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  • Is there an app or script that will extract .rar files for Mac Os X...?

    - by smileemee
    ...after it completed download that runs similiar to how zip files unextract itself when the download finishes and actually throws the zipped copy in the trash? I usually use unrax but lately with all these .rar files, the cool zip extractor is not very helpful right now. A script with automator? anything to make this easier when extracting compressed files such as .rar. Thank you. Running Mac OS X 10.6

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  • Which process is using a port in OS X?

    - by Zubair
    I'm trying to start a program in OS X and I get the message: Port already in use: 8080 I tried LSOF to find out who is using the port but it doesn't have the information. Is there any way I can find out who is using this port so that I can then kill the process?

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  • ASP.NET Javascript Error :: sys.webforms.pagerequestmanagerservererrorexception

    - by Andy Evans
    I have an ASP.NET site that uses JQuery and ASP.NET UpdatePanel and ScriptManager. On one page in particular, I get a javascript error: sys.webforms.pagerequestmanagerservererrorexception: Index and length must refer to a location within the string. Parameter name:length ScriptResourse.axd Code: 0 Here's what's in the master page: <asp:ScriptManager runat="server" ID="ScriptMgr"></asp:ScriptManager> <asp:UpdatePanel runat="server" ID="UpdatePanelMaster"> <ContentTemplate> </ContentTemplate> </asp:UpdatePanel> In the page in question: <asp:Content ID="ContentHeadEdit" ContentPlaceHolderID="ContentHeadMaster" Runat="Server"> <script type="text/javascript"> $(document).ready(function() { $('#<%= ButtonSave.ClientID %>').button(); Sys.WebForms.PageRequestManager.getInstance().add_endRequest(EndRequestHandler); function EndRequestHandler(sender, args) { $('#<%= ButtonSave.ClientID %>').button(); } }); </script> </asp:Content>

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  • Does JSON.js cause conflicts with Sys.Serialization.JavaScriptSerializer.serialize

    - by David Robbins
    I am using Telerik controls in my webforms and want to serialize object on the client. Since I am getting a stackoverflow error with Sys.Serialization.JavaScriptSerializer.deserialize I wanted to try JSON. With both JSON and and the MS library I get "Sys.Application is undefined." Has anyone encountered this what did you do as a work around? EDIT I am serializing my object on a parent page and passing them via an argument to a child window. The child window is in an IFRAME tag. The object can be used in the child page, but I receive the stackoverflow error when I serialize it. The object is an Array of objects.

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  • Sys undefined for HTTPS url in IE8

    - by Dhana
    I just discovered a rather peculiar issue in IE8 for a HTTPS link. Every time the page tries to access the HTTPS link, it produces an error. This happens only in IE8 and nothing else. Any idea what's going on? I found some items that said that means the files were not loaded, hence the issue and tried some fixes recommended, but they haven't worked so far. This is a .NET site by the way. https://www.beckshoes.com/cart/cart.aspx Message: 'Sys' is undefined Line: 70 Char: 1 Code: 0 URI: https://www.beckshoes.com/cart/cart.aspx Message: 'Sys' is undefined Line: 319 Char: 1 Code: 0 URI: https://www.beckshoes.com/cart/cart.aspx

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  • View a SYS.XMLTYPE returned from an Oracle function, using PL/SQL

    - by caerphilly
    I have an Oracle function that dynamically creates an XML document, and returns it in a SYS.XMLTYPE value. I want to run a query from SQL Developer that calls that function and prints the document (either via a select, or dbms_output - I don't care). But all the examples/documentation seem to refer to querying XML columns in tables, and I can't seem to get the syntax right for my particular usage. I'd like something like this: declare x SYS.XMLTYPE; begin x := my_package.my_function(); select x.getclobval() from x; -- doesn't work! end; How can I print out the value of the XML type variable 'x' in the above code?

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  • Can someone please explain to me the basic function of Intents in the Android OS?

    - by K-RAN
    I'm new to programming applications for the Android OS. As far as general architecture of the OS goes, I understand that processes are implemented as Linux processes and that each one is sandboxed. However, I'm utterly confused on the IPCs and syscalls (if any) used. I know that the IBinder is a form of this; parcels are sent back and forth between processes and Bundles are array forms of parcels (?). But even that is still unfamiliar to me. Same with Intents. All in all, I don't understand what kinds of IPCs are implemented and how. Could someone briefly explain to me the specific methods used by user level applications in Android OS to communicate with each other and the OS? I've done kernel programming and played with various IPCs in Linux (Ubuntu and Debian) so it would help immensely if this was all explained in relation to what I'm familiar with... Thanks in advance!

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  • Full GC real time is much more that user+sys times

    - by Stas
    Hi. We have a Web Java based application running on JBoss with allowed maximum heap size of about 1.2 GB (total machine physical memory is 2 GB). At some point the application stops responding (to clients) for several minutes. After some analysis we found out that the culprit is the Full GC. Here's an excerpt from the verbose GC log: 74477.402: [Full GC [PSYoungGen: 3648K-0K(332160K)] [PSOldGen: 778476K-589497K(819200K)] 782124K-589497K(1151360K) [PSPermGen: 102671K-102671K(171328K)], 646.1546860 secs] [Times: user=3.84 sys=3.72, real=646.17 secs] What I don't understand is how is it possible that the real time spent on Full GC is about 11 minutes (646 seconds), while user+sys times are just 7.5 seconds. 7.5 seconds sound to me much more logical time to spend for cleaning <200 MB from the old generation. Where does all the other time go? Thanks a lot.

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  • JavaOne in Brazil

    - by janice.heiss(at)oracle.com
    JavaOne in Brazil, currently taking place in Sao Paolo, is one event I'd love to attend. I once heard "father of Java" James Gosling talk about Java developers throughout the world. He observed that there were good developers everywhere. It was not the case, he said, that that the really good developers are in one place and the not-so-good developers are in another. He encountered excellent developers everywhere. Then he paused and said that the craziest developers were definitely the Brazilians. As anyone who knows James would realize, this was meant as high praise. He said the Brazilians would work through the night on projects and were very enthusiastic and spontaneous - features that Brazilian culture is known for. Brazilian developers are responsible for creating one of the most impressive uses of Java ever - the applications that run the Brazilian health services. Starting from scratch they created a system that enables an expert doctor in Rio to look at an X-Ray of a patient near the Amazon and offer advice. One of the main architects of this was Java Champion Fabinane Nardon the distinguished Brazilian Java architect and open-source evangelist. As she writes in her blog:"In 2003, I was invited to assemble a team and architect a Public Healthcare Information System for the city of São Paulo, the largest in Latin America, with 14 million inhabitants. The resulting software had 2.5 million of lines of code and it was created, from specification to production, in only 10 months. At the time, the software was considered the largest J2EE application in the world and was featured in several articles, as this one. As a result, we won the Duke's Choice Award in 2005 during JavaOne, the largest development conference in the world. At the time, Sun Microsystems make a short documentary about our work." "In 2007, a lightning struck twice and I was again invited to assemble a new team and architect an even larger information system for healthcare. And thus I became CTO and one of the founders of Zilics Healthcare Information Systems. "In 2010, I started to research and work on Cloud Computing technology and became leader of the LSI-TEC Cloud Computing group. LSI-TEC is a research laboratory in the University of Sao Paulo, one of the best in Brazil. Thus, I became one of the ghost writers behind the popular Cloud Computing Twitter @the_cloud."You can see and hear Nardon in a 4 minute documentary on Java and the Brazilian health care system produced by Sun Microsystems. And you can listen to a September 2010 podcast with Nardon and her fellow Brazilian Java Champion Bruno Souza (known in Brazil as "Java Man") here at 11:10 minutes into the podcast.Next year, I'll hope to be reporting in Brazil at JavaOne!

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  • Mac OS X = UNIX? [closed]

    - by lee
    Possible Duplicate: How Unix is Mac OS X? People always said ubuntu, linux is UNIX based OS, then how about mac os x? from mac os x terminal i can see most of the unix command are available does it mean mac os also built on UNIX?

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  • Why does Mac OS X ignore my Windows NTFS and Share permissions?

    - by Michael
    Mac OS X Snow Leopard Windows Server 2003 Windows Folder "Videos" Share Permissions on Videos - Everyone NTFS Permissions on Videos - System (Full Control) - Domain Users (Modify) - Domain Admins (Full Control) Mac user Michael is a part of the Domain Users group. He connects to Videos using cifs://server/Videos and authenticates with his username Michael. Michael copies over a file "dance dance baby.avi". User Jon opens the Videos folder but cannot see the dance dance baby.avi file. Checking the dance dance baby.avi file permissions here is what I see: Everyone - Read, Write Domain Admins - Full Control Michael - Read, Write Owner of File - Michael So here's my question, how come when Michael copies a file over from a Mac, the permissions on the file get changed even though Michael has no rights to change permissions? If the same file is copied over from a Windows machine, it just inherits the proper permissions from the parent Video folder. Am I missing something? Are my permissions wrong? Thanks. Michael

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  • Is is possible to hide label colors in list view in Mac OS 10.X Finder? I use labels as star ratings

    - by Andrew Swift
    I have modified the first five label names in OS X Lion to be ? through ?????. This way I can easily tag photos etc. in the finder. However, I find that Apple's rendering of the colored bars is horrendously ugly. I'd like to keep the labels (to be able to sort by label) but not see the colors. I know it was possible to change the colors with Label X, but Unsanity has not issued an update for Lion. I don't need to change the colors, just find a way to hide them in the finder.

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  • How is a Chromebook better than using Chrome browser on another OS?

    - by bigpopakap
    I'm looking into getting a Chromebook as a lightweight device to use for basic functions. I'm fully aware of some of the limitations like no native apps, limited functionality while offline (though this is getting better with Google Drive's offline capabilities). Currently, I have a Windows machine on which I've installed Google Drive, so files in that folder are synced. And I use Chrome as my browser. So I have access to all the same apps (Drive, Calendar, Gmail, Google Music, etc.). Is there any advantage to having a Chromebook over my current setup, other than the speed of the lightweight Chrome OS?

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  • What are 'damaged files' on external hard drive (HFS format for OS X)?

    - by dtlussier
    I have an external HD formatted to default HFS (Mac OS Extended - Journaled) and very once and a while I get a folder called DamagedFiles in the root of the volume. The folder contains a collection of links to files on the drive. In general the files seem fine as I am for example able to open the images or text files without a problem. Is this serious? What can I do to fix this problem? Any advice would be great as I couldn't find anything on here or via Google that addressed this problem in particular. Many thanks.

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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. 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  • Android : Google étend le champs d'application de l'API "Fragments" pour lutter contre la fragmentation de l'OS

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