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  • complex cron job schedule

    - by Bob
    I know I can do this if I call a script to check, but I am curious if I can do this with just the cron. I need to run a job once/year on the first Saturday of July If that Saturday is July 4th, run it July 5th.

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  • SQL Server performance on VSphere 4.0

    - by Charles
    We are having a performance issue that we cannot explain with our VMWare environment and I am hoping someone here may be able to help. We have a web application that uses a databases backend. We have an SQL 2005 Cluster setup on Windows 2003 R2 between a physical node and a virtual node. Both physical servers are identical 2950's with 2x Xeaon x5460 Quad Core CPUs and 64GB of memory, 16GB allocated to the OS. We are utilizing an iSCSI San for all cluster disks. The problem is this, when utilizing the application under a repeated stress testing that adds CPUs to the cluster nodes, the Physical node scales from 1 pCPU to 8 pCPUs, meaning we see continued performance increases. When testing the node running Vsphere, we have the expected 12% performance hit for being virtual but we still scale from 1 vCPU to 4 vCPUs like the physical but beyond this performance drops off, by the time we get to 8 vCPUs we are seeing performance numbers worse than at 4 vCPUs. Again, both nodes are configured identically in terms of hardware, Guest OS, SQL Configurations etc and there is no traffic other than the testing on the system. There are no other VMs on the virtual server so there should be no competition for resources. We have contacted VMWare for help but they have not really been any suggesting things like setting SQL Processor Affinity which, while being helpful would have the same net effect on each box and should not change our results in the least. We have looked at all of VMWare's SQL Tuning guides with regards to VSphere with no benefit, please help!

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  • Performance data collection for short-running, ephemeral servers

    - by ErikA
    We're building a medical image processing software stack, currently hosted on various AWS resources. As part of this application, we have a handful of long-running servers (database, load balancers, web application, etc.). Collecting performance data on those servers is quite simple - my go-to- recipe of Nagios (for monitoring/notifications) and Munin (for collection of performance data and displaying trends) will work just fine. However - as part of this application, we are constantly starting up and terminating compute instances on EC2. In typical usage, these compute instances start up, configure themselves, receive a job from a message queue, and then get to work processing that job, which takes anywhere from 15 minutes to over 8 hours. After job completion, these instances get terminated, never to be heard from again. What is a decent strategy for collecting performance data on these short-lived instances? I don't necessarily need monitoring on them - if they fail for whatever reason, our application will detect this and handle re-starting the job on another instance or raising the flag so an administrator can take a look at things. However, it still would be useful to collect information like CPU (user, idle, iowait, etc.), memory usage, network traffic, disk read/write data, etc. In our internal database, we track the instance ID of the machine that runs each job, and it would be quite helpful to be able to look up performance data for a specific instance ID for troubleshooting and profiling. Munin doesn't seem like a great candidate, as it requires maintaining a list of munin nodes in a text file - far from ideal for an environment with a high amount of churn, and for the short amount of time each node will be running, I'd rather keep the full-resolution data indefinitely than have RRD water down the data over time. In the end, my guess is that this will require a monitoring engine that: uses a database (MySQL, SQLite, etc.) for configuration and data storage exposes an API for adding/removing hosts and services Are there other things I should be thinking about when evaluating options? Perhaps I'm over-thinking this, though, and just ought to run sar at 1-minute intervals on these short-lived instances and collect the sar db files prior to termination.

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  • Bad performance with Linux software RAID5 and LUKS encryption

    - by Philipp Wendler
    I have set up a Linux software RAID5 on three hard drives and want to encrypt it with cryptsetup/LUKS. My tests showed that the encryption leads to a massive performance decrease that I cannot explain. The RAID5 is able to write 187 MB/s [1] without encryption. With encryption on top of it, write speed is down to about 40 MB/s. The RAID has a chunk size of 512K and a write intent bitmap. I used -c aes-xts-plain -s 512 --align-payload=2048 as the parameters for cryptsetup luksFormat, so the payload should be aligned to 2048 blocks of 512 bytes (i.e., 1MB). cryptsetup luksDump shows a payload offset of 4096. So I think the alignment is correct and fits to the RAID chunk size. The CPU is not the bottleneck, as it has hardware support for AES (aesni_intel). If I write on another drive (an SSD with LVM) that is also encrypted, I do have a write speed of 150 MB/s. top shows that the CPU usage is indeed very low, only the RAID5 xor takes 14%. I also tried putting a filesystem (ext4) directly on the unencrypted RAID so see if the layering is problem. The filesystem decreases the performance a little bit as expected, but by far not that much (write speed varying, but 100 MB/s). Summary: Disks + RAID5: good Disks + RAID5 + ext4: good Disks + RAID5 + encryption: bad SSD + encryption + LVM + ext4: good The read performance is not affected by the encryption, it is 207 MB/s without and 205 MB/s with encryption (also showing that CPU power is not the problem). What can I do to improve the write performance of the encrypted RAID? [1] All speed measurements were done with several runs of dd if=/dev/zero of=DEV bs=100M count=100 (i.e., writing 10G in blocks of 100M). Edit: If this helps: I'm using Ubuntu 11.04 64bit with Linux 2.6.38. Edit2: The performance stays approximately the same if I pass a block size of 4KB, 1MB or 10MB to dd.

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  • Verify that a cron job has completed

    - by skylarking
    Is there a command that can be run to verify that a users cron job has run successfully? Platform is Ubuntu 8.04 LTS. I have scripts in /home/useraccount/bin/ running crontab -l while logged in as user results in: # m h dom mon dow command @hourly /home/useraccount/bin/script_1 @hourly /home/locateruser/bin/script_2 I realize scripts could send email or write to a log with a timestamp, but wondering if there is just a way to verify it ran from the command line.

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  • My CRON job won't run, any ideas?

    - by bbeckford
    I've installed the following CRON job using 'crontab -e' through putty on my server, but it won't run and I have no idea why. This is the line I'm putting in and saving using 'crontab -e': 00 09-18 * * 1-5 /usr/bin/php5 /home/a/v/ava/public_html/p/app_availability_updates_flush.php It's a simple script I want to run on the hour during business hours. When I use 'crontab -l' it prints the following: 00 09-18 * * 1-5 /usr/bin/php5 /home/a/v/ava/public_html/p/app_availability_updates_flush.phproot@ds6639:~# Does that look right?

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  • linux cron job error

    - by bell
    I have setup a cron job to run a php file every 30 minutes, lynx -source public_html/scripts/file.php the result comes through to an email but seems to get this error Can't Access `file://localhost/home/username/public_html/scripts/file.php' Alert!: Unable to access document. lynx: Can't access startfile any advice would be much appreciated

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  • SQL SERVER – Introduction to Wait Stats and Wait Types – Wait Type – Day 1 of 28

    - by pinaldave
    I have been working a lot on Wait Stats and Wait Types recently. Last Year, I requested blog readers to send me their respective server’s wait stats. I appreciate their kind response as I have received  Wait stats from my readers. I took each of the results and carefully analyzed them. I provided necessary feedback to the person who sent me his wait stats and wait types. Based on the feedbacks I got, many of the readers have tuned their server. After a while I got further feedbacks on my recommendations and again, I collected wait stats. I recorded the wait stats and my recommendations and did further research. At some point at time, there were more than 10 different round trips of the recommendations and suggestions. Finally, after six month of working my hands on performance tuning, I have collected some real world wisdom because of this. Now I plan to share my findings with all of you over here. Before anything else, please note that all of these are based on my personal observations and opinions. They may or may not match the theory available at other places. Some of the suggestions may not match your situation. Remember, every server is different and consequently, there is more than one solution to a particular problem. However, this series is written with kept wait stats in mind. While I was working on various performance tuning consultations, I did many more things than just tuning wait stats. Today we will discuss how to capture the wait stats. I use the script diagnostic script created by my friend and SQL Server Expert Glenn Berry to collect wait stats. Here is the script to collect the wait stats: -- Isolate top waits for server instance since last restart or statistics clear WITH Waits AS (SELECT wait_type, wait_time_ms / 1000. AS wait_time_s, 100. * wait_time_ms / SUM(wait_time_ms) OVER() AS pct, ROW_NUMBER() OVER(ORDER BY wait_time_ms DESC) AS rn FROM sys.dm_os_wait_stats WHERE wait_type NOT IN ('CLR_SEMAPHORE','LAZYWRITER_SLEEP','RESOURCE_QUEUE','SLEEP_TASK' ,'SLEEP_SYSTEMTASK','SQLTRACE_BUFFER_FLUSH','WAITFOR', 'LOGMGR_QUEUE','CHECKPOINT_QUEUE' ,'REQUEST_FOR_DEADLOCK_SEARCH','XE_TIMER_EVENT','BROKER_TO_FLUSH','BROKER_TASK_STOP','CLR_MANUAL_EVENT' ,'CLR_AUTO_EVENT','DISPATCHER_QUEUE_SEMAPHORE', 'FT_IFTS_SCHEDULER_IDLE_WAIT' ,'XE_DISPATCHER_WAIT', 'XE_DISPATCHER_JOIN', 'SQLTRACE_INCREMENTAL_FLUSH_SLEEP')) SELECT W1.wait_type, CAST(W1.wait_time_s AS DECIMAL(12, 2)) AS wait_time_s, CAST(W1.pct AS DECIMAL(12, 2)) AS pct, CAST(SUM(W2.pct) AS DECIMAL(12, 2)) AS running_pct FROM Waits AS W1 INNER JOIN Waits AS W2 ON W2.rn <= W1.rn GROUP BY W1.rn, W1.wait_type, W1.wait_time_s, W1.pct HAVING SUM(W2.pct) - W1.pct < 99 OPTION (RECOMPILE); -- percentage threshold GO This script uses Dynamic Management View sys.dm_os_wait_stats to collect the wait stats. It omits the system-related wait stats which are not useful to diagnose performance-related bottleneck. Additionally, not OPTION (RECOMPILE) at the end of the DMV will ensure that every time the query runs, it retrieves new data and not the cached data. This dynamic management view collects all the information since the time when the SQL Server services have been restarted. You can also manually clear the wait stats using the following command: DBCC SQLPERF('sys.dm_os_wait_stats', CLEAR); Once the wait stats are collected, we can start analysis them and try to see what is causing any particular wait stats to achieve higher percentages than the others. Many waits stats are related to one another. When the CPU pressure is high, all the CPU-related wait stats show up on top. But when that is fixed, all the wait stats related to the CPU start showing reasonable percentages. It is difficult to have a sure solution, but there are good indications and good suggestions on how to solve this. I will keep this blog post updated as I will post more details about wait stats and how I reduce them. The reference to Book On Line is over here. Of course, I have selected February to run this Wait Stats series. I am already cheating by having the smallest month to run this series. :) Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: DMV, Pinal Dave, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • SQL Server – SafePeak “Logon Trigger” Feature for Managing Data Access

    - by pinaldave
    Lately I received an interesting question about the abilities of SafePeak for SQL Server acceleration software: Q: “I would like to use SafePeak to make my CRM application faster. It is an application we bought from some vendor, after a while it became slow and we can’t reprogram it. SafePeak automated caching sounds like an easy and good solution for us. But, in my application there are many servers and different other applications services that address its main database, and some even change data, and I feel that there is a chance that some servers that during the connection process we may miss some. Is there a way to ensure that SafePeak will be aware of all connections to the SQL Server, so its cache will remain intact?” Interesting question, as I remember that SafePeak (http://www.safepeak.com/Product/SafePeak-Overview) likes that all traffic to the database will go thru it. I decided to check out the features of SafePeak latest version (2.1) and seek for an answer there. A: Indeed I found SafePeak has a feature they call “Logon Trigger” and is designed for that purpose. It is located in the user interface, under: Settings -> SQL instances management  ->  [your instance]  ->  [Logon Trigger] tab. From here you activate / deactivate it and control a white-list of enabled server IPs and Login names that SafePeak will ignore them. Click to Enlarge After activation of the “logon trigger” Safepeak server is notified by the SQL Server itself on each new opened connection. Safepeak monitors those connections and decides if there is something to do with them or not. On a typical installation SafePeak likes all application and users connections to go via SafePeak – this way it knows about data and schema updates immediately (real time). With activation of the safepeak “logon trigger”  a special CLR trigger is deployed on the SQL server and notifies Safepeak on any connection that has not arrived via SafePeak. In such cases Safepeak can act to clear and lock the cache or to ignore it. This feature enables to make sure SafePeak will be aware of all connections so SafePeak cache will maintain exactly correct all times. So even if a user, like a DBA will connect to the SQL Server not via SafePeak, SafePeak will know about it and take actions. The notification does not impact the work of that connection, the user or application still continue to do whatever they planned to do. Note: I found that activation of logon trigger in SafePeak requires that SafePeak SQL login will have the next permissions: 1) CONTROL SERVER; 2) VIEW SERVER STATE; 3) And the SQL Server instance is CLR enabled; Seeing SafePeak in action, I can say SafePeak brings fantastic resource for those who seek to get performance for SQL Server critical apps. SafePeak promises to accelerate SQL Server applications in just several hours of installation, automatic learning and some optimization configuration (no code changes!!!). If better application and database performance means better business to you – I suggest you to download and try SafePeak. The solution of SafePeak is indeed unique, and the questions I receive are very interesting. Have any more questions on SafePeak? Please leave your question as a comment and I will try to get an answer for you. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Measuring ASP.NET and SharePoint output cache

    - by DigiMortal
    During ASP.NET output caching week in my local blog I wrote about how to measure ASP.NET output cache. As my posting was based on real work and real-life results then I thought that this posting is maybe interesting to you too. So here you can read what I did, how I did and what was the result. Introduction Caching is not effective without measuring it. As MVP Henn Sarv said in one of his sessions then you will get what you measure. And right he is. Lately I measured caching on local Microsoft community portal to make sure that our caching strategy is good enough in environment where this system lives. In this posting I will show you how to start measuring the cache of your web applications. Although the application measured is built on SharePoint Server publishing infrastructure, all those counters have same meaning as similar counters under pure ASP.NET applications. Measured counters I used Performance Monitor and the following performance counters (their names are similar on ASP.NET and SharePoint WCMS): Total number of objects added – how much objects were added to output cache. Total object discards – how much objects were deleted from output cache. Cache hit count – how many times requests were served by cache. Cache hit ratio – percent of requests served from cache. The first three counters are cumulative while last one is coefficient. You can use also other counters to measure the full effect of caching (memory, processor, disk I/O, network load etc before and after caching). Measuring process The measuring I describe here started from freshly restarted web server. I measured application during 12 hours that covered also time ranges when users are most active. The time range does not include late evening hours and night because there is nothing to measure during these hours. During measuring we performed no maintenance or administrative tasks on server. All tasks performed were related to usual daily content management and content monitoring. Also we had no advertisement campaigns or other promotions running at same time. The results You can see the results on following graphic.   Total number of objects added   Total object discards   Cache hit count   Cache hit ratio You can see that adds and discards are growing in same tempo. It is good because cache expires and not so popular items are not kept in memory. If there are more popular content then the these lines may have bigger distance between them. Cache hit count grows faster and this shows that more and more content is served from cache. In current case it shows that cache is filled optimally and we can do even better if we tune caches more. The site contains also pages that are discarded when some subsite changes (page was added/modified/deleted) and one modification may affect about four or five pages. This may also decrease cache hit count because during day the site gets about 5-10 new pages. Cache hit ratio is currently extremely good. The suggested minimum is about 85% but after some tuning and measuring I achieved 98.7% as a result. This is due to the fact that new pages are most often requested and after new pages are added the older ones are requested only sometimes. So they get discarded from cache and only some of these will return sometimes back to cache. Although this may also indicate the need for additional SEO work the result is very well in technical means. Conclusion Measuring ASP.NET output cache is not complex thing to do and you can start by measuring performance of cache as a start. Later you can move on and measure caching effect to other counters such as disk I/O, network, processors etc. What you have to achieve is optimal cache that is not full of items asked only couple of times per day (you can avoid this by not using too long cache durations). After some tuning you should be able to boost cache hit ratio up to at least 85%.

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  • Essbase BSO Data Fragmentation

    - by Ann Donahue
    Essbase BSO Data Fragmentation Data fragmentation naturally occurs in Essbase Block Storage (BSO) databases where there are a lot of end user data updates, incremental data loads, many lock and send, and/or many calculations executed.  If an Essbase database starts to experience performance slow-downs, this is an indication that there may be too much fragmentation.  See Chapter 54 Improving Essbase Performance in the Essbase DBA Guide for more details on measuring and eliminating fragmentation: http://docs.oracle.com/cd/E17236_01/epm.1112/esb_dbag/daprcset.html Fragmentation is likely to occur in the following situations: Read/write databases that users are constantly updating data Databases that execute calculations around the clock Databases that frequently update and recalculate dense members Data loads that are poorly designed Databases that contain a significant number of Dynamic Calc and Store members Databases that use an isolation level of uncommitted access with commit block set to zero There are two types of data block fragmentation Free space tracking, which is measured using the Average Fragmentation Quotient statistic. Block order on disk, which is measured using the Average Cluster Ratio statistic. Average Fragmentation Quotient The Average Fragmentation Quotient ratio measures free space in a given database.  As you update and calculate data, empty spaces occur when a block can no longer fit in its original space and will either append at the end of the file or fit in another empty space that is large enough.  These empty spaces take up space in the .PAG files.  The higher the number the more empty spaces you have, therefore, the bigger the .PAG file and the longer it takes to traverse through the .PAG file to get to a particular record.  An Average Fragmentation Quotient value of 3.174765 means the database is 3% fragmented with free space. Average Cluster Ratio Average Cluster Ratio describes the order the blocks actually exist in the database. An Average Cluster Ratio number of 1 means all the blocks are ordered in the correct sequence in the order of the Outline.  As you load data and calculate data blocks, the sequence can start to be out of order.  This is because when you write to a block it may not be able to place back in the exact same spot in the database that it existed before.  The lower this number the more out of order it becomes and the more it affects performance.  An Average Cluster Ratio value of 1 means no fragmentation.  Any value lower than 1 i.e. 0.01032828 means the data blocks are getting further out of order from the outline order. Eliminating Data Block Fragmentation Both types of data block fragmentation can be removed by doing a dense restructure or export/clear/import of the data.  There are two types of dense restructure: 1. Implicit Restructures Implicit dense restructure happens when outline changes are done using EAS Outline Editor or Dimension Build. Essbase restructures create new .PAG files restructuring the data blocks in the .PAG files. When Essbase restructures the data blocks, it regenerates the index automatically so that index entries point to the new data blocks. Empty blocks are NOT removed with implicit restructures. 2. Explicit Restructures Explicit dense restructure happens when a manual initiation of the database restructure is executed. An explicit dense restructure is a full restructure which comprises of a dense restructure as outlined above plus the removal of empty blocks Empty Blocks vs. Fragmentation The existence of empty blocks is not considered fragmentation.  Empty blocks can be created through calc scripts or formulas.  An empty block will add to an existing database block count and will be included in the block counts of the database properties.  There are no statistics for empty blocks.  The only way to determine if empty blocks exist in an Essbase database is to record your current block count, export the entire database, clear the database then import the exported data.  If the block count decreased, the difference is the number of empty blocks that had existed in the database.

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  • Why is mesh baking causing huge performance spikes?

    - by jellyfication
    A couple of seconds into the gameplay on my Android device, I see huge performance spikes caused by "Mesh.Bake Scaled Mesh PhysX CollisionData" In my game, a whole level is a parent object containing multiple ridigbodies with mesh colliders. Every FixedUpdate(), my parent object rotates around the player. Rotating the world causes mesh scaling. Here is the code that handles world rotation. private void Update() { input.update(); Vector3 currentInput = input.GetDirection(); worldParent.rotation = initialRotation; worldParent.DetachChildren(); worldParent.position = transform.position; world.parent = worldParent; worldParent.Rotate(Vector3.right, currentInput.x * 50f); worldParent.Rotate(Vector3.forward, currentInput.z * 50f); } How can I get rid of mesh scaling ? Mesh.Bake physx seems to take effect after some time, is it possible to disable this function ? The profiler looks like this: Bottom-left panel shows data before spikes, the right after

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  • Oracle President Mark Hurd Highlights How Data-driven HR Decisions Help Maximize Business Performance

    - by Scott Ewart
    HR Intelligence Can Help Companies Win the Race for Talent Today during a keynote at Taleo World 2012, Oracle President Mark Hurd outlined the ways that executives can use HR intelligence to help them make better business decisions, shape the future of their organizations and improve the bottom line. He highlighted that talent management is one of the top three focus areas for CEOs, and explained how HR intelligence can help drive decisions to meet business objectives. Hurd urged HR leaders to use data to make fact-based decisions about hiring, talent management and succession to drive strategic growth. To win the race for talent, Hurd explained that organizations need powerful technology that provides fact-based valuable insight that is needed to proactively manage talent, drive strategic initiatives that promote innovation, and enhance business performance. To view the full story and press release, click here.

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  • Performance considerations for common SQL queries

    - by Jim Giercyk
    Originally posted on: http://geekswithblogs.net/NibblesAndBits/archive/2013/10/16/performance-considerations-for-common-sql-queries.aspxSQL offers many different methods to produce the same results.  There is a never-ending debate between SQL developers as to the “best way” or the “most efficient way” to render a result set.  Sometimes these disputes even come to blows….well, I am a lover, not a fighter, so I decided to collect some data that will prove which way is the best and most efficient.  For the queries below, I downloaded the test database from SQLSkills:  http://www.sqlskills.com/sql-server-resources/sql-server-demos/.  There isn’t a lot of data, but enough to prove my point: dbo.member has 10,000 records, and dbo.payment has 15,554.  Our result set contains 6,706 records. The following queries produce an identical result set; the result set contains aggregate payment information for each member who has made more than 1 payment from the dbo.payment table and the first and last name of the member from the dbo.member table.   /*************/ /* Sub Query  */ /*************/ SELECT  a.[Member Number] ,         m.lastname ,         m.firstname ,         a.[Number Of Payments] ,         a.[Average Payment] ,         a.[Total Paid] FROM    ( SELECT    member_no 'Member Number' ,                     AVG(payment_amt) 'Average Payment' ,                     SUM(payment_amt) 'Total Paid' ,                     COUNT(Payment_No) 'Number Of Payments'           FROM      dbo.payment           GROUP BY  member_no           HAVING    COUNT(Payment_No) > 1         ) a         JOIN dbo.member m ON a.[Member Number] = m.member_no         /***************/ /* Cross Apply  */ /***************/ SELECT  ca.[Member Number] ,         m.lastname ,         m.firstname ,         ca.[Number Of Payments] ,         ca.[Average Payment] ,         ca.[Total Paid] FROM    dbo.member m         CROSS APPLY ( SELECT    member_no 'Member Number' ,                                 AVG(payment_amt) 'Average Payment' ,                                 SUM(payment_amt) 'Total Paid' ,                                 COUNT(Payment_No) 'Number Of Payments'                       FROM      dbo.payment                       WHERE     member_no = m.member_no                       GROUP BY  member_no                       HAVING    COUNT(Payment_No) > 1                     ) ca /********/                    /* CTEs  */ /********/ ; WITH    Payments           AS ( SELECT   member_no 'Member Number' ,                         AVG(payment_amt) 'Average Payment' ,                         SUM(payment_amt) 'Total Paid' ,                         COUNT(Payment_No) 'Number Of Payments'                FROM     dbo.payment                GROUP BY member_no                HAVING   COUNT(Payment_No) > 1              ),         MemberInfo           AS ( SELECT   p.[Member Number] ,                         m.lastname ,                         m.firstname ,                         p.[Number Of Payments] ,                         p.[Average Payment] ,                         p.[Total Paid]                FROM     dbo.member m                         JOIN Payments p ON m.member_no = p.[Member Number]              )     SELECT  *     FROM    MemberInfo /************************/ /* SELECT with Grouping   */ /************************/ SELECT  p.member_no 'Member Number' ,         m.lastname ,         m.firstname ,         COUNT(Payment_No) 'Number Of Payments' ,         AVG(payment_amt) 'Average Payment' ,         SUM(payment_amt) 'Total Paid' FROM    dbo.payment p         JOIN dbo.member m ON m.member_no = p.member_no GROUP BY p.member_no ,         m.lastname ,         m.firstname HAVING  COUNT(Payment_No) > 1   We can see what is going on in SQL’s brain by looking at the execution plan.  The Execution Plan will demonstrate which steps and in what order SQL executes those steps, and what percentage of batch time each query takes.  SO….if I execute all 4 of these queries in a single batch, I will get an idea of the relative time SQL takes to execute them, and how it renders the Execution Plan.  We can settle this once and for all.  Here is what SQL did with these queries:   Not only did the queries take the same amount of time to execute, SQL generated the same Execution Plan for each of them.  Everybody is right…..I guess we can all finally go to lunch together!  But wait a second, I may not be a fighter, but I AM an instigator.     Let’s see how a table variable stacks up.  Here is the code I executed: /********************/ /*  Table Variable  */ /********************/ DECLARE @AggregateTable TABLE     (       member_no INT ,       AveragePayment MONEY ,       TotalPaid MONEY ,       NumberOfPayments MONEY     ) INSERT  @AggregateTable         SELECT  member_no 'Member Number' ,                 AVG(payment_amt) 'Average Payment' ,                 SUM(payment_amt) 'Total Paid' ,                 COUNT(Payment_No) 'Number Of Payments'         FROM    dbo.payment         GROUP BY member_no         HAVING  COUNT(Payment_No) > 1   SELECT  at.member_no 'Member Number' ,         m.lastname ,         m.firstname ,         at.NumberOfPayments 'Number Of Payments' ,         at.AveragePayment 'Average Payment' ,         at.TotalPaid 'Total Paid' FROM    @AggregateTable at         JOIN dbo.member m ON m.member_no = at.member_no In the interest of keeping things in groupings of 4, I removed the last query from the previous batch and added the table variable query.  Here’s what I got:     Since we first insert into the table variable, then we read from it, the Execution Plan renders 2 steps.  BUT, the combination of the 2 steps is only 22% of the batch.  It is actually faster than the other methods even though it is treated as 2 separate queries in the Execution Plan.  The argument I often hear against Table Variables is that SQL only estimates 1 row for the table size in the Execution Plan.  While this is true, the estimate does not come in to play until you read from the table variable.  In this case, the table variable had 6,706 rows, but it still outperformed the other queries.  People argue that table variables should only be used for hash or lookup tables.  The fact is, you have control of what you put IN to the variable, so as long as you keep it within reason, these results suggest that a table variable is a viable alternative to sub-queries. If anyone does volume testing on this theory, I would be interested in the results.  My suspicion is that there is a breaking point where efficiency goes down the tubes immediately, and it would be interesting to see where the threshold is. Coding SQL is a matter of style.  If you’ve been around since they introduced DB2, you were probably taught a little differently than a recent computer science graduate.  If you have a company standard, I strongly recommend you follow it.    If you do not have a standard, generally speaking, there is no right or wrong answer when talking about the efficiency of these types of queries, and certainly no hard-and-fast rule.  Volume and infrastructure will dictate a lot when it comes to performance, so your results may vary in your environment.  Download the database and try it!

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  • Crystal Reports: 5 Tests for Top Performance

    Your masterpiece report is now complete. It doesn't just meet your customer’s expectations, it blows them out of the water. All they want is a beautifully-summarized report that can be displayed in a myriad of ways. Then disaster strikes! You try to run the report for a month against the live database and not the two days worth of test data you used for development, then your report’s runtime goes from twenty seconds to two hours. Every Crystal Reports developer has experienced this situation and it can be one of the most frustrating aspects of report design. Thankfully there are a variety of things that can be done to combat bad performance, any one of which can reap huge benefits...

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  • Is there any performance comparison between Perl web frameworks?

    - by DVK
    I have seen mentions (which sounded like unsubstantiated opinions, and dated ones at that) that Embperl is the fastest Perl web framework. I was wondering if there's a consensus on the relative speed of the major stable Perl web frameworks, or ideally, some sort of fact-based performance comparisons between implementations of the same sample webapps, or individual functionalities (e.g. session handling or form data processing), etc...?

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  • JBoss AS Performance Tuning de Francesco Marchioni, critique par Gomes Rodrigues Antonio

    Bonjour, Vous pouvez trouver sur http://java.developpez.com/livres/?p...L9781849514026 la critique de l'excellent livre "JBoss AS Performance Tuning" [IMG]http://images-eu.amazon.com/images/P/184951402X.01.LZZZZZZZ.jpg[/IMG] Comme il couvre plus que seulement le tuning de JBoss, je préfère mettre cette discussion ici A propos du livre, il couvre la création d'un test de charge avec Jmeter, le tuning de JBoss, le profiling de l'application et de la JVM, de l'OS ... Il se lit plutôt bien et on y trouve pas mal d'informations Si vous avez un avis sur ce livre, je serais intéressé de le connaitre...

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  • Performance: recursion vs. iteration in Javascript

    - by mastazi
    I have read recently some articles (e.g. http://dailyjs.com/2012/09/14/functional-programming/) about the functional aspects of Javascript and the relationship between Scheme and Javascript (the latter was influenced by the first, which is a functional language, while the O-O aspects are inherited from Self which is a prototyping-based language). However my question is more specific: I was wondering if there are metrics about the performance of recursion vs. iteration in Javascript. I know that in some languages (where by design iteration performs better) the difference is minimal because the interpreter / compiler converts recursion into iteration, however I guess that probably this is not the case of Javascript since it is, at least partially, a functional language.

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  • .NET 4: &ldquo;Slim&rdquo;-style performance boost!

    - by Vitus
    RTM version of .NET 4 and Visual Studio 2010 is available, and now we can do some test with it. Parallel Extensions is one of the most valuable part of .NET 4.0. It’s a set of good tools for easily consuming multicore hardware power. And it also contains some “upgraded” sync primitives – Slim-version. For example, it include updated variant of widely known ManualResetEvent. For people, who don’t know about it: you can sync concurrency execution of some pieces of code with this sync primitive. Instance of ManualResetEvent can be in 2 states: signaled and non-signaled. Transition between it possible by Set() and Reset() methods call. Some shortly explanation: Thread 1 Thread 2 Time mre.Reset(); mre.WaitOne(); //code execution 0 //wating //code execution 1 //wating //code execution 2 //wating //code execution 3 //wating mre.Set(); 4 //code execution //… 5 Upgraded version of this primitive is ManualResetEventSlim. The idea in decreasing performance cost in case, when only 1 thread use it. Main concept in the “hybrid sync schema”, which can be done as following:   internal sealed class SimpleHybridLock : IDisposable { private Int32 m_waiters = 0; private AutoResetEvent m_waiterLock = new AutoResetEvent(false);   public void Enter() { if (Interlocked.Increment(ref m_waiters) == 1) return; m_waiterLock.WaitOne(); }   public void Leave() { if (Interlocked.Decrement(ref m_waiters) == 0) return; m_waiterLock.Set(); }   public void Dispose() { m_waiterLock.Dispose(); } } It’s a sample from Jeffry Richter’s book “CLR via C#”, 3rd edition. Primitive SimpleHybridLock have two public methods: Enter() and Leave(). You can put your concurrency-critical code between calls of these methods, and it would executed in only one thread at the moment. Code is really simple: first thread, called Enter(), increase counter. Second thread also increase counter, and suspend while m_waiterLock is not signaled. So, if we don’t have concurrent access to our lock, “heavy” methods WaitOne() and Set() will not called. It’s can give some performance bonus. ManualResetEvent use the similar idea. Of course, it have more “smart” technics inside, like a checking of recursive calls, and so on. I want to know a real difference between classic ManualResetEvent realization, and new –Slim. I wrote a simple “benchmark”: class Program { static void Main(string[] args) { ManualResetEventSlim mres = new ManualResetEventSlim(false); ManualResetEventSlim mres2 = new ManualResetEventSlim(false);   ManualResetEvent mre = new ManualResetEvent(false);   long total = 0; int COUNT = 50;   for (int i = 0; i < COUNT; i++) { mres2.Reset(); Stopwatch sw = Stopwatch.StartNew();   ThreadPool.QueueUserWorkItem((obj) => { //Method(mres, true); Method2(mre, true); mres2.Set(); }); //Method(mres, false); Method2(mre, false);   mres2.Wait(); sw.Stop();   Console.WriteLine("Pass {0}: {1} ms", i, sw.ElapsedMilliseconds); total += sw.ElapsedMilliseconds; }   Console.WriteLine(); Console.WriteLine("==============================="); Console.WriteLine("Done in average=" + total / (double)COUNT); Console.ReadLine(); }   private static void Method(ManualResetEventSlim mre, bool value) { for (int i = 0; i < 9000000; i++) { if (value) { mre.Set(); } else { mre.Reset(); } } }   private static void Method2(ManualResetEvent mre, bool value) { for (int i = 0; i < 9000000; i++) { if (value) { mre.Set(); } else { mre.Reset(); } } } } I use 2 concurrent thread (the main thread and one from thread pool) for setting and resetting ManualResetEvents, and try to run test COUNT times, and calculate average execution time. Here is the results (I get it on my dual core notebook with T7250 CPU and Windows 7 x64): ManualResetEvent ManualResetEventSlim Difference is obvious and serious – in 10 times! So, I think preferable way is using ManualResetEventSlim, because not always on calling Set() and Reset() will be called “heavy” methods for working with Windows kernel-mode objects. It’s a small and nice improvement! ;)

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