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  • SQL query performance optimization (TimesTen)

    - by Sergey Mikhanov
    Hi community, I need some help with TimesTen DB query optimization. I made some measures with Java profiler and found the code section that takes most of the time (this code section executes the SQL query). What is strange that this query becomes expensive only for some specific input data. Here’s the example. We have two tables that we are querying, one represents the objects we want to fetch (T_PROFILEGROUP), another represents the many-to-many link from some other table (T_PROFILECONTEXT_PROFILEGROUPS). We are not querying linked table. These are the queries that I executed with DB profiler running (they are the same except for the ID): Command> select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272; < 1169655247309537280 > < 1169655249792565248 > < 1464837997699399681 > 3 rows found. Command> select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1466585677823868928; < 1169655247309537280 > 1 row found. This is what I have in the profiler: 12:14:31.147 1 SQL 2L 6C 10825P Preparing: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272 12:14:31.147 2 SQL 4L 6C 10825P sbSqlCmdCompile ()(E): (Found already compiled version: refCount:01, bucket:47) cmdType:100, cmdNum:1146695. 12:14:31.147 3 SQL 4L 6C 10825P Opening: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272; 12:14:31.147 4 SQL 4L 6C 10825P Fetching: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272; 12:14:31.148 5 SQL 4L 6C 10825P Fetching: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272; 12:14:31.148 6 SQL 4L 6C 10825P Fetching: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272; 12:14:31.228 7 SQL 4L 6C 10825P Fetching: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272; 12:14:31.228 8 SQL 4L 6C 10825P Closing: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1464837998949302272; 12:14:35.243 9 SQL 2L 6C 10825P Preparing: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1466585677823868928 12:14:35.243 10 SQL 4L 6C 10825P sbSqlCmdCompile ()(E): (Found already compiled version: refCount:01, bucket:44) cmdType:100, cmdNum:1146697. 12:14:35.243 11 SQL 4L 6C 10825P Opening: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1466585677823868928; 12:14:35.243 12 SQL 4L 6C 10825P Fetching: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1466585677823868928; 12:14:35.243 13 SQL 4L 6C 10825P Fetching: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1466585677823868928; 12:14:35.243 14 SQL 4L 6C 10825P Closing: select G.M_ID from T_PROFILECONTEXT_PROFILEGROUPS CG, T_PROFILEGROUP G where CG.M_ID_EID = G.M_ID and CG.M_ID_OID = 1466585677823868928; It’s clear that the first query took almost 100ms, while the second was executed instantly. It’s not about queries precompilation (the first one is precompiled too, as same queries happened earlier). We have DB indices for all columns used here: T_PROFILEGROUP.M_ID, T_PROFILECONTEXT_PROFILEGROUPS.M_ID_OID and T_PROFILECONTEXT_PROFILEGROUPS.M_ID_EID. My questions are: Why querying the same set of tables yields such a different performance for different parameters? Which indices are involved here? Is there any way to improve this simple query and/or the DB to make it faster? UPDATE: to give the feeling of size: Command> select count(*) from T_PROFILEGROUP; < 183840 > 1 row found. Command> select count(*) from T_PROFILECONTEXT_PROFILEGROUPS; < 2279104 > 1 row found.

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  • Performance experiences for running Windows 7 on a Thin-Client?

    - by Peter Bernier
    Has anyone else tried installing Windows 7 on thin-client hardware? I'd be very interested to hear about other people's experiences and what sort of hardware tweaks they had to do to get it to work. (Yes, I realize this is completely unsupported.. half the fun of playing with machines and beta/RC versions is trying out unsupported scenarios. :) ) I managed to get Windows 7 installed on a modified Wyse 9450 Thin-Client and while the performance isn't great, it is usable, particularly as an RDP workstation. Before installing 7, I added another 256Mb of ram (512 total), a 60G laptop hard-drive and a PCI videocard to the 9450 (this was in order to increase the supported screen resolution). I basically did this in order to see whether or not it was possible to get 7 installed on such minimal hardware, and see what the performance would be. For a 550Mhz processor, I was reasonably impressed. I've been using the machine for RDP for the last couple of days and it actually seems slightly snappier than the default Windows XP embedded install (although this is more likely the result of the extra hardware). I'll be running some more tests later on as I'm curious to see particularl whether the streaming video performance will improve. I'd love to hear about anyone's experiences getting 7 to work on extremely low-powered hardware. Particularly any sort of tweaks that you've discovered in order to increase performance..

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  • To what extent is size a factor in SSD performance?

    - by artif
    To what extent is the size of an SSD a factor in its performance? In my mind, correct me if I'm wrong, a bigger SSD should be, everything else being equal, faster than a smaller one. A bigger SSD would have more erase blocks and thus more leeway for the FTL (flash translation layer) to do garbage collection optimization. Also there would be more time before TRIM became necessary. I see on Wikipedia that it remarks that "The performance of the SSD can scale with the number of parallel NAND flash chips used in the device" so it seems throughput also increases significantly. Also many SSDs contain internal caches of some sort and presumably those caches are larger for correspondingly large SSDs. But supposing this effect exists, I would like a quantitative analysis. Does throughput increase linearly? How much is garbage collection impacted, if at all? Does latency stay the same? And so on. Would the performance of a 8 GB SSD be significantly different from, for example, an 80 GB SSD assuming both used high quality chips, controllers, etc? Are there any resources (webpages, research papers, presentations, books, etc) that discuss correlations between SSD performance (4 KB random write speed, latency, maximum sequential throughput, etc) and size? I realize this does not really sound like a programming question but it is relevant for what I'm working on (using flash for caching hard drive data) which does involve programming. If there is a better place to ask this question, eg a more hardware oriented site, what would that be? Something like the equivalent of stack overflow (or perhaps a forum) for in-depth questions on hardware interfaces, internals, etc would be appreciated.

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  • Performance experiences for running Windows 7 on a Thin-Client?

    - by Peter Bernier
    Has anyone else tried installing Windows 7 on thin-client hardware? I'd be very interested to hear about other people's experiences and what sort of hardware tweaks they had to do to get it to work. (Yes, I realize this is completely unsupported.. half the fun of playing with machines and beta/RC versions is trying out unsupported scenarios. :) ) I managed to get Windows 7 installed on a modified Wyse 9450 Thin-Client and while the performance isn't great, it is usable, particularly as an RDP workstation. Before installing 7, I added another 256Mb of ram (512 total), a 60G laptop hard-drive and a PCI videocard to the 9450 (this was in order to increase the supported screen resolution). I basically did this in order to see whether or not it was possible to get 7 installed on such minimal hardware, and see what the performance would be. For a 550Mhz processor, I was reasonably impressed. I've been using the machine for RDP for the last couple of days and it actually seems slightly snappier than the default Windows XP embedded install (although this is more likely the result of the extra hardware). I'll be running some more tests later on as I'm curious to see particularl whether the streaming video performance will improve. I'd love to hear about anyone's experiences getting 7 to work on extremely low-powered hardware. Particularly any sort of tweaks that you've discovered in order to increase performance..

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  • LINQ To objects: Quicker ideas?

    - by SDReyes
    Do you see a better approach to obtain and concatenate item.Number in a single string? Current: var numbers = new StringBuilder( ); // group is the result of a previous group by var basenumbers = group.Select( item => item.Number ); basenumbers.Aggregate ( numbers, ( res, element ) => res.AppendFormat( "{0:00}", element ) );

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  • Data in two databases, eager spool resulting in query

    - by Valkyrie
    I have two databases in SQL2k5: one that holds a large amount of static data (SQL Database 1) (never updated but frequently inserted into) and one that holds relational data (SQL Database 2) related to the static data. They're separated mainly because of corporate guidelines and business requirements: assume for the following problem that combining them is not practical. There are places in SQLDB2 that PKs in SQLDB1 are referenced; triggers control the referential integrity, since cross-database relationships are troublesome in SQL Server. BUT, because of the large amount of data in SQLDB1, I'm getting eager spools on queries that join from the Id in SQLDB2 that references the data in SQLDB1. (With me so far? Maybe an example will help:) SELECT t.Id, t.Name, t2.Company FROM SQLDB1.table t INNER JOIN SQLDB2.table t2 ON t.Id = t2.FKId This query results in a eager spool that's 84% of the load of the query; the table in SQLDB1 has 35M rows, so it's completely choking this query. I can't create a view on the table in SQLDB1 and use that as my FK/index; it doesn't want me to create a constraint based on a view. Anyone have any idea how I can fix this huge bottleneck? (Short of putting the static data in the first db: believe me, I've argued that one until I'm blue in the face to no avail.) Thanks! valkyrie Edit: also can't create an indexed view because you can't put schemabinding on a view that references a table outside the database where the view resides. Dang it.

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  • Oracle T4CPreparedStatement memory leaks?

    - by Jay
    A little background on the application that I am gonna talk about in the next few lines: XYZ is a data masking workbench eclipse RCP application: You give it a source table column, and a target table column, it would apply a trasformation (encryption/shuffling/etc) and copy the row data from source table to target table. Now, when I mask n tables at a time, n threads are launched by this app. Here is the issue: I have run into a production issue on first roll out of the above said app. Unfortunately, I don't have any logs to get to the root. However, I tried to run this app in test region and do a stress test. When I collected .hprof files and ran 'em through an analyzer (yourKit), I noticed that objects of oracle.jdbc.driver.T4CPreparedStatement was retaining heap. The analysis also tells me that one of my classes is holding a reference to this preparedstatement object and thereby, n threads have n such objects. T4CPreparedStatement seemed to have character arrays: lastBoundChars and bindChars each of size char[300000]. So, I researched a bit (google!), obtained ojdbc6.jar and tried decompiling T4CPreparedStatement. I see that T4CPreparedStatement extends OraclePreparedStatement, which dynamically manages array size of lastBoundChars and bindChars. So, my questions here are: Have you ever run into an issue like this? Do you know the significance of lastBoundChars / bindChars? I am new to profiling, so do you think I am not doing it correct? (I also ran the hprofs through MAT - and this was the main identified issue - so, I don't really think I could be wrong?) I have found something similar on the web here: http://forums.oracle.com/forums/thread.jspa?messageID=2860681 Appreciate your suggestions / advice.

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  • Best Practise for Stopwatch in multi processors machine?

    - by Ahmed Said
    I found a good question for measuring function performance, and the answers recommend to use Stopwatch as follows Stopwatch sw = new Stopwatch(); sw.Start(); //DoWork sw.Stop(); //take sw.Elapsed But is this valid if you are running under multi processors machine? the thread can be switched to another processor, can it? Also the same thing should be in Enviroment.TickCount. If the answer is yes should I wrap my code inside BeginThreadAffinity as follows Thread.BeginThreadAffinity(); Stopwatch sw = new Stopwatch(); sw.Start(); //DoWork sw.Stop(); //take sw.Elapsed Thread.EndThreadAffinity(); P.S The switching can occur over the thread level not only the processor level, for example if the function is running in another thread so the system can switch it to another processor, if that happens, will the Stopwatch be valid after this switching? I am not using Stopwatch for perfromance measurement only but also to simulate timer function using Thread.Sleep (to prevent call overlapping)

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  • How to scale MySQL with multiple machines?

    - by erotsppa
    I have a web app running LAMP. We recently have an increase in load and is now looking at solutions to scale. Scaling apache is pretty easy we are just going to have multiple multiple machines hosting it and round robin the incoming traffic. However, each instance of apache will talk with MySQL and eventually MySQL will be overloaded. How to scale MySQL across multiple machines in this setup? I have already looked at this but specifically we need the updates from the DB available immediately so I don't think replication is a good strategy here? Also hopefully this can be done with minimal code change. PS. We have around a 1:1 read-write ratio.

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  • Missing 'DomContentLoaded' and 'load' time information in Firebug's Net Panel.

    - by stony_dreams
    Hello, Firebug is awesome in reporting the relative time when an HTTP request was made with respect to the 'DomContentLoaded' and 'load' time. However, once the 'load' event occurs (seen by the red line on the timeline), the requests thereafter do not have any information about how later they occurred with respect to the two events. To confuse things, these requests (usually at the bottom of the timeline) appear to have started right at the beginning of the page load. Could somebody shed some light on what should i infer when i see such entries in the timeline which do not have information about the 'DomContentLoaded' and 'load' event times and appear to have occurred after the page load event, still net panel shows that they started at the beginning? Thanks!

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  • How well does Scala Perform Comapred to Java?

    - by Teja Kantamneni
    The Question actually says it all. The reason behind this question is I am about to start a small side project and want to do it in Scala. I am learning scala for the past one month and now I am comfortable working with it. The scala compiler itself is pretty slow (unless you use fsc). So how well does it perform on JVM? I previously worked on groovy and I had seen sometimes over performed than java. My Question is how well scala perform on JVM compared to Java. I know scala has some very good features(FP, dynamic lang, statically typed...) but end of the day we need the performance...

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  • Very different I/O performance in C++ on Windows

    - by Mr.Gate
    Hi all, I'm a new user and my english is not so good so I hope to be clear. We're facing a performance problem using large files (1GB or more) expecially (as it seems) when you try to grow them in size. Anyway... to verify our sensations we tryed the following (on Win 7 64Bit, 4core, 8GB Ram, 32 bit code compiled with VC2008) a) Open an unexisting file. Write it from the beginning up to 1Gb in 1Mb slots. Now you have a 1Gb file. Now randomize 10000 positions within that file, seek to that position and write 50 bytes in each position, no matter what you write. Close the file and look at the results. Time to create the file is quite fast (about 0.3"), time to write 10000 times is fast all the same (about 0.03"). Very good, this is the beginnig. Now try something else... b) Open an unexisting file, seek to 1Gb-1byte and write just 1 byte. Now you have another 1Gb file. Follow the next steps exactly same way of case 'a', close the file and look at the results. Time to create the file is the faster you can imagine (about 0.00009") but write time is something you can't believe.... about 90"!!!!! b.1) Open an unexisting file, don't write any byte. Act as before, ramdomizing, seeking and writing, close the file and look at the result. Time to write is long all the same: about 90"!!!!! Ok... this is quite amazing. But there's more! c) Open again the file you crated in case 'a', don't truncate it... randomize again 10000 positions and act as before. You're fast as before, about 0,03" to write 10000 times. This sounds Ok... try another step. d) Now open the file you created in case 'b', don't truncate it... randomize again 10000 positions and act as before. You're slow again and again, but the time is reduced to... 45"!! Maybe, trying again, the time will reduce. I actually wonder why... Any Idea? The following is part of the code I used to test what I told in previuos cases (you'll have to change someting in order to have a clean compilation, I just cut & paste from some source code, sorry). The sample can read and write, in random, ordered or reverse ordered mode, but write only in random order is the clearest test. We tryed using std::fstream but also using directly CreateFile(), WriteFile() and so on the results are the same (even if std::fstream is actually a little slower). Parameters for case 'a' = -f_tempdir_\casea.dat -n10000 -t -p -w Parameters for case 'b' = -f_tempdir_\caseb.dat -n10000 -t -v -w Parameters for case 'b.1' = -f_tempdir_\caseb.dat -n10000 -t -w Parameters for case 'c' = -f_tempdir_\casea.dat -n10000 -w Parameters for case 'd' = -f_tempdir_\caseb.dat -n10000 -w Run the test (and even others) and see... // iotest.cpp : Defines the entry point for the console application. // #include <windows.h> #include <iostream> #include <set> #include <vector> #include "stdafx.h" double RealTime_Microsecs() { LARGE_INTEGER fr = {0, 0}; LARGE_INTEGER ti = {0, 0}; double time = 0.0; QueryPerformanceCounter(&ti); QueryPerformanceFrequency(&fr); time = (double) ti.QuadPart / (double) fr.QuadPart; return time; } int main(int argc, char* argv[]) { std::string sFileName ; size_t stSize, stTimes, stBytes ; int retval = 0 ; char *p = NULL ; char *pPattern = NULL ; char *pReadBuf = NULL ; try { // Default stSize = 1<<30 ; // 1Gb stTimes = 1000 ; stBytes = 50 ; bool bTruncate = false ; bool bPre = false ; bool bPreFast = false ; bool bOrdered = false ; bool bReverse = false ; bool bWriteOnly = false ; // Comsumo i parametri for(int index=1; index < argc; ++index) { if ( '-' != argv[index][0] ) throw ; switch(argv[index][1]) { case 'f': sFileName = argv[index]+2 ; break ; case 's': stSize = xw::str::strtol(argv[index]+2) ; break ; case 'n': stTimes = xw::str::strtol(argv[index]+2) ; break ; case 'b':stBytes = xw::str::strtol(argv[index]+2) ; break ; case 't': bTruncate = true ; break ; case 'p' : bPre = true, bPreFast = false ; break ; case 'v' : bPreFast = true, bPre = false ; break ; case 'o' : bOrdered = true, bReverse = false ; break ; case 'r' : bReverse = true, bOrdered = false ; break ; case 'w' : bWriteOnly = true ; break ; default: throw ; break ; } } if ( sFileName.empty() ) { std::cout << "Usage: -f<File Name> -s<File Size> -n<Number of Reads and Writes> -b<Bytes per Read and Write> -t -p -v -o -r -w" << std::endl ; std::cout << "-t truncates the file, -p pre load the file, -v pre load 'veloce', -o writes in order mode, -r write in reverse order mode, -w Write Only" << std::endl ; std::cout << "Default: 1Gb, 1000 times, 50 bytes" << std::endl ; throw ; } if ( !stSize || !stTimes || !stBytes ) { std::cout << "Invalid Parameters" << std::endl ; return -1 ; } size_t stBestSize = 0x00100000 ; std::fstream fFile ; fFile.open(sFileName.c_str(), std::ios_base::binary|std::ios_base::out|std::ios_base::in|(bTruncate?std::ios_base::trunc:0)) ; p = new char[stBestSize] ; pPattern = new char[stBytes] ; pReadBuf = new char[stBytes] ; memset(p, 0, stBestSize) ; memset(pPattern, (int)(stBytes&0x000000ff), stBytes) ; double dTime = RealTime_Microsecs() ; size_t stCopySize, stSizeToCopy = stSize ; if ( bPre ) { do { stCopySize = std::min(stSizeToCopy, stBestSize) ; fFile.write(p, stCopySize) ; stSizeToCopy -= stCopySize ; } while (stSizeToCopy) ; std::cout << "Creating time is: " << xw::str::itoa(RealTime_Microsecs()-dTime, 5, 'f') << std::endl ; } else if ( bPreFast ) { fFile.seekp(stSize-1) ; fFile.write(p, 1) ; std::cout << "Creating Fast time is: " << xw::str::itoa(RealTime_Microsecs()-dTime, 5, 'f') << std::endl ; } size_t stPos ; ::srand((unsigned int)dTime) ; double dReadTime, dWriteTime ; stCopySize = stTimes ; std::vector<size_t> inVect ; std::vector<size_t> outVect ; std::set<size_t> outSet ; std::set<size_t> inSet ; // Prepare vector and set do { stPos = (size_t)(::rand()<<16) % stSize ; outVect.push_back(stPos) ; outSet.insert(stPos) ; stPos = (size_t)(::rand()<<16) % stSize ; inVect.push_back(stPos) ; inSet.insert(stPos) ; } while (--stCopySize) ; // Write & read using vectors if ( !bReverse && !bOrdered ) { std::vector<size_t>::iterator outI, inI ; outI = outVect.begin() ; inI = inVect.begin() ; stCopySize = stTimes ; dReadTime = 0.0 ; dWriteTime = 0.0 ; do { dTime = RealTime_Microsecs() ; fFile.seekp(*outI) ; fFile.write(pPattern, stBytes) ; dWriteTime += RealTime_Microsecs() - dTime ; ++outI ; if ( !bWriteOnly ) { dTime = RealTime_Microsecs() ; fFile.seekg(*inI) ; fFile.read(pReadBuf, stBytes) ; dReadTime += RealTime_Microsecs() - dTime ; ++inI ; } } while (--stCopySize) ; std::cout << "Write time is " << xw::str::itoa(dWriteTime, 5, 'f') << " (Ave: " << xw::str::itoa(dWriteTime/stTimes, 10, 'f') << ")" << std::endl ; if ( !bWriteOnly ) { std::cout << "Read time is " << xw::str::itoa(dReadTime, 5, 'f') << " (Ave: " << xw::str::itoa(dReadTime/stTimes, 10, 'f') << ")" << std::endl ; } } // End // Write in order if ( bOrdered ) { std::set<size_t>::iterator i = outSet.begin() ; dWriteTime = 0.0 ; stCopySize = 0 ; for(; i != outSet.end(); ++i) { stPos = *i ; dTime = RealTime_Microsecs() ; fFile.seekp(stPos) ; fFile.write(pPattern, stBytes) ; dWriteTime += RealTime_Microsecs() - dTime ; ++stCopySize ; } std::cout << "Ordered Write time is " << xw::str::itoa(dWriteTime, 5, 'f') << " in " << xw::str::itoa(stCopySize) << " (Ave: " << xw::str::itoa(dWriteTime/stCopySize, 10, 'f') << ")" << std::endl ; if ( !bWriteOnly ) { i = inSet.begin() ; dReadTime = 0.0 ; stCopySize = 0 ; for(; i != inSet.end(); ++i) { stPos = *i ; dTime = RealTime_Microsecs() ; fFile.seekg(stPos) ; fFile.read(pReadBuf, stBytes) ; dReadTime += RealTime_Microsecs() - dTime ; ++stCopySize ; } std::cout << "Ordered Read time is " << xw::str::itoa(dReadTime, 5, 'f') << " in " << xw::str::itoa(stCopySize) << " (Ave: " << xw::str::itoa(dReadTime/stCopySize, 10, 'f') << ")" << std::endl ; } }// End // Write in reverse order if ( bReverse ) { std::set<size_t>::reverse_iterator i = outSet.rbegin() ; dWriteTime = 0.0 ; stCopySize = 0 ; for(; i != outSet.rend(); ++i) { stPos = *i ; dTime = RealTime_Microsecs() ; fFile.seekp(stPos) ; fFile.write(pPattern, stBytes) ; dWriteTime += RealTime_Microsecs() - dTime ; ++stCopySize ; } std::cout << "Reverse ordered Write time is " << xw::str::itoa(dWriteTime, 5, 'f') << " in " << xw::str::itoa(stCopySize) << " (Ave: " << xw::str::itoa(dWriteTime/stCopySize, 10, 'f') << ")" << std::endl ; if ( !bWriteOnly ) { i = inSet.rbegin() ; dReadTime = 0.0 ; stCopySize = 0 ; for(; i != inSet.rend(); ++i) { stPos = *i ; dTime = RealTime_Microsecs() ; fFile.seekg(stPos) ; fFile.read(pReadBuf, stBytes) ; dReadTime += RealTime_Microsecs() - dTime ; ++stCopySize ; } std::cout << "Reverse ordered Read time is " << xw::str::itoa(dReadTime, 5, 'f') << " in " << xw::str::itoa(stCopySize) << " (Ave: " << xw::str::itoa(dReadTime/stCopySize, 10, 'f') << ")" << std::endl ; } }// End dTime = RealTime_Microsecs() ; fFile.close() ; std::cout << "Flush/Close Time is " << xw::str::itoa(RealTime_Microsecs()-dTime, 5, 'f') << std::endl ; std::cout << "Program Terminated" << std::endl ; } catch(...) { std::cout << "Something wrong or wrong parameters" << std::endl ; retval = -1 ; } if ( p ) delete []p ; if ( pPattern ) delete []pPattern ; if ( pReadBuf ) delete []pReadBuf ; return retval ; }

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  • Approximate timings for various operations on a "typical desktop PC" anno 2010

    - by knorv
    In the article "Teach Yourself Programming in Ten Years" Peter Norvig (Director of Research, Google) gives the following approximate timings for various operations on a typical 1GHz PC back in 2001: execute single instruction = 1 nanosec = (1/1,000,000,000) sec fetch word from L1 cache memory = 2 nanosec fetch word from main memory = 10 nanosec fetch word from consecutive disk location = 200 nanosec fetch word from new disk location (seek) = 8,000,000 nanosec = 8 millisec What would the corresponding timings be for your definition of a typical PC desktop anno 2010?

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  • Is count(*) really expensive ?

    - by Anil Namde
    I have a page where I have 4 tabs displaying 4 different reports based off different tables. I obtain the row count of each table using a select count(*) from <table> query and display number of rows available in each table on the tabs. As a result, each page postback causes 5 count(*) queries to be executed (4 to get counts and 1 for pagination) and 1 query for getting the report content. Now my question is: are count(*) queries really expensive -- should I keep the row counts (at least those that are displayed on the tab) in the view state of page instead of querying multiple times? How expensive are COUNT(*) queries ?

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  • Very different IO performance in C/C++

    - by Roberto Tirabassi
    Hi all, I'm a new user and my english is not so good so I hope to be clear. We're facing a performance problem using large files (1GB or more) expecially (as it seems) when you try to grow them in size. Anyway... to verify our sensations we tryed the following (on Win 7 64Bit, 4core, 8GB Ram, 32 bit code compiled with VC2008) a) Open an unexisting file. Write it from the beginning up to 1Gb in 1Mb slots. Now you have a 1Gb file. Now randomize 10000 positions within that file, seek to that position and write 50 bytes in each position, no matter what you write. Close the file and look at the results. Time to create the file is quite fast (about 0.3"), time to write 10000 times is fast all the same (about 0.03"). Very good, this is the beginnig. Now try something else... b) Open an unexisting file, seek to 1Gb-1byte and write just 1 byte. Now you have another 1Gb file. Follow the next steps exactly same way of case 'a', close the file and look at the results. Time to create the file is the faster you can imagine (about 0.00009") but write time is something you can't believe.... about 90"!!!!! b.1) Open an unexisting file, don't write any byte. Act as before, ramdomizing, seeking and writing, close the file and look at the result. Time to write is long all the same: about 90"!!!!! Ok... this is quite amazing. But there's more! c) Open again the file you crated in case 'a', don't truncate it... randomize again 10000 positions and act as before. You're fast as before, about 0,03" to write 10000 times. This sounds Ok... try another step. d) Now open the file you created in case 'b', don't truncate it... randomize again 10000 positions and act as before. You're slow again and again, but the time is reduced to... 45"!! Maybe, trying again, the time will reduce. I actually wonder why... Any Idea? The following is part of the code I used to test what I told in previuos cases (you'll have to change someting in order to have a clean compilation, I just cut & paste from some source code, sorry). The sample can read and write, in random, ordered or reverse ordered mode, but write only in random order is the clearest test. We tryed using std::fstream but also using directly CreateFile(), WriteFile() and so on the results are the same (even if std::fstream is actually a little slower). Parameters for case 'a' = -f_tempdir_\casea.dat -n10000 -t -p -w Parameters for case 'b' = -f_tempdir_\caseb.dat -n10000 -t -v -w Parameters for case 'b.1' = -f_tempdir_\caseb.dat -n10000 -t -w Parameters for case 'c' = -f_tempdir_\casea.dat -n10000 -w Parameters for case 'd' = -f_tempdir_\caseb.dat -n10000 -w Run the test (and even others) and see... // iotest.cpp : Defines the entry point for the console application. // #include <windows.h> #include <iostream> #include <set> #include <vector> #include "stdafx.h" double RealTime_Microsecs() { LARGE_INTEGER fr = {0, 0}; LARGE_INTEGER ti = {0, 0}; double time = 0.0; QueryPerformanceCounter(&ti); QueryPerformanceFrequency(&fr); time = (double) ti.QuadPart / (double) fr.QuadPart; return time; } int main(int argc, char* argv[]) { std::string sFileName ; size_t stSize, stTimes, stBytes ; int retval = 0 ; char *p = NULL ; char *pPattern = NULL ; char *pReadBuf = NULL ; try { // Default stSize = 1<<30 ; // 1Gb stTimes = 1000 ; stBytes = 50 ; bool bTruncate = false ; bool bPre = false ; bool bPreFast = false ; bool bOrdered = false ; bool bReverse = false ; bool bWriteOnly = false ; // Comsumo i parametri for(int index=1; index < argc; ++index) { if ( '-' != argv[index][0] ) throw ; switch(argv[index][1]) { case 'f': sFileName = argv[index]+2 ; break ; case 's': stSize = xw::str::strtol(argv[index]+2) ; break ; case 'n': stTimes = xw::str::strtol(argv[index]+2) ; break ; case 'b':stBytes = xw::str::strtol(argv[index]+2) ; break ; case 't': bTruncate = true ; break ; case 'p' : bPre = true, bPreFast = false ; break ; case 'v' : bPreFast = true, bPre = false ; break ; case 'o' : bOrdered = true, bReverse = false ; break ; case 'r' : bReverse = true, bOrdered = false ; break ; case 'w' : bWriteOnly = true ; break ; default: throw ; break ; } } if ( sFileName.empty() ) { std::cout << "Usage: -f<File Name> -s<File Size> -n<Number of Reads and Writes> -b<Bytes per Read and Write> -t -p -v -o -r -w" << std::endl ; std::cout << "-t truncates the file, -p pre load the file, -v pre load 'veloce', -o writes in order mode, -r write in reverse order mode, -w Write Only" << std::endl ; std::cout << "Default: 1Gb, 1000 times, 50 bytes" << std::endl ; throw ; } if ( !stSize || !stTimes || !stBytes ) { std::cout << "Invalid Parameters" << std::endl ; return -1 ; } size_t stBestSize = 0x00100000 ; std::fstream fFile ; fFile.open(sFileName.c_str(), std::ios_base::binary|std::ios_base::out|std::ios_base::in|(bTruncate?std::ios_base::trunc:0)) ; p = new char[stBestSize] ; pPattern = new char[stBytes] ; pReadBuf = new char[stBytes] ; memset(p, 0, stBestSize) ; memset(pPattern, (int)(stBytes&0x000000ff), stBytes) ; double dTime = RealTime_Microsecs() ; size_t stCopySize, stSizeToCopy = stSize ; if ( bPre ) { do { stCopySize = std::min(stSizeToCopy, stBestSize) ; fFile.write(p, stCopySize) ; stSizeToCopy -= stCopySize ; } while (stSizeToCopy) ; std::cout << "Creating time is: " << xw::str::itoa(RealTime_Microsecs()-dTime, 5, 'f') << std::endl ; } else if ( bPreFast ) { fFile.seekp(stSize-1) ; fFile.write(p, 1) ; std::cout << "Creating Fast time is: " << xw::str::itoa(RealTime_Microsecs()-dTime, 5, 'f') << std::endl ; } size_t stPos ; ::srand((unsigned int)dTime) ; double dReadTime, dWriteTime ; stCopySize = stTimes ; std::vector<size_t> inVect ; std::vector<size_t> outVect ; std::set<size_t> outSet ; std::set<size_t> inSet ; // Prepare vector and set do { stPos = (size_t)(::rand()<<16) % stSize ; outVect.push_back(stPos) ; outSet.insert(stPos) ; stPos = (size_t)(::rand()<<16) % stSize ; inVect.push_back(stPos) ; inSet.insert(stPos) ; } while (--stCopySize) ; // Write & read using vectors if ( !bReverse && !bOrdered ) { std::vector<size_t>::iterator outI, inI ; outI = outVect.begin() ; inI = inVect.begin() ; stCopySize = stTimes ; dReadTime = 0.0 ; dWriteTime = 0.0 ; do { dTime = RealTime_Microsecs() ; fFile.seekp(*outI) ; fFile.write(pPattern, stBytes) ; dWriteTime += RealTime_Microsecs() - dTime ; ++outI ; if ( !bWriteOnly ) { dTime = RealTime_Microsecs() ; fFile.seekg(*inI) ; fFile.read(pReadBuf, stBytes) ; dReadTime += RealTime_Microsecs() - dTime ; ++inI ; } } while (--stCopySize) ; std::cout << "Write time is " << xw::str::itoa(dWriteTime, 5, 'f') << " (Ave: " << xw::str::itoa(dWriteTime/stTimes, 10, 'f') << ")" << std::endl ; if ( !bWriteOnly ) { std::cout << "Read time is " << xw::str::itoa(dReadTime, 5, 'f') << " (Ave: " << xw::str::itoa(dReadTime/stTimes, 10, 'f') << ")" << std::endl ; } } // End // Write in order if ( bOrdered ) { std::set<size_t>::iterator i = outSet.begin() ; dWriteTime = 0.0 ; stCopySize = 0 ; for(; i != outSet.end(); ++i) { stPos = *i ; dTime = RealTime_Microsecs() ; fFile.seekp(stPos) ; fFile.write(pPattern, stBytes) ; dWriteTime += RealTime_Microsecs() - dTime ; ++stCopySize ; } std::cout << "Ordered Write time is " << xw::str::itoa(dWriteTime, 5, 'f') << " in " << xw::str::itoa(stCopySize) << " (Ave: " << xw::str::itoa(dWriteTime/stCopySize, 10, 'f') << ")" << std::endl ; if ( !bWriteOnly ) { i = inSet.begin() ; dReadTime = 0.0 ; stCopySize = 0 ; for(; i != inSet.end(); ++i) { stPos = *i ; dTime = RealTime_Microsecs() ; fFile.seekg(stPos) ; fFile.read(pReadBuf, stBytes) ; dReadTime += RealTime_Microsecs() - dTime ; ++stCopySize ; } std::cout << "Ordered Read time is " << xw::str::itoa(dReadTime, 5, 'f') << " in " << xw::str::itoa(stCopySize) << " (Ave: " << xw::str::itoa(dReadTime/stCopySize, 10, 'f') << ")" << std::endl ; } }// End // Write in reverse order if ( bReverse ) { std::set<size_t>::reverse_iterator i = outSet.rbegin() ; dWriteTime = 0.0 ; stCopySize = 0 ; for(; i != outSet.rend(); ++i) { stPos = *i ; dTime = RealTime_Microsecs() ; fFile.seekp(stPos) ; fFile.write(pPattern, stBytes) ; dWriteTime += RealTime_Microsecs() - dTime ; ++stCopySize ; } std::cout << "Reverse ordered Write time is " << xw::str::itoa(dWriteTime, 5, 'f') << " in " << xw::str::itoa(stCopySize) << " (Ave: " << xw::str::itoa(dWriteTime/stCopySize, 10, 'f') << ")" << std::endl ; if ( !bWriteOnly ) { i = inSet.rbegin() ; dReadTime = 0.0 ; stCopySize = 0 ; for(; i != inSet.rend(); ++i) { stPos = *i ; dTime = RealTime_Microsecs() ; fFile.seekg(stPos) ; fFile.read(pReadBuf, stBytes) ; dReadTime += RealTime_Microsecs() - dTime ; ++stCopySize ; } std::cout << "Reverse ordered Read time is " << xw::str::itoa(dReadTime, 5, 'f') << " in " << xw::str::itoa(stCopySize) << " (Ave: " << xw::str::itoa(dReadTime/stCopySize, 10, 'f') << ")" << std::endl ; } }// End dTime = RealTime_Microsecs() ; fFile.close() ; std::cout << "Flush/Close Time is " << xw::str::itoa(RealTime_Microsecs()-dTime, 5, 'f') << std::endl ; std::cout << "Program Terminated" << std::endl ; } catch(...) { std::cout << "Something wrong or wrong parameters" << std::endl ; retval = -1 ; } if ( p ) delete []p ; if ( pPattern ) delete []pPattern ; if ( pReadBuf ) delete []pReadBuf ; return retval ; }

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  • How optimize queries with fully qualified names in t-sql?

    - by tomaszs
    Whe I call: select * from Database.dbo.Table where NAME = 'cat' It takes: 200 ms And when I change database to Database in Management Studio and call it without fully qualified name it's much faster: select * from Table where NAME = 'cat' It takes: 17 ms Is there any way to make fully qualified queries faster without changing database?

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  • Oracle: Difference in execution plans between databases

    - by Will
    Hello, I am comparing queries my development and production database. They are both Oracle 9i, but almost every single query has a completely different execution plan depending on the database. All tables/indexes are the same, but the dev database has about 1/10th the rows for each table. On production, the query execution plan it picks for most queries is different from development, and the cost is somtimes 1000x higher. Queries on production also seem to be not using the correct indexes for queries in some cases (full table access). I have ran dbms_utility.analyze schema on both databases recently as well in the hopes the CBO would figure something out. Is there some other underlying oracle configuration that could be causing this? I am a developer mostly so this kind of DBA analysis is fairly confusing at first..

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  • Free eBook: 45 Database Performance Tips for Developers

    - by TATWORTH
    Originally posted on: http://geekswithblogs.net/TATWORTH/archive/2014/05/25/free-ebook-45-database-performance-tips-for-developers.aspxAt http://www.red-gate.com/products/sql-development/sql-prompt/entrypage/sql-performance-tips-ebook, RedGate are offering a free E-Book, “45 Database Performance Tips for Developers” They also offer on the same page, a 14-day trial of SQL Prompt, an intellisence-style add-on for SSMS (SQL Server Management Studio).

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  • Optimizing MySQL Database Operations for Better Performance

    - by Antoinette O'Sullivan
    If you are responsible for a MySQL Database, you make choices based on your priorities; cost, security and performance. To learn more about improving performance, take the MySQL Performance Tuning course.  In this 4-day instructor-led course you will learn practical, safe and highly efficient ways to optimize performance for the MySQL Server. It will help you develop the skills needed to use tools for monitoring, evaluating and tuning MySQL. You can take this course via the following delivery methods:Training-on-Demand: Take this course at your own pace, starting training within 24 hours of registration. Live-Virtual Event: Follow a live-event from your own desk; no travel required. You can choose from a selection of events to suit your timezone. In-Class Event: Travel to an education center to take this course. Below is a selection of events already on the schedule.  Location  Date  Delivery Language  London, England  26 November 2013  English  Toulouse, France  18 November 2013 French   Rome, Italy  2 December 2013  Italian  Riga, Latvia  3 March 2014  Latvian  Jakarta Barat, Indonesia 10 December 2013  English   Tokyo, Japan  17 April 2014  Japanese  Pasig City, Philippines 9 December 2013   English  Bangkok, Thailand  4 November 2013  English To register for this course or to learn more about the authentic MySQL curriculum, go to http://education.oracle.com/mysql. To see what an expert has to say about MySQL Performance, read Dimitri's blog.

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  • Welcome Oracle Data Integration 12c: Simplified, Future-Ready Solutions with Extreme Performance

    - by Irem Radzik
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 The big day for the Oracle Data Integration team has finally arrived! It is my honor to introduce you to Oracle Data Integration 12c. Today we announced the general availability of 12c release for Oracle’s key data integration products: Oracle Data Integrator 12c and Oracle GoldenGate 12c. The new release delivers extreme performance, increase IT productivity, and simplify deployment, while helping IT organizations to keep pace with new data-oriented technology trends including cloud computing, big data analytics, real-time business intelligence. With the 12c release Oracle becomes the new leader in the data integration and replication technologies as no other vendor offers such a complete set of data integration capabilities for pervasive, continuous access to trusted data across Oracle platforms as well as third-party systems and applications. Oracle Data Integration 12c release addresses data-driven organizations’ critical and evolving data integration requirements under 3 key themes: Future-Ready Solutions Extreme Performance Fast Time-to-Value       There are many new features that support these key differentiators for Oracle Data Integrator 12c and for Oracle GoldenGate 12c. In this first 12c blog post, I will highlight only a few:·Future-Ready Solutions to Support Current and Emerging Initiatives: Oracle Data Integration offer robust and reliable solutions for key technology trends including cloud computing, big data analytics, real-time business intelligence and continuous data availability. Via the tight integration with Oracle’s database, middleware, and application offerings Oracle Data Integration will continue to support the new features and capabilities right away as these products evolve and provide advance features. E    Extreme Performance: Both GoldenGate and Data Integrator are known for their high performance. The new release widens the gap even further against competition. Oracle GoldenGate 12c’s Integrated Delivery feature enables higher throughput via a special application programming interface into Oracle Database. As mentioned in the press release, customers already report up to 5X higher performance compared to earlier versions of GoldenGate. Oracle Data Integrator 12c introduces parallelism that significantly increases its performance as well. Fast Time-to-Value via Higher IT Productivity and Simplified Solutions:  Oracle Data Integrator 12c’s new flow-based declarative UI brings superior developer productivity, ease of use, and ultimately fast time to market for end users.  It also gives the ability to seamlessly reuse mapping logic speeds development.Oracle GoldenGate 12c ‘s Integrated Delivery feature automatically optimally tunes the process, saving time while improving performance. This is just a quick glimpse into Oracle Data Integrator 12c and Oracle GoldenGate 12c. On November 12th we will reveal much more about the new release in our video webcast "Introducing 12c for Oracle Data Integration". Our customer and partner speakers, including SolarWorld, BT, Rittman Mead will join us in launching the new release. Please join us at this free event to learn more from our executives about the 12c release, hear our customers’ perspectives on the new features, and ask your questions to our experts in the live Q&A. Also, please continue to follow our blogs, tweets, and Facebook updates as we unveil more about the new features of the latest release. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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  • URL Rewrite 2.0 Performance

    - by The Official Microsoft IIS Site
    Do performance work it is easy when you have the right tools for measuring gains or lost. I will share some thoughts about how to improve performance during rewriting, but please keep in mind that any change you do must be well thought and with performance Read More......( read more ) Read More......(read more)

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  • Getting baseline and performance stats - the easy way.

    - by fatherjack
    OK, pretty much any DBA worth their salt has read Brent Ozar's (Blog | Twitter) blog about getting a baseline of your server's performance counters and then getting the same counters at regular intervals afterwards so that you can track performance trends and evidence how you are making your servers faster or cope with extra load without costing your boss any money for hardware upgrades. No? well, go read it now. I can wait a while as there is a great video there too...http://www.brentozar.com/archive/2006/12/dba-101-using-perfmon-for-sql-performance-tuning/,...(read more)

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  • How to erase a SSD to restore factory performance in Linux?

    - by Andy B
    Due to big performance issues with an mdraid-1 array I'd like to pull down from the array one of the devices (Samsung 840 Pro), erase it to restore factory performance and re-add it to the array. The reason I want to do this to one of the SSDs is because the poor performance seems to be related to one specific SSD out of the two (although they are the same brand, model and firmware ver). But how do I erase a SSD from Linux? I mention that hdparm indicates that both drives are frozen at this time. Maybe because they are part of an md array? Thanks in advance!

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  • Performance Improvements: Caching

    Caching can greatly improve performance but it can also lull you into a false sense of security. In some cases it can even make the performance worse. If you haven't already, then now is the time to learn the issues and limitations of caching so that you can truly improve performance.

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