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  • Efficient way to delete a line from a text file (C#)

    - by Valentin Vasilyev
    Hello. I need to delete a certain line from a text file. What is the most efficient way of doing this? File can be potentially large(over million records). Thank you. UPDATE: below is the code I'm currently using, but I'm not sure if it is good. internal void DeleteMarkedEntries() { string tempPath=Path.GetTempFileName(); using (var reader = new StreamReader(logPath)) { using (var writer = new StreamWriter(File.OpenWrite(tempPath))) { int counter = 0; while (!reader.EndOfStream) { if (!_deletedLines.Contains(counter)) { writer.WriteLine(reader.ReadLine()); } ++counter; } } } if (File.Exists(tempPath)) { File.Delete(logPath); File.Move(tempPath, logPath); } }

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  • .NET WebService IPC - Should it be done to minimise some expensive operations?

    - by Kyle
    I'm looking at a few different approaches to a problem: Client requests work, some stuff gets done, and a result (ok/error) is returned. A .NET web service definitely seems like the way to go, my only issue is that the "stuff" will involve building up and tearing down a session for each request. Does abstracting the "stuff" out to an app (which would keep a single session active, and process the request from the web service) seem like the right way to go? (and if so, what communication method) The work time is negligible, my concern is the hammering the transaction servers in question will probably get if I create/drop a session for each job. Is some form of IPC or socket based communication a feasible solution here? Thoughts/comments/experiences much appreciated. Edit: After a bit more research, it seems like hosting a WCF service in a Windows Service is probably a better way to go...

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  • Should I go with Varnish instead of nginx?

    - by gotts
    I really like nginx. But recently I've found that varnish gives you an opportunity to implement smart caching revers proxy layer(with URL purging). I have a cluster of mongrels which are pretty resource-intensive so if this caching layer can remove some load from mongrels this can be a great thing. I didn't find a way to implement the caching layer(with for application pages; static content is cacheable of course) same with nginx.. Should I use Varnish instead? What would you recommend?

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  • Prepending to a multi-gigabyte file.

    - by dafmetal
    What would be the most performant way to prepend a single character to a multi-gigabyte file (in my practical case, a 40GB file). There is no limitation on the implementation to do this. Meaning it can be through a tool, a shell script, a program in any programming language, ...

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  • Best method to select an object from another unknown jQuery object

    - by Yosi
    Lets say I have a jQuery object/collection stored in a variable named obj, which should contain a DOM element with an id named target. I don't know in advance if target will be a child in obj, i.e.: obj = $('<div id="parent"><div id="target"></div></div>'); or if obj equals target, i.e.: obj = $('<div id="target"></div>'); or if target is a top-level element inside obj, i.e.: obj = $('<div id="target"/><span id="other"/>'); I need a way to select target from obj, but I don't know in advance when to use .find and when to use .filter. What would be the fastest and/or most concise method of extracting target from obj? What I've come up with is: var $target = obj.find("#target").add(obj.filter("#target")); UPDATE I'm adding solutions to a JSPERF test page to see which one is the best. Currently my solution is still the fastest. Here is the link, please run the tests so that we'll have more data: http://jsperf.com/jquery-selecting-objects

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  • Why are difference lists more efficient than regular concatenation?

    - by Craig Innes
    I am currently working my way through the Learn you a haskell book online, and have come to a chapter where the author is explaining that some list concatenations can be ineffiecient: For example ((((a ++ b) ++ c) ++ d) ++ e) ++ f Is supposedly inefficient. The solution the author comes up with is to use 'difference lists' defined as newtype DiffList a = DiffList {getDiffList :: [a] -> [a] } instance Monoid (DiffList a) where mempty = DiffList (\xs -> [] ++ xs) (DiffList f) `mappend` (DiffList g) = DiffList (\xs -> f (g xs)) I am struggling to understand why DiffList is more computationally efficient than a simple concatenation in some cases. Could someone explain to me in simple terms why the above example is so inefficient, and in what way the DiffList solves this problem?

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  • one two-directed tcp socket OR two one-directed? (linux, high volume, low latency)

    - by osgx
    Hello I need to send (interchange) a high volume of data periodically with the lowest possible latency between 2 machines. The network is rather fast (e.g. 1Gbit or even 2G+). Os is linux. Is it be faster with using 1 tcp socket (for send and recv) or with using 2 uni-directed tcp sockets? The test for this task is very like NetPIPE network benchmark - measure latency and bandwidth for sizes from 2^1 up to 2^13 bytes, each size sent and received 3 times at least (in teal task the number of sends is greater. both processes will be sending and receiving, like ping-pong maybe). The benefit of 2 uni-directed connections come from linux: http://lxr.linux.no/linux+v2.6.18/net/ipv4/tcp_input.c#L3847 3847/* 3848 * TCP receive function for the ESTABLISHED state. 3849 * 3850 * It is split into a fast path and a slow path. The fast path is 3851 * disabled when: ... 3859 * - Data is sent in both directions. Fast path only supports pure senders 3860 * or pure receivers (this means either the sequence number or the ack 3861 * value must stay constant) ... 3863 * 3864 * When these conditions are not satisfied it drops into a standard 3865 * receive procedure patterned after RFC793 to handle all cases. 3866 * The first three cases are guaranteed by proper pred_flags setting, 3867 * the rest is checked inline. Fast processing is turned on in 3868 * tcp_data_queue when everything is OK. All other conditions for disabling fast path is false. And only not-unidirected socket stops kernel from fastpath in receive

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  • Django: Update order attribute for objects in a queryset

    - by lazerscience
    I'm having a attribute on my model to allow the user to order the objects. I have to update the element's order depending on a list, that contains the object's ids in the new order; right now I'm iterating over the whole queryset and set one objects after the other. What would be the easiest/fastest way to do the same with the whole queryset? def update_ordering(model, order): """ order is in the form [id,id,id,id] for example: [8,4,5,1,3] """ id_to_order = dict((order[i], i) for i in range(len(order))) for x in model.objects.all(): x.order = id_to_order[x.id] x.save()

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  • Approach for altering Primary Key from GUID to BigInt in SQL Server related tables

    - by Tom
    I have two tables with 10-20 million rows that have GUID primary keys and at leat 12 tables related via foreign key. The base tables have 10-20 indexes each. We are moving from GUID to BigInt primary keys. I'm wondering if anyone has any suggestions on an approach. Right now this is the approach I'm pondering: Drop all indexes and fkeys on all the tables involved. Add 'NewPrimaryKey' column to each table Make the key identity on the two base tables Script the data change "update table x, set NewPrimaryKey = y where OldPrimaryKey = z Rename the original primarykey to 'oldprimarykey' Rename the 'NewPrimaryKey' column 'PrimaryKey' Script back all the indexes and fkeys Does this seem like a good approach? Does anyone know of a tool or script that would help with this? TD: Edited per additional information. See this blog post that addresses an approach when the GUID is the Primary: http://www.sqlmag.com/blogs/sql-server-questions-answered/sql-server-questions-answered/tabid/1977/entryid/12749/Default.aspx

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  • Can this loop be sped up in pure Python?

    - by Noctis Skytower
    I was trying out an experiment with Python, trying to find out how many times it could add one to an integer in one minute's time. Assuming two computers are the same except for the speed of the CPUs, this should give an estimate of how fast some CPU operations may take for the computer in question. The code below is an example of a test designed to fulfill the requirements given above. This version is about 20% faster than the first attempt and 150% faster than the third attempt. Can anyone make any suggestions as to how to get the most additions in a minute's time span? Higher numbers are desireable. EDIT: This experiment is being written in Python 3.1 and is 15% faster than the fourth speed-up attempt. def start(seconds): import time, _thread def stop(seconds, signal): time.sleep(seconds) signal.pop() total, signal = 0, [None] _thread.start_new_thread(stop, (seconds, signal)) while signal: total += 1 return total if __name__ == '__main__': print('Testing the CPU speed ...') print('Relative speed:', start(60))

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  • Preventing a heavy process from sinking in the swap file

    - by eran
    Our service tends to fall asleep during the nights on our client's server, and then have a hard time waking up. What seems to happen is that the process heap, which is sometimes several hundreds of MB, is moved to the swap file. This happens at night, when our service is not used, and others are scheduled to run (DB backups, AV scans etc). When this happens, after a few hours of inactivity the first call to the service takes up to a few minutes (consequent calls take seconds). I'm quite certain it's an issue of virtual memory management, and I really hate the idea of forcing the OS to keep our service in the physical memory. I know doing that will hurt other processes on the server, and decrease the overall server throughput. Having that said, our clients just want our app to be responsive. They don't care if nightly jobs take longer. I vaguely remember there's a way to force Windows to keep pages on the physical memory, but I really hate that idea. I'm leaning more towards some internal or external watchdog that will initiate higher-level functionalities (there is already some internal scheduler that does very little, and makes no difference). If there were a 3rd party tool that provided that kind of service is would have been just as good. I'd love to hear any comments, recommendations and common solutions to this kind of problem. The service is written in VC2005 and runs on Windows servers.

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  • Measuring debug vs release of ASP.NET applications

    - by Alex Angas
    A question at work came up about building ASP.NET applications in release vs debug mode. When researching further (particularly on SO), general advice is that setting <compilation debug="true"> in web.config has a much bigger impact. Has anyone done any testing to get some actual numbers about this? Here's the sort of information I'm looking for (which may give away my experience with testing such things): Execution time | Debug build | Release build -------------------+---------------+--------------- Debug web.config | average 1 | average 2 Retail web.config | average 3 | average 4 Max memory usage | Debug build | Release build -------------------+---------------+--------------- Debug web.config | average 1 | average 2 Retail web.config | average 3 | average 4 Output file size | Debug build | Release build -------------------+---------------+--------------- | size 1 | size 2

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  • How do I make this nested for loop, testing sums of cubes, more efficient?

    - by Brian J. Fink
    I'm trying to iterate through all the combinations of pairs of positive long integers in Java and testing the sum of their cubes to discover if it's a Fibonacci number. I'm currently doing this by using the value of the outer loop variable as the inner loop's upper limit, with the effect being that the outer loop runs a little slower each time. Initially it appeared to run very quickly--I was up to 10 digits within minutes. But now after 2 full days of continuous execution, I'm only somewhere in the middle range of 15 digits. At this rate it may end up taking a whole year just to finish running this program. The code for the program is below: import java.lang.*; import java.math.*; public class FindFib { public static void main(String args[]) { long uLimit=9223372036854775807L; //long maximum value BigDecimal PHI=new BigDecimal(1D+Math.sqrt(5D)/2D); //Golden Ratio for(long a=1;a<=uLimit;a++) //Outer Loop, 1 to maximum for(long b=1;b<=a;b++) //Inner Loop, 1 to current outer { //Cube the numbers and add BigDecimal c=BigDecimal.valueOf(a).pow(3).add(BigDecimal.valueOf(b).pow(3)); System.out.print(c+" "); //Output result //Upper and lower limits of interval for Mobius test: [c*PHI-1/c,c*PHI+1/c] BigDecimal d=c.multiply(PHI).subtract(BigDecimal.ONE.divide(c,BigDecimal.ROUND_HALF_UP)), e=c.multiply(PHI).add(BigDecimal.ONE.divide(c,BigDecimal.ROUND_HALF_UP)); //Mobius test: if integer in interval (floor values unequal) Fibonacci number! if (d.toBigInteger().compareTo(e.toBigInteger())!=0) System.out.println(); //Line feed else System.out.print("\r"); //Carriage return instead } //Display final message System.out.println("\rDone. "); } } Now the use of BigDecimal and BigInteger was delibrate; I need them to get the necessary precision. Is there anything other than my variable types that I could change to gain better efficiency?

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  • Jmeter- HTTP Cache Manager, Unable to cache everything what it is being cached by Browser

    - by chinmay brahma
    I used HTTP Chache Manager to Cache files which are being cached in browser. I am successful of doing it for some of the pages. Number of files being cached in Jmeter is equal to Number of files being cached by browser. But in some cases : I found number files being cached is lesser than the files being cached by browser. Using Jmeter I found only 5 files are being cached but in real browser 12 files are getting cached. Thanks in advance

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  • Database structure for ecommerce site

    - by imanc
    Hey Guys, I have been tasked with designing an ecommerce solution. The aspect that is causing me the most problems is the database. Currently the site consists of 10+ country based shops each with their own database (all residing on the same mysql instance). For the new site I'd rather all these shop databases be merged into one database so that all tables (products, orders, customers etc.) have a shop_id field. From a programming perspective this seems to make the most sense as we won't have to manage data across multiple databases. Currently the entire site generates about 120k orders a year, but is experiencing fairly heavy growth and we need to design a solution that will scale. In 5 years there may be more than a million orders per year and a database that contains 5 years order history (archiving maybe a solution here). The question is - do we use a single database, or do we keep the database-per-shop structure? I am currently trying to find supporting evidence for either avenue. The company I am designing the solution for prefer the per-shop database structure because they believe it will allow the sites to scale. But my argument is that the shop's database probably won't get that busy over the next few years that they exceed the capacity of a mysql database and a "no expenses spared" hardware set-up. I am wondering if anyone has any advice either way? Does anyone have experience with websites / ecommerce sites that have tables containing millions of records? I know there is probably not a clear answer here, but at what stage do we have too many records or too large table files to have a fast loading site? Also, if anyone has any advice on sources of information - books, websites, etc. where I can do further research, it would be highly appreciated! Cheers, imanc

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  • How to get the size of a binary tree ?

    - by Andrei Ciobanu
    I have a very simple binary tree structure, something like: struct nmbintree_s { unsigned int size; int (*cmp)(const void *e1, const void *e2); void (*destructor)(void *data); nmbintree_node *root; }; struct nmbintree_node_s { void *data; struct nmbintree_node_s *right; struct nmbintree_node_s *left; }; Sometimes i need to extract a 'tree' from another and i need to get the size to the 'extracted tree' in order to update the size of the initial 'tree' . I was thinking on two approaches: 1) Using a recursive function, something like: unsigned int nmbintree_size(struct nmbintree_node* node) { if (node==NULL) { return(0); } return( nmbintree_size(node->left) + nmbintree_size(node->right) + 1 ); } 2) A preorder / inorder / postorder traversal done in an iterative way (using stack / queue) + counting the nodes. What approach do you think is more 'memory failure proof' / performant ? Any other suggestions / tips ? NOTE: I am probably going to use this implementation in the future for small projects of mine. So I don't want to unexpectedly fail :).

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  • What limits scaling in this simple OpenMP program?

    - by Douglas B. Staple
    I'm trying to understand limits to parallelization on a 48-core system (4xAMD Opteron 6348, 2.8 Ghz, 12 cores per CPU). I wrote this tiny OpenMP code to test the speedup in what I thought would be the best possible situation (the task is embarrassingly parallel): // Compile with: gcc scaling.c -std=c99 -fopenmp -O3 #include <stdio.h> #include <stdint.h> int main(){ const uint64_t umin=1; const uint64_t umax=10000000000LL; double sum=0.; #pragma omp parallel for reduction(+:sum) for(uint64_t u=umin; u<umax; u++) sum+=1./u/u; printf("%e\n", sum); } I was surprised to find that the scaling is highly nonlinear. It takes about 2.9s for the code to run with 48 threads, 3.1s with 36 threads, 3.7s with 24 threads, 4.9s with 12 threads, and 57s for the code to run with 1 thread. Unfortunately I have to say that there is one process running on the computer using 100% of one core, so that might be affecting it. It's not my process, so I can't end it to test the difference, but somehow I doubt that's making the difference between a 19~20x speedup and the ideal 48x speedup. To make sure it wasn't an OpenMP issue, I ran two copies of the program at the same time with 24 threads each (one with umin=1, umax=5000000000, and the other with umin=5000000000, umax=10000000000). In that case both copies of the program finish after 2.9s, so it's exactly the same as running 48 threads with a single instance of the program. What's preventing linear scaling with this simple program?

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  • Fastest XML parser for small, simple documents in Java

    - by Varkhan
    I have to objectify very simple and small XML documents (less than 1k, and it's almost SGML: no namespaces, plain UTF-8, you name it...), read from a stream, in Java. I am using JAXP to process the data from my stream into a Document object. I have tried Xerces, it's way too big and slow... I am using Dom4j, but I am still spending way too much time in org.dom4j.io.SAXReader. Does anybody out there have any suggestion on a faster, more efficient implementation, keeping in mind I have very tough CPU and memory constraints? [Edit 1] Keep in mind that my documents are very small, so the overhead of staring the parser can be important. For instance I am spending as much time in org.xml.sax.helpers.XMLReaderFactory.createXMLReader as in org.dom4j.io.SAXReader.read [Edit 2] The result has to be in Dom format, as I pass the document to decision tools that do arbitrary processing on it, like switching code based on the value of arbitrary XPaths, but also extracting lists of values packed as children of a predefined node. [Edit 3] In any case I eventually need to load/parse the complete document, since all the information it contains is going to be used at some point. (This question is related to, but different from, http://stackoverflow.com/questions/373833/best-xml-parser-for-java )

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  • Are closures in javascript recompiled

    - by Discodancer
    Let's say we have this code (forget about prototypes for a moment): function A(){ var foo = 1; this.method = function(){ return foo; } } var a = new A(); is the inner function recompiled each time the function A is run? Or is it better (and why) to do it like this: function method = function(){ return this.foo; } function A(){ this.foo = 1; this.method = method; } var a = new A(); Or are the javascript engines smart enough not to create a new 'method' function every time? Specifically Google's v8 and node.js. Also, any general recommendations on when to use which technique are welcome. In my specific example, it really suits me to use the first example, but I know thath the outer function will be instantiated many times.

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  • one two-directed tcp socket of two one-directed? (linux, high volume, low latency)

    - by osgx
    Hello I need to send (interchange) a high volume of data periodically with the lowest possible latency between 2 machines. The network is rather fast (e.g. 1Gbit or even 2G+). Os is linux. Is it be faster with using 1 tcp socket (for send and recv) or with using 2 uni-directed tcp sockets? The test for this task is very like NetPIPE network benchmark - measure latency and bandwidth for sizes from 2^1 up to 2^13 bytes, each size sent and received 3 times at least (in teal task the number of sends is greater. both processes will be sending and receiving, like ping-pong maybe). The benefit of 2 uni-directed connections come from linux: http://lxr.linux.no/linux+v2.6.18/net/ipv4/tcp_input.c#L3847 3847/* 3848 * TCP receive function for the ESTABLISHED state. 3849 * 3850 * It is split into a fast path and a slow path. The fast path is 3851 * disabled when: ... 3859 * - Data is sent in both directions. Fast path only supports pure senders 3860 * or pure receivers (this means either the sequence number or the ack 3861 * value must stay constant) ... 3863 * 3864 * When these conditions are not satisfied it drops into a standard 3865 * receive procedure patterned after RFC793 to handle all cases. 3866 * The first three cases are guaranteed by proper pred_flags setting, 3867 * the rest is checked inline. Fast processing is turned on in 3868 * tcp_data_queue when everything is OK. All other conditions for disabling fast path is false. And only not-unidirected socket stops kernel from fastpath in receive

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  • oprofile unable to produce call graph

    - by aaa
    hello I am trying to use oprofile to generate call graph. Compiler is g++, platform is linux x86-64, linker is gfortran C++ code is compiled with -fno- omit-frame-pointer. oprofile is started with --callgraph=25. report I run with --callgraph. the call graph is produced but it's only includes self time, which is not much use what am I missing?

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  • How to not over-use jQuery?

    - by Fedyashev Nikita
    Typical jQuery over-use: $('button').click(function() { alert('Button clicked: ' + $(this).attr('id')); }); Which can be simplified to: $('button').click(function() { alert('Button clicked: ' + this.id); }); Which is way faster. Can you give me any more examples of similar jQuery over-use?

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  • Creating C++ client app for some abstract windows server - how to manage TCP connection to server speed?

    - by Kabumbus
    So we have some server with some address port and ip. we are developing that server so we can implement on it what ever we need for help. What are standard/best practices for data transfer speed management between C++ windows client app and server (C++)? My main point is in how to get how much data can be uploaded/downloaded from/to client via his low speed network to my relatively super fast server. (I need it for set up of his live stream Audio/Video bit rate) My try on explaining number 3. We do not care how fast is our server. It is always faster than needed. We care about client tyring to stream out to our server his media. he streams encoded (via ffmpeg) live video data to our server. But he has say ADSL with 500kb/s of outgoing traffic. Also he uses some ICQ or what so ever so he has less than 500 kb/s per second. And he wants to stream live video! So we need to set up our ffmpeg to encode video with respect to the bit rate user can provide. We develop server side and client side. We need a way of finding out how much user can upload per second currently (so value can change dynamically over time)

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