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  • Query Execution Plan - When is the Where clause executed?

    - by Alex
    I have a query like this (created by LINQ): SELECT [t0].[Id], [t0].[CreationDate], [t0].[CreatorId] FROM [dbo].[DataFTS]('test', 100) AS [t0] WHERE [t0].[CreatorId] = 1 ORDER BY [t0].[RANK] DataFTS is a full-text search table valued function. The query execution plan looks like this: SELECT (0%) - Sort (23%) - Nested Loops (Inner Join) (1%) - Sort (Top N Sort) (25%) - Stream Aggregate (0%) - Stream Aggregate (0%) - Compute Scalar (0%) - Table Valued Function (FullTextMatch) (13%) | | - Clustered Index Seek (38%) Does this mean that the WHERE clause ([CreatorId] = 1) is executed prior to the TVF ( full text search) or after the full text search? Thank you.

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  • Why isn't INT more efficient than UNIQUEIDENTIFIER (according to the execution plan)?

    - by ck
    I have a parent table and child table where the columns that join them together are the UNIQUEIDENTIFIER type. The child table has a clustered index on the column that joins it to the parent table (its PK, which is also clustered). I have created a copy of both of these tables but changed the relationship columns to be INTs instead, have rebuilt the indexes so that they are essentially the same structure and can be queried in the same way. When I query for a known 20 records from the parent table, pulling in all the related records from the child tables, I get identical query costs across both, i.e. 50/50 cost for the batches. If this is true, then my giant project to change all of the tables like this appears to be pointless, other than speeding up inserts. Can anyone provide any light on the situation? EDIT: The question is not about which is more efficient, but why is the query execution plan showing both queries as having the same cost?

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  • More CPU cores may not always lead to better performance – MAXDOP and query memory distribution in spotlight

    - by sqlworkshops
    More hardware normally delivers better performance, but there are exceptions where it can hinder performance. Understanding these exceptions and working around it is a major part of SQL Server performance tuning.   When a memory allocating query executes in parallel, SQL Server distributes memory to each task that is executing part of the query in parallel. In our example the sort operator that executes in parallel divides the memory across all tasks assuming even distribution of rows. Common memory allocating queries are that perform Sort and do Hash Match operations like Hash Join or Hash Aggregation or Hash Union.   In reality, how often are column values evenly distributed, think about an example; are employees working for your company distributed evenly across all the Zip codes or mainly concentrated in the headquarters? What happens when you sort result set based on Zip codes? Do all products in the catalog sell equally or are few products hot selling items?   One of my customers tested the below example on a 24 core server with various MAXDOP settings and here are the results:MAXDOP 1: CPU time = 1185 ms, elapsed time = 1188 msMAXDOP 4: CPU time = 1981 ms, elapsed time = 1568 msMAXDOP 8: CPU time = 1918 ms, elapsed time = 1619 msMAXDOP 12: CPU time = 2367 ms, elapsed time = 2258 msMAXDOP 16: CPU time = 2540 ms, elapsed time = 2579 msMAXDOP 20: CPU time = 2470 ms, elapsed time = 2534 msMAXDOP 0: CPU time = 2809 ms, elapsed time = 2721 ms - all 24 cores.In the above test, when the data was evenly distributed, the elapsed time of parallel query was always lower than serial query.   Why does the query get slower and slower with more CPU cores / higher MAXDOP? Maybe you can answer this question after reading the article; let me know: [email protected].   Well you get the point, let’s see an example.   The best way to learn is to practice. To create the below tables and reproduce the behavior, join the mailing list by using this link: www.sqlworkshops.com/ml and I will send you the table creation script.   Let’s update the Employees table with 49 out of 50 employees located in Zip code 2001. update Employees set Zip = EmployeeID / 400 + 1 where EmployeeID % 50 = 1 update Employees set Zip = 2001 where EmployeeID % 50 != 1 go update statistics Employees with fullscan go   Let’s create the temporary table #FireDrill with all possible Zip codes. drop table #FireDrill go create table #FireDrill (Zip int primary key) insert into #FireDrill select distinct Zip from Employees update statistics #FireDrill with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --First serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) goThe query took 1011 ms to complete.   The execution plan shows the 77816 KB of memory was granted while the estimated rows were 799624.  No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 1912 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 799624.  The estimated number of rows between serial and parallel plan are the same. The parallel plan has slightly more memory granted due to additional overhead. Sort properties shows the rows are unevenly distributed over the 4 threads.   Sort Warnings in SQL Server Profiler.   Intermediate Summary: The reason for the higher duration with parallel plan was sort spill. This is due to uneven distribution of employees over Zip codes, especially concentration of 49 out of 50 employees in Zip code 2001. Now let’s update the Employees table and distribute employees evenly across all Zip codes.   update Employees set Zip = EmployeeID / 400 + 1 go update statistics Employees with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go   The query took 751 ms to complete.  The execution plan shows the 77816 KB of memory was granted while the estimated rows were 784707.  No Sort Warnings in SQL Server Profiler.   Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 661 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 784707.  Sort properties shows the rows are evenly distributed over the 4 threads. No Sort Warnings in SQL Server Profiler.    Intermediate Summary: When employees were distributed unevenly, concentrated on 1 Zip code, parallel sort spilled while serial sort performed well without spilling to tempdb. When the employees were distributed evenly across all Zip codes, parallel sort and serial sort did not spill to tempdb. This shows uneven data distribution may affect the performance of some parallel queries negatively. For detailed discussion of memory allocation, refer to webcasts available at www.sqlworkshops.com/webcasts.     Some of you might conclude from the above execution times that parallel query is not faster even when there is no spill. Below you can see when we are joining limited amount of Zip codes, parallel query will be fasted since it can use Bitmap Filtering.   Let’s update the Employees table with 49 out of 50 employees located in Zip code 2001. update Employees set Zip = EmployeeID / 400 + 1 where EmployeeID % 50 = 1 update Employees set Zip = 2001 where EmployeeID % 50 != 1 go update statistics Employees with fullscan go  Let’s create the temporary table #FireDrill with limited Zip codes. drop table #FireDrill go create table #FireDrill (Zip int primary key) insert into #FireDrill select distinct Zip       from Employees where Zip between 1800 and 2001 update statistics #FireDrill with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go The query took 989 ms to complete.  The execution plan shows the 77816 KB of memory was granted while the estimated rows were 785594. No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 1799 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 785594.  Sort Warnings in SQL Server Profiler.    The estimated number of rows between serial and parallel plan are the same. The parallel plan has slightly more memory granted due to additional overhead.  Intermediate Summary: The reason for the higher duration with parallel plan even with limited amount of Zip codes was sort spill. This is due to uneven distribution of employees over Zip codes, especially concentration of 49 out of 50 employees in Zip code 2001.   Now let’s update the Employees table and distribute employees evenly across all Zip codes. update Employees set Zip = EmployeeID / 400 + 1 go update statistics Employees with fullscan go Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go The query took 250  ms to complete.  The execution plan shows the 9016 KB of memory was granted while the estimated rows were 79973.8.  No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0.  --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 85 ms to complete.  The execution plan shows the 13152 KB of memory was granted while the estimated rows were 784707.  No Sort Warnings in SQL Server Profiler.    Here you see, parallel query is much faster than serial query since SQL Server is using Bitmap Filtering to eliminate rows before the hash join.   Parallel queries are very good for performance, but in some cases it can hinder performance. If one identifies the reason for these hindrances, then it is possible to get the best out of parallelism. I covered many aspects of monitoring and tuning parallel queries in webcasts (www.sqlworkshops.com/webcasts) and articles (www.sqlworkshops.com/articles). I suggest you to watch the webcasts and read the articles to better understand how to identify and tune parallel query performance issues.   Summary: One has to avoid sort spill over tempdb and the chances of spills are higher when a query executes in parallel with uneven data distribution. Parallel query brings its own advantage, reduced elapsed time and reduced work with Bitmap Filtering. So it is important to understand how to avoid spills over tempdb and when to execute a query in parallel.   I explain these concepts with detailed examples in my webcasts (www.sqlworkshops.com/webcasts), I recommend you to watch them. The best way to learn is to practice. To create the above tables and reproduce the behavior, join the mailing list at www.sqlworkshops.com/ml and I will send you the relevant SQL Scripts.   Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   Disclaimer and copyright information:This article refers to organizations and products that may be the trademarks or registered trademarks of their various owners. Copyright of this article belongs to R Meyyappan / www.sqlworkshops.com. You may freely use the ideas and concepts discussed in this article with acknowledgement (www.sqlworkshops.com), but you may not claim any of it as your own work. This article is for informational purposes only; you use any of the suggestions given here entirely at your own risk.   Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   R Meyyappan [email protected] LinkedIn: http://at.linkedin.com/in/rmeyyappan  

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  • MERGE Bug with Filtered Indexes

    - by Paul White
    A MERGE statement can fail, and incorrectly report a unique key violation when: The target table uses a unique filtered index; and No key column of the filtered index is updated; and A column from the filtering condition is updated; and Transient key violations are possible Example Tables Say we have two tables, one that is the target of a MERGE statement, and another that contains updates to be applied to the target.  The target table contains three columns, an integer primary key, a single character alternate key, and a status code column.  A filtered unique index exists on the alternate key, but is only enforced where the status code is ‘a’: CREATE TABLE #Target ( pk integer NOT NULL, ak character(1) NOT NULL, status_code character(1) NOT NULL,   PRIMARY KEY (pk) );   CREATE UNIQUE INDEX uq1 ON #Target (ak) INCLUDE (status_code) WHERE status_code = 'a'; The changes table contains just an integer primary key (to identify the target row to change) and the new status code: CREATE TABLE #Changes ( pk integer NOT NULL, status_code character(1) NOT NULL,   PRIMARY KEY (pk) ); Sample Data The sample data for the example is: INSERT #Target (pk, ak, status_code) VALUES (1, 'A', 'a'), (2, 'B', 'a'), (3, 'C', 'a'), (4, 'A', 'd');   INSERT #Changes (pk, status_code) VALUES (1, 'd'), (4, 'a');          Target                     Changes +-----------------------+    +------------------+ ¦ pk ¦ ak ¦ status_code ¦    ¦ pk ¦ status_code ¦ ¦----+----+-------------¦    ¦----+-------------¦ ¦  1 ¦ A  ¦ a           ¦    ¦  1 ¦ d           ¦ ¦  2 ¦ B  ¦ a           ¦    ¦  4 ¦ a           ¦ ¦  3 ¦ C  ¦ a           ¦    +------------------+ ¦  4 ¦ A  ¦ d           ¦ +-----------------------+ The target table’s alternate key (ak) column is unique, for rows where status_code = ‘a’.  Applying the changes to the target will change row 1 from status ‘a’ to status ‘d’, and row 4 from status ‘d’ to status ‘a’.  The result of applying all the changes will still satisfy the filtered unique index, because the ‘A’ in row 1 will be deleted from the index and the ‘A’ in row 4 will be added. Merge Test One Let’s now execute a MERGE statement to apply the changes: MERGE #Target AS t USING #Changes AS c ON c.pk = t.pk WHEN MATCHED AND c.status_code <> t.status_code THEN UPDATE SET status_code = c.status_code; The MERGE changes the two target rows as expected.  The updated target table now contains: +-----------------------+ ¦ pk ¦ ak ¦ status_code ¦ ¦----+----+-------------¦ ¦  1 ¦ A  ¦ d           ¦ <—changed from ‘a’ ¦  2 ¦ B  ¦ a           ¦ ¦  3 ¦ C  ¦ a           ¦ ¦  4 ¦ A  ¦ a           ¦ <—changed from ‘d’ +-----------------------+ Merge Test Two Now let’s repopulate the changes table to reverse the updates we just performed: TRUNCATE TABLE #Changes;   INSERT #Changes (pk, status_code) VALUES (1, 'a'), (4, 'd'); This will change row 1 back to status ‘a’ and row 4 back to status ‘d’.  As a reminder, the current state of the tables is:          Target                        Changes +-----------------------+    +------------------+ ¦ pk ¦ ak ¦ status_code ¦    ¦ pk ¦ status_code ¦ ¦----+----+-------------¦    ¦----+-------------¦ ¦  1 ¦ A  ¦ d           ¦    ¦  1 ¦ a           ¦ ¦  2 ¦ B  ¦ a           ¦    ¦  4 ¦ d           ¦ ¦  3 ¦ C  ¦ a           ¦    +------------------+ ¦  4 ¦ A  ¦ a           ¦ +-----------------------+ We execute the same MERGE statement: MERGE #Target AS t USING #Changes AS c ON c.pk = t.pk WHEN MATCHED AND c.status_code <> t.status_code THEN UPDATE SET status_code = c.status_code; However this time we receive the following message: Msg 2601, Level 14, State 1, Line 1 Cannot insert duplicate key row in object 'dbo.#Target' with unique index 'uq1'. The duplicate key value is (A). The statement has been terminated. Applying the changes using UPDATE Let’s now rewrite the MERGE to use UPDATE instead: UPDATE t SET status_code = c.status_code FROM #Target AS t JOIN #Changes AS c ON t.pk = c.pk WHERE c.status_code <> t.status_code; This query succeeds where the MERGE failed.  The two rows are updated as expected: +-----------------------+ ¦ pk ¦ ak ¦ status_code ¦ ¦----+----+-------------¦ ¦  1 ¦ A  ¦ a           ¦ <—changed back to ‘a’ ¦  2 ¦ B  ¦ a           ¦ ¦  3 ¦ C  ¦ a           ¦ ¦  4 ¦ A  ¦ d           ¦ <—changed back to ‘d’ +-----------------------+ What went wrong with the MERGE? In this test, the MERGE query execution happens to apply the changes in the order of the ‘pk’ column. In test one, this was not a problem: row 1 is removed from the unique filtered index by changing status_code from ‘a’ to ‘d’ before row 4 is added.  At no point does the table contain two rows where ak = ‘A’ and status_code = ‘a’. In test two, however, the first change was to change row 1 from status ‘d’ to status ‘a’.  This change means there would be two rows in the filtered unique index where ak = ‘A’ (both row 1 and row 4 meet the index filtering criteria ‘status_code = a’). The storage engine does not allow the query processor to violate a unique key (unless IGNORE_DUP_KEY is ON, but that is a different story, and doesn’t apply to MERGE in any case).  This strict rule applies regardless of the fact that if all changes were applied, there would be no unique key violation (row 4 would eventually be changed from ‘a’ to ‘d’, removing it from the filtered unique index, and resolving the key violation). Why it went wrong The query optimizer usually detects when this sort of temporary uniqueness violation could occur, and builds a plan that avoids the issue.  I wrote about this a couple of years ago in my post Beware Sneaky Reads with Unique Indexes (you can read more about the details on pages 495-497 of Microsoft SQL Server 2008 Internals or in Craig Freedman’s blog post on maintaining unique indexes).  To summarize though, the optimizer introduces Split, Filter, Sort, and Collapse operators into the query plan to: Split each row update into delete followed by an inserts Filter out rows that would not change the index (due to the filter on the index, or a non-updating update) Sort the resulting stream by index key, with deletes before inserts Collapse delete/insert pairs on the same index key back into an update The effect of all this is that only net changes are applied to an index (as one or more insert, update, and/or delete operations).  In this case, the net effect is a single update of the filtered unique index: changing the row for ak = ‘A’ from pk = 4 to pk = 1.  In case that is less than 100% clear, let’s look at the operation in test two again:          Target                     Changes                   Result +-----------------------+    +------------------+    +-----------------------+ ¦ pk ¦ ak ¦ status_code ¦    ¦ pk ¦ status_code ¦    ¦ pk ¦ ak ¦ status_code ¦ ¦----+----+-------------¦    ¦----+-------------¦    ¦----+----+-------------¦ ¦  1 ¦ A  ¦ d           ¦    ¦  1 ¦ d           ¦    ¦  1 ¦ A  ¦ a           ¦ ¦  2 ¦ B  ¦ a           ¦    ¦  4 ¦ a           ¦    ¦  2 ¦ B  ¦ a           ¦ ¦  3 ¦ C  ¦ a           ¦    +------------------+    ¦  3 ¦ C  ¦ a           ¦ ¦  4 ¦ A  ¦ a           ¦                            ¦  4 ¦ A  ¦ d           ¦ +-----------------------+                            +-----------------------+ From the filtered index’s point of view (filtered for status_code = ‘a’ and shown in nonclustered index key order) the overall effect of the query is:   Before           After +---------+    +---------+ ¦ pk ¦ ak ¦    ¦ pk ¦ ak ¦ ¦----+----¦    ¦----+----¦ ¦  4 ¦ A  ¦    ¦  1 ¦ A  ¦ ¦  2 ¦ B  ¦    ¦  2 ¦ B  ¦ ¦  3 ¦ C  ¦    ¦  3 ¦ C  ¦ +---------+    +---------+ The single net change there is a change of pk from 4 to 1 for the nonclustered index entry ak = ‘A’.  This is the magic performed by the split, sort, and collapse.  Notice in particular how the original changes to the index key (on the ‘ak’ column) have been transformed into an update of a non-key column (pk is included in the nonclustered index).  By not updating any nonclustered index keys, we are guaranteed to avoid transient key violations. The Execution Plans The estimated MERGE execution plan that produces the incorrect key-violation error looks like this (click to enlarge in a new window): The successful UPDATE execution plan is (click to enlarge in a new window): The MERGE execution plan is a narrow (per-row) update.  The single Clustered Index Merge operator maintains both the clustered index and the filtered nonclustered index.  The UPDATE plan is a wide (per-index) update.  The clustered index is maintained first, then the Split, Filter, Sort, Collapse sequence is applied before the nonclustered index is separately maintained. There is always a wide update plan for any query that modifies the database. The narrow form is a performance optimization where the number of rows is expected to be relatively small, and is not available for all operations.  One of the operations that should disallow a narrow plan is maintaining a unique index where intermediate key violations could occur. Workarounds The MERGE can be made to work (producing a wide update plan with split, sort, and collapse) by: Adding all columns referenced in the filtered index’s WHERE clause to the index key (INCLUDE is not sufficient); or Executing the query with trace flag 8790 set e.g. OPTION (QUERYTRACEON 8790). Undocumented trace flag 8790 forces a wide update plan for any data-changing query (remember that a wide update plan is always possible).  Either change will produce a successfully-executing wide update plan for the MERGE that failed previously. Conclusion The optimizer fails to spot the possibility of transient unique key violations with MERGE under the conditions listed at the start of this post.  It incorrectly chooses a narrow plan for the MERGE, which cannot provide the protection of a split/sort/collapse sequence for the nonclustered index maintenance. The MERGE plan may fail at execution time depending on the order in which rows are processed, and the distribution of data in the database.  Worse, a previously solid MERGE query may suddenly start to fail unpredictably if a filtered unique index is added to the merge target table at any point. Connect bug filed here Tests performed on SQL Server 2012 SP1 CUI (build 11.0.3321) x64 Developer Edition © 2012 Paul White – All Rights Reserved Twitter: @SQL_Kiwi Email: [email protected]

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  • What is a ‘best practice’ backup plan for a website?

    - by HollerTrain
    I have a website which is very large and has a large user-base. I am trying to think of a 'best practice' way to create a back up or mirror website, so if something happens on domain.com, I can quickly point the site to backup.domain.com via 401 redirect. This would give me time to troubleshoot domain.com while everyone is viewing backup.domain.com and not knowing the difference. Is my method the ideal method, or have you enacted better methods to creating a backup site? I don't want to have the site go down and then get yelled at every minute while I'm trying to fix it. Ideally I would just 'flip the switch' and it would redirect the user to a backup. Any insight would be greatly appreciated.

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  • What is a 'best practice' backup plan for a website?

    - by HollerTrain
    I have a website which is very large and has a large user-base. I am trying to think of a 'best practice' way to create a back up or mirror website, so if something happens on domain.com, I can quickly point the site to backup.domain.com via 401 redirect. This would give me time to troubleshoot domain.com while everyone is viewing backup.domain.com and not knowing the difference. Is my method the ideal method, or have you enacted better methods to creating a backup site? I don't want to have the site go down and then get yelled at every minute while I'm trying to fix it. Ideally I would just 'flip the switch' and it would redirect the user to a backup. Any insight would be greatly appreciated.

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  • Rewriting Apache URLs to use only paths and set response headers

    - by jabley
    I have apache httpd in front of an application running in Tomcat. The application exposes URLs of the form: /path/to/images?id={an-image-id} The entities returned by such URLs are images (even though URIs are opaque, I find human-friendly ones are easier to work with!). The application does not set caching directives on the image response, so I've added that via Apache. # LocationMatch to set caching directives on image responses <LocationMatch "^/path/to/images$"> # Can't have Set-Cookie on response, otherwise the downstream caching proxy # won't cache! Header unset Set-Cookie # Mark the response as cacheable. Header append Cache-Control "max-age=8640000" </LocationMatch> Note that I can't use ExpiresByType since not all images served by the app have versioned URIs. I know that ones served by the /path/to/images resource handler are versioned URIs though, which don't perform any sort of content negotiation, and thus are ripe for Far Future Expires management. This is working well for us. Now a requirement has come up to put something else in front of the app (in this case, Amazon CloudFront) to further distribute and cache some of the content. Amazon CloudFront will not pass query string parameters through to my origin server. I thought I would be able to work around this, by changing my apache config appropriately: # Rewrite to map new Amazon CloudFront friendly URIs to the application resources RewriteRule ^/new/path/to/images/([0-9]+) /path/to/images?id=$1 [PT] # LocationMatch to set caching directives on image responses <LocationMatch "^/path/to/images$"> # Can't have Set-Cookie on response, otherwise the downstream caching proxy # won't cache! Header unset Set-Cookie # Mark the response as cacheable. Header append Cache-Control "max-age=8640000" </LocationMatch> This works fine in terms of serving the content, but there are no longer caching directives with the response. I've tried playing around with [PT], [P] for the RewriteRule, and adding a new LocationMatch directive: # Rewrite to map new Amazon CloudFront friendly URIs to the application resources # /new/path/to/images/12345 -> /path/to/images?id=12345 RewriteRule ^/new/path/to/images/([0-9]+) /path/to/images?id=$1 [PT] # LocationMatch to set caching directives on image responses <LocationMatch "^/path/to/images$"> # Can't have Set-Cookie on response, otherwise the downstream caching proxy # won't cache! Header unset Set-Cookie # Mark the response as cacheable. Header append Cache-Control "max-age=8640000" </LocationMatch> <LocationMatch "^/new/path/to/images/"> # Can't have Set-Cookie on response, otherwise the downstream caching proxy # won't cache! Header unset Set-Cookie # Mark the response as cacheable. Header append Cache-Control "max-age=8640000" </LocationMatch> Unfortunately, I'm still unable to get the Cache-Control header added to the response with the new URL format. Please point out what I'm missing to get /new/path/to/images/12345 returning a 200 response with a Cache-Control: max-age=8640000 header. Pointers as to how to debug apache like this would be appreciated as well!

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  • Proliant server will not accept new hard disks in RAID 1+0?

    - by Leigh
    I have a HP ProLiant DL380 G5, I have two logical drives configured with RAID. I have one logical drive RAID 1+0 with two 72 gb 10k sas 1 port spare no 376597-001. I had one hard disk fail and ordered a replacement. The configuration utility showed error and would not rebuild the RAID. I presumed a hard disk fault and ordered a replacement again. In the mean time I put the original failed disk back in the server and this started rebuilding. Currently shows ok status however in the log I can see hardware errors. The new disk has come and I again have the same problem of not accepting the hard disk. I have updated the P400 controller with the latest firmware 7.24 , but still no luck. The only difference I can see is the original drive has firmware 0103 (same as the RAID drive) and the new one has HPD2. Any advice would be appreciated. Thanks in advance Logs from server ctrl all show config Smart Array P400 in Slot 1 (sn: PAFGK0P9VWO0UQ) array A (SAS, Unused Space: 0 MB) logicaldrive 1 (68.5 GB, RAID 1, Interim Recovery Mode) physicaldrive 2I:1:1 (port 2I:box 1:bay 1, SAS, 73.5 GB, OK) physicaldrive 2I:1:2 (port 2I:box 1:bay 2, SAS, 72 GB, Failed array B (SAS, Unused Space: 0 MB) logicaldrive 2 (558.7 GB, RAID 5, OK) physicaldrive 1I:1:5 (port 1I:box 1:bay 5, SAS, 300 GB, OK) physicaldrive 2I:1:3 (port 2I:box 1:bay 3, SAS, 300 GB, OK) physicaldrive 2I:1:4 (port 2I:box 1:bay 4, SAS, 300 GB, OK) ctrl all show config detail Smart Array P400 in Slot 1 Bus Interface: PCI Slot: 1 Serial Number: PAFGK0P9VWO0UQ Cache Serial Number: PA82C0J9VWL8I7 RAID 6 (ADG) Status: Disabled Controller Status: OK Hardware Revision: E Firmware Version: 7.24 Rebuild Priority: Medium Expand Priority: Medium Surface Scan Delay: 15 secs Surface Scan Mode: Idle Wait for Cache Room: Disabled Surface Analysis Inconsistency Notification: Disabled Post Prompt Timeout: 0 secs Cache Board Present: True Cache Status: OK Cache Status Details: A cache error was detected. Run more information. Cache Ratio: 100% Read / 0% Write Drive Write Cache: Disabled Total Cache Size: 256 MB Total Cache Memory Available: 208 MB No-Battery Write Cache: Disabled Battery/Capacitor Count: 0 SATA NCQ Supported: True Array: A Interface Type: SAS Unused Space: 0 MB Status: Failed Physical Drive Array Type: Data One of the drives on this array have failed or has Logical Drive: 1 Size: 68.5 GB Fault Tolerance: RAID 1 Heads: 255 Sectors Per Track: 32 Cylinders: 17594 Strip Size: 128 KB Full Stripe Size: 128 KB Status: Interim Recovery Mode Caching: Enabled Unique Identifier: 600508B10010503956574F305551 Disk Name: \\.\PhysicalDrive0 Mount Points: C:\ 68.5 GB Logical Drive Label: A0100539PAFGK0P9VWO0UQ0E93 Mirror Group 0: physicaldrive 2I:1:2 (port 2I:box 1:bay 2, S Mirror Group 1: physicaldrive 2I:1:1 (port 2I:box 1:bay 1, S Drive Type: Data physicaldrive 2I:1:1 Port: 2I Box: 1 Bay: 1 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 73.5 GB Rotational Speed: 10000 Firmware Revision: 0103 Serial Number: B379P8C006RK Model: HP DG072A9B7 PHY Count: 2 PHY Transfer Rate: Unknown, Unknown physicaldrive 2I:1:2 Port: 2I Box: 1 Bay: 2 Status: Failed Drive Type: Data Drive Interface Type: SAS Size: 72 GB Rotational Speed: 15000 Firmware Revision: HPD9 Serial Number: D5A1PCA04SL01244 Model: HP EH0072FARUA PHY Count: 2 PHY Transfer Rate: Unknown, Unknown Array: B Interface Type: SAS Unused Space: 0 MB Status: OK Array Type: Data Logical Drive: 2 Size: 558.7 GB Fault Tolerance: RAID 5 Heads: 255 Sectors Per Track: 32 Cylinders: 65535 Strip Size: 64 KB Full Stripe Size: 128 KB Status: OK Caching: Enabled Parity Initialization Status: Initialization Co Unique Identifier: 600508B10010503956574F305551 Disk Name: \\.\PhysicalDrive1 Mount Points: E:\ 558.7 GB Logical Drive Label: AF14FD12PAFGK0P9VWO0UQD007 Drive Type: Data physicaldrive 1I:1:5 Port: 1I Box: 1 Bay: 5 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 300 GB Rotational Speed: 10000 Firmware Revision: HPD4 Serial Number: 3SE07QH300009923X1X3 Model: HP DG0300BALVP Current Temperature (C): 32 Maximum Temperature (C): 45 PHY Count: 2 PHY Transfer Rate: Unknown, Unknown physicaldrive 2I:1:3 Port: 2I Box: 1 Bay: 3 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 300 GB Rotational Speed: 10000 Firmware Revision: HPD4 Serial Number: 3SE0AHVH00009924P8F3 Model: HP DG0300BALVP Current Temperature (C): 34 Maximum Temperature (C): 47 PHY Count: 2 PHY Transfer Rate: Unknown, Unknown physicaldrive 2I:1:4 Port: 2I Box: 1 Bay: 4 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 300 GB Rotational Speed: 10000 Firmware Revision: HPD4 Serial Number: 3SE08NAK00009924KWD6 Model: HP DG0300BALVP Current Temperature (C): 35 Maximum Temperature (C): 47 PHY Count: 2 PHY Transfer Rate: Unknown, Unknown

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  • How to store 250TB of data and develop a backup/recovery plan?

    - by luccio
    I'm really new to this topic, so big apology for stupid questions. I have a school project and I want to know how to store 250TB of data with life-cycle for 18 months. It means every record is stored for 18 months and after this period of time can be deleted. There are 2 issues: store data backup data Due to amount of data I will probably need to combine data tapes and hard drives. I'd like to have "fast" access to 3 month old data, so ~42TB on disk. I really don't know what RAID should I use, or is here any better solution than combining disk and data tapes? Thanks for any advice, article, anything. I'm getting lost.

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  • Zend_Cache save from command line and access from browser

    - by Krishna Sunuwar
    May be I am this is super-easy but I couldnt' figure out way. I have script running in command line which save cache using Zend_Cache $frontendOptions = array( 'lifetime' => NULL, 'automatic_serialization' => true ); $backendOptions = array( 'cache_dir' => "/home/tmp/cache" ); $cache = Zend_Cache::factory('Core', 'File', $frontendOptions, $backendOptions); $vars = Array("id1" => "12121", "id2" => "2232"); $cache->save($vars, "p_11"); I can access saved cache from command line: $cache->load("p_11"); In above both case, I have app.php file that run in command line using php-cli. Now, I want to access p_11 cache using browser something like http://mytestserve.lan/test_cache.php I have create object with cache factory like above. All the parameters are same as above. However when I try to load cache p_11, i do not variables set from command line. What went wrong?

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  • SQL SERVER – Database Dynamic Caching by Automatic SQL Server Performance Acceleration

    - by pinaldave
    My second look at SafePeak’s new version (2.1) revealed to me few additional interesting features. For those of you who hadn’t read my previous reviews SafePeak and not familiar with it, here is a quick brief: SafePeak is in business of accelerating performance of SQL Server applications, as well as their scalability, without making code changes to the applications or to the databases. SafePeak performs database dynamic caching, by caching in memory result sets of queries and stored procedures while keeping all those cache correct and up to date. Cached queries are retrieved from the SafePeak RAM in microsecond speed and not send to the SQL Server. The application gets much faster results (100-500 micro seconds), the load on the SQL Server is reduced (less CPU and IO) and the application or the infrastructure gets better scalability. SafePeak solution is hosted either within your cloud servers, hosted servers or your enterprise servers, as part of the application architecture. Connection of the application is done via change of connection strings or adding reroute line in the c:\windows\system32\drivers\etc\hosts file on all application servers. For those who would like to learn more on SafePeak architecture and how it works, I suggest to read this vendor’s webpage: SafePeak Architecture. More interesting new features in SafePeak 2.1 In my previous review of SafePeak new I covered the first 4 things I noticed in the new SafePeak (check out my article “SQLAuthority News – SafePeak Releases a Major Update: SafePeak version 2.1 for SQL Server Performance Acceleration”): Cache setup and fine-tuning – a critical part for getting good caching results Database templates Choosing which database to cache Monitoring and analysis options by SafePeak Since then I had a chance to play with SafePeak some more and here is what I found. 5. Analysis of SQL Performance (present and history): In SafePeak v.2.1 the tools for understanding of performance became more comprehensive. Every 15 minutes SafePeak creates and updates various performance statistics. Each query (or a procedure execute) that arrives to SafePeak gets a SQL pattern, and after it is used again there are statistics for such pattern. An important part of this product is that it understands the dependencies of every pattern (list of tables, views, user defined functions and procs). From this understanding SafePeak creates important analysis information on performance of every object: response time from the database, response time from SafePeak cache, average response time, percent of traffic and break down of behavior. One of the interesting things this behavior column shows is how often the object is actually pdated. The break down analysis allows knowing the above information for: queries and procedures, tables, views, databases and even instances level. The data is show now on all arriving queries, both read queries (that can be cached), but also any types of updates like DMLs, DDLs, DCLs, and even session settings queries. The stats are being updated every 15 minutes and SafePeak dashboard allows going back in time and investigating what happened within any time frame. 6. Logon trigger, for making sure nothing corrupts SafePeak cache data If you have an application with many parts, many servers many possible locations that can actually update the database, or the SQL Server is accessible to many DBAs or software engineers, each can access some database directly and do some changes without going thru SafePeak – this can create a potential corruption of the data stored in SafePeak cache. To make sure SafePeak cache is correct it needs to get all updates to arrive to SafePeak, and if a DBA will access the database directly and do some changes, for example, then SafePeak will simply not know about it and will not clean SafePeak cache. In the new version, SafePeak brought a new feature called “Logon Trigger” to solve the above challenge. By special click of a button SafePeak can deploy a special server logon trigger (with a CLR object) on your SQL Server that actually monitors all connections and informs SafePeak on any connection that is coming not from SafePeak. In SafePeak dashboard there is an interface that allows to control which logins can be ignored based on login names and IPs, while the rest will invoke cache cleanup of SafePeak and actually locks SafePeak cache until this connection will not be closed. Important to note, that this does not interrupt any logins, only informs SafePeak on such connection. On the Dashboard screen in SafePeak you will be able to see those connections and then decide what to do with them. Configuration of this feature in SafePeak dashboard can be done here: Settings -> SQL instances management -> click on instance -> Logon Trigger tab. Other features: 7. User management ability to grant permissions to someone without changing its configuration and only use SafePeak as performance analysis tool. 8. Better reports for analysis of performance using 15 minute resolution charts. 9. Caching of client cursors 10. Support for IPv6 Summary SafePeak is a great SQL Server performance acceleration solution for users who want immediate results for sites with performance, scalability and peak spikes challenges. Especially if your apps are packaged or 3rd party, since no code changes are done. SafePeak can significantly increase response times, by reducing network roundtrip to the database, decreasing CPU resource usage, eliminating I/O and storage access. SafePeak team provides a free fully functional trial www.safepeak.com/download and actually provides a one-on-one assistance during such trial. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: About Me, Pinal Dave, PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology

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  • Hash Sum Mismatch using preseed (Ubuntu Server 12.04)

    - by xorma
    My install through Preseed fails at around 80% on Select and Install Software. In VT-4, I can see Hash Sum mismatch errors. This may be because I am going through a firewall which is caching files. There is no-cache option for apt but I can't seem to get it to work with Preseed. Have tried: d-i debian-installer/no-cache string true d-i apt-setup/no-cache boolean true d-i preseed/early_command string mkdir -p /target/etc/apt/apt.conf.d; echo "Acquire::http {No-Cache=True;};" > /target/etc/apt/apt.conf.d/no-cache but none of these are working. It appears that the early_command occurs too early so is over written once install starts. I'm not sure if the other commands are even correct. Anyone know what is the correct way of disabling achieving this through Preseed?

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  • Local Data Cache - How do I refresh the local db when I add fields to remote db?

    - by Chu
    I'm using a Local Data Cache in an ASP.NET 3.5 environment. I made a change in my main database by adding a new field. I double click on my .SYNC file in my project to startup the Local Data Cache wizard again. The wizard starts and I click OK with the hopes that it'll re-query my database and add the new field to the local database file. Instead, I get an error saying "Synchronizing the databae failed with the message: Unable to enumerate changes at the DbServerSyncProvider..." The only way I know to get things working again is to delete the .SYNC file along with the local database and start it from scratch. There's got to be an easier way... anyone know it?

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  • Better performance to Query the DB or Cache small result sets?

    - by user169867
    Say I need to populate 4 or 5 dropdowns w/ items from a database. Each drop down will have < 15 items in it. These items almost never change. Now I could query the DB each time the page is accessed or I could grab the values from a custom class that would check to see if they already exist in ASP.Net's cache and only if they don't query the DB to update the cache. It's trivial for me to write but I'm unsure if the performace would be better or not. I think it would be (although not likely anything huge). What do you think?

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  • Observer not clearing cache in Rails 2.3.2 - please help.

    - by Jason
    Hi, We are using Rails 2.3.2, Ruby 1.8 & memcache. In my Posts controller I have: cache_sweeper Company::Caching::Sweepers::PostSweeper, :only => [:save_post] I have created the following module: module Company module Caching module Sweepers class PostSweeper < ActionController::Caching::Sweeper observe Post def after_save(post) Rails.cache.delete("post_" + post.permalink) end end end end end but when the save_post method is invoked, the cache is never deleted. Just hoping someone can see what I am doing wrong here. Thanks.

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  • Coherence Query Performance in Large Clusters

    - by jpurdy
    Large clusters (measured in terms of the number of storage-enabled members participating in the largest cache services) may introduce challenges when issuing queries. There is no particular cluster size threshold for this, rather a gradually increasing tendency for issues to arise. The most obvious challenges are that a client's perceived query latency will be determined by the slowest responder (more likely to be a factor in larger clusters) as well as the fact that adding additional cache servers will not increase query throughput if the query processing is not compute-bound (which would generally be the case for most indexed queries). If the data set can take advantage of the partition affinity features of Coherence, then the application can use a PartitionedFilter to target a query to a single server (using partition affinity to ensure that all data is in a single partition). If this can not be done, then avoiding an excessive number of cache server JVMs will help, as will ensuring that each cache server has sufficient CPU resources available and is also properly configured to minimize GC pauses (the most common cause of a slow-responding cache server).

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  • How Do I Rollout WP-Cache To 1000 WordPress Blogs?

    - by Volomike
    My client has 1000 WordPress blogs hosted on a server for customers. Each one is in its own domain through cpanel and SuPHP, running in CGI mode on Apache2.2. Now he wants me (I'm the PHP programmer) to get WP-Cache loaded out on each of these blogs and not just activated, but enabled. He also wants the timeout value set to 2 days instead of the default setting. I have root on LAMP. What is the preferred way to roll out an update to each blog such that on a page view, it sees if WP-Cache is enabled or not. If not, it needs to copy it out from a central source, activate it, and then enable it along with the different timeout value being used.

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  • Can a proxy server cache SSL GETs? If not, would response body encryption suffice?

    - by Damian Hickey
    Can a (||any) proxy server cache content that is requested by a client over https? As the proxy server can't see the querystring, or the http headers, I reckon they can't. I'm considering a desktop application, run by a number of people behind their companies proxy. This application may access services across the internet and I'd like to take advantage of the in-built internet caching infrastructure for 'reads'. If the caching proxy servers can't cache SSL delivered content, would simply encrypting the content of a response be a viable option? I am considering all GET requests that we wish to be cachable be requested over http with the body encrypted using asymmetric encryption, where each client has the decryption key. Anytime we wish to perform a GET that is not cachable, or a POST operation, it will be performed over SSL.

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  • Basic memcached question

    - by Aadith
    I have been reading up on distributed hashing. I learnt that consistent hashing is used for distributing the keys among cache machines. I also learnt that, a key is duplicated on mutiple caches to handle failure of cache hosts. But what I have come across on memcached doesn't seem to be in alignment with all this. I read that all cache nodes are independent of each other and that if a cache goes down, requests go to DB. Theres no mention of cache miss on a host resulting in the host directing the request to another host which could either be holding the key or is nearer to the key. Can you please tell me how these two fit together? Is memcached a very preliminary form of distributed hashing which doesnt have much sophistication?

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  • J2EE/EJB + service locator: is it safe to cache EJB Home lookup result ?

    - by Guillaume
    In a J2EE application, we are using EJB2 in weblogic. To avoid losing time building the initial context and looking up EJB Home interface, I'm considering the Service Locator Pattern. But after a few search on the web I found that event if this pattern is often recommended for the InitialContext caching, there are some negative opinion about the EJB Home caching. Questions: Is it safe to cache EJB Home lookup result ? What will happen if one my cluster node is no more working ? What will happen if I install a new version of the EJB without refreshing the service locator's cache ?

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  • In Linux, how can I completely disregard the contents of /etc/ld.so.cache?

    - by BillyBBone
    Hi, For the purposes of prototyping a new set of shared libraries in a development sandbox (to which I don't have root access), I'd like to know how to execute a binary while completely overriding the contents of /etc/ld.so.cache, so that none of the system libraries get loaded. How can this be done? I have looked at mechanisms like setting the LD_LIBRARY_PATH environment variable or launching the program wrapped inside /lib/ld-linux.so, but these methods all seem to supplement the loading of libraries from /etc/ld.so.cache, but not override it completely. Help?

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  • Hold most of the object in cache/memory insted of database?

    - by feiroox
    Hi All, It just occurred to me why not to have most of the objects in a cache(memory) when an application start. if it's not that large web application. Or to have a settings for how much I want to put in the cache/memory. I just guess it could require to have something like below 1 GB RAM or a lot less. Everything in order to speed up the application even more by not querying database. Is it good idea?

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