed time 243MB memory grant 195MB tempdb usage 192MB estimated sort set 25,043 logical reads Sort Warning Test 2 – VARCHAR(MAX) We’ll now run exactly the same test (with the additional monitoring) on the table using a VARCHAR(MAX) padding column: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TM.id, TM.padding FROM dbo.TestMAX AS TM ORDER BY NEWID() OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; This time the query takes around 8 seconds to complete (3 seconds longer than Test 1).  Notice that the estimated row and data sizes are very slightly larger, and the overall memory grant has also increased very slightly to 245MB.  The most marked difference is in the amount of tempdb space used – this query wrote almost 391MB of sort run data to the physical tempdb file.  Don’t draw any general conclusions about VARCHAR(MAX) versus CHAR from this – I chose the length of the data specifically to expose this edge case.  In most cases, VARCHAR(MAX) performs very similarly to CHAR – I just wanted to make test 2 a bit more exciting. MAX Performance Summary: 8 seconds elapsed time 245MB memory grant 391MB tempdb usage 193MB estimated sort set 25,043 logical reads Sort warning Test 3 – TEXT The same test again, but using the deprecated TEXT data type for the padding column: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TT.id, TT.padding FROM dbo.TestTEXT AS TT ORDER BY NEWID() OPTION (MAXDOP 1, RECOMPILE) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; This time the query runs in 500ms.  If you look at the metrics we have been checking so far, it’s not hard to understand why: TEXT Performance Summary: 0.5 seconds elapsed time 9MB memory grant 5MB tempdb usage 5MB estimated sort set 207 logical reads 596 LOB logical reads Sort warning SQL Server’s memory grant algorithm still underestimates the memory needed to perform the sorting operation, but the size of the data to sort is so much smaller (5MB versus 193MB previously) that the spilled sort doesn’t matter very much.  Why is the data size so much smaller?  The query still produces the correct results – including the large amount of data held in the padding column – so what magic is being performed here? TEXT versus MAX Storage The answer lies in how columns of the TEXT data type are stored.  By default, TEXT data is stored off-row in separate LOB pages – which explains why this is the first query we have seen that records LOB logical reads in its STATISTICS IO output.  You may recall from my last post that LOB data leaves an in-row pointer to the separate storage structure holding the LOB data. SQL Server can see that the full LOB value is not required by the query plan until results are returned, so instead of passing the full LOB value down the plan from the Clustered Index Scan, it passes the small in-row structure instead.  SQL Server estimates that each row coming from the scan will be 79 bytes long – 11 bytes for row overhead, 4 bytes for the integer id column, and 64 bytes for the LOB pointer (in fact the pointer is rather smaller – usually 16 bytes – but the details of that don’t really matter right now). OK, so this query is much more efficient because it is sorting a very much smaller data set – SQL Server delays retrieving the LOB data itself until after the Sort starts producing its 150 rows.  The question that normally arises at this point is: Why doesn’t SQL Server use the same trick when the padding column is defined as VARCHAR(MAX)? The answer is connected with the fact that if the actual size of the VARCHAR(MAX) data is 8000 bytes or less, it is usually stored in-row in exactly the same way as for a VARCHAR(8000) column – MAX data only moves off-row into LOB storage when it exceeds 8000 bytes.  The default behaviour of the TEXT type is to be stored off-row by default, unless the ‘text in row’ table option is set suitably and there is room on the page.  There is an analogous (but opposite) setting to control the storage of MAX data – the ‘large value types out of row’ table option.  By enabling this option for a table, MAX data will be stored off-row (in a LOB structure) instead of in-row.  SQL Server Books Online has good coverage of both options in the topic In Row Data. The MAXOOR Table The essential difference, then, is that MAX defaults to in-row storage, and TEXT defaults to off-row (LOB) storage.  You might be thinking that we could get the same benefits seen for the TEXT data type by storing the VARCHAR(MAX) values off row – so let’s look at that option now.  This script creates a fourth table, with the VARCHAR(MAX) data stored off-row in LOB pages: CREATE TABLE dbo.TestMAXOOR ( id INTEGER IDENTITY (1,1) NOT NULL, padding VARCHAR(MAX) NOT NULL,   CONSTRAINT [PK dbo.TestMAXOOR (id)] PRIMARY KEY CLUSTERED (id), ) ; EXECUTE sys.sp_tableoption @TableNamePattern = N'dbo.TestMAXOOR', @OptionName = 'large value types out of row', @OptionValue = 'true' ; SELECT large_value_types_out_of_row FROM sys.tables WHERE [schema_id] = SCHEMA_ID(N'dbo') AND name = N'TestMAXOOR' ; INSERT INTO dbo.TestMAXOOR WITH (TABLOCKX) ( padding ) SELECT SPACE(0) FROM dbo.TestCHAR ORDER BY id ; UPDATE TM WITH (TABLOCK) SET padding.WRITE (TC.padding, NULL, NULL) FROM dbo.TestMAXOOR AS TM JOIN dbo.TestCHAR AS TC ON TC.id = TM.id ; EXECUTE sys.sp_spaceused @objname = 'dbo.TestMAXOOR' ; CHECKPOINT ; Test 4 – MAXOOR We can now re-run our test on the MAXOOR (MAX out of row) table: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) MO.id, MO.padding FROM dbo.TestMAXOOR AS MO ORDER BY NEWID() OPTION (MAXDOP 1, RECOMPILE) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; TEXT Performance Summary: 0.3 seconds elapsed time 245MB memory grant 0MB tempdb usage 193MB estimated sort set 207 logical reads 446 LOB logical reads No sort warning The query runs very quickly – slightly faster than Test 3, and without spilling the sort to tempdb (there is no sort warning in the trace, and the monitoring query shows zero tempdb usage by this query).  SQL Server is passing the in-row pointer structure down the plan and only looking up the LOB value on the output side of the sort. The Hidden Problem There is still a huge problem with this query though – it requires a 245MB memory grant.  No wonder the sort doesn’t spill to tempdb now – 245MB is about 20 times more memory than this query actually requires to sort 50,000 records containing LOB data pointers.  Notice that the estimated row and data sizes in the plan are the same as in test 2 (where the MAX data was stored in-row). The optimizer assumes that MAX data is stored in-row, regardless of the sp_tableoption setting ‘large value types out of row’.  Why?  Because this option is dynamic – changing it does not immediately force all MAX data in the table in-row or off-row, only when data is added or actually changed.  SQL Server does not keep statistics to show how much MAX or TEXT data is currently in-row, and how much is stored in LOB pages.  This is an annoying limitation, and one which I hope will be addressed in a future version of the product. So why should we worry about this?  Excessive memory grants reduce concurrency and may result in queries waiting on the RESOURCE_SEMAPHORE wait type while they wait for memory they do not need.  245MB is an awful lot of memory, especially on 32-bit versions where memory grants cannot use AWE-mapped memory.  Even on a 64-bit server with plenty of memory, do you really want a single query to consume 0.25GB of memory unnecessarily?  That’s 32,000 8KB pages that might be put to much better use. The Solution The answer is not to use the TEXT data type for the padding column.  That solution happens to have better performance characteristics for this specific query, but it still results in a spilled sort, and it is hard to recommend the use of a data type which is scheduled for removal.  I hope it is clear to you that the fundamental problem here is that SQL Server sorts the whole set arriving at a Sort operator.  Clearly, it is not efficient to sort the whole table in memory just to return 150 rows in a random order. The TEXT example was more efficient because it dramatically reduced the size of the set that needed to be sorted.  We can do the same thing by selecting 150 unique keys from the table at random (sorting by NEWID() for example) and only then retrieving the large padding column values for just the 150 rows we need.  The following script implements that idea for all four tables: SET STATISTICS IO ON ; WITH TestTable AS ( SELECT * FROM dbo.TestCHAR ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id = ANY (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestMAX ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestTEXT ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestMAXOOR ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; All four queries now return results in much less than a second, with memory grants between 6 and 12MB, and without spilling to tempdb.  The small remaining inefficiency is in reading the id column values from the clustered primary key index.  As a clustered index, it contains all the in-row data at its leaf.  The CHAR and VARCHAR(MAX) tables store the padding column in-row, so id values are separated by a 3999-character column, plus row overhead.  The TEXT and MAXOOR tables store the padding values off-row, so id values in the clustered index leaf are separated by the much-smaller off-row pointer structure.  This difference is reflected in the number of logical page reads performed by the four queries: Table 'TestCHAR' logical reads 25511 lob logical reads 000 Table 'TestMAX'. logical reads 25511 lob logical reads 000 Table 'TestTEXT' logical reads 00412 lob logical reads 597 Table 'TestMAXOOR' logical reads 00413 lob logical reads 446 We can increase the density of the id values by creating a separate nonclustered index on the id column only.  This is the same key as the clustered index, of course, but the nonclustered index will not include the rest of the in-row column data. CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestCHAR (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestMAX (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestTEXT (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestMAXOOR (id); The four queries can now use the very dense nonclustered index to quickly scan the id values, sort them by NEWID(), select the 150 ids we want, and then look up the padding data.  The logical reads with the new indexes in place are: Table 'TestCHAR' logical reads 835 lob logical reads 0 Table 'TestMAX' logical reads 835 lob logical reads 0 Table 'TestTEXT' logical reads 686 lob logical reads 597 Table 'TestMAXOOR' logical reads 686 lob logical reads 448 With the new index, all four queries use the same query plan (click to enlarge): Performance Summary: 0.3 seconds elapsed time 6MB memory grant 0MB tempdb usage 1MB sort set 835 logical reads (CHAR, MAX) 686 logical reads (TEXT, MAXOOR) 597 LOB logical reads (TEXT) 448 LOB logical reads (MAXOOR) No sort warning I’ll leave it as an exercise for the reader to work out why trying to eliminate the Key Lookup by adding the padding column to the new nonclustered indexes would be a daft idea Conclusion This post is not about tuning queries that access columns containing big strings.  It isn’t about the internal differences between TEXT and MAX data types either.  It isn’t even about the cool use of UPDATE .WRITE used in the MAXOOR table load.  No, this post is about something else: Many developers might not have tuned our starting example query at all – 5 seconds isn’t that bad, and the original query plan looks reasonable at first glance.  Perhaps the NEWID() function would have been blamed for ‘just being slow’ – who knows.  5 seconds isn’t awful – unless your users expect sub-second responses – but using 250MB of memory and writing 200MB to tempdb certainly is!  If ten sessions ran that query at the same time in production that’s 2.5GB of memory usage and 2GB hitting tempdb.  Of course, not all queries can be rewritten to avoid large memory grants and sort spills using the key-lookup technique in this post, but that’s not the point either. The point of this post is that a basic understanding of execution plans is not enough.  Tuning for logical reads and adding covering indexes is not enough.  If you want to produce high-quality, scalable TSQL that won’t get you paged as soon as it hits production, you need a deep understanding of execution plans, and as much accurate, deep knowledge about SQL Server as you can lay your hands on.  The advanced database developer has a wide range of tools to use in writing queries that perform well in a range of circumstances. By the way, the examples in this post were written for SQL Server 2008.  They will run on 2005 and demonstrate the same principles, but you won’t get the same figures I did because 2005 had a rather nasty bug in the Top N Sort operator.  Fair warning: if you do decide to run the scripts on a 2005 instance (particularly the parallel query) do it before you head out for lunch… This post is dedicated to the people of Christchurch, New Zealand. © 2011 Paul White email: @[email protected] twitter: @SQL_Kiwi

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  • Website Health Check - Keyword Blunders - Part 2

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  • Guru Of the Week n° 44 : copie sur écriture - deuxième partie, un article de Herb Sutter traduit par la rédaction C++

    Second article consacré à l'idiome "copie sur écriture" suite à la première partie. Dans cet article, Herb Sutter développe l'exemple d'une classe de chaîne de caractères utilisant cet idiome. Guru Of the Week n° 44 : copie sur écriture - deuxième partie Que pensez-vous de la différence entre std::string et la classe proposée dans cet article ? Retrouver l'ensemble des Guru of the Week sur la page d'index....

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  • Azure Deployment - Be careful adding a Remote Desktop connection to deployments that you want to swap staging with live…

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    Adding Remote Desktop capability adds an external endpoint onto the deployment, meaning it may have more endpoints that your current live deployment.  When there is a difference in the number of endpoints between a staging and live deployment, you can’t swap them in the Azure portal.  Oops. So you have to the remote capability to your live deployment first if you want to do this… more later – joel

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  • Grandfather’s Tales – Why You Always Plug Directly into the Modem [Humorous Comic]

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    Note: Comic contains some language that may be considered inappropriate. The tale of the troll router, or, how I learned to love plugging directly into the modem [via Reddit] How to Own Your Own Website (Even If You Can’t Build One) Pt 1 What’s the Difference Between Sleep and Hibernate in Windows? Screenshot Tour: XBMC 11 Eden Rocks Improved iOS Support, AirPlay, and Even a Custom XBMC OS

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