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  • What's best performance way to constantly change image on WP7?

    - by AlRodriguez
    I'm trying to make my own type of remote desktop for WP7. I have a WCF service that returns an image on what's on the target machine's screen. Here's the WCF Server Code: // Method to load desktop image Bitmap image = new Bitmap( ViewSize.Width, ViewSize.Height ); Graphics g = Graphics.FromImage( image ); g.CopyFromScreen( Position.X, Position.Y, 0, 0, ViewSize ); g.Dispose( ); return image; // Convert image to byte[] which is returned to client using ( MemoryStream ms = new MemoryStream( ) ) { Bitmap image = screenGrabber.LoadScreenImage( ); image.Save( ms, ImageFormat.Jpeg ); imageArray = ms.ToArray( ); } Here's the code for the WP7 client: MemoryStream stream = new MemoryStream( data ); BitmapImage image = new BitmapImage( ); image.SetSource( stream ); BackgroundImage.Source = image; The BackgroundImage variable is an Image control. I'm noticing this freeze on the emulator after a short while, and will eventually crash from an OutOfMemoryException. This is already pretty slow ( images show up a good half second later than what's on the screen ), and I'm wondering if there's a better/faster way of doing this? Any help would be great. Thanks in advance.

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  • Why do dicts of defaultdict(int)'s use so much memory? (and other simple python performance question

    - by dukhat
    import numpy as num from collections import defaultdict topKeys = range(16384) keys = range(8192) table = dict((k,defaultdict(int)) for k in topKeys) dat = num.zeros((16384,8192), dtype="int32") print "looping begins" #how much memory should this use? I think it shouldn't use more that a few #times the memory required to hold (16384*8192) int32's (512 mb), but #it uses 11 GB! for k in topKeys: for j in keys: dat[k,j] = table[k][j] print "done" What is going on here? Furthermore, this similar script takes eons to run compared to the first one, and also uses an absurd quantity of memory. topKeys = range(16384) keys = range(8192) table = [(j,0) for k in topKeys for j in keys] I guess python ints might be 64 bit ints, which would account for some of this, but do these relatively natural and simple constructions really produce such a massive overhead? I guess these scripts show that they do, so my question is: what exactly is causing the high memory usage in the first script and the long runtime and high memory usage of the second script and is there any way to avoid these costs?

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  • Basics of Join Factorization

    - by Hong Su
    We continue our series on optimizer transformations with a post that describes the Join Factorization transformation. The Join Factorization transformation was introduced in Oracle 11g Release 2 and applies to UNION ALL queries. Union all queries are commonly used in database applications, especially in data integration applications. In many scenarios the branches in a UNION All query share a common processing, i.e, refer to the same tables. In the current Oracle execution strategy, each branch of a UNION ALL query is evaluated independently, which leads to repetitive processing, including data access and join. The join factorization transformation offers an opportunity to share the common computations across the UNION ALL branches. Currently, join factorization only factorizes common references to base tables only, i.e, not views. Consider a simple example of query Q1. Q1:    select t1.c1, t2.c2    from t1, t2, t3    where t1.c1 = t2.c1 and t1.c1 > 1 and t2.c2 = 2 and t2.c2 = t3.c2   union all    select t1.c1, t2.c2    from t1, t2, t4    where t1.c1 = t2.c1 and t1.c1 > 1 and t2.c3 = t4.c3; Table t1 appears in both the branches. As does the filter predicates on t1 (t1.c1 > 1) and the join predicates involving t1 (t1.c1 = t2.c1). Nevertheless, without any transformation, the scan (and the filtering) on t1 has to be done twice, once per branch. Such a query may benefit from join factorization which can transform Q1 into Q2 as follows: Q2:    select t1.c1, VW_JF_1.item_2    from t1, (select t2.c1 item_1, t2.c2 item_2                   from t2, t3                    where t2.c2 = t3.c2 and t2.c2 = 2                                  union all                   select t2.c1 item_1, t2.c2 item_2                   from t2, t4                    where t2.c3 = t4.c3) VW_JF_1    where t1.c1 = VW_JF_1.item_1 and t1.c1 > 1; In Q2, t1 is "factorized" and thus the table scan and the filtering on t1 is done only once (it's shared). If t1 is large, then avoiding one extra scan of t1 can lead to a huge performance improvement. Another benefit of join factorization is that it can open up more join orders. Let's look at query Q3. Q3:    select *    from t5, (select t1.c1, t2.c2                  from t1, t2, t3                  where t1.c1 = t2.c1 and t1.c1 > 1 and t2.c2 = 2 and t2.c2 = t3.c2                 union all                  select t1.c1, t2.c2                  from t1, t2, t4                  where t1.c1 = t2.c1 and t1.c1 > 1 and t2.c3 = t4.c3) V;   where t5.c1 = V.c1 In Q3, view V is same as Q1. Before join factorization, t1, t2 and t3 must be joined first before they can be joined with t5. But if join factorization factorizes t1 from view V, t1 can then be joined with t5. This opens up new join orders. That being said, join factorization imposes certain join orders. For example, in Q2, t2 and t3 appear in the first branch of the UNION ALL query in view VW_JF_1. T2 must be joined with t3 before it can be joined with t1 which is outside of the VW_JF_1 view. The imposed join order may not necessarily be the best join order. For this reason, join factorization is performed under cost-based transformation framework; this means that we cost the plans with and without join factorization and choose the cheapest plan. Note that if the branches in UNION ALL have DISTINCT clauses, join factorization is not valid. For example, Q4 is NOT semantically equivalent to Q5.   Q4:     select distinct t1.*      from t1, t2      where t1.c1 = t2.c1  union all      select distinct t1.*      from t1, t2      where t1.c1 = t2.c1 Q5:    select distinct t1.*     from t1, (select t2.c1 item_1                   from t2                union all                   select t2.c1 item_1                  from t2) VW_JF_1     where t1.c1 = VW_JF_1.item_1 Q4 might return more rows than Q5. Q5's results are guaranteed to be duplicate free because of the DISTINCT key word at the top level while Q4's results might contain duplicates.   The examples given so far involve inner joins only. Join factorization is also supported in outer join, anti join and semi join. But only the right tables of outer join, anti join and semi joins can be factorized. It is not semantically correct to factorize the left table of outer join, anti join or semi join. For example, Q6 is NOT semantically equivalent to Q7. Q6:     select t1.c1, t2.c2    from t1, t2    where t1.c1 = t2.c1(+) and t2.c2 (+) = 2  union all    select t1.c1, t2.c2    from t1, t2      where t1.c1 = t2.c1(+) and t2.c2 (+) = 3 Q7:     select t1.c1, VW_JF_1.item_2    from t1, (select t2.c1 item_1, t2.c2 item_2                  from t2                  where t2.c2 = 2                union all                  select t2.c1 item_1, t2.c2 item_2                  from t2                                                                                                    where t2.c2 = 3) VW_JF_1       where t1.c1 = VW_JF_1.item_1(+)                                                                  However, the right side of an outer join can be factorized. For example, join factorization can transform Q8 to Q9 by factorizing t2, which is the right table of an outer join. Q8:    select t1.c2, t2.c2    from t1, t2      where t1.c1 = t2.c1 (+) and t1.c1 = 1 union all    select t1.c2, t2.c2    from t1, t2    where t1.c1 = t2.c1(+) and t1.c1 = 2 Q9:   select VW_JF_1.item_2, t2.c2   from t2,             (select t1.c1 item_1, t1.c2 item_2            from t1            where t1.c1 = 1           union all            select t1.c1 item_1, t1.c2 item_2            from t1            where t1.c1 = 2) VW_JF_1   where VW_JF_1.item_1 = t2.c1(+) All of the examples in this blog show factorizing a single table from two branches. This is just for ease of illustration. Join factorization can factorize multiple tables and from more than two UNION ALL branches.  SummaryJoin factorization is a cost-based transformation. It can factorize common computations from branches in a UNION ALL query which can lead to huge performance improvement. 

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  • How to target SCOM 2007 R2 monitor to monitor only one server

    - by Trondh
    Hi, This might be basic, but hopefully someone can help me: We have a well-working SCOM 2007 R2 implementation monitoring our Microsoft infrastructure. Now, on one of these servers there's an event (logged to the eventlog) that I need to be alerted on. I have created a group and put this one windows server in it. Then, I created a monitor with simple event detection, entered the event id and used the group name as "monitor target". This doesnt work - the monitor doesn't show up in health explorer at all. However, If I create the monitor with "Windows computers" as target it works, but that means I'll have to disable the monitor, and then enable it for the group, which is cumbersome and slightly illogical to me. Is this by design, or am I doing something wrong?

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  • How to improve Windows Aero desktop performance?

    - by Click Ok
    Sincerely I don't understand why in Windows Experience ratings, the "Game Graphics" in my pc is 5.0 and "Graphic Elements" (windows aero desktop performance) is 3.9. How it is possible? My VGA is nice for games but bad for Windows Desktop? What I can do to improve windows aero desktop performance?

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  • SQL SERVER – Index Created on View not Used Often – Observation of the View – Part 2

    - by pinaldave
    Earlier, I have written an article about SQL SERVER – Index Created on View not Used Often – Observation of the View. I received an email from one of the readers, asking if there would no problems when we create the Index on the base table. Well, we need to discuss this situation in two different cases. Before proceeding to the discussion, I strongly suggest you read my earlier articles. To avoid the duplication, I am not going to repeat the code and explanation over here. In all the earlier cases, I have explained in detail how Index created on the View is not utilized. SQL SERVER – Index Created on View not Used Often – Limitation of the View 12 SQL SERVER – Index Created on View not Used Often – Observation of the View SQL SERVER – Indexed View always Use Index on Table As per earlier blog posts, so far we have done the following: Create a Table Create a View Create Index On View Write SELECT with ORDER BY on View However, the blog reader who emailed me suggests the extension of the said logic, which is as follows: Create a Table Create a View Create Index On View Write SELECT with ORDER BY on View Create Index on the Base Table Write SELECT with ORDER BY on View After doing the last two steps, the question is “Will the query on the View utilize the Index on the View, or will it still use the Index of the base table?“ Let us first run the Create example. USE tempdb GO IF EXISTS (SELECT * FROM sys.views WHERE OBJECT_ID = OBJECT_ID(N'[dbo].[SampleView]')) DROP VIEW [dbo].[SampleView] GO IF EXISTS (SELECT * FROM sys.objects WHERE OBJECT_ID = OBJECT_ID(N'[dbo].[mySampleTable]') AND TYPE IN (N'U')) DROP TABLE [dbo].[mySampleTable] GO -- Create SampleTable CREATE TABLE mySampleTable (ID1 INT, ID2 INT, SomeData VARCHAR(100)) INSERT INTO mySampleTable (ID1,ID2,SomeData) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY o1.name), ROW_NUMBER() OVER (ORDER BY o2.name), o2.name FROM sys.all_objects o1 CROSS JOIN sys.all_objects o2 GO -- Create View CREATE VIEW SampleView WITH SCHEMABINDING AS SELECT ID1,ID2,SomeData FROM dbo.mySampleTable GO -- Create Index on View CREATE UNIQUE CLUSTERED INDEX [IX_ViewSample] ON [dbo].[SampleView] ( ID2 ASC ) GO -- Select from view SELECT ID1,ID2,SomeData FROM SampleView ORDER BY ID2 GO -- Create Index on Original Table -- On Column ID1 CREATE UNIQUE CLUSTERED INDEX [IX_OriginalTable] ON mySampleTable ( ID1 ASC ) GO -- On Column ID2 CREATE UNIQUE NONCLUSTERED INDEX [IX_OriginalTable_ID2] ON mySampleTable ( ID2 ) GO -- Select from view SELECT ID1,ID2,SomeData FROM SampleView ORDER BY ID2 GO Now let us see the execution plans for both of the SELECT statement. Before Index on Base Table (with Index on View): After Index on Base Table (with Index on View): Looking at both executions, it is very clear that with or without, the View is using Indexes. Alright, I have written 11 disadvantages of the Views. Now I have written one case where the View is using Indexes. Anybody who says that I am being harsh on Views can say now that I found one place where Index on View can be helpful. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL View, SQLServer, T SQL, Technology

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  • Linux AMD-FX 8350 temperature monitoring

    - by HyperDevil
    I’m trying to get the CPU temperature for my AMD-FX8350 on Debian Squeeze. I ran sensors-detect and then sensors, but I only get my motherboard sensors (it8720-isa-0228). There are three temperature values there but I assume those are not for the CPU. it8720-isa-0228 Adapter: ISA adapter in0: +1.36 V (min = +0.00 V, max = +4.08 V) in1: +1.50 V (min = +0.00 V, max = +4.08 V) in2: +3.38 V (min = +0.00 V, max = +4.08 V) in3: +2.93 V (min = +0.00 V, max = +4.08 V) in4: +3.07 V (min = +0.00 V, max = +4.08 V) in5: +4.08 V (min = +0.00 V, max = +4.08 V) in6: +4.08 V (min = +0.00 V, max = +4.08 V) in7: +2.93 V (min = +0.00 V, max = +4.08 V) Vbat: +3.01 V fan1: 3375 RPM (min = 10 RPM) fan2: 0 RPM (min = 0 RPM) fan3: 1730 RPM (min = 10 RPM) fan5: 0 RPM (min = 0 RPM) temp1: +27.0°C (low = +127.0°C, high = +127.0°C) sensor = thermistor temp2: +53.0°C (low = +127.0°C, high = +127.0°C) sensor = thermal diode temp3: +65.0°C (low = +127.0°C, high = +90.0°C) sensor = thermal diode cpu0_vid: +0.000 V Is there anything I am missing? I also loaded the K8temp and K10temp modules and ran sensor-detect without any results. I do see this message in dmesg: hwmon-vid: Unknown VRM version of your x86 CPU

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  • OpenNMS monitoring SAP

    - by HannesFostie
    I was wondering if anyone had any experience plugging SAP into their OpenNMS installation. Mostly looking for experiences, perhaps Nagios comparisons, or some more concrete information on what is being monitored and how you did it. Go into as much details as you like. I am currently in the process of evaluating both Nagios and OpenNMS and the possibilities with SAP might be the deciding factor here. Sadly, I didn't find a whole lot on google on the subject.

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  • SQL SERVER – Index Created on View not Used Often – Observation of the View

    - by pinaldave
    I always enjoy writing about concepts on Views. Views are frequently used concepts, and so it’s not surprising that I have seen so many misconceptions about this subject. To clear such misconceptions, I have previously written the article SQL SERVER – The Limitations of the Views – Eleven and more…. I also wrote a follow up article wherein I demonstrated that without even creating index on the basic table, the query on the View will not use the View. You can read about this demonstration over here: SQL SERVER – Index Created on View not Used Often – Limitation of the View 12. I promised in that post that I would also write an article where I would demonstrate the condition where the Index will be used. I got many responses suggesting that I can do that with using NOEXPAND; I agree. I have already written about this in my original summary article. Here is a way for you to see how Index created on View can be utilized. We will do the following steps on this exercise: Create a Table Create a View Create Index On View Write SELECT with ORDER BY on View USE tempdb GO IF EXISTS (SELECT * FROM sys.views WHERE OBJECT_ID = OBJECT_ID(N'[dbo].[SampleView]')) DROP VIEW [dbo].[SampleView] GO IF EXISTS (SELECT * FROM sys.objects WHERE OBJECT_ID = OBJECT_ID(N'[dbo].[mySampleTable]') AND TYPE IN (N'U')) DROP TABLE [dbo].[mySampleTable] GO -- Create SampleTable CREATE TABLE mySampleTable (ID1 INT, ID2 INT, SomeData VARCHAR(100)) INSERT INTO mySampleTable (ID1,ID2,SomeData) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY o1.name), ROW_NUMBER() OVER (ORDER BY o2.name), o2.name FROM sys.all_objects o1 CROSS JOIN sys.all_objects o2 GO -- Create View CREATE VIEW SampleView WITH SCHEMABINDING AS SELECT ID1,ID2,SomeData FROM dbo.mySampleTable GO -- Create Index on View CREATE UNIQUE CLUSTERED INDEX [IX_ViewSample] ON [dbo].[SampleView] ( ID2 ASC ) GO -- Select from view SELECT ID1,ID2,SomeData FROM SampleView ORDER BY ID2 GO When we check the execution plan for this , we find it clearly that the Index created on the View is utilized. ORDER BY clause uses the Index created on the View. I hope this makes the puzzle simpler on how the Index is used on the View. Again, I strongly recommend reading my earlier series about the limitations of the Views found here: SQL SERVER – The Limitations of the Views – Eleven and more…. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL View, T SQL, Technology

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  • Web Site Performance and Assembly Versioning – Part 3 Versioning Combined Files Using Mercurial

    - by capgpilk
    Minification and Concatination of JavaScript and CSS Files Versioning Combined Files Using Subversion Versioning Combined Files Using Mercurial – this post I have worked on a project recently where there was a need to version the system (library dll, css and javascript files) by date and Mercurial revision number. This was in the format:- 0.12.524.407 {major}.{year}.{month}{date}.{mercurial revision} Each time there is an internal build using the CI server, it would label the files using this format. When it came time to do a major release, it became v1.{year}.{month}{date}.{mercurial revision}, with each public release having a major version increment. Also as a requirement, each assembly also had to have a new GUID on each build. So like in previous posts, we need to edit the csproj file, and add a couple of Default targets. 1: <?xml version="1.0" encoding="utf-8"?> 2: <Project ToolsVersion="4.0" DefaultTargets="Hg-Revision;AssemblyInfo;Build" 3: xmlns="http://schemas.microsoft.com/developer/msbuild/2003"> 4: <PropertyGroup> Right below the closing tag of the entire project we add our two targets, the first is to get the Mercurial revision number. We first need to import the tasks for MSBuild which can be downloaded from http://msbuildhg.codeplex.com/ 1: <Import Project="..\Tools\MSBuild.Mercurial\MSBuild.Mercurial.Tasks" />   1: <Target Name="Hg-Revision"> 2: <HgVersion LocalPath="$(MSBuildProjectDirectory)" Timeout="5000" 3: LibraryLocation="C:\TortoiseHg\"> 4: <Output TaskParameter="Revision" PropertyName="Revision" /> 5: </HgVersion> 6: <Message Text="Last revision from HG: $(Revision)" /> 7: </Target> With the main Mercurial files being located at c:\TortoiseHg To get a valid GUID we need to escape from the csproj markup and call some c# code which we put in a property group for later reference. 1: <PropertyGroup> 2: <GuidGenFunction> 3: <![CDATA[ 4: public static string ScriptMain() { 5: return System.Guid.NewGuid().ToString().ToUpper(); 6: } 7: ]]> 8: </GuidGenFunction> 9: </PropertyGroup> Now we add in our target for generating the GUID. 1: <Target Name="AssemblyInfo"> 2: <Script Language="C#" Code="$(GuidGenFunction)"> 3: <Output TaskParameter="ReturnValue" PropertyName="NewGuid" /> 4: </Script> 5: <Time Format="yy"> 6: <Output TaskParameter="FormattedTime" PropertyName="year" /> 7: </Time> 8: <Time Format="Mdd"> 9: <Output TaskParameter="FormattedTime" PropertyName="daymonth" /> 10: </Time> 11: <AssemblyInfo CodeLanguage="CS" OutputFile="Properties\AssemblyInfo.cs" 12: AssemblyTitle="name" AssemblyDescription="description" 13: AssemblyCompany="none" AssemblyProduct="product" 14: AssemblyCopyright="Copyright ©" 15: ComVisible="false" CLSCompliant="true" Guid="$(NewGuid)" 16: AssemblyVersion="$(Major).$(year).$(daymonth).$(Revision)" 17: AssemblyFileVersion="$(Major).$(year).$(daymonth).$(Revision)" /> 18: </Target> So this will give use an AssemblyInfo.cs file like this just prior to calling the Build task:- 1: using System; 2: using System.Reflection; 3: using System.Runtime.CompilerServices; 4: using System.Runtime.InteropServices; 5:  6: [assembly: AssemblyTitle("name")] 7: [assembly: AssemblyDescription("description")] 8: [assembly: AssemblyCompany("none")] 9: [assembly: AssemblyProduct("product")] 10: [assembly: AssemblyCopyright("Copyright ©")] 11: [assembly: ComVisible(false)] 12: [assembly: CLSCompliant(true)] 13: [assembly: Guid("9C2C130E-40EF-4A20-B7AC-A23BA4B5F2B7")] 14: [assembly: AssemblyVersion("0.12.524.407")] 15: [assembly: AssemblyFileVersion("0.12.524.407")] Therefore giving us the correct version for the assembly. This can be referenced within your project whether web or Windows based like this:- 1: public static string AppVersion() 2: { 3: return Assembly.GetExecutingAssembly().GetName().Version.ToString(); 4: } As mentioned in previous posts in this series, you can label css and javascript files using this version number and the GetAssemblyIdentity task from the main MSBuild task library build into the .Net framework. 1: <GetAssemblyIdentity AssemblyFiles="bin\TheAssemblyFile.dll"> 2: <Output TaskParameter="Assemblies" ItemName="MyAssemblyIdentities" /> 3: </GetAssemblyIdentity> Then use this to write out the files:- 1: <WriteLinestoFile 2: File="Client\site-style-%(MyAssemblyIdentities.Version).combined.min.css" 3: Lines="@(CSSLinesSite)" Overwrite="true" />

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  • Websphere - Performance Monitoring Infrastructure servlet login GET

    - by virtual-lab
    I am trying to make an http call to the WebSphere PMI servlet. Websphere has security enabled and therefore I am asked to enter user credentials in order to display the xml. What actually doesn't work as I expect is that username and password in the url are not recognized and the BASIC authorization form is displayed. Obviously it doesn't work from a third party application point of view, I need to pass those variables as GET request. Any suggestion?

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  • Monitoring slow nginx/unicorn requests

    - by injekt
    I'm currently using Nginx to proxy requests to a Unicorn server running a Sinatra application. The application only has a couple of routes defined, those of which make fairly simple (non costly) queries to a PostgreSQL database, and finally return data in JSON format, these services are being monitored by God. I'm currently experiencing extremely slow response times from this application server. I have another two Unicorn servers being proxied via Nginx, and these are responding perfectly fine, so I think I can rule out any wrong doing from Nginx. Here is my God configuration: # God configuration APP_ROOT = File.expand_path '../', File.dirname(__FILE__) God.watch do |w| w.name = "app_name" w.interval = 30.seconds # default w.start = "cd #{APP_ROOT} && unicorn -c #{APP_ROOT}/config/unicorn.rb -D" # -QUIT = graceful shutdown, waits for workers to finish their current request before finishing w.stop = "kill -QUIT `cat #{APP_ROOT}/tmp/unicorn.pid`" w.restart = "kill -USR2 `cat #{APP_ROOT}/tmp/unicorn.pid`" w.start_grace = 10.seconds w.restart_grace = 10.seconds w.pid_file = "#{APP_ROOT}/tmp/unicorn.pid" # User under which to run the process w.uid = 'web' w.gid = 'web' # Cleanup the pid file (this is needed for processes running as a daemon) w.behavior(:clean_pid_file) # Conditions under which to start the process w.start_if do |start| start.condition(:process_running) do |c| c.interval = 5.seconds c.running = false end end # Conditions under which to restart the process w.restart_if do |restart| restart.condition(:memory_usage) do |c| c.above = 150.megabytes c.times = [3, 5] # 3 out of 5 intervals end restart.condition(:cpu_usage) do |c| c.above = 50.percent c.times = 5 end end w.lifecycle do |on| on.condition(:flapping) do |c| c.to_state = [:start, :restart] c.times = 5 c.within = 5.minute c.transition = :unmonitored c.retry_in = 10.minutes c.retry_times = 5 c.retry_within = 2.hours end end end Here is my Unicorn configuration: # Unicorn configuration file APP_ROOT = File.expand_path '../', File.dirname(__FILE__) worker_processes 8 preload_app true pid "#{APP_ROOT}/tmp/unicorn.pid" listen 8001 stderr_path "#{APP_ROOT}/log/unicorn.stderr.log" stdout_path "#{APP_ROOT}/log/unicorn.stdout.log" before_fork do |server, worker| old_pid = "#{APP_ROOT}/tmp/unicorn.pid.oldbin" if File.exists?(old_pid) && server.pid != old_pid begin Process.kill("QUIT", File.read(old_pid).to_i) rescue Errno::ENOENT, Errno::ESRCH # someone else did our job for us end end end I have checked God status logs but it appears CPU and Memory Usage are never out of bounds. I also have something to kill high memory workers, which can be found on the GitHub blog page here. When running a tail -f on the Unicorn logs I see some requests, but they're far and few between, when I was at around 60-100 a second before this trouble seemed to have arrived. This log also shows workers being reaped and started as expected. So my question is, how would I go about debugging this? What are the next steps I should be taking? I'm extremely baffled that the server will sometimes respond quickly, but at others time it's very slow, for long periods of time (which may or may not be peak traffic times). Any advice is much appreciated.

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  • Performance Gains using Indexed Views and Computed Columns

    - by NeilHambly
    Hello This is a quick follow-up blog to the Presention I gave last night @ the London UG Meeting ( 17th March 2010 ) It was a great evening and we had a big full house (over 120 Registered for this event), due to time constraints we had I was unable to spend enough time on this topic to really give it justice or any the myriad of questions that arose form the session, I will be gathering all my material and putting a comprehensive BLOG entry on this topic in the next couple of days.. In the meantime here is the slides from last night if you wanted to again review it or if you where not @ the meeting If you wish to contact me then please feel free to send me emails @ [email protected] Finally  - a quick thanks to Tony Rogerson for allowing me to be a Presenter last night (so we know who we can blame !)  and all the other presenters for thier support Watch this space Folks more to follow soon.. 

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  • The JRockit Performance Counters

    - by Marcus Hirt
    Every now and then I get a question regarding what the attributes in the PerfCounters dynamic MBean represent. Now, all the MBeans under the oracle.jrockit.management (bea.jrockit.management pre R28) domain are part of what we call JMXMAPI (the JRockit JMX based Management API), which is unsupported. Therefore there is no official documentation for the API. I did however write a bit about JMXMAPI in my recent JRockit book, Oracle JRockit: The Definitive Guide. The information in the table below is from that book: Counter Description java.cls.loadedClasses The number of classes loaded since the start of the JVM. java.cls.unloadedClasses The number of classes unloaded since the start of the JVM. java.property.java.class.path The class path of the JVM. java.property.java.endorsed.dirs The endorsed dirs. See the Endorsed Standards Override Mechanism. java.property.java.ext.dirs The ext dirs, which are searched for jars that should be automatically put on the classpath. See the Java documentation for java.ext.dirs. java.property.java.home The root of the JDK or JRE installation. java.property.java.library.path The library path used to find user libraries. java.property.java.vm.version The JRockit version. java.rt.vmArgs The list of VM arguments. java.threads.daemon The number of running daemon threads. java.threads.live The total number of running threads. java.threads.livePeak The peak number of threads that has been running since JRockit was started. java.threads.nonDaemon The number of non-daemon threads running. java.threads.started The total number of threads started since the start of JRockit. jrockit.gc.latest.heapSize The current heap size in bytes. jrockit.gc.latest.nurserySize The current nursery size in bytes. jrockit.gc.latest.oc.compaction.time How long, in ticks, the last compaction lasted. Reset to 0 if compaction is skipped. jrockit.gc.latest.oc.heapUsedAfter Used heap at the end of the last OC, in bytes. jrockit.gc.latest.oc.heapUsedBefore Used heap at the start of the last OC, in bytes. jrockit.gc.latest.oc.number The number of OCs that have occurred so far. jrockit.gc.latest.oc.sumOfPauses The paused time for the last OC, in ticks. jrockit.gc.latest.oc.time The time the last OC took, in ticks. jrockit.gc.latest.yc.sumOfPauses The paused time for the last YC, in ticks. jrockit.gc.latest.yc.time The time the last YC took, in ticks. jrockit.gc.max.oc.individualPause The longest OC pause so far, in ticks. jrockit.gc.max.yc.individualPause The longest YC pause so far, in ticks. jrockit.gc.total.oc.compaction.externalAborted Number of aborted external compactions so far. jrockit.gc.total.oc.compaction.internalAborted Number of aborted internal compactions so far. jrockit.gc.total.oc.compaction.internalSkipped Number of skipped internal compactions so far. jrockit.gc.total.oc.compaction.time The total time spent doing compaction so far, in ticks. jrockit.gc.total.oc.ompaction.externalSkipped Number of skipped external compactions so far. jrockit.gc.total.oc.pauseTime The sum of all OC pause times so far, in ticks. jrockit.gc.total.oc.time The total time spent doing OC so far, in ticks. jrockit.gc.total.pageFaults The number of page faults that have occurred during GC so far. jrockit.gc.total.yc.pauseTime The sum of all YC pause times, in ticks. jrockit.gc.total.yc.promotedObjects The number of objects that all YCs have promoted. jrockit.gc.total.yc.promotedSize The total number of bytes that all YCs have promoted, in bytes. jrockit.gc.total.yc.time The total time spent doing YC, in ticks. oracle.ci.jit.count The number of methods JIT compiled. oracle.ci.jit.timeTotal The total time spent JIT compiling, in ticks. oracle.ci.opt.count The number of methods optimized. oracle.ci.opt.timeTotal The total time spent optimizing, in ticks. oracle.rt.counterFrequency Used to convert ticks values to seconds. Note that many of these counters are excellent choices for attributes to plot in the Management Console. Also note that many values are in ticks – to convert them to seconds, divide by the value in the oracle.rt.counterFrequency counter.

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  • ASP.NET/mono performance on Linux

    - by Quandary
    Anybody knows how asp.net/mono performance is on Linux ? I mean, which server gives you the best performance/delivery time (Apache/Apache2, xsp2, lighthttp, nginx, other) ? Since all asp.net goes via xsp2, I'd say xsp2 would certainly be fastest, but it's probably missing a lot of features, which lighthttp offers (e.g. mod_dosevasive, URL-rewriting, etc.).

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  • SQL SERVER – SQL Server High Availability Options – Notes from the Field #032

    - by Pinal Dave
    [Notes from Pinal]: When it is about High Availability or Disaster Recovery, I often see people getting confused. There are so many options available that when the user has to select what is the most optimal solution for their organization they are often confused. Most of the people even know the salient features of various options, but when they have to figure out one single option to use they are often not sure which option to use. I like to give ask my dear friend time all these kinds of complicated questions. He has a skill to make a complex subject very simple and easy to understand. Linchpin People are database coaches and wellness experts for a data driven world. In this 26th episode of the Notes from the Fields series database expert Tim Radney (partner at Linchpin People) explains in a very simple words the best High Availability Option for your SQL Server.  Working with SQL Server a common challenge we are faced with is providing the maximum uptime possible.  To meet these demands we have to design a solution to provide High Availability (HA). Microsoft SQL Server depending on your edition provides you with several options.  This could be database mirroring, log shipping, failover clusters, availability groups or replication. Each possible solution comes with pro’s and con’s.  Not anyone one solution fits all scenarios so understanding which solution meets which need is important.  As with anything IT related, you need to fully understand your requirements before trying to solution the problem.  When it comes to building an HA solution, you need to understand the risk your organization needs to mitigate the most. I have found that most are concerned about hardware failure and OS failures. Other common concerns are data corruption or storage issues.  For data corruption or storage issues you can mitigate those concerns by having a second copy of the databases. That can be accomplished with database mirroring, log shipping, replication or availability groups with a secondary replica.  Failover clustering and virtualization with shared storage do not provide redundancy of the data. I recently created a chart outlining some pros and cons of each of the technologies that I posted on my blog. I like to use this chart to help illustrate how each technology provides a certain number of benefits.  Each of these solutions carries with it some level of cost and complexity.  As a database professional we should all be familiar with these technologies so we can make the best possible choice for our organization. If you want me to take a look at your server and its settings, or if your server is facing any issue we can Fix Your SQL Server. Note: Tim has also written an excellent book on SQL Backup and Recovery, a must have for everyone. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Notes from the Field, PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: Shrinking Database

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  • Event system architecture for networking when performance is concerned

    - by Vandell
    How should I design a system for an action game (think in Golden Axe) where events can happen remotely? I'm using TCP for this because the client is in flash. There's so many options, I can make a binary protocol (I don't like this idea, I found it to be too hard to mantain) but I was also thinking that passing jsons through clients and server can be slow (Is that a exaggerated concern?). What about the internal architecture for the server? And for the client? I'm really lost, If it's a question that is too big, please indicate me some material so I can formulate a better question next time.

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  • OBIEE 11.1.1 - How to configure HTTP compression / caching on Oracle BI Mobile app

    - by Ahmed Awan
     Applies to: OBIEE 11.1.1.5 Supported Physical Devices and OS: The Oracle BI Mobile application with HTTP compression / caching configurations is tested on following devices: iPhone 4S, 4, 3GS. iPad 2 and 1. Note these devices must be running the latest version of the iOS version, i.e. iOS 4.2.1 / iOS 5 is also supported. Configuring Pre-requisites: Prior to configuration, the Oracle Web tier software must be installed on server, as described in product documentation i.e. Enterprise Deployment Guide for Oracle Business Intelligence in Section 3.2, "Installing Oracle HTTP Server." The steps for configuring the compression and caching on Oracle HTTP Server are described in this PA blog at http://blogs.oracle.com/pa/entry/obiee_11g_user_interface_ui and in support Doc ID 1312299.1. Configuration Steps in Oracle BI Mobile application: 1. Download the BI Mobile app from the Apple iTunes App Store. The link is http://itunes.apple.com/us/app/oracle-business-intelligence/id434559909?mt=8 . 2. Add Server for example http://pew801.us.oracle.com:7777/analytics/ , here is how your “Server Setting” screen should look like on your OBI Mobile app:                                 Performance Gain Test (using Oracle® HTTP Server with OBIEE) The test with/without HTTP compression / caching was conducted on iPhone 4S / iPad 2 to measure the throughput (i.e. total bytes received) for Oracle® Business Intelligence Enterprise Edition. Below table shows the throughput comparison before and after using HTTP compression / caching for SampleApp using “QuickStart” dashboard accessing reports i.e. Overview, Details, Published Reporting and Scorecard. Testing shows that total bytes received were reduced from 2.3 MB to 723 KB. a. Test Results > Without HTTP Compression / Caching setting - Total Throughput (in Bytes) captured below: Total Bytes Statistics:        b. Test Results > With HTTP Compression / Caching settings - Total Throughput (in Bytes) captured below: Total Bytes Statistics:      

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