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  • Any worker agent monitors for appliance based load balancers?

    - by Zethris
    Looking to find out to what extent an appliance load balancer can monitor servers for both failover (say for example a service like apache tomcat fails) and load balancing? Right now it looks like it's just port monitoring/connection tracking and healthcheck urls that it will heartbeat and detect as down if it doesn't come back with a finished request. We are looking at the Kemp 3500 or Loadbalancer.org solutions. Is there any sort of web application level monitoring/load balancing that these load balancers can offer that can more directly interact with the servers it's balancing?

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  • SQL Server Express performance issue

    - by Developer IT
    Hi folks ! I know my questions will sound silly and probably nobody will have perfect answer but since I am in a complete dead-end with the situation it will make me feel better to post it here. So... I have a SQL Server Express database that's 500 Mb. It contains 5 tables and maybe 30 stored procedure. This database is use to store articles and is use for the Developer It web site. Normally the web pages load quickly, let's say 2 ou 3 sec. BUT, sqlserver process uses 100% of the processor for those 2 or 3 sec. I try to find which stored procedure was the problem and I could not find one. It seems like every read into the table dans contains the articles (there are about 155,000 of them and 20 or so gets added every 15 minutes). I added few index but without luck... It is because the table is full text indexed ? Should I have order with the primary key instead of date ? I never had any problems with ordering by dates.... Should I use dynamic SQL ? Should I add the primary key into the url of the articles ? Should I use mutiple indexes for seperate columns or one big index ? I you want more details or code bits, just ask for it. Basicly, every little hint is much apreciated. Thanks.

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  • Neo4j 1.9.4 (REST Server,CYPHER) performance issue

    - by user2968943
    I have Neo4j 1.9.4 installed on 24 core 24Gb ram (centos) machine and for most queries CPU usage spikes goes to 200% with only few concurrent requests. Domain: some sort of social application where few types of nodes(profiles) with 3-30 text/array properties and 36 relationship types with at least 3 properties. Most of nodes currently has ~300-500 relationships. Current data set footprint(from console): LogicalLogSize=4294907 (32MB) ArrayStoreSize=1675520 (12MB) NodeStoreSize=1342170 (10MB) PropertyStoreSize=1739548 (13MB) RelationshipStoreSize=6395202 (48MB) StringStoreSize=1478400 (11MB) which is IMHO really small. most queries looks like this one(with more or less WITH .. MATCH .. statements and few queries with variable length relations but the often fast): START targetUser=node({id}), currentUser=node({current}) MATCH targetUser-[contact:InContactsRelation]->n, n-[:InLocationRelation]->l, n-[:InCategoryRelation]->c WITH currentUser, targetUser,n, l,c, contact.fav is not null as inFavorites MATCH n<-[followers?:InContactsRelation]-() WITH currentUser, targetUser,n, l,c,inFavorites, COUNT(followers) as numFollowers RETURN id(n) as id, n.name? as name, n.title? as title, n._class as _class, n.avatar? as avatar, n.avatar_type? as avatar_type, l.name as location__name, c.name as category__name, true as isInContacts, inFavorites as isInFavorites, numFollowers it runs in ~1s-3s(for first run) and ~1s-70ms (for consecutive and it depends on query) and there is about 5-10 queries runs for each impression. Another interesting behavior is when i try run query from console(neo4j) on my local machine many consecutive times(just press ctrl+enter for few seconds) it has almost constant execution time but when i do it on server it goes slower exponentially and i guess it somehow related with my problem. Problem: So my problem is that neo4j is very CPU greedy(for 24 core machine its may be not an issue but its obviously overkill for small project). First time i used AWS EC2 m1.large instance but over all performance was bad, during testing, CPU always was over 100%. Some relevant parts of configuration: neostore.nodestore.db.mapped_memory=1280M wrapper.java.maxmemory=8192 note: I already tried configuration where all memory related parameters where HIGH and it didn't worked(no change at all). Question: Where to digg? configuration? scheme? queries? what i'm doing wrong? if need more info(logs, configs) just ask ;)

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  • Improving performance in this query

    - by Luiz Gustavo F. Gama
    I have 3 tables with user logins: sis_login = administrators tb_rb_estrutura = coordinators tb_usuario = clients I created a VIEW to unite all these users by separating them by levels, as follows: create view `login_names` as select `n1`.`cod_login` as `id`, '1' as `level`, `n1`.`nom_user` as `name` from `dados`.`sis_login` `n1` union all select `n2`.`id` as `id`, '2' as `level`, `n2`.`nom_funcionario` as `name` from `tb_rb_estrutura` `n2` union all select `n3`.`cod_usuario` as `id`, '3' as `level`, `n3`.`dsc_nome` as `name` from `tb_usuario` `n3`; So, can occur up to three ids repeated for different users, which is why I separated by levels. This VIEW is just to return me user name, according to his id and level. considering it has about 500,000 registered users, this view takes about 1 second to load. too much time, but is becomes very small when I need to return the latest posts on the forum of my website. The tables of the forums return the user id and level, then look for a name in this VIEW. I have registered 18 forums. When I run the query, it takes one second for each forum = 18 seconds. OMG. This page loads every time somebody enter my website. This is my query: select `x`.`forum_id`, `x`.`topic_id`, `l`.`nome` from ( select `t`.`forum_id`, `t`.`topic_id`, `t`.`data`, `t`.`user_id`, `t`.`user_level` from `tb_forum_topics` `t` union all select `a`.`forum_id`, `a`.`topic_id`, `a`.`data`, `a`.`user_id`, `a`.`user_level` from `tb_forum_answers` `a` ) `x` left outer join `login_names` `l` on `l`.`id` = `x`.`user_id` and `l`.`level` = `x`.`user_level` group by `x`.`forum_id` asc USING EXPLAIN: id select_type table type possible_keys key key_len ref rows Extra 1 PRIMARY <derived2> ALL NULL NULL NULL NULL 6 Using temporary; Using filesort 1 PRIMARY <derived4> ALL NULL NULL NULL NULL 530415 4 DERIVED n1 ALL NULL NULL NULL NULL 114 5 UNION n2 ALL NULL NULL NULL NULL 2 6 UNION n3 ALL NULL NULL NULL NULL 530299 NULL UNION RESULT ALL NULL NULL NULL NULL NULL 2 DERIVED t ALL NULL NULL NULL NULL 3 3 UNION r ALL NULL NULL NULL NULL 3 NULL UNION RESULT ALL NULL NULL NULL NULL NULL Somebody can help me or give a suggestion?

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  • how to check if internal storage file has any data

    - by user3720291
    public class Save extends Activity { int levels = 2; int data_block = 1024; //char[] data = new char[] {'0', '0'}; String blankval = "0"; String targetval = "0"; String temp; String tempwrite; String string = "null"; TextView tex1; TextView tex2; @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.save); Intent intent = getIntent(); Bundle b = intent.getExtras(); tex1 = (TextView) findViewById(R.id.textView1); tex2 = (TextView) findViewById(R.id.textView2); if(b!=null) { string =(String) b.get("string"); } loadprev(); save(); } public void save() { if (string.equals("Blank")) blankval = "1"; if (string.equals("Target")) targetval = "1"; temp = blankval + targetval; try { FileOutputStream fos = openFileOutput("data.gds", MODE_PRIVATE); fos.write(temp.getBytes()); fos.close(); } catch (FileNotFoundException e) {e.printStackTrace();} catch (IOException e) {e.printStackTrace();} tex1.setText(blankval); tex2.setText(targetval); } public void loadprev() { String final_data = ""; try { FileInputStream fis = openFileInput("data.gds"); InputStreamReader isr = new InputStreamReader(fis); char[] data = new char[data_block]; int size; while((size = isr.read(data))>0) { String read_data = String.copyValueOf(data, 0, size); final_data += read_data; data = new char[data_block]; } } catch (FileNotFoundException e) {e.printStackTrace();} catch (IOException e) {e.printStackTrace();} char[] tempread = final_data.toCharArray();; blankval = "" + tempread[0]; targetval = "" + tempread[1]; } } After much tinkering i have finally managed to get my save/load function to work, but it does have an error, pretty much i got it to work then i did a fresh reintall deleting data.gds, afterwards the save/load function crashes because the data.gds file has no previous values. can i use a if statment to check if data.gds has any values in it, if so how do i do it and if not, then what could i use instead?

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  • Improve file transfer speed between Windows PCs and servers

    - by Geotarget
    I've setup a server which I've connected to multiple PCs in my workplace. Sadly, data transfer speeds are at max 3 MB/sec per connection which works out slow for file transfers, especially when transferring large files. I'm using Windows filesharing and the server is a Windows Server 2008 (2 Ghz CPU, 1 GB RAM) and the client PCs mostly running Windows 7. How can I detect bottlenecks in my network and improve file sharing speed within the network?

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  • Sybase PowerDesigner Change Many (Find/Replace/Convert) Data Item's Data Types

    - by Andy
    Hello, I have a relatively large Conceptual Data Model in PowerDesigner. After generating a Physical Data Model and seeing the DBMS data types, I need to update all of data types(NUMBER/TEXT) for each data item. I'd like to either do a find/replace within the Conceptual Data Model or somehow map to different data types when creating the Physical Data Model. Ex. Change the auto conversion of Text - Clob, to Text - NVARCHAR(20). Thanks!

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  • Is Linq Faster, Slower or the same?

    - by Vaccano
    Is this: Box boxToFind = AllBoxes.Where(box => box.BoxNumber == boxToMatchTo.BagNumber); Faster or slower than this: Box boxToFind ; foreach (Box box in AllBoxes) { if (box.BoxNumber == boxToMatchTo.BoxNumber) { boxToFind = box; } } Both give me the result I am looking for (boxToFind). This is going to run on a mobile device that I need to be performance conscientious of.

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  • Possible reasons for high CPU load of taskmgr.exe process on VM?

    - by mjn
    On a VMware virtual machine which has severe performance problems I can see a constant average of 20+ percent CPU load for the TASKMGR.EXE (task manager) process. The apps running on this server have lower load, around 4 to 10 percent average. The VM is running Windows 2003 Server Standard with 3.75 GB assigned RAM. I suspect that the task manager CPU load has something to do with other VM instances on the VMWare server but could not see a similar value on internal ESXi systems (the problematic VM runs in the customers IT).

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  • are there any useful datasets available on the web for data mining?

    - by niko
    Hi, Does anyone know any good resource where example (real) data can be downloaded for experimenting statistics and machine learning techniques such as decision trees etc? Currently I am studying machine learning techniques and it would be very helpful to have real data for evaluating the accuracy of various tools. If anyone knows any good resource (perhaps csv, xls files or any other format) I would be very thankful for a suggestion.

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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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  • Why can't I access a webserver through a load balancer on my local network?

    - by Karptonite
    When I try to use curl (or wget, lynx, etc) to connect from a server on our local network to our website, which is on a local server behind a CoyotePoint load balancer, curl fails. Ping does not have this problem. When I curl directly to any of the servers behind that load balancer (from and to the same local network), I also have no problem. It doesn't matter whether the local server I'm curling from is behind the load balancer or not. Does anyone have any idea why I can't access my webserver through the load balancer on my local network?

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  • C# Performance on Errors

    - by pm_2
    It would appear that catching an error is slower that performing a check prior to the error (for example a TryParse). The related questions that prompt this observation are here and here. Can anyone tell me why this is so - why is it more costly to catch an error that to perform one or many checks of the data to prevent the error?

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  • SQL SERVER – Faster SQL Server Databases and Applications – Power and Control with SafePeak Caching Options

    - by Pinal Dave
    Update: This blog post is written based on the SafePeak, which is available for free download. Today, I’d like to examine more closely one of my preferred technologies for accelerating SQL Server databases, SafePeak. Safepeak’s software provides a variety of advanced data caching options, techniques and tools to accelerate the performance and scalability of SQL Server databases and applications. I’d like to look more closely at some of these options, as some of these capabilities could help you address lagging database and performance on your systems. To better understand the available options, it is best to start by understanding the difference between the usual “Basic Caching” vs. SafePeak’s “Dynamic Caching”. Basic Caching Basic Caching (or the stale and static cache) is an ability to put the results from a query into cache for a certain period of time. It is based on TTL, or Time-to-live, and is designed to stay in cache no matter what happens to the data. For example, although the actual data can be modified due to DML commands (update/insert/delete), the cache will still hold the same obsolete query data. Meaning that with the Basic Caching is really static / stale cache.  As you can tell, this approach has its limitations. Dynamic Caching Dynamic Caching (or the non-stale cache) is an ability to put the results from a query into cache while maintaining the cache transaction awareness looking for possible data modifications. The modifications can come as a result of: DML commands (update/insert/delete), indirect modifications due to triggers on other tables, executions of stored procedures with internal DML commands complex cases of stored procedures with multiple levels of internal stored procedures logic. When data modification commands arrive, the caching system identifies the related cache items and evicts them from cache immediately. In the dynamic caching option the TTL setting still exists, although its importance is reduced, since the main factor for cache invalidation (or cache eviction) become the actual data updates commands. Now that we have a basic understanding of the differences between “basic” and “dynamic” caching, let’s dive in deeper. SafePeak: A comprehensive and versatile caching platform SafePeak comes with a wide range of caching options. Some of SafePeak’s caching options are automated, while others require manual configuration. Together they provide a complete solution for IT and Data managers to reach excellent performance acceleration and application scalability for  a wide range of business cases and applications. Automated caching of SQL Queries: Fully/semi-automated caching of all “read” SQL queries, containing any types of data, including Blobs, XMLs, Texts as well as all other standard data types. SafePeak automatically analyzes the incoming queries, categorizes them into SQL Patterns, identifying directly and indirectly accessed tables, views, functions and stored procedures; Automated caching of Stored Procedures: Fully or semi-automated caching of all read” stored procedures, including procedures with complex sub-procedure logic as well as procedures with complex dynamic SQL code. All procedures are analyzed in advance by SafePeak’s  Metadata-Learning process, their SQL schemas are parsed – resulting with a full understanding of the underlying code, objects dependencies (tables, views, functions, sub-procedures) enabling automated or semi-automated (manually review and activate by a mouse-click) cache activation, with full understanding of the transaction logic for cache real-time invalidation; Transaction aware cache: Automated cache awareness for SQL transactions (SQL and in-procs); Dynamic SQL Caching: Procedures with dynamic SQL are pre-parsed, enabling easy cache configuration, eliminating SQL Server load for parsing time and delivering high response time value even in most complicated use-cases; Fully Automated Caching: SQL Patterns (including SQL queries and stored procedures) that are categorized by SafePeak as “read and deterministic” are automatically activated for caching; Semi-Automated Caching: SQL Patterns categorized as “Read and Non deterministic” are patterns of SQL queries and stored procedures that contain reference to non-deterministic functions, like getdate(). Such SQL Patterns are reviewed by the SafePeak administrator and in usually most of them are activated manually for caching (point and click activation); Fully Dynamic Caching: Automated detection of all dependent tables in each SQL Pattern, with automated real-time eviction of the relevant cache items in the event of “write” commands (a DML or a stored procedure) to one of relevant tables. A default setting; Semi Dynamic Caching: A manual cache configuration option enabling reducing the sensitivity of specific SQL Patterns to “write” commands to certain tables/views. An optimization technique relevant for cases when the query data is either known to be static (like archive order details), or when the application sensitivity to fresh data is not critical and can be stale for short period of time (gaining better performance and reduced load); Scheduled Cache Eviction: A manual cache configuration option enabling scheduling SQL Pattern cache eviction based on certain time(s) during a day. A very useful optimization technique when (for example) certain SQL Patterns can be cached but are time sensitive. Example: “select customers that today is their birthday”, an SQL with getdate() function, which can and should be cached, but the data stays relevant only until 00:00 (midnight); Parsing Exceptions Management: Stored procedures that were not fully parsed by SafePeak (due to too complex dynamic SQL or unfamiliar syntax), are signed as “Dynamic Objects” with highest transaction safety settings (such as: Full global cache eviction, DDL Check = lock cache and check for schema changes, and more). The SafePeak solution points the user to the Dynamic Objects that are important for cache effectiveness, provides easy configuration interface, allowing you to improve cache hits and reduce cache global evictions. Usually this is the first configuration in a deployment; Overriding Settings of Stored Procedures: Override the settings of stored procedures (or other object types) for cache optimization. For example, in case a stored procedure SP1 has an “insert” into table T1, it will not be allowed to be cached. However, it is possible that T1 is just a “logging or instrumentation” table left by developers. By overriding the settings a user can allow caching of the problematic stored procedure; Advanced Cache Warm-Up: Creating an XML-based list of queries and stored procedure (with lists of parameters) for periodically automated pre-fetching and caching. An advanced tool allowing you to handle more rare but very performance sensitive queries pre-fetch them into cache allowing high performance for users’ data access; Configuration Driven by Deep SQL Analytics: All SQL queries are continuously logged and analyzed, providing users with deep SQL Analytics and Performance Monitoring. Reduce troubleshooting from days to minutes with database objects and SQL Patterns heat-map. The performance driven configuration helps you to focus on the most important settings that bring you the highest performance gains. Use of SafePeak SQL Analytics allows continuous performance monitoring and analysis, easy identification of bottlenecks of both real-time and historical data; Cloud Ready: Available for instant deployment on Amazon Web Services (AWS). As you can see, there are many options to configure SafePeak’s SQL Server database and application acceleration caching technology to best fit a lot of situations. If you’re not familiar with their technology, they offer free-trial software you can download that comes with a free “help session” to help get you started. You can access the free trial here. Also, SafePeak is available to use on Amazon Cloud. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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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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  • 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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  • Performance issues with repeatable loops as control part

    - by djerry
    Hey guys, In my application, i need to show made calls to the user. The user can arrange some filters, according to what they want to see. The problem is that i find it quite hard to filter the calls without losing performance. This is what i am using now : private void ProcessFilterChoice() { _filteredCalls = ServiceConnector.ServiceConnector.SingletonServiceConnector.Proxy.GetAllCalls().ToList(); if (cboOutgoingIncoming.SelectedIndex > -1) GetFilterPartOutgoingIncoming(); if (cboInternExtern.SelectedIndex > -1) GetFilterPartInternExtern(); if (cboDateFilter.SelectedIndex > -1) GetFilteredCallsByDate(); wbPdf.Source = null; btnPrint.Content = "Pdf preview"; } private void GetFilterPartOutgoingIncoming() { if (cboOutgoingIncoming.SelectedItem.ToString().Equals("Outgoing")) for (int i = _filteredCalls.Count - 1; i > -1; i--) { if (_filteredCalls[i].Caller.E164.Length > 4 || _filteredCalls[i].Caller.E164.Equals("0")) _filteredCalls.RemoveAt(i); } else if (cboOutgoingIncoming.SelectedItem.ToString().Equals("Incoming")) for (int i = _filteredCalls.Count - 1; i > -1; i--) { if (_filteredCalls[i].Called.E164.Length > 4 || _filteredCalls[i].Called.E164.Equals("0")) _filteredCalls.RemoveAt(i); } } private void GetFilterPartInternExtern() { if (cboInternExtern.SelectedItem.ToString().Equals("Intern")) for (int i = _filteredCalls.Count - 1; i > -1; i--) { if (_filteredCalls[i].Called.E164.Length > 4 || _filteredCalls[i].Caller.E164.Length > 4 || _filteredCalls[i].Caller.E164.Equals("0")) _filteredCalls.RemoveAt(i); } else if (cboInternExtern.SelectedItem.ToString().Equals("Extern")) for (int i = _filteredCalls.Count - 1; i > -1; i--) { if ((_filteredCalls[i].Called.E164.Length < 5 && _filteredCalls[i].Caller.E164.Length < 5) || _filteredCalls[i].Called.E164.Equals("0")) _filteredCalls.RemoveAt(i); } } private void GetFilteredCallsByDate() { DateTime period = DateTime.Now; switch (cboDateFilter.SelectedItem.ToString()) { case "Today": period = DateTime.Today; break; case "Last week": period = DateTime.Today.Subtract(new TimeSpan(7, 0, 0, 0)); break; case "Last month": period = DateTime.Today.AddMonths(-1); break; case "Last year": period = DateTime.Today.AddYears(-1); break; default: return; } for (int i = _filteredCalls.Count - 1; i > -1; i--) { if (_filteredCalls[i].Start < period) _filteredCalls.RemoveAt(i); } } _filtered calls is a list of "calls". Calls is a class that looks like this : [DataContract] public class Call { private User caller, called; private DateTime start, end; private string conferenceId; private int id; private bool isNew = false; [DataMember] public bool IsNew { get { return isNew; } set { isNew = value; } } [DataMember] public int Id { get { return id; } set { id = value; } } [DataMember] public string ConferenceId { get { return conferenceId; } set { conferenceId = value; } } [DataMember] public DateTime End { get { return end; } set { end = value; } } [DataMember] public DateTime Start { get { return start; } set { start = value; } } [DataMember] public User Called { get { return called; } set { called = value; } } [DataMember] public User Caller { get { return caller; } set { caller = value; } } Can anyone direct me to a better solution or make some suggestions.

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  • fast load big object graph from DB

    - by Famos
    Hi I have my own data structure written in C# like: public class ElectricScheme { public List<Element> Elements { get; set; } public List<Net> Nets { get; set; } } public class Element { public string IdName { get; set; } public string Func { get; set; } public string Name { get; set; } public BaseElementType Type { get; set; } public List<Pin> Pins { get; set; } } public class Pin { public string IdName { get; set; } public string Name { get; set; } public BasePinType PinType { get; set; } public BasePinDirection PinDirection { get; set; } } public class Net { public string IdName { get; set; } public string Name { get; set; } public List<Tuple<Element,Pin>> ConnectionPoints { get; set; } } Where Elements count ~19000, each element contain =3 Pin, Nets count ~20000, each net contain =3 pair (Element, Pin) Parse txt (file size ~17mb) file takes 5 minutes. Serilization / Deserialization by default serializer ~3 minutes. Load from DB 20 minutes and not loaded... I use Entity Framework like public ElectricScheme LoadScheme(int schemeId) { var eScheme = (from s in container.ElectricSchemesSet where s.IdElectricScheme.Equals(schemeId) select s).FirstOrDefault(); if (eScheme == null) return null; container.LoadProperty(eScheme, "Elements"); container.LoadProperty(eScheme, "Nets"); container.LoadProperty(eScheme, "Elements.Pins"); return eScheme; } The problem is dependencies between Element and Pin... (for ~19000 elements ~95000 pins) Any ideas?

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  • Big Data: Size isn’t everything

    - by Simon Elliston Ball
    Big Data has a big problem; it’s the word “Big”. These days, a quick Google search will uncover terabytes of negative opinion about the futility of relying on huge volumes of data to produce magical, meaningful insight. There are also many clichéd but correct assertions about the difficulties of correlation versus causation, in massive data sets. In reading some of these pieces, I begin to understand how climatologists must feel when people complain ironically about “global warming” during snowfall. Big Data has a name problem. There is a lot more to it than size. Shape, Speed, and…err…Veracity are also key elements (now I understand why Gartner and the gang went with V’s instead of S’s). The need to handle data of different shapes (Variety) is not new. Data developers have always had to mold strange-shaped data into our reporting systems, integrating with semi-structured sources, and even straying into full-text searching. However, what we lacked was an easy way to add semi-structured and unstructured data to our arsenal. New “Big Data” tools such as MongoDB, and other NoSQL (Not Only SQL) databases, or a graph database like Neo4J, fill this gap. Still, to many, they simply introduce noise to the clean signal that is their sensibly normalized data structures. What about speed (Velocity)? It’s not just high frequency trading that generates data faster than a single system can handle. Many other applications need to make trade-offs that traditional databases won’t, in order to cope with high data insert speeds, or to extract quickly the required information from data streams. Unfortunately, many people equate Big Data with the Hadoop platform, whose batch driven queries and job processing queues have little to do with “velocity”. StreamInsight, Esper and Tibco BusinessEvents are examples of Big Data tools designed to handle high-velocity data streams. Again, the name doesn’t do the discipline of Big Data any favors. Ultimately, though, does analyzing fast moving data produce insights as useful as the ones we get through a more considered approach, enabled by traditional BI? Finally, we have Veracity and Value. In many ways, these additions to the classic Volume, Velocity and Variety trio acknowledge the criticism that without high-quality data and genuinely valuable outputs then data, big or otherwise, is worthless. As a discipline, Big Data has recognized this, and data quality and cleaning tools are starting to appear to support it. Rather than simply decrying the irrelevance of Volume, we need as a profession to focus how to improve Veracity and Value. Perhaps we should just declare the ‘Big’ silent, embrace these new data tools and help develop better practices for their use, just as we did the good old RDBMS? What does Big Data mean to you? Which V gives your business the most pain, or the most value? Do you see these new tools as a useful addition to the BI toolbox, or are they just enabling a dangerous trend to find ghosts in the noise?

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  • How to load with "simplegame" mode after cooking?

    - by Emran Bayati
    i got a problem after i cook my game in Frontend (udk) ! Every thing is ok during the Cook operation i mean i compiled my scripts,it's ok there's no problem in this step,and i cooked my packages it's ok too ! no problem just like last step. so before i package the whole game;i'll lunch it and see if every thing is ok ! but when i load the game it'll load whit the Unreal tournament default game type ! i mean i don't want it to load with this type ! i want it to load with the "simplegame" game type ! i need to say that i set game type to "simplegame" in the Editor from View world Properties Game Type! but still it's loading with Ut game mode when i cook the game ! I just want load my game in "Simplegame" mode after cooked. if any one can help ! plz help ! Tnx Alot Emran Bayati

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  • Know your Data Lineage

    - by Simon Elliston Ball
    An academic paper without the footnotes isn’t an academic paper. Journalists wouldn’t base a news article on facts that they can’t verify. So why would anyone publish reports without being able to say where the data has come from and be confident of its quality, in other words, without knowing its lineage. (sometimes referred to as ‘provenance’ or ‘pedigree’) The number and variety of data sources, both traditional and new, increases inexorably. Data comes clean or dirty, processed or raw, unimpeachable or entirely fabricated. On its journey to our report, from its source, the data can travel through a network of interconnected pipes, passing through numerous distinct systems, each managed by different people. At each point along the pipeline, it can be changed, filtered, aggregated and combined. When the data finally emerges, how can we be sure that it is right? How can we be certain that no part of the data collection was based on incorrect assumptions, that key data points haven’t been left out, or that the sources are good? Even when we’re using data science to give us an approximate or probable answer, we cannot have any confidence in the results without confidence in the data from which it came. You need to know what has been done to your data, where it came from, and who is responsible for each stage of the analysis. This information represents your data lineage; it is your stack-trace. If you’re an analyst, suspicious of a number, it tells you why the number is there and how it got there. If you’re a developer, working on a pipeline, it provides the context you need to track down the bug. If you’re a manager, or an auditor, it lets you know the right things are being done. Lineage tracking is part of good data governance. Most audit and lineage systems require you to buy into their whole structure. If you are using Hadoop for your data storage and processing, then tools like Falcon allow you to track lineage, as long as you are using Falcon to write and run the pipeline. It can mean learning a new way of running your jobs (or using some sort of proxy), and even a distinct way of writing your queries. Other Hadoop tools provide a lot of operational and audit information, spread throughout the many logs produced by Hive, Sqoop, MapReduce and all the various moving parts that make up the eco-system. To get a full picture of what’s going on in your Hadoop system you need to capture both Falcon lineage and the data-exhaust of other tools that Falcon can’t orchestrate. However, the problem is bigger even that that. Often, Hadoop is just one piece in a larger processing workflow. The next step of the challenge is how you bind together the lineage metadata describing what happened before and after Hadoop, where ‘after’ could be  a data analysis environment like R, an application, or even directly into an end-user tool such as Tableau or Excel. One possibility is to push as much as you can of your key analytics into Hadoop, but would you give up the power, and familiarity of your existing tools in return for a reliable way of tracking lineage? Lineage and auditing should work consistently, automatically and quietly, allowing users to access their data with any tool they require to use. The real solution, therefore, is to create a consistent method by which to bring lineage data from these data various disparate sources into the data analysis platform that you use, rather than being forced to use the tool that manages the pipeline for the lineage and a different tool for the data analysis. The key is to keep your logs, keep your audit data, from every source, bring them together and use the data analysis tools to trace the paths from raw data to the answer that data analysis provides.

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  • Visual studio: Package 'CcVsiLanService Package' failed to load

    - by Engin Bulanik
    During my compile I had this error string. But the compile finished successfully anyway. Microsoft (R) Visual Studio Version 9.0.30729.1. Copyright (C) Microsoft Corp. All rights reserved. Package 'CcVsiLanService Package' failed to load. Package 'CcVsiLanService Package' failed to load. Package 'CcVsiLanService Package' failed to load. Package 'CcVsiLanService Package' failed to load. Package 'CcVsiLanService Package' failed to load. I googled it but could not find any useful information about CcVsiLanService. Do you have any idea what it is? Why anyone needs it?

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  • Propel-load-data is causing an error

    - by Jon Winstanley
    I am trying to load fixtures but myproject is erroring at the CLI and starting the indexer process. I have tried: Rebuilding the schema and model Emptying the database and starting again Clearing the cache Validating the YML file and trying much simpler data-dumps My platform is Symfony 1.0 on Windows Some also seems to have had the same issue in the past. C:\web\my_project>symfony propel-load-data backend >> propel load data from "C:\web\my_project\data\fixtures" PHP Warning: session_start(): Cannot send session cookie - headers already sent by (output started at C:\php\PEAR\symfony\vendor\pake\pakeFunction.php:366) in C:\php\PEAR\symfony\storage\sfSessionStorage.class.php on line 77 Warning: session_start(): Cannot send session cookie - headers already sent by (output started at C:\php\PEAR\symfony\vendor\pake\pakeFunction.php:366) in C:\php\PEAR\symfony\storage\sfSessionStorage.class.php on line 77 PHP Warning: session_start(): Cannot send session cache limiter - headers already sent (output started at C:\php\PEAR\symfony\vendor\pake\pakeFunction.php:366) in C:\php\PEAR\symfony\storage\sfSessionStorage.class.php on line 77 Warning: session_start(): Cannot send session cache limiter - headers already sent (output started at C:\php\PEAR\symfony\vendor\pake\pakeFunction.php:366) in C:\php\PEAR\symfony\storage\sfSessionStorage.class.php on line 77

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  • What the performance impact of enabling WebSphere PMI

    - by Andrew Whitehouse
    I am currently looking at some JProfiler traces from our WebSphere-based application, and am noticing that a significant amount of CPU time is being spent in the class com.ibm.io.async.AsyncLibrary.getCompletionData2. I am guessing, but I am wondering whether this is PMI-related (and we do have this enabled). My knowledge of PMI is limited, as this is managed by another team. Is it expected that PMI can have this sort of impact? (If so) Is the only option to turn it off completely? Or are there some types of data capture that have a particularly high overhead?

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  • Web service performance testing plan, Microsoft .NET WS, SQL

    - by zxed
    Trying to answer a question to come up with a testing plan. It has to do with using a website and/or webservice that queries a sql server to get data and display to user. * Solution must be able to handle an estimated 2000 users, approximately 700 concurrent users, 10,000 + website hits a month. Database calls should handle 100,000 queries via the website/webservice a month. The system is used at multiple times during a 24 hour period; however networking and bandwidth traffic decreases after 5 pm * two windows 2003 servers are used, one for web, another for sql. Both are located in the same room. User access is varied and users can be far/near (its a centralized system), users access via www

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