Although the Active MQ website already gives a pithy, to-the-point explanation of ActiveMQ, I would like to add some more context to their definition. From the ActiveMQ project’s website: “ActiveMQ is an open sourced implementation of JMS 1.1 as part of the J2EE 1.4 specification.” Here’s my take: ActiveMQ is an open-source, messaging software which can serve as the backbone for an architecture of distributed applications built upon messaging. The creators of ActiveMQ were driven to create this open-source project for two main reasons: The available existing solutions at the time were proprietary/very expensive Developers with the Apache Software Foundation were working on a fully J2EE compliant application server (Geronimo) and they needed a JMS solution that had a license compatible with Apache’s licensing. Since its inception, ActiveMQ has turned into a strong competitor of the commercial alternatives, such as WebSphereMQ, EMS/TIBCO and SonicMQ and is deployed in production in some of the top companies in industries ranging from financial services to retail. Using messaging as an integration or communication style leads to many benefits such as: Allowing applications built with different languages and on different operating systems to integrate with each other Location transparency – client applications don’t need to know where the service applications are located Reliable communication – the producers/consumers of messages don’t have to be available at the same time, or certain segments along the route of the message can go down and come back up without impacting the message getting to the service/consumer Scaling – can scale horizontally by adding more services that can handle the messages if too many messages are arriving Asynchronous communication – a client can fire a message and continue other processing instead of blocking until the service has sent a response; it can handle the response message only when the message is ready Reduced coupling – the assumptions made by the clients and services are greatly reduced as a result of the previous 5 benefits. A service can change details about itself, including its location, protocol, and availability, without affecting or disrupting the client. Please see Gregor Hohpe’s description about messaging or the book he and Bobby Woolf wrote about messaging-based enterprise application integration. There are other advantages as well (hopefully someone can add other benefits or drawbacks in the comments), and ActiveMQ is a free, open-source software that can facilitate delivering those advantages and has proven to be highly reliable and scalable in production environments.

Oracle says it’s 9,000,000. Wikipedia claims it’s 10,000,000. And the guys from NumberOf.net seem to be the most precise - they know that there are exactly 9,007,346 Java developers out there. Nice numbers. I have used those articles as reference points while speaking about the potential market size for our memory leak detection tool. But something in these numbers has bothered me for years - there is no trustworthy and public analysis behind those numbers. Its just conjured up from thin air. So I finally thought I would do something about it and try to figure it out for good. It proved out to be a challenging task. After all - with more than seven billion people on our planet I couldn't call everyone and ask them. Well, maybe I could, but if every call would take on average 20 seconds I would need at least 4,439 years to complete the survey. If I would not sleep nor eat nor rest. So I had to use other ways for estimation. After playing around with different sources of information, I decided to dig into four of them for a closer look: Labour statistics provided by different governments Language popularity sites such as Tiobe and Langpop Employment portals using Indeed.com and Monster.com Download numbers on popular Java tools and libraries - namely Eclipse and Tomcat. Using that information I wanted to estimate the number using three different calculations - based on language popularity indexes, labour statistics and download figures. So, here we go. How many programmers could there be in total? World population is currently above seven billion. Out of those seven billion we can leave out sub-Saharan Africa (900M) and rural Asia (about 50% of its 2.2B population) as negligible. This leaves us with approximately 5 billion people living in regions where overall economical and cultural background can be considered suitable for software industries to spawn. Now, out of those 5,000,000,000 how many could be actually developing software? A good answer at StackExchange gives us some pointers as to where we can find information on the percentage of software developers in different countries. Using the US, Japan, Canada, the EU27 and the UK as a baseline we can estimate that 0.82% of the population is employed as a software developer or programmer: Country Population Developers % Canada 33,476,688 387,000 1.16% EU27 502,486,499 5,900,000 1.17% Japan 127,799,000 1,016,929 0.80% UK 63,162,000 333,000 0.53% US 313,931,000 1,336,300 0.43% Weighted average: 0.86% 0.86% out of five billion is 43,000,000. Lets remember this number, as it will be used as a baseline in following calculations. Popularity contests In the popularity contest we will use two channels for the source of data - the TIOBE index and the Langpop one. Other sources such as Dataist figures were hard to interpret, so we’ll stick just to those two. For the background - the TIOBE ratings are calculated by counting hits of the most popular search engines. The search query that is used is +" programming", e.g. +“Java programming” in our case. Langpop uses more sources for input besides search engine queries - in equal weights it traces open job positions, book titles, search engine results, the number of open source projects and other data to calculate its popularity score. Simplifying TIOBE and Langpop results, we can conclude that according to TIOBE 17% and according to Langpop ~15% of the programmers in the world are using Java. Averaging those numbers we can say that around 16% out of the 43,000,000 developers in the world use Java. This translates to 6,880,000 Java developers out there. Job portals Job portals, especially when considering both available positions and uploaded resumes, are definitely a good source of information. The larger ones also provide nice reports on labour market, which we will dig into next. Note that we used Indeed.com and Monster.com - if you can point us towards more and/or better sources of information, we would be glad to correct our calculations. But using this analysis from Monster.com and the aggregated statistics from Indeed.com we can say that ~18% of Monster.com applicants can program in Java and ~16% of open engineering / programming positions scanned by Indeed.com are looking for Java talent. Averaging those numbers we arrive at 17%. Which out of 41,000,000 programmers in total would translate to 7,310,000 Java guys and girls in the world. Software downloads Every Java developer uses something to build the application. Well, we expect them to use at least a JVM and a compiler. If you happen to know anyone who can get away without those two, please let us know. We would hire him immediately. But most of us tend to use more than just a compiler and a virtual machine. We use IDEs, application servers, build tools, etc. So we figured that we would look into the publicly available download numbers of these tools and try to estimate the number of developers from the download numbers. When calculating the total number of developers from estimated number of users, we take into account the market share of the corresponding software. To estimate the market share we use Zeroturnaround’s statistics gathered in the spring of 2012. Eclipse downloads. Eclipse Juno was released on June 27 and has been downloaded 1,200,000 times during the first 20 days. Looking into the historical data published by eclipse.org we can predict that Juno will be downloaded approximately 8,000,000 times in total. Last four major Eclipse releases have all been released using a yearly release calendar and all the releases took place in June: Juno - 8,000,000 (in a year, expecting the trend to continue. Currently has 1,200,000 downloads in first 20 days). Indigo - 6,000,000 downloads Helios - 4,100,000 downloads Galileo - 2,200,000 downloads Averaging Juno estimates and Indigo results, we can say that Eclipse is downloaded approximately 7,000,000 times a year. Using the Zeroturnaround’s statistics, we expect 68% of Java developers to use Eclipse as a (primary) IDE. If we now make a bold claim that each Java developer on Eclipse will download the IDE exactly once a year, expect the number of downloads per year to be 7,000,000 and consider that 32% of Java developers do not use Eclipse at all, we come to a conclusion that there should be 10,300,00 Java developers in total. Apache Tomcat downloads. Vadim Gritsenko has put together some nice statistics on top of Apache logs. From there we can see that during the last year Tomcat has been downloaded approximately 550,000 times/month. This gives us a yearly total of 6,600,000 Tomcat downloads. Applying now statistics from the same report used for calculating Eclipse’s market share we can estimate that 59% of Java developers are using Tomcat as one of their development platform. If we now again make a bold claim that each Java developer on Tomcat will download every major release exactly once and consider that 41% of Java developers do not use Tomcat, we reach to conclusion that there should be 11,186,000 Java developers out there. Averaging the numbers from Eclipse and Tomcat downloads, we end up with 10,743,000 Java developers. Conclusions We used three different sources for estimation - popularity contests, job market analysis and download numbers of popular Java development infrastructure products. The numbers varied quite a bit - from 6,880,000 to 10,743,000. Aggressively averaging the three numbers we can conclude that there are 8,311,000 Java developers out there. Not quite as much as Oracle or Wikipedia think, but still enough to build a business that provides developing tools for the Java community. Lies. Damn lies. And statistics.

While writing list navigation and search features in websites today there is a constant need to find/replace and play with query string elements, so that you can easily manipulate these mystical items while you’re carrying them around in your website’s URLs. I have a few little methods I’ve used over the years and carry with me project to project, and this post is putting them on the record for easy access later. I have a secret. This post is actually more aimed at an audience of 'myself', and my ability to have an easy bit of source code to call upon when I’m on the go looking for a quick solution to cut and paste – as most of my blog posts are. But you, dear reader, you get to share in this benefit with me by pulling from the awesomeness within this post as well. Solution: .Net c# When doing this with c# you have a few pretty cool features up your sleeve. One of these is HttpUtility.ParseQueryString(urlPath) framework method. This static method allows you to extract a NameValueCollection that is editable from a given query string. Why is this cool? Because it allows you to very easily play with the query string collection like it is any other NameValueCollection – with Add() and Remove() methods. This makes it incredibly powerful. Quick & Dirty code beware! The code I’m pasting below is far from being the most elegant solution, i seem to have misplaced my nicer piece of code and am in too much of a rush to find it right now (sorry). Until i find my nicer solution, the method below will get you by – whether you have a hatred for ternary’s or not. public static string ReplaceQueryStringParam(string currentPageUrl, string paramToReplace, string newValue) { string urlWithoutQuery = currentPageUrl.IndexOf('?') >= 0 ? currentPageUrl.Substring(0, currentPageUrl.IndexOf('?')) : currentPageUrl; string queryString = currentPageUrl.IndexOf('?') >= 0 ? currentPageUrl.Substring(currentPageUrl.IndexOf('?')) : null; var queryParamList = queryString != null ? HttpUtility.ParseQueryString(queryString) : HttpUtility.ParseQueryString(string.Empty); if (queryParamList[paramToReplace] != null) { queryParamList[paramToReplace] = newValue; } else { queryParamList.Add(paramToReplace, newValue); } return String.Format("{0}?{1}", urlWithoutQuery, queryParamList); } To call this, you can do the following: // var currentUrl = HttpContext.Current.Request.Url; var currentUrl = "http://www.mysite.com/mypage?category=cool-products&sort=price&page=3"; // change the my sort-by param named"sort" to "name" var newUrlWithChangedSort = ReplaceQueryStringParam(currentUrl, "sort", "name"); Solution: JavaScript The second part of this post includes a JavaScript solution, as you never know when you have to do this on the client side. function replaceQueryString(url, param, value) { if (url.lastIndexOf('?') <= 0) url = url + "?"; var re = new RegExp("([?|&])" + param + "=.*?(&|$)", "i"); if (url.match(re)) return url.replace(re, '$1' + param + "=" + value + '$2'); else return url.substring(url.length - 1) == '?' ? url + param + "=" + value : url + '&' + param + "=" + value; } And to use the above code in your client-side javascript simply write something along the lines of: //var currentUrl = self.location; var currentUrl = "http://www.mysite.com/mypage?category=cool-products&sort=price&page=3"; // change the my sort-by param named"sort" to "name" var newUrlWithChangedSort = replaceQueryString(currentUrl, "sort", "name"); Easy – now next time you need to knock something together, instead of writing it yourself, you can simply cut & paste mine!

Autoscaling your webserver tier is typically straightforward. Image your apache server with source code or without, then sync down files from S3 upon spinup. Roll that image into the autoscale configuration and you’re all set. With the database tier though, things can be a bit tricky. The typical configuration we see is to have a single master database where your application writes. But scaling out or horizontally on Amazon EC2 should be as easy as adding more slaves, right? Why not automate that process? Below we’ve set out to answer some of the questions you’re likely to face when setting up slaves against your master. We’ve included instructions on building an AMI that automatically spins up as a slave. Fancy! How can I autoscale my database tier? Build an auto-starting MySQL slave against your master. Configure those to spinup. Amazon’s autoscaling loadbalancer is one option, another is to use a roll-your-own solution, monitoring thresholds on servers, and spinning up or dropping off slaves as necessary. Does an AWS snapshot capture subvolume data or just the SIZE of the attached volume? In fact, if you have an attached EBS volume and you create an new AMI off of that, you will capture the entire root volume, plus your attached volume data. In fact we find this a great way to create an auto-building slave in the cloud. How do I freeze MySQL during AWS snapshot? mysql> flush tables with read lock;mysql> system xfs_freeze -f /data At this point you can use the Amazon web console, ylastic, or ec2-create-image API call to do so from the command line. When the server you are imaging off of above restarts – as it will do by default – it will start with /data partition unfrozen and mysql’s tables unlocked again. Voila! If you’re not using xfs for your /data filesystem, you should be. It’s fast! The xfsprogs docs seem to indicate this may also work with foreign filesystems. Check the docs for details. How do I build an AMI mysql slave that autoconnects to master? Install mysql_serverid script below. Configure mysql to use your /data EBS mount. Set all your my.cnf settings including server_id Configure the instance as a slave in the normal way. When using GRANT to create the ‘rep’ user on master, specify the host with a subnet wildcard. For example ’10.20.%’. That will subsequently allow any 10.20.x.y servers to connect and replicate. Point the slave at the master. When all is running properly, edit the my.cnf file and remove server_id. Don’t restart mysql. Freeze the filesystem as described above. Use the Amazon console, ylastic or API call to create your new image. Test it of course, to make sure it spins up, sets server_id and connects to master. Make a change in the test schema, and verify that it propagates to all slaves. How do I set server_id uniquely? As you hopefully already know, in MySQL replication environment each node requires a unique server_id setting. In my Amazon Machine Images, I want the server to startup and if it doesn’t find the server_id in the /etc/my.cnf file, to add it there, correctly! Is that so much to ask? Here’s what I did. Fire up your editor of choice and drop in this bit of code: #!/bin/shif grep -q “server_id” /etc/my.cnf then : # do nothing – it’s already set else # extract numeric component from hostname – should be internet IP in Amazon environment export server_id=`echo $HOSTNAME | sed ‘s/[^0-9]*//g’` echo “server_id=$server_id” >> /etc/my.cnf # restart mysql /etc/init.d/mysql restart fi Save that snippet at /root/mysql_serverid. Also be sure to make it executable: $ chmod +x /root/mysql_serverid Then just append it to your /etc/rc.local file with an editor or echo: $ echo "/root/mysql_serverid" >> /etc/rc.local Assuming your my.cnf file does *NOT* contain the server_id setting when you re-image, then it’ll set this automagically each time you spinup a new server off of that AMI. Nice! Can you easily slave off of a slave? How? It’s not terribly different from slaving off of a normal master. A. First enable slave updates. The setting is not dynamic, so if you don’t already have it set, you’ll have to restart your slave. log_slave_updates=true B. Get an initial snapshot of your slave data. You can do that the locking way: mysql> flush tables with read lock;mysql> show master status\G; mysql> system mysqldump -A > full_slave_dump.mysql mysql> unlock tables; You may also choose to use Percona’s excellent xtrabackup utility to create hotbackups without locking any tables. We are very lucky to have an open-source tool like this at our disposal. MySQL Enterprise Backup from Oracle Corp can also do this. C. On the slave, seed the database with your dump created above. $ mysql < full_slave_dump.mysql D. Now point your slave to the original slave. mysql> change master to master_user='rep', master_password='rep', master_host='192.168.0.1', master_log_file='server-bin-log.000004', master_log_pos=399;mysql> start slave; mysql> show slave status\G; Slave master is set as an IP address. Is there another way? It’s possible to use hostnames in MySQL replication, however it’s not recommended. Why? Because of the wacky world of DNS. Suffice it to say MySQL has to do a lot of work to resolve those names into IP addresses. A hickup in DNS can interrupt all MySQL services potentially as sessions will fail to authenticate. To avoid this problem do two things: A. Set this parameter in my.cnf skip_name_resolve = true Remove entries in mysql.user table where hostname is not an IP address. Those entries will be invalid for authentication after setting the above parameter. Doesn’t RDS take care of all of this for me? RDS is Amazon’s Relational Database Service which is built on MySQL. Amazon’s RDS solution presents MySQL as a service which brings certain benefits to administrators and startups: Simpler administration. Nuts and bolts are handled for you. Push-button replication. No more struggling with the nuances and issues of MySQL’s replication management. Simplicity of administration of course has it’s downsides. Depending on your environment, these may or may not be dealbreakers. No access to the slow query log. This is huge. The single best tool for troubleshooting slow database response is this log file. Queries are a large part of keeping a relational database server healthy and happy, and without this facility, you are severely limited. Locked in downtime window When you signup for RDS, you must define a thirty minute maintenance window. This is a weekly window during which your instance *COULD* be unavailable. When you host yourself, you may not require as much downtime at all, especially if you’re using master-master mysql and zero-downtime configuration. Can’t use Percona Server to host your MySQL data. You won’t be able to do this in RDS. Percona server is a high performance distribution of MySQL which typically rolls in serious performance tweaks and updates before they make it to community addition. Well worth the effort to consider it. No access to filesystem, server metrics & command line. Again for troubleshooting problems, these are crucial. Gathering data about what’s really happening on the server is how you begin to diagnose and troubleshoot a server stall or pileup. You are beholden to Amazon’s support services if things go awry. That’s because you won’t have access to the raw iron to diagnose and troubleshoot things yourself. Want to call in an outside consultant to help you debug or troubleshoot? You’ll have your hands tied without access to the underlying server. You can’t replicate to a non-RDS database. Have your own datacenter connected to Amazon via VPC? Want to replication to a cloud server? RDS won’t fit the bill. You’ll have to roll your own – as we’ve described above. And if you want to replicate to an alternate cloud provider, again RDS won’t work for you. Related posts: Deploying MySQL on Amazon EC2 – 8 Best Practices Review: Host Your Web Site In The Cloud, Amazon Web Services Made Easy 5 Ways to Boost MySQL Scalability Top MySQL DBA interview questions (Part 2) MySQL Cluster In The Cloud – Managers Guide

Changing your perspective even a small amount can have profound effects on how you approach your system. Let’s say you’re writing a web application in Java. In the system you deal with orders, customers and products. As a web application, your classes include staples like PersonController, PersonRepository, CustomerController and OrderService. How do you organize your classes into packages? There are two fundamental ways to structure your packages. Either you can focus on the logical tiers, like com.brodwall.myapp.controllers, com.brodwall.myapp.domain or perhaps com.brodwall.myapp.services.customer. Or you can focus on the domain contexts, like com.brodwall.myapp.customer, com.brodwall.myapp.orders and com.brodwall.myapp.products. The first approach is by far the most prevalent. In my view, it’s also the least helpful. Here are some ways your thinking changes if you structure your packages around domain concepts, rather than technological tiers: First, and most fundamentally, your mental model will now be aligned with that of the users of your system. If you’re asked to implement a typical feature, it is now more likely to be focused around a strict subset of the packages of your system. For example, adding a new field to a form will at least affect the presentation logic, entity and persistence layer for the corresponding domain concept. If your packages are organized around tiers, this change will hit all over your system. In a word: A system organized around features, rather than technologies, have higher coherence. This technical term means that a large percentage of a the dependencies of a class are located close to that class. Secondly, organizing around domain concepts will give you more options when your software grows. When a package contains tens of classes, you may want to split it up in several packages. The discussion can itself be enlightening. “Maybe we should separate out the customer address classes into a com.brodwall.myapp.customer.address package. It seems to have a bit of a life on its own.” “Yeah, and maybe we can use the same classes for other places we need addresses, such as suppliers?” “Cool, so com.brodwall.myapp.address, then?” Or maybe you decide that order status codes and payment status codes deserve to be in the “com.brodwall.myapp.order.codes” package. On the other hand, what options do you have for splitting up com.brodwall.myapp.controllers? You could create subpackages for customer, orders and products, but these subpackages may only have one or possibly two classes each. Finally, and perhaps most intriguingly, using domain concepts for packages allows you to vary the design according on a case by case basis. Maybe you really need a OrderService which coordinates the payment and shipping of an order, while ProductController only needs basic create-retrieve-update-delete functionality with a repository. A ProductService would just get in the way. If ProductService is missing from the com.brodwall.myapp.services package, this may be confusing or at the very least give you a nagging feeling that something is wrong. On the other hand, if there’s no Controller in the com.brodwall.myapp.product package, it doesn’t matter much. Also, most systems have some good parts and some not-so-good parts. If your Services package is not working for you, there’s not much you can do. But if the Products package is rotten, you can throw it out and reimplement it without the whole system being thrown into a state of chaos. By putting the classes needed to implement a feature together with each other and apart from the classes needed to implement other features, developers can be pragmatic and innovative when developing one feature without negatively affecting other features. The flip side of this is that most developers are more comfortable with some technologies in the application and less comfortable with other technologies. Organizing around features instead of technologies force each developer to consider a larger set of technological challenges. Some programmers take this as a motivating challenge to learn, while others, it seems, would rather not have to learn something new. If it were my money being spend to create features, I know what kind of developer I would want. Trivial changes can have large effects. By organizing your software around features, you get a more coherent system that allows for growth. It may challenge your developers, but it drives down the number of hand-offs needed to implement a feature and it challenges the developers to improve the parts of the application they are working on. See also my blog post on Architecture as tidying up.

screen real estate is important to me. especially working on a small notebook screen i want to get the most out of it. and i know: all the cool (and fancy) ui items in eclipse have a price. so how to get more space for important things like my source files? eclipse has feature to hide the toolbar completely. for this i simply use the context menu and select ‘hide toolbar’: hide toolbar while this is great, there is one little problem: how to get it back? obviously there is no toolbar any more where i could use a context menu like ‘show toolbar’ . the solution: there is a menu item for this under window > show toolbar: show toolbar menu happy toolbaring

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

This article is part 3 of our NoClassDefFoundError troubleshooting series. As I mentioned in my first article, there are many possible issues that can lead to a NoClassDefFoundError. This article will focus and describe one of the most common causes of this problem: failure of a Java class static initializer block or variable. A sample Java program will be provided and I encourage you to compile and run this example from your workstation in order to properly replicate and understand this type of NoClassDefFoundError problem. Java static initializer revisited The Java programming language provides you with the capability to “statically” initialize variables or a block of code. This is achieved via the “static” variable identifier or the usage of a static {} block at the header of a Java class. Static initializers are guaranteed to be executed only once in the JVM life cycle and are Thread safe by design which make their usage quite appealing for static data initialization such as internal object caches, loggers etc. What is the problem? I will repeat again, static initializers are guaranteed to be executed only once in the JVM life cycle…This means that such code is executed at the Class loading time and never executed again until you restart your JVM. Now what happens if the code executed at that time (@Class loading time) terminates with an unhandled Exception? Welcome to the java.lang.NoClassDefFoundError problem case #2! NoClassDefFoundError problem case 2 – static initializer failure This type of problem is occurring following the failure of static initializer code combined with successive attempts to create a new instance of the affected (non-loaded) class. Sample Java program The following simple Java program is split as per below: The main Java program NoClassDefFoundErrorSimulator The affected Java class ClassA ClassA provides you with a ON/OFF switch allowing you the replicate the type of problem that you want to study This program is simply attempting to create a new instance of ClassA 3 times (one after each other). It will demonstrate that an initial failure of either a static variable or static block initializer combined with successive attempt to create a new instance of the affected class triggers java.lang.NoClassDefFoundError. #### NoClassDefFoundErrorSimulator.java package org.ph.javaee.tools.jdk7.training2; /** * NoClassDefFoundErrorSimulator * @author Pierre-Hugues Charbonneau * */ public class NoClassDefFoundErrorSimulator { /** * @param args */ public static void main(String[] args) { System.out.println("java.lang.NoClassDefFoundError Simulator - Training 2"); System.out.println("Author: Pierre-Hugues Charbonneau"); System.out.println("http://javaeesupportpatterns.blogspot.com\n\n"); try { // Create a new instance of ClassA (attempt #1) System.out.println("FIRST attempt to create a new instance of ClassA...\n"); ClassA classA = new ClassA(); } catch (Throwable any) { any.printStackTrace(); } try { // Create a new instance of ClassA (attempt #2) System.out.println("\nSECOND attempt to create a new instance of ClassA...\n"); ClassA classA = new ClassA(); } catch (Throwable any) { any.printStackTrace(); } try { // Create a new instance of ClassA (attempt #3) System.out.println("\nTHIRD attempt to create a new instance of ClassA...\n"); ClassA classA = new ClassA(); } catch (Throwable any) { any.printStackTrace(); } System.out.println("\n\ndone!"); } } #### ClassA.java package org.ph.javaee.tools.jdk7.training2; /** * ClassA * @author Pierre-Hugues Charbonneau * */ public class ClassA { private final static String CLAZZ = ClassA.class.getName(); // Problem replication switch ON/OFF private final static boolean REPLICATE_PROBLEM1 = true; // static variable initializer private final static boolean REPLICATE_PROBLEM2 = false; // static block{} initializer // Static variable executed at Class loading time private static String staticVariable = initStaticVariable(); // Static initializer block executed at Class loading time static { // Static block code execution... if (REPLICATE_PROBLEM2) throw new IllegalStateException("ClassA.static{}: Internal Error!"); } public ClassA() { System.out.println("Creating a new instance of "+ClassA.class.getName()+"..."); } /** * * @return */ private static String initStaticVariable() { String stringData = ""; if (REPLICATE_PROBLEM1) throw new IllegalStateException("ClassA.initStaticVariable(): Internal Error!"); return stringData; } } Problem reproduction In order to replicate the problem, we will simply “voluntary” trigger a failure of the static initializer code. Please simply enable the problem type that you want to study e.g. either static variable or static block initializer failure: // Problem replication switch ON (true) / OFF (false) private final static boolean REPLICATE_PROBLEM1 = true; // static variable initializer private final static boolean REPLICATE_PROBLEM2 = false; // static block{} initializer Now, let’s run the program with both switch at OFF (both boolean values at false) ## Baseline (normal execution) java.lang.NoClassDefFoundError Simulator - Training 2 Author: Pierre-Hugues Charbonneau http://javaeesupportpatterns.blogspot.com FIRST attempt to create a new instance of ClassA... Creating a new instance of org.ph.javaee.tools.jdk7.training2.ClassA... SECOND attempt to create a new instance of ClassA... Creating a new instance of org.ph.javaee.tools.jdk7.training2.ClassA... THIRD attempt to create a new instance of ClassA... Creating a new instance of org.ph.javaee.tools.jdk7.training2.ClassA... done! For the initial run (baseline), the main program was able to create 3 instances of ClassA successfully with no problem. ## Problem reproduction run (static variable initializer failure) java.lang.NoClassDefFoundError Simulator - Training 2 Author: Pierre-Hugues Charbonneau http://javaeesupportpatterns.blogspot.com FIRST attempt to create a new instance of ClassA... java.lang.ExceptionInInitializerError at org.ph.javaee.tools.jdk7.training2.NoClassDefFoundErrorSimulator.main(NoClassDefFoundErrorSimulator.java:21) Caused by: java.lang.IllegalStateException: ClassA.initStaticVariable(): Internal Error! at org.ph.javaee.tools.jdk7.training2.ClassA.initStaticVariable(ClassA.java:37) at org.ph.javaee.tools.jdk7.training2.ClassA.(ClassA.java:16) ... 1 more SECOND attempt to create a new instance of ClassA... java.lang.NoClassDefFoundError: Could not initialize class org.ph.javaee.tools.jdk7.training2.ClassA at org.ph.javaee.tools.jdk7.training2.NoClassDefFoundErrorSimulator.main(NoClassDefFoundErrorSimulator.java:30) THIRD attempt to create a new instance of ClassA... java.lang.NoClassDefFoundError: Could not initialize class org.ph.javaee.tools.jdk7.training2.ClassA at org.ph.javaee.tools.jdk7.training2.NoClassDefFoundErrorSimulator.main(NoClassDefFoundErrorSimulator.java:39) done! ## Problem reproduction run (static block initializer failure) java.lang.NoClassDefFoundError Simulator - Training 2 Author: Pierre-Hugues Charbonneau http://javaeesupportpatterns.blogspot.com FIRST attempt to create a new instance of ClassA... java.lang.ExceptionInInitializerError at org.ph.javaee.tools.jdk7.training2.NoClassDefFoundErrorSimulator.main(NoClassDefFoundErrorSimulator.java:21) Caused by: java.lang.IllegalStateException: ClassA.static{}: Internal Error! at org.ph.javaee.tools.jdk7.training2.ClassA.(ClassA.java:22) ... 1 more SECOND attempt to create a new instance of ClassA... java.lang.NoClassDefFoundError: Could not initialize class org.ph.javaee.tools.jdk7.training2.ClassA at org.ph.javaee.tools.jdk7.training2.NoClassDefFoundErrorSimulator.main(NoClassDefFoundErrorSimulator.java:30) THIRD attempt to create a new instance of ClassA... java.lang.NoClassDefFoundError: Could not initialize class org.ph.javaee.tools.jdk7.training2.ClassA at org.ph.javaee.tools.jdk7.training2.NoClassDefFoundErrorSimulator.main(NoClassDefFoundErrorSimulator.java:39) done! What happened? As you can see, the first attempt to create a new instance of ClassA did trigger a java.lang.ExceptionInInitializerError. This exception indicates the failure of our static initializer for our static variable & bloc which is exactly what we wanted to achieve. The key point to understand at this point is that this failure did prevent the whole class loading of ClassA. As you can see, attempt #2 and attempt #3 both generated a java.lang.NoClassDefFoundError, why? Well since the first attempt failed, class loading of ClassA was prevented. Successive attempts to create a new instance of ClassA within the current ClassLoader did generate java.lang.NoClassDefFoundError over and over since ClassA was not found within current ClassLoader. As you can see, in this problem context, the NoClassDefFoundError is just a symptom or consequence of another problem. The original problem is the ExceptionInInitializerError triggered following the failure of the static initializer code. This clearly demonstrates the importance of proper error handling and logging when using Java static initializers. Recommendations and resolution strategies Now find below my recommendations and resolution strategies for NoClassDefFoundError problem case 2: - Review the java.lang.NoClassDefFoundError error and identify the missing Java class - Perform a code walkthrough of the affected class and determine if it contains static initializer code (variables & static block) - Review your server and application logs and determine if any error (e.g. ExceptionInInitializerError) originates from the static initializer code - Once confirmed, analyze the code further and determine the root cause of the initializer code failure. You may need to add some extra logging along with proper error handling to prevent and better handle future failures of your static initializer code going forward Please feel free to post any question or comment. The part 4 will start coverage of NoClassDefFoundError problems related to class loader problems.

Determination of proper Java Heap size for a production system is not a straightforward exercise. In my Java EE enterprise experience, I have seen multiple performance problem cases due to inadequate Java Heap capacity and tuning. This article will provide you with 5 tips that can help you determine optimal Java Heap size, as a starting point, for your current or new production environment. Some of these tips are also very useful regarding the prevention and resolution of java.lang.OutOfMemoryError problems; including memory leaks. Please note that these tips are intended to “help you” determine proper Java Heap size. Since each IT environment is unique, you are actually in the best position to determine precisely the required Java Heap specifications of your client’s environment. Some of these tips may also not be applicable in the context of a very small Java standalone application but I still recommend you to read the entire article. Future articles will include tips on how to choose the proper Java VM garbage collector type for your environment and applications. #1 – JVM: you always fear what you don't understand How can you expect to configure, tune and troubleshoot something that you don’t understand? You may never have the chance to write and improve Java VM specifications but you are still free to learn its foundation in order to improve your knowledge and troubleshooting skills. Some may disagree, but from my perspective, the thinking that Java programmers are not required to know the internal JVM memory management is an illusion. Java Heap tuning and troubleshooting can especially be a challenge for Java & Java EE beginners. Find below a typical scenario: - Your client production environment is facing OutOfMemoryError on a regular basis and causing lot of business impact. Your support team is under pressure to resolve this problem - A quick Google search allows you to find examples of similar problems and you now believe (and assume) that you are facing the same problem - You then grab JVM -Xms and -Xmx values from another person OutOfMemoryError problem case, hoping to quickly resolve your client’s problem - You then proceed and implement the same tuning to your environment. 2 days later you realize problem is still happening (even worse or little better)…the struggle continues… What went wrong? - You failed to first acquire proper understanding of the root cause of your problem - You may also have failed to properly understand your production environment at a deeper level (specifications, load situation etc.). Web searches is a great way to learn and share knowledge but you have to perform your own due diligence and root cause analysis - You may also be lacking some basic knowledge of the JVM and its internal memory management, preventing you to connect all the dots together My #1 tip and recommendation to you is to learn and understand the basic JVM principles along with its different memory spaces. Such knowledge is critical as it will allow you to make valid recommendations to your clients and properly understand the possible impact and risk associated with future tuning considerations. Now find below a quick high level reference guide for the Java VM: The Java VM memory is split up to 3 memory spaces: The Java Heap. Applicable for all JVM vendors, usually split between YoungGen (nursery) & OldGen (tenured) spaces. The PermGen (permanent generation). Applicable to the Sun HotSpot VM only (PermGen space will be removed in future Java 7 or Java 8 updates) The Native Heap (C-Heap). Applicable for all JVM vendors. I recommend that you review each article below, including Sun white paper on the HotSpot Java memory management. I also encourage you to download and look at the OpenJDK implementation. ## Sun HotSpot VM http://javaeesupportpatterns.blogspot.com/2011/08/java-heap-space-hotspot-vm.html ## IBM VM http://javaeesupportpatterns.blogspot.com/2012/02/java-heap-space-ibm-vm.html ## Oracle JRockit VM http://javaeesupportpatterns.blogspot.com/2012/02/java-heap-space-jrockit-vm.html ## Sun (Oracle) – Java memory management white paper http://java.sun.com/j2se/reference/whitepapers/memorymanagement_whitepaper.pdf ## OpenJDK – Open-source Java implementation http://openjdk.java.net/ As you can see, the Java VM memory management is more complex than just setting up the biggest value possible via –Xmx. You have to look at all angles, including your native and PermGen space requirement along with physical memory availability (and # of CPU cores) from your physical host(s). It can get especially tricky for 32-bit JVM since the Java Heap and native Heap are in a race. The bigger your Java Heap, smaller the native Heap. Attempting to setup a large Heap for a 32-bit VM e.g .2.5 GB+ increases risk of native OutOfMemoryError depending of your application(s) footprint, number of Threads etc. 64-bit JVM resolves this problem but you are still limited to physical resources availability and garbage collection overhead (cost of major GC collections go up with size). The bottom line is that the bigger is not always the better so please do not assume that you can run all your 20 Java EE applications on a single 16 GB 64-bit JVM process. #2 – Data and application is king: review your static footprint requirement Your application(s) along with its associated data will dictate the Java Heap footprint requirement. By static memory, I mean “predictable” memory requirements as per below. - Determine how many different applications you are planning to deploy to a single JVM process e.g. number of EAR files, WAR files, jar files etc. The more applications you deploy to a single JVM, higher demand on native Heap - Determine how many Java classes will be potentially loaded at runtime; including third part API’s. The more class loaders and classes that you load at runtime, higher demand on the HotSpot VM PermGen space and internal JIT related optimization objects - Determine data cache footprint e.g. internal cache data structures loaded by your application (and third party API’s) such as cached data from a database, data read from a file etc. The more data caching that you use, higher demand on the Java Heap OldGen space - Determine the number of Threads that your middleware is allowed to create. This is very important since Java threads require enough native memory or OutOfMemoryError will be thrown For example, you will need much more native memory and PermGen space if you are planning to deploy 10 separate EAR applications on a single JVM process vs. only 2 or 3. Data caching not serialized to a disk or database will require extra memory from the OldGen space. Try to come up with reasonable estimates of the static memory footprint requirement. This will be very useful to setup some starting point JVM capacity figures before your true measurement exercise (e.g. tip #4). For 32-bit JVM, I usually do not recommend a Java Heap size high than 2 GB (-Xms2048m, -Xmx2048m) since you need enough memory for PermGen and native Heap for your Java EE applications and threads. This assessment is especially important since too many applications deployed in a single 32-bit JVM process can easily lead to native Heap depletion; especially in a multi threads environment. For a 64-bit JVM, a Java Heap size of 3 GB or 4 GB per JVM process is usually my recommended starting point. #3 – Business traffic set the rules: review your dynamic footprint requirement Your business traffic will typically dictate your dynamic memory footprint. Concurrent users & requests generate the JVM GC “heartbeat” that you can observe from various monitoring tools due to very frequent creation and garbage collections of short & long lived objects. As you saw from the above JVM diagram, a typical ratio of YoungGen vs. OldGen is 1:3 or 33%. For a typical 32-bit JVM, a Java Heap size setup at 2 GB (using generational & concurrent collector) will typically allocate 500 MB for YoungGen space and 1.5 GB for the OldGen space. Minimizing the frequency of major GC collections is a key aspect for optimal performance so it is very important that you understand and estimate how much memory you need during your peak volume. Again, your type of application and data will dictate how much memory you need. Shopping cart type of applications (long lived objects) involving large and non-serialized session data typically need large Java Heap and lot of OldGen space. Stateless and XML processing heavy applications (lot of short lived objects) require proper YoungGen space in order to minimize frequency of major collections. Example: - You have 5 EAR applications (~2 thousands of Java classes) to deploy (which include middleware code as well…) - Your native heap requirement is estimated at 1 GB (has to be large enough to handle Threads creation etc.) - Your PermGen space is estimated at 512 MB - Your internal static data caching is estimated at 500 MB - Your total forecast traffic is 5000 concurrent users at peak hours - Each user session data footprint is estimated at 500 K - Total footprint requirement for session data alone is 2.5 GB under peak volume As you can see, with such requirement, there is no way you can have all this traffic sent to a single JVM 32-bit process. A typical solution involves splitting (tip #5) traffic across a few JVM processes and / or physical host (assuming you have enough hardware and CPU cores available). However, for this example, given the high demand on static memory and to ensure a scalable environment in the long run, I would also recommend 64-bit VM but with a smaller Java Heap as a starting point such as 3 GB to minimize the GC cost. You definitely want to have extra buffer for the OldGen space so I typically recommend up to 50% memory footprint post major collection in order to keep the frequency of Full GC low and enough buffer for fail-over scenarios. Most of the time, your business traffic will drive most of your memory footprint, unless you need significant amount of data caching to achieve proper performance which is typical for portal (media) heavy applications. Too much data caching should raise a yellow flag that you may need to revisit some design elements sooner than later. #4 – Don’t guess it, measure it! At this point you should: - Understand the basic JVM principles and memory spaces - Have a deep view and understanding of all applications along with their characteristics (size, type, dynamic traffic, stateless vs. stateful objects, internal memory caches etc.) - Have a very good view or forecast on the business traffic (# of concurrent users etc.) and for each application - Some ideas if you need a 64-bit VM or not and which JVM settings to start with - Some ideas if you need more than one JVM (middleware) processes But wait, your work is not done yet. While this above information is crucial and great for you to come up with “best guess” Java Heap settings, it is always best and recommended to simulate your application(s) behaviour and validate the Java Heap memory requirement via proper profiling, load & performance testing. You can learn and take advantage of tools such as JProfiler (future articles will include tutorials on JProfiler). From my perspective, learning how to use a profiler is the best way to properly understand your application memory footprint. Another approach I use for existing production environments is heap dump analysis using the Eclipse MAT tool. Heap Dump analysis is very powerful and allow you to view and understand the entire memory footprint of the Java Heap, including class loader related data and is a must do exercise in any memory footprint analysis; especially memory leaks. Java profilers and heap dump analysis tools allow you to understand and validate your application memory footprint, including detection and resolution of memory leaks. Load and performance testing is also a must since this will allow you to validate your earlier estimates by simulating your forecast concurrent users. It will also expose your application bottlenecks and allow you to further fine tune your JVM settings. You can use tools such as Apache JMeter which is very easy to learn and use or explore other commercial products. Finally, I have seen quite often Java EE environments running perfectly fine until the day where one piece of the infrastructure start to fail e.g. hardware failure. Suddenly the environment is running at reduced capacity (reduced # of JVM processes) and the whole environment goes down. What happened? There are many scenarios that can lead to domino effects but lack of JVM tuning and capacity to handle fail-over (short term extra load) is very common. If your JVM processes are running at 80%+ OldGen space capacity with frequent garbage collections, how can you expect to handle any fail-over scenario? Your load and performance testing exercise performed earlier should simulate such scenario and you should adjust your tuning settings properly so your Java Heap has enough buffer to handle extra load (extra objects) at short term. This is mainly applicable for the dynamic memory footprint since fail-over means redirecting a certain % of your concurrent users to the available JVM processes (middleware instances). #5 – Divide and conquer At this point you have performed dozens of load testing iterations. You know that your JVM is not leaking memory. Your application memory footprint cannot be reduced any further. You tried several tuning strategies such as using a large 64-bit Java Heap space of 10 GB+, multiple GC policies but still not finding your performance level acceptable? In my experience I found that, with current JVM specifications, proper vertical and horizontal scaling which involved creating a few JVM processes per physical host and across several hosts will give you the throughput and capacity that you are looking for. Your IT environment will also more fault tolerant if you break your application list in a few logical silos, with their own JVM process, Threads and tuning values. This “divide and conquer” strategy involves splitting your application(s) traffic to multiple JVM processes and will provide you with: - Reduced Java Heap size per JVM process (both static & dynamic footprint) - Reduced complexity of JVM tuning - Reduced GC elapsed and pause time per JVM process - Increased redundancy and fail-over capabilities - Aligned with latest Cloud and IT virtualization strategies The bottom line is that when you find yourself spending too much time in tuning that single elephant 64-bit JVM process, it is time to revisit your middleware and JVM deployment strategy and take advantage of vertical & horizontal scaling. This implementation strategy is more taxing for the hardware but will really pay off in the long run. Please provide any comment and share your experience on JVM Heap sizing and tuning.

in this heat map example, web site’s performance data is overlay with color, where red means “slowest” and green means “fastest” (with various color grades in between). i added this heat map example because the chart data was difficult to “crunch”, to analyze for patterns; adding color made this easier. download the excel sheet here . otherwise, see the below “before” and “after”. before: raw chart data: after: heat map: side by side: the chart data is the arithmetic mean average of six weeks’ worth of web site response time measurements (in milliseconds), broken down by day of week and by hour of day. i then used excel’s built-in /conditional formatting/color scales/red – yellow – green color scale/. document complete / onload: _the following is optional reading material._ here are some potential fact statements we get from looking at this heat map: 01. the weekends are the “fastest”. 02. the weekdays are the “slowest”. 03. response time slows starting at around 07:00 am pt and response time speeds starting at around 07:00 pm pt. 04. the “slowest” (i.e. the most red) “day of week”/”hour of day” combination is wednesday at 05:00 pm pt. 05. the “fastest” (i.e. the most green) “day of week”/”hour of day” combination is wednesday at 02:00 am pt. 06. interestingly enough, the “fastest” and “slowest” “day of week”/”hour of day” combination was on a wednesday. 07. friday afternoons were “faster” when compared to other weekday afternoons (insert comment here re: folks getting ready for the weekend!). download the excel file here: https://docs.google.com/open?id=0b9n5sarv4oonmedtotlvohnib0e ******************************************************************************** i used his model to chart the performance heat map of google search (search for a keyword) and decided to compare the wait time (1st byte) and their search query time: google’s wait time vs. google search query time here is the same for etsy’s home page, wait time and the etsy server time: etsy’s wait time vs. etsy’s server time (data from june 1 2012 to july 13 2012, time in est) i used these 2 examples mainly because not only we are plotting a catchpoint metric but also overlaying customer telemetry coming from google (how long it takes for google to spit the results) or etsy (how long it takes for their server to build the html). what would be cool is to get traffic volume during those days and hours to correlate the performance metrics and usage pattern. but what these chart show is clearly a performance degradation during certain days and hours of day that are driven by volume, users, usage… enjoy the summer and try to keep your web performance cool! thank you leo for sharing this!

I had a relational database, that I wanted to migrate to cassandra. Cassandra's sstableloader provides option to load the existing data from flat files to a cassandra ring. Hence this can be used as a way to migrate data in relational databases to cassandra, as most relational databases let us export the data into flat files. sqoop gives the option to do this effectively. Interestingly, DataStax Enterprise provides everything we want in the big data space as a package. This includes, cassandra, hadoop, hive, pig, sqoop, and mahout, which comes handy in this case. Under the resources directory, you may find the cassandra, dse, hadoop, hive, log4j-appender, mahout, pig, solr, sqoop, and tomcat specific configurations. For example, from resources/hadoop/bin, you may format the hadoop name node using ./hadoop namenode -format as usual. * Download and extract DataStax Enterprise binary archive (dse-2.1-bin.tar.gz). * Follow the documentation, which is also available as a PDF. * Migrating a relational database to cassandra is documented and is also blogged. * Before starting DataStax, make sure that the JAVA_HOME is set. This also can be set directly on conf/hadoop-env.sh. * Include the connector to the relational database into a location reachable by sqoop. I put mysql-connector-java-5.1.12-bin.jar under resources/sqoop. * Set the environment $ bin/dse-env.sh * Start DataStax Enterprise, as an Analytics node. $ sudo bin/dse cassandra -t where cassandra starts the Cassandra process plus CassandraFS and the -t option starts the Hadoop JobTracker and TaskTracker processes. if you start without the -t flag, the below exception will be thrown during the further operations that are discussed below. No jobtracker found Unable to run : jobtracker not found Hence do not miss the -t flag. * Start cassandra cli to view the cassandra keyrings and you will be able to view the data in cassandra, once you migrate using sqoop as given below. $ bin/cassandra-cli -host localhost -port 9160 Confirm that it is connected to the test cluster that is created on the port 9160, by the below from the CLI. [default@unknown] describe cluster; Cluster Information: Snitch: com.datastax.bdp.snitch.DseDelegateSnitch Partitioner: org.apache.cassandra.dht.RandomPartitioner Schema versions: f5a19a50-b616-11e1-0000-45b29245ddff: [127.0.1.1] If you have missed mentioning the host/port (starting the cli by just bin/cassandra-cli) or given it wrong, you will get the response as "Not connected to a cassandra instance." $ bin/dse sqoop import --connect jdbc:mysql://127.0.0.1:3306/shopping_cart_db --username root --password root --table Category --split-by categoryName --cassandra-keyspace shopping_cart_db --cassandra-column-family Category_cf --cassandra-row-key categoryName --cassandra-thrift-host localhost --cassandra-create-schema Above command will now migrate the table "Category" in the shopping_cart_db with the primary key categoryName, into a cassandra keyspace named shopping_cart, with the cassandra row key categoryName. You may use the --direct mysql specific option, which is faster. In my above command, I have everything runs on localhost. +--------------+-------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +--------------+-------------+------+-----+---------+-------+ | categoryName | varchar(50) | NO | PRI | NULL | | | description | text | YES | | NULL | | | image | blob | YES | | NULL | | +--------------+-------------+------+-----+---------+-------+ This also creates the respective java class (Category.java), inside the working directory. To import all the tables in the database, instead of a single table. $ bin/dse sqoop import-all-tables -m 1 --connect jdbc:mysql://127.0.0.1:3306/shopping_cart_db --username root --password root --cassandra-thrift-host localhost --cassandra-create-schema --direct Here "-m 1" tag ensures a sequential import. If not specified, the below exception will be thrown. ERROR tool.ImportAllTablesTool: Error during import: No primary key could be found for table Category. Please specify one with --split-by or perform a sequential import with '-m 1'. To check whether the keyspace is created, [default@unknown] show keyspaces; ................ Keyspace: shopping_cart_db: Replication Strategy: org.apache.cassandra.locator.SimpleStrategy Durable Writes: true Options: [replication_factor:1] Column Families: ColumnFamily: Category_cf Key Validation Class: org.apache.cassandra.db.marshal.UTF8Type Default column value validator: org.apache.cassandra.db.marshal.UTF8Type Columns sorted by: org.apache.cassandra.db.marshal.UTF8Type Row cache size / save period in seconds / keys to save : 0.0/0/all Row Cache Provider: org.apache.cassandra.cache.SerializingCacheProvider Key cache size / save period in seconds: 200000.0/14400 GC grace seconds: 864000 Compaction min/max thresholds: 4/32 Read repair chance: 1.0 Replicate on write: true Bloom Filter FP chance: default Built indexes: [] Compaction Strategy: org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy ............. [default@unknown] describe shopping_cart_db; Keyspace: shopping_cart_db: Replication Strategy: org.apache.cassandra.locator.SimpleStrategy Durable Writes: true Options: [replication_factor:1] Column Families: ColumnFamily: Category_cf Key Validation Class: org.apache.cassandra.db.marshal.UTF8Type Default column value validator: org.apache.cassandra.db.marshal.UTF8Type Columns sorted by: org.apache.cassandra.db.marshal.UTF8Type Row cache size / save period in seconds / keys to save : 0.0/0/all Row Cache Provider: org.apache.cassandra.cache.SerializingCacheProvider Key cache size / save period in seconds: 200000.0/14400 GC grace seconds: 864000 Compaction min/max thresholds: 4/32 Read repair chance: 1.0 Replicate on write: true Bloom Filter FP chance: default Built indexes: [] Compaction Strategy: org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy You may also use hive to view the databases created in cassandra, in an sql-like manner. * Start Hive $ bin/dse hive hive> show databases; OK default shopping_cart_db When the entire database is imported as above, separate java classes will be created for each of the tables. $ bin/dse sqoop import-all-tables -m 1 --connect jdbc:mysql://127.0.0.1:3306/shopping_cart_db --username root --password root --cassandra-thrift-host localhost --cassandra-create-schema --direct 12/06/15 15:42:11 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead. 12/06/15 15:42:11 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset. 12/06/15 15:42:11 INFO tool.CodeGenTool: Beginning code generation 12/06/15 15:42:11 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `Category` AS t LIMIT 1 12/06/15 15:42:11 INFO orm.CompilationManager: HADOOP_HOME is /home/pradeeban/programs/dse-2.1/resources/hadoop/bin/.. Note: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Category.java uses or overrides a deprecated API. Note: Recompile with -Xlint:deprecation for details. 12/06/15 15:42:13 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Category.jar 12/06/15 15:42:13 INFO manager.DirectMySQLManager: Beginning mysqldump fast path import 12/06/15 15:42:13 INFO mapreduce.ImportJobBase: Beginning import of Category 12/06/15 15:42:14 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 12/06/15 15:42:15 INFO mapred.JobClient: Running job: job_201206151241_0007 12/06/15 15:42:16 INFO mapred.JobClient: map 0% reduce 0% 12/06/15 15:42:25 INFO mapred.JobClient: map 100% reduce 0% 12/06/15 15:42:25 INFO mapred.JobClient: Job complete: job_201206151241_0007 12/06/15 15:42:25 INFO mapred.JobClient: Counters: 18 12/06/15 15:42:25 INFO mapred.JobClient: Job Counters 12/06/15 15:42:25 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=6480 12/06/15 15:42:25 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 12/06/15 15:42:25 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 12/06/15 15:42:25 INFO mapred.JobClient: Launched map tasks=1 12/06/15 15:42:25 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=0 12/06/15 15:42:25 INFO mapred.JobClient: File Output Format Counters 12/06/15 15:42:25 INFO mapred.JobClient: Bytes Written=2848 12/06/15 15:42:25 INFO mapred.JobClient: FileSystemCounters 12/06/15 15:42:25 INFO mapred.JobClient: FILE_BYTES_WRITTEN=21419 12/06/15 15:42:25 INFO mapred.JobClient: CFS_BYTES_WRITTEN=2848 12/06/15 15:42:25 INFO mapred.JobClient: CFS_BYTES_READ=87 12/06/15 15:42:25 INFO mapred.JobClient: File Input Format Counters 12/06/15 15:42:25 INFO mapred.JobClient: Bytes Read=0 12/06/15 15:42:25 INFO mapred.JobClient: Map-Reduce Framework 12/06/15 15:42:25 INFO mapred.JobClient: Map input records=1 12/06/15 15:42:25 INFO mapred.JobClient: Physical memory (bytes) snapshot=119435264 12/06/15 15:42:25 INFO mapred.JobClient: Spilled Records=0 12/06/15 15:42:25 INFO mapred.JobClient: CPU time spent (ms)=630 12/06/15 15:42:25 INFO mapred.JobClient: Total committed heap usage (bytes)=121241600 12/06/15 15:42:25 INFO mapred.JobClient: Virtual memory (bytes) snapshot=2085318656 12/06/15 15:42:25 INFO mapred.JobClient: Map output records=36 12/06/15 15:42:25 INFO mapred.JobClient: SPLIT_RAW_BYTES=87 12/06/15 15:42:25 INFO mapreduce.ImportJobBase: Transferred 0 bytes in 11.4492 seconds (0 bytes/sec) 12/06/15 15:42:25 INFO mapreduce.ImportJobBase: Retrieved 36 records. 12/06/15 15:42:25 INFO tool.CodeGenTool: Beginning code generation 12/06/15 15:42:25 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `Customer` AS t LIMIT 1 12/06/15 15:42:25 INFO orm.CompilationManager: HADOOP_HOME is /home/pradeeban/programs/dse-2.1/resources/hadoop/bin/.. Note: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Customer.java uses or overrides a deprecated API. Note: Recompile with -Xlint:deprecation for details. 12/06/15 15:42:25 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Customer.jar 12/06/15 15:42:26 INFO manager.DirectMySQLManager: Beginning mysqldump fast path import 12/06/15 15:42:26 INFO mapreduce.ImportJobBase: Beginning import of Customer 12/06/15 15:42:26 INFO mapred.JobClient: Running job: job_201206151241_0008 12/06/15 15:42:27 INFO mapred.JobClient: map 0% reduce 0% 12/06/15 15:42:35 INFO mapred.JobClient: map 100% reduce 0% 12/06/15 15:42:35 INFO mapred.JobClient: Job complete: job_201206151241_0008 12/06/15 15:42:35 INFO mapred.JobClient: Counters: 17 12/06/15 15:42:35 INFO mapred.JobClient: Job Counters 12/06/15 15:42:35 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=6009 12/06/15 15:42:35 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 12/06/15 15:42:35 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 12/06/15 15:42:35 INFO mapred.JobClient: Launched map tasks=1 12/06/15 15:42:35 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=0 12/06/15 15:42:35 INFO mapred.JobClient: File Output Format Counters 12/06/15 15:42:35 INFO mapred.JobClient: Bytes Written=0 12/06/15 15:42:35 INFO mapred.JobClient: FileSystemCounters 12/06/15 15:42:35 INFO mapred.JobClient: FILE_BYTES_WRITTEN=21489 12/06/15 15:42:35 INFO mapred.JobClient: CFS_BYTES_READ=87 12/06/15 15:42:35 INFO mapred.JobClient: File Input Format Counters 12/06/15 15:42:35 INFO mapred.JobClient: Bytes Read=0 12/06/15 15:42:35 INFO mapred.JobClient: Map-Reduce Framework 12/06/15 15:42:35 INFO mapred.JobClient: Map input records=1 12/06/15 15:42:35 INFO mapred.JobClient: Physical memory (bytes) snapshot=164855808 12/06/15 15:42:35 INFO mapred.JobClient: Spilled Records=0 12/06/15 15:42:35 INFO mapred.JobClient: CPU time spent (ms)=510 12/06/15 15:42:35 INFO mapred.JobClient: Total committed heap usage (bytes)=121241600 12/06/15 15:42:35 INFO mapred.JobClient: Virtual memory (bytes) snapshot=2082869248 12/06/15 15:42:35 INFO mapred.JobClient: Map output records=0 12/06/15 15:42:35 INFO mapred.JobClient: SPLIT_RAW_BYTES=87 12/06/15 15:42:35 INFO mapreduce.ImportJobBase: Transferred 0 bytes in 9.3143 seconds (0 bytes/sec) 12/06/15 15:42:35 INFO mapreduce.ImportJobBase: Retrieved 0 records. 12/06/15 15:42:35 INFO tool.CodeGenTool: Beginning code generation 12/06/15 15:42:35 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `OrderEntry` AS t LIMIT 1 12/06/15 15:42:35 INFO orm.CompilationManager: HADOOP_HOME is /home/pradeeban/programs/dse-2.1/resources/hadoop/bin/.. Note: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/OrderEntry.java uses or overrides a deprecated API. Note: Recompile with -Xlint:deprecation for details. 12/06/15 15:42:35 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/OrderEntry.jar 12/06/15 15:42:36 INFO manager.DirectMySQLManager: Beginning mysqldump fast path import 12/06/15 15:42:36 INFO mapreduce.ImportJobBase: Beginning import of OrderEntry 12/06/15 15:42:36 INFO mapred.JobClient: Running job: job_201206151241_0009 12/06/15 15:42:37 INFO mapred.JobClient: map 0% reduce 0% 12/06/15 15:42:45 INFO mapred.JobClient: map 100% reduce 0% 12/06/15 15:42:45 INFO mapred.JobClient: Job complete: job_201206151241_0009 12/06/15 15:42:45 INFO mapred.JobClient: Counters: 17 12/06/15 15:42:45 INFO mapred.JobClient: Job Counters 12/06/15 15:42:45 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=6381 12/06/15 15:42:45 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 12/06/15 15:42:45 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 12/06/15 15:42:45 INFO mapred.JobClient: Launched map tasks=1 12/06/15 15:42:45 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=0 12/06/15 15:42:45 INFO mapred.JobClient: File Output Format Counters 12/06/15 15:42:45 INFO mapred.JobClient: Bytes Written=0 12/06/15 15:42:45 INFO mapred.JobClient: FileSystemCounters 12/06/15 15:42:45 INFO mapred.JobClient: FILE_BYTES_WRITTEN=21569 12/06/15 15:42:45 INFO mapred.JobClient: CFS_BYTES_READ=87 12/06/15 15:42:45 INFO mapred.JobClient: File Input Format Counters 12/06/15 15:42:45 INFO mapred.JobClient: Bytes Read=0 12/06/15 15:42:45 INFO mapred.JobClient: Map-Reduce Framework 12/06/15 15:42:45 INFO mapred.JobClient: Map input records=1 12/06/15 15:42:45 INFO mapred.JobClient: Physical memory (bytes) snapshot=137252864 12/06/15 15:42:45 INFO mapred.JobClient: Spilled Records=0 12/06/15 15:42:45 INFO mapred.JobClient: CPU time spent (ms)=520 12/06/15 15:42:45 INFO mapred.JobClient: Total committed heap usage (bytes)=121241600 12/06/15 15:42:45 INFO mapred.JobClient: Virtual memory (bytes) snapshot=2014703616 12/06/15 15:42:45 INFO mapred.JobClient: Map output records=0 12/06/15 15:42:45 INFO mapred.JobClient: SPLIT_RAW_BYTES=87 12/06/15 15:42:45 INFO mapreduce.ImportJobBase: Transferred 0 bytes in 9.2859 seconds (0 bytes/sec) 12/06/15 15:42:45 INFO mapreduce.ImportJobBase: Retrieved 0 records. 12/06/15 15:42:45 INFO tool.CodeGenTool: Beginning code generation 12/06/15 15:42:45 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `OrderItem` AS t LIMIT 1 12/06/15 15:42:45 INFO orm.CompilationManager: HADOOP_HOME is /home/pradeeban/programs/dse-2.1/resources/hadoop/bin/.. Note: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/OrderItem.java uses or overrides a deprecated API. Note: Recompile with -Xlint:deprecation for details. 12/06/15 15:42:45 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/OrderItem.jar 12/06/15 15:42:46 WARN manager.CatalogQueryManager: The table OrderItem contains a multi-column primary key. Sqoop will default to the column orderNumber only for this job. 12/06/15 15:42:46 INFO manager.DirectMySQLManager: Beginning mysqldump fast path import 12/06/15 15:42:46 INFO mapreduce.ImportJobBase: Beginning import of OrderItem 12/06/15 15:42:46 INFO mapred.JobClient: Running job: job_201206151241_0010 12/06/15 15:42:47 INFO mapred.JobClient: map 0% reduce 0% 12/06/15 15:42:55 INFO mapred.JobClient: map 100% reduce 0% 12/06/15 15:42:55 INFO mapred.JobClient: Job complete: job_201206151241_0010 12/06/15 15:42:55 INFO mapred.JobClient: Counters: 17 12/06/15 15:42:55 INFO mapred.JobClient: Job Counters 12/06/15 15:42:55 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=5949 12/06/15 15:42:55 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 12/06/15 15:42:55 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 12/06/15 15:42:55 INFO mapred.JobClient: Launched map tasks=1 12/06/15 15:42:55 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=0 12/06/15 15:42:55 INFO mapred.JobClient: File Output Format Counters 12/06/15 15:42:55 INFO mapred.JobClient: Bytes Written=0 12/06/15 15:42:55 INFO mapred.JobClient: FileSystemCounters 12/06/15 15:42:55 INFO mapred.JobClient: FILE_BYTES_WRITTEN=21524 12/06/15 15:42:55 INFO mapred.JobClient: CFS_BYTES_READ=87 12/06/15 15:42:55 INFO mapred.JobClient: File Input Format Counters 12/06/15 15:42:55 INFO mapred.JobClient: Bytes Read=0 12/06/15 15:42:55 INFO mapred.JobClient: Map-Reduce Framework 12/06/15 15:42:55 INFO mapred.JobClient: Map input records=1 12/06/15 15:42:55 INFO mapred.JobClient: Physical memory (bytes) snapshot=116674560 12/06/15 15:42:55 INFO mapred.JobClient: Spilled Records=0 12/06/15 15:42:55 INFO mapred.JobClient: CPU time spent (ms)=590 12/06/15 15:42:55 INFO mapred.JobClient: Total committed heap usage (bytes)=121241600 12/06/15 15:42:55 INFO mapred.JobClient: Virtual memory (bytes) snapshot=2014703616 12/06/15 15:42:55 INFO mapred.JobClient: Map output records=0 12/06/15 15:42:55 INFO mapred.JobClient: SPLIT_RAW_BYTES=87 12/06/15 15:42:55 INFO mapreduce.ImportJobBase: Transferred 0 bytes in 9.2539 seconds (0 bytes/sec) 12/06/15 15:42:55 INFO mapreduce.ImportJobBase: Retrieved 0 records. 12/06/15 15:42:55 INFO tool.CodeGenTool: Beginning code generation 12/06/15 15:42:55 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `Payment` AS t LIMIT 1 12/06/15 15:42:55 INFO orm.CompilationManager: HADOOP_HOME is /home/pradeeban/programs/dse-2.1/resources/hadoop/bin/.. Note: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Payment.java uses or overrides a deprecated API. Note: Recompile with -Xlint:deprecation for details. 12/06/15 15:42:55 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Payment.jar 12/06/15 15:42:56 WARN manager.CatalogQueryManager: The table Payment contains a multi-column primary key. Sqoop will default to the column orderNumber only for this job. 12/06/15 15:42:56 INFO manager.DirectMySQLManager: Beginning mysqldump fast path import 12/06/15 15:42:56 INFO mapreduce.ImportJobBase: Beginning import of Payment 12/06/15 15:42:56 INFO mapred.JobClient: Running job: job_201206151241_0011 12/06/15 15:42:57 INFO mapred.JobClient: map 0% reduce 0% 12/06/15 15:43:05 INFO mapred.JobClient: map 100% reduce 0% 12/06/15 15:43:05 INFO mapred.JobClient: Job complete: job_201206151241_0011 12/06/15 15:43:05 INFO mapred.JobClient: Counters: 17 12/06/15 15:43:05 INFO mapred.JobClient: Job Counters 12/06/15 15:43:05 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=5914 12/06/15 15:43:05 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 12/06/15 15:43:05 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 12/06/15 15:43:05 INFO mapred.JobClient: Launched map tasks=1 12/06/15 15:43:05 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=0 12/06/15 15:43:05 INFO mapred.JobClient: File Output Format Counters 12/06/15 15:43:05 INFO mapred.JobClient: Bytes Written=0 12/06/15 15:43:05 INFO mapred.JobClient: FileSystemCounters 12/06/15 15:43:05 INFO mapred.JobClient: FILE_BYTES_WRITTEN=21518 12/06/15 15:43:05 INFO mapred.JobClient: CFS_BYTES_READ=87 12/06/15 15:43:05 INFO mapred.JobClient: File Input Format Counters 12/06/15 15:43:05 INFO mapred.JobClient: Bytes Read=0 12/06/15 15:43:05 INFO mapred.JobClient: Map-Reduce Framework 12/06/15 15:43:05 INFO mapred.JobClient: Map input records=1 12/06/15 15:43:05 INFO mapred.JobClient: Physical memory (bytes) snapshot=137998336 12/06/15 15:43:05 INFO mapred.JobClient: Spilled Records=0 12/06/15 15:43:05 INFO mapred.JobClient: CPU time spent (ms)=520 12/06/15 15:43:05 INFO mapred.JobClient: Total committed heap usage (bytes)=121241600 12/06/15 15:43:05 INFO mapred.JobClient: Virtual memory (bytes) snapshot=2082865152 12/06/15 15:43:05 INFO mapred.JobClient: Map output records=0 12/06/15 15:43:05 INFO mapred.JobClient: SPLIT_RAW_BYTES=87 12/06/15 15:43:05 INFO mapreduce.ImportJobBase: Transferred 0 bytes in 9.2642 seconds (0 bytes/sec) 12/06/15 15:43:05 INFO mapreduce.ImportJobBase: Retrieved 0 records. 12/06/15 15:43:05 INFO tool.CodeGenTool: Beginning code generation 12/06/15 15:43:05 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `Product` AS t LIMIT 1 12/06/15 15:43:06 INFO orm.CompilationManager: HADOOP_HOME is /home/pradeeban/programs/dse-2.1/resources/hadoop/bin/.. Note: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Product.java uses or overrides a deprecated API. Note: Recompile with -Xlint:deprecation for details. 12/06/15 15:43:06 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Product.jar 12/06/15 15:43:06 INFO manager.DirectMySQLManager: Beginning mysqldump fast path import 12/06/15 15:43:06 INFO mapreduce.ImportJobBase: Beginning import of Product 12/06/15 15:43:07 INFO mapred.JobClient: Running job: job_201206151241_0012 12/06/15 15:43:08 INFO mapred.JobClient: map 0% reduce 0% 12/06/15 15:43:16 INFO mapred.JobClient: map 100% reduce 0% 12/06/15 15:43:16 INFO mapred.JobClient: Job complete: job_201206151241_0012 12/06/15 15:43:16 INFO mapred.JobClient: Counters: 18 12/06/15 15:43:16 INFO mapred.JobClient: Job Counters 12/06/15 15:43:16 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=5961 12/06/15 15:43:16 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 12/06/15 15:43:16 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 12/06/15 15:43:16 INFO mapred.JobClient: Launched map tasks=1 12/06/15 15:43:16 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=0 12/06/15 15:43:16 INFO mapred.JobClient: File Output Format Counters 12/06/15 15:43:16 INFO mapred.JobClient: Bytes Written=248262 12/06/15 15:43:16 INFO mapred.JobClient: FileSystemCounters 12/06/15 15:43:16 INFO mapred.JobClient: FILE_BYTES_WRITTEN=21527 12/06/15 15:43:16 INFO mapred.JobClient: CFS_BYTES_WRITTEN=248262 12/06/15 15:43:16 INFO mapred.JobClient: CFS_BYTES_READ=87 12/06/15 15:43:16 INFO mapred.JobClient: File Input Format Counters 12/06/15 15:43:16 INFO mapred.JobClient: Bytes Read=0 12/06/15 15:43:16 INFO mapred.JobClient: Map-Reduce Framework 12/06/15 15:43:16 INFO mapred.JobClient: Map input records=1 12/06/15 15:43:16 INFO mapred.JobClient: Physical memory (bytes) snapshot=144871424 12/06/15 15:43:16 INFO mapred.JobClient: Spilled Records=0 12/06/15 15:43:16 INFO mapred.JobClient: CPU time spent (ms)=1030 12/06/15 15:43:16 INFO mapred.JobClient: Total committed heap usage (bytes)=121241600 12/06/15 15:43:16 INFO mapred.JobClient: Virtual memory (bytes) snapshot=2085318656 12/06/15 15:43:16 INFO mapred.JobClient: Map output records=300 12/06/15 15:43:16 INFO mapred.JobClient: SPLIT_RAW_BYTES=87 12/06/15 15:43:16 INFO mapreduce.ImportJobBase: Transferred 0 bytes in 9.2613 seconds (0 bytes/sec) 12/06/15 15:43:16 INFO mapreduce.ImportJobBase: Retrieved 300 records. I found DataStax an interesting project to explore more. I have blogged on the issues that I faced on this as a learner, and how easily can they be fixed - Issues that you may encounter during the migration to Cassandra using DataStax/Sqoop and the fixes.

Katas are small exercises that are repeatedly executed to improve one aspect of our programming capabilities - being design, automation, or a practice like Test-Driven Development. The Roman numerals kata attempts to create a conversion mechanism from integers to a Roman representation (a string): 1, 2 and 3 become I, II and III respectively. 5 and 10 become V and X respectively. 6 become VI, as symbols are additive. 4 becomes IV, as symbols are used subtractively (in this case subtracting 1 from 5) to avoid repeating a symbol more than three times in a row. If you have never tried the kata and want to do so in the future, I urge you to pick it up before reading further as I would spoil some of the experience for you. I recently executed the kata as a TDD exercise by myself and in a workshop in the local PHP user group in Milan. We reached two different solutions, and I want to analyze here why, and which is preferrable with regards to certain concerns. The solution is not the point of a kata: the experience (and what it teaches you) is; however, the roads you can take are part of the experience. The hardcore TDD solution When "pure" TDD is teached with this kata, one test is added at the time and production code is modified to make this test pass along with all the previously introduced ones. Then, a arbitrary long refactoring phase is performed to improve the code and prepare it to evolve further. Most of the solutions I found on the web (also in the form of videos) to compare them with mine went like this : private static final int[] VALUES = { 1000, 900, 500, 400, 100, 90, 50, 40, 10, 9, 5, 4, 1 }; private static final String[] SYMBOLS = { "M", "CM", "D", "CD", "C", "XC", "L", "XL", "X", "IX", "V", "IV", "I" }; public static String arabicToRoman(int arabic) { StringBuilder result = new StringBuilder(); int remaining = arabic; for (int i = 0; i < VALUES.length; i++) { remaining = appendRomanNumerals(remaining, VALUES[i], SYMBOLS[i], result); } return result.toString(); } I was not really satisfied with these kind of solutions as they are essentially an optimized lookup table more than a conversion algorithm. There is lots of duplication in this code: symbols like I and X are repeated in more than one subtractive form (IV and IX), and the subtractive rule itself is scattered among the values of the SYMBOLS array. If rules were to change (which does not happen in a kata but does in the real world), it would be difficult to adapt this code to cover Etruscan or Greek numerals, that have different models of subtractivity and symbol reuse. Again and again, TDD proved not sufficient (but facilitating) for algorithm generation; at least, for generating an algorithm well-factored enough to present changeable parts instead of a single procedure or a lookup table. How the domain comes into play If applying a pure red-green-refactor cycle cannot help us, what should we do? I took a look at Wikipedia, which is a good source to get acquainted with the domain of numerical systems (I stick to the Roman one however). Thus I discovered some interesting facts: the United States National Institute of Standards and Technology could find no authority that could describe if the year 1999 should be written as MDCCCCLXXXXVIIII, MCMXCIX, or MIM.[2] Despite the lack of standardization, an additional set of rules has been frequently applied for the last few hundred years Only one small-value symbol may be subtracted from any large-value symbol. The symbols "I", "X", "C", and "M" can be repeated three times in succession, but no more. (They may appear more than three times if they appear non-sequentially, such as XXXIX.) "D", "L", and "V" can never be repeated. "I" can be subtracted from "V" and "X" only. "X" can be subtracted from "L" and "C" only. "C" can be subtracted from "D" and "M" only. "V", "L", and "D" can never be subtracted. A number written in Arabic numerals can be broken into digits. For example, 1903 is composed of 1, 9, 0, and 3. To write the Roman numeral, each of the non-zero digits should be treated separately. In the above example, 1,000 = M, 900 = CM, and 3 = III. Therefore, 1903 = MCMIII. (All quotes are from http://en.wikipedia.org/wiki/Roman_numerals,) This knowledge is represented as a set of requirements, or can be seen as domain rules. By taking the time to find a source of knowledge about the domain of my application, I have extracted some relevant bits that can greatly simplify my model of the problem: We have clear requirements! Acceptance criteria are clearly defined as there are multiple possibilities for the numerical conversion. (1) There is a mechanism that is repeated across orders of magnitude: I is subtracted from 5 and 10, but X is subtracted from 50 and 100, and so on (2, 3, 4). We can divide the Arabic number into different ciphers and convert them separately, keeping in mind their position. Converting 42 becomes converting 40 and 2 plus a concatenation: 'XL' plus 'II'. Yet I couldn't find a single solution published online that leverages this important property to improve the code. Analysis-based solution Thus I set out to create a more flexible solution to the Roman numerals conversion problem. In this case I used only functions instead of objects to keep the solution clear for our user group audience, but I suspect that by introducing one or more classes such as OrderOfMagnitude and RomanCipher the code could speak for itself more clearly. I published on Github both this solution of mine and the classical one that we reached at the user group. function romanCipher($firstSymbol, $middleSymbol, $lastSymbol) { return function($number) use ($firstSymbol, $middleSymbol, $lastSymbol) { if ($number == 0) { return ''; } $symbols = array(1 => $firstSymbol, 5 => $middleSymbol, 10 => $lastSymbol); $roman = ''; foreach ($symbols as $denomination => $symbol) { if ($number == $denomination - 1) { return $firstSymbol . $symbol; } } foreach (array_reverse($symbols, true) as $denomination => $symbol) { while ($number >= $denomination) { $roman .= $symbol; $number -= $denomination; } } return $roman; }; } class RomanNumeralsTest extends PHPUnit_Framework_TestCase { public function setUp() { $this->toRoman = function($number) { $ciphers = array( '1' => romanCipher('I', 'V', 'X'), '2' => romanCipher('X', 'L', 'C'), '3' => romanCipher('C', 'D', 'M'), '4' => romanCipher('M', '?', '?'), ); $arabic = (string) $number; $fullRoman = ''; for ($i = 0; $i < strlen($arabic); $i++) { $position = strlen($arabic) - $i; $cipher = $ciphers[$position]; $fullRoman .= $cipher($arabic{$i}); } return $fullRoman; }; } ... The code is longer than the classic solution; but isn't well-factored code always a bit longer than a brute force version? I also found out that I could write more focused tests that collected domain properties; while refactoring I renamed tests from: public function test4IsConvertedToIV() ... public function test124ISConvertedToCXXIV() to: public function testSmallerSymbolsOnTheLeftAreSubtractive() ... public function testEvenNumbersGreaterThan100CanBeDividedIntoCiphers() { $this->assertEquals('C' . 'XX' . 'IV', $this->toRoman(124)); $this->assertEquals('CM' . 'XC' . 'IX', $this->toRoman(999)); } Conclusions I presented TDD at our workshop as a facilitating condition for writing clean code, but not as a sufficient one. Analysis is not dead and writing tests or jumping into coding some spike is not the only way you have to approach a problem. To be fair, the pure TDD approach let everyone reach a working solution, covered by automated tests; in many utility components of your application, such a result proves useful as you can write code that works and clean it up as much as you need (and no further). If numerical conversions aren't your core domain, you can just go for the brute force code where its complexity is manageable; just like you could go for bubble sort in case you needed to sort only a dozen of elements.

The last couple of days I've been playing around with three.js and geo information. I wanted to be able to render map/geo data (e.g. in geojson format) inside the three.js scene. That way I have another dimension I could use to show a specific metric instead of just using the color in a 2D map. In this article I'll show you how you can do this. The example we'll create shows a 3D map of the Netherlands, rendered in Three.js, that uses a color to indicate the population density per municipality and the height of each municipality represents the actual number of residents. Or if you can look at a working example. This information is based on open data available from the Dutch government. If you look at the source from the example, you can see the json we use for this. For more information on geojson and how to parse it see the other articles I did on this subject: Using d3.js to visualize GIS Election site part 1: Basics with Knockout.js, Bootstrap and d3.js To get this working we'll take the following steps: Load the input geo data Setup a three.js scene Convert the input data to a Three.js path using d3.js Set the color and height of the Three.js object Render everything Just a reminder to see everything working, just look at the example. Load the input geo data D3.js has support to load json and directly transform it to an SVG path. Though this is a convenient way, I only needed the path data, not the complete SVG elements. So to load json I just used jquery's json support. // get the data jQuery.getJSON('data/cities.json', function(data, textStatus, jqXHR) { .. }); This will load the data and pass it in the data object to the supplied function. Setup a three.js scene Before we do anything with the data lets first setup a basic Three.js scene. // Set up the three.js scene. This is the most basic setup without // any special stuff function initScene() { // set the scene size var WIDTH = 600, HEIGHT = 600; // set some camera attributes var VIEW_ANGLE = 45, ASPECT = WIDTH / HEIGHT, NEAR = 0.1, FAR = 10000; // create a WebGL renderer, camera, and a scene renderer = new THREE.WebGLRenderer({antialias:true}); camera = new THREE.PerspectiveCamera(VIEW_ANGLE, ASPECT, NEAR, FAR); scene = new THREE.Scene(); // add and position the camera at a fixed position scene.add(camera); camera.position.z = 550; camera.position.x = 0; camera.position.y = 550; camera.lookAt( scene.position ); // start the renderer, and black background renderer.setSize(WIDTH, HEIGHT); renderer.setClearColor(0x000); // add the render target to the page $("#chart").append(renderer.domElement); // add a light at a specific position var pointLight = new THREE.PointLight(0xFFFFFF); scene.add(pointLight); pointLight.position.x = 800; pointLight.position.y = 800; pointLight.position.z = 800; // add a base plane on which we'll render our map var planeGeo = new THREE.PlaneGeometry(10000, 10000, 10, 10); var planeMat = new THREE.MeshLambertMaterial({color: 0x666699}); var plane = new THREE.Mesh(planeGeo, planeMat); // rotate it to correct position plane.rotation.x = -Math.PI/2; scene.add(plane); } Nothing to special, the comments inline should nicely explain what we're doing here. Next it gets more interesting. Convert the input data to a Three.js path using d3.js What we need to do next is convert our geojson input format to a THREE.Path that we can use in our scene. Three.js itself doesn't support geojson or SVG for that matter. Luckily though someone already started work on integrating d3.js with three.js. This project is called "d3-threeD" (sources can be found on github here). With this extension you can automagically render SVG elements in 3D directly from D3.js. Cool stuff, but it didn't allow me any control over how the elements were rendered. It does however contain a function we can use for our scenario. If you look through the source code of this project you'll find a method called "transformSVGPath". This method converts an SVG path string to a Three.Shape element. Unfortunately this method isn't exposed, but that's quickly solved by adding this to the d3-threeD.js file: // at the top var transformSVGPathExposed; ... // within the d3threeD(exports) function transformSVGPathExposed = transformSVGPath; This way we can call this method separately. Now that we have a way to transform an SVG path to a Three.js shape, we only need to convert the geojson to an SVG string and pass it to this function. We can use the geo functionaly from D3.js for this: geons.geoConfig = function() { this.TRANSLATE_0 = appConstants.TRANSLATE_0; this.TRANSLATE_1 = appConstants.TRANSLATE_1; this.SCALE = appConstants.SCALE; this.mercator = d3.geo.mercator(); this.path = d3.geo.path().projection(this.mercator); this.setupGeo = function() { var translate = this.mercator.translate(); translate[0] = this.TRANSLATE_0; translate[1] = this.TRANSLATE_1; this.mercator.translate(translate); this.mercator.scale(this.SCALE); } } The path variable from the previous piece of code can now be used like this: var feature = geo.path(geoFeature); To convert a geojson element to an SVG path. So how does this look combined? // add the loaded gis object (in geojson format) to the map function addGeoObject() { // keep track of rendered objects var meshes = []; ... // convert to mesh and calculate values for (var i = 0 ; i < data.features.length ; i++) { var geoFeature = data.features[i] var feature = geo.path(geoFeature); // we only need to convert it to a three.js path var mesh = transformSVGPathExposed(feature); // add to array meshes.push(mesh); ... } As you can see we iterate over the data.features list (this contains all the geojson representations of the municipalities). Each municipality is converted to an svg string, and each svg string is converted to a mesh. This mesh is a Three.js object that we can render on the scene. Set the color and height of the Three.js object Now we just need to set the height and the color of the Three.js shape and add it to the scene. The extended addGeoObject method now looks like this: // add the loaded gis object (in geojson format) to the map function addGeoObject() { // keep track of rendered objects var meshes = []; var averageValues = []; var totalValues = []; // keep track of min and max, used to color the objects var maxValueAverage = 0; var minValueAverage = -1; // keep track of max and min of total value var maxValueTotal = 0; var minValueTotal = -1; // convert to mesh and calculate values for (var i = 0 ; i < data.features.length ; i++) { var geoFeature = data.features[i] var feature = geo.path(geoFeature); // we only need to convert it to a three.js path var mesh = transformSVGPathExposed(feature); // add to array meshes.push(mesh); // we get a property from the json object and use it // to determine the color later on var value = parseInt(geoFeature.properties.bev_dichth); if (value > maxValueAverage) maxValueAverage = value; if (value < minValueAverage || minValueAverage == -1) minValueAverage = value; averageValues.push(value); // and we get the max values to determine height later on. value = parseInt(geoFeature.properties.aant_inw); if (value > maxValueTotal) maxValueTotal = value; if (value < minValueTotal || minValueTotal == -1) minValueTotal = value; totalValues.push(value); } // we've got our paths now extrude them to a height and add a color for (var i = 0 ; i < averageValues.length ; i++) { // create material color based on average var scale = ((averageValues[i] - minValueAverage) / (maxValueAverage - minValueAverage)) * 255; var mathColor = gradient(Math.round(scale),255); var material = new THREE.MeshLambertMaterial({ color: mathColor }); // create extrude based on total var extrude = ((totalValues[i] - minValueTotal) / (maxValueTotal - minValueTotal)) * 100; var shape3d = meshes[i].extrude({amount: Math.round(extrude), bevelEnabled: false}); // create a mesh based on material and extruded shape var toAdd = new THREE.Mesh(shape3d, material); // rotate and position the elements nicely in the center toAdd.rotation.x = Math.PI/2; toAdd.translateX(-490); toAdd.translateZ(50); toAdd.translateY(extrude/2); // add to scene scene.add(toAdd); } } // simple gradient function function gradient(length, maxLength) { var i = (length * 255 / maxLength); var r = i; var g = 255-(i); var b = 0; var rgb = b | (g << 8) | (r << 16); return rgb; } A big piece of code, but not that complex. What we do here is we keep track of two values for each municipality: the population density and the total population. These values are used to respectively calculate the color (using the gradient function) and the height. The height is used in the Three.js extrude function which converts our 2D Three.Js path to a 3D shape. The color is used to define a material. This shape and material is used to create the Mesh that we add to the scene. Render everything All that is left is to render everything. For this example we're not interested in animations or anything so we can make a single call to the renderer: renderer.render( scene, camera ); And the result is as you saw in the beginning. The following image shows a different example. This time we once again show the population density, but now the height represents the land area of the municipality. I'm currently creating a new set of geojson data, but this time for the whole of Europe. So in the next couple of weeks expect some articles using maps of Europe.

Apache Thrift is a RPC framework founded by facebook and now it is an Apache project. Thrift lets you define data types and service interfaces in a language neutral definition file. That definition file is used as the input for the compiler to generate code for building RPC clients and servers that communicate over different programming languages. You can refer Thrift white paper also. According to the official web site Apache Thrift is a, software framework, for scalable cross-language services development, combines a software stack with a code generation engine to build services that work efficiently and seamlessly between C++, Java, Python, PHP, Ruby, Erlang, Perl, Haskell, C#, Cocoa, JavaScript, Node.js, Smalltalk, OCaml and Delphi and other languages. Image courtesy wikipedia Installing Apache Thrift in Windows Installation Thrift can be a tiresome process. But for windows the compiler is available as a prebuilt exe. Download thrift.exe and add it into your environment variables. Writing Thrift definition file (.thrift file) Writing the Thrift definition file becomes really easy once you get used to it. I found this tutorial quite useful to begin with. Example definition file (add.thrift) namespace java com.eviac.blog.samples.thrift.server // defines the namespace typedef i32 int //typedefs to get convenient names for your types service AdditionService { // defines the service to add two numbers int add(1:int n1, 2:int n2), //defines a method } Compiling Thrift definition file To compile the .thrift file use the following command. thrift --gen For my example the command is, thrift --gen java add.thrift After performing the command, inside gen-java directory you'll find the source codes which is useful for building RPC clients and server. In my example it will create a java code called AdditionService.java Writing a service handler Service handler class is required to implement the AdditionService.Iface interface. Example service handler (AdditionServiceHandler.java) package com.eviac.blog.samples.thrift.server; import org.apache.thrift.TException; public class AdditionServiceHandler implements AdditionService.Iface { @Override public int add(int n1, int n2) throws TException { return n1 + n2; } } Writing a simple server Following is an example code to initiate a simple thrift server. To enable the multithreaded server uncomment the commented parts of the example code. Example server (MyServer.java) package com.eviac.blog.samples.thrift.server; import org.apache.thrift.transport.TServerSocket; import org.apache.thrift.transport.TServerTransport; import org.apache.thrift.server.TServer; import org.apache.thrift.server.TServer.Args; import org.apache.thrift.server.TSimpleServer; public class MyServer { public static void StartsimpleServer(AdditionService.Processor processor) { try { TServerTransport serverTransport = new TServerSocket(9090); TServer server = new TSimpleServer( new Args(serverTransport).processor(processor)); // Use this for a multithreaded server // TServer server = new TThreadPoolServer(new // TThreadPoolServer.Args(serverTransport).processor(processor)); System.out.println("Starting the simple server..."); server.serve(); } catch (Exception e) { e.printStackTrace(); } } public static void main(String[] args) { StartsimpleServer(new AdditionService.Processor(new AdditionServiceHandler())); } } Writing the client Following is an example java client code which consumes the service provided by AdditionService. Example client code (AdditionClient.java) package com.eviac.blog.samples.thrift.client; import org.apache.thrift.TException; import org.apache.thrift.protocol.TBinaryProtocol; import org.apache.thrift.protocol.TProtocol; import org.apache.thrift.transport.TSocket; import org.apache.thrift.transport.TTransport; import org.apache.thrift.transport.TTransportException; public class AdditionClient { public static void main(String[] args) { try { TTransport transport; transport = new TSocket("localhost", 9090); transport.open(); TProtocol protocol = new TBinaryProtocol(transport); AdditionService.Client client = new AdditionService.Client(protocol); System.out.println(client.add(100, 200)); transport.close(); } catch (TTransportException e) { e.printStackTrace(); } catch (TException x) { x.printStackTrace(); } } } Run the server code(MyServer.java). It should output following and will listen to the requests. Starting the simple server... Then run the client code(AdditionClient.java). It should output following. 300

If you have already used the ASP.NET ListView and DataPager controls, you know how easily you can display your data using custom templates and provide pagination functionality. You can do that in only few minutes. The ListView and DataPager controls work perfectly fine in combination with DataSource controls (SqlDataSource, LinqDataSource, ObjectDataSource etc.), however if you use them with custom data collection, without using Data Source controls, you may find some unexpected behavior and have to add some little more code to make it work. Note: I saw question related to this issue asked multiple times in different asp.net forums, so I thought it would be nice to document it here. Lets create a working demo together… 1. Create sample ASP.NET Web Application project, add new ASPX page. 2. Add ListView control and modify the markup so that you will end up having this: ( ) No data The ListView control has three templates defined: LayoutTemplate – where we define the way we want to represent our data. We have PlaceHolder where data from ItemTemplate will be placed. ListView recognizes this automatically. ItemTemplate – where the ListView control will show the items from the data source. It makes automatically iterating over each item in the collection (same as any other data source control) EmptyDataTemplate – If the collection has no data, this template will be displayed. 3. Add DataPager control and modify the markup in the following way: We add the DataPager control, associate it with the ListView1 control and add the PageSize property. After that, we need to define where we put the field type and button type. If we were binding from Data Source Control (SqlDataSource, LinqDataSource or any other…) this would be it and the ListView and DataPager would work perfectly fine. However, if we bind custom data collection to the ListView without using Data Source controls, we will have problems with the pagination. Lets add custom data in our C# code and bind it to the ListView. 4. C# code adding custom data collection - Define Product class public class Product { public string Name { get; set; } public decimal Price { get; set; } public string Currency { get; set; } } - Define method that will create List of products (sample data) List SampleData() { List p = new List(); p.Add(new Product() { Name = "Microsoft Windows 7", Price = 70, Currency = "USD" }); p.Add(new Product() { Name = "HP ProBook", Price = 320, Currency = "USD" }); p.Add(new Product() { Name = "Microsoft Office Home", Price = 60, Currency = "USD" }); p.Add(new Product() { Name = "NOKIA N900", Price = 350, Currency = "USD" }); p.Add(new Product() { Name = "BlackBerry Storm", Price = 100, Currency = "USD" }); p.Add(new Product() { Name = "Apple iPhone", Price = 400, Currency = "USD" }); p.Add(new Product() { Name = "HTC myTouch", Price = 200, Currency = "USD" }); return p; } This method should be part of the ASPX page class (e.g. inside _Default page class if your page is Default.aspx) - Bind the sample data to ListView on Page_Load protected void Page_Load(object sender, EventArgs e) { if (!IsPostBack) { BindListView(); } } void BindListView() { ListView1.DataSource = SampleData(); ListView1.DataBind(); } Now, run the project and you should see the data displayed where only the first five items will be shown. The data pager should have two pages (1 2) and you will be able to click the second page to navigate to the last two items in the `collection. Now, if you click on page 2, you will see it won’t display the last two items automatically, instead you will have to click again on page 2 to see the last two items. After, if you click on page 1, you will encounter another problem where the five items are displayed, but the data for the last two items in the ListView are shown (see print screen bellow) If you notice in the previous two pictures, the behavior doesn’t seem to work properly. The problem here is that DataPager doesn’t know about current ListView page changing property. Therefore, we should explicitly set the DataPager Page Properties in the ListView’s PagePropertiesChanging event. Here is what we need to do: 1. Add OnPagePropertiesChanging event to ListView control 2. Implement the ListView1_PagePropertiesChanging method protected void ListView1_PagePropertiesChanging(object sender, PagePropertiesChangingEventArgs e) { //set current page startindex, max rows and rebind to false lvDataPager1.SetPageProperties(e.StartRowIndex, e.MaximumRows, false); //rebind List View BindListView(); } Now, if you test the functionality, it should work properly. Hope this was helpful. Regards, Hajan

If we want to create a checksum for a file in Gradle we can use the ANT checksum task. We assign the file name to the file property of the task. Then the checksum task will generate a file with the checksum in the same directory. Suppose we have a Java project and want to generate a checksum for the JAR file that is generated with the jar task. We can add an extra action to the jar task with the doLast() method. We pass a closure to this method and in the closure we define the action to create a checksum. The file name of the JAR file is accessible via the archivePath property: apply plugin: 'java' archivesBaseName = 'checksum-sample' jar.doLast { task -> ant.checksum file: task.archivePath } When we invoke the jar task we get an extra file in the build/libs directory with the name checksum-sample.jar.MD5. This file contains the checksum for the file checksum-sample.jar. We can make sure in our Gradle build file that all archive tasks (like Zip, Jar, War) generate a checksum file for the archive file of that task. In the following build file we use the withType() method in the afterEvaluate() method to add the checksum task to all tasks of type Zip. This means also task types that extend the Zip task, like Jar, War and Ear, will get the extra action. We also create extra archives in our project to show we don't have to configure them explicitly with the checksum task, but that Gradle will add the action automatically. apply plugin: 'java' archivesBaseName = 'checksum-sample' task sourceJar(type: Jar) { from sourceSets.main.allSource classifier = 'sources' } task javadocJar(type: Jar, dependsOn: 'javadoc') { from javadoc.destinationDir classifier = 'javadoc' } artifacts { archives sourceJar, javadocJar } tasks.withType(Zip) { task -> task.doLast { ant.checksum file: it.archivePath } } When we invoke the assemble task and look at the file that are generated we get the following command-line output: $ gradle assemble :compileJava :processResources UP-TO-DATE :classes :jar :javadoc :javadocJar :sourceJar :assemble BUILD SUCCESSFUL Total time: 4.161 secs $ ls -1 build/libs checksum-sample-javadoc.jar checksum-sample-javadoc.jar.MD5 checksum-sample-sources.jar checksum-sample-sources.jar.MD5 checksum-sample.jar checksum-sample.jar.MD5

Java Message Service is a mechanism for integrating applications in a loosely coupled, flexible manner and delivers data asynchronously across applications.

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/

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We all know how financially important it is for your app’s server architecture to handle peaks of load. This article discusses 5 tips for improving PHP Web performance.