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  • Slow query with unexpected scan

    - by zerkms
    Hello I have this query: SELECT * FROM SAMPLE SAMPLE INNER JOIN TEST TEST ON SAMPLE.SAMPLE_NUMBER = TEST.SAMPLE_NUMBER INNER JOIN RESULT RESULT ON TEST.TEST_NUMBER = RESULT . TEST_NUMBER WHERE SAMPLED_DATE BETWEEN '2010-03-17 09:00' AND '2010-03-17 12:00' the biggest table here is RESULT, contains 11.1M records. The left 2 tables about 1M. this query works slowly (more than 10 minutes) and returns about 800 records. executing plan shows clustered index scan over all 11M records. RESULT.TEST_NUMBER is a clustered primary key. if I change 2010-03-17 09:00 to 2010-03-17 10:00 - i get about 40 records. it executes for 300ms. and plan shows clustered index seek if i replace * in SELECT clause to RESULT.TEST_NUMBER (covered with index) - then all become fast in first case too. this points to hdd io issues, but doesn't clarifies changing plan. so, any ideas?

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  • Sql Server 2000 Stored Procedure Prevent Parallelism or something?

    - by user187305
    I have a huge disgusting stored procedure that wasn't slow a couple months ago, but now is. I barely know what this thing does and I am in no way interested in rewriting it. I do know that if I take the body of the stored procedure and then declare/set the values of the parameters and run it in query analyzer that it runs more than 20x faster. From the internet, I've read that this is probably due to a bad cached query plan. So, I've tried running the sp with "WITH RECOMPILE" after the EXEC and I've also tried putting the "WITH RECOMPLE" inside the sp, but neither of those helped even a little bit. When I look at the execution plan of the sp vs the query, the biggest difference is that the sp has "Parallelism" operations all over the place and the query doesn't have any. Can this be the cause of the difference in speeds? Thank you, any ideas would be great... I'm stuck.

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  • Windows Workflow runs very slowlyh on my DEV machine

    - by Joon
    I am developing an app using WF hosted in IIS as WCF services as a business layer. This runs quickly on any machine running Windows Server 2008 R2, but very slowly on our dev machines, running Windows XP SP3. Yesterday, the workflows were as fast on my dev machine as they are on the server for the whole day. Today, they are back to running slowly again (I rebooted overnight) Has anyone else experienced this problem with workflows running slowly on IIS in XP? What did you do to fix it?

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  • C#/WPF FileSystemWatcher on every extension on every path

    - by BlueMan
    I need FileSystemWatcher, that can observing same specific paths, and specific extensions. But the paths could by dozens, hundreds or maybe thousand (hope not :P), the same with extensions. The paths and ext are added by user. Creating hundreds of FileSystemWatcher it's not good idea, isn't it? So - how to do it? Is it possible to watch/observing every device (HDDs, SD flash, pendrives, etc.)? Will it be efficient? I don't think so... . Every changing Windows log file, scanning file by antyvirus program - it could realy slow down my program with SystemWatcher :(

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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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  • Quickest way to compare a bunch of array or list of values.

    - by zapping
    Can you please let me know on the quickest and efficient way to compare a large set of values. Its like there are a list of parent codes(string) and each code has a series of child values(string). The child lists have to be compared with each other and find out duplicates and count how many times they repeat. code1(code1_value1, code1_value2, code3_value3, ..., code1_valueN); code2(code2_value1, code1_value2, code2_value3, ..., code2_valueN); code3(code2_value1, code3_value2, code3_value3, ..., code3_valueN); . . . codeN(codeN_value1, codeN_value2, codeN_value3, ..., codeN_valueN); The lists are huge say like there are 100 parent codes and each has about 250 values in them. There will not be duplicates within a code list. Doing it in java and the solution i could figure out is. Store the values of first set of code in as codeMap.put(codeValue, duplicateCount). The count initialized to 0. Then compare the rest of the values with this. If its in the map then increment the count otherwise append it to the map. The downfall of this is to get the duplicates. Another iteration needs to be performed on a very large list. An alternative is to maintain another hashmap for duplicates like duplicateCodeMap.put(codeValue, duplicateCount) and change the initial hashmap to codeMap.put(codeValue, codeValue). Speed is what is requirement. Hope one of you can help me with it.

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  • Are there any tools to optimize the number of consumer and producer threads on a JMS queue?

    - by lindelof
    I'm working on an application that is distributed over two JBoss instances and that produces/consumes JMS messages on several JMS queues. When we configured the application we had to determine which threading model we would use, in particular the number of producing and consuming threads per queue. We have done this in a rather ad-hoc fashion but after reading the most recent columns by Herb Sutter in Dr Dobbs (in particular this one) I would like to size our threads in a more rigorous manner. Are there any methods/tools to measure the throughput of JMS queues (in particular JBoss Messaging queues) as a function of the number of producing/consuming threads?

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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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  • Should I aim for fewer HTTP requests or more cacheable CSS files?

    - by Jonathan Hanson
    We're being told that fewer HTTP requests per page load is a Good Thing. The extreme form of that for CSS would be to have a single, unique CSS file per page, with any shared site-wide styles duplicated in each file. But there's a trade off there. If you have separate shared global CSS files, they can be cached once when the front page is loaded and then re-used on multiple pages, thereby reducing the necessary size of the page-specific CSS files. So which is better in real-world practice? Shorter CSS files through multiple discrete CSS files that are cacheable, or fewer HTTP requests through fewer-but-larger CSS files?

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  • Why does Go compile quickly?

    - by Evan Kroske
    I've Googled and poked around the Go website, but I can't seem to find an explanation for Go's extraordinary build times. Are they products of the language features (or lack thereof), a highly optimized compiler, or something else? I'm not trying to promote Go; I'm just curious.

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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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  • How to profile object creation in Java?

    - by gooli
    The system I work with is creating a whole lot of objects and garbage collecting them all the time which results in a very steeply jagged graph of heap consumption. I would like to know which objects are being generated to tune the code, but I can't figure out a way to dump the heap at the moment the garbage collection starts. When I tried to initiate dumpHeap via JConsole manually at random times, I always got results after GC finished its run, and didn't get any useful data. Any notes on how to track down excessive temporary object creation are welcome.

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  • Time complexity O() of isPalindrome()

    - by Aran
    I have this method, isPalindrome(), and I am trying to find the time complexity of it, and also rewrite the code more efficiently. boolean isPalindrome(String s) { boolean bP = true; for(int i=0; i<s.length(); i++) { if(s.charAt(i) != s.charAt(s.length()-i-1)) { bP = false; } } return bP; } Now I know this code checks the string's characters to see whether it is the same as the one before it and if it is then it doesn't change bP. And I think I know that the operations are s.length(), s.charAt(i) and s.charAt(s.length()-i-!)). Making the time-complexity O(N + 3), I think? This correct, if not what is it and how is that figured out. Also to make this more efficient, would it be good to store the character in temporary strings?

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  • SQL query: how to translate IN() into a JOIN?

    - by tangens
    I have a lot of SQL queries like this: SELECT o.Id, o.attrib1, o.attrib2 FROM table1 o WHERE o.Id IN ( SELECT DISTINCT Id FROM table1, table2, table3 WHERE ... ) These queries have to run on different database engines (MySql, Oracle, DB2, MS-Sql, Hypersonic), so I can only use common SQL syntax. Here I read, that with MySql the IN statement isn't optimized and it's really slow, so I want to switch this into a JOIN. I tried: SELECT o.Id, o.attrib1, o.attrib2 FROM table1 o, table2, table3 WHERE ... But this does not take into account the DISTINCT keyword. Question: How do I get rid of the duplicate rows using the JOIN approach?

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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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  • 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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  • Definition of Connect, Processing, Waiting in apache bench.

    - by rpatel
    When I run apache bench I get results like: Command: abs.exe -v 3 -n 10 -c 1 https://mysite Connection Times (ms) min mean[+/-sd] median max Connect: 203 213 8.1 219 219 Processing: 78 177 88.1 172 359 Waiting: 78 169 84.6 156 344 Total: 281 389 86.7 391 563 I can't seem to find the definition of Connect, Processing and Waiting. What do those numbers mean?

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  • How to clear APC cache entries?

    - by lo_fye
    I need to clear all APC cache entries when I deploy a new version of the site. APC.php has a button for clearing all opcode caches, but I don't see buttons for clearing all User Entries, or all System Entries, or all Per-Directory Entries. Is it possible to clear all cache entries via the command-line, or some other way?

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  • Decent profiler for Windows?

    - by olliej
    Does windows have any decent sampling (eg. non-instrumenting) profilers available? Preferably something akin to Shark on MacOS, although i am willing to accept that i am going to have to pay for such a profiler on windows. I've tried the profiler in VS Team Suite and was not overly impressed, and was wondering if there were any other good ones. [Edit: Erk, i forgot to say this is for C/C++, rather than .NET -- sorry for any confusion]

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  • XSLT 1.0: restrict entries in a nodeset

    - by Mike
    Hi, Being relatively new to XSLT I have what I hope is a simple question. I have some flat XML files, which can be pretty big (eg. 7MB) that I need to make 'more hierarchical'. For example, the flat XML might look like this: <D0011> .... .... and it should end up looking like this: <D0011> .... .... I have a working XSLT for this, and it essentially gets a nodeset of all the b elements and then uses the 'following-sibling' axis to get a nodeset of the nodes following the current b node (ie. following-sibling::*[position() =$nodePos]). Then recursion is used to add the siblings into the result tree until another b element is found (I have parameterised it of course, to make it more generic). I also have a solution that just sends the position in the XML of the next b node and selects the nodes after that one after the other (using recursion) via a *[position() = $nodePos] selection. The problem is that the time to execute the transformation increases unacceptably with the size of the XML file. Looking into it with XML Spy it seems that it is the 'following-sibling' and 'position()=' that take the time in the two respective methods. What I really need is a way of restricting the number of nodes in the above selections, so fewer comparisons are performed: every time the position is tested, every node in the nodeset is tested to see if its position is the right one. Is there a way to do that ? Any other suggestions ? Thanks, Mike

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  • how to optimize an oracle query that has to_char in where clause for date

    - by panorama12
    I have a table that contains about 49403459 records. I want to query the table on a date range. say 04/10/2010 to 04/10/2010. However, the dates are stored in the table as format 10-APR-10 10.15.06.000000 AM (time stamp). As a result. When I do: SELECT bunch,of,stuff,create_date FROM myTable WHERE TO_CHAR (create_date,'MM/DD/YYYY)' >= '04/10/2010' AND TO_CHAR (create_date, 'MM/DD/YYYY' <= '04/10/2010' I get 529 rows but in 255.59 seconds! which is because I guess I am doing to_char on EACH record. However, When I do SELECT bunch,of,stuff,create_date FROM myTable WHERE create_date >= to_date('04/10/2010','MM/DD/YYYY') AND create_date <= to_date('04/10/2010','MM/DD/YYYY') then I get 0 results in 0.14 seconds. How can I make this query fast and still get valid (529) results?? At this point I can not change indexes. Right now I think index is created on create_date column

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  • Why the difference in speed?

    - by AngryHacker
    Consider this code: function Foo(ds as OtherDLL.BaseObj) dim lngRowIndex as long dim lngColIndex as long for lngRowIndex = 1 to ubound(ds.Data, 2) for lngColIndex = 1 to ds.Columns.Count Debug.Print ds.Data(lngRowIndex, lngColIndex) next next end function OK, a little context. Parameter ds is of type OtherDLL.BaseObj which is defined in a referenced ActiveX DLL. ds.Data is a variant 2-dimensional array (one dimension carries the data, the other one carries the column index. ds.Columns is a Collection of columns in 'ds.Data`. Assuming there are at least 400 rows of data and 25 columns, this code takes about 15 seconds to run on my machine. Kind of unbelievable. However if I copy the variant array to a local variable, so: function Foo(ds as OtherDLL.BaseObj) dim lngRowIndex as long dim lngColIndex as long dim v as variant v = ds.Data for lngRowIndex = 1 to ubound(v, 2) for lngColIndex = 1 to ds.Columns.Count Debug.Print v(lngRowIndex, lngColIndex) next next end function the entire thing processes in barely any noticeable time (basically close to 0). Why?

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