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  • Is there a USB 3.0 memory stick / thumb drive?

    - by jasondavis
    The new PC I just finished building has USB 3.0 support. I use a USB stick/thumb drive all the time on my PC for stuff, is there an equivalent available anywhere for USB 3.0? Please list one product per post, and provide a link to the manufacturer's product page. Also see the eSATA version of this question.

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  • Does uwsgi workers share a common memory ? [ With Nginx ]

    - by Yugal Jindle
    I have configured my Nginx with Django uwsgi. When the django server starts, it reads a 5MB file from the hard-disk. Now, Without Nginx with Django default server python manage.py runserver = Runs immediately and starts serving pages. Problem: With Nginx as the server It takes very long time and several HTTP 504 before it start serving pages. So, How does uwsgi workers work with Nginx ? I have: 4 Workers 512 Threads each So, is the 5MB file getting read 512 * 4 times ? Is there a possible work around for this in Nginx / Uwsgi ?

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  • Optimizing Solaris 11 SHA-1 on Intel Processors

    - by danx
    SHA-1 is a "hash" or "digest" operation that produces a 160 bit (20 byte) checksum value on arbitrary data, such as a file. It is intended to uniquely identify text and to verify it hasn't been modified. Max Locktyukhin and others at Intel have improved the performance of the SHA-1 digest algorithm using multiple techniques. This code has been incorporated into Solaris 11 and is available in the Solaris Crypto Framework via the libmd(3LIB), the industry-standard libpkcs11(3LIB) library, and Solaris kernel module sha1. The optimized code is used automatically on systems with a x86 CPU supporting SSSE3 (Intel Supplemental SSSE3). Intel microprocessor architectures that support SSSE3 include Nehalem, Westmere, Sandy Bridge microprocessor families. Further optimizations are available for microprocessors that support AVX (such as Sandy Bridge). Although SHA-1 is considered obsolete because of weaknesses found in the SHA-1 algorithm—NIST recommends using at least SHA-256, SHA-1 is still widely used and will be with us for awhile more. Collisions (the same SHA-1 result for two different inputs) can be found with moderate effort. SHA-1 is used heavily though in SSL/TLS, for example. And SHA-1 is stronger than the older MD5 digest algorithm, another digest option defined in SSL/TLS. Optimizations Review SHA-1 operates by reading an arbitrary amount of data. The data is read in 512 bit (64 byte) blocks (the last block is padded in a specific way to ensure it's a full 64 bytes). Each 64 byte block has 80 "rounds" of calculations (consisting of a mixture of "ROTATE-LEFT", "AND", and "XOR") applied to the block. Each round produces a 32-bit intermediate result, called W[i]. Here's what each round operates: The first 16 rounds, rounds 0 to 15, read the 512 bit block 32 bits at-a-time. These 32 bits is used as input to the round. The remaining rounds, rounds 16 to 79, use the results from the previous rounds as input. Specifically for round i it XORs the results of rounds i-3, i-8, i-14, and i-16 and rotates the result left 1 bit. The remaining calculations for the round is a series of AND, XOR, and ROTATE-LEFT operators on the 32-bit input and some constants. The 32-bit result is saved as W[i] for round i. The 32-bit result of the final round, W[79], is the SHA-1 checksum. Optimization: Vectorization The first 16 rounds can be vectorized (computed in parallel) because they don't depend on the output of a previous round. As for the remaining rounds, because of step 2 above, computing round i depends on the results of round i-3, W[i-3], one can vectorize 3 rounds at-a-time. Max Locktyukhin found through simple factoring, explained in detail in his article referenced below, that the dependencies of round i on the results of rounds i-3, i-8, i-14, and i-16 can be replaced instead with dependencies on the results of rounds i-6, i-16, i-28, and i-32. That is, instead of initializing intermediate result W[i] with: W[i] = (W[i-3] XOR W[i-8] XOR W[i-14] XOR W[i-16]) ROTATE-LEFT 1 Initialize W[i] as follows: W[i] = (W[i-6] XOR W[i-16] XOR W[i-28] XOR W[i-32]) ROTATE-LEFT 2 That means that 6 rounds could be vectorized at once, with no additional calculations, instead of just 3! This optimization is independent of Intel or any other microprocessor architecture, although the microprocessor has to support vectorization to use it, and exploits one of the weaknesses of SHA-1. Optimization: SSSE3 Intel SSSE3 makes use of 16 %xmm registers, each 128 bits wide. The 4 32-bit inputs to a round, W[i-6], W[i-16], W[i-28], W[i-32], all fit in one %xmm register. The following code snippet, from Max Locktyukhin's article, converted to ATT assembly syntax, computes 4 rounds in parallel with just a dozen or so SSSE3 instructions: movdqa W_minus_04, W_TMP pxor W_minus_28, W // W equals W[i-32:i-29] before XOR // W = W[i-32:i-29] ^ W[i-28:i-25] palignr $8, W_minus_08, W_TMP // W_TMP = W[i-6:i-3], combined from // W[i-4:i-1] and W[i-8:i-5] vectors pxor W_minus_16, W // W = (W[i-32:i-29] ^ W[i-28:i-25]) ^ W[i-16:i-13] pxor W_TMP, W // W = (W[i-32:i-29] ^ W[i-28:i-25] ^ W[i-16:i-13]) ^ W[i-6:i-3]) movdqa W, W_TMP // 4 dwords in W are rotated left by 2 psrld $30, W // rotate left by 2 W = (W >> 30) | (W << 2) pslld $2, W_TMP por W, W_TMP movdqa W_TMP, W // four new W values W[i:i+3] are now calculated paddd (K_XMM), W_TMP // adding 4 current round's values of K movdqa W_TMP, (WK(i)) // storing for downstream GPR instructions to read A window of the 32 previous results, W[i-1] to W[i-32] is saved in memory on the stack. This is best illustrated with a chart. Without vectorization, computing the rounds is like this (each "R" represents 1 round of SHA-1 computation): RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR With vectorization, 4 rounds can be computed in parallel: RRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRR Optimization: AVX The new "Sandy Bridge" microprocessor architecture, which supports AVX, allows another interesting optimization. SSSE3 instructions have two operands, a input and an output. AVX allows three operands, two inputs and an output. In many cases two SSSE3 instructions can be combined into one AVX instruction. The difference is best illustrated with an example. Consider these two instructions from the snippet above: pxor W_minus_16, W // W = (W[i-32:i-29] ^ W[i-28:i-25]) ^ W[i-16:i-13] pxor W_TMP, W // W = (W[i-32:i-29] ^ W[i-28:i-25] ^ W[i-16:i-13]) ^ W[i-6:i-3]) With AVX they can be combined in one instruction: vpxor W_minus_16, W, W_TMP // W = (W[i-32:i-29] ^ W[i-28:i-25] ^ W[i-16:i-13]) ^ W[i-6:i-3]) This optimization is also in Solaris, although Sandy Bridge-based systems aren't widely available yet. As an exercise for the reader, AVX also has 256-bit media registers, %ymm0 - %ymm15 (a superset of 128-bit %xmm0 - %xmm15). Can %ymm registers be used to parallelize the code even more? Optimization: Solaris-specific In addition to using the Intel code described above, I performed other minor optimizations to the Solaris SHA-1 code: Increased the digest(1) and mac(1) command's buffer size from 4K to 64K, as previously done for decrypt(1) and encrypt(1). This size is well suited for ZFS file systems, but helps for other file systems as well. Optimized encode functions, which byte swap the input and output data, to copy/byte-swap 4 or 8 bytes at-a-time instead of 1 byte-at-a-time. Enhanced the Solaris mdb(1) and kmdb(1) debuggers to display all 16 %xmm and %ymm registers (mdb "$x" command). Previously they only displayed the first 8 that are available in 32-bit mode. Can't optimize if you can't debug :-). Changed the SHA-1 code to allow processing in "chunks" greater than 2 Gigabytes (64-bits) Performance I measured performance on a Sun Ultra 27 (which has a Nehalem-class Xeon 5500 Intel W3570 microprocessor @3.2GHz). Turbo mode is disabled for consistent performance measurement. Graphs are better than words and numbers, so here they are: The first graph shows the Solaris digest(1) command before and after the optimizations discussed here, contained in libmd(3LIB). I ran the digest command on a half GByte file in swapfs (/tmp) and execution time decreased from 1.35 seconds to 0.98 seconds. The second graph shows the the results of an internal microbenchmark that uses the Solaris libpkcs11(3LIB) library. The operations are on a 128 byte buffer with 10,000 iterations. The results show operations increased from 320,000 to 416,000 operations per second. Finally the third graph shows the results of an internal kernel microbenchmark that uses the Solaris /kernel/crypto/amd64/sha1 module. The operations are on a 64Kbyte buffer with 100 iterations. third graph shows the results of an internal kernel microbenchmark that uses the Solaris /kernel/crypto/amd64/sha1 module. The operations are on a 64Kbyte buffer with 100 iterations. The results show for 1 kernel thread, operations increased from 410 to 600 MBytes/second. For 8 kernel threads, operations increase from 1540 to 1940 MBytes/second. Availability This code is in Solaris 11 FCS. It is available in the 64-bit libmd(3LIB) library for 64-bit programs and is in the Solaris kernel. You must be running hardware that supports Intel's SSSE3 instructions (for example, Intel Nehalem, Westmere, or Sandy Bridge microprocessor architectures). The easiest way to determine if SSSE3 is available is with the isainfo(1) command. For example, nehalem $ isainfo -v $ isainfo -v 64-bit amd64 applications sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov amd_sysc cx8 tsc fpu 32-bit i386 applications sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov sep cx8 tsc fpu If the output also shows "avx", the Solaris executes the even-more optimized 3-operand AVX instructions for SHA-1 mentioned above: sandybridge $ isainfo -v 64-bit amd64 applications avx xsave pclmulqdq aes sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov amd_sysc cx8 tsc fpu 32-bit i386 applications avx xsave pclmulqdq aes sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov sep cx8 tsc fpu No special configuration or setup is needed to take advantage of this code. Solaris libraries and kernel automatically determine if it's running on SSSE3 or AVX-capable machines and execute the correctly-tuned code for that microprocessor. Summary The Solaris 11 Crypto Framework, via the sha1 kernel module and libmd(3LIB) and libpkcs11(3LIB) libraries, incorporated a useful SHA-1 optimization from Intel for SSSE3-capable microprocessors. As with other Solaris optimizations, they come automatically "under the hood" with the current Solaris release. References "Improving the Performance of the Secure Hash Algorithm (SHA-1)" by Max Locktyukhin (Intel, March 2010). The source for these SHA-1 optimizations used in Solaris "SHA-1", Wikipedia Good overview of SHA-1 FIPS 180-1 SHA-1 standard (FIPS, 1995) NIST Comments on Cryptanalytic Attacks on SHA-1 (2005, revised 2006)

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  • Java Compiler: Optimization of "cascaded" ifs and best practices?

    - by jens
    Hello, does the Java Compiler optimize a statement like this if (a == true) { if (b == true) { if (c == true) { if(d == true) { //code to process stands here } } } } to if (a == true && b==true && c==true && d == true) So thats my first question: Do both take exactly the same "CPU Cycles" or is the first variant "slowlier". My Second questin is, is the first variant with the cascaded if considered bad programming style as it is so verbose? (I like the first variant as I can better logically group my expressions and better comment them (my if statements are more complex than in the example), but maybe thats bad proramming style?) and even slowlier, thats why I am asking... Thanks Jens

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  • Java map / nio / NFS issue causing a VM fault: "a fault occurred in a recent unsafe memory access op

    - by Matthew Bloch
    I have written a parser class for a particular binary format (nfdump if anyone is interested) which uses java.nio's MappedByteBuffer to read through files of a few GB each. The binary format is just a series of headers and mostly fixed-size binary records, which are fed out to the called by calling nextRecord(), which pushes on the state machine, returning null when it's done. It performs well. It works on a development machine. On my production host, it can run for a few minutes or hours, but always seems to throw "java.lang.InternalError: a fault occurred in a recent unsafe memory access operation in compiled Java code", fingering one of the Map.getInt, getShort methods, i.e. a read operation in the map. The uncontroversial (?) code that sets up the map is this: /** Set up the map from the given filename and position */ protected void open() throws IOException { // Set up buffer, is this all the flexibility we'll need? channel = new FileInputStream(file).getChannel(); MappedByteBuffer map1 = channel.map(FileChannel.MapMode.READ_ONLY, 0, channel.size()); map1.load(); // we want the whole thing, plus seems to reduce frequency of crashes? map = map1; // assumes the host writing the files is little-endian (x86), ought to be configurable map.order(java.nio.ByteOrder.LITTLE_ENDIAN); map.position(position); } and then I use the various map.get* methods to read shorts, ints, longs and other sequences of bytes, before hitting the end of the file and closing the map. I've never seen the exception thrown on my development host. But the significant point of difference between my production host and development is that on the former, I am reading sequences of these files over NFS (probably 6-8TB eventually, still growing). On my dev machine, I have a smaller selection of these files locally (60GB), but when it blows up on the production host it's usually well before it gets to 60GB of data. Both machines are running java 1.6.0_20-b02, though the production host is running Debian/lenny, the dev host is Ubuntu/karmic. I'm not convinced that will make any difference. Both machines have 16GB RAM, and are running with the same java heap settings. I take the view that if there is a bug in my code, there is enough of a bug in the JVM not to throw me a proper exception! But I think it is just a particular JVM implementation bug due to interactions between NFS and mmap, possibly a recurrence of 6244515 which is officially fixed. I already tried adding in a "load" call to force the MappedByteBuffer to load its contents into RAM - this seemed to delay the error in the one test run I've done, but not prevent it. Or it could be coincidence that was the longest it had gone before crashing! If you've read this far and have done this kind of thing with java.nio before, what would your instinct be? Right now mine is to rewrite it without nio :)

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  • Java - Error Message Help

    - by Brian
    In the Code, mem is a of Class Memory and getMDR and getMAR ruturn ints. When I try to compile the code I get the following errors.....how can I fix this? Computer.java:25: write(int,int) in Memory cannot be applied to (int) Input.getInt(mem.write(cpu.getMDR())); ^ Computer.java:28: write(int,int) in Memory cannot be applied to (int) mem.write(cpu.getMAR()); Here is the code for Computer: class Computer{ private Cpu cpu; private Input in; private OutPut out; private Memory mem; public Computer() { Memory mem = new Memory(100); Input in = new Input(); OutPut out = new OutPut(); Cpu cpu = new Cpu(); System.out.println(in.getInt()); } public void run() { cpu.reset(); cpu.setMDR(mem.read(cpu.getMAR())); cpu.fetch2(); while (!cpu.stop()) { cpu.decode(); if (cpu.OutFlag()) OutPut.display(mem.read(cpu.getMAR())); if (cpu.InFlag()) Input.getInt(mem.write(cpu.getMDR())); if (cpu.StoreFlag()) { mem.write(cpu.getMAR()); cpu.getMDR(); } else { cpu.setMDR(mem.read(cpu.getMAR())); cpu.execute(); cpu.fetch(); cpu.setMDR(mem.read(cpu.getMAR())); cpu.fetch2(); } } } Here is the code for Memory: class Memory{ private MemEl[] memArray; private int size; public Memory(int s) {size = s; memArray = new MemEl[s]; for(int i = 0; i < s; i++) memArray[i] = new MemEl(); } public void write (int loc, int val) {if (loc >=0 && loc < size) memArray[loc].write(val); else System.out.println("Index Not in Domain"); } public int read (int loc) {return memArray[loc].read(); } public void dump() { for(int i = 0; i < size; i++) if(i%1 == 0) System.out.println(memArray[i].read()); else System.out.print(memArray[i].read()); } } Here is the code for getMAR and getMDR: public int getMAR() { return ir.getOpcode(); } public int getMDR() { return mdr.read(); }

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  • javascript simple object creation test: opera leaks?

    - by joe
    Hi, I am trying to figure out certain memory leak conditions in javascript on a few browsers. Currently I'm only testing FF 3.6, Opera 10.10, and Safari 4.0.3. I've started with a fairly simple test, and can confirm no memory leaks in Firefox and Safari. But Opera just takes memory and never gives it back. What gives? Here's the test: <html> <head> <script type="text/javascript"> window.onload = init; //window.onunload = cleanup; var a=[]; function init() { var d = document.createElement('div'); d.innerHTML = "page loading..."; document.body.appendChild(d); for (var i=0; i<400000; i++) { a[i] = new Obj("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"); } d.innerHTML = "PAGE LOADED"; } function cleanup() { for (var i=0; i<400000; i++) { a[i] = null; } } function Obj(msg) { this.msg=msg; } </script> </head> <body> </body> </html> I shouldn't need the cleanup() call on window.unload, but tried that also. No luck. As you can see this is simple JS, no circular DOM links, no closures. I monitor the memory usage using 'top' on Mac 10.4.11. Memory usage spikes up on page load, as expected. In FF and Safari reloading the page does not use any further memory, and all memory is returned when the window (tab) is closed. In Opera, memory spikes on load, and seems to also spike further on each reload (but not always...). But regardless of reload, memory never goes back down below the initial load spike. I had hoped this was a no-brainer test that all browsers would pass, so I could move on to more "interesting" conditions. Am I doing something wrong here? Or is this a known Opera issue? Thanks! -joe

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  • Is it possible to perform Google Website Optimization on URL Rewritten pages?

    - by digiguru
    I have a format of pages that I want to perform an A/B comparison on using google website optimizer. the URLs look as follows - the first page I want to compare... <mywebsite.com>/request1/([a-zA-Z0-9\-]*)_([0-9]+).htm vs <mywebsite.com>/request2/([a-zA-Z0-9\-]*)_([0-9]+).htm the goal page is <mywebsite.com>/request-sent.htm How can I set this up in google website optimizer? If it's not possible, are there alternative solutions available for doing such comparison reports online?

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  • deleting element objects of a std vector using erase : a) memory handling and b) better way?

    - by memC
    hi, I have a vec_A that stores instances of class A as: vec_A.push_back(A()); I want to remove some elements in the vector at a later stage and have two questions: a) The element is deleted as: vec_A.erase(iterator) Is there any additional code I need to add to make sure that there is no memory leak? . b) Assume that condition if(num <5) is if num is among a specific numberList. Given this, is there a better way to delete the elements of a vector than what I am illustrating below? #include<vector> #include<stdio.h> #include<iostream> class A { public: int getNumber(); A(int val); ~A(){}; private: int num; }; A::A(int val){ num = val; }; int A::getNumber(){ return num; }; int main(){ int i =0; int num; std::vector<A> vec_A; std::vector<A>::iterator iter; for ( i = 0; i < 10; i++){ vec_A.push_back(A(i)); } iter = vec_A.begin(); while(iter != vec_A.end()){ std::cout << "\n --------------------------"; std::cout << "\n Size before erase =" << vec_A.size(); num = iter->getNumber() ; std::cout << "\n num = "<<num; if (num < 5){ vec_A.erase(iter); } else{ iter++; } std::cout << "\n size after erase =" << vec_A.size(); } std::cout << "\nPress RETURN to continue..."; std::cin.get(); return 0; }

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  • DB Interface Design Optimization: Is it better to optimise for Fewer requests of smaller data size?

    - by Overflow
    The prevailing wisdom in webservices/web requests in general is to design your api such that you use as few requests as possible, and that each request returns therefore as much data as is needed In database design, the accepted wisdom is to design your queries to minimise size over the network, as opposed to minimizing the number of queries. They are both remote calls, so what gives?

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  • JQuery Optimization: Is there any way to speed up the rendering of the FlexSelect control?

    - by Sephrial
    Greetings, I am new to jQuery, and I have a performance problem with the FlexSelect control where it takes about 5 seconds to render the dropdown control (in the renderDropdown() function). The dropdown list contains about 5000 element. I believe all the runtime is attributed to the following block of code: var list = this.dropdownList.html(""); $.each(this.results, function() { list.append($("<li/>").html(this.name)); }); Question: Are there any alternatives that would build this list of elements in a more inefficient manner?

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  • Hot to get rid of memory allocations/deallocations in swig wrappers?

    - by Dmitriy Matveev
    I want to use swig for generation of read-only wrappers for a complex object. The object which I want to wrap will always be existent while I will read it. And also I will only use my wrappers at the time that object is existent, thus I don't need any memory management from SWIG. For following swig interface: %module test %immutable; %inline %{ struct Foo { int a; }; struct Bar { int b; Foo f; }; %} I will have a wrappers which will have a lot of garbage in generated interfaces and do useless work which will reduce performance in my case. Generated java wrapper for Bar class will be like this: public class Bar { private long swigCPtr; protected boolean swigCMemOwn; protected Bar(long cPtr, boolean cMemoryOwn) { swigCMemOwn = cMemoryOwn; swigCPtr = cPtr; } protected static long getCPtr(Bar obj) { return (obj == null) ? 0 : obj.swigCPtr; } protected void finalize() { delete(); } public synchronized void delete() { if (swigCPtr != 0) { if (swigCMemOwn) { swigCMemOwn = false; testJNI.delete_Bar(swigCPtr); } swigCPtr = 0; } } public int getB() { return testJNI.Bar_b_get(swigCPtr, this); } public Foo getF() { return new Foo(testJNI.Bar_f_get(swigCPtr, this), true); } public Bar() { this(testJNI.new_Bar(), true); } } I don't need 'swigCMemOwn' field in my wrapper since it always will be false. All code related to this field will also be useless. There are also unnecessary logic in native code: SWIGEXPORT jlong JNICALL Java_some_testJNI_Bar_1f_1get(JNIEnv *jenv, jclass jcls, jlong jarg1, jobject jarg1_) { jlong jresult = 0 ; struct Bar *arg1 = (struct Bar *) 0 ; Foo result; (void)jenv; (void)jcls; (void)jarg1_; arg1 = *(struct Bar **)&jarg1; result = ((arg1)->f); { Foo * resultptr = (Foo *) malloc(sizeof(Foo)); memmove(resultptr, &result, sizeof(Foo)); *(Foo **)&jresult = resultptr; } return jresult; } I don't need these calls to malloc and memmove. I want to force swig to resolve both of these problems, but don't know how. Is it possible?

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  • db optimization - have a total field or query table?

    - by Dorian Fife
    I have an app where users get points for actions they perform - either 1 point for an easy action or 2 for a difficult one. I wish to display to the user the total number of points he got in my app and the points obtained this week (since Monday at midnight). I have a table that records all actions, along with their time and number of points. I have two alternatives and I'm not sure which is better: Every time the user sees the report perform a query and sum the points the user got Add two fields to each user that records the number of points obtained so far (total and weekly). The weekly points value will be set to 0 every Monday at midnight. The first option is easier, but I'm afraid that as I'll get many users and actions queries will take a long time. The second option bares the risk of inconsistency between the table of actions and the summary values. I'm very interested in what you think is the best alternative here. Thanks, Dorian

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  • Oracle Query Optimization: Why is My Second Query Faster?

    - by Patrick Cuff
    I was having some performance issues with an Oracle query, so I downloaded a trial of the Quest SQL Optimizer for Oracle, which made some changes that dramatically improved the query's performance. I'm not exactly sure why the recommended query had such an improvement; can anyone provide an explanation? Before: SELECT t1.version_id, t1.id, t2.field1, t3.person_id, t2.id FROM table1 t1, table2 t2, table3 t3 WHERE t1.id = t2.id AND t1.version_id = t2.version_id AND t2.id = 123 AND t1.version_id = t3.version_id AND t1.VERSION_NAME <> 'AA' order by t1.id Plan Cost: 831 Elapsed Time: 00:00:21.40 Number of Records: 40,717 After: SELECT /*+ USE_NL_WITH_INDEX(t1) */ t1.version_id, t1.id, t2.field1, t3.person_id, t2.id FROM table2 t2, table3 t3, table1 t1 WHERE t1.id = t2.id + 0 AND t1.version_id = t2.version_id + 0 AND t2.id = 123 AND t1.version_id = t3.version_id + 0 AND t1.VERSION_NAME || '' <> 'AA' AND t3.version_id = t2.version_id + 0 order by t1.id Plan Cost: 686 Elapsed Time: 00:00:00.95 Number of Records: 40,717 Questions: Why does re-arranging the order of the tables in the FROM clause help? Why does adding + 0 to the WHERE clause comparisons help? Why does || '' <> 'AA' in the WHERE clause VERSION_NAME comparison help? Is this a more efficient way of handling possible nulls on this column?

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  • VS 2010 IDE 2GB limt

    - by user561732
    I am using VS 2010 on a win 7 64 bit system with 8 GB of memory. My application is 32 bit. While in the VS 2010 .Net IDE, the app shows up in the Windows task manager as "MyApp.vshost.exe *32" while the VS IDE itself shows up as "devenv.exe *32". I checked and it appears that the VS 2010 IDE file (devenv.exe) is complied with the /LargeAddressAware flag. However, when debugging large models, the IDE fails with an Out of memory exception. In the Windows Task manager, the "MyApp.vshost.exe *32" process indicates about 1400 MB of memory usage (while the "devenv.exe *32" process is well under 500 MB). Is it possible to set the "MyApp.vshost.exe *32" process to be /LargeAddressAware in order to avoid this out of memory situation? If so, how can this be done in the IDE. While setting the final application binary to be /LargeAddressAware would work, I still need to be able to debug the app in the IDE with these type of large models. I should also note that my app has a deep object hierarchy with many collections that together required a lot of memory. However, my issue is not related to trying to create say 1 large array that requires greater then 2 GB of memory etc. I should note that I am able to run the same app in the VB6 IDE and not get an out of memory situation as long as the VB6 IDE is made /LargeAddressAware. In the case of VB6, the IDE and the app being debugged are part of the same process (and not split into 2 as is the case with VS 2010.) The VB6 process can be larger then 3 GB without running into out of memory issues. Ultimately, my objective is to have my app run completely in 64 bit to access more memory. I am hoping that in such cases, the IDE will allow the debugging process to exceed 2 GB without crashing (and certainly more then 1.4 GB as is the current case). However, for now, while 95% of my app is 64 bit, I am calling a legacy COM 32 bit DLL and as such, my entire app is forced to still run in 32 bit mode until I replace that DLL.

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  • Bad performance function in PHP. With large files memory blows up! How can I refactor?

    - by André
    Hi I have a function that strips out lines from files. I'm handling with large files(more than 100Mb). I have the PHP Memory with 256MB but the function that handles with the strip out of lines blows up with a 100MB CSV File. What the function must do is this: Originally I have the CSV like: Copyright (c) 2007 MaxMind LLC. All Rights Reserved. locId,country,region,city,postalCode,latitude,longitude,metroCode,areaCode 1,"O1","","","",0.0000,0.0000,, 2,"AP","","","",35.0000,105.0000,, 3,"EU","","","",47.0000,8.0000,, 4,"AD","","","",42.5000,1.5000,, 5,"AE","","","",24.0000,54.0000,, 6,"AF","","","",33.0000,65.0000,, 7,"AG","","","",17.0500,-61.8000,, 8,"AI","","","",18.2500,-63.1667,, 9,"AL","","","",41.0000,20.0000,, When I pass the CSV file to this function I got: locId,country,region,city,postalCode,latitude,longitude,metroCode,areaCode 1,"O1","","","",0.0000,0.0000,, 2,"AP","","","",35.0000,105.0000,, 3,"EU","","","",47.0000,8.0000,, 4,"AD","","","",42.5000,1.5000,, 5,"AE","","","",24.0000,54.0000,, 6,"AF","","","",33.0000,65.0000,, 7,"AG","","","",17.0500,-61.8000,, 8,"AI","","","",18.2500,-63.1667,, 9,"AL","","","",41.0000,20.0000,, It only strips out the first line, nothing more. The problem is the performance of this function with large files, it blows up the memory. The function is: public function deleteLine($line_no, $csvFileName) { // this function strips a specific line from a file // if a line is stripped, functions returns True else false // // e.g. // deleteLine(-1, xyz.csv); // strip last line // deleteLine(1, xyz.csv); // strip first line // Assigna o nome do ficheiro $filename = $csvFileName; $strip_return=FALSE; $data=file($filename); $pipe=fopen($filename,'w'); $size=count($data); if($line_no==-1) $skip=$size-1; else $skip=$line_no-1; for($line=0;$line<$size;$line++) if($line!=$skip) fputs($pipe,$data[$line]); else $strip_return=TRUE; return $strip_return; } It is possible to refactor this function to not blow up with the 256MB PHP Memory? Give me some clues. Best Regards,

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  • Doing XML extracts with XSLT without having to read the whole DOM tree into memory?

    - by Thorbjørn Ravn Andersen
    I have a situation where I want to extract some information from some very large but regular XML files (just had to do it with a 500 Mb file), and where XSLT would be perfect. Unfortunately those XSLT implementations I am aware of (except the most expensive version of Saxon) does not support only having the necessary part of the DOM read in but reads in the whole tree. This cause the computer to swap to death. The XPath in question is //m/e[contains(.,'foobar') so it is essentially just a grep. Is there an XSLT implementation which can do this? Or an XSLT implementation which given suitable "advice" can do this trick of pruning away the parts in memory which will not be needed again? I'd prefer a Java implementation but both Windows and Linux are viable native platforms. EDIT: The input XML looks like: <log> <!-- Fri Jun 26 12:09:27 CEST 2009 --> <e h='12:09:27,284' l='org.apache.catalina.session.ManagerBase' z='1246010967284' t='ContainerBackgroundProcessor[StandardEngine[Catalina]]' v='10000'> <m>Registering Catalina:type=Manager,path=/axsWHSweb-20090626,host=localhost</m></e> <e h='12:09:27,284' l='org.apache.catalina.session.ManagerBase' z='1246010967284' t='ContainerBackgroundProcessor[StandardEngine[Catalina]]' v='10000'> <m>Force random number initialization starting</m></e> <e h='12:09:27,284' l='org.apache.catalina.session.ManagerBase' z='1246010967284' t='ContainerBackgroundProcessor[StandardEngine[Catalina]]' v='10000'> <m>Getting message digest component for algorithm MD5</m></e> <e h='12:09:27,284' l='org.apache.catalina.session.ManagerBase' z='1246010967284' t='ContainerBackgroundProcessor[StandardEngine[Catalina]]' v='10000'> <m>Completed getting message digest component</m></e> <e h='12:09:27,284' l='org.apache.catalina.session.ManagerBase' z='1246010967284' t='ContainerBackgroundProcessor[StandardEngine[Catalina]]' v='10000'> <m>getDigest() 0</m></e> ...... </log> Essentialy I want to select some m-nodes (and I know the XPath is wrong for that, it was just a quick hack), but maintain the XML layout. EDIT: It appears that STX may be what I am looking for (I can live with another transformation language), and that Joost is an implementation hereof. Any experiences? EDIT: I found that Saxon 6.5.4 with -Xmx1500m could load my XML, so this allowed me to use my XPaths right now. This is just a lucky stroke so I'd still like to solve this generically - this means scriptable which in turn means no handcrafted Java filtering first. EDIT: Oh, by the way. This is a log file very similar to what is generated by the log4j XMLLayout. The reason for XML is to be able to do exactly this, namely do queries on the log. This is the initial try, hence the simple question. Later I'd like to be able to ask more complex questions - therefore I'd like the query language to be able to handle the input file.

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  • jQuery memory game: if $('.opened').length; == number run function.

    - by Carl Papworth
    So I'm trying to change the a.heart when there is td.opened == 24. I'm not sure what's going wrong though since nothings happening. HTML: <body> <header> <div id="headerTitle"><a href="index.html">&lt;html<span class="heart">&hearts;</span>ve&gt;</a> </div> <div id="help"> <h2>?</h2> <div id="helpInfo"> <p>How many tiles are there? Let's see [calculating] 25...</p> </div> </div> </header> <div id="reward"> <div id="rewardContainer"> <div id="rewardBG" class="heart">&hearts; </div> <p>OMG, this must be luv<br><a href="index.html" class="exit">x</a></p> </div> </div> <div id="pageWrap"> <div id="mainContent"> <!-- DON'T BE A CHEATER !--> <table id="memory"> <tr> <td class="pair1"><a>&Psi;</a></td> <td class="pair2"><a>&para;</a></td> <td class="pair3"><a>&Xi;</a></td> <td class="pair1"><a>&Psi;</a></td> <td class="pair4"><a >&otimes;</a></td> </tr> <tr> <td class="pair5"><a>&spades;</a></td> <td class="pair6"><a >&Phi;</a></td> <td class="pair7"><a>&sect;</a></td> <td class="pair8"><a>&clubs;</a></td> <td class="pair4"><a>&otimes;</a></td> </tr> <tr> <td class="pair9"><a>&Omega;</a></td> <td class="pair2"><a>&para;</a></td> <td id="goal"> <a href="#reward" class="heart">&hearts;</a> </td> <td class="pair10"><a>&copy;</a></td> <td class="pair9"><a>&Omega;</a></td> </tr> <tr> <td class="pair11"><a>&there4;</a></td> <td class="pair8"><a>&clubs;</a></td> <td class="pair12"><a>&dagger;</a></td> <td class="pair6"><a>&Phi;</a></td> <td class="pair11"><a>&there4;</a></td> </tr> <tr> <td><a class="pair12">&dagger;</a></td> <td><a class="pair5">&spades;</a></td> <td><a class="pair10">&copy;</a></td> <td><a class="pair3">&Xi;</a></td> <td><a class="pair7">&sect;</a></td> </tr> </table> <!-- DON'T BE A CHEATER !--> </div> </div> <!-- END Page Wrap --> <footer> <div class="heartCollection"> <p>collect us if u need luv:<p> <ul> <li><a id="collection1">&hearts;</a></li> <li><a id="collection2">&hearts;</a></li> <li><a id="collection3">&hearts;</a></li> <li><a id="collection4">&hearts;</a></li> <li><a id="collection5">&hearts;</a></li> <li><a id="collection6">&hearts;</a></li> </ul> </div> <div class="credits">with love from Popm0uth ©2012</div> </footer> </body> </html> Javascript: var thisCard = $(this).text(); var activeCard = $('.active').text(); var openedCards = $('.opened').length; $(document).ready(function() { $('a.heart').css('color', '#CCCCCC'); $('a.heart').off('click'); function reset(){ $('td').removeClass('opened'); $('a').removeClass('visible'); $('td').removeClass('active'); }; $('td').click(openCard); function openCard(){ $(this).addClass('opened'); $(this).find('a').addClass('visible'); if ($(".active")[0]){ if ($(this).text() != $('.active').text()) { setTimeout(function(){ reset(); }, 1000); } else { $('.active').removeClass('active'); } } else { $(this).addClass("active"); } if (openedCards == 24){ $(".active").removeClass("active"); $("a.heart").css('color', '#ff63ff'); $("a.heart").off('click'); } } });

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  • Advanced Data Source Engine coming to Telerik Reporting Q1 2010

    This is the final blog post from the pre-release series. In it we are going to share with you some of the updates coming to our reporting solution in Q1 2010. A new Declarative Data Source Engine will be added to Telerik Reporting, that will allow full control over data management, and deliver significant gains in rendering performance and memory consumption. Some of the engines new features will be: Data source parameters - those parameters will be used to limit data retrieved from the data source to just the data needed for the report. Data source parameters are processed on the data source side, however only queried data is fetched to the reporting engine, rather than the full data source. This leads to lower memory consumption, because data operations are performed on queried data only, rather than on all data. As a result, only the queried data needs to be stored in the memory vs. the whole dataset, which was the case with the old approach Support for stored procedures - they will assist in achieving a consistent implementation of logic across applications, and are especially practical for performing repetitive tasks. A stored procedure stores the SQL statements and logic, which can then be executed in different reports and/or applications. Stored Procedures will not only save development time, but they will also improve performance, because each stored procedure is compiled on the data base server once, and then is reutilized. In Telerik Reporting, the stored procedure will also be parameterized, where elements of the SQL statement will be bound to parameters. These parameterized SQL queries will be handled through the data source parameters, and are evaluated at run time. Using parameterized SQL queries will improve the performance and decrease the memory footprint of your application, because they will be applied directly on the database server and only the necessary data will be downloaded on the middle tier or client machine; Calculated fields through expressions - with the help of the new reporting engine you will be able to use field values in formulas to come up with a calculated field. A calculated field is a user defined field that is computed "on the fly" and does not exist in the data source, but can perform calculations using the data of the data source object it belongs to. Calculated fields are very handy for adding frequently used formulas to your reports; Improved performance and optimized in-memory OLAP engine - the new data source will come with several improvements in how aggregates are calculated, and memory is managed. As a result, you may experience between 30% (for simpler reports) and 400% (for calculation-intensive reports) in rendering performance, and about 50% decrease in memory consumption. Full design time support through wizards - Declarative data sources are a great advance and will save developers countless hours of coding. In Q1 2010, and true to Telerik Reportings essence, using the new data source engine and its features requires little to no coding, because we have extended most of the wizards to support the new functionality. The newly extended wizards are available in VS2005/VS2008/VS2010 design-time. More features will be revealed on the product's what's new page when the new version is officially released in a few days. Also make sure you attend the free webinar on Thursday, March 11th that will be dedicated to the updates in Telerik Reporting Q1 2010. Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Sun Fire X4270 M3 SAP Enhancement Package 4 for SAP ERP 6.0 (Unicode) Two-Tier Standard Sales and Distribution (SD) Benchmark

    - by Brian
    Oracle's Sun Fire X4270 M3 server achieved 8,320 SAP SD Benchmark users running SAP enhancement package 4 for SAP ERP 6.0 with unicode software using Oracle Database 11g and Oracle Solaris 10. The Sun Fire X4270 M3 server using Oracle Database 11g and Oracle Solaris 10 beat both IBM Flex System x240 and IBM System x3650 M4 server running DB2 9.7 and Windows Server 2008 R2 Enterprise Edition. The Sun Fire X4270 M3 server running Oracle Database 11g and Oracle Solaris 10 beat the HP ProLiant BL460c Gen8 server using SQL Server 2008 and Windows Server 2008 R2 Enterprise Edition by 6%. The Sun Fire X4270 M3 server using Oracle Database 11g and Oracle Solaris 10 beat Cisco UCS C240 M3 server running SQL Server 2008 and Windows Server 2008 R2 Datacenter Edition by 9%. The Sun Fire X4270 M3 server running Oracle Database 11g and Oracle Solaris 10 beat the Fujitsu PRIMERGY RX300 S7 server using SQL Server 2008 and Windows Server 2008 R2 Enterprise Edition by 10%. Performance Landscape SAP-SD 2-Tier Performance Table (in decreasing performance order). SAP ERP 6.0 Enhancement Pack 4 (Unicode) Results (benchmark version from January 2009 to April 2012) System OS Database Users SAPERP/ECCRelease SAPS SAPS/Proc Date Sun Fire X4270 M3 2xIntel Xeon E5-2690 @2.90GHz 128 GB Oracle Solaris 10 Oracle Database 11g 8,320 20096.0 EP4(Unicode) 45,570 22,785 10-Apr-12 IBM Flex System x240 2xIntel Xeon E5-2690 @2.90GHz 128 GB Windows Server 2008 R2 EE DB2 9.7 7,960 20096.0 EP4(Unicode) 43,520 21,760 11-Apr-12 HP ProLiant BL460c Gen8 2xIntel Xeon E5-2690 @2.90GHz 128 GB Windows Server 2008 R2 EE SQL Server 2008 7,865 20096.0 EP4(Unicode) 42,920 21,460 29-Mar-12 IBM System x3650 M4 2xIntel Xeon E5-2690 @2.90GHz 128 GB Windows Server 2008 R2 EE DB2 9.7 7,855 20096.0 EP4(Unicode) 42,880 21,440 06-Mar-12 Cisco UCS C240 M3 2xIntel Xeon E5-2690 @2.90GHz 128 GB Windows Server 2008 R2 DE SQL Server 2008 7,635 20096.0 EP4(Unicode) 41,800 20,900 06-Mar-12 Fujitsu PRIMERGY RX300 S7 2xIntel Xeon E5-2690 @2.90GHz 128 GB Windows Server 2008 R2 EE SQL Server 2008 7,570 20096.0 EP4(Unicode) 41,320 20,660 06-Mar-12 Complete benchmark results may be found at the SAP benchmark website http://www.sap.com/benchmark. Configuration and Results Summary Hardware Configuration: Sun Fire X4270 M3 2 x 2.90 GHz Intel Xeon E5-2690 processors 128 GB memory Sun StorageTek 6540 with 4 * 16 * 300GB 15Krpm 4Gb FC-AL Software Configuration: Oracle Solaris 10 Oracle Database 11g SAP enhancement package 4 for SAP ERP 6.0 (Unicode) Certified Results (published by SAP): Number of benchmark users: 8,320 Average dialog response time: 0.95 seconds Throughput: Fully processed order line: 911,330 Dialog steps/hour: 2,734,000 SAPS: 45,570 SAP Certification: 2012014 Benchmark Description The SAP Standard Application SD (Sales and Distribution) Benchmark is a two-tier ERP business test that is indicative of full business workloads of complete order processing and invoice processing, and demonstrates the ability to run both the application and database software on a single system. The SAP Standard Application SD Benchmark represents the critical tasks performed in real-world ERP business environments. SAP is one of the premier world-wide ERP application providers, and maintains a suite of benchmark tests to demonstrate the performance of competitive systems on the various SAP products. See Also SAP Benchmark Website Sun Fire X4270 M3 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Two-tier SAP Sales and Distribution (SD) standard SAP SD benchmark based on SAP enhancement package 4 for SAP ERP 6.0 (Unicode) application benchmark as of 04/11/12: Sun Fire X4270 M3 (2 processors, 16 cores, 32 threads) 8,320 SAP SD Users, 2 x 2.90 GHz Intel Xeon E5-2690, 128 GB memory, Oracle 11g, Solaris 10, Cert# 2012014. IBM Flex System x240 (2 processors, 16 cores, 32 threads) 7,960 SAP SD Users, 2 x 2.90 GHz Intel Xeon E5-2690, 128 GB memory, DB2 9.7, Windows Server 2008 R2 EE, Cert# 2012016. IBM System x3650 M4 (2 processors, 16 cores, 32 threads) 7,855 SAP SD Users, 2 x 2.90 GHz Intel Xeon E5-2690, 128 GB memory, DB2 9.7, Windows Server 2008 R2 EE, Cert# 2012010. Cisco UCS C240 M3 (2 processors, 16 cores, 32 threads) 7,635 SAP SD Users, 2 x 2.90 GHz Intel Xeon E5-2690, 128 GB memory, SQL Server 2008, Windows Server 2008 R2 DE, Cert# 2012011. Fujitsu PRIMERGY RX300 S7 (2 processors, 16 cores, 32 threads) 7,570 SAP SD Users, 2 x 2.90 GHz Intel Xeon E5-2690, 128 GB memory, SQL Server 2008, Windows Server 2008 R2 EE, Cert# 2012008. HP ProLiant DL380p Gen8 (2 processors, 16 cores, 32 threads) 7,865 SAP SD Users, 2 x 2.90 GHz Intel Xeon E5-2690, 128 GB memory, SQL Server 2008, Windows Server 2008 R2 EE, Cert# 2012012. SAP, R/3, reg TM of SAP AG in Germany and other countries. More info www.sap.com/benchmark

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  • How to find and fix performance problems in ORM powered applications

    - by FransBouma
    Once in a while we get requests about how to fix performance problems with our framework. As it comes down to following the same steps and looking into the same things every single time, I decided to write a blogpost about it instead, so more people can learn from this and solve performance problems in their O/R mapper powered applications. In some parts it's focused on LLBLGen Pro but it's also usable for other O/R mapping frameworks, as the vast majority of performance problems in O/R mapper powered applications are not specific for a certain O/R mapper framework. Too often, the developer looks at the wrong part of the application, trying to fix what isn't a problem in that part, and getting frustrated that 'things are so slow with <insert your favorite framework X here>'. I'm in the O/R mapper business for a long time now (almost 10 years, full time) and as it's a small world, we O/R mapper developers know almost all tricks to pull off by now: we all know what to do to make task ABC faster and what compromises (because there are almost always compromises) to deal with if we decide to make ABC faster that way. Some O/R mapper frameworks are faster in X, others in Y, but you can be sure the difference is mainly a result of a compromise some developers are willing to deal with and others aren't. That's why the O/R mapper frameworks on the market today are different in many ways, even though they all fetch and save entities from and to a database. I'm not suggesting there's no room for improvement in today's O/R mapper frameworks, there always is, but it's not a matter of 'the slowness of the application is caused by the O/R mapper' anymore. Perhaps query generation can be optimized a bit here, row materialization can be optimized a bit there, but it's mainly coming down to milliseconds. Still worth it if you're a framework developer, but it's not much compared to the time spend inside databases and in user code: if a complete fetch takes 40ms or 50ms (from call to entity object collection), it won't make a difference for your application as that 10ms difference won't be noticed. That's why it's very important to find the real locations of the problems so developers can fix them properly and don't get frustrated because their quest to get a fast, performing application failed. Performance tuning basics and rules Finding and fixing performance problems in any application is a strict procedure with four prescribed steps: isolate, analyze, interpret and fix, in that order. It's key that you don't skip a step nor make assumptions: these steps help you find the reason of a problem which seems to be there, and how to fix it or leave it as-is. Skipping a step, or when you assume things will be bad/slow without doing analysis will lead to the path of premature optimization and won't actually solve your problems, only create new ones. The most important rule of finding and fixing performance problems in software is that you have to understand what 'performance problem' actually means. Most developers will say "when a piece of software / code is slow, you have a performance problem". But is that actually the case? If I write a Linq query which will aggregate, group and sort 5 million rows from several tables to produce a resultset of 10 rows, it might take more than a couple of milliseconds before that resultset is ready to be consumed by other logic. If I solely look at the Linq query, the code consuming the resultset of the 10 rows and then look at the time it takes to complete the whole procedure, it will appear to me to be slow: all that time taken to produce and consume 10 rows? But if you look closer, if you analyze and interpret the situation, you'll see it does a tremendous amount of work, and in that light it might even be extremely fast. With every performance problem you encounter, always do realize that what you're trying to solve is perhaps not a technical problem at all, but a perception problem. The second most important rule you have to understand is based on the old saying "Penny wise, Pound Foolish": the part which takes e.g. 5% of the total time T for a given task isn't worth optimizing if you have another part which takes a much larger part of the total time T for that same given task. Optimizing parts which are relatively insignificant for the total time taken is not going to bring you better results overall, even if you totally optimize that part away. This is the core reason why analysis of the complete set of application parts which participate in a given task is key to being successful in solving performance problems: No analysis -> no problem -> no solution. One warning up front: hunting for performance will always include making compromises. Fast software can be made maintainable, but if you want to squeeze as much performance out of your software, you will inevitably be faced with the dilemma of compromising one or more from the group {readability, maintainability, features} for the extra performance you think you'll gain. It's then up to you to decide whether it's worth it. In almost all cases it's not. The reason for this is simple: the vast majority of performance problems can be solved by implementing the proper algorithms, the ones with proven Big O-characteristics so you know the performance you'll get plus you know the algorithm will work. The time taken by the algorithm implementing code is inevitable: you already implemented the best algorithm. You might find some optimizations on the technical level but in general these are minor. Let's look at the four steps to see how they guide us through the quest to find and fix performance problems. Isolate The first thing you need to do is to isolate the areas in your application which are assumed to be slow. For example, if your application is a web application and a given page is taking several seconds or even minutes to load, it's a good candidate to check out. It's important to start with the isolate step because it allows you to focus on a single code path per area with a clear begin and end and ignore the rest. The rest of the steps are taken per identified problematic area. Keep in mind that isolation focuses on tasks in an application, not code snippets. A task is something that's started in your application by either another task or the user, or another program, and has a beginning and an end. You can see a task as a piece of functionality offered by your application.  Analyze Once you've determined the problem areas, you have to perform analysis on the code paths of each area, to see where the performance problems occur and which areas are not the problem. This is a multi-layered effort: an application which uses an O/R mapper typically consists of multiple parts: there's likely some kind of interface (web, webservice, windows etc.), a part which controls the interface and business logic, the O/R mapper part and the RDBMS, all connected with either a network or inter-process connections provided by the OS or other means. Each of these parts, including the connectivity plumbing, eat up a part of the total time it takes to complete a task, e.g. load a webpage with all orders of a given customer X. To understand which parts participate in the task / area we're investigating and how much they contribute to the total time taken to complete the task, analysis of each participating task is essential. Start with the code you wrote which starts the task, analyze the code and track the path it follows through your application. What does the code do along the way, verify whether it's correct or not. Analyze whether you have implemented the right algorithms in your code for this particular area. Remember we're looking at one area at a time, which means we're ignoring all other code paths, just the code path of the current problematic area, from begin to end and back. Don't dig in and start optimizing at the code level just yet. We're just analyzing. If your analysis reveals big architectural stupidity, it's perhaps a good idea to rethink the architecture at this point. For the rest, we're analyzing which means we collect data about what could be wrong, for each participating part of the complete application. Reviewing the code you wrote is a good tool to get deeper understanding of what is going on for a given task but ultimately it lacks precision and overview what really happens: humans aren't good code interpreters, computers are. We therefore need to utilize tools to get deeper understanding about which parts contribute how much time to the total task, triggered by which other parts and for example how many times are they called. There are two different kind of tools which are necessary: .NET profilers and O/R mapper / RDBMS profilers. .NET profiling .NET profilers (e.g. dotTrace by JetBrains or Ants by Red Gate software) show exactly which pieces of code are called, how many times they're called, and the time it took to run that piece of code, at the method level and sometimes even at the line level. The .NET profilers are essential tools for understanding whether the time taken to complete a given task / area in your application is consumed by .NET code, where exactly in your code, the path to that code, how many times that code was called by other code and thus reveals where hotspots are located: the areas where a solution can be found. Importantly, they also reveal which areas can be left alone: remember our penny wise pound foolish saying: if a profiler reveals that a group of methods are fast, or don't contribute much to the total time taken for a given task, ignore them. Even if the code in them is perhaps complex and looks like a candidate for optimization: you can work all day on that, it won't matter.  As we're focusing on a single area of the application, it's best to start profiling right before you actually activate the task/area. Most .NET profilers support this by starting the application without starting the profiling procedure just yet. You navigate to the particular part which is slow, start profiling in the profiler, in your application you perform the actions which are considered slow, and afterwards you get a snapshot in the profiler. The snapshot contains the data collected by the profiler during the slow action, so most data is produced by code in the area to investigate. This is important, because it allows you to stay focused on a single area. O/R mapper and RDBMS profiling .NET profilers give you a good insight in the .NET side of things, but not in the RDBMS side of the application. As this article is about O/R mapper powered applications, we're also looking at databases, and the software making it possible to consume the database in your application: the O/R mapper. To understand which parts of the O/R mapper and database participate how much to the total time taken for task T, we need different tools. There are two kind of tools focusing on O/R mappers and database performance profiling: O/R mapper profilers and RDBMS profilers. For O/R mapper profilers, you can look at LLBLGen Prof by hibernating rhinos or the Linq to Sql/LLBLGen Pro profiler by Huagati. Hibernating rhinos also have profilers for other O/R mappers like NHibernate (NHProf) and Entity Framework (EFProf) and work the same as LLBLGen Prof. For RDBMS profilers, you have to look whether the RDBMS vendor has a profiler. For example for SQL Server, the profiler is shipped with SQL Server, for Oracle it's build into the RDBMS, however there are also 3rd party tools. Which tool you're using isn't really important, what's important is that you get insight in which queries are executed during the task / area we're currently focused on and how long they took. Here, the O/R mapper profilers have an advantage as they collect the time it took to execute the query from the application's perspective so they also collect the time it took to transport data across the network. This is important because a query which returns a massive resultset or a resultset with large blob/clob/ntext/image fields takes more time to get transported across the network than a small resultset and a database profiler doesn't take this into account most of the time. Another tool to use in this case, which is more low level and not all O/R mappers support it (though LLBLGen Pro and NHibernate as well do) is tracing: most O/R mappers offer some form of tracing or logging system which you can use to collect the SQL generated and executed and often also other activity behind the scenes. While tracing can produce a tremendous amount of data in some cases, it also gives insight in what's going on. Interpret After we've completed the analysis step it's time to look at the data we've collected. We've done code reviews to see whether we've done anything stupid and which parts actually take place and if the proper algorithms have been implemented. We've done .NET profiling to see which parts are choke points and how much time they contribute to the total time taken to complete the task we're investigating. We've performed O/R mapper profiling and RDBMS profiling to see which queries were executed during the task, how many queries were generated and executed and how long they took to complete, including network transportation. All this data reveals two things: which parts are big contributors to the total time taken and which parts are irrelevant. Both aspects are very important. The parts which are irrelevant (i.e. don't contribute significantly to the total time taken) can be ignored from now on, we won't look at them. The parts which contribute a lot to the total time taken are important to look at. We now have to first look at the .NET profiler results, to see whether the time taken is consumed in our own code, in .NET framework code, in the O/R mapper itself or somewhere else. For example if most of the time is consumed by DbCommand.ExecuteReader, the time it took to complete the task is depending on the time the data is fetched from the database. If there was just 1 query executed, according to tracing or O/R mapper profilers / RDBMS profilers, check whether that query is optimal, uses indexes or has to deal with a lot of data. Interpret means that you follow the path from begin to end through the data collected and determine where, along the path, the most time is contributed. It also means that you have to check whether this was expected or is totally unexpected. My previous example of the 10 row resultset of a query which groups millions of rows will likely reveal that a long time is spend inside the database and almost no time is spend in the .NET code, meaning the RDBMS part contributes the most to the total time taken, the rest is compared to that time, irrelevant. Considering the vastness of the source data set, it's expected this will take some time. However, does it need tweaking? Perhaps all possible tweaks are already in place. In the interpret step you then have to decide that further action in this area is necessary or not, based on what the analysis results show: if the analysis results were unexpected and in the area where the most time is contributed to the total time taken is room for improvement, action should be taken. If not, you can only accept the situation and move on. In all cases, document your decision together with the analysis you've done. If you decide that the perceived performance problem is actually expected due to the nature of the task performed, it's essential that in the future when someone else looks at the application and starts asking questions you can answer them properly and new analysis is only necessary if situations changed. Fix After interpreting the analysis results you've concluded that some areas need adjustment. This is the fix step: you're actively correcting the performance problem with proper action targeted at the real cause. In many cases related to O/R mapper powered applications it means you'll use different features of the O/R mapper to achieve the same goal, or apply optimizations at the RDBMS level. It could also mean you apply caching inside your application (compromise memory consumption over performance) to avoid unnecessary re-querying data and re-consuming the results. After applying a change, it's key you re-do the analysis and interpretation steps: compare the results and expectations with what you had before, to see whether your actions had any effect or whether it moved the problem to a different part of the application. Don't fall into the trap to do partly analysis: do the full analysis again: .NET profiling and O/R mapper / RDBMS profiling. It might very well be that the changes you've made make one part faster but another part significantly slower, in such a way that the overall problem hasn't changed at all. Performance tuning is dealing with compromises and making choices: to use one feature over the other, to accept a higher memory footprint, to go away from the strict-OO path and execute queries directly onto the RDBMS, these are choices and compromises which will cross your path if you want to fix performance problems with respect to O/R mappers or data-access and databases in general. In most cases it's not a big issue: alternatives are often good choices too and the compromises aren't that hard to deal with. What is important is that you document why you made a choice, a compromise: which analysis data, which interpretation led you to the choice made. This is key for good maintainability in the years to come. Most common performance problems with O/R mappers Below is an incomplete list of common performance problems related to data-access / O/R mappers / RDBMS code. It will help you with fixing the hotspots you found in the interpretation step. SELECT N+1: (Lazy-loading specific). Lazy loading triggered performance bottlenecks. Consider a list of Orders bound to a grid. You have a Field mapped onto a related field in Order, Customer.CompanyName. Showing this column in the grid will make the grid fetch (indirectly) for each row the Customer row. This means you'll get for the single list not 1 query (for the orders) but 1+(the number of orders shown) queries. To solve this: use eager loading using a prefetch path to fetch the customers with the orders. SELECT N+1 is easy to spot with an O/R mapper profiler or RDBMS profiler: if you see a lot of identical queries executed at once, you have this problem. Prefetch paths using many path nodes or sorting, or limiting. Eager loading problem. Prefetch paths can help with performance, but as 1 query is fetched per node, it can be the number of data fetched in a child node is bigger than you think. Also consider that data in every node is merged on the client within the parent. This is fast, but it also can take some time if you fetch massive amounts of entities. If you keep fetches small, you can use tuning parameters like the ParameterizedPrefetchPathThreshold setting to get more optimal queries. Deep inheritance hierarchies of type Target Per Entity/Type. If you use inheritance of type Target per Entity / Type (each type in the inheritance hierarchy is mapped onto its own table/view), fetches will join subtype- and supertype tables in many cases, which can lead to a lot of performance problems if the hierarchy has many types. With this problem, keep inheritance to a minimum if possible, or switch to a hierarchy of type Target Per Hierarchy, which means all entities in the inheritance hierarchy are mapped onto the same table/view. Of course this has its own set of drawbacks, but it's a compromise you might want to take. Fetching massive amounts of data by fetching large lists of entities. LLBLGen Pro supports paging (and limiting the # of rows returned), which is often key to process through large sets of data. Use paging on the RDBMS if possible (so a query is executed which returns only the rows in the page requested). When using paging in a web application, be sure that you switch server-side paging on on the datasourcecontrol used. In this case, paging on the grid alone is not enough: this can lead to fetching a lot of data which is then loaded into the grid and paged there. Keep note that analyzing queries for paging could lead to the false assumption that paging doesn't occur, e.g. when the query contains a field of type ntext/image/clob/blob and DISTINCT can't be applied while it should have (e.g. due to a join): the datareader will do DISTINCT filtering on the client. this is a little slower but it does perform paging functionality on the data-reader so it won't fetch all rows even if the query suggests it does. Fetch massive amounts of data because blob/clob/ntext/image fields aren't excluded. LLBLGen Pro supports field exclusion for queries. You can exclude fields (also in prefetch paths) per query to avoid fetching all fields of an entity, e.g. when you don't need them for the logic consuming the resultset. Excluding fields can greatly reduce the amount of time spend on data-transport across the network. Use this optimization if you see that there's a big difference between query execution time on the RDBMS and the time reported by the .NET profiler for the ExecuteReader method call. Doing client-side aggregates/scalar calculations by consuming a lot of data. If possible, try to formulate a scalar query or group by query using the projection system or GetScalar functionality of LLBLGen Pro to do data consumption on the RDBMS server. It's far more efficient to process data on the RDBMS server than to first load it all in memory, then traverse the data in-memory to calculate a value. Using .ToList() constructs inside linq queries. It might be you use .ToList() somewhere in a Linq query which makes the query be run partially in-memory. Example: var q = from c in metaData.Customers.ToList() where c.Country=="Norway" select c; This will actually fetch all customers in-memory and do an in-memory filtering, as the linq query is defined on an IEnumerable<T>, and not on the IQueryable<T>. Linq is nice, but it can often be a bit unclear where some parts of a Linq query might run. Fetching all entities to delete into memory first. To delete a set of entities it's rather inefficient to first fetch them all into memory and then delete them one by one. It's more efficient to execute a DELETE FROM ... WHERE query on the database directly to delete the entities in one go. LLBLGen Pro supports this feature, and so do some other O/R mappers. It's not always possible to do this operation in the context of an O/R mapper however: if an O/R mapper relies on a cache, these kind of operations are likely not supported because they make it impossible to track whether an entity is actually removed from the DB and thus can be removed from the cache. Fetching all entities to update with an expression into memory first. Similar to the previous point: it is more efficient to update a set of entities directly with a single UPDATE query using an expression instead of fetching the entities into memory first and then updating the entities in a loop, and afterwards saving them. It might however be a compromise you don't want to take as it is working around the idea of having an object graph in memory which is manipulated and instead makes the code fully aware there's a RDBMS somewhere. Conclusion Performance tuning is almost always about compromises and making choices. It's also about knowing where to look and how the systems in play behave and should behave. The four steps I provided should help you stay focused on the real problem and lead you towards the solution. Knowing how to optimally use the systems participating in your own code (.NET framework, O/R mapper, RDBMS, network/services) is key for success as well as knowing what's going on inside the application you built. I hope you'll find this guide useful in tracking down performance problems and dealing with them in a useful way.  

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  • Transparency and AlphaBlending

    - by TechTwaddle
    In this post we'll look at the AlphaBlend() api and how it can be used for semi-transparent blitting. AlphaBlend() takes a source device context and a destination device context (DC) and combines the bits in such a way that it gives a transparent effect. Follow the links for the msdn documentation. So lets take a image like, and AlphaBlend() it on our window. The code to do so is below, (under the WM_PAINT message of WndProc) HBITMAP hBitmap=NULL, hBitmapOld=NULL; HDC hMemDC=NULL; BLENDFUNCTION bf; hdc = BeginPaint(hWnd, &ps); hMemDC = CreateCompatibleDC(hdc); hBitmap = LoadBitmap(g_hInst, MAKEINTRESOURCE(IDB_BITMAP1)); hBitmapOld = SelectObject(hMemDC, hBitmap); bf.BlendOp = AC_SRC_OVER; bf.BlendFlags = 0; bf.SourceConstantAlpha = 80; //transparency value between 0-255 bf.AlphaFormat = 0;    AlphaBlend(hdc, 0, 25, 240, 100, hMemDC, 0, 0, 240, 100, bf); SelectObject(hMemDC, hBitmapOld); DeleteDC(hMemDC); DeleteObject(hBitmap); EndPaint(hWnd, &ps);   The code above creates a memory DC (hMemDC) using CreateCompatibleDC(), loads a bitmap onto the memory DC and AlphaBlends it on the device DC (hdc), with a transparency value of 80. The result is: Pretty simple till now. Now lets try to do something a little more exciting. Lets get two images involved, each overlapping the other, giving a better demonstration of transparency. I am also going to add a few buttons so that the user can increase or decrease the transparency by clicking on the buttons. Since this is the first time I played around with GDI apis, I ran into something that everybody runs into sometime or the other, flickering. When clicking the buttons the images would flicker a lot, I figured out why and used something called double buffering to avoid flickering. We will look at both my first implementation and the second implementation just to give the concept a little more depth and perspective. A few pre-conditions before I dive into the code: - hBitmap and hBitmap2 are handles to the two images obtained using LoadBitmap(), these variables are global and are initialized under WM_CREATE - The two buttons in the application are labeled Opaque++ (make more opaque, less transparent) and Opaque-- (make less opaque, more transparent) - DrawPics(HWND hWnd, int step=0); is the function called to draw the images on the screen. This is called from under WM_PAINT and also when the buttons are clicked. When Opaque++ is clicked the 'step' value passed to DrawPics() is +20 and when Opaque-- is clicked the 'step' value is -20. The default value of 'step' is 0 Now lets take a look at my first implementation: //this funciton causes flicker, cos it draws directly to screen several times void DrawPics(HWND hWnd, int step) {     HDC hdc=NULL, hMemDC=NULL;     BLENDFUNCTION bf;     static UINT32 transparency = 100;     //no point in drawing when transparency is 0 and user clicks Opaque--     if (transparency == 0 && step < 0)         return;     //no point in drawing when transparency is 240 (opaque) and user clicks Opaque++     if (transparency == 240 && step > 0)         return;         hdc = GetDC(hWnd);     if (!hdc)         return;     //create a memory DC     hMemDC = CreateCompatibleDC(hdc);     if (!hMemDC)     {         ReleaseDC(hWnd, hdc);         return;     }     //while increasing transparency, clear the contents of screen     if (step < 0)     {         RECT rect = {0, 0, 240, 200};         FillRect(hdc, &rect, (HBRUSH)GetStockObject(WHITE_BRUSH));     }     SelectObject(hMemDC, hBitmap2);     BitBlt(hdc, 0, 25, 240, 100, hMemDC, 0, 0, SRCCOPY);         SelectObject(hMemDC, hBitmap);     transparency += step;     if (transparency >= 240)         transparency = 240;     if (transparency <= 0)         transparency = 0;     bf.BlendOp = AC_SRC_OVER;     bf.BlendFlags = 0;     bf.SourceConstantAlpha = transparency;     bf.AlphaFormat = 0;            AlphaBlend(hdc, 0, 75, 240, 100, hMemDC, 0, 0, 240, 100, bf);     DeleteDC(hMemDC);     ReleaseDC(hWnd, hdc); }   In the code above, we first get the window DC using GetDC() and create a memory DC using CreateCompatibleDC(). Then we select hBitmap2 onto the memory DC and Blt it on the window DC (hdc). Next, we select the other image, hBitmap, onto memory DC and AlphaBlend() it over window DC. As I told you before, this implementation causes flickering because it draws directly on the screen (hdc) several times. The video below shows what happens when the buttons were clicked rapidly: Well, the video recording tool I use captures only 15 frames per second and so the flickering is not visible in the video. So you're gonna have to trust me on this, it flickers (; To solve this problem we make sure that the drawing to the screen happens only once and to do that we create an additional memory DC, hTempDC. We perform all our drawing on this memory DC and finally when it is ready we Blt hTempDC on hdc, and the images are displayed in one go. Here is the code for our new DrawPics() function: //no flicker void DrawPics(HWND hWnd, int step) {     HDC hdc=NULL, hMemDC=NULL, hTempDC=NULL;     BLENDFUNCTION bf;     HBITMAP hBitmapTemp=NULL, hBitmapOld=NULL;     static UINT32 transparency = 100;     //no point in drawing when transparency is 0 and user clicks Opaque--     if (transparency == 0 && step < 0)         return;     //no point in drawing when transparency is 240 (opaque) and user clicks Opaque++     if (transparency == 240 && step > 0)         return;         hdc = GetDC(hWnd);     if (!hdc)         return;     hMemDC = CreateCompatibleDC(hdc);     hTempDC = CreateCompatibleDC(hdc);     hBitmapTemp = CreateCompatibleBitmap(hdc, 240, 150);     hBitmapOld = (HBITMAP)SelectObject(hTempDC, hBitmapTemp);     if (!hMemDC)     {         ReleaseDC(hWnd, hdc);         return;     }     //while increasing transparency, clear the contents     if (step < 0)     {         RECT rect = {0, 0, 240, 150};         FillRect(hTempDC, &rect, (HBRUSH)GetStockObject(WHITE_BRUSH));     }     SelectObject(hMemDC, hBitmap2);     //Blt hBitmap2 directly to hTempDC     BitBlt(hTempDC, 0, 0, 240, 100, hMemDC, 0, 0, SRCCOPY);         SelectObject(hMemDC, hBitmap);     transparency += step;     if (transparency >= 240)         transparency = 240;     if (transparency <= 0)         transparency = 0;     bf.BlendOp = AC_SRC_OVER;     bf.BlendFlags = 0;     bf.SourceConstantAlpha = transparency;     bf.AlphaFormat = 0;            AlphaBlend(hTempDC, 0, 50, 240, 100, hMemDC, 0, 0, 240, 100, bf);     //now hTempDC is ready, blt it directly on hdc     BitBlt(hdc, 0, 25, 240, 150, hTempDC, 0, 0, SRCCOPY);     SelectObject(hTempDC, hBitmapOld);     DeleteObject(hBitmapTemp);     DeleteDC(hMemDC);     DeleteDC(hTempDC);     ReleaseDC(hWnd, hdc); }   This function is very similar to the first version, except for the use of hTempDC. Another point to note is the use of CreateCompatibleBitmap(). When a memory device context is created using CreateCompatibleDC(), the context is exactly one monochrome pixel high and one monochrome pixel wide. So in order for us to draw anything onto hTempDC, we first have to set a bitmap on it. We use CreateCompatibleBitmap() to create a bitmap of required dimension (240x150 above), and then select this bitmap onto hTempDC. Think of it as utilizing an extra canvas, drawing everything on the canvas and finally transferring the contents to the display in one scoop. And with this version the flickering is gone, video follows:   If you want the entire solutions source code then leave a message, I will share the code over SkyDrive.

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  • Create PDF document using iTextSharp in ASP.Net 4.0 and MemoryMappedFile

    - by sreejukg
    In this article I am going to demonstrate how ASP.Net developers can programmatically create PDF documents using iTextSharp. iTextSharp is a software component, that allows developers to programmatically create or manipulate PDF documents. Also this article discusses the process of creating in-memory file, read/write data from/to the in-memory file utilizing the new feature MemoryMappedFile. I have a database of users, where I need to send a notice to all my users as a PDF document. The sending mail part of it is not covered in this article. The PDF document will contain the company letter head, to make it more official. I have a list of users stored in a database table named “tblusers”. For each user I need to send customized message addressed to them personally. The database structure for the users is give below. id Title Full Name 1 Mr. Sreeju Nair K. G. 2 Dr. Alberto Mathews 3 Prof. Venketachalam Now I am going to generate the pdf document that contains some message to the user, in the following format. Dear <Title> <FullName>, The message for the user. Regards, Administrator Also I have an image, bg.jpg that contains the background for the document generated. I have created .Net 4.0 empty web application project named “iTextSharpSample”. First thing I need to do is to download the iTextSharp dll from the source forge. You can find the url for the download here. http://sourceforge.net/projects/itextsharp/files/ I have extracted the Zip file and added the itextsharp.dll as a reference to my project. Also I have added a web form named default.aspx to my project. After doing all this, the solution explorer have the following view. In the default.aspx page, I inserted one grid view and associated it with a SQL Data source control that bind data from tblusers. I have added a button column in the grid view with text “generate pdf”. The output of the page in the browser is as follows. Now I am going to create a pdf document when the user clicking on the Generate PDF button. As I mentioned before, I am going to work with the file in memory, I am not going to create a file in the disk. I added an event handler for button by specifying onrowcommand event handler. My gridview source looks like <asp:GridView ID="GridView1" runat="server" AutoGenerateColumns="False" DataSourceID="SqlDataSource1" Width="481px" CellPadding="4" ForeColor="#333333" GridLines="None" onrowcommand="Generate_PDF" > ………………………………………………………………………….. ………………………………………………………………………….. </asp:GridView> In the code behind, I wrote the corresponding event handler. protected void Generate_PDF(object sender, GridViewCommandEventArgs e) { // The button click event handler code. // I am going to explain the code for this section in the remaining part of the article } The Generate_PDF method is straight forward, It get the title, fullname and message to some variables, then create the pdf using these variables. The code for getting data from the grid view is as follows // get the row index stored in the CommandArgument property int index = Convert.ToInt32(e.CommandArgument); // get the GridViewRow where the command is raised GridViewRow selectedRow = ((GridView)e.CommandSource).Rows[index]; string title = selectedRow.Cells[1].Text; string fullname = selectedRow.Cells[2].Text; string msg = @"There are some changes in the company policy, due to this matter you need to submit your latest address to us. Please update your contact details / personnal details by visiting the member area of the website. ................................... "; since I don’t want to save the file in the disk, I am going the new feature introduced in .Net framework 4, called Memory-Mapped Files. Using Memory-Mapped mapped file, you can created non-persisted memory mapped files, that are not associated with a file in a disk. So I am going to create a temporary file in memory, add the pdf content to it, then write it to the output stream. To read more about MemoryMappedFile, read this msdn article http://msdn.microsoft.com/en-us/library/dd997372.aspx The below portion of the code using MemoryMappedFile object to create a test pdf document in memory and perform read/write operation on file. The CreateViewStream() object will give you a stream that can be used to read or write data to/from file. The code is very straight forward and I included comment so that you can understand the code. using (MemoryMappedFile mmf = MemoryMappedFile.CreateNew("test1.pdf", 1000000)) { // Create a new pdf document object using the constructor. The parameters passed are document size, left margin, right margin, top margin and bottom margin. iTextSharp.text.Document d = new iTextSharp.text.Document(PageSize.A4, 72,72,172,72); //get an instance of the memory mapped file to stream object so that user can write to this using (MemoryMappedViewStream stream = mmf.CreateViewStream()) { // associate the document to the stream. PdfWriter.GetInstance(d, stream); /* add an image as bg*/ iTextSharp.text.Image jpg = iTextSharp.text.Image.GetInstance(Server.MapPath("Image/bg.png")); jpg.Alignment = iTextSharp.text.Image.UNDERLYING; jpg.SetAbsolutePosition(0, 0); //this is the size of my background letter head image. the size is in points. this will fit to A4 size document. jpg.ScaleToFit(595, 842); d.Open(); d.Add(jpg); d.Add(new Paragraph(String.Format("Dear {0} {1},", title, fullname))); d.Add(new Paragraph("\n")); d.Add(new Paragraph(msg)); d.Add(new Paragraph("\n")); d.Add(new Paragraph(String.Format("Administrator"))); d.Close(); } //read the file data byte[] b; using (MemoryMappedViewStream stream = mmf.CreateViewStream()) { BinaryReader rdr = new BinaryReader(stream); b = new byte[mmf.CreateViewStream().Length]; rdr.Read(b, 0, (int)mmf.CreateViewStream().Length); } Response.Clear(); Response.ContentType = "Application/pdf"; Response.BinaryWrite(b); Response.End(); } Press ctrl + f5 to run the application. First I got the user list. Click on the generate pdf icon. The created looks as follows. Summary: Creating pdf document using iTextSharp is easy. You will get lot of information while surfing the www. Some useful resources and references are mentioned below http://itextsharp.com/ http://www.mikesdotnetting.com/Article/82/iTextSharp-Adding-Text-with-Chunks-Phrases-and-Paragraphs http://somewebguy.wordpress.com/2009/05/08/itextsharp-simplify-your-html-to-pdf-creation/ Hope you enjoyed the article.

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