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• PHP code's performance test

  - by jasmine

  What is the best way for test my php code's performance?

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• Python performance improvement request for winkler

  - by Martlark

  I'm a python n00b and I'd like some suggestions on how to improve the algorithm to improve the performance of this method to compute the Jaro-Winkler distance of two names. def winklerCompareP(str1, str2): """Return approximate string comparator measure (between 0.0 and 1.0) USAGE: score = winkler(str1, str2) ARGUMENTS: str1 The first string str2 The second string DESCRIPTION: As described in 'An Application of the Fellegi-Sunter Model of Record Linkage to the 1990 U.S. Decennial Census' by William E. Winkler and Yves Thibaudeau. Based on the 'jaro' string comparator, but modifies it according to whether the first few characters are the same or not. """ # Quick check if the strings are the same - - - - - - - - - - - - - - - - - - # jaro_winkler_marker_char = chr(1) if (str1 == str2): return 1.0 len1 = len(str1) len2 = len(str2) halflen = max(len1,len2) / 2 - 1 ass1 = '' # Characters assigned in str1 ass2 = '' # Characters assigned in str2 #ass1 = '' #ass2 = '' workstr1 = str1 workstr2 = str2 common1 = 0 # Number of common characters common2 = 0 #print "'len1', str1[i], start, end, index, ass1, workstr2, common1" # Analyse the first string - - - - - - - - - - - - - - - - - - - - - - - - - # for i in range(len1): start = max(0,i-halflen) end = min(i+halflen+1,len2) index = workstr2.find(str1[i],start,end) #print 'len1', str1[i], start, end, index, ass1, workstr2, common1 if (index > -1): # Found common character common1 += 1 #ass1 += str1[i] ass1 = ass1 + str1[i] workstr2 = workstr2[:index]+jaro_winkler_marker_char+workstr2[index+1:] #print "str1 analyse result", ass1, common1 #print "str1 analyse result", ass1, common1 # Analyse the second string - - - - - - - - - - - - - - - - - - - - - - - - - # for i in range(len2): start = max(0,i-halflen) end = min(i+halflen+1,len1) index = workstr1.find(str2[i],start,end) #print 'len2', str2[i], start, end, index, ass1, workstr1, common2 if (index > -1): # Found common character common2 += 1 #ass2 += str2[i] ass2 = ass2 + str2[i] workstr1 = workstr1[:index]+jaro_winkler_marker_char+workstr1[index+1:] if (common1 != common2): print('Winkler: Wrong common values for strings "%s" and "%s"' % \ (str1, str2) + ', common1: %i, common2: %i' % (common1, common2) + \ ', common should be the same.') common1 = float(common1+common2) / 2.0 ##### This is just a fix ##### if (common1 == 0): return 0.0 # Compute number of transpositions - - - - - - - - - - - - - - - - - - - - - # transposition = 0 for i in range(len(ass1)): if (ass1[i] != ass2[i]): transposition += 1 transposition = transposition / 2.0 # Now compute how many characters are common at beginning - - - - - - - - - - # minlen = min(len1,len2) for same in range(minlen+1): if (str1[:same] != str2[:same]): break same -= 1 if (same > 4): same = 4 common1 = float(common1) w = 1./3.*(common1 / float(len1) + common1 / float(len2) + (common1-transposition) / common1) wn = w + same*0.1 * (1.0 - w) return wn

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• MySQL Normalization stored procedure performance

  - by srkiNZ84

  Hi, I've written a stored procedure in MySQL to take values currently in a table and to "Normalize" them. This means that for each value passed to the stored procedure, it checks whether the value is already in the table. If it is, then it stores the id of that row in a variable. If the value is not in the table, it stores the newly inserted value's id. The stored procedure then takes the id's and inserts them into a table which is equivalent to the original de-normailized table, but this table is fully normalized and consists of mainly foreign keys. My problem with this design is that the stored procedure takes approximately 10ms or so to return, which is too long when you're trying to work through some 10million records. My suspicion is that the performance is to do with the way in which I'm doing the inserts. i.e. INSERT INTO TableA (first_value) VALUES (argument_from_sp) ON DUPLICATE KEY UPDATE id=LAST_INSERT_ID(id); SET @TableAId = LAST_INSERT_ID(); The "ON DUPLICATE KEY UPDATE" is a bit of a hack, due to the fact that on a duplicate key I don't want to update anything but rather just return the id value of the row. If you miss this step though, the LAST_INSERT_ID() function returns the wrong value when you're trying to run the "SET ..." statement. Does anyone know of a better way to do this in MySQL? Thank you

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• Which memory related Tomcat JVM startup parameters are worth tuning?

  - by knorv

  I'm trying to understand the fine art of tuning Tomcat memory settings. In this quest I have the following three questions: Which memory related JVM startup parameters are worth setting when running Tomcat? Why? What are useful rule-of-thumbs when fine-tuning the memory settings for a Tomcat installation? How do you monitor the memory consumption of your live Tomcat installation?

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• What's the format of Real World Performance Day?

  - by william.hardie

  A question that has cropped a lot of late is "what's the format of Real World Performance Day?" Not an unreasonable question you might think. Sure enough, a quick check of the Independent Oracle User Group's website tells us a bit about the Real World Performance Day event, but no formal agenda? This was one of the questions I posed to Tom Kyte (one of the main presenters) in our recent podcast. Tom tells us that this isn't your traditional event where one speaker follows another with loads of slides. In fact, the Real World Performance Day features Tom and fellow Oracle performance experts - Andrew Holdsworth and Graham Wood - continuously on stage throughout the day. All three will be discussing database performance challenges and solutions from development, architectural design and management perspectives. There's going to be multi-terabyte demos on show, less of the traditional slides, and more interactive debate and discussion going on. Tune-in and hear what else Tom has to say about this fairly unique event!

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• Analysing Group & Individual Member Performance -RUP

  - by user23871

  I am writing a report which requires the analysis of performance of each individual team member. This is for a software development project developed using the Unified Process (UP). I was just wondering if there are any existing group & individual appraisal metrics used so I don't have to reinvent the wheel... EDIT This is by no means correct but something like: Individual Contribution (IC) = time spent (individual) / time spent (total) = Performance = ? (should use individual contribution (IC) combined with something to gain a measure of overall performance).... Maybe I am talking complete hash and I know generally its really difficult to analyse performance with numbers but any mathematicians out there that can lend a hand or know a somewhat more accurate method of analysing performance than arbitrary marking (e.g. 8 out 10)

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• Strange performance behaviour for 64 bit modulo operation

  - by codymanix

  The last three of these method calls take approx. double the time than the first four. The only difference is that their arguments doesn't fit in integer anymore. But should this matter? The parameter is declared to be long, so it should use long for calculation anyway. Does the modulo operation use another algorithm for numbersmaxint? I am using amd athlon64 3200+, winxp sp3 and vs2008. Stopwatch sw = new Stopwatch(); TestLong(sw, int.MaxValue - 3l); TestLong(sw, int.MaxValue - 2l); TestLong(sw, int.MaxValue - 1l); TestLong(sw, int.MaxValue); TestLong(sw, int.MaxValue + 1l); TestLong(sw, int.MaxValue + 2l); TestLong(sw, int.MaxValue + 3l); Console.ReadLine(); static void TestLong(Stopwatch sw, long num) { long n = 0; sw.Reset(); sw.Start(); for (long i = 3; i < 20000000; i++) { n += num % i; } sw.Stop(); Console.WriteLine(sw.Elapsed); } EDIT: I now tried the same with C and the issue does not occur here, all modulo operations take the same time, in release and in debug mode with and without optimizations turned on: #include "stdafx.h" #include "time.h" #include "limits.h" static void TestLong(long long num) { long long n = 0; clock_t t = clock(); for (long long i = 3; i < 20000000LL*100; i++) { n += num % i; } printf("%d - %lld\n", clock()-t, n); } int main() { printf("%i %i %i %i\n\n", sizeof (int), sizeof(long), sizeof(long long), sizeof(void*)); TestLong(3); TestLong(10); TestLong(131); TestLong(INT_MAX - 1L); TestLong(UINT_MAX +1LL); TestLong(INT_MAX + 1LL); TestLong(LLONG_MAX-1LL); getchar(); return 0; } EDIT2: Thanks for the great suggestions. I found that both .net and c (in debug as well as in release mode) does't not use atomically cpu instructions to calculate the remainder but they call a function that does. In the c program I could get the name of it which is "_allrem". It also displayed full source comments for this file so I found the information that this algorithm special cases the 32bit divisors instead of dividends which was the case in the .net application. I also found out that the performance of the c program really is only affected by the value of the divisor but not the dividend. Another test showed that the performance of the remainder function in the .net program depends on both the dividend and divisor. BTW: Even simple additions of long long values are calculated by a consecutive add and adc instructions. So even if my processor calls itself 64bit, it really isn't :( EDIT3: I now ran the c app on a windows 7 x64 edition, compiled with visual studio 2010. The funny thing is, the performance behavior stays the same, although now (I checked the assembly source) true 64 bit instructions are used.

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• C# performance varying due to memory

  - by user1107474

  Hope this is a valid post here, its a combination of C# issues and hardware. I am benchmarking our server because we have found problems with the performance of our quant library (written in C#). I have simulated the same performance issues with some simple C# code- performing very heavy memory-usage. The code below is in a function which is spawned from a threadpool, up to a maximum of 32 threads (because our server has 4x CPUs x 8 cores each). This is all on .Net 3.5 The problem is that we are getting wildly differing performance. I run the below function 1000 times. The average time taken for the code to run could be, say, 3.5s, but the fastest will only be 1.2s and the slowest will be 7s- for the exact same function! I have graphed the memory usage against the timings and there doesnt appear to be any correlation with the GC kicking in. One thing I did notice is that when running in a single thread the timings are identical and there is no wild deviation. I have also tested CPU-bound algorithms and the timings are identical too. This has made us wonder if the memory bus just cannot cope. I was wondering could this be another .net or C# problem, or is it something related to our hardware? Would this be the same experience if I had used C++, or Java?? We are using 4x Intel x7550 with 32GB ram. Is there any way around this problem in general? Stopwatch watch = new Stopwatch(); watch.Start(); List<byte> list1 = new List<byte>(); List<byte> list2 = new List<byte>(); List<byte> list3 = new List<byte>(); int Size1 = 10000000; int Size2 = 2 * Size1; int Size3 = Size1; for (int i = 0; i < Size1; i++) { list1.Add(57); } for (int i = 0; i < Size2; i = i + 2) { list2.Add(56); } for (int i = 0; i < Size3; i++) { byte temp = list1.ElementAt(i); byte temp2 = list2.ElementAt(i); list3.Add(temp); list2[i] = temp; list1[i] = temp2; } watch.Stop(); (the code is just meant to stress out the memory) I would include the threadpool code, but we used a non-standard threadpool library. EDIT: I have reduced "size1" to 100000, which basically doesn't use much memory and I still get a lot of jitter. This suggests it's not the amount of memory being transferred, but the frequency of memory grabs?

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• High accuracy cpu timers

  - by John Robertson

  An expert in highly optimized code once told me that an important part of his strategy was the availability of extremely high performance timers on the CPU. Does anyone know what those are and how one can access them to test various code optimizations? While I am interested regardless, I also wanted to ask whether it is possible to access them from something higher than assembly (or with only a little assembly) via visual studio C++?

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• AS 400 Performance from .Net iSeries Provider

  - by Nathan

  Hey all, First off, I am not an AS 400 guy - at all. So please forgive me for asking any noobish questions here. Basically, I am working on a .Net application that needs to access the AS400 for some real-time data. Although I have the system working, I am getting very different performance results between queries. Typically, when I make the 1st request against a SPROC on the AS400, I am seeing ~ 14 seconds to get the full data set. After that initial call, any subsequent calls usually only take ~ 1 second to return. This performance improvement remains for ~ 20 mins or so before it takes 14 seconds again. The interesting part with this is that, if the stored procedure is executed directly on the iSeries Navigator, it always returns within milliseconds (no change in response time). I wonder if it is a caching / execution plan issue but I can only apply my SQL SERVER logic to the AS400, which is not always a match. Any suggestions on what I can do to recieve a more consistant response time or simply insight as to why the AS400 is acting in this manner when I was using the iSeries Data Provider for .Net? Is there a better access method that I should use? Just in case, here's the code I am using to connect to the AS400 Dim Conn As New IBM.Data.DB2.iSeries.iDB2Connection(ConnectionString) Dim Cmd As New IBM.Data.DB2.iSeries.iDB2Command("SPROC_NAME_HERE", Conn) Cmd.CommandType = CommandType.StoredProcedure Using Conn Conn.Open() Dim Reader = Cmd.ExecuteReader() Using Reader While Reader.Read() 'Do Something End While Reader.Close() End Using Conn.Close() End Using

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• MySQL performance - 100Mb ethernet vs 1Gb ethernet

  - by Rob Penridge

  Hi All I've just started a new job and noticed that the analysts computers are connected to the network at 100Mbps. The ODBC queries we run against the MySQL server can easily return 500MB+ and it seems at times when the servers are under high load the DBAs kill low priority jobs as they are taking too long to run. My question is this... How much of this server time is spent executing the request, and how much time is spent returning the data to the client? Could the query speeds be improved by upgrading the network connections to 1Gbps? (Updated for the why): The database in question was built to accomodate reporting needs and contains massive amounts of data. We usually work with subsets of this data at a granular level in external applications such as SAS or Excel, hence the reason for the large amounts of data being transmitted. The queries are not poorly structured - they are very simple and the appropriate joins/indexes etc are being used. I've removed 'query' from the Title of the post as I realised this question is more to do with general MySQL performance rather than query related performance. I was kind of hoping that someone with a Gigabit connection may be able to actually quantify some results for me here by running a query that returns a decent amount of data, then they could limit their connection speed to 100Mb and rerun the same query. Hopefully this could be done in an environment where loads are reasonably stable so as not to skew the results. If ethernet speed can improve the situation I wanted some quantifiable results to help argue my case for upgrading the network connections. Thanks Rob

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• Java performance problem with LinkedBlockingQueue

  - by lofthouses

  Hello, this is my first post on stackoverflow...i hope someone can help me i have a big performance regression with Java 6 LinkedBlockingQueue. In the first thread i generate some objects which i push in to the queue In the second thread i pull these objects out. The performance regression occurs when the take() method of the LinkedBlockingQueue is called frequently. I monitored the whole program and the take() method claimed the most time overall. And the throughput goes from ~58Mb/s to 0.9Mb/s... the queue pop and take methods ar called with a static method from this class public class C_myMessageQueue { private static final LinkedBlockingQueue<C_myMessageObject> x_queue = new LinkedBlockingQueue<C_myMessageObject>( 50000 ); /** * @param message * @throws InterruptedException * @throws NullPointerException */ public static void addMyMessage( C_myMessageObject message ) throws InterruptedException, NullPointerException { x_queue.put( message ); } /** * @return Die erste message der MesseageQueue * @throws InterruptedException */ public static C_myMessageObject getMyMessage() throws InterruptedException { return x_queue.take(); } } how can i tune the take() method to accomplish at least 25Mb/s, or is there a other class i can use which will block when the "queue" is full or empty. kind regards Bart P.S.: sorry for my bad english, i'm from germany ;)

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• Reporting System architecture for better performance

  - by pauloya

  Hi, We have a product that runs Sql Server Express 2005 and uses mainly ASP.NET. The database has around 200 tables, with a few (4 or 5) that can grow from 300 to 5000 rows per day and keep a history of 5 years, so they can grow to have 10 million rows. We have built a reporting platform, that allows customers to build reports based on templates, fields and filters. We face performance problems almost since the beginning, we try to keep reports display under 10 seconds but some of them go up to 25 seconds (specially on those customers with long history). We keep checking indexes and trying to improve the queries but we get the feeling that there's only so much we can do. Off course the fact that the queries are generated dynamically doesn't help with the optimization. We also added a few tables that keep redundant data, but then we have the added problem of maintaining this data up to date, and also Sql Express has a limit on the size of databases. We are now facing a point where we have to decide if we want to give up real time reports, or maybe cut the history to be able to have better performance. I would like to ask what is the recommended approach for this kind of systems. Also, should we start looking for third party tools/platforms? I know OLAP can be an option but can we make it work on Sql Server Express, or at least with a license that is cheap enough to distribute to thousands of deployments? Thanks

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• Poor LLVM JIT performance

  - by Paul J. Lucas

  I have a legacy C++ application that constructs a tree of C++ objects. I want to use LLVM to call class constructors to create said tree. The generated LLVM code is fairly straight-forward and looks repeated sequences of: ; ... %11 = getelementptr [11 x i8*]* %Value_array1, i64 0, i64 1 %12 = call i8* @T_string_M_new_A_2Pv(i8* %heap, i8* getelementptr inbounds ([10 x i8]* @0, i64 0, i64 0)) %13 = call i8* @T_QueryLoc_M_new_A_2Pv4i(i8* %heap, i8* %12, i32 1, i32 1, i32 4, i32 5) %14 = call i8* @T_GlobalEnvironment_M_getItemFactory_A_Pv(i8* %heap) %15 = call i8* @T_xs_integer_M_new_A_Pvl(i8* %heap, i64 2) %16 = call i8* @T_ItemFactory_M_createInteger_A_3Pv(i8* %heap, i8* %14, i8* %15) %17 = call i8* @T_SingletonIterator_M_new_A_4Pv(i8* %heap, i8* %2, i8* %13, i8* %16) store i8* %17, i8** %11, align 8 ; ... Where each T_ function is a C "thunk" that calls some C++ constructor, e.g.: void* T_string_M_new_A_2Pv( void *v_value ) { string *const value = static_cast<string*>( v_value ); return new string( value ); } The thunks are necessary, of course, because LLVM knows nothing about C++. The T_ functions are added to the ExecutionEngine in use via ExecutionEngine::addGlobalMapping(). When this code is JIT'd, the performance of the JIT'ing itself is very poor. I've generated a call-graph using kcachegrind. I don't understand all the numbers (and this PDF seems not to include commas where it should), but if you look at the left fork, the bottom two ovals, Schedule... is called 16K times and setHeightToAtLeas... is called 37K times. On the right fork, RAGreed... is called 35K times. Those are far too many calls to anything for what's mostly a simple sequence of call LLVM instructions. Something seems horribly wrong. Any ideas on how to improve the performance of the JIT'ing?

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• Performance of java on different hardware?

  - by tangens

  In another SO question I asked why my java programs run faster on AMD than on Intel machines. But it seems that I'm the only one who has observed this. Now I would like to invite you to share the numbers of your local java performance with the SO community. I observed a big performance difference when watching the startup of JBoss on different hardware, so I set this program as the base for this comparison. For participation please download JBoss 5.1.0.GA and run: jboss-5.1.0.GA/bin/run.sh (or run.bat) This starts a standard configuration of JBoss without any extra applications. Then look for the last line of the start procedure which looks like this: [ServerImpl] JBoss (Microcontainer) [5.1.0.GA (build: SVNTag=JBoss_5_1_0_GA date=200905221634)] Started in 25s:264ms Please repeat this procedure until the printed time is somewhat stable and post this line together with some comments on your hardware (I used cpu-z to get the infos) and operating system like this: java version: 1.6.0_13 OS: Windows XP Board: ASUS M4A78T-E Processor: AMD Phenom II X3 720, 2.8 GHz RAM: 2*2 GB DDR3 (labeled 1333 MHz) GPU: NVIDIA GeForce 9400 GT disc: Seagate 1.5 TB (ST31500341AS) Use your votes to bring the fastest configuration to the top. I'm very curious about the results. EDIT: Up to now only a few members have shared their results. I'd really be interested in the results obtained with some other architectures. If someone works with a MAC (desktop) or runs an Intel i7 with less than 3 GHz, please once start JBoss and share your results. It will only take a few minutes.

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• Memory Bandwidth Performance for Modern Machines

  - by porgarmingduod

  I'm designing a real-time system that occasionally has to duplicate a large amount of memory. The memory consists of non-tiny regions, so I expect the copying performance will be fairly close to the maximum bandwidth the relevant components (CPU, RAM, MB) can do. This led me to wonder what kind of raw memory bandwidth modern commodity machine can muster? My aging Core2Duo gives me 1.5 GB/s if I use 1 thread to memcpy() (and understandably less if I memcpy() with both cores simultaneously.) While 1.5 GB is a fair amount of data, the real-time application I'm working on will have have something like 1/50th of a second, which means 30 MB. Basically, almost nothing. And perhaps worst of all, as I add multiple cores, I can process a lot more data without any increased performance for the needed duplication step. But a low-end Core2Due isn't exactly hot stuff these days. Are there any sites with information, such as actual benchmarks, on raw memory bandwidth on current and near-future hardware? Furthermore, for duplicating large amounts of data in memory, are there any shortcuts, or is memcpy() as good as it will get? Given a bunch of cores with nothing to do but duplicate as much memory as possible in a short amount of time, what's the best I can do?

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• Lucene (.NET) Document stucture and performance suggestions.

  - by Josh Handel

  Hello, I am indexing about 100M documents that consist of a few string identifiers and a hundred or so numaric terms.. I won't be doing range queries, so I haven't dugg too deep into Numaric Field but I'm not thinking its the right choose here. My problem is that the query performance degrades quickly when I start adding OR criteria to my query.. All my queries are on specific numaric terms.. So a document looks like StringField:[someString] and N DataField:[someNumber].. I then query it with something like DataField:((+1 +(2 3)) (+75 +(3 5 52)) (+99 +88 +(102 155 199))). Currently these queries take about 7 to 16 seconds to run on my laptop.. I would like to make sure thats really the best they can do.. I am open to suggestions on field structure and query structure :-). Thanks Josh PS: I have already read over all the other lucene performance discussions on here, and on the Lucene wiki and at lucid imiagination... I'm a bit further down the rabbit hole then that...

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• Testing performance of queries in mysl

  - by Unreason

  I am trying to setup a script that would test performance of queries on a development mysql server. Here are more details: I have root access I am the only user accessing the server Mostly interested in InnoDB performance The queries I am optimizing are mostly search queries (SELECT ... LIKE '%xy%') What I want to do is to create reliable testing environment for measuring the speed of a single query, free from dependencies on other variables. Till now I have been using SQL_NO_CACHE, but sometimes the results of such tests also show caching behaviour - taking much longer to execute on the first run and taking less time on subsequent runs. If someone can explain this behaviour in full detail I might stick to using SQL_NO_CACHE; I do believe that it might be due to file system cache and/or caching of indexes used to execute the query, as this post explains. It is not clear to me when Buffer Pool and Key Buffer get invalidated or how they might interfere with testing. So, short of restarting mysql server, how would you recommend to setup an environment that would be reliable in determining if one query performs better then the other?

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• Divide and conquer of large objects for GC performance

  - by Aperion

  At my work we're discussing different approaches to cleaning up a large amount of managed ~50-100MB memory.There are two approaches on the table (read: two senior devs can't agree) and not having the experience the rest of the team is unsure of what approach is more desirable, performance or maintainability. The data being collected is many small items, ~30000 which in turn contains other items, all objects are managed. There is a lot of references between these objects including event handlers but not to outside objects. We'll call this large group of objects and references as a single entity called a blob. Approach #1: Make sure all references to objects in the blob are severed and let the GC handle the blob and all the connections. Approach #2: Implement IDisposable on these objects then call dispose on these objects and set references to Nothing and remove handlers. The theory behind the second approach is since the large longer lived objects take longer to cleanup in the GC. So, by cutting the large objects into smaller bite size morsels the garbage collector will processes them faster, thus a performance gain. So I think the basic question is this: Does breaking apart large groups of interconnected objects optimize data for garbage collection or is better to keep them together and rely on the garbage collection algorithms to processes the data for you? I feel this is a case of pre-optimization, but I do not know enough of the GC to know what does help or hinder it.

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• Performance Difference between HttpContext user and Thread user

  - by atrueresistance

  I am wondering what the difference between HttpContext.Current.User.Identity.Name.ToString.ToLower and Thread.CurrentPrincipal.Identity.Name.ToString.ToLower. Both methods grab the username in my asp.net 3.5 web service. I decided to figure out if there was any difference in performance using a little program. Running from full Stop to Start Debugging in every run. Dim st As DateTime = DateAndTime.Now Try 'user = HttpContext.Current.User.Identity.Name.ToString.ToLower user = Thread.CurrentPrincipal.Identity.Name.ToString.ToLower Dim dif As TimeSpan = Now.Subtract(st) Dim break As String = "nothing" Catch ex As Exception user = "Undefined" End Try I set a breakpoint on break to read the value of dif. The results were the same for both methods. dif.Milliseconds 0 Integer dif.Ticks 0 Long Using a longer duration, loop 5,000 times results in these figures. Thread Method run 1 dif.Milliseconds 125 Integer dif.Ticks 1250000 Long run 2 dif.Milliseconds 0 Integer dif.Ticks 0 Long run 3 dif.Milliseconds 0 Integer dif.Ticks 0 Long HttpContext Method run 1 dif.Milliseconds 15 Integer dif.Ticks 156250 Long run 2 dif.Milliseconds 156 Integer dif.Ticks 1562500 Long run 3 dif.Milliseconds 0 Integer dif.Ticks 0 Long So I guess what is more prefered, or more compliant with webservice standards? If there is some type of a performance advantage, I can't really tell. Which one scales to larger environments easier?

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• SQL Server performance issue.

  - by Jit

  Hi Friends, I have been trying to analyze performance issue with SQL Server 2005. We have 30 jobs, one for each databases (30 databases, one per each client). The jobs run at early morning at an interval of 5 minutes. When I run the job individually for testing, for most of the databases it finishes in 7 to 9 minutes. But when these jobs run at early morning, I see few jobs taking 2 to 3 hours to finish and the same takes few minutes as mentioned above if ran independently. We dont have any other job scheduled during that time, other than these 30 jobs. If we restart the server then for 2 or so days all the jobs finishes in few minutes, but over the period of time (from 3rd day suddenly), few jobs start taking hours to finish. What could be the possible reason of performance degradation over the period of time? I verified all the SPs and we uses temp tables and I made sure none of the temp table is left without dropping at the end of SP. Let me know what are the possible reasons for such behavior. Appreciate your time and help. Thanks

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• Poor performance using RMI-proxies with Swing components

  - by Patrick

  I'm having huge performance issues when I add RMI proxy references to a Java Swing JList-component. I'm retrieving a list of user Profiles with RMI from a server. The retrieval itself takes just a second or so, so that's acceptable under the circumstances. However, when I try to add these proxies to a JList, with the help of a custom ListModel and a CellRenderer, it takes between 30-60 seconds to add about 180 objects. Since it is a list of users' names, it's preferrable to present them alphabetically. The biggest performance hit is when I sort the elements as they get added to the ListModel. Since the list will always be sorted, I opted to use the built-in Collections.binarySearch() to find the correct position for the next element to be added, and the comparator uses two methods that are defined by the Profile interface, namely getFirstName() and getLastName(). Is there any way to speed this process up, or am I simply implementing it the wrong way? Or is this a "feature" of RMI? I'd really love to be able to cache some of the data of the remote objects locally, to minimize the remote method calls.

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• Google app engine: Poor Performance with JDO + Datastore

  - by Bosh

  I have a simple data model that includes USERS: store basic information (key, name, phone # etc) RELATIONS: describe, e.g. a friendship between two users (supplying a relationship_type + two user keys) I'm getting very poor performance, for instance, if I try to print the first names of all of a user's friends. Say the user has 500 friends: I can fetch the list of friend user_ids very easily in a single query. But then, to pull out first names, I have to do 500 back-and-forth trips to the Datastore, each of which seems to take on the order of 30 ms. If this were SQL, I'd just do a JOIN and get the answer out fast. I understand there are rudimentary facilities for performing joins across un-owned relations in a relaxed implementation of JDO (as described at http://gae-java-persistence.blogspot.com) but they sound experimental and non-standard (e.g. my code won't work in any other JDO implementation). Is this really my best bet? Otherwise, how do people extract satisfactory performance from JDO/Datastore in this kind of (very common) situation? -Bosh

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• Silverlight performance with many loaded controls

  - by gius

  I have a SL application with many DataGrids (from Silverlight Toolkit), each on its own view. If several DataGrids are opened, changing between views (TabItems, for example) takes a long time (few seconds) and it freezes the whole application (UI thread). The more DataGrids are loaded, the longer the change takes. These DataGrids that slow the UI chanage might be on other places in the app and not even visible at that moment. But once they are opened (and loaded with data), they slow showing other DataGrids. Note that DataGrids are NOT disposed and then recreated again, they still remain in memory, only their parent control is being hidden and visible again. I have profiled the application. It shows that agcore.dll's SetValue function is the bottleneck. Unfortunately, debug symbols are not available for this Silverlight native library responsible for drawing. The problem is not in the DataGrid control - I tried to replace it with XCeed's grid and the performance when changing views is even worse. Do you have any idea how to solve this problem? Why more opened controls slow down other controls? I have created a sample that shows this issue: http://cenud.cz/PerfTest.zip UPDATE: Using VS11 profiler on the sample provided suggests that the problem could be in MeasureOverride being called many times (for each DataGridCell, I guess). But still, why is it slower as more controls are loaded elsewhere? Is there a way to improve the performance?

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• Mysql Performance Question - Essentially about normalizing efficiency

  - by freqmode

  Hi there. Just a quick question about database performance. I'll outline my site purpose below as background. I'm creating a dictionary site that saves the words users define to a database. What I'm wondering is whether or not to create a words table for each user or to keep one massive words table. This site will be used for entire schools so the single words table would be massive! The database structure is as follows: A user table with: User_ID PRIMARY KEY Username First Last Password Email Country Research Standings SendInfo Donated JoinedOn LastLogin Logins Correct Attempts Admin Active And one word table with: User_ID PRIMARY KEY Word Vocab Spell Defined DefinedAttempted Spelled SpelledAttempted Sentenced SentencedAttempted So what I'm asking is , performance-wise, should I create a new table for each user when they join the site - each user could have hundreds or thousands of words over time? Or is it better to have one massive table with thousands and thousands of records and filter by User_ID. I don't think I'll perform many table joins. My gut feeling is to create a new table for each user, but I thought I'd ask for expert advice! Thanks in advance.

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