Search Results

Search found 97855 results on 3915 pages for 'code performance'.

Page 43/3915 | < Previous Page | 39 40 41 42 43 44 45 46 47 48 49 50  | Next Page >

  • Make The Web Fast - The HAR Show: Capturing and Analyzing performance data with HTTP Archive format

    Make The Web Fast - The HAR Show: Capturing and Analyzing performance data with HTTP Archive format Need a flexible format to record, export, and analyze network performance data? Well, that's exactly what the HTTP Archive format (HAR) is designed to do! Even better, did you know that Chrome DevTools supports it? In this episode we'll take a deep dive into the format (as you'll see, its very simple), and explore the many different ways it can help you capture and analyze your sites performance. Join +Ilya Grigorik and +Peter Lubbers to find out how to capture HAR network traces in Chrome, visualize the data via an online tool, share the reports with your clients and coworkers, automate the logging and capture of HAR data for your build scripts, and even adapt it to server-side analysis use cases! Yes, a rapid fire session of awesome demos - see you there. From: GoogleDevelopers Views: 0 6 ratings Time: 00:00 More in Science & Technology

    Read the article

  • Performance issues when using SSD for a developer notebook (WAMP/LAMP stack)?

    - by András Szepesházi
    I'm a web application developer using my notebook as a standalone development environment (WAMP stack). I just switched from a Core2-duo Vista 32 bit notebook with 2Gb RAM and SATA HDD, to an i5-2520M Win7 64 bit with 4Gb RAM and 128 GB SDD (Corsair P3 128). My initial experience was what I expected, fast boot, quick load of all the applications (Eclipse takes now 5 seconds as opposed to 30s on my old notebook), overall great experience. Then I started to build up my development stack, both as LAMP (using VirtualBox with a debian guest) and WAMP (windows native apache + mysql + php). I wanted to compare those two. This still all worked great out, then I started to pull in my projects to these stacks. And here came the nasty surprise, one of those projects produced a lot worse response times than on my old notebook (that was true for both the VirtualBox and WAMP stack). Apache, php and mysql configurations were practically identical in all environments. I started to do a lot of benchmarking and profiling, and here is what I've found: All general benchmarks (Performance Test 7.0, HDTune Pro, wPrime2 and some more) gave a big advantage to the new notebook. Nothing surprising here. Disc specific tests showed that read/write operations peaked around 380M/160M for the SSD, and all the different sized block operations also performed very well. Started apache performance benchmarking with Apache Benchmark for a small static html file (10 concurrent threads, 500 iterations). Old notebook: min 47ms, median 111ms, max 156ms New WAMP stack: min 71ms, median 135ms, max 296ms New LAMP stack (in VirtualBox): min 6ms, median 46ms, max 175ms Right here I don't get why the native WAMP stack performed so bad, but at least the LAMP environment brought the expected speed. Apache performance measurement for non-cached php content. The php runs a loop of 1000 and generates sha1(uniqid()) inisde. Again, 10 concurrent threads, 500 iterations were used for the benchmark. Old notebook: min 0ms, median 39ms, max 218ms New WAMP stack: min 20ms, median 61ms, max 186ms New LAMP stack (in VirtualBox): min 124ms, median 704ms, max 2463ms What the hell? The new LAMP performed miserably, and even the new native WAMP was outperformed by the old notebook. php + mysql test. The test consists of connecting to a database and reading a single record form a table using INNER JOIN on 3 more (indexed) tables, repeated 100 times within a loop. Databases were identical. 10 concurrent threads, 100 iterations were used for the benchmark. Old notebook: min 1201ms, median 1734ms, max 3728ms New WAMP stack: min 367ms, median 675ms, max 1893ms New LAMP stack (in VirtualBox): min 1410ms, median 3659ms, max 5045ms And the same test with concurrency set to 1 (instead of 10): Old notebook: min 1201ms, median 1261ms, max 1357ms New WAMP stack: min 399ms, median 483ms, max 539ms New LAMP stack (in VirtualBox): min 285ms, median 348ms, max 444ms Strictly for my purposes, as I'm using a self contained development environment (= low concurrency) I could be satisfied with the second test's result. Though I have no idea why the VirtualBox environment performed so bad with higher concurrency. Finally I performed a test of including many php files. The application that I mentioned at the beginning, the one that was performing so bad, has a heavy bootstrap, loads hundreds of small library and configuration files while initializing. So this test does nothing else just includes about 100 files. Concurrency set to 1, 100 iterations: Old notebook: min 140ms, median 168ms, max 406ms New WAMP stack: min 434ms, median 488ms, max 604ms New LAMP stack (in VirtualBox): min 413ms, median 1040ms, max 1921ms Even if I consider that VirtualBox reached those files via shared folders, and that slows things down a bit, I still don't see how could the old notebook outperform so heavily both new configurations. And I think this is the real root of the slow performance, as the application uses even more includes, and the whole bootstrap will occur several times within a page request (for each ajax call, for example). To sum it up, here I am with a brand new high-performance notebook that loads the same page in 20 seconds, that my old notebook can do in 5-7 seconds. Needless to say, I'm not a very happy person right now. Why do you think I experience these poor performance values? What are my options to remedy this situation?

    Read the article

  • How to efficiently map tokens to code in a script interpreter?

    - by lithander
    I'm writing an interpreter for a simple scripting language where each line is a complete, executable command. (Like the instructions in assembler) When parsing a line I have to map the requested command to actual code. My current solution looks like this: std::string op, param1, param2; //parse line, identify op, param1, param2 ... //call command specific code if(op == "MOV") ExecuteMov(AsNumber(param1)); else if(op == "ROT") ExecuteRot(AsNumber(param1)); else if(op == "SZE") ExecuteSze(AsNumber(param1)); else if(op == "POS") ExecutePos((AsNumber(param1), AsNumber(param2)); else if(op == "DIR") ExecuteDir((AsNumber(param1), AsNumber(param2)); else if(op == "SET") ExecuteSet(param1, AsNumber(param2)); else if(op == "EVL") ... The more commands are supported the more string comparisions I'll have to do to identify and call the associated method. Can you point me to a more efficient implementation in the described scenario?

    Read the article

  • Implementing set of processes in a stored procedure or through the code?

    - by just_name
    I want to know what's the suitable method to implement the following case (best practice). If i make a set of processes like this : 1- select data from set of DB tables. 2- loop on the selected result . 3- Make some checks on each iteration . 4- Insert the result in another table . Implementing the previous steps in a stored procedure or in a transaction through my code (asp.net) . ? Concerning the performance , security and reliability issues .

    Read the article

  • How do I maintain a really poorly written code base?

    - by onlineapplab.com
    Recently I got hired to work on existing web application because of NDA I'm not at liberty to disclose any details but this application is working online in sort of a beta testing stage before official launch. We have a few hundred users right now but this number is supposed to significantly increase after official launch. The application is written in PHP (but it is irrelevant to my question) and is running on a dual xeon processor standalone server with severe performance problems. I have seen a lot of bad PHP code but this really sets new standards, especially knowing how much time and money was invested in developing it. it is as badly coded as possible there is PHP, HTML, SQL mixed together and code is repeated whenever it is necessary (especially SQL queries). there are not any functions used, not mentioning any OOP there are four versions of the app (desktop, iPhone, Android + other mobile) each version has pretty much the same functionality but was created by copying the whole code base, so now there are some differences between each version and it is really hard to maintain the database is really badly designed, which is causing severe performance problems also for fixing some errors in PHP code there is a lot of database triggers used which are updating data on SELECT and on INSERT so any testing is a nightmare Basically, any sin of a bad programming you can imagine is there for example it is not only possible to use SQL injections in literally every place but you can log into app if you use a login which doesn't exist and an empty password. The team which created this app is not working on it any more and there is an outsourced team which suggested that there are some problems but was never willing to deal with the elephant in the room partially because they've got a very comfortable contract and partially due to lack of skills (just my opinion). My job was supposed to be fixing some performance problems and extending existing functionality but first thing I was asked to do was a review of the existing code base. I've made my review and it was quite a shock for the management but my conclusions were after some time finally confirmed by other programmers. Management made it clear that it is not possible to start rewriting this app from scratch (which in my opinion should be done). We have to maintain its operable state and at the same time fix performance errors and extend the functionality. My question is, as I don't want just to patch the existing code, how to transform this into properly written app while keeping the existing code working at the same time? My plan is: Unify four existing versions into common code base (fixing only most obvious errors). Redesign db and use triggers to populate it with data (so data will be maintained in two formats at the same time) All new functionality will be written as separate project. Step by step transfer existing functionality into the new project After some time everything will be in the new project Some explanation about #2, right now it is practically impossible to make any updates in existing db any change requires reviewing whole code and making changes in many places. Is such plan feasible at all? Another solution is to walk away and leave the headache to someone else.

    Read the article

  • What are the Crappy Code Games - Why should I attend?

    - by simonsabin
    This is part of a series on the Crappy Code Games The background Who can enter? What are the challenges? What are the prizes? Why should I attend? Tips on how to win Why should I attend? The crappy code games isn’t all about winning, its also about having a good time and learning about SQL Server. Even if you don’t want to enter the competition I think its valuable for you to come along to the heats and the final in Brighton as a chance to meat SQL Server bods and also to learn about SQL Server and...(read more)

    Read the article

  • Google I/O 2010 - Architecting for performance with GWT

    Google I/O 2010 - Architecting for performance with GWT Google I/O 2010 - Architecting for performance with GWT GWT 201 Joel Webber, Adam Schuck Modern web applications are quickly evolving to an architecture that has to account for the performance characteristics of the client, the server, and the global network connecting them. Should you render HTML on the server or build DOM structures with JS in the browser, or both? This session discusses this, as well as several other key architectural considerations to keep in mind when building your Next Big Thing. For all I/O 2010 sessions, please go to code.google.com From: GoogleDevelopers Views: 9 1 ratings Time: 01:01:09 More in Science & Technology

    Read the article

  • How can you know what is w3wp.exe doing? (or how to diagnose a performance problem)

    - by Daniel Magliola
    I'm having a performance problem in a site we've made, and I'm not exactly sure how to start diagnosing it. The short description is: We have a very small site (http://hearablog.com) with very little traffic, in a crappy dedicated server, CPU is always very high, sometimes it stays at 100% for minutes, and w3wp.exe is taking most of it. A typical scenario is w3wp.exe takes 60%, and SQL Server takes about 30%. Our DB is pretty small too. Long description and more details: The site is hosted in a very crappy server by Cari.Net. From the beginning we had the feeling that the server didn't quite behave correctly, like some things would take just too long, so this could be a configuration problem from the get go. It may also be that we are getting a virtual server while we're supposed to have a dedicated one, although we have no evidence that'd indicate this, except for the fact that the server tends to be quite slow. The server is Windows 2008 Standard 64-bit, with SQL 2008 Express Hardware is a Celeron 2.80 GHz, 1Gb RAM The website is developed in ASP.Net MVC, using Entity Framework for data access. Now, this is pretty crappy hardware, but i've had other servers with these guys, with equivalent (or worse) HW, and performance is much better than this one. That said, the other servers have W2003 and SQL2005, and I'm using ASP.Net "WebForms" 2.0, no MVC, no LINQ, no EF; so I'm not sure whether going to 2008 / the other stuff means a big performance penalty is expected. I'm serving MP3 files (5-20 Mb) regularly, which is a slightly unusual load, maybe that is causing some kind of problems? Would that cause w3wp to use a lot of CPU? Disk usage seems very low. Memory is usually around 90%, but disk usage seems to indicate it's not paging much. I get tons of e-mails every day about SQL timeouts, for queries taking over 30 seconds, although all our queries are pretty straightforward (or should be, but EF may be screwing it up). This is what resource monitor looks like in one of these "sprints" of 100% CPU, in case there's anything useful there. And a snapshot of some performance counters: Now, what confuses me very much is that CPU usage of w3wp is just so high. It shouldn't be doing much really... So my questions are... Is there any way of finding out "what" it is doing? Maybe even profile it? Any performance counters I should be looking at? Is this to be expected given this hardware/software configuration? Is this could be cause by some kind of configuration failure, where would you start looking? Thank you VERY much. Daniel Magliola

    Read the article

  • Is this slow WPF TextBlock performance expected?

    - by Ben Schoepke
    Hi, I am doing some benchmarking to determine if I can use WPF for a new product. However, early performance results are disappointing. I made a quick app that uses data binding to display a bunch of random text inside of a list box every 100 ms and it was eating up ~15% CPU. So I made another quick app that skipped the data binding/data template scheme and does nothing but update 10 TextBlocks that are inside of a ListBox every 100 ms (the actual product wouldn't require 100 ms updates, more like 500 ms max, but this is a stress test). I'm still seeing ~10-15% CPU usage. Why is this so high? Is it because of all the garbage strings? Here's the XAML: <Grid> <ListBox x:Name="numericsListBox"> <ListBox.Resources> <Style TargetType="TextBlock"> <Setter Property="FontSize" Value="48"/> <Setter Property="Width" Value="300"/> </Style> </ListBox.Resources> <TextBlock/> <TextBlock/> <TextBlock/> <TextBlock/> <TextBlock/> <TextBlock/> <TextBlock/> <TextBlock/> <TextBlock/> <TextBlock/> </ListBox> </Grid> Here's the code behind: public partial class Window1 : Window { private int _count = 0; public Window1() { InitializeComponent(); } private void OnLoad(object sender, RoutedEventArgs e) { var t = new DispatcherTimer(TimeSpan.FromSeconds(0.1), DispatcherPriority.Normal, UpdateNumerics, Dispatcher); t.Start(); } private void UpdateNumerics(object sender, EventArgs e) { ++_count; foreach (object textBlock in numericsListBox.Items) { var t = textBlock as TextBlock; if (t != null) t.Text = _count.ToString(); } } } Any ideas for a better way to quickly render text? My computer: XP SP3, 2.26 GHz Core 2 Duo, 4 GB RAM, Intel 4500 HD integrated graphics. And that is an order of magnitude beefier than the hardware I'd need to develop for in the real product.

    Read the article

  • Performance of tokenizing CSS in PHP

    - by Boldewyn
    This is a noob question from someone who hasn't written a parser/lexer ever before. I'm writing a tokenizer/parser for CSS in PHP (please don't repeat with 'OMG, why in PHP?'). The syntax is written down by the W3C neatly here (CSS2.1) and here (CSS3, draft). It's a list of 21 possible tokens, that all (but two) cannot be represented as static strings. My current approach is to loop through an array containing the 21 patterns over and over again, do an if (preg_match()) and reduce the source string match by match. In principle this works really good. However, for a 1000 lines CSS string this takes something between 2 and 8 seconds, which is too much for my project. Now I'm banging my head how other parsers tokenize and parse CSS in fractions of seconds. OK, C is always faster than PHP, but nonetheless, are there any obvious D'Oh! s that I fell into? I made some optimizations, like checking for '@', '#' or '"' as the first char of the remaining string and applying only the relevant regexp then, but this hadn't brought any great performance boosts. My code (snippet) so far: $TOKENS = array( 'IDENT' => '...regexp...', 'ATKEYWORD' => '@...regexp...', 'String' => '"...regexp..."|\'...regexp...\'', //... ); $string = '...CSS source string...'; $stream = array(); // we reduce $string token by token while ($string != '') { $string = ltrim($string, " \t\r\n\f"); // unconsumed whitespace at the // start is insignificant but doing a trim reduces exec time by 25% $matches = array(); // loop through all possible tokens foreach ($TOKENS as $t => $p) { // The '&' is used as delimiter, because it isn't used anywhere in // the token regexps if (preg_match('&^'.$p.'&Su', $string, $matches)) { $stream[] = array($t, $matches[0]); $string = substr($string, strlen($matches[0])); // Yay! We found one that matches! continue 2; } } // if we come here, we have a syntax error and handle it somehow } // result: an array $stream consisting of arrays with // 0 => type of token // 1 => token content

    Read the article

  • Basis for claim that the number of bugs per line of code is constant regardless of the language used

    - by Matt R
    I've heard people say (although I can't recall who in particular) that the number of bugs per line of code is roughly constant regardless of what language is used. What is the research that backs this up? Edited to add: I don't have access to it, but apparently the authors of this paper "asked the question whether the number of bugs per lines of code (LOC) is the same for programs written in different programming languages or not."

    Read the article

  • Performance of looping over an Unboxed array in Haskell

    - by Joey Adams
    First of all, it's great. However, I came across a situation where my benchmarks turned up weird results. I am new to Haskell, and this is first time I've gotten my hands dirty with mutable arrays and Monads. The code below is based on this example. I wrote a generic monadic for function that takes numbers and a step function rather than a range (like forM_ does). I compared using my generic for function (Loop A) against embedding an equivalent recursive function (Loop B). Having Loop A is noticeably faster than having Loop B. Weirder, having both Loop A and B together is faster than having Loop B by itself (but slightly slower than Loop A by itself). Some possible explanations I can think of for the discrepancies. Note that these are just guesses: Something I haven't learned yet about how Haskell extracts results from monadic functions. Loop B faults the array in a less cache efficient manner than Loop A. Why? I made a dumb mistake; Loop A and Loop B are actually different. Note that in all 3 cases of having either or both Loop A and Loop B, the program produces the same output. Here is the code. I tested it with ghc -O2 for.hs using GHC version 6.10.4 . import Control.Monad import Control.Monad.ST import Data.Array.IArray import Data.Array.MArray import Data.Array.ST import Data.Array.Unboxed for :: (Num a, Ord a, Monad m) => a -> a -> (a -> a) -> (a -> m b) -> m () for start end step f = loop start where loop i | i <= end = do f i loop (step i) | otherwise = return () primesToNA :: Int -> UArray Int Bool primesToNA n = runSTUArray $ do a <- newArray (2,n) True :: ST s (STUArray s Int Bool) let sr = floor . (sqrt::Double->Double) . fromIntegral $ n+1 -- Loop A for 4 n (+ 2) $ \j -> writeArray a j False -- Loop B let f i | i <= n = do writeArray a i False f (i+2) | otherwise = return () in f 4 forM_ [3,5..sr] $ \i -> do si <- readArray a i when si $ forM_ [i*i,i*i+i+i..n] $ \j -> writeArray a j False return a primesTo :: Int -> [Int] primesTo n = [i | (i,p) <- assocs . primesToNA $ n, p] main = print $ primesTo 30000000

    Read the article

  • 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 ;)

    Read the article

  • Performance Problem with Clojure Array

    - by dbyrne
    This piece of code is very slow. Execution from the slime-repl on my netbook takes a couple minutes. Am I doing something wrong? (def test-array (make-array Integer/TYPE 400 400 3)) (doseq [x (range 400), y (range 400), z (range 3)] (aset test-array x y z 0))

    Read the article

  • weird performance in C++ (VC 2010)

    - by raicuandi
    Hello, I have this loop written in C++, that compiled with MSVC2010 takes a long time to run. (300ms) for (int i=0; i<h; i++) { for (int j=0; j<w; j++) { if (buf[i*w+j] > 0) { const int sy = max(0, i - hr); const int ey = min(h, i + hr + 1); const int sx = max(0, j - hr); const int ex = min(w, j + hr + 1); float val = 0; for (int k=sy; k < ey; k++) { for (int m=sx; m < ex; m++) { val += original[k*w + m] * ds[k - i + hr][m - j + hr]; } } heat_map[i*w + j] = val; } } } It seemed a bit strange to me, so I did some tests then changed a few bits to inline assembly: (specifically, the code that sums "val") for (int i=0; i<h; i++) { for (int j=0; j<w; j++) { if (buf[i*w+j] > 0) { const int sy = max(0, i - hr); const int ey = min(h, i + hr + 1); const int sx = max(0, j - hr); const int ex = min(w, j + hr + 1); __asm { fldz } for (int k=sy; k < ey; k++) { for (int m=sx; m < ex; m++) { float val = original[k*w + m] * ds[k - i + hr][m - j + hr]; __asm { fld val fadd } } } float val1; __asm { fstp val1 } heat_map[i*w + j] = val1; } } } Now it runs in half the time, 150ms. It does exactly the same thing, but why is it twice as quick? In both cases it was run in Release mode with optimizations on. Am I doing anything wrong in my original C++ code?

    Read the article

  • 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++?

    Read the article

  • 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.

    Read the article

  • 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

    Read the article

  • Loading PNGs into OpenGL performance issues - Java & JOGL much slower than C# & Tao.OpenGL

    - by Edward Cresswell
    I am noticing a large performance difference between Java & JOGL and C# & Tao.OpenGL when both loading PNGs from storage into memory, and when loading that BufferedImage (java) or Bitmap (C# - both are PNGs on hard drive) 'into' OpenGL. This difference is quite large, so I assumed I was doing something wrong, however after quite a lot of searching and trying different loading techniques I've been unable to reduce this difference. With Java I get an image loaded in 248ms and loaded into OpenGL in 728ms The same on C# takes 54ms to load the image, and 34ms to load/create texture. The image in question above is a PNG containing transparency, sized 7200x255, used for a 2D animated sprite. I realise the size is really quite ridiculous and am considering cutting up the sprite, however the large difference is still there (and confusing). On the Java side the code looks like this: BufferedImage image = ImageIO.read(new File(fileName)); texture = TextureIO.newTexture(image, false); texture.setTexParameteri(GL.GL_TEXTURE_MIN_FILTER, GL.GL_LINEAR); texture.setTexParameteri(GL.GL_TEXTURE_MAG_FILTER, GL.GL_LINEAR); The C# code uses: Bitmap t = new Bitmap(fileName); t.RotateFlip(RotateFlipType.RotateNoneFlipY); Rectangle r = new Rectangle(0, 0, t.Width, t.Height); BitmapData bd = t.LockBits(r, ImageLockMode.ReadOnly, PixelFormat.Format32bppArgb); Gl.glBindTexture(Gl.GL_TEXTURE_2D, tID); Gl.glTexImage2D(Gl.GL_TEXTURE_2D, 0, Gl.GL_RGBA, t.Width, t.Height, 0, Gl.GL_BGRA, Gl.GL_UNSIGNED_BYTE, bd.Scan0); Gl.glTexParameteri(Gl.GL_TEXTURE_2D, Gl.GL_TEXTURE_MIN_FILTER, Gl.GL_LINEAR); Gl.glTexParameteri(Gl.GL_TEXTURE_2D, Gl.GL_TEXTURE_MAG_FILTER, Gl.GL_LINEAR); t.UnlockBits(bd); t.Dispose(); After quite a lot of testing I can only come to the conclusion that Java/JOGL is just slower here - PNG reading might not be as quick, or that I'm still doing something wrong. Thanks. Edit2: I have found that creating a new BufferedImage with format TYPE_INT_ARGB_PRE decreases OpenGL texture load time by almost half - this includes having to create the new BufferedImage, getting the Graphics2D from it and then rendering the previously loaded image to it. Edit3: Benchmark results for 5 variations. I wrote a small benchmarking tool, the following results come from loading a set of 33 pngs, most are very wide, 5 times. testStart: ImageIO.read(file) -> TextureIO.newTexture(image) result: avg = 10250ms, total = 51251 testStart: ImageIO.read(bis) -> TextureIO.newTexture(image) result: avg = 10029ms, total = 50147 testStart: ImageIO.read(file) -> TextureIO.newTexture(argbImage) result: avg = 5343ms, total = 26717 testStart: ImageIO.read(bis) -> TextureIO.newTexture(argbImage) result: avg = 5534ms, total = 27673 testStart: TextureIO.newTexture(file) result: avg = 10395ms, total = 51979 ImageIO.read(bis) refers to the technique described in James Branigan's answer below. argbImage refers to the technique described in my previous edit: img = ImageIO.read(file); argbImg = new BufferedImage(img.getWidth(), img.getHeight(), TYPE_INT_ARGB_PRE); g = argbImg.createGraphics(); g.drawImage(img, 0, 0, null); texture = TextureIO.newTexture(argbImg, false); Any more methods of loading (either images from file, or images to OpenGL) would be appreciated, I will update these benchmarks.

    Read the article

  • Odd performance with C# Asynchronous server socket

    - by The.Anti.9
    I'm working on a web server in C# and I have it running on Asynchronous socket calls. The weird thing is that for some reason, when you start loading pages, the 3rd request is where the browser won't connect. It just keeps saying "Connecting..." and doesn't ever stop. If I hit stop. and then refresh, it will load again, but if I try another time after that it does the thing where it doesn't load again. And it continues in that cycle. I'm not really sure what is making it do that. The code is kind of hacked together from a couple of examples and some old code I had. Any miscellaneous tips would be helpful as well. Heres my little Listener class that handles everything (pastied here. thought it might be easier to read this way) using System; using System.Collections.Generic; using System.Net; using System.Net.Sockets; using System.Text; using System.Threading; namespace irek.Server { public class Listener { private int port; private Socket server; private Byte[] data = new Byte[2048]; static ManualResetEvent allDone = new ManualResetEvent(false); public Listener(int _port) { port = _port; } public void Run() { server = new Socket(AddressFamily.InterNetwork, SocketType.Stream, ProtocolType.Tcp); IPEndPoint iep = new IPEndPoint(IPAddress.Any, port); server.Bind(iep); Console.WriteLine("Server Initialized."); server.Listen(5); Console.WriteLine("Listening..."); while (true) { allDone.Reset(); server.BeginAccept(new AsyncCallback(AcceptCon), server); allDone.WaitOne(); } } private void AcceptCon(IAsyncResult iar) { allDone.Set(); Socket s = (Socket)iar.AsyncState; Socket s2 = s.EndAccept(iar); SocketStateObject state = new SocketStateObject(); state.workSocket = s2; s2.BeginReceive(state.buffer, 0, SocketStateObject.BUFFER_SIZE, 0, new AsyncCallback(Read), state); } private void Read(IAsyncResult iar) { try { SocketStateObject state = (SocketStateObject)iar.AsyncState; Socket s = state.workSocket; int read = s.EndReceive(iar); if (read > 0) { state.sb.Append(Encoding.ASCII.GetString(state.buffer, 0, read)); if (s.Available > 0) { s.BeginReceive(state.buffer, 0, SocketStateObject.BUFFER_SIZE, 0, new AsyncCallback(Read), state); return; } } if (state.sb.Length > 1) { string requestString = state.sb.ToString(); // HANDLE REQUEST HERE // Temporary response string resp = "<h1>It Works!</h1>"; string head = "HTTP/1.1 200 OK\r\nContent-Type: text/html;\r\nServer: irek\r\nContent-Length:"+resp.Length+"\r\n\r\n"; byte[] answer = Encoding.ASCII.GetBytes(head+resp); // end temp. state.workSocket.BeginSend(answer, 0, answer.Length, SocketFlags.None, new AsyncCallback(Send), state.workSocket); } } catch (Exception) { return; } } private void Send(IAsyncResult iar) { try { SocketStateObject state = (SocketStateObject)iar.AsyncState; int sent = state.workSocket.EndSend(iar); state.workSocket.Shutdown(SocketShutdown.Both); state.workSocket.Close(); } catch (Exception) { } return; } } } And my SocketStateObject: public class SocketStateObject { public Socket workSocket = null; public const int BUFFER_SIZE = 1024; public byte[] buffer = new byte[BUFFER_SIZE]; public StringBuilder sb = new StringBuilder(); }

    Read the article

  • 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 ; }

    Read the article

  • 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.

    Read the article

  • 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 ; }

    Read the article

  • Ado.net performance:What does SNIReadSync do?

    - by Beatles1692
    We have a query that takes 2 seconds to run in Sql Server Management Studio but it takes 13 seconds to be shown on a client screen. I used dotTrace to profile my source code and noticed there is this SNIReadSync method (part of ADO.net assemblies)that takes a lot of time to do its job(9 seconds).I ran my source over server so I could omit the network effects and the result was the same. It doesn't matter if I'm using OleDBConnection or SqlConnection. It doesn't matter if I'm using a DataReader or a DataSet. Connection pooling does not solve this issue(as my result shows). I googled this issue and I couldn't find an answer to the question that what this method is actually doing and how we can improve it. here's what I found on StakOverFlow that's not helpful either: http://stackoverflow.com/questions/1610874/snireadsync-executing-between-120-500-ms-for-a-simple-query-what-do-i-look-for

    Read the article

< Previous Page | 39 40 41 42 43 44 45 46 47 48 49 50  | Next Page >