Spring for Apache Hadoop is a Spring project to support writing applications that can benefit of the integration of Spring Framework and Hadoop. This post describes how to use Spring Data Apache Hadoop in an Amazon EC2 environment using the “Hello World” equivalent of Hadoop programming – a Wordcount application. 1./ Launch an Amazon Web Services EC2 instance. - Navigate to AWS EC2 Console (“https://console.aws.amazon.com/ec2/home”): - Select Launch Instance then Classic Wizzard and click on Continue. My test environment was a “Basic Amazon Linux AMI 2011.09″ 32-bit., Instant type: Micro (t1.micro , 613 MB), Security group quick-start-1 that enables ssh to be used for login. Select your existing key pair (or create a new one). Obviously you can select another AMI and instance types depending on your favourite flavour. (Should you vote for Windows 2008 based instance, you also need to have cygwin installed as an additional Hadoop prerequisite beside Java JDK and ssh, see “Install Apache Hadoop” section) 2./ Download Apache Hadoop - as of writing this article, 1.0.0 is the latest stable version of Apache Hadoop, that is what was used for testing purposes. I downloaded hadoop-1.0.0.tar.gz and copied it into /home/ec2-user directory using pscp command from my PC running Windows: c:\downloads>pscp -i mykey.ppk hadoop-1.0.0.tar.gz [email protected]:/home/ec2-user (the computer name above – ec2-ipaddress-region-compute.amazonaws.com – can be found on AWS EC2 console, Instance Description, public DNS field) 3./ Install Apache Hadoop: As prerequisites, you need to have Java JDK 1.6 and ssh installed, see Apache Single-Node Setup Guide. (ssh is automatically installed with Basic Amazon AMI). Then install hadoop itself: $ cd ~ # change directory to ec2-user home (/home/ec2-user) $ tar xvzf hadoop-1.0.0.tar.gz $ ln -s hadoop-1.0.0 hadoop $ cd hadoop/conf $ vi hadoop-env.sh # edit as below export JAVA_HOME=/opt/jdk1.6.0_29 $ vi core-site.xml # edit as below – this defines the namenode to be running on localhost and listeing to port 9000. fs.default.name hdfs://localhost:9000 $ vi hdsf-site.xml # edit as below this defines that file system replicate is 1 (in production environment it is supposed to be 3 by default) dfs.replication 1 $ vi mapred-site.xml # edit as below – this defines the jobtracker to be running on localhost and listeing to port 9001. mapred.job.tracker localhost:9001 $ cd ~/hadoop $ bin/hadoop namenode -format $ bin/start-all.sh At this stage all hadoop jobs are running in pseudo distributed mode, you can verify it by running: $ ps -ef | grep java You should see 5 java processes: namenode, secondarynamenode, datanode, jobtracker and tasktracker. 4./ Install Spring Data Hadoop Download Spring Data Hadoop package from SpringSource community download site. As of writing this article, the latest stable version is spring-data-hadoop-1.0.0.M1.zip. $ cd ~ $ tar xzvf spring-data-hadoop-1.0.0.M1.zip $ ln -s spring-data-hadoop-1.0.0.M1 spring-data-hadoop 5./ Build and Run Spring Data Hadoop Wordcount example $ cd spring-data-hadoop/spring-data-hadoop-1.0.0.M1/samples/wordcount Spring Data Hadoop is using gradle as build tool. Check build.grandle build file. The original version packaged in the tar.gz file does not compile, it complains about thrift, version 0.2.0 and jdo2-api, version2.3-ec. Add datanucleus.org maven repository to the build.gradle file to support jdo2-api (http://www.datanucleus.org/downloads/maven2/) . Unfortunatelly, there seems to be no maven repo for thrift 0.2.0 . You should download thrift 0.2.0.jar and thrift.0.2.0.pom file e.g. from this repo: “http://people.apache.org/~rawson/repo“ and then add it to local maven repo. $ mvn install:install-file -DgroupId=org.apache.thrift -DartifactId=thrift -Dversion=0.2.0 -Dfile=thrift-0.2.0.jar -Dpackaging=jar $ vi build.grandle # modify the build file to refer to datanucleus maven repo for jdo2-api and the local repo for thrift repositories { // Public Spring artefacts mavenCentral() maven { url “http://repo.springsource.org/libs-release” } maven { url “http://repo.springsource.org/libs-milestone” } maven { url “http://repo.springsource.org/libs-snapshot” } maven { url “http://www.datanucleus.org/downloads/maven2/” } maven { url “file:///home/ec2-user/.m2/repository” } } I also modified the META-INF/spring/context.xml file in order to run hadoop file system commands manually: $ cd /home/ec2-user/spring-data-hadoop/spring-data-hadoop-1.0.0.M1/samples/wordcount/src/main/resources $vi META-INF/spring/context.xml # remove clean-script and also the dependency on it for JobRunner. xmlns=”http://www.springframework.org/schema/beans” xmlns:xsi=”http://www.w3.org/2001/XMLSchema-instance” xmlns:context=”http://www.springframework.org/schema/context” xmlns:hdp=”http://www.springframework.org/schema/hadoop” xmlns:p=”http://www.springframework.org/schema/p” xsi:schemaLocation=”http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd http://www.springframework.org/schema/hadoop http://www.springframework.org/schema/hadoop/spring-hadoop.xsd”> fs.default.name=${hd.fs} Copy the sample file – nietzsche-chapter-1.txt – to Hadoop file system (/user/ec2-user-/input directory) $ cd src/main/resources/data $ hadoop fs -mkdir /user/ec2-user/input $ hadoop fs -put nietzsche-chapter-1.txt /user/ec2-user/input/data $ cd ../../../.. # go back to samples/wordcount directory $ ../gradlew Verify the result: $ hadoop fs -cat /user/ec2-user/output/part-r-00000 | more “AWAY 1 “BY 1 “Beyond 1 “By 2 “Cheers 1 “DE 1 “Everywhere 1 “FROM” 1 “Flatterers 1 “Freedom 1

i switch between programming languages quite a bit; i often wondered what happens when having to deal with the different syntaxes, does the syntax allow you to be more expressive or faster at coding in one language or another. i don't really know about that; but what i do know what keys are pressed when writing with different programming languages. this might be something interesting for people who are deciding to select a programming language might look into, here is a post on the answer to the aged question of: which programming language should i learn? as far as i can tell languages with a wider focused spread across the keyboard are usually syntaxes we usually associate with ugly languages (ugly to read and code). ex. shell and perl. you might argue that the variables names being used will alter the results, but as most languages programming have conventions for naming but we can assume a decent spread for variable names. i don’t offer conclusions, just poorly layout the facts. although the heat map does miss out on things like shift and caps. ex. in perl with the dollar sign. ($) whitespace hasn’t been taken into consideration (tabs and spaces) which would have been a cool thing to see. the data that was used to gather this information was spread amongst various popular github projects. javascript shell java c c++ ruby python php perl objc lisp lisp code here was written by paul graham. references heatmap.js http://www.patrick-wied.at/projects/heatmap-keyboard/

Ah!! The name is everywhere, carried with the wind. Apache Hadoop!! The BIG DATA crunching platform! We all know how alien it can be at start too! Phew!! :o Its my personal experience, nearly 11 months before, I was trying to install HBase, I faced few issues! The problem was version compatibility. Ex: "HBase some x.version" with "Hadoop some y.version". This is a real issue because you will never know which package of what version blends well with the other, unless, someone has tested it. This testing again depends on the environment where they have set up and could be another issue. There was a pressing demand for the management of distributions and then comes an open source project which attempts to create a fully integrated and tested Big Data management distribution, "Apache Bigtop". Goals of Apache Bigtop: -Packaging -Deployment -Integration Testing of all the sub-projects of Hadoop. This project aims at system as a whole, than the individual project. I love the way Doug Cutting quoted in the Keynote, back then, wherein he expressed the similarity between Hadoop and Linux kernel,and the corresponding similarity between the big stack of Hadoop ( Hive, Hbase, Pig, Avro, etc.) and the fully operational operating systems with its distributions (RedHat, Ubuntu, Fedora, Debian etc.). This is an awesome analogy! :) Life is made easy with Bigtop: Bigtop Hadoop distribution artifacts won't make you feel that you live in an alien world! After installing, you will get a chance to blend a Hadoop cluster in any mode, with the sub-projects of it. Its all for you to garnish next! :) Setup Of Bigtop and Installing Hadoop: It's time to welcome all your packages home. [I also mean /home/..] ;) I've tested on Ubuntu 11.04 and here goes a quick and easy installation process. Step 1: Installing the GNU Privacy Guard key, a key management system to access all public key directories. wget -O- http://www.apache.org/dist/incubator/bigtop/bigtop-0.3.0-incubating/repos/GPG-KEY-bigtop | sudo apt-key add - Step 2: Get the repo file from the link http://www.apache.org/dist/incubator/bigtop/bigtop-0.3.0-incubating/repos/ubuntu/bigtop.list sudo wget -O /etc/apt/sources.list.d/bigtop.listhttp://www.apache.org/dist/incubator/bigtop/bigtop-0.3.0-incubating/repos/ubuntu/bigtop.list sudo gedit /etc/apt/sources.list.d/bigtop.list uncomment the mirror link near by. The first link worked for me. deb http://apache.01link.hk/incubator/bigtop/stable/repos/ubuntu/ bigtop contrib Step 3: Updating the apt cache sudo apt-get update Step 4: Checking in the artifacts sudo apt-cache search hadoop Image: Search in the apt cache Step 5: Set your JAVA_HOME export JAVA_HOME=path_to_your_Java export $JAVA_HOME in ~/.bashrc Step 6: Installing the complete Hadoop stack sudo apt-get install hadoop\* Image: (above) Running Hadoop: Step 1: Formatting the namendoe sudo -u hdfs hadoop namenode -format Image : Formatting the namenode Step 2: Starting the Namenode, Datanode, Jobtracker, Tasktracker of Hadoop for i in hadoop-namenode hadoop-datanode hadoop-jobtracker hadoop-tasktracker ; do sudo service $i start ; done Now, the cluster is up and running. Image : Start all the services Step 3: Creating a new directory in hdfs sudo -u hdfs hadoop fs -mkdir /user/bigtop bigtop is the directory name in the user $USER sudo -u hdfs hadoop fs -chown $USER /user/bigtop Image : Create a directory in HDFS Step 4: List the directories in file system hadoop fs -lsr / Image : HDFS directories Step 5: Running a sample pi example hadoop jar /usr/lib/hadoop/hadoop-examples.jar pi 10 1000 Image : Running a sample program Job Completed! Enjoy with your cluster! :) We shall see what more blending could be done with Hadoop (with Hive, Hbase, etc.) in the next post! Until then, Happy Learning!! :):)

As I mentioned in another post about a month ago I’ve been playing around with a neo4j graph in which I have the following relationship between nodes: One thing I wanted to do was work out which node is the most connected on the graph, which would tell me who’s worked with the most people. I started off with the following cypher query: query = " START n = node(*)" query << " MATCH n-[r:colleagues]->c" query << " WHERE n.type? = 'person' and has(n.name)" query << " RETURN n.name, count(r) AS connections" query << " ORDER BY connections DESC" I can then execute that via the neo4j console or through irb using the neography gem like so: > require 'rubygems' > require 'neography' > neo = Neography::Rest.new(:port => 7476) > neo.execute_query query # cut for brevity {"data"=>[["Carlos Villela", 283], ["Mark Needham", 221]], "columns"=>["n.name", "connections"]} That shows me each person and the number of people they’ve worked with but I wanted to be able to see the most connected person in each office . Each person is assigned to an office while they’re working out of that office but people tend to move around so they’ll have links to multiple offices: I put ‘start_date’ and ‘end_date’ properties on the ‘member_of’ relationship and we can work out a person’s current office by finding the ‘member_of’ relationship which doesn’t have an end date defined: query = " START n = node(*)" query << " MATCH n-[r:colleagues]->c, n-[r2:member_of]->office" query << " WHERE n.type? = 'person' and has(n.name) and not(has(r2.end_date))" query << " RETURN n.name, count(r) AS connections, office.name" query << " ORDER BY connections DESC" And now our results look more like this: {"data"=>[["Carlos Villela", 283, "Porto Alegre - Brazil"], ["Mark Needham", 221, "London - UK South"]], "columns"=>["n.name", "connections"]} If we want to restrict that just to return the people for a specific person we can do that as well: query = " START n = node(*)" query << " MATCH n-[r:colleagues]->c, n-[r2:member_of]->office" query << " WHERE n.type? = 'person' and has(n.name) and (not(has(r2.end_date))) and office.name = 'London - UK South'" query << " RETURN n.name, count(r) AS connections, office.name" query << " ORDER BY connections DESC" {"data"=>[["Mark Needham", 221, "London - UK South"]], "columns"=>["n.name", "connections"]} In the current version of cypher we need to put brackets around the not expression otherwise it will apply the not to the rest of the where clause. Another way to get around that would be to put the not part of the where clause at the end of the line. While I am able to work out the most connected person by using these queries I’m not sure that it actually tells you who the most connected person is because it’s heavily biased towards people who have worked on big teams. Some ways to try and account for this are to bias the connectivity in favour of those you have worked longer with and also to give less weight to big teams since you’re less likely to have a strong connection with everyone as you might in a smaller team. I haven’t got onto that yet though!

Innodb can indexes built by sort since Innodb Plugin for MySQL 5.1 which is a lot faster than building them through insertion, especially for tables much larger than memory and large uncorrelated indexes you might be looking at 10x difference or more. Yet for some reason Innodb team has chosen to use very small (just 1MB) and hard coded buffer for this operation, which means almost any such index build operation has to use excessive sort merge passes significantly slowing down index built process. Mark Callaghan and Facebook Team has fixed this in their tree back in early 2011 adding innodb_merge_sort_block_size variable and I was thinking this small patch will be merged to MySQL 5.5 promptly, yet it has not happen to date. Here is example of gains you can expect (courtesy of Alexey Kopytov), using 1Mil rows Sysbench table. Buffer Length | alter table sbtest add key(c) 1MB 34 sec 8MB 26 sec 100MB 21 sec 128MB 17 sec REBUILD 37 sec REBUILD in this table is using “fast_index_creation=0″ which allows to disable fast index creation in Percona Server and force complete table to be rebuilt instead. Looking at this data we can see even for such small table there is possible to improve index creation time 2x by using large buffer. Also we can see we can substantially improve performance even increasing it from 1MB to 8MB, which might be sensible as default as even small systems should be able to allocate 8MB to do alter table. You may be wondering why in this case table rebuild is so close in performance to building index by sort with small buffer – this comes from building index on long character field with very short length, Innodb would use fixed size records for sort space which results in a lot more work done than you would otherwise need. Having some optimization to better deal with this case also would be nice. The table also was fitting in buffer pool completely in this case which means table rebuild could have done fast too. Results are from Percona Server 5.5.24

One of the advantages of code generation is the ability to see how a specific language feature or framework is used. As I discussed in the post NetBeans 7.2 beta: Faster and More Helpful, NetBeans 7.2 beta provides TestNG integration. I did not elaborate further in that post other than a single reference to that feature because I wanted to devote this post to the subject. I use this post to demonstrate how NetBeans 7.2 can be used to help a developer new to TestNG start using this alternative (to JUnit) test framework. NetBeans 7.2's New File wizard makes it easier to create an empty TestNG test case. This is demonstrated in the following screen snapshots that are kicked off by using New File | Unit Tests (note that "New File" is available under the "File" drop-down menu or by right-clicking in the Projects window). Running the TestNG test case creation as shown above leads to the following generated test code. TestNGDemo.java (Generated by NetBeans 7.2) package dustin.examples; import org.testng.annotations.AfterMethod; import org.testng.annotations.AfterClass; import org.testng.annotations.BeforeMethod; import org.testng.annotations.BeforeClass; import org.testng.annotations.Test; import org.testng.Assert; /** * * @author Dustin */ public class TestNGDemo { public TestNGDemo() { } @BeforeClass public void setUpClass() { } @AfterClass public void tearDownClass() { } @BeforeMethod public void setUp() { } @AfterMethod public void tearDown() { } // TODO add test methods here. // The methods must be annotated with annotation @Test. For example: // // @Test // public void hello() {} } The test generated by NetBeans 7.2 includes comments indicate how test methods are added and annotated (similar to modern versions of JUnit). The generated code also shows some annotations for overall test case set up and tear down and for per-test set up and tear down (annotations are similar to JUnit's). NetBeans identifies import statements that are not yet used at this point (import org.testng.annotations.Test; and import org.testng.Assert;), but are likely to be used and so have been included in the generated code. I can add a test method easily to this generated test case. The following code snippet is a test method using TestNG. testIntegerArithmeticMultiplyIntegers() @Test public void testIntegerArithmeticMultiplyIntegers() { final IntegerArithmetic instance = new IntegerArithmetic(); final int[] integers = {4, 5, 6}; final int expectedProduct = 2 * 3 * 4 * 5 * 6; final int product = instance.multiplyIntegers(2, 3, integers); assertEquals(product, expectedProduct); } This, of course, looks very similar to the JUnit equivalent I used against the same IntegerArithmetic class that I used for testing illustrations in the posts Improving On assertEquals with JUnit and Hamcrest and JUnit's Built-in Hamcrest Core Matcher Support. The following screen snapshot shows the output in NetBeans 7.2 beta from right-clicking on the test case class and selecting "Run File" (Shift+F6). The text output of the TestNG run provided in the NetBeans 7.2 beta is reproduced next. [TestNG] Running: Command line suite [VerboseTestNG] RUNNING: Suite: "Command line test" containing "1" Tests (config: null) [VerboseTestNG] INVOKING CONFIGURATION: "Command line test" - @BeforeClass dustin.examples.TestNGDemo.setUpClass() [VerboseTestNG] PASSED CONFIGURATION: "Command line test" - @BeforeClass dustin.examples.TestNGDemo.setUpClass() finished in 33 ms [VerboseTestNG] INVOKING CONFIGURATION: "Command line test" - @BeforeMethod dustin.examples.TestNGDemo.setUp() [VerboseTestNG] PASSED CONFIGURATION: "Command line test" - @BeforeMethod dustin.examples.TestNGDemo.setUp() finished in 2 ms [VerboseTestNG] INVOKING: "Command line test" - dustin.examples.TestNGDemo.testIntegerArithmeticMultiplyIntegers() [VerboseTestNG] PASSED: "Command line test" - dustin.examples.TestNGDemo.testIntegerArithmeticMultiplyIntegers() finished in 12 ms [VerboseTestNG] INVOKING CONFIGURATION: "Command line test" - @AfterMethod dustin.examples.TestNGDemo.tearDown() [VerboseTestNG] PASSED CONFIGURATION: "Command line test" - @AfterMethod dustin.examples.TestNGDemo.tearDown() finished in 1 ms [VerboseTestNG] INVOKING CONFIGURATION: "Command line test" - @AfterClass dustin.examples.TestNGDemo.tearDownClass() [VerboseTestNG] PASSED CONFIGURATION: "Command line test" - @AfterClass dustin.examples.TestNGDemo.tearDownClass() finished in 1 ms [VerboseTestNG] [VerboseTestNG] =============================================== [VerboseTestNG] Command line test [VerboseTestNG] Tests run: 1, Failures: 0, Skips: 0 [VerboseTestNG] =============================================== =============================================== Command line suite Total tests run: 1, Failures: 0, Skips: 0 =============================================== Deleting directory C:\Users\Dustin\AppData\Local\Temp\dustin.examples.TestNGDemo test: BUILD SUCCESSFUL (total time: 2 seconds) The above example shows how easy it is to start using TestNG, especially if one is moving to TestNG from JUnit and is using NetBeans 7.2 beta. Of course, there is much more to TestNG than this, but learning a new framework is typically most difficult at the very beginning and NetBeans 7.2 gets one off to a fast start.

I often use IntelliJ to run applications, and on occasion things go wrong. For example, a thread that wont terminate can cause a running application to become unstoppable via the IntelliJ UI. Usually when this happens I end up running ps aux | grep java and following up with a kill -9 for each process that looks like it might be the one I'm looking for. On good days there's only a few processes; however, things are more complicated when I have several to look through. Last week I noticed that the command used to launch the process printed in the Console window, and, more importantly, the idea.launcher.port is part of that command: e.g. idea.launcher.port=7538. Assuming the port is unique, or even almost unique it's much easier to ps aux | grep for than java.

In how many ways can you retrieve TeamCity artifacts? I say plenty to choose from! If you’re in a world of Java build tools then you can use plain HTTP request, Ant + Ivy, Gradle and Maven to download and use binaries produced by TeamCity build configurations. How? Read on. Build Configuration “id” Before you retrieve artifacts of any build configuration you need to know its "id" which can be seen in a browser when corresponding configuration is browsed. Let’s take IntelliJ IDEA Community Edition project hosted at teamcity.jetbrains.com as an example. Its “Community Dist” build configuration provides a number of artifacts which we’re going to play with. And as can be seen on the screenshot below, its "id" is "bt343". HTTP Anonymous HTTP access is probably the easiest way to fetch TeamCity artifacts, the URL to do so is: http://server/guestAuth/repository/download/// Fot this request to work 3 parameters need to be specified: btN Build configuration "id", as mentioned above. buildNumber Build number or one of predefined constants: "lastSuccessful", "lastPinned", or "lastFinished". For example, you can download periodic IDEA builds from last successful TeamCity execution. artifactName Name of artifact like "ideaIC-118.SNAPSHOT.win.zip". Can also take a form of "artifactName!archivePath" for reading archive’s content, like IDEA’s build file. You can get a list of all artifacts produced in a certain build by requesting a special "teamcity-ivy.xml" artifact generated by TeamCity. Ant + Ivy All artifacts published to TeamCity are accompanied by "teamcity-ivy.xml" Ivy descriptor, effectively making TeamCity an Ivy repository. The code below downloads "core/annotations.jar" from IDEA distribution to "download/ivy" directory: "ivyconf.xml" "ivy.xml" "build.xml" Gradle Identically to Ivy example above it is fairly easy to retrieve TeamCity artifacts with Gradle due to its built-in Ivy support. In addition to downloading the same jar file to "download/gradle" directory with a custom Gradle task let’s use it as "compile" dependency for our Java class, importing IDEA’s @NotNull annotation: "Test.java" import org.jetbrains.annotations.NotNull; public class Test { private final String data; public Test ( @NotNull String data ){ this.data = data; } } "build.gradle" apply plugin: 'java' repositories { ivy { ivyPattern 'http://teamcity.jetbrains.com/guestAuth/repository/download/[module]/[revision]/teamcity-ivy.xml' artifactPattern 'http://teamcity.jetbrains.com/guestAuth/repository/download/[module]/[revision]/[artifact](.[ext])' } } dependencies { compile ( 'org:bt343:lastSuccessful' ){ artifact { name = 'core/annotations' type = 'jar' } } } task copyJar( type: Copy ) { from configurations.compile into "${ project.projectDir }/download/gradle" } Maven The best way to use Maven with TeamCity is by setting up an Artifactory repository manager and its TeamCity plugin. This way artifacts produced by your builds are nicely deployed to Artifactory and can be served from there as from any other remote Maven repository. However, you can still use TeamCity artifacts in Maven without any additional setups. "ivy-maven-plugin" bridges two worlds allowing you to plug Ivy resolvers into Maven’s runtime environment, download dependencies required and add them to corresponding "compile" or "test" scopes. Let’s compile the same Java source from the Gradle example but using Maven this time. "pom.xml" 4.0.0 com.test maven jar 0.1-SNAPSHOT [${project.groupId}:${project.artifactId}:${project.version}] Ivy Maven plugin example com.github.goldin ivy-maven-plugin 0.2.5 get-ivy-artifacts ivy initialize ${project.basedir}/ivyconf.xml ${project.basedir}/ivy.xml ${project.basedir}/download/maven compile When this plugin runs it resolves IDEA annotations artifact using the same "ivyconf.xml" and "ivy.xml" files we’ve seen previously, copies it to "download/maven" directory and adds to "compile" scope so our Java sources can compile. GitHub Project All examples demonstrated are available in my GitHub project. Feel free to clone and run it: git clone git://github.com/evgeny-goldin/teamcity-download-examples.git cd teamcity-download-examples chmod +x run.sh dist/ant/bin/ant gradlew dist/maven/bin/mvn ./run.sh Resources The links below can provide you with more details: TeamCity – Patterns For Accessing Build Artifacts TeamCity – Accessing Server by HTTP TeamCity – Configuring Artifact Dependencies Using Ant Build Script Gradle – Ivy repositories "ivy-maven-plugin" That’s it, you’ve seen it – TeamCity artifacts are perfectly accessible using either of 4 ways: direct HTTP access, Ant + Ivy, Gradle or Maven. Which one do you use? Let me know!

After my article Connection Pooling in a Java Web Application with Glassfish and NetBeans IDE, here are the instructions for Tomcat. Requirements NetBeans IDE (this tutorial uses NetBeans 7) Tomcat (this tutorial uses Tomcat 7 that is bundled within NetBeans) MySQL database MySQL Java Driver Steps Assuming your MySQL database is ready, connect to it and create a database. Lets call it connpool: mysql> create database connpool; Now we create and populate the table from which we will fetch the data: mysql> use connpool; mysql> create table data(id int(5) not null unique auto_increment, name varchar(255) not null); mysql> insert into data(name) values("Fred Flintstone"), ("Pink Panther"), ("Wayne Cramp"), ("Johnny Bravo"), ("Spongebob Squarepants"); That is it for the database part. We now create our web application. In NetBeans IDE, click File → New Project... Select Java Web → Web Application: Click Next and give the project the name TomPool. Click Next Choose the server as Tomcat and, since we are not going to use any frameworks, click Finish. The project will be created and the start page, index.jsp, opened for us in the IDE. Now we create the connection pooling parameters. In the Projects window, expand configuration files and open "context.xml". You will see that the IDE has added this code for us: Delete the last line: and then add the following to the context.xml file. I have explained the sections along the way. Make sure you edit your MySQL username and password appropriately: Next, expand the Web Pages node, right-click WEB-INF → New → Other → XML → XML Document. Click Next and type web for the File Name. Click next and choose Well-Formed Document then Finish. You will now have the file "web.xml": Delete everything in the file and paste this code: MySQL Test App DB Connection connpool javax.sql.DataSource Container That is it for the connection pool. We now edit our code to make use of it. Edit index.jsp by adding this code just after the initial coments but before Edit the section of the page: Data in my Connection Pooled Database Now, we test the connection pool by running the application: If you want to have the one connection pool used in multiple applications, you need to edit the following two files: 1. /conf/web.xml Just before the closing tag, add the code DB Connection connpool javax.sql.DataSource Container 2. /conf/context.xml Just before the closing tag, add the code Now you can use the pool without editing XML files in each of your applications. Just use the sample code as given in index.jsp That's it folks!

In my last two blog posts, I showed examples of using Selenium WebDriver to capture screenshots, and running in a headless (no X-server) mode. This example combines the two solutions to capture screenshots inside a virtual display. To achieve this, I use a combination of Selenium WebDriver and pyvirtualdisplay (which uses xvfb) to run a browser in a virtual display and capture screenshots. the setup you need is: Selenium 2 Python bindings: PyPI pyvirtualdisplay Python package (depends on xvfb): PyPI On Debian/Ubuntu Linux systems, you can install everything with: $ sudo apt-get install python-pip xvfb xserver-xephyr $ sudo pip install selenium once you have it setup, the following code example should work: #!/usr/bin/env python from pyvirtualdisplay import Display from selenium import webdriver display = Display(visible=0, size=(800, 600)) display.start() browser = webdriver.Firefox() browser.get('http://www.google.com') browser.save_screenshot('screenie.png') browser.quit() display.stop() this will: launch a virtual display launch Firefox browser inside the virtual display navigate to google.com capture and save a screenshot close the browser stop the virtual display

A few weeks ago, I co-authored an article (with my colleague Rama Ramani) about how the Screen Actors Guild Awards website migrated its Drupal deployment from LAMP to Windows Azure: Azure Real World: Migrating a Drupal Site from LAMP to Windows Azure. Since then, Rama and another colleague, Jason Roth, have been working on writing up how the SAG Awards website was managed and monitored in Windows Azure. The article below is the fruit of their work…a very interesting/educational read. Overview Drupal is an open source content management system that runs on PHP. Windows Azure offers a flexible platform for hosting, managing, and scaling Drupal deployments. This paper focuses on an approach to host Drupal sites on Windows Azure, based on learning from a BPD Customer Programs Design Win engagement with the Screen Actors Guild Awards Drupal website. This paper covers guidelines and best practices for managing an existing Drupal web site in Windows Azure. For more information on how to migrate Drupal applications to Windows Azure, see Azure Real World: Migrating a Drupal Site from LAMP to Windows Azure. The target audience for this paper is Drupal administrators who have some exposure to Windows Azure. More detailed pointers to Windows Azure content is provided throughout the paper as links. Drupal Application Architecture on Windows Azure Before reviewing the management and monitoring guidelines, it is important to understand the architecture of a typical Drupal deployment on Windows Azure. First, the following diagram displays the basic architecture of Drupal running on Windows and IIS7. In the Windows Server scenario, you could have one or more machines hosting the web site in a farm. Those machines would either persist the site content to the file system or point to other network shares. For Windows Azure, the basic architecture is the same, but there are some differences. In Windows Azure the site is hosted on a web role. A web role instance is hosted on a Windows Server 2008 virtual machine within the Windows Azure datacenter. Like the web farm, you can have multiple instances running the site. But there is no persistence guarantee for the data on the file system. Because of this, much of the shared site content should be stored in Windows Azure Blob storage. This allows them to be highly available and durable. Usually, a large portion of the site caters to static content which lends well to caching. And caching can be applied in a set of places – browser level caching, CDN to cache content in the edge closer to the browser clients, caching in Azure to reduce the load on backend, etc. Finally, the database can be located in SQL Azure. The following diagram shows these differences. For monitoring and management, we will look at Drupal on Windows Azure from three perspectives: Availability: Ensure the web site does not go down and that all tiers are setup correctly. Apply best practices to ensure that the site is deployed across data centers and perform backup operations regularly. Scalability: Correctly handle changes in user load. Understand the performance characteristics of the site. Manageability: Correctly handle updates. Make code and site changes with no downtime when possible. Although some management tasks span one or more of these categories, it is still helpful to discuss Drupal management on Windows Azure within these focus areas. Availability One main goal is that the Drupal site remains running and accessible to all end-users. This involves monitoring both the site and the SQL Azure database that the site depends on. In this section, we will briefly look at monitoring and backup tasks. Other crossover areas that affect availability will be discussed in the next section on scalability. Monitoring With any application, monitoring plays an important role with managing availability. Monitoring data can reveal whether users are successfully using the site or whether computing resources are meeting the demand. Other data reveals error counts and possibly points to issues in a specific tier of the deployment. There are several monitoring tools that can be used. The Windows Azure Management Portal. Windows Azure diagnostic data. Custom monitoring scripts. System Center Operations Manager. Third party tools such as Azure Diagnostics Manager and Azure Storage Explorer. The Windows Azure Management Portal can be used to ensure that your deployments are successful and running. You can also use the portal to manage features such as Remote Desktop so that you can directly connect to machines that are running the Drupal site. Windows Azure diagnostics allows you to collect performance counters and logs off of the web role instances that are running the Drupal site. Although there are many options for configuring diagnostics in Azure, the best solution with Drupal is to use a diagnostics configuration file. The following configuration file demonstrates some basic performance counters that can monitor resources such as memory, processor utilization, and network bandwidth. For more information about setting up diagnostic configuration files, see How to Use the Windows Azure Diagnostics Configuration File. This information is stored locally on each role instance and then transferred to Windows Azure storage per a defined schedule or on-demand. See Getting Started with Storing and Viewing Diagnostic Data in Windows Azure Storage. Various monitoring tools, such as Azure Diagnostics Manager, help you to more easily analyze diagnostic data. Monitoring the performance of the machines hosting the Drupal site is only part of the story. In order to plan properly for both availability and scalability, you should also monitor site traffic, including user load patterns and trends. Standard and custom diagnostic data could contribute to this, but there are also third-party tools that monitor web traffic. For example, if you know that spikes occur in your application during certain days of the week, you could make changes to the application to handle the additional load and increase the availability of the Drupal solution. Backup Tasks To remain highly available, it is important to backup your data as a defense-in-depth strategy for disaster recovery. This is true even though SQL Azure and Windows Azure Storage both implement redundancy to prevent data loss. One obvious reason is that these services cannot prevent administrator error if data is accidentally deleted or incorrectly changed. SQL Azure does not currently have a formal backup technology, although there are many third-party tools and solutions that provide this capability. Usually the database size for a Drupal site is relatively small. In the case of SAG Awards, it was only ~100-150 MB. So performing an entire backup using any strategy was relatively fast. If your database is much larger, you might have to test various backup strategies to find the one that works best. Apart from third-party SQL Azure backup solutions, there are several strategies for obtaining a backup of your data: · Use the Drush tool and the portabledb-export command. · Periodically copy the database using the CREATE DATABASE Transact-SQL command. · Use Data-tier applications (DAC) to assist with backup and restore of the database. SQL Azure backup and data security techniques are described in more detail in the topic, Business Continuity in SQL Azure. Note that bandwidth costs accrue with any backup operation that transfers information outside of the Windows Azure datacenter. To reduce costs, you can copy the database to a database within the same datacenter. Or you can export the data-tier applications to blob storage in the same datacenter. Another potential backup task involves the files in Blob storage. If you keep a master copy of all media files uploaded to Blob storage, then you already have an on-premises backup of those files. However, if multiple administrators are loading files into Blob storage for use on the Drupal site, it is a good idea to enumerate the storage account and to download any new files to a central location. The following PHP script demonstrates how this can be done by backing up all files in Blob storage after a specified modification date. setProxy(true, 'YOUR_PROXY_IF_NEEDED', 80); $blobs = (array)$blobObj->listBlobs(AZURE_STORAGE_CONTAINER, '', '', 35000); backupBlobs($blobs, $blobObj); function backupBlobs($blobs, $blobObj) { foreach ($blobs as $blob) { if (strtotime($blob->lastmodified) >= DEFAULT_BACKUP_FROM_DATE && strtotime($blob->lastmodified) <= DEFAULT_BACKUP_TO_DATE) { $path = pathinfo($blob->name); if ($path['basename'] != '$$$.$$$') { $dir = $path['dirname']; $oldDir = getcwd(); if (handleDirectory($dir)) { chdir($dir); $blobObj->getBlob( AZURE_STORAGE_CONTAINER, $blob->name, $path['basename'] ); chdir($oldDir); } } } } } function handleDirectory($dir) { if (!checkDirExists($dir)) { return mkdir($dir, 0755, true); } return true; } function checkDirExists($dir) { if(file_exists($dir) && is_dir($dir)) { return true; } return false; } ?> This script has a dependency on the Windows Azure SDK for PHP. Also note there are several parameters that you must modify such as the storage account, secret, and backup location. As with SQL Azure, bandwidth and transaction charges apply to a backup script like this. Scalability Drupal sites on Windows Azure can scale as load increased through typical strategies of scale-up, scale-out, and caching. The following sections describe the specifics of how these strategies are implemented in Windows Azure. Typically you make scalability decisions based on monitoring and capacity planning. Monitoring can be done in staging during testing or in production with real-time load. Capacity planning factors in projections for changes in user demand. Scale Up When you configure your web role prior to deployment, you have the option of specifying the Virtual Machine (VM) size, such as Small or ExtraLarge. Each size tier adds additional memory, processing power, and network bandwidth to each instance of your web role. For cost efficiency and smaller units of scale, you can test your application under expected load to find the smallest virtual machine size that meets your requirements. The workload usually in most popular Drupal websites can be separated out into a limited set of Drupal admins making content changes and a large user base who perform mostly read-only workload. End users can be allowed to make ‘writes’, such as uploading blogs or posting in forums, but those changes are not ‘content changes’. Drupal admins are setup to operate without caching so that the writes are made directly to SQL Azure or the corresponding backend database. This workload performs well with Large or ExtraLarge VM sizes. Also, note that the VM size is closely tied to all hardware resources, so if there are many content-rich pages that are streaming content, then the VM size requirements are higher. To make changes to the Virtual Machine size setting, you must change the vmsize attribute of the WebRole element in the service definition file, ServiceDefinition.csdef. A virtual machine size change requires existing applications to be redeployed. Scale Out In addition to the size of each web role instance, you can increase or decrease the number of instances that are running the Drupal site. This spreads the web requests across more servers, enabling the site to handle more users. To change the number of running instances of your web role, see How to Scale Applications by Increasing or Decreasing the Number of Role Instances. Note that some configuration changes can cause your existing web role instances to recycle. You can choose to handle this situation by applying the configuration change and continue running. This is done by handling the RoleEnvironment.Changing event. For more information see, How to Use the RoleEnvironment.Changing Event. A common question for any Windows Azure solution is whether there is some type of built-in automatic scaling. Windows Azure does not provide a service that provides auto-scaling. However, it is possible to create a custom solution that scales Azure services using the Service Management API. For an example of this approach, see An Auto-Scaling Module for PHP Applications in Windows Azure. Caching Caching is an important strategy for scaling Drupal applications on Windows Azure. One reason for this is that SQL Azure implements throttling mechanisms to regulate the load on any one database in the cloud. Code that uses SQL Azure should have robust error handling and retry logic to account for this. For more information, see Error Messages (SQL Azure Database). Because of the potential for load-related throttling as well as for general performance improvement, it is strongly recommended to use caching. Although Windows Azure provides a Caching service, this service does not currently have interoperability with PHP. Because of this, the best solution for caching in Drupal is to use a module that uses an open-source caching technology, such as Memcached. Outside of a specific Drupal module, you can also configure Memcached to work in PHP for Windows Azure. For more information, see Running Memcached on Windows Azure for PHP. Here is also an example of how to get Memcached working in Windows Azure using a plugin: Windows Azure Memcached plugin. In a future paper, we hope to cover this architecture in more detail. For now, here are several design and management considerations related to caching. Area Consideration Design and Implementation For a technology like Memcached, will the cache be collocated (spread across all web role instances)? Or will you attempt to setup a dedicated cache ring with worker roles that only run Memcached? Configuration What memory is required and how will items in the cache be invalidated? Performance and Monitoring What mechanisms will be used to detect the performance and overall health of the cache? For ease of use and cost savings, collocation of the cache across the web role instances of the Drupal site works best. However, this assumes that there is available reserve memory on each instance to apply toward caching. It is possible to increase the virtual machine size setting to increase the amount of available memory on each machine. It is also possible to add additional web role instances to add to the overall memory of the cache while at the same time improving the ability of the web site to respond to load. It is possible to create a dedicated cache cluster in the cloud, but the steps for this are beyond the scope of this paper[RR1] . For Windows Azure Blob storage, there is also a caching feature built into the service called the Content Delivery Network (CDN). CDN provides high-bandwidth access to files in Blob storage by caching copies of the files in edge nodes around the world. Even within a single geographic region, you could see performance improvements as there are many more edge nodes than Windows Azure datacenters. For more information, see Delivering High-Bandwidth Content with the Windows Azure CDN. Manageability It is important to note that each hosted service has a Staging environment and a Production environment. This can be used to manage deployments, because you can load and test and application in staging before performing a VIP swap with production. From a manageability standpoint, Drupal has an advantage on Windows Azure in the way that site content is stored. Because the data necessary to serve pages is stored in the database and blob storage, there is no need to redeploy the application to change the content of the site. Another best practice is to use a separate storage account for diagnostic data than the one that is used for the application itself. This can improve performance and also helps to separate the cost of diagnostic monitoring from the cost of the running application. As mentioned previously, there are several tools that can assist with managing Windows Azure applications. The following table summarizes a few of these choices. Tool Description Windows Azure Management Portal The web interface of the Windows Azure management portal shows deployments, instance counts and properties, and supports many different common management and monitoring tasks. Azure Diagnostics Managerq[RR2] [JR3] A Red Gate Software product that provides advanced monitoring and management of diagnostic data. This tool can be very useful for easily analyzing the performance of the Drupal site to determine appropriate scaling decisions. Azure Storage Explorer A tool created by Neudesic for viewing Windows Azure storage account. This can be useful for viewing both diagnostic data and the files in Blob storage.

See original post at https://muhammadkhojaye.blogspot.com/2012/04/how-to-run-amazon-elastic-mapreduce-job.html Introduction Amazon EMR is a web service which can be used to easily and efficiently process enormous amounts of data. It uses a hosted Hadoop framework running on the web-scale infrastructure of Amazon EC2 and Amazon S3. Amazon EMR removes most of the cumbersome details of Hadoop while taking care of provisioning of Hadoop, running the job flow, terminating the job flow, moving the data between Amazon EC2 and Amazon S3, and optimizing Hadoop. In this tutorial, we will use a developed WordCount Java example using Hadoop and thereafter, we execute our program on Amazon Elastic MapReduce. Prerequisites You must have valid AWS account credentials. You should also have a general familiarity with using the Eclipse IDE before you begin. The reader can also use any other IDE of their choice. Step 1 – Develop MapReduce WordCount Java Program In this section, we are first going to develop a WordCount application. A WordCount program will determine how many times different words appear in a set of files. In Eclipse (or whatever the IDE you are using), Create simple Java Project with the name "WordCount". Create a java class name Map and override the map method as follow, public class Map extends Mapper { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); @Override public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); StringTokenizer tokenizer = new StringTokenizer(line); while (tokenizer.hasMoreTokens()) { word.set(tokenizer.nextToken()); context.write(word, one); } } } Create a java class named Reduce and override the reduce method as shown below, public class Reduce extends Reducer { @Override protected void reduce(Text key, java.lang.Iterable values, org.apache.hadoop.mapreduce.Reducer.Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable value : values) { sum += value.get(); } context.write(key, new IntWritable(sum)); } } Create a java class named WordCount and defined the main method as below, public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = new Job(conf, "wordcount"); job.setJarByClass(WordCount.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); job.setMapperClass(Map.class); job.setReducerClass(Reduce.class); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.waitForCompletion(true); } Export the WordCount program in a jar using eclipse and save it to some location on disk. Make sure that you have provided the Main Class (WordCount.jar) during extraction ofu8u the jar file as shown below. Our jar is ready!!! Step 2 – Upload the WordCount JAR and Input Files to Amazon S3 Now we are going to upload the WordCount jar to Amazon S3. First, go to the following URL: https://console.aws.amazon.com/s3/home Next, click “Create Bucket”, give your bucket a name, and click the “Create” button. Select your new S3 bucket in the left-hand pane. Upload the WordCount JAR and sample input file for counting the words. Step 3 – Running an Elastic MapReduce job Now that the JAR is uploaded into S3, all we need to do is to create a new Job flow. let's execute the steps below. (I encourage readers to check out the following link for details regarding each step, How to Create a Job Flow Using a Custom JAR ) Sign in to the AWS Management Console and open the Amazon Elastic MapReduce console at https://console.aws.amazon.com/elasticmapreduce/ Click Create New Job Flow. In the DEFINE JOB FLOW page, enter the following details, a) Job Flow Name = WordCountJob b) Select Run your own applications) Select Custom JAR in the drop-down list) Click Continue In the SPECIFY PARAMETERS page, enter values in the boxes using the following table as a guide, and then click Continue.JAR Location = bucketName/jarFileLocationJAR Arguments =s3n://bucketName/inputFileLocations3n://bucketName/outputpath Please note that the output path must be unique each time we execute the job. The Hadoop always create a folder with the same name specified here. After executing the job, just wait and monitor your job that runs through the Hadoop flow. You can also look for errors by using the Debug button. The job should be complete within 10 to 15 minutes (can also depend on the size of the input). After completing the job, You can view results in the S3 Browser panel. You can also download the files from S3 and can analyze the outcome of the job. Amazon Elastic MapReduce Resources Amazon Elastic MapReduce Documentation,http://aws.amazon.com/documentation/elasticmapreduce/ Amazon Elastic MapReduce Getting Started Guide,http://docs.amazonwebservices.com/ElasticMapReduce/latest/GettingStartedGuide/ Amazon Elastic MapReduce Developer Guide,http://docs.amazonwebservices.com/ElasticMapReduce/latest/DeveloperGuide/ Apache Hadoop,http://hadoop.apache.org/ See more at https://muhammadkhojaye.blogspot.com/2012/04/how-to-run-amazon-elastic-mapreduce-job.html

How to create a real-time face detection system using HTML5, JavaScript, and OpenCV, leveraging WebRTC for webcam access and WebSockets for client-server communication.

i’ve done a few posts recently using d3.js and now i want to show you how to use two other great javascript libraries to visualize your graphs. we’ll start with sigma.js and soon i’ll do another post with three.js . we’re going to create our graph and group our nodes into five clusters. you’ll notice later on that we’re going to give our clustered nodes colors using rgb values so we’ll be able to see them move around until they find their right place in our layout. we’ll be using two sigma.js plugins, the gefx (graph exchange xml format) parser and the forceatlas2 layout. you can see what a gefx file looks like below. notice it comes from gephi which is an interactive visualization and exploration platform, which runs on all major operating systems, is open source, and is free. ... ... in order to build this file, we will need to get the nodes and edges from the graph and create an xml file. get '/graph.xml' do @nodes = nodes @edges = edges builder :graph end we’ll use cypher to get our nodes and edges: def nodes neo = neography::rest.new cypher_query = " start node = node:nodes_index(type='user')" cypher_query << " return id(node), node" neo.execute_query(cypher_query)["data"].collect{|n| {"id" => n[0]}.merge(n[1]["data"])} end we need the node and relationship ids, so notice i’m using the id() function in both cases. def edges neo = neography::rest.new cypher_query = " start source = node:nodes_index(type='user')" cypher_query << " match source -[rel]-> target" cypher_query << " return id(rel), id(source), id(target)" neo.execute_query(cypher_query)["data"].collect{|n| {"id" => n[0], "source" => n[1], "target" => n[2]} } end so far we have seen graphs represented as json, and we’ve built these manually. today we’ll take advantage of the builder ruby gem to build our graph in xml. xml.instruct! :xml xml.gexf 'xmlns' => "http://www.gephi.org/gexf", 'xmlns:viz' => "http://www.gephi.org/gexf/viz" do xml.graph 'defaultedgetype' => "directed", 'idtype' => "string", 'type' => "static" do xml.nodes :count => @nodes.size do @nodes.each do |n| xml.node :id => n["id"], :label => n["name"] do xml.tag!("viz:size", :value => n["size"]) xml.tag!("viz:color", :b => n["b"], :g => n["g"], :r => n["r"]) xml.tag!("viz:position", :x => n["x"], :y => n["y"]) end end end xml.edges :count => @edges.size do @edges.each do |e| xml.edge:id => e["id"], :source => e["source"], :target => e["target"] end end end end you can get the code on github as usual and see it running live on heroku. you will want to see it live on heroku so you can see the nodes in random positions and then move to form clusters. use your mouse wheel to zoom in, and click and drag to move around. credit goes out to alexis jacomy and mathieu jacomy . you’ve seen me create numerous random graphs, but for completeness here is the code for this graph. notice how i create 5 clusters and for each node i assign half its relationships to other nodes in their cluster and half to random nodes? this is so the forceatlas2 layout plugin clusters our nodes neatly. def create_graph neo = neography::rest.new graph_exists = neo.get_node_properties(1) return if graph_exists && graph_exists['name'] names = 500.times.collect{|x| generate_text} clusters = 5.times.collect{|x| {:r => rand(256), :g => rand(256), :b => rand(256)} } commands = [] names.each_index do |n| cluster = clusters[n % clusters.size] commands << [:create_node, {:name => names[n], :size => 5.0 + rand(20.0), :r => cluster[:r], :g => cluster[:g], :b => cluster[:b], :x => rand(600) - 300, :y => rand(150) - 150 }] end names.each_index do |from| commands << [:add_node_to_index, "nodes_index", "type", "user", "{#{from}"] connected = [] # create clustered relationships members = 20.times.collect{|x| x * 10 + (from % clusters.size)} members.delete(from) rels = 3 rels.times do |x| to = members[x] connected << to commands << [:create_relationship, "follows", "{#{from}", "{#{to}"] unless to == from end # create random relationships rels = 3 rels.times do |x| to = rand(names.size) commands << [:create_relationship, "follows", "{#{from}", "{#{to}"] unless (to == from) || connected.include?(to) end end batch_result = neo.batch *commands end

I love stack traces. Not because I love errors, but the moment they occur, stack trace is priceless source of information. For instance in web application the stack trace shows you the complete request processing path, from HTTP socket, through filters, servlets, controllers, services, DAOs, etc. - up to the place, where an error occurred. You can read them as a good book, where every event has cause and effect. I even implemented some enhancements in the way Logback prints exceptions, see Logging exceptions root cause first. But one thing's been bothering me for a while. The infamous “stack trace from hell" symptom – stack traces containing hundreds of irrelevant, cryptic, often auto-generated methods. AOP frameworks and over-engineered libraries tend to produce insanely long execution traces. Let me show a real-life example. In a sample application I am using the following technology stack: Colours are important. According to framework/layer colour I painted a sample stack trace, caused by exception thrown somewhere deep while trying to fetch data from the database: No longer that pleasant, don't you think? Placing Spring between application and Hibernate in the first diagram was a huge oversimplification. Spring framework is a glue code that wires up and intercepts your business logic with surrounding layers. That is why application code is scattered and interleaved by dozens of lines of technical invocations (see green lines). I put as much stuff as I could into the application (Spring AOP, method-level @Secured annotations, custom aspects and interceptors, etc.) to emphasize the problem – but it is not Spring specific. EJB servers generate equally terrible stack traces (...from hell) between EJB calls. Should I care? Think about it, when you innocently call BookService.listBooks() from BookController.listBooks() do you expect to see this? at com.blogspot.nurkiewicz.BookService.listBooks() at com.blogspot.nurkiewicz.BookService$$FastClassByCGLIB$$e7645040.invoke() at net.sf.cglib.proxy.MethodProxy.invoke() at org.springframework.aop.framework.Cglib2AopProxy$CglibMethodInvocation.invokeJoinpoint() at org.springframework.aop.framework.ReflectiveMethodInvocation.proceed() at org.springframework.aop.aspectj.MethodInvocationProceedingJoinPoint.proceed() at com.blogspot.nurkiewicz.LoggingAspect.logging() at sun.reflect.NativeMethodAccessorImpl.invoke0() at sun.reflect.NativeMethodAccessorImpl.invoke() at sun.reflect.DelegatingMethodAccessorImpl.invoke() at java.lang.reflect.Method.invoke() at org.springframework.aop.aspectj.AbstractAspectJAdvice.invokeAdviceMethodWithGivenArgs() at org.springframework.aop.aspectj.AbstractAspectJAdvice.invokeAdviceMethod() at org.springframework.aop.aspectj.AspectJAroundAdvice.invoke() at org.springframework.aop.framework.ReflectiveMethodInvocation.proceed() at org.springframework.aop.interceptor.AbstractTraceInterceptor.invoke() at org.springframework.aop.framework.ReflectiveMethodInvocation.proceed() at org.springframework.transaction.interceptor.TransactionInterceptor.invoke() at org.springframework.aop.framework.ReflectiveMethodInvocation.proceed() at org.springframework.aop.interceptor.ExposeInvocationInterceptor.invoke() at org.springframework.aop.framework.ReflectiveMethodInvocation.proceed() at org.springframework.aop.framework.Cglib2AopProxy$DynamicAdvisedInterceptor.intercept() at com.blogspot.nurkiewicz.BookService$$EnhancerByCGLIB$$7cb147e4.listBooks() at com.blogspot.nurkiewicz.web.BookController.listBooks() And have you even noticed there is custom aspect in between? That's the thing, there is so much noise in the stack traces nowadays that following the actual business logic is virtually impossible. One of the best troubleshooting tools we have is bloated with irrelevant framework-related stuff we don't need in 99% of the cases. Tools and IDEs are doing a good job of reducing the noise. Eclipse has stack trace filter patterns for Junit, IntelliJ IDEA supports console folding customization. See also: Cleaning noise out of Java stack traces, which inspired me to write this article. So why not having such possibility at the very root – in the logging framework such as Logback? I implemented a very simple enhancement in Logback. Basically you can define a set of stack trace frame patterns that are suppose to be excluded from stack traces. Typically you will use package or class names that you are not interested in seeing. This is a sample logback.xml excerpt with the new feature enabled: %d{HH:mm:ss.SSS} | %-5level | %thread | %logger{1} | %m%n%rEx{full, java.lang.reflect.Method, org.apache.catalina, org.springframework.aop, org.springframework.security, org.springframework.transaction, org.springframework.web, sun.reflect, net.sf.cglib, ByCGLIB } I am a bit extreme in filtering almost whole Spring framework + Java reflection and CGLIB classes. But it is just to give you an impression how much can you get. The very same error after applying my enhancement to Logback: Just as a reminder, green is our application. Finally in one place, finally you can really see what was your code doing when an error occurred: at com.blogspot.nurkiewicz.DefaultBookHelper.findBooks() at com.blogspot.nurkiewicz.BookService.listBooks() at com.blogspot.nurkiewicz.LoggingAspect.logging() at com.blogspot.nurkiewicz.web.BookController.listBooks() Simpler? If you like this feature, I opened a ticket LBCLASSIC-325: Filtering out selected stack trace frames. Vote and discuss. This is only a proof-of-concept, but if you like to have a look at the implementation (improvements are welcome!), it is available under my fork of Logback (around 20 lines of code).

The Map Reduce Framework works in two main phases to process the data, which are the "map" phase and the "reduce" phase.

The one major difference between IDEA and Eclipse is that IDEA "feels context", which effectively makes IDEA intelligent.

a pre-commit hook is a piece of code that runs before every commit and determines whether or not the commit should be accepted. think of it as the gatekeeper to your codebase. want to ensure you didn’t accidentally leave any pdb s in your code? pre-commit hook. want to make sure your javascript is jshint approved? pre-commit hook. want to guarantee clean, readable pep8 -compliant code? pre-commit hook. want to pipe all of the comments in your codebase through strunk & white ? please don’t. the pre-commit hook is just an executable file that runs before every commit. if it exits with zero status, the commit is accepted. if it exits with a non-zero status, the commit is rejected. (note: a pre-commit hook can be bypassed by passing the --no-verify argument.) along with the pre-commit hook there are numerous other git hooks that are available: post-commit, post-merge, pre-receive, and others that can be found here . why most pre-commit hooks are wrong be wary of the above’s example as the majority of pre-commit hooks you’ll see on the web are wrong. most test against whatever files are currently on disk, not what is in the staging area (the files actually being committed). we avoid this in our hook by stashing all changes that are not part of the staging area before running our checks and then popping the changes afterwards. this is very important because a file could be fine on disk while the changes that are being committed are wrong. the code below is the pre-commit hook we use at yipit. our hook is simply a set of checks to be run against any files that have been modified in this commit. each check can be configured to include/exclude particular types of files. it is designed for a django environment, but should be adaptable to other environments with minor changes. note that you need git 1.7.7+ #!/usr/bin/env python import os import re import subprocess import sys modified = re.compile('^(?:m|a)(\s+)(?p.*)') checks = [ { 'output': 'checking for pdbs...', 'command': 'grep -n "import pdb" %s', 'ignore_files': ['.*pre-commit'], 'print_filename': true, }, { 'output': 'checking for ipdbs...', 'command': 'grep -n "import ipdb" %s', 'ignore_files': ['.*pre-commit'], 'print_filename': true, }, { 'output': 'checking for print statements...', 'command': 'grep -n print %s', 'match_files': ['.*\.py$'], 'ignore_files': ['.*migrations.*', '.*management/commands.*', '.*manage.py', '.*/scripts/.*'], 'print_filename': true, }, { 'output': 'checking for console.log()...', 'command': 'grep -n console.log %s', 'match_files': ['.*yipit/.*\.js$'], 'print_filename': true, }, { 'output': 'checking for debugger...', 'command': 'grep -n debugger %s', 'match_files': ['.*\.js$'], 'print_filename': true, }, { 'output': 'running jshint...', # by default, jshint prints 'lint free!' upon success. we want to filter this out. 'command': 'jshint %s | grep -v "lint free!"', 'match_files': ['.*yipit/.*\.js$'], 'print_filename': false, }, { 'output': 'running pyflakes...', 'command': 'pyflakes %s', 'match_files': ['.*\.py$'], 'ignore_files': ['.*settings/.*', '.*manage.py', '.*migrations.*', '.*/terrain/.*'], 'print_filename': false, }, { 'output': 'running pep8...', 'command': 'pep8 -r --ignore=e501,w293 %s', 'match_files': ['.*\.py$'], 'ignore_files': ['.*migrations.*'], 'print_filename': false, }, { 'output': 'checking for sass changes...', 'command': 'sass --quiet --update %s', 'match_files': ['.*\.scss$'], 'print_filename': true, }, ] def matches_file(file_name, match_files): return any(re.compile(match_file).match(file_name) for match_file in match_files) def check_files(files, check): result = 0 print check['output'] for file_name in files: if not 'match_files' in check or matches_file(file_name, check['match_files']): if not 'ignore_files' in check or not matches_file(file_name, check['ignore_files']): process = subprocess.popen(check['command'] % file_name, stdout=subprocess.pipe, stderr=subprocess.pipe, shell=true) out, err = process.communicate() if out or err: if check['print_filename']: prefix = '\t%s:' % file_name else: prefix = '\t' output_lines = ['%s%s' % (prefix, line) for line in out.splitlines()] print '\n'.join(output_lines) if err: print err result = 1 return result def main(all_files): # stash any changes to the working tree that are not going to be committed subprocess.call(['git', 'stash', '-u', '--keep-index'], stdout=subprocess.pipe) files = [] if all_files: for root, dirs, file_names in os.walk('.'): for file_name in file_names: files.append(os.path.join(root, file_name)) else: p = subprocess.popen(['git', 'status', '--porcelain'], stdout=subprocess.pipe) out, err = p.communicate() for line in out.splitlines(): match = modified.match(line) if match: files.append(match.group('name')) result = 0 print 'running django code validator...' return_code = subprocess.call('$virtual_env/bin/python manage.py validate', shell=true) result = return_code or result for check in checks: result = check_files(files, check) or result # unstash changes to the working tree that we had stashed subprocess.call(['git', 'reset', '--hard'], stdout=subprocess.pipe, stderr=subprocess.pipe) subprocess.call(['git', 'stash', 'pop', '-q'], stdout=subprocess.pipe, stderr=subprocess.pipe) sys.exit(result) if __name__ == '__main__': all_files = false if len(sys.argv) > 1 and sys.argv[1] == '--all-files': all_files = true main(all_files) to use this hook or a hook that you create yourself, simply copy the file to .git/hooks/pre-commit inside of your project and make sure that it is executable or add in to your git repo and setup a symlink.

for a while now, we had been operating in the wild west when it comes to building our applications and deploying to production. builds were typically done straight from the developer’s ide and manually deployed to one of our app servers. we had a manual process in place, where the developer would do the following steps. check all project code into subversion and tag build the application. archive the application binary to a network drive deploy to production update our deployment wiki with the date and version number of the app that was just deployed. the problem is that there were occasionally times where one of these steps were missed, and it always seemed to be at a time when we needed to either rollback to the previous version, or branch from the tag to do a bugfix. sometimes the previous version had not been archived to the network, or the developer forgot to tag svn. we were already using jenkins to perform automated builds, so we wanted to look at extending it further to perform release builds. the maven release plug-in provides a good starting point for creating an automated release process. we have also just started using the nexus maven repository and wanted to incorporate that as well to archive our binaries to, rather than archiving them to a network drive. the first step is to set up the project’s pom file with the deploy plugin as well as include configuration information about our nexus and subversion repositories. org.apache.maven.plugins maven-release-plugin 2.2.2 http://mks:8080/svn/jrepo/tags/frameworks/siestaframework the release plugin configuration is pretty straightforward. the configuration takes the subversion url of the location where the tags will reside for this project. the next step is to configure the svn location where the code will be checked out from. scm:svn:http://mks:8080/svn/jrepo/trunk/frameworks/siestaframework http://mks:8080/svn the last step in configuring the project is to set up the location where the binaries will be archived to. in our case, the nexus repository. lynden-java-release lynden release repository http://cisunwk:8081/nexus/content/repositories/lynden-java-release the project is now ready to use the maven release plug-in. the release plugin provides a number of useful goals. release:clean – cleans the workspace in the event the last release process was not successful. release: prepare – performs a number of operations checks to make sure that there are no uncommitted changes. ensures that there are no snapshot dependencies in the pom file, changes the version of the application and removes snapshot from the version. ie 1.0.3-snapshot becomes 1.0.3 run project tests against modified poms commit the modified pom tag the code in subersion increment the version number and append snapshot. ie 1.0.3 becomes 1.0.4-snapshot commit modified pom release: perform – performs the release process checks out the code using the previously defined tag runs the deploy maven goal to move the resulting binary to the repository. putting it all together the last step in this process is to configure jenkins to allow release builds on-demand, meaning we want the user to have to explicitly kick off a release build for this process to take place. we have download and installed the release jenkins plug-in in order to allow developers to kick off release builds from jenkins. the release plug-in will execute tasks after the normal build has finished. below is a screenshot of the configuration of one of our projects. the release build option for the project is enabled by selecting the “configure release build” option in the “build environment” section. the maven release plug-in is activated by adding the goals to the “after successful release build” section. (the –b option enables batch mode so that the release plug-in will not ask the user for input, but use defaults instead.) once the release option has been configured for a project there will be a “release” icon on the left navigation menu for the project. selecting this will kick off a build and then the maven release process, assuming the build succeeds. finally a look at svn and nexus verifies that the build for version 1.0.4 of the siesta-framework project has been tagged in svn and uploaded to nexus. the next steps for this project will be to generate release notes for release builds, and also to automate a deployment pipeline, so that developers can deploy to our test, staging and production servers via jenkins rather than manually from their development workstations. twitter: @robterp blog: http://rterp.wordpress.com

Originally Authored by Dejan Noveski I'm not going to dive into how good or extendible Vim is. If you are reading this article, you probably know that. The thing that makes Vim so good, is the scripting environment behind it called VimL. Using this scripting language, you can write any functionality/plugin you need for Vim. Each plugin you use is written in this language. Here's the best part. You only need very little knowledge of VimL to be able to write plugins, if you know Python (or Ruby). What's a vim plugin anyway A Vim plugin is a .vim script that defines functions, mappings, syntax rules, commands that may, or may not, manipulate the windows, buffers, lines. It is a complete piece of code with some specific functionality. Usually, a plugin consists of several functions mappings command definitions and event hooks. When writing vim plugins with Python, often, everything outside the functions is written in VimL. But those are vim commands and they can be learned fast. In fact, VimL can be learned fast, but using python gives so much flexibility. Think about using urllib/httplib/simplejson for accessing some web service that helps editing in Vim. This is why most of the plugins that work with web services are usually done in VimL+Python. Any prerequisites? You must have vim compiled with +python support. You can check that using the command: vim --version | grep +python Vim package in Ubuntu and it's derivatives comes with +python support. To Work - Vimmit.vim What's better than starting with a simple example? This is a plugin that, when called, will retrieve the homepage of Reddit and will display it in the current buffer. Start by opening "vimmit.vim" file (in vim). Since we are writing python code, its good to check if Vim supports Python: if !has('python') echo "Error: Required vim compiled with +python" finish endif This piece is writen in VimL. It's best if we stick to VimL for things like this, mappings and event hooks. This function will check if Vim has python support or it will end the script with an error message. We continue with the main function Reddit(). This is where we use Python and do the main functionality: " Vim comments start with a double quote. " Function definition is VimL. We can mix VimL and Python in " function definition. function! Reddit() " We start the python code like the next line. python << EOF # the vim module contains everything we need to interface with vim from # python. We need urllib2 for the web service consumer. import vim, urllib2 # we need json for parsing the response import json # we define a timeout that we'll use in the API call. We don't want # users to wait much. TIMEOUT = 20 URL = "http://reddit.com/.json" try: # Get the posts and parse the json response response = urllib2.urlopen(URL, None, TIMEOUT).read() json_response = json.loads(response) posts = json_response.get("data", "").get("children", "") # vim.current.buffer is the current buffer. It's list-like object. # each line is an item in the list. We can loop through them delete # them, alter them etc. # Here we delete all lines in the current buffer del vim.current.buffer[:] # Here we append some lines above. Aesthetics. vim.current.buffer[0] = 80*"-" for post in posts: # In the next few lines, we get the post details post_data = post.get("data", {}) up = post_data.get("ups", 0) down = post_data.get("downs", 0) title = post_data.get("title", "NO TITLE").encode("utf-8") score = post_data.get("score", 0) permalink = post_data.get("permalink").encode("utf-8") url = post_data.get("url").encode("utf-8") comments = post_data.get("num_comments") # And here we append line by line to the buffer. # First the upvotes vim.current.buffer.append("↑ %s"%up) # Then the title and the url vim.current.buffer.append(" %s [%s]"%(title, url,)) # Then the downvotes and number of comments vim.current.buffer.append("↓ %s | comments: %s [%s]"%(down, comments, permalink,)) # And last we append some "-" for visual appeal. vim.current.buffer.append(80*"-") except Exception, e: print e EOF " Here the python code is closed. We can continue writing VimL or python again. endfunction Save the file, source it in vim (:source vimmit.vim) and: :call Reddit() Now, the way we call the function is not so elegant. So we define a command: command! -nargs=0 Reddit call Reddit() We define the command :Reddit to call the function. After adding this, open a new bufer and do :Reddit . Home page will be loaded in the buffer. The -nargs argument states how many arguments the command will take. Function Arguments, Eval and Command Q: How does one access functional arguments? function! SomeName(arg1, arg2, arg3) " Get the first argument by name in VimL let firstarg=a:arg1 " Get the second argument by position in Viml let secondarg=a:1 " Get the arguments in python python << EOF import vim first_argument = vim.eval("a:arg1") #or vim.eval("a:0") second_argument = vim.eval("a:arg2") #or vim.eval("a:1") You can define a function with arbitrary number of arguments by putting "..." instead of argument names. You can access these arguments only by position, and you can mix them with named arguments (arg1, arg2, ...) Q: How can I call Vim commands from Python? vim.command("[vim-command-here]") Q: How to define global variables and access them in VimL and Python? Global vars are prefixed with g:. If you want to define one in your script, best thing to do is check if it exists and if doesn't define it and assign some default value to it: if !exists("g:reddit_apicall_timeout") let g:reddit_apicall_timeout=40 endif You can access it from python using the vim module: TIMEOUT = vim.eval("g:reddit_apicall_timeout") If you want to override this setting, you can write: let g:reddit_apicall_timeout=60 in .vimrc . Additional Notes VimL is pretty easy once you try it. Remember that print works and everything you can do with python, you can do in here. Here you can find the documentation for the vim python module. Vimdoc is the possibly the only resource you will need when writing vim plugins. You can also check this IBM developerWorks article . Now, try to extend "vimmit.vim" so the user is able to choose a subreddit (as a first functional argument). Source: http://brainacle.com/how-to-write-vim-plugins-with-python.html