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  • C# performance analysis- how to count CPU cycles?

    - by Lirik
    Is this a valid way to do performance analysis? I want to get nanosecond accuracy and determine the performance of typecasting: class PerformanceTest { static double last = 0.0; static List<object> numericGenericData = new List<object>(); static List<double> numericTypedData = new List<double>(); static void Main(string[] args) { double totalWithCasting = 0.0; double totalWithoutCasting = 0.0; for (double d = 0.0; d < 1000000.0; ++d) { numericGenericData.Add(d); numericTypedData.Add(d); } Stopwatch stopwatch = new Stopwatch(); for (int i = 0; i < 10; ++i) { stopwatch.Start(); testWithTypecasting(); stopwatch.Stop(); totalWithCasting += stopwatch.ElapsedTicks; stopwatch.Start(); testWithoutTypeCasting(); stopwatch.Stop(); totalWithoutCasting += stopwatch.ElapsedTicks; } Console.WriteLine("Avg with typecasting = {0}", (totalWithCasting/10)); Console.WriteLine("Avg without typecasting = {0}", (totalWithoutCasting/10)); Console.ReadKey(); } static void testWithTypecasting() { foreach (object o in numericGenericData) { last = ((double)o*(double)o)/200; } } static void testWithoutTypeCasting() { foreach (double d in numericTypedData) { last = (d * d)/200; } } } The output is: Avg with typecasting = 468872.3 Avg without typecasting = 501157.9 I'm a little suspicious... it looks like there is nearly no impact on the performance. Is casting really that cheap?

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  • sphinx xmlpipe2 cassandra and ruby 1.9

    - by user369083
    Hi, I start to using cassandra and I want to index my db with sphinx. I wrote ruby script which is used as xmlpipe, and I configure sphinx to use it. source xmlsrc { type = xmlpipe2 xmlpipe_command = /usr/local/bin/ruby /home/httpd/html/app/script/sphinxpipe.rb } When I run script from console output looks fine, but when I run indexer sphinx return error $ indexer test_index Sphinx 0.9.9-release (r2117) Copyright (c) 2001-2009, Andrew Aksyonoff using config file '/usr/local/etc/sphinx.conf'... indexing index 'test_index'... ERROR: index 'test_index': source 'xmlsrc': attribute 'id' required in <sphinx:document> (line=10, pos=0, docid=0). total 0 docs, 0 bytes total 0.000 sec, 0 bytes/sec, 0.00 docs/sec total 0 reads, 0.000 sec, 0.0 kb/call avg, 0.0 msec/call avg total 0 writes, 0.000 sec, 0.0 kb/call avg, 0.0 msec/call avg my script is very simple $stdout.sync = true puts %{<?xml version="1.0" encoding="utf-8"?>} puts %{<sphinx:docset>} puts %{<sphinx:schema>} puts %{<sphinx:field name="body"/>} puts %{</sphinx:schema>} puts %{<sphinx:document id="ba32c02e-79e2-11df-9815-af1b5f766459">} puts %{<body><![CDATA[aaa]]></body>} puts %{</sphinx:document>} puts %{</sphinx:docset>} I use ruby 1.9.2-head, ubuntu 10.04, sphinx 0.9.9 How can I get this to work?

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  • SQL command to get field of a maximum value, without making two select

    - by António Capelo
    I'm starting to learn SQL and I'm working on this exercise: I have a "books" table which holds the info on every book (including price and genre ID). I need to get the name of the genre which has the highest average price. I suppose that I first need to group the prices by genre and then retrieve the name of the highest.. I know that I can get the results GENRE VS COST with the following: select b.genre, round(avg(b.price),2) as cost from books b group by b.genre; My question is, to get the genre with the highest AVG price from that result, do I have to make: select aux.genre from ( select b.genre, round(avg(b.price),2) as cost from books b group by b.genre ) aux where aux.cost = (select max(aux.cost) from ( select b.genre, round(avg(b.price),2) as cost from books l group by b.genre ) aux); Is it bad practice or isn't there another way? I get the correct result but I'm not confortable with creating two times the same selection. I'm not using PL SQL so I can't use variables or anything like that.. Any help will be appreciated. Thanks in advance!

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  • MDX: Filtering a member set by a measure's table values

    - by oyvinro
    I have some numbers in a fact table, and have generated a measure which use the SUM aggregator to summarize the numbers. But the problem is that I only want to sum the numbers that are higher than, say 10. I tried using a generic expression in the measure definition, and that works of course, but the problem is that I need to be able to dynamically set that value, because it's not always 10, meaning users should be able to select it themselves. More specifically, my current MDX looks like this: WITH SET [Email Measures] AS '{[Measures].[Number Of Answered Cases], [Measures].[Max Expedition Time First In Case], [Measures].[Avg Expedition Times First In Case], [Measures].[Number Of Incoming Email Requests], [Measures].[Avg Number Of Emails In Cases], [Measures].[Avg Expedition Times Total],[Measures].[Number Of Answered Incoming Emails]}' SET [Organizations] AS '{[Organization.Id].[860]}' SET [Operators] AS '{[Operator.Id].[3379],[Operator.Id].[3181]}' SET [Email Accounts] AS '{[Email Account.Id].[6]}' MEMBER [Time.Date].[Date Period] AS Aggregate ({[Time.Date].[2008].[11].[11] :[Time.Date].[2009].[1].[2] }) MEMBER [Email.Type].[Email Types] AS Aggregate ({[Email.Type].[0]}) SELECT {[Email Measures]} ON columns, [Operators] ON rows FROM [Email_Fact] WHERE ( [Time.Date].[Date Period] ) Now, the member in question is the calculated member [Avg Expedition Times Total]. This member takes in two measures; [Sum Expedition Times] and [Nr of Expedition Times] and splits one on the other to get the average, all this presently works. However, I want [Sum Expedition Times] to only summarize values over or under a parameter of my/the user's wish. How do I filter the numbers [Sum Expedition Times] iterates through, rather than filtering on the sum that the measure gives me in the end?

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  • Configuring UCM cache to check for external Content Server changes

    - by Martin Deh
    Recently, I was involved in a customer scenario where they were modifying the Content Server's contributor data files directly through Content Server.  This operation of course is completely supported.  However, since the contributor data file was modified through the "backdoor", a running WebCenter Spaces page, which also used the same data file, would not get the updates immediately.  This was due to two reasons.  The first reason is that the Spaces page was using Content Presenter to display the contents of the data file. The second reason is that the Spaces application was using the "cached" version of the data file.  Fortunately, there is a way to configure cache so backdoor changes can be picked up more quickly and automatically. First a brief overview of Content Presenter.  The Content Presenter task flow enables WebCenter Spaces users with Page-Edit permissions to precisely customize the selection and presentation of content in a WebCenter Spaces application.  With Content Presenter, you can select a single item of content, contents under a folder, a list of items, or query for content, and then select a Content Presenter based template to render the content on a page in a Spaces application.  In addition to displaying the folders and the files in a Content Server, Content Presenter integrates with Oracle Site Studio to allow you to create, access, edit, and display Site Studio contributor data files (Content Server Document) in either a Site Studio region template or in a custom Content Presenter display template.  More information about creating Content Presenter Display Template can be found in the OFM Developers Guide for WebCenter Portal. The easiest way to configure the cache is to modify the WebCenter Spaces Content Server service connection setting through Enterprise Manager.  From here, under the Cache Details, there is a section to set the Cache Invalidation Interval.  Basically, this enables the cache to be monitored by the cache "sweeper" utility.  The cache sweeper queries for changes in the Content Server, and then "marks" the object in cache as "dirty".  This causes the application in turn to get a new copy of the document from the Content Server that replaces the cached version.  By default the initial value for the Cache Invalidation Interval is set to 0 (minutes).  This basically means that the sweeper is OFF.  To turn the sweeper ON, just set a value (in minutes).  The mininal value that can be set is 2 (minutes): Just a note.  In some instances, once the value of the Cache Invalidation Interval has been set (and saved) in the Enterprise Manager UI, it becomes "sticky" and the interval value cannot be set back to 0.  The good news is that this value can also be updated throught a WLST command.   The WLST command to run is as follows: setJCRContentServerConnection(appName, name, [socketType, url, serverHost, serverPort, keystoreLocation, keystorePassword, privateKeyAlias, privateKeyPassword, webContextRoot, clientSecurityPolicy, cacheInvalidationInterval, binaryCacheMaxEntrySize, adminUsername, adminPassword, extAppId, timeout, isPrimary, server, applicationVersion]) One way to get the required information for executing the command is to use the listJCRContentServerConnections('webcenter',verbose=true) command.  For example, this is the sample output from the execution: ------------------ UCM ------------------ Connection Name: UCM Connection Type: JCR External Appliction ID: Timeout: (not set) CIS Socket Type: socket CIS Server Hostname: webcenter.oracle.local CIS Server Port: 4444 CIS Keystore Location: CIS Private Key Alias: CIS Web URL: Web Server Context Root: /cs Client Security Policy: Admin User Name: sysadmin Cache Invalidation Interval: 2 Binary Cache Maximum Entry Size: 1024 The Documents primary connection is "UCM" From this information, the completed  setJCRContentServerConnection would be: setJCRContentServerConnection(appName='webcenter',name='UCM', socketType='socket', serverHost='webcenter.oracle.local', serverPort='4444', webContextRoot='/cs', cacheInvalidationInterval='0', binaryCacheMaxEntrySize='1024',adminUsername='sysadmin',isPrimary=1) Note: The Spaces managed server must be restarted for the change to take effect. More information about using WLST for WebCenter can be found here. Once the sweeper is turned ON, only cache objects that have been changed will be invalidated.  To test this out, I will go through a simple scenario.  The first thing to do is configure the Content Server so it can monitor and report on events.  Log into the Content Server console application, and under the Administration menu item, select System Audit Information.  Note: If your console is using the left menu display option, the Administration link will be located there. Under the Tracing Sections Information, add in only "system" and "requestaudit" in the Active Sections.  Check Full Verbose Tracing, check Save, then click the Update button.  Once this is done, select the View Server Output menu option.  This will change the browser view to display the log.  This is all that is needed to configure the Content Server. For example, the following is the View Server Output with the cache invalidation interval set to 2(minutes) Note the time stamp: requestaudit/6 08.30 09:52:26.001  IdcServer-68    GET_FOLDER_HISTORY_REPORT [dUser=sysadmin][IsJava=1] 0.016933999955654144(secs) requestaudit/6 08.30 09:52:26.010  IdcServer-69    GET_FOLDER_HISTORY_REPORT [dUser=sysadmin][IsJava=1] 0.006134999915957451(secs) requestaudit/6 08.30 09:52:26.014  IdcServer-70    GET_DOCUMENT_HISTORY_REPORT [dUser=sysadmin][IsJava=1] 0.004271999932825565(secs) ... other trace info ... requestaudit/6 08.30 09:54:26.002  IdcServer-71    GET_FOLDER_HISTORY_REPORT [dUser=sysadmin][IsJava=1] 0.020323999226093292(secs) requestaudit/6 08.30 09:54:26.011  IdcServer-72    GET_FOLDER_HISTORY_REPORT [dUser=sysadmin][IsJava=1] 0.017928000539541245(secs) requestaudit/6 08.30 09:54:26.017  IdcServer-73    GET_DOCUMENT_HISTORY_REPORT [dUser=sysadmin][IsJava=1] 0.010185999795794487(secs) Now that the tracing logs are reporting correctly, the next step is set up the Spaces app to test the sweeper. I will use 2 different pages that will use Content Presenter task flows.  Each task flow will use a different custom Content Presenter display template, and will be assign 2 different contributor data files (document that will be in the cache).  The pages at run time appear as follows: Initially, when the Space pages containing the content is loaded in the browser for the first time, you can see the tracing information in the Content Server output viewer. requestaudit/6 08.30 11:51:12.030 IdcServer-129 CLEAR_SERVER_OUTPUT [dUser=weblogic] 0.029171999543905258(secs) requestaudit/6 08.30 11:51:12.101 IdcServer-130 GET_SERVER_OUTPUT [dUser=weblogic] 0.025721000507473946(secs) requestaudit/6 08.30 11:51:26.592 IdcServer-131 VCR_GET_DOCUMENT_BY_NAME [dID=919][dDocName=DF_UCMCACHETESTER][dDocTitle=DF_UCMCacheTester][dUser=weblogic][RevisionSelectionMethod=LatestReleased][IsJava=1] 0.21525299549102783(secs) requestaudit/6 08.30 11:51:27.117 IdcServer-132 VCR_GET_CONTENT_TYPES [dUser=sysadmin][IsJava=1] 0.5059549808502197(secs) requestaudit/6 08.30 11:51:27.146 IdcServer-133 VCR_GET_CONTENT_TYPE [dUser=sysadmin][IsJava=1] 0.03360399976372719(secs) requestaudit/6 08.30 11:51:27.169 IdcServer-134 VCR_GET_CONTENT_TYPE [dUser=sysadmin][IsJava=1] 0.008806000463664532(secs) requestaudit/6 08.30 11:51:27.204 IdcServer-135 VCR_GET_CONTENT_TYPE [dUser=sysadmin][IsJava=1] 0.013265999965369701(secs) requestaudit/6 08.30 11:51:27.384 IdcServer-136 VCR_GET_CONTENT_TYPE [dUser=sysadmin][IsJava=1] 0.18119299411773682(secs) requestaudit/6 08.30 11:51:27.533 IdcServer-137 VCR_GET_CONTENT_TYPE [dUser=sysadmin][IsJava=1] 0.1519480049610138(secs) requestaudit/6 08.30 11:51:27.634 IdcServer-138 VCR_GET_CONTENT_TYPE [dUser=sysadmin][IsJava=1] 0.10827399790287018(secs) requestaudit/6 08.30 11:51:27.687 IdcServer-139 VCR_GET_CONTENT_TYPE [dUser=sysadmin][IsJava=1] 0.059702999889850616(secs) requestaudit/6 08.30 11:51:28.271 IdcServer-140 GET_USER_PERMISSIONS [dUser=weblogic][IsJava=1] 0.006703000050038099(secs) requestaudit/6 08.30 11:51:28.285 IdcServer-141 GET_ENVIRONMENT [dUser=sysadmin][IsJava=1] 0.010893999598920345(secs) requestaudit/6 08.30 11:51:30.433 IdcServer-142 GET_SERVER_OUTPUT [dUser=weblogic] 0.017318999394774437(secs) requestaudit/6 08.30 11:51:41.837 IdcServer-143 VCR_GET_DOCUMENT_BY_NAME [dID=508][dDocName=113_ES][dDocTitle=Landing Home][dUser=weblogic][RevisionSelectionMethod=LatestReleased][IsJava=1] 0.15937699377536774(secs) requestaudit/6 08.30 11:51:42.781 IdcServer-144 GET_FILE [dID=326][dDocName=WEBCENTERORACL000315][dDocTitle=Duke][dUser=anonymous][RevisionSelectionMethod=LatestReleased][dSecurityGroup=Public][xCollectionID=0] 0.16288499534130096(secs) The highlighted sections show where the 2 data files DF_UCMCACHETESTER (P1 page) and 113_ES (P2 page) were called by the (Spaces) VCR connection to the Content Server. The most important line to notice is the VCR_GET_DOCUMENT_BY_NAME invocation.  On subsequent refreshes of these 2 pages, you will notice (after you refresh the Content Server's View Server Output) that there are no further traces of the same VCR_GET_DOCUMENT_BY_NAME invocations.  This is because the pages are getting the documents from the cache. The next step is to go through the "backdoor" and change one of the documents through the Content Server console.  This operation can be done by first locating the data file document, and from the Content Information page, select Edit Data File menu option.   This invokes the Site Studio Contributor, where the modifications can be made. Refreshing the Content Server View Server Output, the tracing displays the operations perform on the document.  requestaudit/6 08.30 11:56:59.972 IdcServer-255 SS_CHECKOUT_BY_NAME [dID=922][dDocName=DF_UCMCACHETESTER][dUser=weblogic][dSecurityGroup=Public] 0.05558200180530548(secs) requestaudit/6 08.30 11:57:00.065 IdcServer-256 SS_GET_CONTRIBUTOR_CONFIG [dID=922][dDocName=DF_UCMCACHETESTER][dDocTitle=DF_UCMCacheTester][dUser=weblogic][dSecurityGroup=Public][xCollectionID=0] 0.08632399886846542(secs) requestaudit/6 08.30 11:57:00.470 IdcServer-259 DOC_INFO_BY_NAME [dID=922][dDocName=DF_UCMCACHETESTER][dDocTitle=DF_UCMCacheTester][dUser=weblogic][dSecurityGroup=Public][xCollectionID=0] 0.02268899977207184(secs) requestaudit/6 08.30 11:57:10.177 IdcServer-264 GET_FOLDER_HISTORY_REPORT [dUser=sysadmin][IsJava=1] 0.007652000058442354(secs) requestaudit/6 08.30 11:57:10.181 IdcServer-263 GET_FOLDER_HISTORY_REPORT [dUser=sysadmin][IsJava=1] 0.01868399977684021(secs) requestaudit/6 08.30 11:57:10.187 IdcServer-265 GET_DOCUMENT_HISTORY_REPORT [dUser=sysadmin][IsJava=1] 0.009367000311613083(secs) (internal)/6 08.30 11:57:26.118 IdcServer-266 File to be removed: /oracle/app/admin/domains/webcenter/ucm/cs/vault/~temp/703253295.xml (internal)/6 08.30 11:57:26.121 IdcServer-266 File to be removed: /oracle/app/admin/domains/webcenter/ucm/cs/vault/~temp/703253295.xml requestaudit/6 08.30 11:57:26.122 IdcServer-266 SS_SET_ELEMENT_DATA [dID=923][dDocName=DF_UCMCACHETESTER][dDocTitle=DF_UCMCacheTester][dUser=weblogic][dSecurityGroup=Public][xCollectionID=0][StatusCode=0][StatusMessage=Successfully checked in content item 'DF_UCMCACHETESTER'.] 0.3765290081501007(secs) requestaudit/6 08.30 11:57:30.710 IdcServer-267 DOC_INFO_BY_NAME [dID=923][dDocName=DF_UCMCACHETESTER][dDocTitle=DF_UCMCacheTester][dUser=weblogic][dSecurityGroup=Public][xCollectionID=0] 0.07942699640989304(secs) requestaudit/6 08.30 11:57:30.733 IdcServer-268 SS_GET_CONTRIBUTOR_STRINGS [dUser=weblogic] 0.0044570001773536205(secs) After a few moments and refreshing the P1 page, the updates has been applied. Note: The refresh time may very, since the Cache Invalidation Interval (set to 2 minutes) is not determined by when changes happened.  The sweeper just runs every 2 minutes. Refreshing the Content Server View Server Output, the tracing displays the important information. requestaudit/6 08.30 11:59:10.171 IdcServer-270 GET_FOLDER_HISTORY_REPORT [dUser=sysadmin][IsJava=1] 0.00952600035816431(secs) requestaudit/6 08.30 11:59:10.179 IdcServer-271 GET_FOLDER_HISTORY_REPORT [dUser=sysadmin][IsJava=1] 0.011118999682366848(secs) requestaudit/6 08.30 11:59:10.182 IdcServer-272 GET_DOCUMENT_HISTORY_REPORT [dUser=sysadmin][IsJava=1] 0.007447000127285719(secs) requestaudit/6 08.30 11:59:16.885 IdcServer-273 VCR_GET_DOCUMENT_BY_NAME [dID=923][dDocName=DF_UCMCACHETESTER][dDocTitle=DF_UCMCacheTester][dUser=weblogic][RevisionSelectionMethod=LatestReleased][IsJava=1] 0.0786449983716011(secs) After the specifed interval time the sweeper is invoked, which is noted by the GET_ ... calls.  Since the history has noted the change, the next call is to the VCR_GET_DOCUMENT_BY_NAME to retrieve the new version of the (modifed) data file.  Navigating back to the P2 page, and viewing the server output, there are no further VCR_GET_DOCUMENT_BY_NAME to retrieve the data file.  This simply means that this data file was just retrieved from the cache.   Upon further review of the server output, we can see that there was only 1 request for the VCR_GET_DOCUMENT_BY_NAME: requestaudit/6 08.30 12:08:00.021 Audit Request Monitor Request Audit Report over the last 120 Seconds for server webcenteroraclelocal16200****  requestaudit/6 08.30 12:08:00.021 Audit Request Monitor -Num Requests 8 Errors 0 Reqs/sec. 0.06666944175958633 Avg. Latency (secs) 0.02762500010430813 Max Thread Count 2  requestaudit/6 08.30 12:08:00.021 Audit Request Monitor 1 Service VCR_GET_DOCUMENT_BY_NAME Total Elapsed Time (secs) 0.09200000017881393 Num requests 1 Num errors 0 Avg. Latency (secs) 0.09200000017881393  requestaudit/6 08.30 12:08:00.021 Audit Request Monitor 2 Service GET_PERSONALIZED_JAVASCRIPT Total Elapsed Time (secs) 0.054999999701976776 Num requests 1 Num errors 0 Avg. Latency (secs) 0.054999999701976776  requestaudit/6 08.30 12:08:00.021 Audit Request Monitor 3 Service GET_FOLDER_HISTORY_REPORT Total Elapsed Time (secs) 0.028999999165534973 Num requests 2 Num errors 0 Avg. Latency (secs) 0.014499999582767487  requestaudit/6 08.30 12:08:00.021 Audit Request Monitor 4 Service GET_SERVER_OUTPUT Total Elapsed Time (secs) 0.017999999225139618 Num requests 1 Num errors 0 Avg. Latency (secs) 0.017999999225139618  requestaudit/6 08.30 12:08:00.021 Audit Request Monitor 5 Service GET_FILE Total Elapsed Time (secs) 0.013000000268220901 Num requests 1 Num errors 0 Avg. Latency (secs) 0.013000000268220901  requestaudit/6 08.30 12:08:00.021 Audit Request Monitor ****End Audit Report*****  

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  • Linux buffer cache effect on IO writes?

    - by Patrick LeBoutillier
    I'm copying large files (3 x 30G) between 2 filesystems on a Linux server (kernel 2.6.37, 16 cores, 32G RAM) and I'm getting poor performance. I suspect that the usage of the buffer cache is killing the I/O performance. To try and narrow down the problem I used fio directly on the SAS disk to monitor the performance. Here is the output of 2 fio runs (the first with direct=1, the second one direct=0): Config: [test] rw=write blocksize=32k size=20G filename=/dev/sda # direct=1 Run 1: test: (g=0): rw=write, bs=32K-32K/32K-32K, ioengine=sync, iodepth=1 Starting 1 process Jobs: 1 (f=1): [W] [100.0% done] [0K/205M /s] [0/6K iops] [eta 00m:00s] test: (groupid=0, jobs=1): err= 0: pid=4667 write: io=20,480MB, bw=199MB/s, iops=6,381, runt=102698msec clat (usec): min=104, max=13,388, avg=152.06, stdev=72.43 bw (KB/s) : min=192448, max=213824, per=100.01%, avg=204232.82, stdev=4084.67 cpu : usr=3.37%, sys=16.55%, ctx=655410, majf=0, minf=29 IO depths : 1=100.0%, 2=0.0%, 4=0.0%, 8=0.0%, 16=0.0%, 32=0.0%, >=64=0.0% submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0% complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0% issued r/w: total=0/655360, short=0/0 lat (usec): 250=99.50%, 500=0.45%, 750=0.01%, 1000=0.01% lat (msec): 2=0.01%, 4=0.02%, 10=0.01%, 20=0.01% Run status group 0 (all jobs): WRITE: io=20,480MB, aggrb=199MB/s, minb=204MB/s, maxb=204MB/s, mint=102698msec, maxt=102698msec Disk stats (read/write): sda: ios=0/655238, merge=0/0, ticks=0/79552, in_queue=78640, util=76.55% Run 2: test: (g=0): rw=write, bs=32K-32K/32K-32K, ioengine=sync, iodepth=1 Starting 1 process Jobs: 1 (f=1): [W] [100.0% done] [0K/0K /s] [0/0 iops] [eta 00m:00s] test: (groupid=0, jobs=1): err= 0: pid=4733 write: io=20,480MB, bw=91,265KB/s, iops=2,852, runt=229786msec clat (usec): min=16, max=127K, avg=349.53, stdev=4694.98 bw (KB/s) : min=56013, max=1390016, per=101.47%, avg=92607.31, stdev=167453.17 cpu : usr=0.41%, sys=6.93%, ctx=21128, majf=0, minf=33 IO depths : 1=100.0%, 2=0.0%, 4=0.0%, 8=0.0%, 16=0.0%, 32=0.0%, >=64=0.0% submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0% complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0% issued r/w: total=0/655360, short=0/0 lat (usec): 20=5.53%, 50=93.89%, 100=0.02%, 250=0.01%, 500=0.01% lat (msec): 2=0.01%, 4=0.01%, 10=0.01%, 20=0.01%, 50=0.12% lat (msec): 100=0.38%, 250=0.04% Run status group 0 (all jobs): WRITE: io=20,480MB, aggrb=91,265KB/s, minb=93,455KB/s, maxb=93,455KB/s, mint=229786msec, maxt=229786msec Disk stats (read/write): sda: ios=8/79811, merge=7/7721388, ticks=9/32418456, in_queue=32471983, util=98.98% I'm not knowledgeable enough with fio to interpret the results, but I don't expect the overall performance using the buffer cache to be 50% less than with O_DIRECT. Can someone help me interpret the fio output? Are there any kernel tunings that could fix/minimize the problem? Thanks a lot,

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  • Linux buffer cache effect on IO writes?

    - by Patrick LeBoutillier
    Hi, I'm copying large files (3 x 30G) between 2 filesystems on a Linux server (kernel 2.6.37, 16 cores, 32G RAM) and I'm getting poor performance. I suspect that the usage of the buffer cache is killing the I/O performance. To try and narrow down the problem I used fio directly on the SAS disk to monitor the performance. Here is the output of 2 fio runs (the first with direct=1, the second one direct=0): Config: [test] rw=write blocksize=32k size=20G filename=/dev/sda # direct=1 Run 1: test: (g=0): rw=write, bs=32K-32K/32K-32K, ioengine=sync, iodepth=1 Starting 1 process Jobs: 1 (f=1): [W] [100.0% done] [0K/205M /s] [0/6K iops] [eta 00m:00s] test: (groupid=0, jobs=1): err= 0: pid=4667 write: io=20,480MB, bw=199MB/s, iops=6,381, runt=102698msec clat (usec): min=104, max=13,388, avg=152.06, stdev=72.43 bw (KB/s) : min=192448, max=213824, per=100.01%, avg=204232.82, stdev=4084.67 cpu : usr=3.37%, sys=16.55%, ctx=655410, majf=0, minf=29 IO depths : 1=100.0%, 2=0.0%, 4=0.0%, 8=0.0%, 16=0.0%, 32=0.0%, >=64=0.0% submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0% complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0% issued r/w: total=0/655360, short=0/0 lat (usec): 250=99.50%, 500=0.45%, 750=0.01%, 1000=0.01% lat (msec): 2=0.01%, 4=0.02%, 10=0.01%, 20=0.01% Run status group 0 (all jobs): WRITE: io=20,480MB, aggrb=199MB/s, minb=204MB/s, maxb=204MB/s, mint=102698msec, maxt=102698msec Disk stats (read/write): sda: ios=0/655238, merge=0/0, ticks=0/79552, in_queue=78640, util=76.55% Run 2: test: (g=0): rw=write, bs=32K-32K/32K-32K, ioengine=sync, iodepth=1 Starting 1 process Jobs: 1 (f=1): [W] [100.0% done] [0K/0K /s] [0/0 iops] [eta 00m:00s] test: (groupid=0, jobs=1): err= 0: pid=4733 write: io=20,480MB, bw=91,265KB/s, iops=2,852, runt=229786msec clat (usec): min=16, max=127K, avg=349.53, stdev=4694.98 bw (KB/s) : min=56013, max=1390016, per=101.47%, avg=92607.31, stdev=167453.17 cpu : usr=0.41%, sys=6.93%, ctx=21128, majf=0, minf=33 IO depths : 1=100.0%, 2=0.0%, 4=0.0%, 8=0.0%, 16=0.0%, 32=0.0%, >=64=0.0% submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0% complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0% issued r/w: total=0/655360, short=0/0 lat (usec): 20=5.53%, 50=93.89%, 100=0.02%, 250=0.01%, 500=0.01% lat (msec): 2=0.01%, 4=0.01%, 10=0.01%, 20=0.01%, 50=0.12% lat (msec): 100=0.38%, 250=0.04% Run status group 0 (all jobs): WRITE: io=20,480MB, aggrb=91,265KB/s, minb=93,455KB/s, maxb=93,455KB/s, mint=229786msec, maxt=229786msec Disk stats (read/write): sda: ios=8/79811, merge=7/7721388, ticks=9/32418456, in_queue=32471983, util=98.98% I'm not knowledgeable enough with fio to interpret the results, but I don't expect the overall performance using the buffer cache to be 50% less than with O_DIRECT. Can someone help me interpret the fio output? Are there any kernel tunings that could fix/minimize the problem? Thanks a lot,

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  • HAProxy causing delay

    - by user1221444
    I am trying to configure HAProxy to do load balancing for a custom webserver I created. Right now I am noticing an increasing delay with HAProxy as the size of the return message increases. For example, I ran four different tests, here are the results: Response 15kb through HAProxy: Avg. response time: .34 secs Transacation rate: 763 trans/sec Throughput: 11.08 MB/sec Response 2kb through HAProxy: Avg. response time: .08 secs Transaction rate: 1171 trans / sec Throughput: 2.51 MB/sec Response 15kb directly to server: Avg. response time: .11 sec Transaction rate: 1046 trans/sec throughput: 15.20 MB/sec Response 2kb directly to server: Avg. Response time: .05 secs Transaction rate: 1158 trans/sec Throughput: 2.48 MB/sec All transactions are HTTP requests. As you can see, there seems to be a much bigger difference between response times for when the response is bigger, than when it is smaller. I understand there will be a slight delay when using HAProxy. Not sure if it matters, but the test itself was run using siege. And during the test there was only one server behind the HAProxy(the same that was used in the direct to server tests). Here is my haproxy.config file: global log 127.0.0.1 local0 log 127.0.0.1 local1 notice maxconn 10000 user haproxy group haproxy daemon #debug defaults log global mode http option httplog option dontlognull retries 3 option redispatch option httpclose maxconn 10000 contimeout 10000 clitimeout 50000 srvtimeout 50000 balance roundrobin stats enable stats uri /stats listen lb1 10.1.10.26:80 maxconn 10000 server app1 10.1.10.200:8080 maxconn 5000 I couldn't find much in terms of options in this file that would help my problem. I have heard suggestions that I may have to adjust a few of my sysctl settings. I could not find a lot of information on this however, most documentation is for Linux 2.4 and 2.6 on the sysctl stuff, I am running 3.2(Ubuntu server 12.04), which seems to auto tuning, so I have no clue what I should or shouldn't be changing. Most settings changes I tried had no effect or a negative effect on performance. Just a notice, this is a very preliminary test, and my hope is that at deployment time, my HAProxy will be able to balance 10k-20k requests/sec to many servers, so if anyone could provide information to help me reach that goal, it would be much appreciated. Thank you very much for any information you can provide. And if you need anymore information from me please let me know, I will get you anything I can.

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  • sql select statement with a group by

    - by user85116
    I have data in 2 tables, and I want to create a report. Table A: tableAID (primary key) name Table B: tableBID (primary key) grade tableAID (foreign key, references Table A) There is much more to both tables, but those are the relevant columns. The query I want to run, conceptually, is this: select TableA.name, avg(TableB.grade) where TableB.tableAID = TableA.tableAID The problem of course is that I'm using an aggregate function (avg), and I can rewrite it like this: select avg(grade), tableAID from TableB group by tableAID but then I only get the ID of TableA, whereas I really need that name column which appears in TableA, not just the ID. Is it possible to write a query to do this in one statement, or would I first need to execute the second query I listed, get the list of id's, then query each record in TableA for the name column... seems to me I'm missing something obvious here, but I'm (quite obviously) not an sql guru...

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  • mySQL query : working with INTERVAL and CURDATE

    - by Tristan
    Hello, i'm building a chart and i want to recieve data for each months Here's my first request which is working : SELECT s.GSP_nom AS nom, timestamp, AVG( v.vote + v.prix ) /2 AS avg FROM votes_serveur AS v INNER JOIN serveur AS s ON v.idServ = s.idServ WHERE s.valide =1 AND v.date > CURDATE() -30 GROUP BY s.GSP_nom ORDER BY avg DESC But, in my case i've to write 12 request to recieve datas for the 12 previous months, is there any trick to avoid writing : // example for the previous month AND v.date > CURDATE() -60 AND v.date < CURDATE () -30 I heard about INTERVAL, i went to the mySQL doc but i didn't manage to implement it. Any ideas / example of using INTERVAL please ? Thank you

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  • getting userbase vote average and individual user's vote in the same query?

    - by Andrew Heath
    Here goes: T1 [id] [desc] 1 lovely 2 ugly 3 slender T2 [id] [userid] [vote] 1 1 3 1 2 5 1 3 2 2 1 1 2 2 4 2 3 4 In one query (if possible) I'd like to return: T1.id, T1.desc, AVG(T2.vote), T2.vote (for user viewing the page) I can get the first 3 items with: SELECT T1.id, T1.desc, AVG(T2.vote) FROM T1 LEFT JOIN T2 ON T1.id=T2.id GROUP BY T1.id and I can get the first, second, and fourth items with: SELECT T1.id, T1.desc, T2.vote FROM T1 LEFT JOIN T2 ON T1.id=T2.id WHERE T2.userid='1' GROUP BY T1.id but I'm at a loss as to how to get all four items in one query. I tried inserting a select as the fourth term: SELECT T1.id, T1.desc, AVG(T2.vote), (SELECT T2.vote FROM T2 WHERE T2.userid='1') AS userVote etc etc but I get an error that the select returns more than one row... Help? My reason for wanting to do this in one query instead of two is that I want to be able to sort the data within MySQL rather than one it's been split into a number of arrays.

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  • SQL Server 2005 - Understanding ouput of DBCC SHOWCONTIG

    - by user169743
    I'm seeing some slow performance on a SQL Server 2005 database. I've been doing some research regarding SQL Server performance but I'm having difficulty fully understanding the output of SHOWCONTIG and would be very grateful if someone could have a look and offer some suggestions to improve performance. TABLE level scan performed. Pages Scanned................................: 19348 Extents Scanned..............................: 2427 Extent Switches..............................: 3829 Avg. Pages per Extent........................: 8.0 Scan Density [Best Count:Actual Count].......: 63.16% [2419:3830] Logical Scan Fragmentation ..................: 8.40% Extent Scan Fragmentation ...................: 35.15% Avg. Bytes Free per Page.....................: 938.1 Avg. Page Density (full).....................: 88.41%

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  • MS SQL 2005 - Understanding ouput of DBCC SHOWCONTIG

    - by user169743
    I'm seeing some slow performance on a MS SQL 2005 database. I've been doing some research regarding MS SQL performance but I'm having difficulty fully understanding the output of SHOWCONTIG and would be very grateful if someone could have a look and offer some suggestions to improve performance. TABLE level scan performed. Pages Scanned................................: 19348 Extents Scanned..............................: 2427 Extent Switches..............................: 3829 Avg. Pages per Extent........................: 8.0 Scan Density [Best Count:Actual Count].......: 63.16% [2419:3830] Logical Scan Fragmentation ..................: 8.40% Extent Scan Fragmentation ...................: 35.15% Avg. Bytes Free per Page.....................: 938.1 Avg. Page Density (full).....................: 88.41%

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  • MySQL: Matching inexact values using "ON"

    - by Brad
    I'm way out of my league here... I have a mapping table (table1) to assign particular values (value) to a whole number (map_nu). My second table (table2), is a collection of averages (avg) (I couldn't figure out how to properly make a markdown table, please feel free to edit!) table1: table2: (value)(Map_nu) (avg) ---- ----- 1 1 1.111 1.045 2 1.2 1.09 3 1.33333 1.135 4 1 1.18 5 1.389 1.225 6 1.42 1.27 7 1.07 1.315 8 1.36 9 1.405 10 I need to find a way to match the averages from table2 to the closest value in table1. It only need to match to the 2 digit past the decimal, so I've added the Truncated function SELECT map_nu FROM `table1` JOIN table2 ON TRUNCATE(table1.value,2)=TRUNCATE(table2.avg,2) I still miss the values that don't match the averages exactly. Is there a way to pick the nearest truncated value? Thanks!

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  • SQL SERVER – Fundamentals of Columnstore Index

    - by pinaldave
    There are two kind of storage in database. Row Store and Column Store. Row store does exactly as the name suggests – stores rows of data on a page – and column store stores all the data in a column on the same page. These columns are much easier to search – instead of a query searching all the data in an entire row whether the data is relevant or not, column store queries need only to search much lesser number of the columns. This means major increases in search speed and hard drive use. Additionally, the column store indexes are heavily compressed, which translates to even greater memory and faster searches. I am sure this looks very exciting and it does not mean that you convert every single index from row store to column store index. One has to understand the proper places where to use row store or column store indexes. Let us understand in this article what is the difference in Columnstore type of index. Column store indexes are run by Microsoft’s VertiPaq technology. However, all you really need to know is that this method of storing data is columns on a single page is much faster and more efficient. Creating a column store index is very easy, and you don’t have to learn new syntax to create them. You just need to specify the keyword “COLUMNSTORE” and enter the data as you normally would. Keep in mind that once you add a column store to a table, though, you cannot delete, insert or update the data – it is READ ONLY. However, since column store will be mainly used for data warehousing, this should not be a big problem. You can always use partitioning to avoid rebuilding the index. A columnstore index stores each column in a separate set of disk pages, rather than storing multiple rows per page as data traditionally has been stored. The difference between column store and row store approaches is illustrated below: In case of the row store indexes multiple pages will contain multiple rows of the columns spanning across multiple pages. In case of column store indexes multiple pages will contain multiple single columns. This will lead only the columns needed to solve a query will be fetched from disk. Additionally there is good chance that there will be redundant data in a single column which will further help to compress the data, this will have positive effect on buffer hit rate as most of the data will be in memory and due to same it will not need to be retrieved. Let us see small example of how columnstore index improves the performance of the query on a large table. As a first step let us create databaseset which is large enough to show performance impact of columnstore index. The time taken to create sample database may vary on different computer based on the resources. USE AdventureWorks GO -- Create New Table CREATE TABLE [dbo].[MySalesOrderDetail]( [SalesOrderID] [int] NOT NULL, [SalesOrderDetailID] [int] NOT NULL, [CarrierTrackingNumber] [nvarchar](25) NULL, [OrderQty] [smallint] NOT NULL, [ProductID] [int] NOT NULL, [SpecialOfferID] [int] NOT NULL, [UnitPrice] [money] NOT NULL, [UnitPriceDiscount] [money] NOT NULL, [LineTotal] [numeric](38, 6) NOT NULL, [rowguid] [uniqueidentifier] NOT NULL, [ModifiedDate] [datetime] NOT NULL ) ON [PRIMARY] GO -- Create clustered index CREATE CLUSTERED INDEX [CL_MySalesOrderDetail] ON [dbo].[MySalesOrderDetail] ( [SalesOrderDetailID]) GO -- Create Sample Data Table -- WARNING: This Query may run upto 2-10 minutes based on your systems resources INSERT INTO [dbo].[MySalesOrderDetail] SELECT S1.* FROM Sales.SalesOrderDetail S1 GO 100 Now let us do quick performance test. I have kept STATISTICS IO ON for measuring how much IO following queries take. In my test first I will run query which will use regular index. We will note the IO usage of the query. After that we will create columnstore index and will measure the IO of the same. -- Performance Test -- Comparing Regular Index with ColumnStore Index USE AdventureWorks GO SET STATISTICS IO ON GO -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO -- Table 'MySalesOrderDetail'. Scan count 1, logical reads 342261, physical reads 0, read-ahead reads 0. -- Create ColumnStore Index CREATE NONCLUSTERED COLUMNSTORE INDEX [IX_MySalesOrderDetail_ColumnStore] ON [MySalesOrderDetail] (UnitPrice, OrderQty, ProductID) GO -- Select Table with Columnstore Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO It is very clear from the results that query is performance extremely fast after creating ColumnStore Index. The amount of the pages it has to read to run query is drastically reduced as the column which are needed in the query are stored in the same page and query does not have to go through every single page to read those columns. If we enable execution plan and compare we can see that column store index performance way better than regular index in this case. Let us clean up the database. -- Cleanup DROP INDEX [IX_MySalesOrderDetail_ColumnStore] ON [dbo].[MySalesOrderDetail] GO TRUNCATE TABLE dbo.MySalesOrderDetail GO DROP TABLE dbo.MySalesOrderDetail GO In future posts we will see cases where Columnstore index is not appropriate solution as well few other tricks and tips of the columnstore index. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Index, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Ranking Part III

    - by PointsToShare
    © 2011 By: Dov Trietsch. All rights reserved   Ranking Part III In a previous blogs “Ranking an Introduction” and  “Ranking Part II” , you have already praised me in “Rank the Author” and learned how to create a new element on a page and how to place it where you need it. For this installment, I just added code to keep the number of votes (you vote by clicking one of the stars) and the total vote. Using these two, we can compute the average rating. It’s a small step, but its purpose is to show that we do not need a detailed history in order to compute the average. A running total is sufficient. Please note that once you close the game, you will lose your previous total. In real life, we persist the totals in the list itself. We also keep a list of actual votes, but its purpose is to prevent double votes. If a person has already voted, his user id is already on the list and our program will check for it and bar the person from voting again. This is coded in an event receiver, which is a SharePoint server piece of code. I will show you how to do this part in a subsequent blog. Again, go to the page and look at the code. The gist of it is here. avg, votes, and stars are global variables that I defined before. function sendRate(sel){//I hate long line so I created pieces of the message in their own vars            var s1 = "Your Rating Was: ";            var s2 = ".. ";            var s3 = "\nVotes = ";            var s4 = "\nTotal Stars = ";            var s5 = "\nAverage = ";            var s;            s = parseInt(sel.id.replace("_", '')); // Get the selected star number            votes = parseInt(votes) + 1;            stars = parseInt(stars) + s;            avg = parseFloat(stars) / parseFloat(votes);            alert(s1 + sel.id + s2 +sel.title + s3 + votes + s4 + stars + s5 + avg);} Click on the link to play and examine “Ranking with Stats” That’s all folks!

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  • Bug! Slow Sums and Averages

    - by Paul White
    It’s a curious thing about SQL that the SUM or AVG of no items is not zero, it’s NULL. In this post, you’ll see how this means your SUM and AVG calculations might run at half speed, or worse. As with most of my blog entries though, today’s instalment is not so much about the result, but the journey we take to get there. Before we get started on that, I just want to mention that there’s a problem with the Google Reader feed for this blog, so those of you that use that will have missed two recent entries: Seeking Without Indexes and Advanced TSQL Tuning: Why Internals Knowledge Matters. Accessing the site directly always works of course :) Ok, on to today’s story. Take a look at this query:...(read more)

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  • Windows in StreamInsight: Hopping vs. Snapshot

    - by Roman Schindlauer
    Three weeks ago, we explained the basic concept of windows in StreamInsight: defining sets of events that serve as arguments for set-based operations, like aggregations. Today, we want to discuss the so-called Hopping Windows and compare them with Snapshot Windows. We will compare these two, because they can serve similar purposes with different behaviors; we will discuss the remaining window type, Count Windows, another time. Hopping (and its syntactic-sugar-sister Tumbling) windows are probably the most straightforward windowing concept in StreamInsight. A hopping window is defined by its length, and the offset from one window to the next. They are aligned with some absolute point on the timeline (which can also be given as a parameter to the window) and create sets of events. The diagram below shows an example of a hopping window with length of 1h and hop size (the offset) of 15 minutes, hence creating overlapping windows:   Two aspects in this diagram are important: Since this window is overlapping, an event can fall into more than one windows. If an (interval) event spans a window boundary, its lifetime will be clipped to the window, before it is passed to the set-based operation. That’s the default and currently only available window input policy. (This should only concern you if you are using a time-sensitive user-defined aggregate or operator.) The set-based operation will be applied to each of these sets, yielding a result. This result is: A single scalar value in case of built-in or user-defined aggregates. A subset of the input payloads, in case of the TopK operator. Arbitrary events, when using a user-defined operator. The timestamps of the result are almost always the ones of the windows. Only the user-defined  operator can create new events with timestamps. (However, even these event lifetimes are subject to the window’s output policy, which is currently always to clip to the window end.) Let’s assume we were calculating the sum over some payload field: var result = from window in source.HoppingWindow( TimeSpan.FromHours(1), TimeSpan.FromMinutes(15), HoppingWindowOutputPolicy.ClipToWindowEnd) select new { avg = window.Avg(e => e.Value) }; Now each window is reflected by one result event:   As you can see, the window definition defines the output frequency. No matter how many or few events we got from the input, this hopping window will produce one result every 15 minutes – except for those windows that do not contain any events at all, because StreamInsight window operations are empty-preserving (more about that another time). The “forced” output for every window can become a performance issue if you have a real-time query with many events in a wide group & apply – let me explain: imagine you have a lot of events that you group by and then aggregate within each group – classical streaming pattern. The hopping window produces a result in each group at exactly the same point in time for all groups, since the window boundaries are aligned with the timeline, not with the event timestamps. This means that the query output will become very bursty, delivering the results of all the groups at the same point in time. This becomes especially obvious if the events are long-lasting, spanning multiple windows each, so that the produced result events do not change their value very often. In such a case, a snapshot window can remedy. Snapshot windows are more difficult to explain than hopping windows: they represent those periods in time, when no event changes occur. In other words, if you mark all event start and and times on your timeline, then you are looking at all snapshot window boundaries:   If your events are never overlapping, the snapshot window will not make much sense. It is commonly used together with timestamp modification, which make it a very powerful tool. Or as Allan Mitchell expressed in in a recent tweet: “I used to look at SnapshotWindow() with disdain. Now she is my mistress, the one I turn to in times of trouble and need”. Let’s look at a simple example: I want to compute the average of some value in my events over the last minute. I don’t want this output be produced at fixed intervals, but at soon as it changes (that’s the true event-driven spirit!). The snapshot window will include all currently active event at each point in time, hence we need to extend our original events’ lifetimes into the future: Applying the Snapshot window on these events, it will appear to be “looking back into the past”: If you look at the result produced in this diagram, you can easily prove that, at each point in time, the current event value represents the average of all original input event within the last minute. Here is the LINQ representation of that query, applying the lifetime extension before the snapshot window: var result = from window in source .AlterEventDuration(e => TimeSpan.FromMinutes(1)) .SnapshotWindow(SnapshotWindowOutputPolicy.Clip) select new { avg = window.Avg(e => e.Value) }; With more complex modifications of the event lifetimes you can achieve many more query patterns. For instance “running totals” by keeping the event start times, but snapping their end times to some fixed time, like the end of the day. Each snapshot then “sees” all events that have happened in the respective time period so far. Regards, The StreamInsight Team

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  • Duplicate ping response when running Ubuntu as virtual machine (VMWare)

    - by Stonerain
    I have the following setup: My router - 192.168.0.1 My host computer (Windows 7) - 192.168.0.3 And Ubuntu is running as virtual machine on the host. VMWare network settings is Bridged mode. I've modified Ubuntu network settings in /etc/netowrk/interfaces, set the following config: iface eth0 inet static address 192.168.0.220 netmask 255.255.255.0 network 192.168.0.0 broadcast 192.168.0.255 gateway 192.168.0.1 Internet works correctly, I can install packages. But it gets weird if I try to ping something I get this: PING belpak.by (193.232.248.80) 56(84) bytes of data. From 192.168.0.1 icmp_seq=1 Time to live exceeded From 192.168.0.1 icmp_seq=1 Time to live exceeded From 192.168.0.1 icmp_seq=1 Time to live exceeded From 192.168.0.1 icmp_seq=1 Time to live exceeded From 192.168.0.1 icmp_seq=1 Time to live exceeded 64 bytes from belhost.by (193.232.248.80): icmp_seq=1 ttl=250 time=17.0 ms 64 bytes from belhost.by (193.232.248.80): icmp_seq=1 ttl=249 time=17.0 ms (DUP! ) 64 bytes from belhost.by (193.232.248.80): icmp_seq=1 ttl=248 time=17.0 ms (DUP! ) 64 bytes from belhost.by (193.232.248.80): icmp_seq=1 ttl=247 time=17.0 ms (DUP! ) 64 bytes from belhost.by (193.232.248.80): icmp_seq=1 ttl=246 time=17.0 ms (DUP! ) ^CFrom 192.168.0.1 icmp_seq=2 Time to live exceeded --- belpak.by ping statistics --- 2 packets transmitted, 1 received, +4 duplicates, +6 errors, 50% packet loss, ti me 999ms rtt min/avg/max/mdev = 17.023/17.041/17.048/0.117 ms I think even more interesting are the results of pinging the router itself: stonerain@ubuntu:~$ ping 192.168.0.1 -c 1 PING 192.168.0.1 (192.168.0.1) 56(84) bytes of data. From 192.168.0.3: icmp_seq=1 Redirect Network(New nexthop: 192.168.0.1) 64 bytes from 192.168.0.1: icmp_seq=1 ttl=254 time=6.64 ms --- 192.168.0.1 ping statistics --- 1 packets transmitted, 1 received, 0% packet loss, time 0ms rtt min/avg/max/mdev = 6.644/6.644/6.644/0.000 ms But if I set -c 2: ... 64 bytes from 192.168.0.1: icmp_seq=1 ttl=252 time=13.5 ms (DUP!) 64 bytes from 192.168.0.1: icmp_seq=1 ttl=251 time=13.5 ms (DUP!) 64 bytes from 192.168.0.1: icmp_seq=1 ttl=254 time=13.5 ms (DUP!) 64 bytes from 192.168.0.1: icmp_seq=1 ttl=253 time=13.5 ms (DUP!) 64 bytes from 192.168.0.1: icmp_seq=1 ttl=252 time=13.5 ms (DUP!) 64 bytes from 192.168.0.1: icmp_seq=1 ttl=251 time=13.5 ms (DUP!) From 192.168.0.3: icmp_seq=2 Redirect Network(New nexthop: 192.168.0.1) 64 bytes from 192.168.0.1: icmp_seq=2 ttl=254 time=7.87 ms --- 192.168.0.1 ping statistics --- 2 packets transmitted, 2 received, +256 duplicates, 0% packet loss, time 1002ms rtt min/avg/max/mdev = 6.666/10.141/13.556/2.410 ms Pinging host machine on the other hand works absolutely correctly: no DUPs, no errors. What seems to be the problem and how can I fix it? Thank you.

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  • Fans running very fast on MacBook Pro 8.1 ubuntu 12.04

    - by Tomasz Kacprzak
    I installed Ubuntu 12.04 on Macbook Pro 8.1 and one of the first things I noticed was that the fans were starting to spin very fast every few minutes for 10-30 sec and then going back to normal. That was happening even without any processor load, when completely idle. The fans were usually spinning at 4000 RPM and made much noise. The computer was not getting hotter than usual. When running OSX Lion there was no noise at all, fans almost all the time at 2000 RPM. I spent some time on it and found out that Precise uses a deamon to control the temperature, called macfanctld. You can use /etc/macfanctld.conf to set the configuration. I found out that the high fan speed is not due to the fact that the temperature is getting hot, but because there are two sensors which indicate wrong numbers (you can check that using 'sensors' command ): TW0P: +129.0°C TCTD: +256.0°C TCFC: +0.0°C TMBS: +0.0°C or setting the macfanctld log level to 2: Speed: 4992, *AVG: 56.9C, TC0P: 50.2C, TG0P: 51.5C, Sensors: TB0T:34 TB1T:34 TB2T:33 TC0C:58 TC0D:56 TC0E:59 TC0F:60 TC0P:50 TC1C:58 TC2C:58 TC3C:58 TC4C:57 TCFC:0 TCGC:57 TCSA:53 TCTD:256 TG0D:52 TG0P:52 THSP:42 TM0S:64 TMBS:0 TP0P:54 TPCD:60 TW0P:129 Th1H:51 Th2H:48 Tm0P:40 Ts0P:32 Ts0S:43 Moreover, TCTD was randomly jumping from temperatures of 0 to 256, so this may be the reason for unjustified random fan speeds. macfanctld is taking an average of the sensors including the values above, so the actual AVG temp used to control the fans is wrong, usually biased up, hence high RPM and noise. The workaround solution is to use an option in the macfanctld.conf which allows to ignore the malfunctioning sensors: exclude: 13 16 21 24 After reboot the reported temperatures are usually normal and the fans are working at reasonable speeds. I tested the response of the fans to heavy processor load by asking MATLAB to invert 10000x10000 matrix and the AVG temperature jumped to 63deg, and the fan to max 6200 RPM and then got it back to normal temperature. So I think it is safe so far. There is a expired bug about the failing sensor readings: https://bugs.launchpad.net/ubuntu/+source/linux/+bug/955538 which may be good to open again. My question would be: does anyone know what the failing sensors do and if there is any danger in excluding them? Maybe some better solution to this problem?

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  • Fans running very fast on MacBook Pro 8.1

    - by Tomasz Kacprzak
    I installed Ubuntu 12.04 on Macbook Pro 8.1 and one of the first things I noticed was that the fans were starting to spin very fast every few minutes for 10-30 sec and then going back to normal. That was happening even without any processor load, when completely idle. The fans were usually spinning at 4000 RPM and made much noise. The computer was not getting hotter than usual. When running OSX Lion there was no noise at all, fans almost all the time at 2000 RPM. I spent some time on it and found out that Precise uses a deamon to control the temperature, called macfanctld. You can use /etc/macfanctld.conf to set the configuration. I found out that the high fan speed is not due to the fact that the temperature is getting hot, but because there are two sensors which indicate wrong numbers (you can check that using 'sensors' command ): TW0P: +129.0°C TCTD: +256.0°C TCFC: +0.0°C TMBS: +0.0°C or setting the macfanctld log level to 2: Speed: 4992, *AVG: 56.9C, TC0P: 50.2C, TG0P: 51.5C, Sensors: TB0T:34 TB1T:34 TB2T:33 TC0C:58 TC0D:56 TC0E:59 TC0F:60 TC0P:50 TC1C:58 TC2C:58 TC3C:58 TC4C:57 TCFC:0 TCGC:57 TCSA:53 TCTD:256 TG0D:52 TG0P:52 THSP:42 TM0S:64 TMBS:0 TP0P:54 TPCD:60 TW0P:129 Th1H:51 Th2H:48 Tm0P:40 Ts0P:32 Ts0S:43 Moreover, TCTD was randomly jumping from temperatures of 0 to 256, so this may be the reason for unjustified random fan speeds. macfanctld is taking an average of the sensors including the values above, so the actual AVG temp used to control the fans is wrong, usually biased up, hence high RPM and noise. The workaround solution is to use an option in the macfanctld.conf which allows to ignore the malfunctioning sensors: exclude: 13 16 21 24 After reboot the reported temperatures are usually normal and the fans are working at reasonable speeds. I tested the response of the fans to heavy processor load by asking MATLAB to invert 10000x10000 matrix and the AVG temperature jumped to 63deg, and the fan to max 6200 RPM and then got it back to normal temperature. So I think it is safe so far. There is a expired bug about the failing sensor readings: https://bugs.launchpad.net/ubuntu/+source/linux/+bug/955538 which may be good to open again. My question would be: does anyone know what the failing sensors do and if there is any danger in excluding them? Maybe some better solution to this problem?

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  • PMDB Block Size Choice

    - by Brian Diehl
    Choosing a block size for the P6 PMDB database is not a difficult task. In fact, taking the default of 8k is going to be just fine. Block size is one of those things that is always hotly debated. Everyone has their personal preference and can sight plenty of good reasons for their choice. To add to the confusion, Oracle supports multiple block sizes withing the same instance. So how to decide and what is the justification? Like most OLTP systems, Oracle Primavera P6 has a wide variety of data. A typical table's average row size may be less than 50 bytes or upwards of 500 bytes. There are also several tables with BLOB types but the LOB data tends not to be very large. It is likely that no single block size would be perfect for every table. So how to choose? My preference is for the 8k (8192 bytes) block size. It is a good compromise that is not too small for the wider rows, yet not to big for the thin rows. It is also important to remember that database blocks are the smallest unit of change and caching. I prefer to have more, individual "working units" in my database. For an instance with 4gb of buffer cache, an 8k block will provide 524,288 blocks of cache. The following SQL*Plus script returns the average, median, min, and max rows per block. column "AVG(CNT)" format 999.99 set verify off select avg(cnt), median(cnt), min(cnt), max(cnt), count(*) from ( select dbms_rowid.ROWID_RELATIVE_FNO(rowid) , dbms_rowid.ROWID_BLOCK_NUMBER(rowid) , count(*) cnt from &tab group by dbms_rowid.ROWID_RELATIVE_FNO(rowid) , dbms_rowid.ROWID_BLOCK_NUMBER(rowid) ) Running this for the TASK table, I get this result on a database with an 8k block size. Each activity, on average, has about 19 rows per block. Enter value for tab: task AVG(CNT) MEDIAN(CNT) MIN(CNT) MAX(CNT) COUNT(*) -------- ----------- ---------- ---------- ---------- 18.72 19 3 28 415917 I recommend an 8k block size for the P6 transactional database. All of our internal performance and scalability test are done with this block size. This does not mean that other block sizes will not work. Instead, like many other parameters, this is the safest choice.

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  • Loading PNGs into OpenGL performance issues - Java & JOGL much slower than C# & Tao.OpenGL

    - by Edward Cresswell
    I am noticing a large performance difference between Java & JOGL and C# & Tao.OpenGL when both loading PNGs from storage into memory, and when loading that BufferedImage (java) or Bitmap (C# - both are PNGs on hard drive) 'into' OpenGL. This difference is quite large, so I assumed I was doing something wrong, however after quite a lot of searching and trying different loading techniques I've been unable to reduce this difference. With Java I get an image loaded in 248ms and loaded into OpenGL in 728ms The same on C# takes 54ms to load the image, and 34ms to load/create texture. The image in question above is a PNG containing transparency, sized 7200x255, used for a 2D animated sprite. I realise the size is really quite ridiculous and am considering cutting up the sprite, however the large difference is still there (and confusing). On the Java side the code looks like this: BufferedImage image = ImageIO.read(new File(fileName)); texture = TextureIO.newTexture(image, false); texture.setTexParameteri(GL.GL_TEXTURE_MIN_FILTER, GL.GL_LINEAR); texture.setTexParameteri(GL.GL_TEXTURE_MAG_FILTER, GL.GL_LINEAR); The C# code uses: Bitmap t = new Bitmap(fileName); t.RotateFlip(RotateFlipType.RotateNoneFlipY); Rectangle r = new Rectangle(0, 0, t.Width, t.Height); BitmapData bd = t.LockBits(r, ImageLockMode.ReadOnly, PixelFormat.Format32bppArgb); Gl.glBindTexture(Gl.GL_TEXTURE_2D, tID); Gl.glTexImage2D(Gl.GL_TEXTURE_2D, 0, Gl.GL_RGBA, t.Width, t.Height, 0, Gl.GL_BGRA, Gl.GL_UNSIGNED_BYTE, bd.Scan0); Gl.glTexParameteri(Gl.GL_TEXTURE_2D, Gl.GL_TEXTURE_MIN_FILTER, Gl.GL_LINEAR); Gl.glTexParameteri(Gl.GL_TEXTURE_2D, Gl.GL_TEXTURE_MAG_FILTER, Gl.GL_LINEAR); t.UnlockBits(bd); t.Dispose(); After quite a lot of testing I can only come to the conclusion that Java/JOGL is just slower here - PNG reading might not be as quick, or that I'm still doing something wrong. Thanks. Edit2: I have found that creating a new BufferedImage with format TYPE_INT_ARGB_PRE decreases OpenGL texture load time by almost half - this includes having to create the new BufferedImage, getting the Graphics2D from it and then rendering the previously loaded image to it. Edit3: Benchmark results for 5 variations. I wrote a small benchmarking tool, the following results come from loading a set of 33 pngs, most are very wide, 5 times. testStart: ImageIO.read(file) -> TextureIO.newTexture(image) result: avg = 10250ms, total = 51251 testStart: ImageIO.read(bis) -> TextureIO.newTexture(image) result: avg = 10029ms, total = 50147 testStart: ImageIO.read(file) -> TextureIO.newTexture(argbImage) result: avg = 5343ms, total = 26717 testStart: ImageIO.read(bis) -> TextureIO.newTexture(argbImage) result: avg = 5534ms, total = 27673 testStart: TextureIO.newTexture(file) result: avg = 10395ms, total = 51979 ImageIO.read(bis) refers to the technique described in James Branigan's answer below. argbImage refers to the technique described in my previous edit: img = ImageIO.read(file); argbImg = new BufferedImage(img.getWidth(), img.getHeight(), TYPE_INT_ARGB_PRE); g = argbImg.createGraphics(); g.drawImage(img, 0, 0, null); texture = TextureIO.newTexture(argbImg, false); Any more methods of loading (either images from file, or images to OpenGL) would be appreciated, I will update these benchmarks.

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  • sysbench memory test on ec2 small instance

    - by caribio
    I'm seeing a problem with sysbench memory test (the default version that's compiled in). This is on Ubuntu Maverick, sysbench installed via apt-get install sysbench. Running the same thing on Ubuntu @ Rackspace worked just as expected. While the CPU and I/O tests worked fine on EC2 servers, the memory test just runs without doing anything (notice the 0M in the test results). The instance used was the publicly available 'stock' Ubuntu image with no changes to it: ./ec2-run-instances ami-ccf405a5 --instance-type m1.small --region us-east-1 --key mykey Supplying more arguments (such as: --memory-block-size=1K --memory-total-size=102400M) didn't help. What am I doing wrong? Thanks. sysbench --num-threads=4 --test=memory run sysbench 0.4.12: multi-threaded system evaluation benchmark Running the test with following options: Number of threads: 4 Doing memory operations speed test Memory block size: 1K Memory transfer size: 0M Memory operations type: write Memory scope type: global Threads started! Done. Operations performed: 0 ( 0.00 ops/sec) 0.00 MB transferred (0.00 MB/sec) Test execution summary: total time: 0.0003s total number of events: 0 total time taken by event execution: 0.0000 per-request statistics: min: 18446744073709.55ms avg: 0.00ms max: 0.00ms Threads fairness: events (avg/stddev): 0.0000/0.00 execution time (avg/stddev): 0.0000/0.00

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