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  • Why your Netapp is so slow...

    - by Darius Zanganeh
    Have you ever wondered why your Netapp FAS box is slow and doesn't perform well at large block workloads?  In this blog entry I will give you a little bit of information that will probably help you understand why it’s so slow, why you shouldn't use it for applications that read and write in large blocks like 64k, 128k, 256k ++ etc..  Of course since I work for Oracle at this time, I will show you why the ZS3 storage boxes are excellent choices for these types of workloads. Netapp’s Fundamental Problem The fundamental problem you have running these workloads on Netapp is the backend block size of their WAFL file system.  Every application block on a Netapp FAS ends up in a 4k chunk on a disk. Reference:  Netapp TR-3001 Whitepaper Netapp has proven this lacking large block performance fact in at least two different ways. They have NEVER posted an SPC-2 Benchmark yet they have posted SPC-1 and SPECSFS, both recently. In 2011 they purchased Engenio to try and fill this GAP in their portfolio. Block Size Matters So why does block size matter anyways?  Many applications use large block chunks of data especially in the Big Data movement.  Some examples are SAS Business Analytics, Microsoft SQL, Hadoop HDFS is even 64MB! Now let me boil this down for you.  If an application such MS SQL is writing data in a 64k chunk then before Netapp actually writes it on disk it will have to split it into 16 different 4k writes and 16 different disk IOPS.  When the application later goes to read that 64k chunk the Netapp will have to again do 16 different disk IOPS.  In comparison the ZS3 Storage Appliance can write in variable block sizes ranging from 512b to 1MB.  So if you put the same MSSQL database on a ZS3 you can set the specific LUNs for this database to 64k and then when you do an application read/write it requires only a single disk IO.  That is 16x faster!  But, back to the problem with your Netapp, you will VERY quickly run out of disk IO and hit a wall.  Now all arrays will have some fancy pre fetch algorithm and some nice cache and maybe even flash based cache such as a PAM card in your Netapp but with large block workloads you will usually blow through the cache and still need significant disk IO.  Also because these datasets are usually very large and usually not dedupable they are usually not good candidates for an all flash system.  You can do some simple math in excel and very quickly you will see why it matters.  Here are a couple of READ examples using SAS and MSSQL.  Assume these are the READ IOPS the application needs even after all the fancy cache and algorithms.   Here is an example with 128k blocks.  Notice the numbers of drives on the Netapp! Here is an example with 64k blocks You can easily see that the Oracle ZS3 can do dramatically more work with dramatically less drives.  This doesn't even take into account that the ONTAP system will likely run out of CPU way before you get to these drive numbers so you be buying many more controllers.  So with all that said, lets look at the ZS3 and why you should consider it for any workload your running on Netapp today.  ZS3 World Record Price/Performance in the SPC-2 benchmark ZS3-2 is #1 in Price Performance $12.08ZS3-2 is #3 in Overall Performance 16,212 MBPS Note: The number one overall spot in the world is held by an AFA 33,477 MBPS but at a Price Performance of $29.79.  A customer could purchase 2 x ZS3-2 systems in the benchmark with relatively the same performance and walk away with $600,000 in their pocket.

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  • Code Golf: Shortest Turing-complete interpreter.

    - by ilya n.
    I've just tried to create the smallest possible language interpreter. Would you like to join and try? Rules of the game: You should specify a programming language you're interpreting. If it's a language you invented, it should come with a list of commands in the comments. Your code should start with example program and data assigned to your code and data variables. Your code should end with output of your result. It's preferable that there are debug statements at every intermediate step. Your code should be runnable as written. You can assume that data are 0 and 1s (int, string or boolean, your choice) and output is a single bit. The language should be Turing-complete in the sense that for any algorithm written on a standard model, such as Turing machine, Markov chains, or similar of your choice, it's reasonably obvious (or explained) how to write a program that after being executred by your interpreter performs the algorithm. The length of the code is defined as the length of the code after removal of input part, output part, debug statements and non-necessary whitespaces. Please add the resulting code and its length to the post. You can't use functions that make compiler execute code for you, such as eval(), exec() or similar. This is a Community Wiki, meaning neither the question nor answers get the reputation points from votes. But vote anyway!

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  • How optimize code with introspection + heavy alloc on iPhone

    - by mamcx
    I have a problem. I try to display a UITable that could have 2000-20000 records (typicall numbers.) I have a SQLite database similar to the Apple contacts application. I do all the tricks I know to get a smoth scroll, but I have a problem. I load the data in 50 recods blocks. Then, when the user scroll, request next 50 until finish the list. However, load that 50 records cause a notable "pause" in loading and scrolling. Everything else works fine. I cache the data, have opaque cells, draw it by code, etc... I swap the code loading the same data in dicts and have a performance boost but wonder if I could keep my object oriented aproach and improve the actual code. This is the code I think have the performance problem: -(NSArray *) loadAndFill: (NSString *)sql theClass: (Class)cls { [self openDb]; NSMutableArray *list = [NSMutableArray array]; NSAutoreleasePool *pool = [[NSAutoreleasePool alloc] init]; DbObject *ds; Class myClass = NSClassFromString([DbObject getTableName:cls]); FMResultSet *rs = [self load:sql]; while ([rs next]) { ds = [[myClass alloc] init]; NSDictionary *props = [ds properties]; NSString *fieldType = nil; id fieldValue; for (NSString *fieldName in [props allKeys]) { fieldType = [props objectForKey: fieldName]; fieldValue = [self ValueForField:rs Name:fieldName Type:fieldType]; [ds setValue:fieldValue forKey:fieldName]; } [list addObject :ds]; [ds release]; } [rs close]; [pool drain]; return list; } And I think the main culprit is: -(id) ValueForField: (FMResultSet *)rs Name:(NSString *)fieldName Type:(NSString *)fieldType { id fieldValue = nil; if ([fieldType isEqualToString:@"i"] || // int [fieldType isEqualToString:@"I"] || // unsigned int [fieldType isEqualToString:@"s"] || // short [fieldType isEqualToString:@"S"] || // unsigned short [fieldType isEqualToString:@"f"] || // float [fieldType isEqualToString:@"d"] ) // double { fieldValue = [NSNumber numberWithInt: [rs longForColumn:fieldName]]; } else if ([fieldType isEqualToString:@"B"]) // bool or _Bool { fieldValue = [NSNumber numberWithBool: [rs boolForColumn:fieldName]]; } else if ([fieldType isEqualToString:@"l"] || // long [fieldType isEqualToString:@"L"] || // usigned long [fieldType isEqualToString:@"q"] || // long long [fieldType isEqualToString:@"Q"] ) // unsigned long long { fieldValue = [NSNumber numberWithLong: [rs longForColumn:fieldName]]; } else if ([fieldType isEqualToString:@"c"] || // char [fieldType isEqualToString:@"C"] ) // unsigned char { fieldValue = [rs stringForColumn:fieldName]; //Is really a boolean? if ([fieldValue isEqualToString:@"0"] || [fieldValue isEqualToString:@"1"]) { fieldValue = [NSNumber numberWithInt: [fieldValue intValue]]; } } else if ([fieldType hasPrefix:@"@"] ) // Object { NSString *className = [fieldType substringWithRange:NSMakeRange(2, [fieldType length]-3)]; if ([className isEqualToString:@"NSString"]) { fieldValue = [rs stringForColumn:fieldName]; } else if ([className isEqualToString:@"NSDate"]) { NSDateFormatter* dateFormatter = [[NSDateFormatter alloc] init]; [dateFormatter setDateFormat:@"yyyy-MM-dd'T'HH:mm:ss"]; NSString *theDate = [rs stringForColumn:fieldName]; if (theDate) { fieldValue = [dateFormatter dateFromString: theDate]; } else { fieldValue = nil; } [dateFormatter release]; } else if ([className isEqualToString:@"NSInteger"]) { fieldValue = [NSNumber numberWithInt: [rs intForColumn :fieldName]]; } else if ([className isEqualToString:@"NSDecimalNumber"]) { fieldValue = [rs stringForColumn :fieldName]; if (fieldValue) { fieldValue = [NSDecimalNumber decimalNumberWithString:[rs stringForColumn :fieldName]]; } } else if ([className isEqualToString:@"NSNumber"]) { fieldValue = [NSNumber numberWithDouble: [rs doubleForColumn:fieldName]]; } else { //Is a relationship one-to-one? if (![fieldType hasPrefix:@"NS"]) { id rel = class_createInstance(NSClassFromString(className), sizeof(unsigned)); Class theClass = [rel class]; if ([rel isKindOfClass:[DbObject class]]) { fieldValue = [rel init]; //Load the record... NSInteger Id = [rs intForColumn:[theClass relationName]]; if (Id>0) { [fieldValue release]; Db *db = [Db currentDb]; fieldValue = [db loadById: theClass theId:Id]; } } } else { NSString *error = [NSString stringWithFormat:@"Err Can't get value for field %@ of type %@", fieldName, fieldType]; NSLog(error); NSException *e = [NSException exceptionWithName:@"DBError" reason:error userInfo:nil]; @throw e; } } } return fieldValue; }

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  • Make c# matrix code faster

    - by Wam
    Hi all, Working on some matrix code, I'm concerned of performance issues. here's how it works : I've a IMatrix abstract class (with all matrices operations etc), implemented by a ColumnMatrix class. abstract class IMatrix { public int Rows {get;set;} public int Columns {get;set;} public abstract float At(int row, int column); } class ColumnMatrix : IMatrix { private data[]; public override float At(int row, int column) { return data[row + columns * this.Rows]; } } This class is used a lot across my application, but I'm concerned with performance issues. Testing only read for a 2000000x15 matrix against a jagged array of the same size, I get 1359ms for array access agains 9234ms for matrix access : public void TestAccess() { int iterations = 10; int rows = 2000000; int columns = 15; ColumnMatrix matrix = new ColumnMatrix(rows, columns); for (int i = 0; i < rows; i++) for (int j = 0; j < columns; j++) matrix[i, j] = i + j; float[][] equivalentArray = matrix.ToRowsArray(); TimeSpan totalMatrix = new TimeSpan(0); TimeSpan totalArray = new TimeSpan(0); float total = 0f; for (int iteration = 0; iteration < iterations; iteration++) { total = 0f; DateTime start = DateTime.Now; for (int i = 0; i < rows; i++) for (int j = 0; j < columns; j++) total = matrix.At(i, j); totalMatrix += (DateTime.Now - start); total += 1f; //Ensure total is read at least once. total = total > 0 ? 0f : 0f; start = DateTime.Now; for (int i = 0; i < rows; i++) for (int j = 0; j < columns; j++) total = equivalentArray[i][j]; totalArray += (DateTime.Now - start); } if (total < 0f) logger.Info("Nothing here, just make sure we read total at least once."); logger.InfoFormat("Average time for a {0}x{1} access, matrix : {2}ms", rows, columns, totalMatrix.TotalMilliseconds); logger.InfoFormat("Average time for a {0}x{1} access, array : {2}ms", rows, columns, totalArray.TotalMilliseconds); Assert.IsTrue(true); } So my question : how can I make this thing faster ? Is there any way I can make my ColumnMatrix.At faster ? Cheers !

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  • Code runs 6 times slower with 2 threads than with 1

    - by Edward Bird
    So I have written some code to experiment with threads and do some testing. The code should create some numbers and then find the mean of those numbers. I think it is just easier to show you what I have so far. I was expecting with two threads that the code would run about 2 times as fast. Measuring it with a stopwatch I think it runs about 6 times slower! void findmean(std::vector<double>*, std::size_t, std::size_t, double*); int main(int argn, char** argv) { // Program entry point std::cout << "Generating data..." << std::endl; // Create a vector containing many variables std::vector<double> data; for(uint32_t i = 1; i <= 1024 * 1024 * 128; i ++) data.push_back(i); // Calculate mean using 1 core double mean = 0; std::cout << "Calculating mean, 1 Thread..." << std::endl; findmean(&data, 0, data.size(), &mean); mean /= (double)data.size(); // Print result std::cout << " Mean=" << mean << std::endl; // Repeat, using two threads std::vector<std::thread> thread; std::vector<double> result; result.push_back(0.0); result.push_back(0.0); std::cout << "Calculating mean, 2 Threads..." << std::endl; // Run threads uint32_t halfsize = data.size() / 2; uint32_t A = 0; uint32_t B, C, D; // Split the data into two blocks if(data.size() % 2 == 0) { B = C = D = halfsize; } else if(data.size() % 2 == 1) { B = C = halfsize; D = hsz + 1; } // Run with two threads thread.push_back(std::thread(findmean, &data, A, B, &(result[0]))); thread.push_back(std::thread(findmean, &data, C, D , &(result[1]))); // Join threads thread[0].join(); thread[1].join(); // Calculate result mean = result[0] + result[1]; mean /= (double)data.size(); // Print result std::cout << " Mean=" << mean << std::endl; // Return return EXIT_SUCCESS; } void findmean(std::vector<double>* datavec, std::size_t start, std::size_t length, double* result) { for(uint32_t i = 0; i < length; i ++) { *result += (*datavec).at(start + i); } } I don't think this code is exactly wonderful, if you could suggest ways of improving it then I would be grateful for that also.

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  • 3 Day Level 400 SQL Tuning Workshop 15 March in London, early bird and referral offer

    - by sqlworkshops
    I want to inform you that we have organized the "3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop" in London, United Kingdom during March 15-17, 2011.This is a truly level 400 hands-on workshop and you can find the Agenda, Prerequisite, Goal of the Workshop and Registration information at www.sqlworkshops.com/ruk. Charges are GBP 1800 (VAT excl.). Early bird discount of GBP 125 until 18 February. We are also introducing a new referral plan. If you refer someone who participates in the workshop you will receive an Amazon gift voucher for GBP 125.Feedback from one of the participants who attended our November London workshop:Andrew, Senior SQL Server DBA from UBS, UK, www.ubs.com, November 26, 2010:Rating: In a scale of 1 to 5 please rate each item below (1=Poor & 5=Excellent) Overall I was satisfied with the workshop 5 Instructor maintained the focus of the course 5 Mix of theory and practice was appropriate 5 Instructor answered the questions asked 5 The training facility met the requirement 5 How confident are you with SQL Server 2008 performance tuning 5 Additional comments from Andrew: The course was expertly delivered and backed up with practical examples. At the end of the course I felt my knowledge of SQL Server had been greatly enhanced and was eager to share with my colleagues. I felt there was one prerequisite missing from the course description, an open mind since the course changed some of my core product beliefs. For Additional workshop feedbacks refer to: www.sqlworkshops.com/feedbacks.I will be delivering the Level 300-400 1 Day Microsoft SQL Server 2008 Performance Monitoring and Tuning Seminar at Istanbul and Ankara, Turkey during March. This event is organized by Microsoft Turkey, let me know if you are in Turkey and would like to attend.During September 2010 I delivered this Level 300-400 1 Day Microsoft SQL Server 2008 Performance Monitoring and Tuning Seminar in Zurich, Switzerland organized by Microsoft Switzerland and the feedback was 4.85 out of 5, there were about 100 participants. During November 2010 when I delivered seminar in Lisbon, Portugal organized by Microsoft Portugal, the feedback was 8.30 out of 9, there were 130 participants.Our Mission: Empower customers to fully realize the Performance potential of Microsoft SQL Server without increasing the total cost of ownership (TCO) and achieve high customer satisfaction in every consulting engagement and workshop delivery.Our Business Plan: Provide useful content in webcasts, articles and seminars to get visibility for consulting engagements and workshop delivery opportunity. Help us by forwarding this email to your SQL Server friends and colleagues.Looking forwardR Meyyappan & Team @ www.SQLWorkshops.comLinkedIn: http://at.linkedin.com/in/rmeyyappan

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  • Performance Improvement: Session State

    Performance is critical to today's successful applications and web sites. If you design with an awareness of the session state management challenges you can always change your strategies to match your performance needs.

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  • Performance Improvement: Session State

    Performance is critical to today's successful applications and web sites. If you design with an awareness of the session state management challenges you can always change your strategies to match your performance needs.

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  • FREE eBook: .NET Performance Testing and Optimization (Part 1)

    In this this first part of complete guide to performance profiling, Paul Glavich and Chris Farrell explain why performance testing is a good idea and walk you through everything you need to know to set up a test environment. This comprehensive guide to getting started is an essential handbook to any programmer looking to set up a .NET testing environment and get the best results out of it. Download your free copy now span.fullpost {display:none;}

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  • Google I/O 2012 - Building High Performance Mobile Web Applications

    Google I/O 2012 - Building High Performance Mobile Web Applications Ryan Fioravanti Learn what it takes to build an HTML5 mobile app that will wow your users. This session will focus on speed, offline support, UI layouts, and the tools necessary to set up a productive development environment. Come to this session if you're looking to make a killer mobile web app that stands out amongst the competition. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 33 0 ratings Time: 49:43 More in Science & Technology

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  • Improved Performance on PeopleSoft Combined Benchmark using SPARC T4-4

    - by Brian
    Oracle's SPARC T4-4 server running Oracle's PeopleSoft HCM 9.1 combined online and batch benchmark achieved a world record 18,000 concurrent users experiencing subsecond response time while executing a PeopleSoft Payroll batch job of 500,000 employees in 32.4 minutes. This result was obtained with a SPARC T4-4 server running Oracle Database 11g Release 2, a SPARC T4-4 server running PeopleSoft HCM 9.1 application server and a SPARC T4-2 server running Oracle WebLogic Server in the web tier. The SPARC T4-4 server running the application tier used Oracle Solaris Zones which provide a flexible, scalable and manageable virtualization environment. The average CPU utilization on the SPARC T4-2 server in the web tier was 17%, on the SPARC T4-4 server in the application tier it was 59%, and on the SPARC T4-4 server in the database tier was 47% (online and batch) leaving significant headroom for additional processing across the three tiers. The SPARC T4-4 server used for the database tier hosted Oracle Database 11g Release 2 using Oracle Automatic Storage Management (ASM) for database files management with I/O performance equivalent to raw devices. Performance Landscape Results are presented for the PeopleSoft HRMS Self-Service and Payroll combined benchmark. The new result with 128 streams shows significant improvement in the payroll batch processing time with little impact on the self-service component response time. PeopleSoft HRMS Self-Service and Payroll Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-4 (db) 18,000 0.988 0.539 32.4 128 SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-4 (db) 18,000 0.944 0.503 43.3 64 The following results are for the PeopleSoft HRMS Self-Service benchmark that was previous run. The results are not directly comparable with the combined results because they do not include the payroll component. PeopleSoft HRMS Self-Service 9.1 Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) 2x SPARC T4-2 (db) 18,000 1.048 0.742 N/A N/A The following results are for the PeopleSoft Payroll benchmark that was previous run. The results are not directly comparable with the combined results because they do not include the self-service component. PeopleSoft Payroll (N.A.) 9.1 - 500K Employees (7 Million SQL PayCalc, Unicode) Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-4 (db) N/A N/A N/A 30.84 96 Configuration Summary Application Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 512 GB memory Oracle Solaris 11 11/11 PeopleTools 8.52 PeopleSoft HCM 9.1 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Java Platform, Standard Edition Development Kit 6 Update 32 Database Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 256 GB memory Oracle Solaris 11 11/11 Oracle Database 11g Release 2 PeopleTools 8.52 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Micro Focus Server Express (COBOL v 5.1.00) Web Tier Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory Oracle Solaris 11 11/11 PeopleTools 8.52 Oracle WebLogic Server 10.3.4 Java Platform, Standard Edition Development Kit 6 Update 32 Storage Configuration: 1 x Sun Server X2-4 as a COMSTAR head for data 4 x Intel Xeon X7550, 2.0 GHz 128 GB memory 1 x Sun Storage F5100 Flash Array (80 flash modules) 1 x Sun Storage F5100 Flash Array (40 flash modules) 1 x Sun Fire X4275 as a COMSTAR head for redo logs 12 x 2 TB SAS disks with Niwot Raid controller Benchmark Description This benchmark combines PeopleSoft HCM 9.1 HR Self Service online and PeopleSoft Payroll batch workloads to run on a unified database deployed on Oracle Database 11g Release 2. The PeopleSoft HRSS benchmark kit is a Oracle standard benchmark kit run by all platform vendors to measure the performance. It's an OLTP benchmark where DB SQLs are moderately complex. The results are certified by Oracle and a white paper is published. PeopleSoft HR SS defines a business transaction as a series of HTML pages that guide a user through a particular scenario. Users are defined as corporate Employees, Managers and HR administrators. The benchmark consist of 14 scenarios which emulate users performing typical HCM transactions such as viewing paycheck, promoting and hiring employees, updating employee profile and other typical HCM application transactions. All these transactions are well-defined in the PeopleSoft HR Self-Service 9.1 benchmark kit. This benchmark metric is the weighted average response search/save time for all the transactions. The PeopleSoft 9.1 Payroll (North America) benchmark demonstrates system performance for a range of processing volumes in a specific configuration. This workload represents large batch runs typical of a ERP environment during a mass update. The benchmark measures five application business process run times for a database representing large organization. They are Paysheet Creation, Payroll Calculation, Payroll Confirmation, Print Advice forms, and Create Direct Deposit File. The benchmark metric is the cumulative elapsed time taken to complete the Paysheet Creation, Payroll Calculation and Payroll Confirmation business application processes. The benchmark metrics are taken for each respective benchmark while running simultaneously on the same database back-end. Specifically, the payroll batch processes are started when the online workload reaches steady state (the maximum number of online users) and overlap with online transactions for the duration of the steady state. Key Points and Best Practices Two PeopleSoft Domain sets with 200 application servers each on a SPARC T4-4 server were hosted in 2 separate Oracle Solaris Zones to demonstrate consolidation of multiple application servers, ease of administration and performance tuning. Each Oracle Solaris Zone was bound to a separate processor set, each containing 15 cores (total 120 threads). The default set (1 core from first and third processor socket, total 16 threads) was used for network and disk interrupt handling. This was done to improve performance by reducing memory access latency by using the physical memory closest to the processors and offload I/O interrupt handling to default set threads, freeing up cpu resources for Application Servers threads and balancing application workload across 240 threads. A total of 128 PeopleSoft streams server processes where used on the database node to complete payroll batch job of 500,000 employees in 32.4 minutes. See Also Oracle PeopleSoft Benchmark White Papers oracle.com SPARC T4-2 Server oracle.com OTN SPARC T4-4 Server oracle.com OTN PeopleSoft Enterprise Human Capital Managementoracle.com OTN PeopleSoft Enterprise Human Capital Management (Payroll) oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 8 November 2012.

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  • Tester la performance de votre réseau avec Iperf, un tutoriel par Nicolas Hennion

    Bonjour à tous !La rubrique Réseaux vous propose un article expliquant comment tester les performances du réseau avec Iperf par nicolargo : Tester la performance de votre réseau avec Iperf. Citation: Iperf est un des outils indispensables pour tout administrateur réseau qui se respecte. En effet, ce logiciel de mesure de performance réseau, disponible sur de nombreuses plateformes (Linux, BSD, Mac, Windows?) se présente sous la forme d'une ligne de commande à exécuter sur deux machines...

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  • Data Quality Services Performance Best Practices Guide

    This guide details high-level performance numbers expected and a set of best practices on getting optimal performance when using Data Quality Services (DQS) in SQL Server 2012 with Cumulative Update 1. Schedule Azure backupsRed Gate’s Cloud Services makes it simple to create and schedule backups of your SQL Azure databases to Azure blob storage or Amazon S3. Try it for free today.

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  • A Perspective on Database Performance Tuning

    Fundamentally, database performance tuning is done for two basic reasons, to reduce response time and to reduce resource usage, both of which can apply for any given situation. Julian Stuhler looks at database performance tuning, and why it remains one of the most important topics for any DBA, developer or systems administrator.

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  • Talend vs. SSIS: A Simple Performance Comparison

    With all of the ETL tools in the marketplace, which one is best? Jeff Singleton brings us simple performance comparison pitting SSIS against open source powerhouse Talend. Optimize SQL Server performance“With SQL Monitor, we can be proactive in our optimization process, instead of waiting until a customer reports a problem,” John Trumbul, Sr. Software Engineer. Optimize your servers with a free trial.

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  • A Perspective on Database Performance Tuning

    Fundamentally, database performance tuning is done for two basic reasons, to reduce response time and to reduce resource usage, both of which can apply for any given situation. Julian Stuhler looks at database performance tuning, and why it remains one of the most important topics for any DBA, developer or systems administrator.

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  • What is the way to understand someone else's giant uncommented spaghetti code? [closed]

    - by Anisha Kaul
    Possible Duplicate: I’ve inherited 200K lines of spaghetti code — what now? I have been recently handled a giant multithreaded program with no comments and have been asked to understand what it does, and then to improve it (if possible). Are there some techniques which should be followed when we need to understand someone else's code? OR do we straightaway start from the first function call and go on tracking next function calls? C++ (with multi-threading) on Linux

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  • Will using FAT32 provide better pagefile performance than NTFS?

    - by llazzaro
    Hello, I was discussing with my others personalities, and came up with a conflict. In http://technet.microsoft.com/en-us/library/cc938440.aspx , says that FAT32 is faster when using smaller volumes. Ok separate disk, will give more performance than same disk. But did anyone test this? Scenario 1 : Separate hard disk FAT32 (small volume) Scenario 2 : Separate hard disk NTFS which one will win? minimum gain?

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  • Profiling SharePoint with ANTS Performance Profiler 5.2

    Using ANTS Performance Profiler with SharePoint has, previously, been possible, but not easy. Version 5.2 of ANTS Performance Profiler changes all that, and Chris Allen has put together a straight-forward guide to profiling SharePoint, demonstrating just how much easier it has become.

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  • How do large blobs affect SQL delete performance, and how can I mitigate the impact?

    - by Max Pollack
    I'm currently experiencing a strange issue that my understanding of SQL Server doesn't quite mesh with. We use SQL as our file storage for our internal storage service, and our database has about half a million rows in it. Most of the files (86%) are 1mb or under, but even on fresh copies of our database where we simply populate the table with data for the purposes of a test, it appears that rows with large amounts of data stored in a BLOB frequently cause timeouts when our SQL Server is under load. My understanding of how SQL Server deletes rows is that it's a garbage collection process, i.e. the row is marked as a ghost and the row is later deleted by the ghost cleanup process after the changes are copied to the transaction log. This suggests to me that regardless of the size of the data in the blob, row deletion should be close to instantaneous. However when deleting these rows we are definitely experiencing large numbers of timeouts and astoundingly low performance. In our test data set, its files over 30mb that cause this issue. This is an edge case, we don't frequently encounter these, and even though we're looking into SQL filestream as a solution to some of our problems, we're trying to narrow down where these issues are originating from. We ARE performing our deletes inside of a transaction. We're also performing updates to metadata such as file size stats, but these exist in a separate table away from the file data itself. Hierarchy data is stored in the table that contains the file information. Really, in the end it's not so much what we're doing around the deletes that matters, we just can't find any references to low delete performance on rows that contain a large amount of data in a BLOB. We are trying to determine if this is even an avenue worth exploring, or if it has to be one of our processes around the delete that's causing the issue. Are there any situations in which this could occur? Is it common for a database server to come to the point of complete timeouts when many of these deletes are occurring simultaneously? Is there a way to combat this issue if it exists? (cross-posted from StackOverflow )

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