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  • How to style alternating rows in asp.net mvc

    - by YeahStu
    I am using a <% foreach ...% loop to list a collection of items on my website. I want to add a different background color for the style of alternating rows in my list. I have found a way to do this but I am not satisfied with it as it seems like a hack. Here is what I have done to solve it so far: <table> <% int counter = 0; foreach (var item in Model) { counter++; if (counter % 2 == 0) { %> <tr class="colorfull"> <% } else { %> <tr> <% } %> ... Is there a best practice that I am missing that people are using for this scenario in ASP.NET MVC?

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  • Sorting items in php

    - by c0mrade
    Hello, I have a problem with sorting items in table in PHP. Here is what I want to achieve : Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item How I mean to achieve this, well since I have a for each loop I can insert counter, and say after 5th item is listed write to another column, the thing is I'm not good with tables, I've tried something like : for(...) $counter++; if(($counter%5) == 0){ echo ""; } Not happening .. I hope you understood what I meant .. tnx

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  • Using ftplib for multithread uploads

    - by Arty
    I'm trying to do multithread uploads, but get errors. I guessed that maybe it's impossible to use multithreads with ftplib? Here comes my code: class myThread (threading.Thread): def __init__(self, threadID, src, counter, image_name): self.threadID = threadID self.src = src self.counter = counter self.image_name = image_name threading.Thread.__init__(self) def run(self): uploadFile(self.src, self.image_name) def uploadFile(src, image_name): f = open(src, "rb") ftp.storbinary('STOR ' + image_name, f) f.close() ftp = FTP('host') # connect to host, default port ftp.login() # user anonymous, passwd anonymous@ dirname = "/home/folder/" i = 1 threads = [] for image in os.listdir(dirname): if os.path.isfile(dirname + image): thread = myThread(i , dirname + image, i, image ) thread.start() threads.append( thread ) i += 1 for t in threads: t.join() Get bunch of ftplib errors like raise error_reply, resp error_reply: 200 Type set to I If I try to upload one by one, everything works fine

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  • Representing robot's elbow angle in 3-D

    - by Onkar Deshpande
    I am given coordinates of two points in 3-D viz. shoulder point and object point(to which I am supposed to reach). I am also given the length from my shoulder-to-elbow arm and the length of my forearm. I am trying to solve for the unknown position(the position of the joint elbow). I am using cosine rule to find out the elbow angle. Here is my code - #include <stdio.h> #include <math.h> #include <stdlib.h> struct point { double x, y, z; }; struct angles { double clock_wise; double counter_clock_wise; }; double max(double a, double b) { return (a > b) ? a : b; } /* * Check if the combination can make a triangle by considering the fact that sum * of any two sides of a triangle is greater than the remaining side. The * overlapping condition of links is handled separately in main(). */ int valid_triangle(struct point p0, double l0, struct point p1, double l1) { double dist = sqrt(pow((fabs(p1.z - p0.z)), 2) + pow((fabs(p1.y - p0.y)), 2) + pow((fabs(p1.x - p0.x)), 2)); if((max(dist, l0) == dist) && max(dist, l1) == dist) { return (dist < (l0 + l1)); } else if((max(dist, l0) == l0) && (max(l0, l1) == l0)) { return (l0 < (dist + l1)); } else { return (l1 < (dist + l0)); } } /* * Cosine rule is used to find the elbow angle. Positive value indicates a * counter clockwise angle while negative value indicates a clockwise angle. * Since this problem has at max 2 solutions for any given position of P0 and * P1, I am returning a structure of angles which can be used to consider angles * from both direction viz. clockwise-negative and counter-clockwise-positive */ void return_config(struct point p0, double l0, struct point p1, double l1, struct angles *a) { double dist = sqrt(pow((fabs(p1.z - p0.z)), 2) + pow((fabs(p1.y - p0.y)), 2) + pow((fabs(p1.x - p0.x)), 2)); double degrees = (double) acos((l0 * l0 + l1 * l1 - dist * dist) / (2 * l0 * l1)) * (180.0f / 3.1415f); a->clock_wise = -degrees; a->counter_clock_wise = degrees; } int main() { struct point p0, p1; struct angles a; p0.x = 15, p0.y = 4, p0.z = 0; p1.x = 20, p1.y = 4, p1.z = 0; double l0 = 5, l1 = 8; if(valid_triangle(p0, l0, p1, l1)) { printf("Three lengths can make a valid configuration \n"); return_config(p0, l0, p1, l1, &a); printf("Angle of the elbow point (clockwise) = %lf, (counter clockwise) = %lf \n", a.clock_wise, a.counter_clock_wise); } else { double dist = sqrt(pow((fabs(p1.z - p0.z)), 2) + pow((fabs(p1.y - p0.y)), 2) + pow((fabs(p1.x - p0.x)), 2)); if((dist <= (l0 + l1)) && (dist > l0)) { a.clock_wise = -180.0f; a.counter_clock_wise = 180.0f; printf("Angle of the elbow point (clockwise) = %lf, (counter clockwise) = %lf \n", a.clock_wise, a.counter_clock_wise); } else if((dist <= fabs(l0 - l1)) && (dist < l0)){ a.clock_wise = -0.0f; a.counter_clock_wise = 0.0f; printf("Angle of the elbow point (clockwise) = %lf, (counter clockwise) = %lf \n", a.clock_wise, a.counter_clock_wise); } else printf("Given combination cannot make a valid configuration\n"); } return 0; } However, this solution makes sense only in 2-D. Because clockwise and counter-clockwise are meaningless without an axis and direction of rotation. Returning only an angle is technically correct but it leaves a lot of work for the client of this function to use the result in meaningful way. How can I make the changes to get the axis and direction of rotation ? Also, I want to know how many possible solution could be there for this problem. Please let me know your thoughts ! Any help is highly appreciated ...

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  • Need Help With Simple Counting Bug in iPhone SDK

    - by seanny94
    So I'm basically making an app that adds a count to a number and then displays it each time you tap the button. However, the first tap issued doesn't take any action but adds one (just as planned) on the second tap. I've searched to the ends of the earth looking for the solution with no luck, so I'll see what you guys can make of this. :) #import "MainView.h" @implementation MainView int count = 0; -(void)awakeFromNib { counter.text = @"0"; } - (IBAction)addUnit { if(count >= 999) return; NSString *numValue = [[NSString alloc] initWithFormat:@"%d", count++]; counter.text = numValue; [numValue release]; } - (IBAction)subtractUnit { if(count <= 0) return; NSString *numValue = [[NSString alloc] initWithFormat:@"%d", count--]; counter.text = numValue; [numValue release]; } @end

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  • Grouping items in php

    - by c0mrade
    Hello, I have a problem with sorting items in table in PHP. Here is what I want to achieve : Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item How I mean to achieve this, well since I have a for each loop I can insert counter, and say after 5th item is listed write to another column, the thing is I'm not good with tables, I've tried something like : for(...) $counter++; if(($counter%5) == 0){ echo ""; } Not happening .. I hope you understood what I meant .. tnx

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  • SWIG & C/C++ Python API connected - SEGFAULT

    - by user289637
    Hello, my task is to create dual program. At the beginning I start C program that calls throught C/C++ API of Python some Python method. The called method after that call a function that is created with SWIG. I show you my sample also with backtrace from gdb after I am given Segmentation fault. main.c: #include <Python.h> #include <stdio.h> #include "utils.h" int main(int argc, char** argv) { printf("Calling from C !\n"); increment(); int i; for(i = 0; i < 11; ++i) { Py_Initialize(); PyObject *pname = PyString_FromString("py_function"); PyObject *module = PyImport_Import(pname); PyObject *dict = PyModule_GetDict(module); PyObject *func = PyDict_GetItemString(dict, "ink"); PyObject_CallObject(func, NULL); Py_DECREF(module); Py_DECREF(pname); printf("\tbefore finalize\n"); Py_Finalize(); printf("\tafter finalize\n"); } return 0; } utils.c #include <stdio.h> #include "utils.h" void increment(void) { printf("Incremention counter to: %u\n", ++counter); } py_function.py #!/usr/bin/python2.6 '''py_function.py - Python source designed to demonstrate the use of python embedding''' import utils def ink(): print 'I am gonna increment !' utils.increment() and last think is my Makefile & SWIG configure file Makefile: CC=gcc CFLAGS=-c -g -Wall -std=c99 all: main main: main.o utils.o utils_wrap.o $(CC) main.o utils.o -lpython2.6 -o sample swig -Wall -python -o utils_wrap.c utils.i $(CC) utils.o utils_wrap.o -shared -o _utils.so main.o: main.c $(CC) $(CFLAGS) main.c -I/usr/include/python2.6 -o main.o utils.o: utils.c utils.h $(CC) $(CFLAGS) -fPIC utils.c -o $@ utils_wrap.o: utils_wrap.c $(CC) -c -fPIC utils_wrap.c -I/usr/include/python2.6 -o $@ clean: rm -rf *.o The program is called by ./main and there is output: (gdb) run Starting program: /home/marxin/Programming/python2/sample [Thread debugging using libthread_db enabled] Calling from C ! Incremention counter to: 1 I am gonna increment ! Incremention counter to: 2 before finalize after finalize I am gonna increment ! Incremention counter to: 3 before finalize after finalize I am gonna increment ! Incremention counter to: 4 before finalize after finalize Program received signal SIGSEGV, Segmentation fault. 0xb7ed3e4e in PyObject_Malloc () from /usr/lib/libpython2.6.so.1.0 Backtrace: (gdb) backtrace #0 0xb7ed3e4e in PyObject_Malloc () from /usr/lib/libpython2.6.so.1.0 #1 0xb7ca2b2c in ?? () #2 0xb7f8dd40 in ?? () from /usr/lib/libpython2.6.so.1.0 #3 0xb7eb014c in ?? () from /usr/lib/libpython2.6.so.1.0 #4 0xb7f86ff4 in ?? () from /usr/lib/libpython2.6.so.1.0 #5 0xb7f99820 in ?? () from /usr/lib/libpython2.6.so.1.0 #6 0x00000001 in ?? () #7 0xb7f8dd40 in ?? () from /usr/lib/libpython2.6.so.1.0 #8 0xb7f4f014 in _PyObject_GC_Malloc () from /usr/lib/libpython2.6.so.1.0 #9 0xb7f99820 in ?? () from /usr/lib/libpython2.6.so.1.0 #10 0xb7f4f104 in _PyObject_GC_NewVar () from /usr/lib/libpython2.6.so.1.0 #11 0xb7ee8760 in _PyType_Lookup () from /usr/lib/libpython2.6.so.1.0 #12 0xb7f99820 in ?? () from /usr/lib/libpython2.6.so.1.0 #13 0x00000001 in ?? () #14 0xb7f8dd40 in ?? () from /usr/lib/libpython2.6.so.1.0 #15 0xb7ef13ed in ?? () from /usr/lib/libpython2.6.so.1.0 #16 0xb7f86ff4 in ?? () from /usr/lib/libpython2.6.so.1.0 #17 0x00000001 in ?? () #18 0xbfff0c34 in ?? () #19 0xb7e993c3 in ?? () from /usr/lib/libpython2.6.so.1.0 #20 0x00000001 in ?? () #21 0xbfff0c70 in ?? () #22 0xb7f99da0 in ?? () from /usr/lib/libpython2.6.so.1.0 #23 0xb7f86ff4 in ?? () from /usr/lib/libpython2.6.so.1.0 #24 0xb7f86ff4 in ?? () from /usr/lib/libpython2.6.so.1.0 #25 0x080a6b0c in ?? () #26 0x080a6b0c in ?? () #27 0xb7e99420 in PyObject_CallFunctionObjArgs () from /usr/lib/libpython2.6.so.1.0 #28 0xb7f86ff4 in ?? () from /usr/lib/libpython2.6.so.1.0 #29 0xb7f86ff4 in ?? () from /usr/lib/libpython2.6.so.1.0 #30 0x800e55eb in ?? () #31 0x080a6b0c in ?? () #32 0xb7e9958c in PyObject_IsSubclass () from /usr/lib/libpython2.6.so.1.0 #33 0xb7f8dd40 in ?? () from /usr/lib/libpython2.6.so.1.0 #34 0x080a9020 in ?? () #35 0xb7fb78f0 in PyFPE_counter () from /usr/lib/libpython2.6.so.1.0 #36 0xb7f86ff4 in ?? () from /usr/lib/libpython2.6.so.1.0 #37 0x00000000 in ?? () Thanks for your help and advices, marxin

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  • How to calculate number of leap years between two years in C#

    - by Vlad Bezden
    Hi All, Is there a better way to calculate number of leap years between two years. Assuming I have start date and end date. I have my code, but I think there should be more elegant way. calling code: var numberOfLeapYears = NumberOfLeapYears(startDate.Year + 1, endDate.Year - 1); function itself: private static int NumberOfLeapYears(int startYear, int endYear) { var counter = 0; for (var year = startYear; year <= endYear; year++) counter += DateTime.IsLeapYear(year) ? 1 : 0; return counter; } So if I have startDate = "10/16/2006" and endDate = "4/18/2004" I should only have 1 leap year (2000) in result. Another words startDate's Year and endDate's year should not be calculated, only years in between. Thanks in advance for your help.

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  • implementing keepalives with Java

    - by Bilal
    Hi All, I am biulding a client-server application where I have to implement a keepalive mechanism in order to detect that the client has crashed or not. I have separate threads on both client and server side. the client thread sends a "ping" then sleeps for 3 seconds, while the server reads the BufferedInput Stream and checks whether ping is received, if so it makes the ping counter eqauls zero, else it increments the counter by +1, the server thread then sleeps for 3 seconds, if the ping counter reaches 3, it daclares the client as dead. The problem is that when the server reads the input stream, its a blocking call, and it blocks untill the next ping is received, irrespective of how delayed it is, so the server never detects a missed ping. any suggestions, so that I can read the current value of the stream and it doesn't block if there is nothing on the incoming stream. Thanks,

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  • Access variable value using string representing variable's name

    - by Paul Ridgway
    Hello everyone, If the title was not clear, I will try to clarify what I am asking: Imagine I have a variable called counter, I know I can see its current value by doing something like: std::cout << counter << std::endl; However, assume I have lots of variables and I don't know which I'm going to want to look at until runtime. Does anyone know a way I can fetch the value of a variable by using its name, for example: std::cout << valueOf("counter") << std::endl; I feel being able to do this might make debugging large complex projects easier. Thanks in advance for your time.

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  • Simple loop, which one I would get more performance and which one is recommended? defining a variable inside a loop or outside of it?

    - by Grego
    Variable outside of the loop int number = 0; for(int i = 0; i < 10000; i++){ number = 3 * i; printf("%d",number); } or Variable inside of the loop for(int i = 0; i < 10000; i++){ int number = 3 * i; printf("%d",number); } Which one is recommended and which one is better in performance? Edit: This is just an example to exhibit what I mean, All I wanna know is if defining a variable inside a loop and outside a loop means the same thing , or there's a difference.

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  • C++ Access variable value using string representing variable's name

    - by Paul Ridgway
    Hello everyone, If the title was not clear, I will try to clarify what I am asking: Imagine I have a variable called counter, I know I can see its current value by doing something like: std::cout << counter << std::endl; However, assume I have lots of variables and I don't know which I'm going to want to look at until runtime. Does anyone know a way I can fetch the value of a variable by using its name, for example: std::cout << valueOf("counter") << std::endl; I feel being able to do this might make debugging large complex projects easier. Thanks in advance for your time. PS: Please do not respond with 'Google it', I have, though maybe not with the best query to get the answer I'm looking for...

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  • Java Double Array

    - by user1062058
    I'm having trouble setting up and placing values into an array using a text file containing the floating point numbers 2.1 and 4.3 each number is separated by a space - below is the error I'm getting: Exception in thread "main" java.util.NoSuchElementException import java.util.*; import java.io.*; public class DoubleArray { public static void main(String[] args) throws FileNotFoundException { Scanner in = new Scanner(new FileReader("mytestnumbers.txt")); double [] nums = new double[2]; for (int counter=0; counter < 2; counter++) { int index = 0; index++; nums[index] = in.nextDouble(); } } } Thanks, I'm sure this isn't a hard question to answer... I appreciate your time.

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  • Extract inputs from a pointer to array of characters in C / C++

    - by user2066884
    I am writing a command line utility but I cannot find a way to store the commands and arguments. so far I have the following but I get a Segmentation fault: int main(void) { char *command; char *args[MAX_LINE/2 + 1]; int should_run = 1; do{ cout << "cmd> "; int counter = 0; while(cin >> command) { strcpy(args[counter],command); counter++; } cout << args[0] << "\n"; } }

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  • C# string to combobox issues

    - by Mike
    What i'm trying to do is read in a header row of a file to a combobox. Here is my code: private void button4_Click(object sender, EventArgs e) { string[] startdelim = File.ReadAllLines(textBox1.Text); int counter = 1; foreach (string delim in startdelim) { if (counter == 1) { string removedelim = delim.Replace("\"", ""); string[] lines = removedelim.IndexOf(","); foreach (string line in lines) { comboBox1.Items.Add(line); } } counter++; } for some reason it keeps telling me Error Cannot implicitly convert type 'int' to 'string[]' at string[] lines = removedelim.IndexOf(",");

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  • How to check the type name of an object in derived classes?

    - by Vincenzo
    This is my code: class Base { /* something */ }; class Derived : public Base { /* something */ }; vector<Base*> v; // somebody else initializes it, somewhere int counter = 0; for (vector<Base*>::iterator i=v.begin(); i!=v.end(); ++i) { if (typeof(*i) == "Derived") { // this line is NOT correct counter++; } } cout << "Found " << counter << " derived classes"; One line in the code is NOT correct. How should I write it properly? Many thanks in advance!

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  • SQL SERVER – SSIS Look Up Component – Cache Mode – Notes from the Field #028

    - by Pinal Dave
    [Notes from Pinal]: Lots of people think that SSIS is all about arranging various operations together in one logical flow. Well, the understanding is absolutely correct, but the implementation of the same is not as easy as it seems. Similarly most of the people think lookup component is just component which does look up for additional information and does not pay much attention to it. Due to the same reason they do not pay attention to the same and eventually get very bad performance. Linchpin People are database coaches and wellness experts for a data driven world. In this 28th episode of the Notes from the Fields series database expert Tim Mitchell (partner at Linchpin People) shares very interesting conversation related to how to write a good lookup component with Cache Mode. In SQL Server Integration Services, the lookup component is one of the most frequently used tools for data validation and completion.  The lookup component is provided as a means to virtually join one set of data to another to validate and/or retrieve missing values.  Properly configured, it is reliable and reasonably fast. Among the many settings available on the lookup component, one of the most critical is the cache mode.  This selection will determine whether and how the distinct lookup values are cached during package execution.  It is critical to know how cache modes affect the result of the lookup and the performance of the package, as choosing the wrong setting can lead to poorly performing packages, and in some cases, incorrect results. Full Cache The full cache mode setting is the default cache mode selection in the SSIS lookup transformation.  Like the name implies, full cache mode will cause the lookup transformation to retrieve and store in SSIS cache the entire set of data from the specified lookup location.  As a result, the data flow in which the lookup transformation resides will not start processing any data buffers until all of the rows from the lookup query have been cached in SSIS. The most commonly used cache mode is the full cache setting, and for good reason.  The full cache setting has the most practical applications, and should be considered the go-to cache setting when dealing with an untested set of data. With a moderately sized set of reference data, a lookup transformation using full cache mode usually performs well.  Full cache mode does not require multiple round trips to the database, since the entire reference result set is cached prior to data flow execution. There are a few potential gotchas to be aware of when using full cache mode.  First, you can see some performance issues – memory pressure in particular – when using full cache mode against large sets of reference data.  If the table you use for the lookup is very large (either deep or wide, or perhaps both), there’s going to be a performance cost associated with retrieving and caching all of that data.  Also, keep in mind that when doing a lookup on character data, full cache mode will always do a case-sensitive (and in some cases, space-sensitive) string comparison even if your database is set to a case-insensitive collation.  This is because the in-memory lookup uses a .NET string comparison (which is case- and space-sensitive) as opposed to a database string comparison (which may be case sensitive, depending on collation).  There’s a relatively easy workaround in which you can use the UPPER() or LOWER() function in the pipeline data and the reference data to ensure that case differences do not impact the success of your lookup operation.  Again, neither of these present a reason to avoid full cache mode, but should be used to determine whether full cache mode should be used in a given situation. Full cache mode is ideally useful when one or all of the following conditions exist: The size of the reference data set is small to moderately sized The size of the pipeline data set (the data you are comparing to the lookup table) is large, is unknown at design time, or is unpredictable Each distinct key value(s) in the pipeline data set is expected to be found multiple times in that set of data Partial Cache When using the partial cache setting, lookup values will still be cached, but only as each distinct value is encountered in the data flow.  Initially, each distinct value will be retrieved individually from the specified source, and then cached.  To be clear, this is a row-by-row lookup for each distinct key value(s). This is a less frequently used cache setting because it addresses a narrower set of scenarios.  Because each distinct key value(s) combination requires a relational round trip to the lookup source, performance can be an issue, especially with a large pipeline data set to be compared to the lookup data set.  If you have, for example, a million records from your pipeline data source, you have the potential for doing a million lookup queries against your lookup data source (depending on the number of distinct values in the key column(s)).  Therefore, one has to be keenly aware of the expected row count and value distribution of the pipeline data to safely use partial cache mode. Using partial cache mode is ideally suited for the conditions below: The size of the data in the pipeline (more specifically, the number of distinct key column) is relatively small The size of the lookup data is too large to effectively store in cache The lookup source is well indexed to allow for fast retrieval of row-by-row values No Cache As you might guess, selecting no cache mode will not add any values to the lookup cache in SSIS.  As a result, every single row in the pipeline data set will require a query against the lookup source.  Since no data is cached, it is possible to save a small amount of overhead in SSIS memory in cases where key values are not reused.  In the real world, I don’t see a lot of use of the no cache setting, but I can imagine some edge cases where it might be useful. As such, it’s critical to know your data before choosing this option.  Obviously, performance will be an issue with anything other than small sets of data, as the no cache setting requires row-by-row processing of all of the data in the pipeline. I would recommend considering the no cache mode only when all of the below conditions are true: The reference data set is too large to reasonably be loaded into SSIS memory The pipeline data set is small and is not expected to grow There are expected to be very few or no duplicates of the key values(s) in the pipeline data set (i.e., there would be no benefit from caching these values) Conclusion The cache mode, an often-overlooked setting on the SSIS lookup component, represents an important design decision in your SSIS data flow.  Choosing the right lookup cache mode directly impacts the fidelity of your results and the performance of package execution.  Know how this selection impacts your ETL loads, and you’ll end up with more reliable, faster packages. If you want me to take a look at your server and its settings, or if your server is facing any issue we can Fix Your SQL Server. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Notes from the Field, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: SSIS

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  • Benchmarking MySQL Replication with Multi-Threaded Slaves

    - by Mat Keep
    0 0 1 1145 6530 Homework 54 15 7660 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} The objective of this benchmark is to measure the performance improvement achieved when enabling the Multi-Threaded Slave enhancement delivered as a part MySQL 5.6. As the results demonstrate, Multi-Threaded Slaves delivers 5x higher replication performance based on a configuration with 10 databases/schemas. For real-world deployments, higher replication performance directly translates to: · Improved consistency of reads from slaves (i.e. reduced risk of reading "stale" data) · Reduced risk of data loss should the master fail before replicating all events in its binary log (binlog) The multi-threaded slave splits processing between worker threads based on schema, allowing updates to be applied in parallel, rather than sequentially. This delivers benefits to those workloads that isolate application data using databases - e.g. multi-tenant systems deployed in cloud environments. Multi-Threaded Slaves are just one of many enhancements to replication previewed as part of the MySQL 5.6 Development Release, which include: · Global Transaction Identifiers coupled with MySQL utilities for automatic failover / switchover and slave promotion · Crash Safe Slaves and Binlog · Optimized Row Based Replication · Replication Event Checksums · Time Delayed Replication These and many more are discussed in the “MySQL 5.6 Replication: Enabling the Next Generation of Web & Cloud Services” Developer Zone article  Back to the benchmark - details are as follows. Environment The test environment consisted of two Linux servers: · one running the replication master · one running the replication slave. Only the slave was involved in the actual measurements, and was based on the following configuration: - Hardware: Oracle Sun Fire X4170 M2 Server - CPU: 2 sockets, 6 cores with hyper-threading, 2930 MHz. - OS: 64-bit Oracle Enterprise Linux 6.1 - Memory: 48 GB Test Procedure Initial Setup: Two MySQL servers were started on two different hosts, configured as replication master and slave. 10 sysbench schemas were created, each with a single table: CREATE TABLE `sbtest` (    `id` int(10) unsigned NOT NULL AUTO_INCREMENT,    `k` int(10) unsigned NOT NULL DEFAULT '0',    `c` char(120) NOT NULL DEFAULT '',    `pad` char(60) NOT NULL DEFAULT '',    PRIMARY KEY (`id`),    KEY `k` (`k`) ) ENGINE=InnoDB DEFAULT CHARSET=latin1 10,000 rows were inserted in each of the 10 tables, for a total of 100,000 rows. When the inserts had replicated to the slave, the slave threads were stopped. The slave data directory was copied to a backup location and the slave threads position in the master binlog noted. 10 sysbench clients, each configured with 10 threads, were spawned at the same time to generate a random schema load against each of the 10 schemas on the master. Each sysbench client executed 10,000 "update key" statements: UPDATE sbtest set k=k+1 WHERE id = <random row> In total, this generated 100,000 update statements to later replicate during the test itself. Test Methodology: The number of slave workers to test with was configured using: SET GLOBAL slave_parallel_workers=<workers> Then the slave IO thread was started and the test waited for all the update queries to be copied over to the relay log on the slave. The benchmark clock was started and then the slave SQL thread was started. The test waited for the slave SQL thread to finish executing the 100k update queries, doing "select master_pos_wait()". When master_pos_wait() returned, the benchmark clock was stopped and the duration calculated. The calculated duration from the benchmark clock should be close to the time it took for the SQL thread to execute the 100,000 update queries. The 100k queries divided by this duration gave the benchmark metric, reported as Queries Per Second (QPS). Test Reset: The test-reset cycle was implemented as follows: · the slave was stopped · the slave data directory replaced with the previous backup · the slave restarted with the slave threads replication pointer repositioned to the point before the update queries in the binlog. The test could then be repeated with identical set of queries but a different number of slave worker threads, enabling a fair comparison. The Test-Reset cycle was repeated 3 times for 0-24 number of workers and the QPS metric calculated and averaged for each worker count. MySQL Configuration The relevant configuration settings used for MySQL are as follows: binlog-format=STATEMENT relay-log-info-repository=TABLE master-info-repository=TABLE As described in the test procedure, the slave_parallel_workers setting was modified as part of the test logic. The consequence of changing this setting is: 0 worker threads:    - current (i.e. single threaded) sequential mode    - 1 x IO thread and 1 x SQL thread    - SQL thread both reads and executes the events 1 worker thread:    - sequential mode    - 1 x IO thread, 1 x Coordinator SQL thread and 1 x Worker thread    - coordinator reads the event and hands it to the worker who executes 2+ worker threads:    - parallel execution    - 1 x IO thread, 1 x Coordinator SQL thread and 2+ Worker threads    - coordinator reads events and hands them to the workers who execute them Results Figure 1 below shows that Multi-Threaded Slaves deliver ~5x higher replication performance when configured with 10 worker threads, with the load evenly distributed across our 10 x schemas. This result is compared to the current replication implementation which is based on a single SQL thread only (i.e. zero worker threads). Figure 1: 5x Higher Performance with Multi-Threaded Slaves The following figure shows more detailed results, with QPS sampled and reported as the worker threads are incremented. The raw numbers behind this graph are reported in the Appendix section of this post. Figure 2: Detailed Results As the results above show, the configuration does not scale noticably from 5 to 9 worker threads. When configured with 10 worker threads however, scalability increases significantly. The conclusion therefore is that it is desirable to configure the same number of worker threads as schemas. Other conclusions from the results: · Running with 1 worker compared to zero workers just introduces overhead without the benefit of parallel execution. · As expected, having more workers than schemas adds no visible benefit. Aside from what is shown in the results above, testing also demonstrated that the following settings had a very positive effect on slave performance: relay-log-info-repository=TABLE master-info-repository=TABLE For 5+ workers, it was up to 2.3 times as fast to run with TABLE compared to FILE. Conclusion As the results demonstrate, Multi-Threaded Slaves deliver significant performance increases to MySQL replication when handling multiple schemas. This, and the other replication enhancements introduced in MySQL 5.6 are fully available for you to download and evaluate now from the MySQL Developer site (select Development Release tab). You can learn more about MySQL 5.6 from the documentation  Please don’t hesitate to comment on this or other replication blogs with feedback and questions. Appendix – Detailed Results

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  • How to compile a C++ source code written for Linux/Unix on Windows Vista (code given)

    - by HTMZ
    I have a c++ source code that was written in linux/unix environment by some other author. It gives me errors when i compile it in windows vista environment. I am using Bloodshed Dev C++ v 4.9. please help. #include <iostream.h> #include <map> #include <vector> #include <string> #include <string.h> #include <strstream> #include <unistd.h> #include <stdlib.h> using namespace std; template <class T> class PrefixSpan { private: vector < vector <T> > transaction; vector < pair <T, unsigned int> > pattern; unsigned int minsup; unsigned int minpat; unsigned int maxpat; bool all; bool where; string delimiter; bool verbose; ostream *os; void report (vector <pair <unsigned int, int> > &projected) { if (minpat > pattern.size()) return; // print where & pattern if (where) { *os << "<pattern>" << endl; // what: if (all) { *os << "<freq>" << pattern[pattern.size()-1].second << "</freq>" << endl; *os << "<what>"; for (unsigned int i = 0; i < pattern.size(); i++) *os << (i ? " " : "") << pattern[i].first; } else { *os << "<what>"; for (unsigned int i = 0; i < pattern.size(); i++) *os << (i ? " " : "") << pattern[i].first << delimiter << pattern[i].second; } *os << "</what>" << endl; // where *os << "<where>"; for (unsigned int i = 0; i < projected.size(); i++) *os << (i ? " " : "") << projected[i].first; *os << "</where>" << endl; *os << "</pattern>" << endl; } else { // print found pattern only if (all) { *os << pattern[pattern.size()-1].second; for (unsigned int i = 0; i < pattern.size(); i++) *os << " " << pattern[i].first; } else { for (unsigned int i = 0; i < pattern.size(); i++) *os << (i ? " " : "") << pattern[i].first << delimiter << pattern[i].second; } *os << endl; } } void project (vector <pair <unsigned int, int> > &projected) { if (all) report(projected); map <T, vector <pair <unsigned int, int> > > counter; for (unsigned int i = 0; i < projected.size(); i++) { int pos = projected[i].second; unsigned int id = projected[i].first; unsigned int size = transaction[id].size(); map <T, int> tmp; for (unsigned int j = pos + 1; j < size; j++) { T item = transaction[id][j]; if (tmp.find (item) == tmp.end()) tmp[item] = j ; } for (map <T, int>::iterator k = tmp.begin(); k != tmp.end(); ++k) counter[k->first].push_back (make_pair <unsigned int, int> (id, k->second)); } for (map <T, vector <pair <unsigned int, int> > >::iterator l = counter.begin (); l != counter.end (); ) { if (l->second.size() < minsup) { map <T, vector <pair <unsigned int, int> > >::iterator tmp = l; tmp = l; ++tmp; counter.erase (l); l = tmp; } else { ++l; } } if (! all && counter.size () == 0) { report (projected); return; } for (map <T, vector <pair <unsigned int, int> > >::iterator l = counter.begin (); l != counter.end(); ++l) { if (pattern.size () < maxpat) { pattern.push_back (make_pair <T, unsigned int> (l->first, l->second.size())); project (l->second); pattern.erase (pattern.end()); } } } public: PrefixSpan (unsigned int _minsup = 1, unsigned int _minpat = 1, unsigned int _maxpat = 0xffffffff, bool _all = false, bool _where = false, string _delimiter = "/", bool _verbose = false): minsup(_minsup), minpat (_minpat), maxpat (_maxpat), all(_all), where(_where), delimiter (_delimiter), verbose (_verbose) {}; ~PrefixSpan () {}; istream& read (istream &is) { string line; vector <T> tmp; T item; while (getline (is, line)) { tmp.clear (); istrstream istrs ((char *)line.c_str()); while (istrs >> item) tmp.push_back (item); transaction.push_back (tmp); } return is; } ostream& run (ostream &_os) { os = &_os; if (verbose) *os << transaction.size() << endl; vector <pair <unsigned int, int> > root; for (unsigned int i = 0; i < transaction.size(); i++) root.push_back (make_pair (i, -1)); project (root); return *os; } void clear () { transaction.clear (); pattern.clear (); } }; int main (int argc, char **argv) { extern char *optarg; unsigned int minsup = 1; unsigned int minpat = 1; unsigned int maxpat = 0xffffffff; bool all = false; bool where = false; string delimiter = "/"; bool verbose = false; string type = "string"; int opt; while ((opt = getopt(argc, argv, "awvt:M:m:L:d:")) != -1) { switch(opt) { case 'a': all = true; break; case 'w': where = true; break; case 'v': verbose = true; break; case 'm': minsup = atoi (optarg); break; case 'M': minpat = atoi (optarg); break; case 'L': maxpat = atoi (optarg); break; case 't': type = string (optarg); break; case 'd': delimiter = string (optarg); break; default: cout << "Usage: " << argv[0] << " [-m minsup] [-M minpat] [-L maxpat] [-a] [-w] [-v] [-t type] [-d delimiter] < data .." << endl; return -1; } } if (type == "int") { PrefixSpan<unsigned int> prefixspan (minsup, minpat, maxpat, all, where, delimiter, verbose); prefixspan.read (cin); prefixspan.run (cout); }else if (type == "short") { PrefixSpan<unsigned short> prefixspan (minsup, minpat, maxpat, all, where, delimiter, verbose); prefixspan.read (cin); prefixspan.run (cout); } else if (type == "char") { PrefixSpan<unsigned char> prefixspan (minsup, minpat, maxpat, all, where, delimiter, verbose); prefixspan.read (cin); prefixspan.run (cout); } else if (type == "string") { PrefixSpan<string> prefixspan (minsup, minpat, maxpat, all, where, delimiter, verbose); prefixspan.read (cin); prefixspan.run (cout); } else { cerr << "Unknown Item Type: " << type << " : choose from [string|int|short|char]" << endl; return -1; } return 0; }

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  • Benchmarking Linux flash player and google chrome built in flash player

    - by Fischer
    I use xubuntu 14.04 64 bit, I installed flash player from software center and xubuntu-restricted-extras too Are there any benchmarks on Linux flash player and google chrome built in flash player? I just want to see their performance because in theory google's flash player should be more updated and have better performance than the one we use in Firefox. (that's what I read everywhere) I have chrome latest version installed and Firefox next, and I found that flash videos in Chrome are laggy and they take long time to load. While the same flash videos load much faster in Firefox and I tend to prefer watching flash videos in firefox, especially the long ones because it loads them so much faster. I can't believe these results on my PC, so is there any way to benchmark flash players performance on both browsers? I want to know if it's because of the flash player or the browsers or something else

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  • Mark Hurd and Balaji Yelamanchili present Oracle’s Business Analytics Strategy

    - by Mike.Hallett(at)Oracle-BI&EPM
    Join Mark Hurd and Balaji Yelamanchili as they unveil the latest advances in Oracle’s strategy for placing analytics into the hands of every decision-makers—so that they can see more, think smarter, and act faster. Wednesday, April 4, 2012   at 1.0 pm UK BST / 2.0 pm CET Register HERE today for this online event Agenda Keynote: Oracle’s Business Analytics StrategyMark Hurd, President, Oracle, and Balaji Yelamanchili, Senior Vice President, Analytics and Performance Management, Oracle Plus Breakout Sessions: Achieving Predictable Performance with Oracle Hyperion Enterprise Performance Managemen Explore All Relevant Data—Introducing Oracle Endeca Information Discovery Run Your Business Faster and Smarter with Oracle Business Intelligence Applications on Oracle Exalytics In-Memory Machine Analyzing and Deciding with Big Data

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  • SQL SERVER – Guest Post – Jonathan Kehayias – Wait Type – Day 16 of 28

    - by pinaldave
    Jonathan Kehayias (Blog | Twitter) is a MCITP Database Administrator and Developer, who got started in SQL Server in 2004 as a database developer and report writer in the natural gas industry. After spending two and a half years working in TSQL, in late 2006, he transitioned to the role of SQL Database Administrator. His primary passion is performance tuning, where he frequently rewrites queries for better performance and performs in depth analysis of index implementation and usage. Jonathan blogs regularly on SQLBlog, and was a coauthor of Professional SQL Server 2008 Internals and Troubleshooting. On a personal note, I think Jonathan is extremely positive person. In every conversation with him I have found that he is always eager to help and encourage. Every time he finds something needs to be approved, he has contacted me without hesitation and guided me to improve, change and learn. During all the time, he has not lost his focus to help larger community. I am honored that he has accepted to provide his views on complex subject of Wait Types and Queues. Currently I am reading his series on Extended Events. Here is the guest blog post by Jonathan: SQL Server troubleshooting is all about correlating related pieces of information together to indentify where exactly the root cause of a problem lies. In my daily work as a DBA, I generally get phone calls like, “So and so application is slow, what’s wrong with the SQL Server.” One of the funny things about the letters DBA is that they go so well with Default Blame Acceptor, and I really wish that I knew exactly who the first person was that pointed that out to me, because it really fits at times. A lot of times when I get this call, the problem isn’t related to SQL Server at all, but every now and then in my initial quick checks, something pops up that makes me start looking at things further. The SQL Server is slow, we see a number of tasks waiting on ASYNC_IO_COMPLETION, IO_COMPLETION, or PAGEIOLATCH_* waits in sys.dm_exec_requests and sys.dm_exec_waiting_tasks. These are also some of the highest wait types in sys.dm_os_wait_stats for the server, so it would appear that we have a disk I/O bottleneck on the machine. A quick check of sys.dm_io_virtual_file_stats() and tempdb shows a high write stall rate, while our user databases show high read stall rates on the data files. A quick check of some performance counters and Page Life Expectancy on the server is bouncing up and down in the 50-150 range, the Free Page counter consistently hits zero, and the Free List Stalls/sec counter keeps jumping over 10, but Buffer Cache Hit Ratio is 98-99%. Where exactly is the problem? In this case, which happens to be based on a real scenario I faced a few years back, the problem may not be a disk bottleneck at all; it may very well be a memory pressure issue on the server. A quick check of the system spec’s and it is a dual duo core server with 8GB RAM running SQL Server 2005 SP1 x64 on Windows Server 2003 R2 x64. Max Server memory is configured at 6GB and we think that this should be enough to handle the workload; or is it? This is a unique scenario because there are a couple of things happening inside of this system, and they all relate to what the root cause of the performance problem is on the system. If we were to query sys.dm_exec_query_stats for the TOP 10 queries, by max_physical_reads, max_logical_reads, and max_worker_time, we may be able to find some queries that were using excessive I/O and possibly CPU against the system in their worst single execution. We can also CROSS APPLY to sys.dm_exec_sql_text() and see the statement text, and also CROSS APPLY sys.dm_exec_query_plan() to get the execution plan stored in cache. Ok, quick check, the plans are pretty big, I see some large index seeks, that estimate 2.8GB of data movement between operators, but everything looks like it is optimized the best it can be. Nothing really stands out in the code, and the indexing looks correct, and I should have enough memory to handle this in cache, so it must be a disk I/O problem right? Not exactly! If we were to look at how much memory the plan cache is taking by querying sys.dm_os_memory_clerks for the CACHESTORE_SQLCP and CACHESTORE_OBJCP clerks we might be surprised at what we find. In SQL Server 2005 RTM and SP1, the plan cache was allowed to take up to 75% of the memory under 8GB. I’ll give you a second to go back and read that again. Yes, you read it correctly, it says 75% of the memory under 8GB, but you don’t have to take my word for it, you can validate this by reading Changes in Caching Behavior between SQL Server 2000, SQL Server 2005 RTM and SQL Server 2005 SP2. In this scenario the application uses an entirely adhoc workload against SQL Server and this leads to plan cache bloat, and up to 4.5GB of our 6GB of memory for SQL can be consumed by the plan cache in SQL Server 2005 SP1. This in turn reduces the size of the buffer cache to just 1.5GB, causing our 2.8GB of data movement in this expensive plan to cause complete flushing of the buffer cache, not just once initially, but then another time during the queries execution, resulting in excessive physical I/O from disk. Keep in mind that this is not the only query executing at the time this occurs. Remember the output of sys.dm_io_virtual_file_stats() showed high read stalls on the data files for our user databases versus higher write stalls for tempdb? The memory pressure is also forcing heavier use of tempdb to handle sorting and hashing in the environment as well. The real clue here is the Memory counters for the instance; Page Life Expectancy, Free List Pages, and Free List Stalls/sec. The fact that Page Life Expectancy is fluctuating between 50 and 150 constantly is a sign that the buffer cache is experiencing constant churn of data, once every minute to two and a half minutes. If you add to the Page Life Expectancy counter, the consistent bottoming out of Free List Pages along with Free List Stalls/sec consistently spiking over 10, and you have the perfect memory pressure scenario. All of sudden it may not be that our disk subsystem is the problem, but is instead an innocent bystander and victim. Side Note: The Page Life Expectancy counter dropping briefly and then returning to normal operating values intermittently is not necessarily a sign that the server is under memory pressure. The Books Online and a number of other references will tell you that this counter should remain on average above 300 which is the time in seconds a page will remain in cache before being flushed or aged out. This number, which equates to just five minutes, is incredibly low for modern systems and most published documents pre-date the predominance of 64 bit computing and easy availability to larger amounts of memory in SQL Servers. As food for thought, consider that my personal laptop has more memory in it than most SQL Servers did at the time those numbers were posted. I would argue that today, a system churning the buffer cache every five minutes is in need of some serious tuning or a hardware upgrade. Back to our problem and its investigation: There are two things really wrong with this server; first the plan cache is excessively consuming memory and bloated in size and we need to look at that and second we need to evaluate upgrading the memory to accommodate the workload being performed. In the case of the server I was working on there were a lot of single use plans found in sys.dm_exec_cached_plans (where usecounts=1). Single use plans waste space in the plan cache, especially when they are adhoc plans for statements that had concatenated filter criteria that is not likely to reoccur with any frequency.  SQL Server 2005 doesn’t natively have a way to evict a single plan from cache like SQL Server 2008 does, but MVP Kalen Delaney, showed a hack to evict a single plan by creating a plan guide for the statement and then dropping that plan guide in her blog post Geek City: Clearing a Single Plan from Cache. We could put that hack in place in a job to automate cleaning out all the single use plans periodically, minimizing the size of the plan cache, but a better solution would be to fix the application so that it uses proper parameterized calls to the database. You didn’t write the app, and you can’t change its design? Ok, well you could try to force parameterization to occur by creating and keeping plan guides in place, or we can try forcing parameterization at the database level by using ALTER DATABASE <dbname> SET PARAMETERIZATION FORCED and that might help. If neither of these help, we could periodically dump the plan cache for that database, as discussed as being a problem in Kalen’s blog post referenced above; not an ideal scenario. The other option is to increase the memory on the server to 16GB or 32GB, if the hardware allows it, which will increase the size of the plan cache as well as the buffer cache. In SQL Server 2005 SP1, on a system with 16GB of memory, if we set max server memory to 14GB the plan cache could use at most 9GB  [(8GB*.75)+(6GB*.5)=(6+3)=9GB], leaving 5GB for the buffer cache.  If we went to 32GB of memory and set max server memory to 28GB, the plan cache could use at most 16GB [(8*.75)+(20*.5)=(6+10)=16GB], leaving 12GB for the buffer cache. Thankfully we have SQL Server 2005 Service Pack 2, 3, and 4 these days which include the changes in plan cache sizing discussed in the Changes to Caching Behavior between SQL Server 2000, SQL Server 2005 RTM and SQL Server 2005 SP2 blog post. In real life, when I was troubleshooting this problem, I spent a week trying to chase down the cause of the disk I/O bottleneck with our Server Admin and SAN Admin, and there wasn’t much that could be done immediately there, so I finally asked if we could increase the memory on the server to 16GB, which did fix the problem. It wasn’t until I had this same problem occur on another system that I actually figured out how to really troubleshoot this down to the root cause.  I couldn’t believe the size of the plan cache on the server with 16GB of memory when I actually learned about this and went back to look at it. SQL Server is constantly telling a story to anyone that will listen. As the DBA, you have to sit back and listen to all that it’s telling you and then evaluate the big picture and how all the data you can gather from SQL about performance relate to each other. One of the greatest tools out there is actually a free in the form of Diagnostic Scripts for SQL Server 2005 and 2008, created by MVP Glenn Alan Berry. Glenn’s scripts collect a majority of the information that SQL has to offer for rapid troubleshooting of problems, and he includes a lot of notes about what the outputs of each individual query might be telling you. When I read Pinal’s blog post SQL SERVER – ASYNC_IO_COMPLETION – Wait Type – Day 11 of 28, I noticed that he referenced Checking Memory Related Performance Counters in his post, but there was no real explanation about why checking memory counters is so important when looking at an I/O related wait type. I thought I’d chat with him briefly on Google Talk/Twitter DM and point this out, and offer a couple of other points I noted, so that he could add the information to his blog post if he found it useful.  Instead he asked that I write a guest blog for this. I am honored to be a guest blogger, and to be able to share this kind of information with the community. The information contained in this blog post is a glimpse at how I do troubleshooting almost every day of the week in my own environment. SQL Server provides us with a lot of information about how it is running, and where it may be having problems, it is up to us to play detective and find out how all that information comes together to tell us what’s really the problem. This blog post is written by Jonathan Kehayias (Blog | Twitter). Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: MVP, Pinal Dave, PostADay, Readers Contribution, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • BizTalk Server 2009 - Architecture Options

    - by StuartBrierley
    I recently needed to put forward a proposal for a BizTalk 2009 implementation and as a part of this needed to describe some of the basic architecture options available for consideration.  While I already had an idea of the type of environment that I would be looking to recommend, I felt that presenting a range of options while trying to explain some of the strengths and weaknesses of those options was a good place to start.  These outline architecture options should be equally valid for any version of BizTalk Server from 2004, through 2006 and R2, up to 2009.   The following diagram shows a crude representation of the common implementation options to consider when designing a BizTalk environment.         Each of these options provides differing levels of resilience in the case of failure or disaster, with the later options also providing more scope for performance tuning and scalability.   Some of the options presented above make use of clustering. Clustering may best be described as a technology that automatically allows one physical server to take over the tasks and responsibilities of another physical server that has failed. Given that all computer hardware and software will eventually fail, the goal of clustering is to ensure that mission-critical applications will have little or no downtime when such a failure occurs. Clustering can also be configured to provide load balancing, which should generally lead to performance gains and increased capacity and throughput.   (A) Single Servers   This option is the most basic BizTalk implementation that should be considered. It involves the deployment of a single BizTalk server in conjunction with a single SQL server. This configuration does not provide for any resilience in the case of the failure of either server. It is however the cheapest and easiest to implement option of those available.   Using a single BizTalk server does not provide for the level of performance tuning that is otherwise available when using more than one BizTalk server in a cluster.   The common edition of BizTalk used in single server implementations is the standard edition. It should be noted however that if future demand requires increased capacity for a solution, this BizTalk edition is limited to scaling up the implementation and not scaling out the number of servers in use. Any need to scale out the solution would require an upgrade to the enterprise edition of BizTalk.   (B) Single BizTalk Server with Clustered SQL Servers   This option uses a single BizTalk server with a cluster of SQL servers. By utilising clustered SQL servers we can ensure that there is some resilience to the implementation in respect of the databases that BizTalk relies on to operate. The clustering of two SQL servers is possible with the standard edition but to go beyond this would require the enterprise level edition. While this option offers improved resilience over option (A) it does still present a potential single point of failure at the BizTalk server.   Using a single BizTalk server does not provide for the level of performance tuning that is otherwise available when using more than one BizTalk server in a cluster.   The common edition of BizTalk used in single server implementations is the standard edition. It should be noted however that if future demand requires increased capacity for a solution, this BizTalk edition is limited to scaling up the implementation and not scaling out the number of servers in use. You are also unable to take advantage of multiple message boxes, which would allow us to balance the SQL load in the event of any bottlenecks in this area of the implementation. Any need to scale out the solution would require an upgrade to the enterprise edition of BizTalk.   (C) Clustered BizTalk Servers with Clustered SQL Servers   This option makes use of a cluster of BizTalk servers with a cluster of SQL servers to offer high availability and resilience in the case of failure of either of the server types involved. Clustering of BizTalk is only available with the enterprise edition of the product. Clustering of two SQL servers is possible with the standard edition but to go beyond this would require the enterprise level edition.    The use of a BizTalk cluster also provides for the ability to balance load across the servers and gives more scope for performance tuning any implemented solutions. It is also possible to add more BizTalk servers to an existing cluster, giving scope for scaling out the solution as future demand requires.   This might be seen as the middle cost option, providing a good level of protection in the case of failure, a decent level of future proofing, but at a higher cost than the single BizTalk server implementations.   (D) Clustered BizTalk Servers with Clustered SQL Servers – with disaster recovery/service continuity   This option is similar to that offered by (C) and makes use of a cluster of BizTalk servers with a cluster of SQL servers to offer high availability and resilience in case of failure of either of the server types involved. Clustering of BizTalk is only available with the enterprise edition of the product. Clustering of two SQL servers is possible with the standard edition but to go beyond this would require the enterprise level edition.    As with (C) the use of a BizTalk cluster also provides for the ability to balance load across the servers and gives more scope for performance tuning the implemented solution. It is also possible to add more BizTalk servers to an existing cluster, giving scope for scaling the solution out as future demand requires.   In this scenario however, we would be including some form of disaster recovery or service continuity. An example of this would be making use of multiple sites, with the BizTalk server cluster operating across sites to offer resilience in case of the loss of one or more sites. In this scenario there are options available for the SQL implementation depending on the network implementation; making use of either one cluster per site or a single SQL cluster across the network. A multi-site SQL implementation would require some form of data replication across the sites involved.   This is obviously an expensive and complex option, but does provide an extraordinary amount of protection in the case of failure.

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  • BizTalk host throttling &ndash; Singleton pattern and High database size

    - by S.E.R.
    Originally posted on: http://geekswithblogs.net/SERivas/archive/2013/06/30/biztalk-host-throttling-ndash-singleton-pattern-and-high-database-size.aspxI have worked for some days around the singleton pattern (for those unfamiliar with it, read this post by Victor Fehlberg) and have come across a few very interesting posts, among which one dealt with performance issues (here, also by Victor Fehlberg). Simply put: if you have an orchestration which implements the singleton pattern, then performances will continuously decrease as the orchestration receives and consumes messages, and that behavior is more obvious when the orchestration never ends (ie : it keeps looping and never terminates or completes). As I experienced the same kind of problem (actually I was alerted by SCOM, which told me that the host was being throttled because of High database size), I thought it would be a good idea to dig a little bit a see what happens deep inside BizTalk and thus understand the reasons for this behavior. NOTE: in this article, I will focus on this High database size throttling condition. I will try and work on the other conditions in some not too distant future… Test conditions The singleton orchestration For the purpose of this study, I have created the following orchestration, which is a very basic implementation of a singleton that piles up incoming messages, then does something else when a certain timeout has been reached without receiving another message: Throttling settings I have two distinct hosts : one that hosts the receive port (basic FILE port) : Ports_ReceiveHostone that hosts the orchestration : ProcessingHost In order to emphasize the throttling mechanism, I have modified the throttling settings for each of these hosts are as follows (all other parameters are set to the default value): [Throttling thresholds] Message count in database: 500 (default value : 50000) Evolution of performance counters when submitting messages Since we are investigating the High database size throttling condition, here are the performance counter that we should take a look at (all of them are in the BizTalk:Message Agent performance object): Database sizeHigh database sizeMessage delivery throttling stateMessage publishing throttling stateMessage delivery delay (ms)Message publishing delay (ms)Message delivery throttling state durationMessage publishing throttling state duration (If you are not used to Perfmon, I strongly recommend that you start using it right now: it is a wonderful tool that allows you to open the hood and see what is going on inside BizTalk – and other systems) Database size It is quite obvious that we will start by watching the database size and high database size counters, just to see when the first reaches the configured threshold (500) and when the second rings the alarm. NOTE : During this test I submitted 600 messages, one message at a time every 10ms to see the evolution of the counters we have previously selected. It might not show very well on this screenshot, but here is what happened: From 15:46:50 to 15:47:50, the database size for the Ports_ReceiveHost host (blue line) kept growing until it reached a maximum of 504.At 15:47:50, the high database size alert fires At first I was surprised by this result: why is it the database size of the receiving host that keeps growing since it is the processing host that piles up messages? Actually, it makes total sense. This counter measures the size of the database queue that is being filled by the host, not consumed. Therefore, the high database size alert is raised on the host that fills the queue: Ports_ReceiveHost. More information is available on the Public MPWiki page. Now, looking at the Message publishing throttling state for the receiving host (green line), we can see that a throttling condition has been reached at 15:47:50: We can also see that the Message publishing delay(ms) (blue line) has begun growing slowly from this point. All of this explains why performances keep decreasing when a singleton keeps processing new messages: the database size grows and when it has exceeded the Message count in database threshold, the host is throttled and the publishing delay keeps increasing. Digging further So, what happens to the database queue then? Is it flushed some day or does it keep growing and growing indefinitely? The real question being: will the host be throttled forever because of this singleton? To answer this question, I set the Message count in database threshold to 20 (this value is very low in order not to wait for too long, otherwise I certainly would have fallen asleep in front of my screen) and I submitted 30 messages. The test was started at 18:26. At 18:56 (ie : exactly 30min later) the throttling was stopped and the database size was divided by 2. 30 min later again, the database size had dropped to almost zero: I guess I’ll have to find some documentation and do some more testing before I sort this out! My guess is that some maintenance job is at work here, though I cannot tell which one Digging even further If we take a look at the Message delivery throttling state counter for the processing host, we can see that this host was also throttled during the submission of the 600 documents: The value for the counter was 1, meaning that Message delivery incoming rate for the host instance exceeds the Message delivery outgoing rate * the specified Rate overdrive factor (percent) value. We will see this another day… :) A last word Let’s end this article with a warning: DO NOT CHANGE THE THROTTLING SETTINGS LIGHTLY! The temptation can be great to just bypass throttling by setting very high values for each parameter (or zero in some cases, which simply disables throttling). Nevertheless, always keep in mind that this mechanism is here for a very good reason: prevent your BizTalk infrastructure from exploding!! So whatever you do with those settings, do a lot of testing and benchmarking!

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  • How can Swift be so much faster than Objective-C in these comparisons?

    - by Yellow
    Apple launched its new programming language Swift at WWDC14. In the presentation, they made some performance comparisons between Objective-C and Python. The following is a picture of one of their slides, of a comparison of those three languages performing some complex object sort: There was an even more incredible graph about a performance comparison using the RC4 encryption algorithm. Obviously this is a marketing talk, and they didn't go into detail on how this was implemented in each. I leaves me wondering though: How can a new programming language be so much faster? Are the Objective-C results caused by a bad compiler or is there something less efficient in Objective-C than Swift? How would you explain a 40% performance increase? I understand that garbage collection/automated reference control might produce some additional overhead, but this much?

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