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• Need advice on speeding up tableViewCell photo loading performance

  - by ambertch

  I have around 20 tableview cells that each contain a number (2-5) thumbnail sized pictures (they are VERY small Facebook profile pictures, ex. http://profile.ak.fbcdn.net/hprofile-ak-sf2p/hs254.snc3/23133_201668_2989_q.jpg). Each picture is an UIImageView added to the cell's contentview. Scrolling performance is poor, and measuring the draw time I've found the UIImage rendering is the bottleneck. I've researched/thought of some solutions but as I am new to iphone development I am not sure which strategy to pursue: preload all the images and retrieve them from disk instead of URL when drawing cells (I'm not sure if cell drawing will still be slow, so I want to hold off on the time investment here) Have the cells display a placeholder image from disk, while the picture is asynchronously loaded (this seems to be the best solution, but I'm currently not sure exactly how to do best do this) There's the fast drawing recommendation from Tweetie, but I don't know that will have much affect if it turns out my overhead is in network loading (http://blog.atebits.com/2008/12/fast-scrolling-in-tweetie-with-uitableview/) Thoughts/implementation advice? Thanks!

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• SQL code to insert multiple rows in ms-access table

  - by Thierry

  I'm trying to speed up my code and the bottleneck seems to be the individual insert statements to a Jet MDB from outside Access via ODBC. I need to insert 100 rows at a time and have to repeat that many times. It is possible to insert multiple rows in a table with SQL code? Here is some stuff that I tried but neither of them worked. Any suggestions? INSERT INTO tblSimulation (p, cfYear, cfLocation, Delta, Design, SigmaLoc, Sigma, SampleSize, Intercept) VALUES (0, 2, 8.3, 0, 1, 0.5, 0.2, 220, 3.4), (0, 2.4, 7.8, 0, 1, 0.5, 0.2, 220, 3.4), (0, 2.3, 5.9, 0, 1, 0.5, 0.2, 220, 3.4) INSERT INTO tblSimulation (p, cfYear, cfLocation, Delta, Design, SigmaLoc, Sigma, SampleSize, Intercept) VALUES (0, 2, 8.3, 0, 1, 0.5, 0.2, 220, 3.4) UNION (0, 2.4, 7.8, 0, 1, 0.5, 0.2, 220, 3.4) UNION (0, 2.3, 5.9, 0, 1, 0.5, 0.2, 220, 3.4)

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• Why is thread local storage so slow?

  - by dsimcha

  I'm working on a custom mark-release style memory allocator for the D programming language that works by allocating from thread-local regions. It seems that the thread local storage bottleneck is causing a huge (~50%) slowdown in allocating memory from these regions compared to an otherwise identical single threaded version of the code, even after designing my code to have only one TLS lookup per allocation/deallocation. This is based on allocating/freeing memory a large number of times in a loop, and I'm trying to figure out if it's an artifact of my benchmarking method. My understanding is that thread local storage should basically just involve accessing something through an extra layer of indirection, similar to accessing a variable via a pointer. Is this incorrect? How much overhead does thread-local storage typically have? Note: Although I mention D, I'm also interested in general answers that aren't specific to D, since D's implementation of thread-local storage will likely improve if it is slower than the best implementations.

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• Adding 90000 XElement to XDocument

  - by Jon

  I have a Dictionary<int, MyClass> It contains 100,000 items 10,000 items value is populated whilst 90,000 are null. I have this code: var nullitems = MyInfoCollection.Where(x => x.Value == null).ToList(); nullitems.ForEach(x => LogMissedSequenceError(x.Key + 1)); private void LogMissedSequenceError(long SequenceNumber) { DateTime recordTime = DateTime.Now; var errors = MyXDocument.Descendants("ERRORS").FirstOrDefault(); if (errors != null) { errors.Add( new XElement("ERROR", new XElement("DATETIME", DateTime.Now.ToString("dd/MM/yyyy HH:mm:ss:fff")), new XElement("DETAIL", "No information was read for expected sequence number " + SequenceNumber), new XAttribute("TYPE", "MISSED"), new XElement("PAGEID", SequenceNumber) ) ); } } This seems to take about 2 minutes to complete. I can't seem to find where the bottleneck might be or if this timing sounds about right? Can anyone see anything to why its taking so long?

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• Python lazy property decorator

  - by detly

  Recently I've gone through an existing code base and refactored a lot of instance attributes to be lazy, ie. not be initialised in the constructor but only upon first read. These attributes do not change over the lifetime of the instance, but they're a real bottleneck to calculate that first time and only really accessed for special cases. I find myself typing the following snippet of code over and over again for various attributes across various classes: class testA(object): def __init__(self): self._a = None self._b = None @property def a(self): if self._a is None: # Calculate the attribute now self._a = 7 return self._a @property def b(self): #etc Is there an existing decorator to do this already in Python that I'm simply unaware of? Or, is there a reasonably simple way to define a decorator that does this? I'm working under Python 2.5, but 2.6 answers might still be interesting if they are significantly different.

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• Java replace slow?

  - by cpf

  Hi StackOverflow, I have a Java application that makes heavy use of a large file, to read, process and give through to SolrEmbeddedServer (http://lucene.apache.org/solr/). One of the functions does basic HTML escaping: private String htmlEscape(String input) { return input.replace("&", "&amp;").replace(">", "&gt;").replace("<", "&lt;") .replace("'", "&apos;").replaceAll("\"", "&quot;"); } While profiling the application, the program spends roughly 58% of the time in this function, a total of 47% in replace, and 11% in replaceAll. Now, is the Java replace that slow, or am I on the right path and should I consider the program efficient enough to have its bottleneck in Java and not in my code? (Or am I replacing wrong?) Thanks in advance!

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• large amount of data in many text files - how to process?

  - by Stephen

  Hi, I have large amounts of data (a few terabytes) and accumulating... They are contained in many tab-delimited flat text files (each about 30MB). Most of the task involves reading the data and aggregating (summing/averaging + additional transformations) over observations/rows based on a series of predicate statements, and then saving the output as text, HDF5, or SQLite files, etc. I normally use R for such tasks but I fear this may be a bit large. Some candidate solutions are to 1) write the whole thing in C (or Fortran) 2) import the files (tables) into a relational database directly and then pull off chunks in R or Python (some of the transformations are not amenable for pure SQL solutions) 3) write the whole thing in Python Would (3) be a bad idea? I know you can wrap C routines in Python but in this case since there isn't anything computationally prohibitive (e.g., optimization routines that require many iterative calculations), I think I/O may be as much of a bottleneck as the computation itself. Do you have any recommendations on further considerations or suggestions? Thanks

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• writing a fast parser in python

  - by panzi

  I've written a hands-on recursive pure python parser for a some file format (ARFF) we use in one lecture. Now running my exercise submission is awfully slow. Turns out by far the most time is spent in my parser. It's consuming a lot of CPU time, the HD is not the bottleneck. I wonder what performant ways are there to write a parser in python? I'd rather not rewrite it in C. I tried to use jython, but that decreased performance a lot! The files I parse are partially huge ( 150 MB) with very long lines. My current parser only needs a look-ahead of one character. I'd post the source here but I don't know if that's such a good idea. After all the submission deadline has not jet ended. But then, the focus in this exercise is not the parser. You can choose whatever language you want to use and there already is a parser for Java.

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• How do I get Java to use my multi-core processor?

  - by Rudiger

  I'm using a GZIPInputStream in my program, and I know that the performance would be helped if I could get Java running my program in parallel. In general, is there a command-line option for the standard VM to run on many cores? It's running on just one as it is. Thanks! Edit I'm running plain ol' Java SE 6 update 17 on Windows XP. Would putting the GZIPInputStream on a separate thread explicitly help? No! Do not put the GZIPInputStream on a separate thread! Do NOT multithread I/O! Edit 2 I suppose I/O is the bottleneck, as I'm reading and writing to the same disk... In general, though, is there a way to make GZIPInputStream faster? Or a replacement for GZIPInputStream that runs parallel? Edit 3 Code snippet I used: GZIPInputStream gzip = new GZIPInputStream(new FileInputStream(INPUT_FILENAME)); DataInputStream in = new DataInputStream(new BufferedInputStream(gzip));

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• Windows Azure worker roles: One big job or many small jobs?

  - by Ryan Elkins

  Is there any inherent advantage when using multiple workers to process pieces of procedural code versus processing the entire load? In other words, if my workflow looks like this: Get work from queue0 and do A Store result from A in queue1 Get result from queue 1 and do B Store result from B in queue2 Get result from queue2 and do C Is there an inherent advantage to using 3 workers who each do the entire process themselves versus 3 workers that each do a part of the work (Worker 1 does 1 & 2, worker 2 does 3 & 4, worker 3 does 5). If we only care about working being done (finished with step 5) it would seem that it scales the same way (once you're using at least 3 workers). Maybe the big job is better because workers with that setup have less bottleneck issues?

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• All possible values of int from the smallest to the largest, using Java.

  - by Totophil

  Write a program to print out all possible values of int data type from the smallest to the largest, using Java. Some notable solutions as of 8th of May 2009, 10:44 GMT: 1) Daniel Lew was the first to post correctly working code. 2) Kris has provided the simplest solution for the given problem. 3) Tom Hawtin - tackline, came up arguably with the most elegant solution. 4) mmyers pointed out that printing is likely to become a bottleneck and can be improved through buffering. 5) Jay's brute force approach is notable since, besides defying the core point of programming, the resulting source code takes about 128 GB and will blow compiler limits. As a side note I believe that the answers do demonstrate that it could be a good interview question, as long as the emphasis is not on the ability to remember trivia about the data type overflow and its implications (that can be easily spotted during unit testing), or the way of obtaining MAX and MIN limits (can easily be looked up in the documentation) but rather on the analysis of various ways of dealing with the problem.

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• SQL IO and SAN troubles

  - by James

  We are running two servers with identical software setup but different hardware. The first one is a VM on VMWare on a normal tower server with dual core xeons, 16 GB RAM and a 7200 RPM drive. The second one is a VM on XenServer on a powerful brand new rack server, with 4 core xeons and shared storage. We are running Dynamics AX 2012 and SQL Server 2008 R2. When I insert 15 000 records into a table on the slow tower server (as a test), it does so in 13 seconds. On the fast server it takes 33 seconds. I re-ran these tests several times with the same results. I have a feeling it is some sort of IO bottleneck, so I ran SQLIO on both. Here are the results for the slow tower server: C:\Program Files (x86)\SQLIO>test.bat C:\Program Files (x86)\SQLIO>sqlio -kW -t8 -s120 -o8 -frandom -b8 -BH -LS C:\Tes tFile.dat sqlio v1.5.SG using system counter for latency timings, 14318180 counts per second 8 threads writing for 120 secs to file C:\TestFile.dat using 8KB random IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: C:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 226.97 MBs/sec: 1.77 latency metrics: Min_Latency(ms): 0 Avg_Latency(ms): 281 Max_Latency(ms): 467 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 99 C:\Program Files (x86)\SQLIO>sqlio -kR -t8 -s120 -o8 -frandom -b8 -BH -LS C:\Tes tFile.dat sqlio v1.5.SG using system counter for latency timings, 14318180 counts per second 8 threads reading for 120 secs from file C:\TestFile.dat using 8KB random IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: C:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 91.34 MBs/sec: 0.71 latency metrics: Min_Latency(ms): 14 Avg_Latency(ms): 699 Max_Latency(ms): 1124 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 C:\Program Files (x86)\SQLIO>sqlio -kW -t8 -s120 -o8 -fsequential -b64 -BH -LS C :\TestFile.dat sqlio v1.5.SG using system counter for latency timings, 14318180 counts per second 8 threads writing for 120 secs to file C:\TestFile.dat using 64KB sequential IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: C:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 1094.50 MBs/sec: 68.40 latency metrics: Min_Latency(ms): 0 Avg_Latency(ms): 58 Max_Latency(ms): 467 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 C:\Program Files (x86)\SQLIO>sqlio -kR -t8 -s120 -o8 -fsequential -b64 -BH -LS C :\TestFile.dat sqlio v1.5.SG using system counter for latency timings, 14318180 counts per second 8 threads reading for 120 secs from file C:\TestFile.dat using 64KB sequential IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: C:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 1155.31 MBs/sec: 72.20 latency metrics: Min_Latency(ms): 17 Avg_Latency(ms): 55 Max_Latency(ms): 205 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 Here are the results of the fast rack server: C:\Program Files (x86)\SQLIO>test.bat C:\Program Files (x86)\SQLIO>sqlio -kW -t8 -s120 -o8 -frandom -b8 -BH -LS E:\Tes tFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads writing for 120 secs to file E:\TestFile.dat using 8KB random IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) open_file: CreateFile (E:\TestFile.dat for write): The system cannot find the pa th specified. exiting C:\Program Files (x86)\SQLIO>sqlio -kR -t8 -s120 -o8 -frandom -b8 -BH -LS E:\Tes tFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads reading for 120 secs from file E:\TestFile.dat using 8KB random IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) open_file: CreateFile (E:\TestFile.dat for read): The system cannot find the pat h specified. exiting C:\Program Files (x86)\SQLIO>sqlio -kW -t8 -s120 -o8 -fsequential -b64 -BH -LS E :\TestFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads writing for 120 secs to file E:\TestFile.dat using 64KB sequential IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) open_file: CreateFile (E:\TestFile.dat for write): The system cannot find the pa th specified. exiting C:\Program Files (x86)\SQLIO>sqlio -kR -t8 -s120 -o8 -fsequential -b64 -BH -LS E :\TestFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads reading for 120 secs from file E:\TestFile.dat using 64KB sequential IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) open_file: CreateFile (E:\TestFile.dat for read): The system cannot find the pat h specified. exiting C:\Program Files (x86)\SQLIO>test.bat C:\Program Files (x86)\SQLIO>sqlio -kW -t8 -s120 -o8 -frandom -b8 -BH -LS c:\Tes tFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads writing for 120 secs to file c:\TestFile.dat using 8KB random IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: c:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 2575.77 MBs/sec: 20.12 latency metrics: Min_Latency(ms): 1 Avg_Latency(ms): 24 Max_Latency(ms): 655 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 5 8 9 9 9 8 5 3 1 1 1 1 0 0 0 0 0 0 0 0 0 37 C:\Program Files (x86)\SQLIO>sqlio -kR -t8 -s120 -o8 -frandom -b8 -BH -LS c:\Tes tFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads reading for 120 secs from file c:\TestFile.dat using 8KB random IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: c:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 1141.39 MBs/sec: 8.91 latency metrics: Min_Latency(ms): 1 Avg_Latency(ms): 55 Max_Latency(ms): 652 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 91 C:\Program Files (x86)\SQLIO>sqlio -kW -t8 -s120 -o8 -fsequential -b64 -BH -LS c :\TestFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads writing for 120 secs to file c:\TestFile.dat using 64KB sequential IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: c:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 341.37 MBs/sec: 21.33 latency metrics: Min_Latency(ms): 5 Avg_Latency(ms): 186 Max_Latency(ms): 120037 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 C:\Program Files (x86)\SQLIO>sqlio -kR -t8 -s120 -o8 -fsequential -b64 -BH -LS c :\TestFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads reading for 120 secs from file c:\TestFile.dat using 64KB sequential IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: c:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 1024.07 MBs/sec: 64.00 latency metrics: Min_Latency(ms): 5 Avg_Latency(ms): 61 Max_Latency(ms): 81632 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 Three of the four tests are, to my mind, within reasonable parameters for the rack server. However, the 64 write test is incredibly slow on the rack server. (68 mb/sec on the slow tower vs 21 mb/s on the rack). The read speed for 64k also seems slow. Is this enough to say there is some sort of bottleneck with the shared storage? I need to know if I can take this evidence and say we need to launch an investigation into this. Any help is appreciated.

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• NTFS-compressing Virtual PC disks (on host and/or guest)

  - by nlawalker

  I'm hoping someone here can answer these definitively: Does putting a VHD file in an NTFS-compressed folder on the host improve performance of the virtual machine, diminish performance, or neither? What about using NTFS compression within the guest? Does using compresssion on either the host or the guest lead to any problems like read or write errors? If I were to put a VHD in a compressed folder on the host, would I benefit from compacting it? I've seen references to using NTFS compression on quite a few VPC "tips and tricks" blog posts, and it seems like half of them say to never do it and the other half say that not only does it save disk space but it actually can improve performance if you have a fast CPU and your primary performance bottleneck is the disk.

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• Parameter passing Vs Table Valued Parameters Vs XML to SQL 2008 from .Net Application

  - by Harryboy

  As We are working on a asp .net project there three ways one can update data into database when there are multiple rows updation / insertion required Let's assume we need to update employee education detail (which could be 1,3,5 or 10 records) Method to Update Data Pass value as parameter (Traditional approach), If 10 records are there then 10 round trip required Pass data as xml and write logic inside your stored procedure to get that data from xml and update the table (only single roundtrip required) Use Table valued parameters (only single roundtrip required) Note : Data is available as List, so i need to convert it to xml or any other format if i need to pass. There are no. of places in entire application we need to update data in bulk (or multiple records) I just need your suggestions that Which method will be faster (please mention if there are some other overheads) Manageability or testability concern with any approach Any other bottleneck or issue with any of the approach (Serialization /Deserialization concern or limit on size of the data passing) Any other method you suggest for same operations Thanks

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• What application domains are CPU bound and will tend to benefit from multi-core technologies?

  - by Glomek

  I hear a lot of people talking about the revolution that is coming in programming due to multi-core processors and parallelism, but I can't shake the feeling that for most of us, CPU cycles aren't the bottleneck. Pretty much all of my programs have been I/O bound in one way or another (database, filesystem, network, user interaction, etc.) for a very long time. Now I can think of a few areas where CPU cycles are a limiting factor, like code breaking, graphics, sound, some forms of simulation (weather, physics, etc.), and some forms of mathematical research, but they all seem like fairly specialized application domains. My general impression is that most programs are still I/O bound and that for most of our industry CPUs have been plenty fast for quite a while now. Am I off my rocker? What other application domains are CPU bound today? Do any of them include a large portion of the programming population? In essence, I'm wondering whether the multi-core CPUs will impact very many of us, and if so, how?

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• [Python] How do I read binary pickle data first, then unpickle it?

  - by conradlee

  I'm unpickling a NetworkX object that's about 1GB in size on disk. Although I saved it in the binary format (using protocol 2), it is taking a very long time to unpickle this file---at least half an hour. The system I'm running on has plenty of system memory (128 GB), so that's not the bottleneck. I've read here that pickling can be sped up by first reading the entire file into memory, and then unpickling it (that particular thread refers to python 3.0, which I'm not using, but the point should still be true in python 2.6). How do I first read the binary file, and then unpickle it? I have tried: import cPickle as pickle f = open("big_networkx_graph.pickle","rb") bin_data = f.read() graph_data = pickle.load(bin_data) But this returns: TypeError: argument must have 'read' and 'readline' attributes Any ideas?

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• Speed up math code in C# by writing a C dll?

  - by Projectile Fish

  I have a very large nested for loop in which some multiplications and additions are performed on floating point numbers. for (int i = 0; i < length1; i++) { s = GetS(i); c = GetC(i); for(int j = 0; j < length2; j++) { double oldU = u[j]; u[j] = c * oldU + s * omega[i][j]; omega[i][j] = c * omega[i][j] - s * oldU; } } This loop is taking up the majority of my processing time and is a bottleneck. Would I be likely to see any speed improvements if I rewrite this loop in C and interface to it from C#?

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• Loading a DB table into nested dictionaries in Python

  - by Hossein

  Hi, I have a table in MySql DB which I want to load it to a dictionary in python. the table columns is as follows: id,url,tag,tagCount tagCount is the number of times that a tag has been repeated for a certain url. So in that case I need a nested dictionary, in other words a dictionary of dictionary, to load this table. Because each url have several tags for which there are different tagCounts.the code that I used is this:( the whole table is about 22,000 records ) cursor.execute( ''' SELECT url,tag,tagCount FROM wtp ''') urlTagCount = cursor.fetchall() d = defaultdict(defaultdict) for url,tag,tagCount in urlTagCount: d[url][tag]=tagCount print d first of all I want to know if this is correct.. and if it is why it takes so much time? Is there any faster solutions? I am loading this table into memory to have fast access to get rid of the hassle of slow database operations, but with this slow speed it has become a bottleneck itself, it is even much slower than DB access. and anyone help? thanks

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• C++ Thread Safe Integer

  - by Paul Ridgway

  Hello everyone, I have currently created a C++ class for a thread safe integer which simply stores an integer privately and has public get a set functions which use a boost::mutex to ensure that only one change at a time can be applied to the integer. Is this the most efficient way to do it, I have been informed that mutexes are quite resource intensive? The class is used a lot, very rapidly so it could well be a bottleneck... Googleing C++ Thread Safe Integer returns unclear views and oppinions on the thread safety of integer operations on different architectures. Some say that a 32bit int on a 32bit arch is safe, but 64 on 32 isn't due to 'alignment' Others say it is compiler/OS specific (which I don't doubt). I am using Ubuntu 9.10 on 32 bit machines, some have dual cores and so threads may be executed simultaneously on different cores in some cases and I am using GCC 4.4's g++ compiler. Thanks in advance...

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• Performance testing on .xap files...

  - by Radhi

  Hi All, I want to know that can i use profiler to do performance testing of .xap files. if you have any articles for the same topic please provide it to me. and if there are any other tools available to do this please tell me. in my project we have to check that when we logged into the Silverlight 4 .0 application. the screen takes 5 seconds to load. so i have to check which method is taking time to do this. in our project there are services which calls other services too,, and we have used CAL. so need to identify the bottleneck... please help...

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• MEMORY(HEAP) vs. InnoDB in a Read and Write Environment

  - by Johannes

  I want to program a real-time application using MySQL. It needs a small table (less than 10000 rows) that will be under heavy read (scan) and write (update and some insert/delete) load. I am really speaking of 10000 updates or selects per second. These statements will be executed on only a few (less than 10) open mysql connections. The table is small and does not contain any data that needs to be stored on disk. So I ask which is faster: InnoDB or MEMORY (HEAP)? My thoughts are: Both engines will probably serve SELECTs directly from memory, as even InnoDB will cache the whole table. What about the UPDATEs? (innodb_flush_log_at_trx_commit?) My main concern is the locking behavior: InnoDB row lock vs. MEMORY table lock. Will this present the bottleneck in the MEMORY implementation? Thanks for your thoughts!

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• How can I optimize MVC and IIS pipeline to obtain higher speed?

  - by Andy

  Hi, I am doing performance tweaking of a simple app that uses MVC on IIS 7.5. I have a StopWatch starting up in Application_BeginRequest and I take a snapshot at Controller.OnActionExecuting. So I measure the time spend in the entire IIS pipeline: from request receipt to the moment execution finally gets to my controller. I obtain 700 microseconds on my 3GHz quad-core (project compiled Release x64), and I wonder where the bottleneck is, especially hearing some people say that one can get up to 8000 page loads per second with MVC. How can I optimize MVC and IIS pipeline to obtain higher speed?

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• MEMORY(HEAP) vs. InnoDB in a Read and Write Envirnment

  - by Johannes

  I want to programm a real-time application using MySQL. It needs a small table (less than 10000 rows) that will be under heavy read (scan) and write (update and some insert/delete) load. I am really speaking of 10000 updates or selects per second. These statements will be executed on only a few (less than 10) open mysql connections. The table is small and does not contain any data that needs to be stored on disk. So I ask which is faster: InnoDB or MEMORY (HEAP)? My thoughts are: Both enginges will probably serve SELECTs directly from memory, as even InnoDB will cache the whole table. What about the UPDATAEs? (innodb_flush_log_at_trx_commit?) My main concern is the locking behavior: InnoDB row lock vs. MEMORY table lock. Will this present the bottleneck in the MEMORY implementation? Thanks for your thoughts!

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• Slow Client connection blocks Mongrel

  - by Sanjay

  I have a Apache + Haproxy + Mongrel setup for my rails application. When I hit a particular server page, mongrel takes around 100ms to process the request and I get the page in around 5 secs due to data transmission time on my slow home connection. Now I see that during these 5 secs of data transmission, mongrel does not serve any other request. I am surprised as that means mongrel is serving the response html to the client and is blocked till the client receives it. Shouldn't serving response be the job of Apache? This puts serious bottleneck in the no of requests Mongrel can serve as that would depend on the speed of the client connection. Is there any way that html generated by mongrel is served by apache/haproxy or any other web server like nginx? I wonder how the other high traffic sites are managing it?

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• Why don't scripting languages output Unicode to the Windows console?

  - by hippietrail

  The Windows console has been Unicode aware for at least a decade and perhaps as far back as Windows NT. However for some reason the major cross-platform scripting languages including Perl and Python only ever output various 8-bit encodings, requiring much trouble to work around. Perl gives a "wide character in print" warning, Pythong gives a charmap error and quits. Why on earth after all these years do they not just simply call the Win32 -W APIs that output UTF-16 Unicode instead of forcing everything through the ANSI/codepage bottleneck? Is it just that cross-platform performance is low priority? Is it that the languages use UTF-8 internally and find it too much bother to output UTF-16? Or are the -W APIs inherently broken to such a degree that they can't be used as-is?

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