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  • If I take a large datatype. Will it affect performance in sql server

    - by Shantanu Gupta
    If i takes larger datatype where i know i should have taken datatype that was sufficient for possible values that i will insert into a table will affect any performance in sql server in terms of speed or any other way. eg. IsActive (0,1,2,3) not more than 3 in any case. I know i must take tinyint but due to some reasons consider it as compulsion, i am taking every numeric field as bigint and every character field as nVarchar(Max) Please give statistics if possible, to let me try to overcoming that compulsion. I need some solid analysis that can really make someone rethink before taking any datatype.

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  • Can using non primitive Integer/ Long datatypes too frequently in the application, hurt the performance??

    - by Marcos
    I am using Long/Integer data types very frequently in my application, to build Generic datatypes. I fear that using these wrapper objects instead of primitive data types may be harmful for performance since each time it needs to create objects which is an expensive operation. but also it seems that I have no other choice(when I have to use primtives with generics) rather than just using them. However, still it would be great if you can suggest if there is anything I could do to make it better. or any way if I could just avoid it ?? Also What may be the downsides of this ? Suggestions welcomed!

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  • Improving the performance of an nHibernate Data Access Layer.

    - by Amitabh
    I am working on improving the performance of DataAccess Layer of an existing Asp.Net Web Application. The scenerios are. Its a web based application in Asp.Net. DataAccess layer is built using NHibernate 1.2 and exposed as WCF Service. The Entity class is marked with DataContract. Lazy loading is not used and because of the eager-fetching of the relations there is huge no of database objects are loaded in the memory. No of hits to the database is also high. For example I profiled the application using NHProfiler and there were about 50+ sql calls to load one of the Entity object using the primary key. I also can not change code much as its an existing live application with no NUnit test cases at all. Please can I get some suggestions here?

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  • Performance optimization for mssql: decrease stored procedures execution time or unload the server?

    - by tim
    Hello everybody! We have a web service which provides search over hotels. There is a problem with performance: a single request to the service takes around 5000 ms. Almost all of the time is spent in database by executing storing procedures. During the request our server (mssql2008) consumes ~90% of the processor time. When 2 requests are made in parallel the average time grows and is around 7000 ms. When number of request is increasing, the average time of response is increasing as well. We have 20-30 requests per minute. Which kind of optimization is the best in this case having in mind that the goal is to provide stable response time for the service: 1) Try to decrease the stored procedures execution time 2) Try to find the way how to unload the server It is interesting to hear from people who deal with booking sites. Thanks!

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  • What's the most performance effective way to have a webbrowser inside a class library ?

    - by Xaqron
    I'm developing a class library. Need some data from internet and this cannot be done with HttpWebRequest in my case so I wanna use WebBrowser component. WebBrowser is used for opening a single page and fetch some data from it, so WebBrowser life-time is very short. Running thread is MTA and no message pump or STA thread is available by default (class library is used by an ASP.NET application). How to create a WebBrowser object, run it with a STA thread, fetch data from a web page and finally dispose it with the least performance impact on the application ? I just need the idea/concept and will find details myself. Thanks guys

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  • JavaScript: String Concatenation slow performance? Array.join('')?

    - by NickNick
    I've read that if I have a for loop, I should not use string concation because it's slow. Such as: for (i=0;i<10000000;i++) { str += 'a'; } And instead, I should use Array.join(), since it's much faster: var tmp = []; for (i=0;i<10000000;i++) { tmp.push('a'); } var str = tmp.join(''); However, I have also read that string concatention is ONLY a problem for Internet Explorer and that browsers such as Safari/Chrome, which use Webkit, actually perform FASTER is using string concatention than Array.join(). I've attempting to find a performance comparison between all major browser of string concatenation vs Array.join() and haven't been able to find one. As such, what is faster and more efficient JavaScript code? Using string concatenation or Array.join()?

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  • Continuous Integration for SQL Server Part II – Integration Testing

    - by Ben Rees
    My previous post, on setting up Continuous Integration for SQL Server databases using GitHub, Bamboo and Red Gate’s tools, covered the first two parts of a simple Database Continuous Delivery process: Putting your database in to a source control system, and, Running a continuous integration process, each time changes are checked in. However there is, of course, a lot more to to Continuous Delivery than that. Specifically, in addition to the above: Putting some actual integration tests in to the CI process (otherwise, they don’t really do much, do they!?), Deploying the database changes with a managed, automated approach, Monitoring what you’ve just put live, to make sure you haven’t broken anything. This post will detail how to set up a very simple pipeline for implementing the first of these (continuous integration testing). NB: A lot of the setup in this post is built on top of the configuration from before, so it might be difficult to implement this post without running through part I first. There’ll then be a third post on automated database deployment followed by a final post dealing with the last item – monitoring changes on the live system. In the previous post, I used a mixture of Red Gate products and other 3rd party software – GitHub and Atlassian Bamboo specifically. This was partly because I believe most people work in an heterogeneous environment, using software from different vendors to suit their purposes and I wanted to show how this could work for this process. For example, you could easily substitute Atlassian’s BitBucket or Stash for GitHub, depending on your needs, or use an alternative CI server such as TeamCity, TFS or Jenkins. However, in this, post, I’ll be mostly using Red Gate products only (other than tSQLt). I would do this, firstly because I work for Red Gate. However, I also think that in the area of Database Delivery processes, nobody else has the offerings to implement this process fully – so I didn’t have any choice!   Background on Continuous Delivery For me, a great source of information on what makes a proper Continuous Delivery process is the Jez Humble and David Farley classic: Continuous Delivery – Reliable Software Releases through Build, Test, and Deployment Automation This book is not of course, primarily about databases, and the process I outline here and in the previous article is a gross simplification of what Jez and David describe (not least because it’s that much harder for databases!). However, a lot of the principles that they describe can be equally applied to database development and, I would argue, should be. As I say however, what I describe here is a very simple version of what would be required for a full production process. A couple of useful resources on handling some of these complexities can be found in the following two references: Refactoring Databases – Evolutionary Database Design, by Scott J Ambler and Pramod J. Sadalage Versioning Databases – Branching and Merging, by Scott Allen In particular, I don’t deal at all with the issues of multiple branches and merging of those branches, an issue made particularly acute by the use of GitHub. The other point worth making is that, in the words of Martin Fowler: Continuous Delivery is about keeping your application in a state where it is always able to deploy into production.   I.e. we are not talking about continuously delivery updates to the production database every time someone checks in an amendment to a stored procedure. That is possible (and what Martin calls Continuous Deployment). However, again, that’s more than I describe in this article. And I doubt I need to remind DBAs or Developers to Proceed with Caution!   Integration Testing Back to something practical. The next stage, building on our set up from the previous article, is to add in some integration tests to the process. As I say, the CI process, though interesting, isn’t enormously useful without some sort of test process running. For this we’ll use the tSQLt framework, an open source framework designed specifically for running SQL Server tests. tSQLt is part of Red Gate’s SQL Test found on http://www.red-gate.com/products/sql-development/sql-test/ or can be downloaded separately from www.tsqlt.org - though I’ll provide a step-by-step guide below for setting this up. Getting tSQLt set up via SQL Test Click on the link http://www.red-gate.com/products/sql-development/sql-test/ and click on the blue Download button to download the Red Gate SQL Test product, if not already installed. Follow the install process for SQL Test to install the SQL Server Management Studio (SSMS) plugin on to your machine, if not already installed. Open SSMS. You should now see SQL Test under the Tools menu:   Clicking this link will give you the basic SQL Test dialogue: As yet, though we’ve installed the SQL Test product we haven’t yet installed the tSQLt test framework on to any particular database. To do this, we need to add our RedGateApp database using this dialogue, by clicking on the + Add Database to SQL Test… link, selecting the RedGateApp database and clicking the Add Database link:   In the next screen, SQL Test describes what will be installed on the database for the tSQLt framework. Also in this dialogue, uncheck the “Add SQL Cop tests” option (shown below). SQL Cop is a great set of pre-defined tests that work within the tSQLt framework to check the general health of your SQL Server database. However, we won’t be using them in this particular simple example: Once you’ve clicked on the OK button, the changes described in the dialogue will be made to your database. Some of these are shown in the left-hand-side below: We’ve now installed the framework. However, we haven’t actually created any tests, so this will be the next step. But, before we proceed, we’ve made an update to our database so should, again check this in to source control, adding comments as required:   Also worth a quick check that your build still runs with the new additions!: (And a quick check of the RedGateAppCI database shows that the changes have been made).   Creating and Testing a Unit Test There are, of course, a lot of very interesting unit tests that you could and should set up for a database. The great thing about the tSQLt framework is that you can write these in SQL. The example I’m going to use here is pretty Mickey Mouse – our database table is going to include some email addresses as reference data and I want to check whether these are all in a correct email format. Nothing clever but it illustrates the process and hopefully shows the method by which more interesting tests could be set up. Adding Reference Data to our Database To start, I want to add some reference data to my database, and have this source controlled (as well as the schema). First of all I need to add some data in to my solitary table – this can be done a number of ways, but I’ll do this in SSMS for simplicity: I then add some reference data to my table: Currently this reference data just exists in the database. For proper integration testing, this needs to form part of the source-controlled version of the database – and so needs to be added to the Git repository. This can be done via SQL Source Control, though first a Primary Key needs to be added to the table. Right click the table, select Design, then right-click on the first “id” row. Then click on “Set Primary Key”: NB: once this change is made, click Save to save the change to the table. Then, to source control this reference data, right click on the table (dbo.Email) and selecting the following option:   In the next screen, link the data in the Email table, by selecting it from the list and clicking “save and close”: We should at this point re-commit the changes (both the addition of the Primary Key, and the data) to the Git repo. NB: From here on, I won’t show screenshots for the GitHub side of things – it’s the same each time: whenever a change is made in SQL Source Control and committed to your local folder, you then need to sync this in the GitHub Windows client (as this is where the build server, Bamboo is taking it from). An interesting point to note here, when these changes are committed in SQL Source Control (right-click database and select “Commit Changes to Source Control..”): The display gives a warning about possibly needing a migration script for the “Add Primary Key” step of the changes. This isn’t actually necessary in this case, but this mechanism would allow you to create override scripts to replace the default change scripts created by the SQL Compare engine (which runs underneath SQL Source Control). Ignoring this message (!), we add a comment and commit the changes to Git. I then sync these, run a build (or the build gets run automatically), and check that the data is being deployed over to the target RedGateAppCI database:   Creating and Running the Test As I mention, the test I’m going to use here is a very simple one - are the email addresses in my reference table valid? This isn’t of course, a full test of email validation (I expect the email addresses I’ve chosen here aren’t really the those of the Fab Four) – but just a very basic check of format used. I’ve taken the relevant SQL from this Stack Overflow article. In SSMS select “SQL Test” from the Tools menu, then click on + New Test: In the next screen, give your new test a name, and also enter a name in the Test Class box (test classes are schemas that help you keep things organised). Also check that the database in which the test is going to be created is correct – RedGateApp in this example: Click “Create Test”. After closing a couple of subsequent dialogues, you’ll see a dummy script for the test, that needs filling in:   We now need to define the SQL for our test. As mentioned before, tSQLt allows you to write your unit tests in T-SQL, and the code I’m going to use here is as below. This needs to be copied and pasted in to the query window, to replace the default given by tSQLt: –  Basic email check test ALTER PROCEDURE [MyChecks].[test Check Email Addresses] AS BEGIN SET NOCOUNT ON         Declare @Output VarChar(max)     Set @Output = ”       SELECT  @Output = @Output + Email +Char(13) + Char(10) FROM dbo.Email WHERE email NOT LIKE ‘%_@__%.__%’       If @Output > ”         Begin             Set @Output = Char(13) + Char(10)                           + @Output             EXEC tSQLt.Fail@Output         End   END;   Once this script is entered, hit execute to add the Stored Procedure to the database. Before committing the test to source control,  it’s worth just checking that it works! For a positive test, click on “SQL Test” from the Tools menu, then click Run Tests. You should see output like the following: - a green tick to indicate success! But of course, what we also need to do is test that this is actually doing something by showing a failed test. Edit one of the email addresses in your table to an incorrect format: Now, re-run the same SQL Test as before and you’ll see the following: Great – we now know that our test is really doing something! You’ll also see a useful error message at the bottom of SSMS: (leave the email address as invalid for now, for the next steps). The next stage is to check this new test in to source control again, by right-clicking on the database and checking in the changes with a commit message (and not forgetting to sync in the GitHub client):   Checking that the Tests are Running as Integration Tests After the changes above are made, and after a build has run on Bamboo (manual or automatic), looking at the Stored Procedures for the RedGateAppCI, the SPROC for the new test has been moved over to the database. However this is not exactly what we were after. We didn’t want to just copy objects from one database to another, but actually run the tests as part of the build/integration test process. I.e. we’re continuously checking any changes we make (in this case, to the reference data emails), to ensure we’re not breaking a test that we’ve set up. The behaviour we want to see is that, if we check in static data that is incorrect (as we did in step 9 above) and we have the tSQLt test set up, then our build in Bamboo should fail. However, re-running the build shows the following: - sadly, a successful build! To make sure the tSQLt tests are run as part of the integration test, we need to amend a switch in the Red Gate CI config file. First, navigate to file sqlCI.targets in your working folder: Edit this document, make the following change, save the document, then commit and sync this change in the GitHub client: <!-- tSQLt tests --> <!-- Optional --> <!-- To run tSQLt tests in source control for the database, enter true. --> <enableTsqlt>true</enableTsqlt> Now, if we re-run the build in Bamboo (NB: I’ve moved to a new server here, hence different address and build number): - superb, a broken build!! The error message isn’t great here, so to get more detailed info, click on the full build log link on this page (below the fold). The interesting part of the log shown is towards the bottom. Pulling out this part:   21-Jun-2013 11:35:19 Build FAILED. 21-Jun-2013 11:35:19 21-Jun-2013 11:35:19 "C:\Users\Administrator\bamboo-home\xml-data\build-dir\RGA-RGP-JOB1\sqlCI.proj" (default target) (1) -> 21-Jun-2013 11:35:19 (sqlCI target) -> 21-Jun-2013 11:35:19 EXEC : sqlCI error occurred: RedGate.Deploy.SqlServerDbPackage.Shared.Exceptions.InvalidSqlException: Test Case Summary: 1 test case(s) executed, 0 succeeded, 1 failed, 0 errored. [C:\Users\Administrator\bamboo-home\xml-data\build-dir\RGA-RGP-JOB1\sqlCI.proj] 21-Jun-2013 11:35:19 EXEC : sqlCI error occurred: [MyChecks].[test Check Email Addresses] failed: [C:\Users\Administrator\bamboo-home\xml-data\build-dir\RGA-RGP-JOB1\sqlCI.proj] 21-Jun-2013 11:35:19 EXEC : sqlCI error occurred: ringo.starr@beatles [C:\Users\Administrator\bamboo-home\xml-data\build-dir\RGA-RGP-JOB1\sqlCI.proj] 21-Jun-2013 11:35:19 EXEC : sqlCI error occurred: [C:\Users\Administrator\bamboo-home\xml-data\build-dir\RGA-RGP-JOB1\sqlCI.proj] 21-Jun-2013 11:35:19 EXEC : sqlCI error occurred: +----------------------+ [C:\Users\Administrator\bamboo-home\xml-data\build-dir\RGA-RGP-JOB1\sqlCI.proj] 21-Jun-2013 11:35:19 EXEC : sqlCI error occurred: |Test Execution Summary| [C:\Users\Administrator\bamboo-home\xml-data\build-dir\RGA-RGP-JOB1\sqlCI.proj]   As a final check, we should make sure that, if we now fix this error, the build succeeds. So in SSMS, I’m going to correct the invalid email address, then check this change in to SQL Source Control (with a comment), commit to GitHub, and re-run the build:   This should have fixed the build: It worked! Summary This has been a very quick run through the implementation of CI for databases, including tSQLt tests to test whether your database updates are working. The next post in this series will focus on automated deployment – we’ve tested our database changes, how can we now deploy these to target sites?  

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  • GRUB 2 problem after Mac OS X update

    - by vallllll
    I have a MacBook Pro in dual boot Mac OS X / Ubuntu 12.04 (Precise Pangolin). When I boot it I have a rEFIt menu, and I can chose between Mac OS X and Linux. A few days ago I have updated Mac OS X from 10.7 (Lion) to 10.8 (Mountain Lion) using a .dmg image provided by my company. Since then when I select Linux in rEFIt it says: No bootable device --insert boot disk and press any key I have tried going to rEFIt partitioning tool. This is what I got: As suggested in Mac OSX Mavericks update rEFIT broken I wanted to fix the issue the same way as AndrewM, but I don't have the option "MBR table must be updated". Then I booted on Ubuntu 12.04 CD, chose repair broken system, chose root patition /dev/sda6 as this is where my Ubuntu file system is. I got a shell, but I don't really know how to repair the poblem since if it was just Windows dual boot. A GRUB update would solve the issue, but here I don't know where the GRUB 2 is installed. Here are results from Parted, and it is a bit confusing for me as the Mac partition is the one with boot: As you can see the entry 1 is an EFI system partition and is the boot partition, so I wonder if I should install GRUB there or in sda6, which is the Ubuntu filesystem. I am not sure should I work on rEFIt shell or Ubuntu. Unfortunately, I don't remember where GRUB was before update. UPDATE: using same link above I have tried RoundSparrow hilltx answer and installed rEFInd, but the result is same.... still no bootable device when I select Linux. UPDATE 2: just used alternate CD again, mounted on /dev/sda6 and the ran update-grub. It seemed to wok and started listing all my kernels. But after rebooting several times still no bootable device when I select Linux in rEFInd. UDATE 3: Have tried to boot from Ubuntu cd and select "boot from first available filesystem. I got error and dropped to grub rescue shell. I even followed the indications on this link but was unable to boot as I tried to use sdb6 but no luck UPDATE 4 as per Rob Smith request here is out put from ls -l $(find /EFI -iname "*.efi") *MACOSX -rw-r--r--@ 1 root admin 55048 29 oct 17:44 /EFI/refind/drivers_x64/btrfs_x64.efi -rw-r--r--@ 1 root admin 38888 29 oct 17:44 /EFI/refind/drivers_x64/ext2_x64.efi -rw-r--r--@ 1 root admin 39304 29 oct 17:44 /EFI/refind/drivers_x64/ext4_x64.efi -rw-r--r--@ 1 root admin 43432 29 oct 17:44 /EFI/refind/drivers_x64/hfs_x64.efi -rw-r--r--@ 1 root admin 38984 29 oct 17:44 /EFI/refind/drivers_x64/iso9660_x64.efi -rw-r--r--@ 1 root admin 43656 29 oct 17:44 /EFI/refind/drivers_x64/reiserfs_x64.efi -rw-r--r--@ 1 root admin 175016 29 oct 17:44 /EFI/refind/refind_x64.efi -rw-rw-r-- 1 root admin 73232 7 mar 2010 /EFI/tools/dbounce.efi -rw-rw-r-- 1 root admin 763248 7 mar 2010 /EFI/tools/dhclient.efi -rw-rw-r-- 1 root admin 67024 7 mar 2010 /EFI/tools/drawbox.efi -rw-rw-r-- 1 root admin 71312 7 mar 2010 /EFI/tools/dumpfv.efi -rw-rw-r-- 1 root admin 84848 7 mar 2010 /EFI/tools/dumpprot.efi -rw-rw-r-- 1 root admin 472912 7 mar 2010 /EFI/tools/ed.efi -rw-rw-r-- 1 root admin 143856 7 mar 2010 /EFI/tools/edit.efi -rw-rw-r-- 1 root admin 1801008 7 mar 2010 /EFI/tools/ftp.efi -rw-r--r--@ 1 root admin 47848 29 oct 17:44 /EFI/tools/gptsync_x64.efi -rw-rw-r-- 1 root admin 320560 7 mar 2010 /EFI/tools/hexdump.efi -rw-rw-r-- 1 root admin 286384 7 mar 2010 /EFI/tools/hostname.efi -rw-rw-r-- 1 root admin 534416 7 mar 2010 /EFI/tools/ifconfig.efi -rw-rw-r-- 1 root admin 395344 7 mar 2010 /EFI/tools/loadarg.efi -rw-rw-r-- 1 root admin 587408 7 mar 2010 /EFI/tools/ping.efi -rw-rw-r-- 1 root admin 730416 7 mar 2010 /EFI/tools/pppd.efi -rw-rw-r-- 1 root admin 561360 7 mar 2010 /EFI/tools/route.efi -rw-rw-r-- 1 root admin 1961712 7 mar 2010 /EFI/tools/shell.efi -rw-rw-r-- 1 root admin 750224 7 mar 2010 /EFI/tools/tcpipv4.efi -rw-rw-r-- 1 root admin 4048 7 mar 2010 /EFI/tools/textmode.efi -rw-rw-r-- 1 root admin 320656 7 mar 2010 /EFI/tools/which.efi *LINUX

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  • SQL SEVER – Finding Memory Pressure – External and Internal

    - by pinaldave
    Following query will provide details of external and internal memory pressure. It will return the data how much portion in the existing memory is assigned to what kind of memory type. SELECT TYPE, SUM(single_pages_kb) InternalPressure, SUM(multi_pages_kb) ExtermalPressure FROM sys.dm_os_memory_clerks GROUP BY TYPE ORDER BY SUM(single_pages_kb) DESC, SUM(multi_pages_kb) DESC GO What is your method to find memory pressure? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Are SMART goals useful for programmers?

    - by Craig Schwarze
    Several organisations I know use SMART goals for their programmers. SMART is an acronym for Specific, Measurable, Achievable, Relevant and Time-Bound. They are fairly common in large corporations. My own prior experience with SMART goals has not been all that positive. Have other programmers found them an effective way to measure performance? What are some examples of good SMART goals for programmers (if they exist).

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  • SQL SERVER – Difference Between DATETIME and DATETIME2

    - by pinaldave
    Yesterday I have written a very quick blog post on SQL SERVER – Difference Between GETDATE and SYSDATETIME and I got tremendous response for the same. I suggest you read that blog post before continuing this blog post today. I had asked people to honestly take part and share their view about above two system function. There are few emails as well few comments on the blog post asking question how did I come to know the difference between the same. The answer is real world issues. I was called in for performance tuning consultancy where I was asked very strange question by one developer. Here is the situation he was facing. System had a single table with two different column of datetime. One column was datelastmodified and second column was datefirstmodified. One of the column was DATETIME and another was DATETIME2. Developer was populating them with SYSDATETIME respectively. He was always thinking that the value inserted in the table will be the same. This table was only accessed by INSERT statement and there was no updates done over it in application.One fine day he ran distinct on both of this column and was in for surprise. He always thought that both of the table will have same data, but in fact they had very different data. He presented this scenario to me. I said this can not be possible but when looked at the resultset, I had to agree with him. Here is the simple script generated to demonstrate the problem he was facing. This is just a sample of original table. DECLARE @Intveral INT SET @Intveral = 10000 CREATE TABLE #TimeTable (FirstDate DATETIME, LastDate DATETIME2) WHILE (@Intveral > 0) BEGIN INSERT #TimeTable (FirstDate, LastDate) VALUES (SYSDATETIME(), SYSDATETIME()) SET @Intveral = @Intveral - 1 END GO SELECT COUNT(DISTINCT FirstDate) D_GETDATE, COUNT(DISTINCT LastDate) D_SYSGETDATE FROM #TimeTable GO SELECT DISTINCT a.FirstDate, b.LastDate FROM #TimeTable a INNER JOIN #TimeTable b ON a.FirstDate = b.LastDate GO SELECT * FROM #TimeTable GO DROP TABLE #TimeTable GO Let us see the resultset. You can clearly see from result that SYSDATETIME() does not populate the same value in the both of the field. In fact the value is either rounded down or rounded up in the field which is DATETIME. Event though we are populating the same value, the values are totally different in both the column resulting the SELF JOIN fail and display different DISTINCT values. The best policy is if you are using DATETIME use GETDATE() and if you are suing DATETIME2 use SYSDATETIME() to populate them with current date and time to accurately address the precision. As DATETIME2 is introduced in SQL Server 2008, above script will only work with SQL SErver 2008 and later versions. I hope I have answered few questions asked yesterday. Reference: Pinal Dave (http://www.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL DateTime, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • SQL SERVER – When are Statistics Updated – What triggers Statistics to Update

    - by pinaldave
    If you are an SQL Server Consultant/Trainer involved with Performance Tuning and Query Optimization, I am sure you have faced the following questions many times. When is statistics updated? What is the interval of Statistics update? What is the algorithm behind update statistics? These are the puzzling questions and more. I searched the Internet as well many official MS documents in order to find answers. All of them have provided almost similar algorithm. However, at many places, I have seen a bit of variation in algorithm as well. I have finally compiled the list of various algorithms and decided to share what was the most common “factor” in all of them. I would like to ask for your suggestions as whether following the details, when Statistics is updated, are accurate or not. I will update this blog post with accurate information after receiving your ideas. The answer I have found here is when statistics are expired and not when they are automatically updated. I need your help here to answer when they are updated. Permanent table If the table has no rows, statistics is updated when there is a single change in table. If the number of rows in a table is less than 500, statistics is updated for every 500 changes in table. If the number of rows in table is more than 500, statistics is updated for every 500+20% of rows changes in table. Temporary table If the table has no rows, statistics is updated when there is a single change in table. If the number of rows in table is less than 6, statistics is updated for every 6 changes in table. If the number of rows in table is less than 500, statistics is updated for every 500 changes in table. If the number of rows in table is more than 500, statistics is updated for every 500+20% of rows changes in table. Table variable There is no statistics for Table Variables. If you want to read further about statistics, I suggest that you read the white paper Statistics Used by the Query Optimizer in Microsoft SQL Server 2008. Let me know your opinions about statistics, as well as if there is any update in the above algorithm. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, Readers Question, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: SQL Statistics

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  • SQL SERVER – Checklist for Analyzing Slow-Running Queries

    - by pinaldave
    I am recently working on upgrading my class Microsoft SQL Server 2005/2008 Query Optimization and & Performance Tuning with additional details and more interesting examples. While working on slide deck I realized that I need to have one solid slide which talks about checklist for analyzing slow running queries. A quick search on my saved [...]

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  • Easy Listening = CRM On Demand Podcasts

    - by Anne
    OK, here's my NEW favorite resource for CRM On Demand info -- podcasts! Specifically, the CRM On Demand Podcast site -- signed, sealed, and delivered with humor and know-how. Yes, I admit, I know the cast of characters. But let's face it, sometimes dealing with software is just soooo dry! Not so when discussed by the two main commentators, Louis Peters and Robert Davidson, whom someone once referred to as CRM On Demand's "Click and Clack." (Thought that was too good not to pass along!) Anyhow, another huge plus about the site is the option to listen OR to read. Out walking my dog or doing the dishes? Just turn up the podcast. Listening to music or watching TV? I'll read Louis's entertaining write-ups to glean great info about CRM On Demand in a very short period of time. So that you get a better understanding of why I like this site so much, here's a sampling of what's discussed: Five Things about Books of Business As Louis Peters put it in his entry, when you see "Five Things" in the title, "you'll know you're going to get some concrete advice that you can put to work right away." Well, Louis and Robert do just that, pointing you in the right direction when using Books of Business to segment data. Moving to Indexed Fields - A Rough Guide (only an article, not a podcast) I've read all about performance and even helped develop material around it. But nowhere have I heard indexed custom fields referred to as "super heroes." Louis and Robert use imaginative language to describe the process for moving your data to indexed fields for optimal performance. Data Access QA from the Forums I think that everyone would admit that data access and visibility is the most difficult topic to understand in CRM On Demand. Following up on their previous podcast on the same topic, Louis and Robert answer a few key questions from the many postings on the Oracle CRM On Demand forums. And I bet that the scenarios match many companies' business requirements...maybe even yours! We Need to Talk About Adoption Another expert, Tim Koehler, joins Louis to talk about how to drive user adoption: aligning product usage with business results, communicating why and how to use the product, getting feedback on usability, and so on. Hope I've made my point -- turn to these podcasts to hear knowledgeable folks discuss CRM On Demand tips and tricks in entertaining ways. One podcast is even called "SaaS Talk"!

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  • Make your code gooder with the goodies gem

    - by kerry
    I have decided to publish all my Ruby tools via a gem called ‘goodies’.  To install this gem simply type ‘gem install goodies’. The source is hosted on GitHub.  The first version (0.1) has the Hash object accessors and the String file path utility methods discussed in the previous two posts. Enjoy!   Ruby Goodies @ GitHub Goodies on gemcutter.org

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  • MySQL – Scalability on Amazon RDS: Scale out to multiple RDS instances

    - by Pinal Dave
    Today, I’d like to discuss getting better MySQL scalability on Amazon RDS. The question of the day: “What can you do when a MySQL database needs to scale write-intensive workloads beyond the capabilities of the largest available machine on Amazon RDS?” Let’s take a look. In a typical EC2/RDS set-up, users connect to app servers from their mobile devices and tablets, computers, browsers, etc.  Then app servers connect to an RDS instance (web/cloud services) and in some cases they might leverage some read-only replicas.   Figure 1. A typical RDS instance is a single-instance database, with read replicas.  This is not very good at handling high write-based throughput. As your application becomes more popular you can expect an increasing number of users, more transactions, and more accumulated data.  User interactions can become more challenging as the application adds more sophisticated capabilities. The result of all this positive activity: your MySQL database will inevitably begin to experience scalability pressures. What can you do? Broadly speaking, there are four options available to improve MySQL scalability on RDS. 1. Larger RDS Instances – If you’re not already using the maximum available RDS instance, you can always scale up – to larger hardware.  Bigger CPUs, more compute power, more memory et cetera. But the largest available RDS instance is still limited.  And they get expensive. “High-Memory Quadruple Extra Large DB Instance”: 68 GB of memory 26 ECUs (8 virtual cores with 3.25 ECUs each) 64-bit platform High I/O Capacity Provisioned IOPS Optimized: 1000Mbps 2. Provisioned IOPs – You can get provisioned IOPs and higher throughput on the I/O level. However, there is a hard limit with a maximum instance size and maximum number of provisioned IOPs you can buy from Amazon and you simply cannot scale beyond these hardware specifications. 3. Leverage Read Replicas – If your application permits, you can leverage read replicas to offload some reads from the master databases. But there are a limited number of replicas you can utilize and Amazon generally requires some modifications to your existing application. And read-replicas don’t help with write-intensive applications. 4. Multiple Database Instances – Amazon offers a fourth option: “You can implement partitioning,thereby spreading your data across multiple database Instances” (Link) However, Amazon does not offer any guidance or facilities to help you with this. “Multiple database instances” is not an RDS feature.  And Amazon doesn’t explain how to implement this idea. In fact, when asked, this is the response on an Amazon forum: Q: Is there any documents that describe the partition DB across multiple RDS? I need to use DB with more 1TB but exist a limitation during the create process, but I read in the any FAQ that you need to partition database, but I don’t find any documents that describe it. A: “DB partitioning/sharding is not an official feature of Amazon RDS or MySQL, but a technique to scale out database by using multiple database instances. The appropriate way to split data depends on the characteristics of the application or data set. Therefore, there is no concrete and specific guidance.” So now what? The answer is to scale out with ScaleBase. Amazon RDS with ScaleBase: What you get – MySQL Scalability! ScaleBase is specifically designed to scale out a single MySQL RDS instance into multiple MySQL instances. Critically, this is accomplished with no changes to your application code.  Your application continues to “see” one database.   ScaleBase does all the work of managing and enforcing an optimized data distribution policy to create multiple MySQL instances. With ScaleBase, data distribution, transactions, concurrency control, and two-phase commit are all 100% transparent and 100% ACID-compliant, so applications, services and tooling continue to interact with your distributed RDS as if it were a single MySQL instance. The result: now you can cost-effectively leverage multiple MySQL RDS instance to scale out write-intensive workloads to an unlimited number of users, transactions, and data. Amazon RDS with ScaleBase: What you keep – Everything! And how does this change your Amazon environment? 1. Keep your application, unchanged – There is no change your application development life-cycle at all.  You still use your existing development tools, frameworks and libraries.  Application quality assurance and testing cycles stay the same. And, critically, you stay with an ACID-compliant MySQL environment. 2. Keep your RDS value-added services – The value-added services that you rely on are all still available. Amazon will continue to handle database maintenance and updates for you. You can still leverage High Availability via Multi A-Z.  And, if it benefits youra application throughput, you can still use read replicas. 3. Keep your RDS administration – Finally the RDS monitoring and provisioning tools you rely on still work as they did before. With your one large MySQL instance, now split into multiple instances, you can actually use less expensive, smallersmaller available RDS hardware and continue to see better database performance. Conclusion Amazon RDS is a tremendous service, but it doesn’t offer solutions to scale beyond a single MySQL instance. Larger RDS instances get more expensive.  And when you max-out on the available hardware, you’re stuck.  Amazon recommends scaling out your single instance into multiple instances for transaction-intensive apps, but offers no services or guidance to help you. This is where ScaleBase comes in to save the day. It gives you a simple and effective way to create multiple MySQL RDS instances, while removing all the complexities typically caused by “DIY” sharding andwith no changes to your applications . With ScaleBase you continue to leverage the AWS/RDS ecosystem: commodity hardware and value added services like read replicas, multi A-Z, maintenance/updates and administration with monitoring tools and provisioning. SCALEBASE ON AMAZON If you’re curious to try ScaleBase on Amazon, it can be found here – Download NOW. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: MySQL, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Certificate Revocation checking affecting system performance [migrated]

    - by Colm Clarke
    I have a .NET 3.5 desktop application that had been showing periodic slow downs in functionality whenever the test machine it was on was out of the office. I managed to replicate the error on a machine in the office without an internet connection, but it was only when i used ANTS performance profiler that i got a clearer picture of what was going on. In ANTS I saw a "Waiting for synchronization" taking up to 16 seconds that corresponded to the delay I could see in the application when NHibernate tried to load the System.Data.SqlServerCE.dll assembly. If I tried the action again immediately it would work with no delay but if I left it for 5 minutes then it would be slow to load again the next time I tried it. From my research so far it appears to be because the SqlServerCE dll is signed and so the system is trying to connect to get the certificate revocation lists and timing out. Disabling the "Automatically detect settings" setting in the Internet Options LAN settings makes the problem go away, as does disabling the "Check for publishers certificate revocation". But the admins where this application will be deployed are not going to be happy with the idea of disabling certificate checking on a per machine or per user basis so I really need to get the application level disabling of the CRL check working. There is the well documented bug in .net 2.0 which describes this behaviour, and offers a possible fix with a config file element. <?xml version="1.0" encoding="utf-8"?> <configuration> <runtime> <generatePublisherEvidence enabled="false"/> </runtime> </configuration> This is NOT working for me however even though I am using .net 3.5. The SQLServerCE dll is being loaded dynamically by NHibernate and I wonder if the fact that it's dynamic could somehow be why the setting isn't working, but I don't know how I could check that. Can anyone offer suggestions as to why the config setting might not work? Or is there another way I could disable the check at the application level, perhaps a CAS policy setting that I can use to set an exception for the application when it's installed? Or is there something I can change in the application to up the trust level or something like that? I have also tried using to no advantage ServicePointManager.CheckCertificateRevocationList = false; http://rusanu.com/2009/07/24/fix-slow-application-startup-due-to-code-sign-validation/ I have also tried those registry settings out and unfortunately they didn't help. The dlls that appear to be the cause of the hold up are native SQL Server CE dlls, and looking at the stack traces in ProcMon mscorwks.dll doesn't appear to be involved even though the checks on crypto and cert registry keys are being done under the .NET application. It's definitely still something to do with publisher certificate checking because unticking "Check for publisher revocation certificate" still works but something odd is going on.

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  • Tool to identify (and remove) unnecessary website files?

    - by xanadont
    Inevitably I'll stop using an antiquated css, script, or image file. Especially when a separate designer is tinkering with things and testing out a few versions of images. Before I build one myself, are there any tools out there that will drill through a website and list unlinked files? Specifically, I'm interested in ASP.NET MVC sites, so detecting calls to (and among many other things) @Url.Content(...) is important.

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  • The blocking nature of aggregates

    - by Rob Farley
    I wrote a post recently about how query tuning isn’t just about how quickly the query runs – that if you have something (such as SSIS) that is consuming your data (and probably introducing a bottleneck), then it might be more important to have a query which focuses on getting the first bit of data out. You can read that post here.  In particular, we looked at two operators that could be used to ensure that a query returns only Distinct rows. and The Sort operator pulls in all the data, sorts it (discarding duplicates), and then pushes out the remaining rows. The Hash Match operator performs a Hashing function on each row as it comes in, and then looks to see if it’s created a Hash it’s seen before. If not, it pushes the row out. The Sort method is quicker, but has to wait until it’s gathered all the data before it can do the sort, and therefore blocks the data flow. But that was my last post. This one’s a bit different. This post is going to look at how Aggregate functions work, which ties nicely into this month’s T-SQL Tuesday. I’ve frequently explained about the fact that DISTINCT and GROUP BY are essentially the same function, although DISTINCT is the poorer cousin because you have less control over it, and you can’t apply aggregate functions. Just like the operators used for Distinct, there are different flavours of Aggregate operators – coming in blocking and non-blocking varieties. The example I like to use to explain this is a pile of playing cards. If I’m handed a pile of cards and asked to count how many cards there are in each suit, it’s going to help if the cards are already ordered. Suppose I’m playing a game of Bridge, I can easily glance at my hand and count how many there are in each suit, because I keep the pile of cards in order. Moving from left to right, I could tell you I have four Hearts in my hand, even before I’ve got to the end. By telling you that I have four Hearts as soon as I know, I demonstrate the principle of a non-blocking operation. This is known as a Stream Aggregate operation. It requires input which is sorted by whichever columns the grouping is on, and it will release a row as soon as the group changes – when I encounter a Spade, I know I don’t have any more Hearts in my hand. Alternatively, if the pile of cards are not sorted, I won’t know how many Hearts I have until I’ve looked through all the cards. In fact, to count them, I basically need to put them into little piles, and when I’ve finished making all those piles, I can count how many there are in each. Because I don’t know any of the final numbers until I’ve seen all the cards, this is blocking. This performs the aggregate function using a Hash Match. Observant readers will remember this from my Distinct example. You might remember that my earlier Hash Match operation – used for Distinct Flow – wasn’t blocking. But this one is. They’re essentially doing a similar operation, applying a Hash function to some data and seeing if the set of values have been seen before, but before, it needs more information than the mere existence of a new set of values, it needs to consider how many of them there are. A lot is dependent here on whether the data coming out of the source is sorted or not, and this is largely determined by the indexes that are being used. If you look in the Properties of an Index Scan, you’ll be able to see whether the order of the data is required by the plan. A property called Ordered will demonstrate this. In this particular example, the second plan is significantly faster, but is dependent on having ordered data. In fact, if I force a Stream Aggregate on unordered data (which I’m doing by telling it to use a different index), a Sort operation is needed, which makes my plan a lot slower. This is all very straight-forward stuff, and information that most people are fully aware of. I’m sure you’ve all read my good friend Paul White (@sql_kiwi)’s post on how the Query Optimizer chooses which type of aggregate function to apply. But let’s take a look at SQL Server Integration Services. SSIS gives us a Aggregate transformation for use in Data Flow Tasks, but it’s described as Blocking. The definitive article on Performance Tuning SSIS uses Sort and Aggregate as examples of Blocking Transformations. I’ve just shown you that Aggregate operations used by the Query Optimizer are not always blocking, but that the SSIS Aggregate component is an example of a blocking transformation. But is it always the case? After all, there are plenty of SSIS Performance Tuning talks out there that describe the value of sorted data in Data Flow Tasks, describing the IsSorted property that can be set through the Advanced Editor of your Source component. And so I set about testing the Aggregate transformation in SSIS, to prove for sure whether providing Sorted data would let the Aggregate transform behave like a Stream Aggregate. (Of course, I knew the answer already, but it helps to be able to demonstrate these things). A query that will produce a million rows in order was in order. Let me rephrase. I used a query which produced the numbers from 1 to 1000000, in a single field, ordered. The IsSorted flag was set on the source output, with the only column as SortKey 1. Performing an Aggregate function over this (counting the number of rows per distinct number) should produce an additional column with 1 in it. If this were being done in T-SQL, the ordered data would allow a Stream Aggregate to be used. In fact, if the Query Optimizer saw that the field had a Unique Index on it, it would be able to skip the Aggregate function completely, and just insert the value 1. This is a shortcut I wouldn’t be expecting from SSIS, but certainly the Stream behaviour would be nice. Unfortunately, it’s not the case. As you can see from the screenshots above, the data is pouring into the Aggregate function, and not being released until all million rows have been seen. It’s not doing a Stream Aggregate at all. This is expected behaviour. (I put that in bold, because I want you to realise this.) An SSIS transformation is a piece of code that runs. It’s a physical operation. When you write T-SQL and ask for an aggregation to be done, it’s a logical operation. The physical operation is either a Stream Aggregate or a Hash Match. In SSIS, you’re telling the system that you want a generic Aggregation, that will have to work with whatever data is passed in. I’m not saying that it wouldn’t be possible to make a sometimes-blocking aggregation component in SSIS. A Custom Component could be created which could detect whether the SortKeys columns of the input matched the Grouping columns of the Aggregation, and either call the blocking code or the non-blocking code as appropriate. One day I’ll make one of those, and publish it on my blog. I’ve done it before with a Script Component, but as Script components are single-use, I was able to handle the data knowing everything about my data flow already. As per my previous post – there are a lot of aspects in which tuning SSIS and tuning execution plans use similar concepts. In both situations, it really helps to have a feel for what’s going on behind the scenes. Considering whether an operation is blocking or not is extremely relevant to performance, and that it’s not always obvious from the surface. In a future post, I’ll show the impact of blocking v non-blocking and synchronous v asynchronous components in SSIS, using some of LobsterPot’s Script Components and Custom Components as examples. When I get that sorted, I’ll make a Stream Aggregate component available for download.

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  • The blocking nature of aggregates

    - by Rob Farley
    I wrote a post recently about how query tuning isn’t just about how quickly the query runs – that if you have something (such as SSIS) that is consuming your data (and probably introducing a bottleneck), then it might be more important to have a query which focuses on getting the first bit of data out. You can read that post here.  In particular, we looked at two operators that could be used to ensure that a query returns only Distinct rows. and The Sort operator pulls in all the data, sorts it (discarding duplicates), and then pushes out the remaining rows. The Hash Match operator performs a Hashing function on each row as it comes in, and then looks to see if it’s created a Hash it’s seen before. If not, it pushes the row out. The Sort method is quicker, but has to wait until it’s gathered all the data before it can do the sort, and therefore blocks the data flow. But that was my last post. This one’s a bit different. This post is going to look at how Aggregate functions work, which ties nicely into this month’s T-SQL Tuesday. I’ve frequently explained about the fact that DISTINCT and GROUP BY are essentially the same function, although DISTINCT is the poorer cousin because you have less control over it, and you can’t apply aggregate functions. Just like the operators used for Distinct, there are different flavours of Aggregate operators – coming in blocking and non-blocking varieties. The example I like to use to explain this is a pile of playing cards. If I’m handed a pile of cards and asked to count how many cards there are in each suit, it’s going to help if the cards are already ordered. Suppose I’m playing a game of Bridge, I can easily glance at my hand and count how many there are in each suit, because I keep the pile of cards in order. Moving from left to right, I could tell you I have four Hearts in my hand, even before I’ve got to the end. By telling you that I have four Hearts as soon as I know, I demonstrate the principle of a non-blocking operation. This is known as a Stream Aggregate operation. It requires input which is sorted by whichever columns the grouping is on, and it will release a row as soon as the group changes – when I encounter a Spade, I know I don’t have any more Hearts in my hand. Alternatively, if the pile of cards are not sorted, I won’t know how many Hearts I have until I’ve looked through all the cards. In fact, to count them, I basically need to put them into little piles, and when I’ve finished making all those piles, I can count how many there are in each. Because I don’t know any of the final numbers until I’ve seen all the cards, this is blocking. This performs the aggregate function using a Hash Match. Observant readers will remember this from my Distinct example. You might remember that my earlier Hash Match operation – used for Distinct Flow – wasn’t blocking. But this one is. They’re essentially doing a similar operation, applying a Hash function to some data and seeing if the set of values have been seen before, but before, it needs more information than the mere existence of a new set of values, it needs to consider how many of them there are. A lot is dependent here on whether the data coming out of the source is sorted or not, and this is largely determined by the indexes that are being used. If you look in the Properties of an Index Scan, you’ll be able to see whether the order of the data is required by the plan. A property called Ordered will demonstrate this. In this particular example, the second plan is significantly faster, but is dependent on having ordered data. In fact, if I force a Stream Aggregate on unordered data (which I’m doing by telling it to use a different index), a Sort operation is needed, which makes my plan a lot slower. This is all very straight-forward stuff, and information that most people are fully aware of. I’m sure you’ve all read my good friend Paul White (@sql_kiwi)’s post on how the Query Optimizer chooses which type of aggregate function to apply. But let’s take a look at SQL Server Integration Services. SSIS gives us a Aggregate transformation for use in Data Flow Tasks, but it’s described as Blocking. The definitive article on Performance Tuning SSIS uses Sort and Aggregate as examples of Blocking Transformations. I’ve just shown you that Aggregate operations used by the Query Optimizer are not always blocking, but that the SSIS Aggregate component is an example of a blocking transformation. But is it always the case? After all, there are plenty of SSIS Performance Tuning talks out there that describe the value of sorted data in Data Flow Tasks, describing the IsSorted property that can be set through the Advanced Editor of your Source component. And so I set about testing the Aggregate transformation in SSIS, to prove for sure whether providing Sorted data would let the Aggregate transform behave like a Stream Aggregate. (Of course, I knew the answer already, but it helps to be able to demonstrate these things). A query that will produce a million rows in order was in order. Let me rephrase. I used a query which produced the numbers from 1 to 1000000, in a single field, ordered. The IsSorted flag was set on the source output, with the only column as SortKey 1. Performing an Aggregate function over this (counting the number of rows per distinct number) should produce an additional column with 1 in it. If this were being done in T-SQL, the ordered data would allow a Stream Aggregate to be used. In fact, if the Query Optimizer saw that the field had a Unique Index on it, it would be able to skip the Aggregate function completely, and just insert the value 1. This is a shortcut I wouldn’t be expecting from SSIS, but certainly the Stream behaviour would be nice. Unfortunately, it’s not the case. As you can see from the screenshots above, the data is pouring into the Aggregate function, and not being released until all million rows have been seen. It’s not doing a Stream Aggregate at all. This is expected behaviour. (I put that in bold, because I want you to realise this.) An SSIS transformation is a piece of code that runs. It’s a physical operation. When you write T-SQL and ask for an aggregation to be done, it’s a logical operation. The physical operation is either a Stream Aggregate or a Hash Match. In SSIS, you’re telling the system that you want a generic Aggregation, that will have to work with whatever data is passed in. I’m not saying that it wouldn’t be possible to make a sometimes-blocking aggregation component in SSIS. A Custom Component could be created which could detect whether the SortKeys columns of the input matched the Grouping columns of the Aggregation, and either call the blocking code or the non-blocking code as appropriate. One day I’ll make one of those, and publish it on my blog. I’ve done it before with a Script Component, but as Script components are single-use, I was able to handle the data knowing everything about my data flow already. As per my previous post – there are a lot of aspects in which tuning SSIS and tuning execution plans use similar concepts. In both situations, it really helps to have a feel for what’s going on behind the scenes. Considering whether an operation is blocking or not is extremely relevant to performance, and that it’s not always obvious from the surface. In a future post, I’ll show the impact of blocking v non-blocking and synchronous v asynchronous components in SSIS, using some of LobsterPot’s Script Components and Custom Components as examples. When I get that sorted, I’ll make a Stream Aggregate component available for download.

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  • Can Campaign URL tags cause a soft 404 error?

    - by user35306
    I was checking out one of my company's website's Webmaster Tools to analyze the cause behind some soft 404 errors and discovered that a few of the older errors had affiliate mp referral tags listed as the relative URLs. Since these are older problems and I don't seem too many of them coming up in the last few months I don't think it's still a problem. I'm just curious if it's possible to cause a soft 404 by improperly copying the campaign or referral tag into the URL.

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  • How to Create Custom SharePoint Workflows in Visual Studio 2008

    Whereas simple workflows are possible using Microsoft Office SharePoint Designer, you will soon reach the point where you will need to use Visual Studio. In the third article in Charles' introduction to Workflows in Sharepoint, he demonstrates how to create a workflow from scratch using Visual Studio, and discusses the relative merits of the two tools for this sort of development work.

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  • Extreme Performance and Scale Delivered by SOA on Oracle Exalogic

    - by J Swaroop
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Calibri","sans-serif"; mso-ascii- mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi- mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Calibri","sans-serif"; mso-ascii- mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi- mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Demands to incorporate internet-scale applications, data, and social media traffic with existing IT infrastructure require extreme availability, reliability, and scalability. In this session on industrial-strength SOA, learn how Oracle Exalogic and Oracle Exadata engineered systems address these requirements. Topics covered: (1) how SOA and BPM benefit from “hardware and software engineered for each other,” (2) how Oracle Exadata provides the data tier with unparalleled scalability and performance for SOA and BPM running on Oracle Exalogic (3) customer case studies (4) best practices and topology guidelines (5) information on tools that help operate, manage, provision, and deploy—to help reduce overall TCO. Extreme engineering at its best! Session details: 10/2/12 (Tuesday) 11:45 AM - Moscone South -308

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  • SQLRally and SQLRally - Session material

    - by Hugo Kornelis
    I had a great week last week. First at SQLRally Nordic , in Stockholm, where I presented a session on how improvements to the OVER clause can help you simplify queries in SQL Server 2012 enormously. And then I continued straight on into SQLRally Amsterdam , where I delivered a session on the performance implications of using user-defined functions in T-SQL. I understand that both events will make my slides and demo code downloadable from their website, but this may take a while. So those who do not...(read more)

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