Axis2 uses CommonsHTTPTransportSender by default, which is based on commons-httpclient-3.1. At transport level, there’re two types of timeouts that can be set: 1. Socket Timeout 2. Connection Timeout Here’s how you can configure the above ones: Way #1: Configuring timeouts in axis2.xml Socket Timeout some_integer_value Connection timeout some_integer_value Way #2: Configuring timeouts in code … Options options = new Options(); options.setProperty(HTTPConstants.SO_TIMEOUT, new Integer(timeOutInMilliSeconds)); options.setProperty(HTTPConstants.CONNECTION_TIMEOUT, new Integer(timeOutInMilliSeconds)); // or options.setTimeOutInMilliSeconds(timeOutInMilliSeconds); … Real-life code: How to set timeout for a Axis2 Stub? long timeout = 2 * 60 * 1000; // Two minutes Stub stub = new TestStub(); stub._getServiceClient().getOptions().setTimeOutInMilliSeconds(soTimeout); //or long timeout = 2 * 60 * 1000; // Two minutes Stub stub = new TestStub(); stub._getServiceClient().getOptions().setProperty( HTTPConstants.SO_TIMEOUT, new Integer(timeOutInMilliSeconds)); stub._getServiceClient().getOptions().setProperty( HTTPConstants.CONNECTION_TIMEOUT, new Integer(timeOutInMilliSeconds));

There are two ways to speedup UNIONs in a MySQL database. First use UNION ALL if at all possible, and second try to push down your conditions. 1. UNION ALL is much faster than UNION How does a UNION work? Imagine you have two tables for shirts. The short_sleeve table looks like this: blue green gray black And long_sleeve another that looks like this: red green yellow blue Related: Why Generalists are Better at Scaling the Web If you UNION those two tables, first MySQL will sort the combined set into a temp table like this: black blue blue gray green green red yellow Once it’s done this sort, it can easily remove the duplicate blue & duplicate green for this resulting set: black blue gray green red yellow See also: Mythical MySQL DBA – the talent drought. Why does it do this? UNION is defined that way in SQL. Duplicates must be removed and this is an efficient way for the MySQL engine to remove them. Combine results, sort, remove duplicates and return the set. Queries with UNION can be accelerated in two ways. Switch to UNION ALL or try to push ORDER BY, LIMIT and WHERE conditions inside each subquery. You’ll be glad you did! What if we did UNION ALL? The result would look like this: blue green gray black red green yellow blue Read this: MySQL DBA Interview & Hiring Guide. It doesn’t have to sort, and doesn’t have to remove duplicates. If you imagine combining two 10 million row tables, and don’t have to sort, this speedup can be HUGE. 2. Use Push-down Conditions to speedup UNION in MySQL Imagine with our example above the shirts have a design date, the year they were released. Yes we’re keeping this example very simple to illustrate the concept. Here is the short_sleeve table: blue 2013 green 2013 green 2012 gray 2011 black 2009 black 2011 And long_sleeve table looks like this: red 2012 red 2013 green 2011 yellow 2010 blue 2011 For 2013 designs could combine them like this: (SELECT type, release FROM short_sleeve) UNION (SELECT type, release FROM long_sleeve); WHERE release >=2013; See also: 5 More Things Deadly to Scalability and the original 5 Things Toxic to Scalability.. Here the WHERE clause works on this 11 record temp table: black 2009 black 2011 blue 2011 blue 2013 gray 2011 green 2013 green 2012 green 2011 red 2012 red 2013 yellow 2010 But it would be much faster to move the WHERE inside each subquery like this: (SELECT type, release FROM short_sleeve WHERE release >=2013) UNION (SELECT type, release FROM long_sleeve WHERE release >=2013); That would be operating on a combined 3 record table. Faster to sort & remove duplicates. Smaller result sets cache better too, providing a pay forward dividend. That’s what performance optimization is all about! Read this: RDS or MySQL – 10 Use Cases. Remember multi-million row sets in each part of this query will quickly illustrate the optimization. We’re using very small results to make visualizing easier. You can also use this optimization for ORDER BY and for LIMIT conditions. By reducing the number of records returned by EACH PART of the UNION, you reduce the work that happens at the stage where they are all combined. If you’re seeing some UNION queries in your slow query log, I suggest you try this optimization out and see if you can tweak it.

NetBeans IDE 7.3.1 is an update to NetBeans IDE 7.3 and includes the following highlights: Support for Java EE 7 development Deployment to GlassFish 4 Support for major Java EE 7 specifications: JSF 2.2, JPA 2.1, JAX-RS 2.0, WebSocket 1.0 and more Support for WebLogic 12.1.2 and JBoss 7.x Integration of recent patches There are two ways to get the recent changes: To use the new Java EE 7 support, it is recommended to download and install NetBeans IDE 7.3.1. To get only the integration of recent patches: Launch your current installation of NetBeans IDE 7.3. An update notification will appear in the IDE. Click the notification box to install the updates. OR to perform the update manually, in the IDE select Help-->Check for Updates. NetBeans IDE 7.3.1 is available in English, Brazilian Portuguese, Japanese, Russian, and Simplified Chinese.

RPC/encoded is a vestige from before SOAP objects were defined with XML Schema. It’s not widely supported anymore. You will need to generate the stubs using Apache Axis 1.0, which is from the same era. java org.apache.axis.wsdl.WSDL2Java http://someurl?WSDL You will need the following jars or equivalents in the -cp classpath param: axis-1.4.jar commons-logging-1.1.ja commons-discovery-0.2.jar jaxrpc-1.1.jar saaj-1.1.jar wsdl4j-1.4.jar activation-1.1.jar mail-1.4.jar This will generate similar stubs to wsimport. Alternatively, if you are not using the parts of the schema that require rpc/encoded, you can download a copy of the WSDL and comment out those bits. Then run wsimport against the local file. If you look at the WSDL, the following bits are using rpc/encoded: Sources 1. http://bitkickers.blogspot.com/2008/12/rpcencoded-web-services-on-java-16.html 2. http://stackoverflow.com/questions/412772/java-rpc-encoded-wsdls-are-not-supported-in-jaxws-2-0

I’ve recently had the need to automate configuration of Nginx on an Ubuntu server. Of course, in UNIX land we like to use SSH (Secure Shell) to log into our servers and manage them remotely. Wouldn’t it be nice, I thought, if there was a managed SSH library somewhere so that I could automate logging onto my Ubuntu server, run various commands and transfer files. A short Google turned up SSH.NET by the somewhat mysterious Olegkap (at least I couldn’t find out anything else about them) which turned out to be just what I wanted. Here’s the blurb on the CodePlex site: “This project was inspired by Sharp.SSH library which was ported from java and it seems like was not supported for quite some time. This library is complete rewrite using .NET 4.0, without any third party dependencies and to utilize the parallelism as much as possible to allow best performance I can get.” It does exactly what it says on the tin. It’s on NuGet, so you can grab it with: PM> Install-Package SSH.NET Here’s how you run a remote command. First you need to build a ConnectionInfo object: public ConnectionInfo CreateConnectionInfo() { const string privateKeyFilePath = @"C:\some\private\key.pem"; ConnectionInfo connectionInfo; using (var stream = new FileStream(privateKeyFilePath, FileMode.Open, FileAccess.Read)) { var privateKeyFile = new PrivateKeyFile(stream); AuthenticationMethod authenticationMethod = new PrivateKeyAuthenticationMethod("ubuntu", privateKeyFile); connectionInfo = new ConnectionInfo( "my.server.com", "ubuntu", authenticationMethod); } return connectionInfo; } Then you simply create an SshClient instance and run commands: public void Connect() { using (var ssh = new SshClient(CreateConnectionInfo())) { ssh.Connect(); var command = ssh.CreateCommand("uptime"); var result = command.Execute(); Console.Out.WriteLine(result); ssh.Disconnect(); } } Here I’m running the ‘uptime’ command which output this when I ran it just now: 14:37:46 up 22 days, 3:59, 0 users, load average: 0.08, 0.03, 0.05 To transfer a file, just use the ScpClient: public void GetConfigurationFiles() { using (var scp = new ScpClient(CreateNginxServerConnectionInfo())) { scp.Connect(); scp.Download("/etc/nginx/", new DirectoryInfo(@"D:\Temp\ScpDownloadTest")); scp.Disconnect(); } } Which grabs all my Nginx configuration and transfers it to a directory tree on my windows machine. All in all a very nice little library that’s been working well for me so far. Give it a try if you need to interact with a UNIX-like machine from .NET code.

Serialization is a form of persistence: serialized data survives the process and the RAM where it was created and can be reconstituted inside different processes and machines that live in a different time or place. Sometimes serialization is a poor form of persistence in fact, one that confuses the boundary between the different schemas the data can fit in. However, what I found useful in the last years of development is to institute a strict separation: serialize Value Objects, Entities, and everything that represents the state of the application. Meanwhile, use Dependency Injection over services that are part of a larger object graph and never serialize this second kind of objects. In the discussion that follows, I make the assumption that serialization and deserialization occur on the same machine (e.g. like for web-oriented sessions.) The problem with serialization, which work transparently most of the time, is the need to serialize service objects instead of limiting the procedure to data structures. How can you store such objects? Not options Some options to solve this problems are really not options. Serialization by itself will fail because of the staleness of the references contained in these objects. For example, in PHP trying to serialize a database connections composed by a Repository or DAO object will rightly fail with an exception. Whenever an object represents a resource of the current machine, it cannot usually be serialized except in the case when the only resource involved is RAM. If the resource is disk space or other running processes such as a database daemon, the reconstitution of the object in another place and time will fail and it's best to just stop the developer immediately during storage. Quasi-options Some solutions to the problem try to avoid the staleness problem by serializing objects without their resources, and make them regrab a new version of them on deserialization. In PHP for example, this can be done with the __sleep() and __wakeup() magic methods, called automatically during serialization and deserializaton respectively. This deserialization mechanism introduces a dependency from the serialized Entity to external services: such a dependency is already in place when building the object the first time (passing the XService in the constructor) but it is aggravated when deserializing (depending on a XServiceFactory instead of just an XService). An improvement, from the dependencies point of view, is to reattach collaborators to deserialized objects like you would for other persistence-related tasks. For example, EntityRepository can inject the missing pieces of Entity every time its find() method is called. However, there is still another option, which is the most resilient from the modelling point of view and not only that of dependency management: injecting non-serializable collaborators through the stack. Objects can collaborate even without keeping field references to each other, and injecting dependencies as parameters move the dependency starting point from the server to the client object (which may or may not be desirable). What is most important is that Entities are relieved of having to manage external references in any context, not only that of persistence and in particular serialization. The metaphor for the 3rd option Misko Hevery likes to say: have you ever seen a credit card able to charge itself? If a CreditCard is an Entity in your domain, it would be very strange to keeping a wire attached to your wallet wherever you go. With the first option, you have the card spring a wire when it is taken out of the wallet, like in horror movies. This intelligent cable tries as its best to attach to the nearest Point of Sale (a bad case of bluetooth I think). With Repositories in mind, you're not dealing with automated wires anymore, but you're still attaching cables between cards and fixed devices. In reality, cards collaborate with the PoS in a fast process that does not last more than a few seconds. Actually, sometimes they don't touch it at all, as in all Internet-based purchases. Keeping services around to deal with external dependencies does not mean the API of your Domain Model has to be biased towards service objects: pos.charge(creditCard); // can equivalently be: creditCard.chargeOn(pos); This is a form of Double Dispatch since there are two objects collaborating and you can dispatch (send messages) to both, being polimorphic by substituting both objects. The sequence of calls is: client -> creditCard -> pos The client object still looks at CreditCard as a behaviorally complete object, but it is clear which dependency is necessary to run each use case (CreditCard method). You can persist a CreditCard easily and send it over the wire to caches or databases. When it comes the time to charge, it is the client that has to bring forward a service able to connect to a bank.

[This blog was syndicated from http://blog.grallandco.com] Introduction When I was looking for a more effective way to create my cluster I asked some sysadmins which tools I should use to do it. The answer I got during OSDC was not Puppet, nor Chef, but wasAnsible. This article shows you how you can easily configure and create a Couchbase cluster deployed and many linux boxes...and the only thing you need on these boxes is an SSH Server! Thanks to Jan-Piet Mens that was one of the person that convinced me to use Ansible and answered questions I had about Ansible. You can watch the demonstration below, and/or look at all the details in the next paragraph. Ansible Ansible is an open-source software that allows administrator to configure and manage many computers over SSH. I won't go in all the details about the installation, just follow the steps documented in the Getting Started Guide. As you can see from this guide, you just need Python and few other libraries and clone Ansible project from Github. So I am expecting that you have Ansible working with your various servers on which you want to deploy Couchbase. Also for this first scripts I am using root on my server to do all the operations. So be sure you have register the root ssh keys to your administration server, from where you are running the Ansible scripts. Create a Couchbase Cluster So before going into the details of the Ansible script it is interesting to explain how you create a Couchbase Cluster. So here are the 5 steps to create and configure a cluster: Install Couchbase on each nodes of the cluster, as documented here. Take one of the node and "initialize" the cluster, using cluster-init command. Add the other nodes to the cluster, using server-add command. Rebalance, using rebalance command. Create a Bucket, using bucket-create command. So the goal now is to create an Ansible Playbook that executes these steps for you. Ansible Playbook for Couchbase The first think you need is to have the list of hosts you want to target, so I have create a hosts file that contains all my server organized in 2 groups: [couchbase-main] vm1.grallandco.com [couchbase-nodes] vm2.grallandco.com vm3.grallandco.com The group [couchbase-main] group is just one of the node that will drive the installation and configuration, as you probably already know, Couchbase does not have any master... All nodes in the cluster are identical. To ease the configuration of the cluster, I have create another file that contains all parameters that must be sent to all the various commands. This file is located in the group_vars/all see the section Splitting Out Host and Group Specific Data in the documentation. # Adminisrator user and password admin_user: Administrator admin_password: password # ram quota for the cluster cluster_ram_quota: 1024 # bucket and replicas bucket_name: ansible bucket_ram_quota: 512 num_replicas: 2 Use this file to configure your cluster. Let's describe the playbook file : - name: Couchbase Installation hosts: all user: root tasks: - name: download Couchbase package get_url: url=http://packages.couchbase.com/releases/2.0.1/couchbase-server-enterprise_x86_64_2.0.1.deb dest=~/. - name: Install dependencies apt: pkg=libssl0.9.8 state=present - name: Install Couchbase .deb file on all machines shell: dpkg -i ~/couchbase-server-enterprise_x86_64_2.0.1.deb As expected, the installation has to be done on all servers as root then we need to execute 3 tasks: Download the product, the get_url command will only download the file if not already present Install the dependencies with the apt command, the state=present allows the system to only install this package if not already present Install Couchbase with a simple shell command. (here I am not checking if Couchbase is already installed) So we have now installed Couchbase on all the nodes. Let's now configure the first node and add the others: - name: Initialize the cluster and add the nodes to the cluster hosts: couchbase-main user: root tasks: - name: Configure main node shell: /opt/couchbase/bin/couchbase-cli cluster-init -c 127.0.0.1:8091 --cluster-init-username=${admin_user} --cluster-init-password=${admin_password} --cluster-init-port=8091 --cluster-init-ramsize=${cluster_ram_quota} - name: Create shell script for configuring main node action: template src=couchbase-add-node.j2 dest=/tmp/addnodes.sh mode=750 - name: Launch config script action: shell /tmp/addnodes.sh - name: Rebalance the cluster shell: /opt/couchbase/bin/couchbase-cli rebalance -c 127.0.0.1:8091 -u ${admin_user} -p ${admin_password} - name: create bucket ${bucket_name} with ${num_replicas} replicas shell: /opt/couchbase/bin/couchbase-cli bucket-create -c 127.0.0.1:8091 --bucket=${bucket_name} --bucket-type=couchbase --bucket-port=11211 --bucket-ramsize=${bucket_ram_quota} --bucket-replica=${num_replicas} -u ${admin_user} -p ${admin_password} Now we need to execute specific taks on the "main" server: Initialization of the cluster using the Couchbase CLI, on line 06 and 07 Then the system needs to ask all other server to join the cluster. For this the system needs to get the various IP and for each IP address execute the add-server command with the IP address. As far as I know it is not possible to get the IP address from the main playbook YAML file, so I ask the system to generate a shell script to add each node and execute the script. This is done from the line 09 to 13. To generate the shell script, I use Ansible Template, the template is available in the couchbase-add-node.j2 file. {% for host in groups['couchbase-nodes'] %} /opt/couchbase/bin/couchbase-cli server-add -c 127.0.0.1:8091 -u ${admin_user} -p ${admin_password} --server-add={{ hostvars[host]['ansible_eth0']['ipv4']['address'] }:8091 --server-add-username=${admin_user} --server-add-password=${admin_password} {% endfor %} As you can see this script loop on each server in the [couchbase-nodes] group and use its IP address to add the node to the cluster. Finally the script rebalance the cluster (line 16) and add a new bucket (line 19). You are now ready to execute the playbook using the following command : ./bin/ansible-playbook -i ./couchbase/hosts ./couchbase/couchbase.yml -vv I am adding the -vv parameter to allow you to see more information about what's happening during the execution of the script. This will execute all the commands described in the playbook, and after few seconds you will have a new cluster ready to be used! You can for example open a browser and go to the Couchase Administration Console and check that your cluster is configured as expected. As you can see it is really easy and fast to create a new cluster using Ansible. I have also create a script to uninstall properly the cluster.. just launch ./bin/ansible-playbook -i ./couchbase/hosts ./couchbase/couchbase-uninstall.yml

Hadoop provides a Java native API to support file system operations..

avro has a little-known gem of a feature which allows you to control which fields in an avro record are used for partitioning , sorting and grouping in mapreduce. the following figure gives a quick refresher as to what these terms mean. oh, and don’t take the placement of the “sorting” literally - sorting actually occurs on both the map and reduce side - but it’s always performed in the context of a specific partition (i.e. for a specific reducer). by default all the fields in an avro map output key are used for partitioning, sorting and grouping in mapreduce. let’s walk through an example and see how this works. you’ll begin with a simple schema github source : {"type": "record", "name": "com.alexholmes.avro.weathernoignore", "doc": "a weather reading.", "fields": [ {"name": "station", "type": "string"}, {"name": "time", "type": "long"}, {"name": "temp", "type": "int"}, {"name": "counter", "type": "int", "default": 0} ] } we’re going to see what happens when we run this code against a small sample data set, which we’ll generate using avro code github source : file input = tmpfolder.newfile("input.txt"); avrofiles.createfile(input, weathernoignore.schema$, arrays.aslist( weathernoignore.newbuilder().setstation("sfo").settime(1).settemp(3).build(), weathernoignore.newbuilder().setstation("iad").settime(1).settemp(1).build(), weathernoignore.newbuilder().setstation("sfo").settime(2).settemp(1).build(), weathernoignore.newbuilder().setstation("sfo").settime(1).settemp(2).build(), weathernoignore.newbuilder().setstation("sfo").settime(1).settemp(1).build() ).toarray()); to understand how avro is partitioning, sorting and grouping the data, we’ll write an identity mapper and reducer, with a small enhancement to the reducer to increment the counter field for each record we see in an individual reducer instance github source : package com.alexholmes.avro.sort.basic; import com.alexholmes.avro.weathernoignore; import org.apache.avro.mapred.avrokey; import org.apache.avro.mapred.avrovalue; import org.apache.avro.mapreduce.avrojob; import org.apache.avro.mapreduce.avrokeyinputformat; import org.apache.avro.mapreduce.avrokeyoutputformat; import org.apache.hadoop.fs.path; import org.apache.hadoop.io.nullwritable; import org.apache.hadoop.mapreduce.job; import org.apache.hadoop.mapreduce.mapper; import org.apache.hadoop.mapreduce.reducer; import org.apache.hadoop.mapreduce.lib.input.fileinputformat; import org.apache.hadoop.mapreduce.lib.output.fileoutputformat; import java.io.ioexception; public class avrosort { private static class sortmapper extends mapper, nullwritable, avrokey, avrovalue> { @override protected void map(avrokey key, nullwritable value, context context) throws ioexception, interruptedexception { context.write(key, new avrovalue(key.datum())); } } private static class sortreducer extends reducer, avrovalue, avrokey, nullwritable> { @override protected void reduce(avrokey key, iterable> values, context context) throws ioexception, interruptedexception { int counter = 1; for (avrovalue weathernoignore : values) { weathernoignore.datum().setcounter(counter++); context.write(new avrokey(weathernoignore.datum()), nullwritable.get()); } } } public boolean runmapreduce(final job job, path inputpath, path outputpath) throws exception { fileinputformat.setinputpaths(job, inputpath); job.setinputformatclass(avrokeyinputformat.class); avrojob.setinputkeyschema(job, weathernoignore.schema$); job.setmapperclass(sortmapper.class); avrojob.setmapoutputkeyschema(job, weathernoignore.schema$); avrojob.setmapoutputvalueschema(job, weathernoignore.schema$); job.setreducerclass(sortreducer.class); avrojob.setoutputkeyschema(job, weathernoignore.schema$); job.setoutputformatclass(avrokeyoutputformat.class); fileoutputformat.setoutputpath(job, outputpath); return job.waitforcompletion(true); } } if you look at the output of the job below, you’ll see that the output is sorted across all the fields, and that the sorting is in field ordinal order. what this means is that when mapreduce is sorting these records, it compares the station field first, then the time field second, and so on according to the ordering of the fields in the avro schema. this is pretty much what you’d expect if you write your own complex writable type, and your comparator compared all the fields in order. {"station": "iad", "time": 1, "temp": 1, "counter": 1} {"station": "sfo", "time": 1, "temp": 1, "counter": 1} {"station": "sfo", "time": 1, "temp": 2, "counter": 1} {"station": "sfo", "time": 1, "temp": 3, "counter": 1} {"station": "sfo", "time": 2, "temp": 1, "counter": 1} oh, and before we move on notice that the value for the counter field is always 1 , meaning that each reducer was only fed a single key/vaue pair, which makes sense since our identity mapper only emitted a single value for each key, the keys are unique, and the mapreduce partitioner, sorter and grouper were using all the fields in the record. excluding fields for sorting avro gives us the ability to indicate that specific fields should be ignored when performing ordering functions. in mapreduce these fields are ignored for sorting/partitioning and grouping in mapreduce, which basically means that we have the ability to perform secondary sorting. let’s examine the following schema github source : {"type": "record", "name": "com.alexholmes.avro.weather", "doc": "a weather reading.", "fields": [ {"name": "station", "type": "string"}, {"name": "time", "type": "long"}, {"name": "temp", "type": "int", "order": "ignore"}, {"name": "counter", "type": "int", "order": "ignore", "default": 0} ] } it’s pretty much identical to the first schema, the only difference being that the last two fields are flagged as being “ignored” for sorting/partitioning/grouping. let’s run the same (other than modified to work with the different schema) mapreduce code github source as above against this new schema and examine the outputs. {"station": "iad", "time": 1, "temp": 1, "counter": 1} {"station": "sfo", "time": 1, "temp": 3, "counter": 1} {"station": "sfo", "time": 1, "temp": 2, "counter": 2} {"station": "sfo", "time": 1, "temp": 1, "counter": 3} {"station": "sfo", "time": 2, "temp": 1, "counter": 1} there are a couple of notable differences between this output, and the output from the previous schema which didn’t have any ignored fields. first, it’s clear that the temp field isn’t being used in the sorting, which makes sense since we specified that it should be ignored in the schema. however, more interestingly, note the value of the counter field. all records that had identical station and time values went to the same reducer invocation, evidenced by the increasing value of counter . this is essentially secondary sort! now, all of this greatness isn’t without some limitations: you can’t support two mapreduce jobs that use the same avro key, but have different sorting/partitioning/grouping requirements. although it’s conceivable that you could create a new instance of the avro schema and set the ignored flags for these fields yourself. the partitioner, sorter and grouping functions in mapreduce all work off of the same fields (i.e. they all ignore fields that set as ignored in the schema). this means that your options for secondary sorting are limited. for example, you wouldn’t be able to partition all stations to the same reducer, and then group by station and time. ordering uses a field’s ordinal position to determine its order within the overall set of fields to be ordered. in other words, in a two-field record, the first field is always compared before the second. there’s no way to change this behavior other than flipping the order of the fields in the record. having said all of that - the “ignoring fields” feature for sorting is pretty awesome, and something that will no doubt come in handy in my future mapreduce work.

You can easily tell Maven to include the version of the artifact and its Git/SVN/… revision in the JAR manifest file and then access that information at runtime via getClass().getPackage.getImplementationVersion(). (All credit goes to Markus Krüger and other colleagues.) Include Maven artifact version in the manifest (Note: You will actually not want to use it, if you also want to include a SCM revision; see below.) pom.xml: ... org.apache.maven.plugins maven-jar-plugin ... true true ... ... The resulting MANIFEST.MF of the JAR file will then include the following entries, with values from the indicated properties: Built-By: ${user.name} Build-Jdk: ${java.version} Specification-Title: ${project.name} Specification-Version: ${project.version} Specification-Vendor: ${project.organization.name Implementation-Title: ${project.name} Implementation-Version: ${project.version} Implementation-Vendor-Id: ${project.groupId} Implementation-Vendor: ${project.organization.name} (Specification-Vendor and Implementation-Vendor come from the POM’s organization/name.) Include SCM revision For this you can either use the Build Number Maven plugin that produces the property ${buildNumber}, or retrieve it from environment variables passed by Jenkinsor Hudson (SVN_REVISION for Subversion, GIT_COMMIT for Git). For git alone, you could also use the maven-git-commit-id-plugin that can either replace strings such as ${git.commit.id} in existing resource files (using maven’s resource filtering, which you must enable) with the actual values or output all of them into a git.properties file. Let’s use the buildnumber-maven-plugin and create the manifest entries explicitely, containing the build number (i.e. revision) org.codehaus.mojo buildnumber-maven-plugin 1.2 validate create false false org.apache.maven.plugins maven-jar-plugin 2.4 ${project.name} ${project.version} ${buildNumber} ... Accessing the version & revision As mentioned above, you can access the manifest entries from your code via getClass().getPackage.getImplementationVersion() andgetClass().getPackage.getImplementationTitle(). References SO: How to get Maven Artifact version at runtime? Maven Archiver documentation

In this blog post, I will present a simple tutorial on uploading a large file to Amazon S3 as fast as the network supports. Amazon S3 is clustered storage service of Amazon. It is designed to make web-scale computing easier. Amazon S3 provides a simple web services interface that can be used to store and retrieve any amount of data, at any time, from anywhere on the web. It gives any developer access to the same highly scalable, reliable, secure, fast, inexpensive infrastructure that Amazon uses to run its own global network of web sites. The service aims to maximize benefits of scale and to pass those benefits on to developers. For using Amazon services, you'll need your AWS access key identifiers, which AWS assigned you when you created your AWS account. The following are the AWS access key identifiers: Access Key ID (a 20-character, alphanumeric sequence) For example: 022QF06E7MXBSH9DHM02 Secret Access Key (a 40-character sequence) For example: kWcrlUX5JEDGM/LtmEENI/aVmYvHNif5zB+d9+ct Caution Your Secret Access Key is a secret, which only you and AWS should know. It is important to keep it confidential to protect your account. Store it securely in a safe place. Never include it in your requests to AWS, and never e-mail it to anyone. Do not share it outside your organization, even if an inquiry appears to come from AWS or Amazon.com. No one who legitimately represents Amazon will ever ask you for your Secret Access Key. The Access Key ID is associated with your AWS account. You include it in AWS service requests to identify yourself as the sender of the request. The Access Key ID is not a secret, and anyone could use your Access Key ID in requests to AWS. To provide proof that you truly are the sender of the request, you also include a digital signature calculated using your Secret Access Key. The sample code handles this for you. Your Access Key ID and Secret Access Key are displayed to you when you create your AWS account. They are not e-mailed to you. If you need to see them again, you can view them at any time from your AWS account. To get your AWS access key identifiers Go to the Amazon Web Services web site at http://aws.amazon.com. Point to Your Account and click Security Credentials. Log in to your AWS account. The Security Credentials page is displayed. Your Access Key ID is displayed in the Access Identifiers section of the page. To display your Secret Access Key, click Show in the Secret Access Key column. You can use your Amazon keys from a properties file in your application. Here is a sample for properties file containing Amazon keys: # Fill in your AWS Access Key ID and Secret Access Key # http://aws.amazon.com/security-credentials accessKey = secretKey = Here is sample AmazonUtil class for getting AWS Credentials from properties file. public class AmazonUtil { private static final Logger logger = LogUtil.getLogger(); private static final String AWS_CREDENTIALS_CONFIG_FILE_PATH = ConfigUtil.CONFIG_DIRECTORY_PATH + File.separator + "aws-credentials.properties"; private static AWSCredentials awsCredentials; static { init(); } private AmazonUtil() { } private static void init() { try { awsCredentials = new PropertiesCredentials(IOUtil.getResourceAsStream(AWS_CREDENTIALS_CONFIG_FILE_PATH)); } catch (IOException e) { logger.error("Unable to initialize AWS Credentials from " + AWS_CREDENTIALS_CONFIG_FILE_PATH); } } public static AWSCredentials getAwsCredentials() { return awsCredentials; } } Amazon S3 has Multipart Upload service which allows faster, more flexible uploads into Amazon S3. Multipart Upload allows you to upload a single object as a set of parts. After all parts of your object are uploaded, Amazon S3 then presents the data as a single object. With this feature you can create parallel uploads, pause and resume an object upload, and begin uploads before you know the total object size. For more information on Multipart Upload, review the Amazon S3 Developer Guide In this tutorial, my sample application uploads each file parts to Amazon S3 with different threads for using network throughput as possible as much. Each file part is associated with a thread and each thread uploads its associated part with Amazon S3 API. Figure 1. Amazon S3 Parallel Multi-Part File Upload Mechanism Amazon S3 API suppots MultiPart File Upload in this way: 1. Send a MultipartUploadRequest to Amazon. 2. Get a response containing a unique id for this upload operation. 3. For i in ${partCount} 3.1. Calculate size and offset of split-i in whole file. 3.2. Build a UploadPartRequest with file offset, size of current split and unique upload id. 3.3. Give this request to a thread and starts upload by running thread. 3.3.1. Send associated UploadPartRequest to Amazon. 3.3.2. Get response after successful upload and save ETag property of response. 4. Wait all threads to terminate 5. Get ETags (ETag is an identifier for successfully completed uploads) of all terminated threads. 6. Send a CompleteMultipartUploadRequest to Amazon with unique upload id and all ETags. So Amazon joins all file parts as target objects. Here is implementation: public class AmazonS3Util { private static final Logger logger = LogUtil.getLogger(); public static final long DEFAULT_FILE_PART_SIZE = 5 * 1024 * 1024; // 5MB public static long FILE_PART_SIZE = DEFAULT_FILE_PART_SIZE; private static AmazonS3 s3Client; private static TransferManager transferManager; static { init(); } private AmazonS3Util() { } private static void init() { // ... s3Client = new AmazonS3Client(AmazonUtil.getAwsCredentials()); transferManager = new TransferManager(AmazonUtil.getAwsCredentials()); } // ... public static void putObjectAsMultiPart(String bucketName, File file) { putObjectAsMultiPart(bucketName, file, FILE_PART_SIZE); } public static void putObjectAsMultiPart(String bucketName, File file, long partSize) { List partETags = new ArrayList(); List uploaders = new ArrayList(); // Step 1: Initialize. InitiateMultipartUploadRequest initRequest = new InitiateMultipartUploadRequest(bucketName, file.getName()); InitiateMultipartUploadResult initResponse = s3Client.initiateMultipartUpload(initRequest); long contentLength = file.length(); try { // Step 2: Upload parts. long filePosition = 0; for (int i = 1; filePosition < contentLength; i++) { // Last part can be less than part size. Adjust part size. partSize = Math.min(partSize, (contentLength - filePosition)); // Create request to upload a part. UploadPartRequest uploadRequest = new UploadPartRequest(). withBucketName(bucketName).withKey(file.getName()). withUploadId(initResponse.getUploadId()).withPartNumber(i). withFileOffset(filePosition). withFile(file). withPartSize(partSize); uploadRequest.setProgressListener(new UploadProgressListener(file, i, partSize)); // Upload part and add response to our list. MultiPartFileUploader uploader = new MultiPartFileUploader(uploadRequest); uploaders.add(uploader); uploader.upload(); filePosition += partSize; } for (MultiPartFileUploader uploader : uploaders) { uploader.join(); partETags.add(uploader.getPartETag()); } // Step 3: complete. CompleteMultipartUploadRequest compRequest = new CompleteMultipartUploadRequest(bucketName, file.getName(), initResponse.getUploadId(), partETags); s3Client.completeMultipartUpload(compRequest); } catch (Throwable t) { logger.error("Unable to put object as multipart to Amazon S3 for file " + file.getName(), t); s3Client.abortMultipartUpload( new AbortMultipartUploadRequest( bucketName, file.getName(), initResponse.getUploadId())); } } // ... private static class UploadProgressListener implements ProgressListener { File file; int partNo; long partLength; UploadProgressListener(File file) { this.file = file; } @SuppressWarnings("unused") UploadProgressListener(File file, int partNo) { this(file, partNo, 0); } UploadProgressListener(File file, int partNo, long partLength) { this.file = file; this.partNo = partNo; this.partLength = partLength; } @Override public void progressChanged(ProgressEvent progressEvent) { switch (progressEvent.getEventCode()) { case ProgressEvent.STARTED_EVENT_CODE: logger.info("Upload started for file " + "\"" + file.getName() + "\""); break; case ProgressEvent.COMPLETED_EVENT_CODE: logger.info("Upload completed for file " + "\"" + file.getName() + "\"" + ", " + file.length() + " bytes data has been transferred"); break; case ProgressEvent.FAILED_EVENT_CODE: logger.info("Upload failed for file " + "\"" + file.getName() + "\"" + ", " + progressEvent.getBytesTransfered() + " bytes data has been transferred"); break; case ProgressEvent.CANCELED_EVENT_CODE: logger.info("Upload cancelled for file " + "\"" + file.getName() + "\"" + ", " + progressEvent.getBytesTransfered() + " bytes data has been transferred"); break; case ProgressEvent.PART_STARTED_EVENT_CODE: logger.info("Upload started at " + partNo + ". part for file " + "\"" + file.getName() + "\""); break; case ProgressEvent.PART_COMPLETED_EVENT_CODE: logger.info("Upload completed at " + partNo + ". part for file " + "\"" + file.getName() + "\"" + ", " + (partLength > 0 ? partLength : progressEvent.getBytesTransfered()) + " bytes data has been transferred"); break; case ProgressEvent.PART_FAILED_EVENT_CODE: logger.info("Upload failed at " + partNo + ". part for file " + "\"" + file.getName() + "\"" + ", " + progressEvent.getBytesTransfered() + " bytes data has been transferred"); break; } } } private static class MultiPartFileUploader extends Thread { private UploadPartRequest uploadRequest; private PartETag partETag; MultiPartFileUploader(UploadPartRequest uploadRequest) { this.s3Client = s3Client; this.uploadRequest = uploadRequest; } @Override public void run() { partETag = s3Client.uploadPart(uploadRequest).getPartETag(); } private PartETag getPartETag() { return partETag; } private void upload() { start(); } } }

Avro has the ability to generate Java code from Avro schema, IDL and protocol files. Avro also has a plugin which allows you to generate these Java sources directly from Maven, which is a good idea as it avoids issues that can arise if your schema/protocol files stray from the checked-in code generated equivalents. Today I created a simple GitHub project called avro-maven because I had to fiddle a bit to get Avro and Maven to play nice. The GitHub project is self-contained and also has a README which goes over the basics. In this post I’ll go over how to use Maven to generate code for schema, IDL and protocol files. pom.xml updates to support the Avro plugin Avro schema files only define types, whereas IDL and protocol files model types as well as RPC semantics such as messages. The only difference between IDL and protocol files is that IDL files are Avro’s DSL for specifying RPC, versus protocol files are the same in JSON form. Each type of file has an entry that can be used in the goals element as can be seen below. All three can be used together, or if you only have schema files you can safely remove the protocol and idl-protocol entries (and vice-versa). org.apache.avro avro-maven-plugin ${avro.version} generate-sources schema protocol idl-protocol ... org.apache.avro avro ${avro.version} org.apache.avro avro-maven-plugin ${avro.version} org.apache.avro avro-compiler ${avro.version} org.apache.avro avro-ipc ${avro.version} By default the plugin assumes that your Avro sources are located in ${basedir}/src/main/avro, and that you want your generated sources to be written to ${project.build.directory}/generated-sources/avro, where ${project.build.directory} is typically the target directory. Keep reading if you want to change any of these settings. Avro configurables Luckily Avro’s Maven plugin offers the ability to customize various code generation settings. The following table shows the configurables that can be used for any of the schema, IDL and protocol code generators. Configurable Default value Description sourceDirectory ${basedir}/src/main/avro The Avro source directory for schema, protocol and IDL files. outputDirectory ${project.build.directory}/generated-sources/avro The directory where Avro writes code-generated sources. testSourceDirectory ${basedir}/src/test/avro The input directory containing any Avro files used in testing. testOutputDirectory ${project.build.directory}/generated-test-sources/avro The output directory where Avro writes code-generated files for your testing purposes. fieldVisibility PUBLIC_DEPRECATED Determines the accessibility of fields (e.g. whether they are public or private). Must be one of PUBLIC, PUBLIC_DEPRECATED or PRIVATE. PUBLIC_DEPRECATED merely adds a deprecated annotation to each field, e.g. "@Deprecated public long time". In addition, the includes and testIncludes configurables can also be used to specify alternative file extensions to the defaults, which are **/*.avsc, **/*.avpr and **/*.avdl for schema, protocol and IDL files respectively. Let’s look at an example of how we can specify all of these options for schema compilation. org.apache.avro avro-maven-plugin ${avro.version} generate-sources schema ${project.basedir}/src/main/myavro/ ${project.basedir}/src/main/java/ ${project.basedir}/src/main/myavro/ ${project.basedir}/src/test/java/ PRIVATE **/*.avro **/*.test As a reminder everything covered in this blog article can be seen in action in the GitHub repo at https://github.com/alexholmes/avro-maven.

In our previous article we have explained on how to protect the data while it is in transit through Transport Layer Security (TLS)/Secured Socket Layer (SSL). Now let us try to understand how to apply security mechanism for a JEE 6 based web application using LDAP server for authentication. Objective: • Configure a LDAP realm in the JEE Application Server • Apply JEE security to a sample web application. Products used: IDE: Netbeans 7.2 Java Development Kit (JDK): Version 6 Glassfish server: 3.1 Authentication Mechanism: Form Based authentication Authentication server: LDAP OpenDS v2.2 Apply JEE security to the sample web application: The JEE web applications can be secured either through Declarative security or Programmatic security. Declarative security can be implemented in JEE applications by using annotations or through deployment descriptor. This type of security mechanism is used when the roles and authentication process is simple, when it can make use of existing security providers (even external like LDAP, Kerberos). Programmatic security provides additional security mechanism when declarative security is not sufficient for the application in context. It is used when we require custom made security and when rich set of roles, authentication is required. Configure Realm in the Glassfish Application Server Before we configure a realm in the Glassfish Application server you will need to install and configure an LDAP server which we will be using for our project. You can get the complete instructions in the following article: “How to install and configure LDAP server”. Once the installation is successful start your Glassfish server and go to the admin console. Create a new LDAP Realm. Create new LDAP Realm Add the configuration settings as per the configurations set up done for the LDAP server. Glassfish Web App LDAP Realm JAAS Context – identifier which will be used in the application module to connect with the LDAP server. (e.g. ldapRealm) Directory – LDAP server URL path (e.g. ldap://localhost:389) Base DN: Distinguished name in the LDAP directory identifying the location of the user data. Applying JEE security to the web application Create a sample web application as per the following structure: SampleWebApp Directory Form based authentication mechanism will be used for authentication of the users. JEE Login and Authentication Let us explain the whole process with help of above diagram and the code. Set up a sample web application in Netbeans IDE. SampleWebApp in Netbeans IDE SampleWebApp Configuration Step 1: As explained in the above diagram a client browser tries to request for a protected resource from the websitehttp://{samplewebsite.com}/{contextroot}/index.jsp. The webserver goes into the web configuration file and figures out that the requested resource is protected. web.xml Code SecurityConstraint Secured resources /* GeneralUser Administrator NONE Step 2: The webserver presents the Login.jsp as a part of the Form based authentication mechanism to the client. These configurations are checked from the web configuration file. web.xml FORM ldapRealm /Login.jsp /LoginError.jsp Step 3: The client submits the login form to the web server. When the servers finds that the form action is “j_security_check” it processes the request to authenticate the client’s credential. The jsp form must contain the login elements j_username and j_password which will allow the web server to invoke the login authentication mechanism. Login.jsp username: password: While processing the request the webserver will send the authentication request to the LDAP server since LDAP realm is used in the login-config. The LDAP server will authenticate the user based on the username and password stored in the LDAP repository. Step 4: If the authentication is successful the secured resource (in this case index.jsp) is returned to the client and the container uses a session id to identify a login session for the client. The container maintains the login session with a cookie containing the session-id. The server sends this cookie back to the client, and as long as the client is able to show this cookie for subsequent requests, then the container easily recognize the client and hence maintains the session for this client. Step 5: Only if the authentication is unsuccessful the user will be redirected to the LoginError.jsp as per the configuration in the web.xml. /LoginError.jsp This shows how to apply form based security authentication to a sample web application. Now let us get a brief look on the secured resource which is used for this project. In this project the secured resource is index.jsp which accepts a username and forwards the request to LoginServlet. Login servlet dispatches the request to Success.jsp which then prints the username to the client. index.jsp Please type your name LoginServlet.java protected void processRequest(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { response.setContentType("text/html;charset=UTF-8"); PrintWriter out = response.getWriter(); try { RequestDispatcher requestDispatcher = getServletConfig().getServletContext(). getRequestDispatcher("/Success.jsp"); requestDispatcher.forward(request, response); } finally { out.close(); } } Success.jsp You have been successfully logged in as ${param.username} web.xml LoginServlet com.login.LoginServlet LoginServlet /LoginServlet You can download the complete working code from the below link. SampleWebApp-Code Download Hope our readers have enjoyed this article. Keep watching this space for more articles on JEE security.

With the release of ASP.NET MVC 4 one of the exciting features packed in the release was ASP.NET Web API.

A few days ago I was going through a project's Maven dependencies, removing unused junk, checking jar file version numbers adding a little dependency management and generally tidying up (yes, I know that this isn't something we often get time to do, but even Maven dependencies can be a form of technical debt). After recompiling and running the unit tests I ran some end to end tests only to find that the whole thing fell apart... Big time. The exception I got was the usual one that all Spring developers get, a java.lang.IllegalStateException: Failed to load ApplicationContext ...exception. This is nothing new and as a Spring developer you find the problem, which is usually a missing bean definition and move on. Only this time it was something different, and that's because the cause was: java.lang.NoSuchFieldError: NULL ...which gives you no clues about what's going wrong. Now I knew that I'd been messing around with the project's dependencies, so I must have broken something somewhere. It turned out that it was a transient dependency problem. I was using Spring Security version 3.1.1-RELEASE, which is built using version 3.0.7-RELEASE of the Spring core libraries and not as you'd expect version 3.1.1-RELEASE. This meant that I'd ended up with different and incompatible versions of some of the Spring libraries on my classpath. You may well wonder why the Guys at Spring Security build their code with version 3.0.7-RELEASE and they say that this is intentional and that it's to do with backwards compatibility issues. As Rob Winch, Spring Security Lead at SpringSource, says: "Spring Security uses 3.0.x (intentionally to support users that require it). For this reason, if you build with Maven and want to use Spring 3.1 you must either exclude the Spring dependencies in your maven pom, explicitly add the Spring 3.1 dependencies to your pom, or add a dependency management section to your pom. This is not a bug. Even if Spring Security was changed to use Spring 3.1 by default, the users using Spring 3.0 would encounter the same problem. The reason this occurs is due to the algorithm that Maven uses to resolve transitive dependency versions [1]" Once you know how, the problem is easy to spot. If you're using STS/eclipse you can easily examine Maven dependencies using the POM editor. The fix is simple too, all you need to do is to explicitly define the wayward Spring libraries in your POM. For example: org.springframework spring-core 3.1.1-RELEASE Finally, you can check that it's fixed using STS/eclipse's POM file editor, where you'll see that the unwanted version is now labelled as "omitted". [1] http://maven.apache.org/guides/introduction/introduction-to-dependency-mechanism.html#Transitive_Dependencies

If you’re uploading blobs by splitting blobs into blocks and you get the error – The specified blob or block content is invalid, then this post is for you. Short Version If you’re uploading blobs by splitting blobs into blocks and you get the above mentioned error, ensure that your block ids of your blocks are of same length. If the block ids of your blocks are of different length, you’ll get this error. Long Version Now for the longer version of this post . A few days back I was working with storage client library especially around uploading blobs in chunks and with one particular blob I was constantly getting the error – The specified blob or block content is invalid. I tried numerous combinations even resorting to REST API directly but to no avail. It only happened with just one blob. Furthermore if I uploaded the same blob without splitting it into blocks, all was well. I was at my wits’ end. Tried searching the Internet for this error but could not find a conclusive answer to my problem. After much trial and error, I was able to simulate the same problem on other blobs as well. Here’s how you can recreate it: Start uploading the blob by splitting it into blocks. For block id, let’s do a 7 character long string e.g. intValue.ToString(“d7”). This will ensure that my block ids would be “0000001”, “0000002”, …, ”0000010” ….. After one or two blocks are uploaded, cancel the operation. Now re-upload the blob by splitting it into blocks. However this time for block id, let’s do a 6 character long string e.g. intValue.ToString(“d6”). You’ll get the error as soon as you try to upload the 1st block. Possible Solutions Now that we know the root cause of this problem, let’s look at some of the possible solutions to solve this problem. Wait out One possible solution is to wait out. I know its lame but still a possible solution. We know that Windows Azure Blob Storage Service keeps all uncommitted blocks for a duration of 7 days and if within 7 days those uncommitted blocks are not committed, the storage service purges them. I wish storage service provided some mechanism to purge uncommitted blocks programmatically. Commit uncommitted blocks You could possibly commit the blocks which are in uncommitted state so that at least you get a blob (which would not be the blob we wanted to upload in the first place). You can then delete that blob and re-upload the blob by specifying block ids which are of same length. To fetch the list of uncommitted blocks, if you’re using REST API directly you can perform “Get Block List” operation and pass “blocklisttype=uncommitted” as one of the query string parameters. If you’re using storage client library (assuming you’re using the version 2.x of .Net storage client library), you can do something like the code below: private static List GetUncommittedBlockIds(CloudBlockBlob blob) { var sasUri = blob.GetSharedAccessSignature(new SharedAccessBlobPolicy() { SharedAccessExpiryTime = DateTime.UtcNow.AddMinutes(5), Permissions = SharedAccessBlobPermissions.Read, }); var blobUri = new Uri(string.Format("{0}{1}", blob.Uri, sasUri)); List uncommittedBlockIds = new List(); var request = BlobHttpWebRequestFactory.GetBlockList(blobUri, null, null, BlockListingFilter.Uncommitted, null, null); //request.Headers.Add("Authorization", using (var resp = (HttpWebResponse)request.GetResponse()) { using (var stream = resp.GetResponseStream()) { var getBlockListResponse = new GetBlockListResponse(stream); var blocks = getBlockListResponse.Blocks; foreach (var block in blocks.Where(b => !b.Committed)) { uncommittedBlockIds.Add(Encoding.UTF8.GetString(Convert.FromBase64String(block.Name))); } } } return uncommittedBlockIds; } A few things to keep in mind here: Microsoft.WindowsAzure.Storage.Blob namespace does not have the capability to get the list of uncommitted blocks. You would need to make use ofMicrosoft.WindowsAzure.Storage.Blob.Protocol namespace. Because we’re kind of invoking the REST API by executing an HttpWebRequest, I created a shared access signature on the blob so that I don’t have to create “Authorization” header. Fetch uncommitted blocks to see block id length You could fetch the list of uncommitted blocks just to find out the length of the block id used. You could then use that block id length for your new upload session and do the upload. Please see the code snippet above to find this information. Upload another blob with same name without splitting it into blocks You could also upload another blob with the same name without splitting it into blocks. It could very well be a zero byte blob. That way your uncommitted block list will be wiped clean. Then you could delete that dummy blob and re-upload the actual blob. A Few Words About Blocks Since we’re talking about blocks, I thought it might be useful to mention a few points about them: Blocks and block related operations are only applicable for “Block Blobs”. Duh!! You’ll get an error if you’re trying to do these operations on a “Page Blob”. For uploading large blobs, it is recommended that you split your blob into blocks. In fact if your blob size is more than 64 MB, then you have to split it into blocks. Minimum size of a block is 1 Byte and the maximum size of a block is 4 MB. It is recommended that you choose a block size based on your internet connectivity and number of parallel threads you want use to upload these blocks. A blob can be split into a maximum of 50000 blocks. It’s important to remember this limitation because you are reminded of this limit when you’re trying to upload 50001st block. The length of all the block ids must be same. So if you’re using an integer value to denote block id, you make sure that you pad that integer value with “0” so that you get same length. So you could do something likeint.ToString(“d6”). When passing the block id as a parameter, it must be Base64 encoded. While the order in which the blocks are uploaded is not important, the order is important when you commit the block list because that’s when the blob is constructed by the service. For example, let’s say you’re uploading a blob by splitting it into 5 blocks (with ids “000001”, “000002”, “000003”, “000004”, and “000005”). You could upload these blocks in any order – 000004, 000001, 000003, 000005, 000002 however when you commit the block list, ensure that the block ids are passed in proper order i.e. 000001, 000002, 000003, 000004, 000005. Summary That’s it for this post. I hope you’ve found this information useful. I spent considerable amount of time trying to fix this problem so I hope it will help some folks out. As always, if you find any issues with the post please let me know and I’ll fix it ASAP.

this month, my fellow it pro technical evangelists and i are authoring a new series of articles on 20 key scenarios with windows azure infrastructure services . check out the list of articles here: http://mythoughtsonit.com/2013/05/20-key-scenarios-with-windows-azure-infrastructure-services/ . web-based distributed authoring and versioning, or webdav, is a set of protocols based on http that allows end-users to map a network drive over http and edit content and files stored on the web server. when webdav was first offered on microsoft server i had evaluated it and decided it did not perform well enough for me. the webdav extension to iis was completely rewritten back in the server 2008 timeframe and is worth taking a look at again. in this article i will guide you step by step through the process of setting up webdav on server 2012 in a windows azure iaas environment. this will give you a solid performing file share on the internet over port 80 and the http protocol. first you need an azure account. you can setup a free trail of azure. details can be found here: http://mythoughtsonit.com/2013/04/step-by-step-guide-to-setting-up-a-windows-azure-free-trial/ second provision a server 2012 machine. watch a video of what to do here: third open port 80 to this new server: in the azure portal select your 2012 server and choose the “endpoints” tab on the top. click “add endpoint” at the bottom of the screen enter the endpoint information for port 80 to port 80 done. next we need to install the iis webserver and webdav. installing webdav on iis 8.0 start server manager and go to “add roles and features” under server roles – add the web server (iis) role click through the wizard until you come to the role services section. then find and select “webdav publishing” and “windows authentication” click next and then install when the install is finished you are ready to move on to the next section. configuring iis 8 for webdav after the installation finishes you need to configure the box for access. start the iis manager tool. choose the “default web site” on the left side. then click on “authentication” open the windows authentication option and enable it. open the “webdav authoring rules” create a webdav rule. i choose to allow all users access to all content. a better security practice is to limit what users can use the service. it’s your data so you decide. make sure webdav is enabled and that your access rule is set: that is it… now your ready to access your webdav file share! test and insure you can hit the web server by using your browser: because you opened port 80 and installed iis 8 you should see the default web page when you browse to your servers internet dns name. example: http://yourdomainname.cloudapp.net/ how to map a drive to your webdav server: there are two ways i use to connect to the webdav server how to map a drive to your webdav server from the win 8 gui: from windows explorer, right click on “computer” and select “map a network drive” map your network drive by entering the address to your server example: http://yourdomainname.cloudapp.net/ i selected “connect using different credentials” because my workstation was not joined to the server in anyway and i needed to use an account in the servers local sam database. hit “finish” and enter your credentials. now you will have a connected drive that you can access from windows explorer or any tool via the drive mapping. how to map a drive to your webdav server from a cmd box: 1. hit windows start and type: cmd 2. enter the command: net use [drive letter] [url] example: net use e: http://yourdomainname.cloudapp.net/

This post will be for WSO2 ESB Filter Mediator and it will be cover simple usecase with basic Filter Mediator functions. It can be used for XPath filtering of messages. There are two modes of operation Specifies the XPath (boolean expression), return true or false XPath will be matched against the regular expression return true or false Syntax mediator+ Usecase I have services call 'BusService' where I can give rootId (road name) and get list bus number that going on that root. In the same services it have some train deatils also. When client call busService it must give busService and also If client ask for train details rather bus system must give it also. Here is busServices calls request: root1 respond: root1Colombo Negombo Galle getTraingNo request: root1 respond: 12-Colombo 13-Muthu 01-Bange Now I have write simple WSO2 ESB proxy with filter mediator. 1. Download wso2 esb 4.6.0 2. Start wso2 esb /bin/wso2server.bat (offset 1) Other services expose in wso2 AS in offset 0 3. Go to https://localhost:9444/carbon/ 4. Then Create "Pass Through Proxy" 5. Here I am adding WSO2 Filter Mediator Specify As: XPath or a Regular expression. XPath: XPath expression if you selected the "Specify As" option to "XPath". Source: which is going match with the reguilar expression Regex: Regular expression to match with the source value. 6. In Here I am filtering for the action of the WS request and it log the client request is it bus or train request? Here is proxy Source View 6. Go to https://localhost:9444/services/transportProxy?tryit# Make bus request and train request and see console log You can improve this usecase with some WSO2 ESB mediator if you wish!!

apache camel with apache tomcat provides a low-cost and lightweight integration framework. is apache camel with apache tomcat a good fit for your project requirements? apache tomcat is known for it’s ease-of-use and minimal footprint when building servlet and javaserver page applications, while apache camel is known for supporting enterprise integration patterns, routing and mediation rules in a variety of domain-specific languages, including a java-based fluent api, spring or blueprint xml configuration files, and a scala dsl. developers and architects find a straightforward learning curve when using apache camel’s java based dsl, yet they find better tools exist when building simple connections or implementing large integration projects. see kai wahner’s writeup on lightweight frameworks . for larger integration projects requiring reliable messaging, scalability, eventing, business process execution, or web agent hosting, selecting an enterprise service bus provides a better fit . kai has another good article placing esb and integration suites in context. apache camel is often integrated with activemq, servicemix, or fuse to obtain additional capabilities required to deliver medium to complex integration projects. the wso2 esb team is looking to embrace the simplicity of apache camel (by incorporating the project similar to embedding apache cxf ), and extend with multi-tenancy, failover, performance, and scalability enhancements. similar to redhat jboss fuse, wso2 esb delivers service container clustering and reliable failover functions. in addition to extensive mediation primitives, the products provide service monitoring and management support not available in the basic apache camel with apache tomcat combination. to combat server proliferation, wso2 esb inherently supports multi-tenancy. the multi-tenancy goes beyond simple tomcat virtual domains by using osgi class loaders and security managers to provide adequate tenant isolation and separate administration console interfaces. a single wso2 esb instance can support multiple business units with appropriate data, logic, and execution isolation. springsource, mulesoft, and wso2 have extended apache tomcat to provide better server management and ability to install features within the integration platform. wso2 esb can install over 100+ features (e.g. business process execution, complex event execution, business activity monitoring) into the integration platform. from a performance perspective, apache camel with apache tomcat depends on the tomcat transport to provide high performant message transfer. the wso2 esb pass through transport and binary relay transports are optimized to provide the best streaming, non-blocking performance by tightly integrating the transport and mediation layers. camel + tomcat depends on what ever the tomcat transport support but i believe esb pt and nhttp transports are preforming efficiently here but i also don’t have any reference. if you install apache camel on top of apache tomcat then you are not going to get the same performance and scalability. the latest esb performance benchmarks are posted for reference and replication.

This blog does not try to compare all available Web Services Development Frameworks but focuses only on three popular approaches