Spring Integration (SI) is a framework enabling a collection of individual applications to integrate together to deliver a business enterprise system. The framework is essentially a lightweight messaging system that enables spring based applications to communicate with one another and supports integration with external systems via declarative adaptors. It is based on the 'filters and pipes' design architecture. A key feature of it is that it achieves this integration in a minimally intrusive way. The framework is built on 3 main components: Messages Encapsulate the data to be transferred from one place to another. They comprise of a header (holds meta data such as message-id, timestamp, etc) and a payload (your data typically in the form of a POJO). Channels Provide a mechanism to transport messages from one endpoint to another. Represents the pipes in the pipes & filters architecture. SI offers two types of channels, namely Pollable and Subscribable Channels. The former rely on consumers to periodically check for messages whereas the latter is directly responsible for notifying registered consumers when messages become available. Endpoints Consumer/Producer of messages. Performs some action based on the payload. Endpoints come in various flavours, each performing a different function. These include Transformers (transform data), Routers (route data), Filters (filter data), Splitter (splits messages), Aggregator (aggregates group of messages into single message), Service Activator (connecting messages to Services) and Channel Adapters (connect channels to external applications). The basic idea behind the SI framework is that applications communicate with each other by sending/receiving messages. These messages would typically contain the information (payload) required by the next application in the process pipeline. The transport of messages from one application to another is performed by Channel components. The Endpoints perform some action based on the payload. This could be routing the messages to another endpoint or processing the payload itself. The objective of this post is to provide an introduction to Spring Integration. To help achieve this, I developed a sample application which will be discussed below. The source for this sample application is available at here. The project was built and run using spring-integration-4.0.0, maven 3.2.1 and jdk1.6. The main dependency is for the relevant spring-integration jar as declared in the pom.xml: org.springframework.integration spring-integration-stream 4.0.0.RC1 I ran the application using the maven exec plugin. This allows me to clean, package and run the application by invoking mvn clean package exec:java -P OnlineShop from the command line. Developing a sample application: Tabernus My goal as usual was to build something very simple which would help me to become familiar with key concepts of this framework and to this end I've knocked up a simple app which does not connect up individual systems but rather invokes methods on a POJO. Extending this to actual working applications shouldn't be too difficult. The scenario I'm going to model revolves around purchasing items from an online store (Tabernus). This store only sells 3 types of items: Books, Music CDs, and software. During a Sale, the owners have decided to apply different discounts based on the item type. In this instance books, music and software benefit from discounts of 5%, 10%, and 15% respectively. The following diagram shows our domain entities. The class diagram shows that a Customer can place an Order comprising of a number of OrderItems which are of type Book, MusicCD or Software. The problem I need to solve is to design a system which can interrogate each Order and apply the correct discount based on the item type. Subsequently it should be able to compute the total cost of the order once the discounts have been applied. To model this using Spring Integration we need the following pipeline The above diagram shows various components most of which can be divided into 2 categories, channels (blue cylinder shapes) and endpoints (rectangular boxes). The exception to this is the Poller component whose purpose is to enable the various endpoints to function correctly and discussion of it will be given later. We'll start off by briefly covering the various stages in this pipeline as indicated by the numbers in red. Following this we will delve deeper into how we build this pipeline using the SI framework. The pipeline is comprised of 6 major stages as reflected by the numbers in the diagram, The Gateway component represents the entry point to the messaging system. All new Orders will be submitted to this component which will in turn wrap them as messages and place them into the channel appropriately named ordersChannel. Using the Splitter component - each Order is decomposed into a collection of it's constituent OrderItem instances. Each of these is wrapped in a Message and placed in the orderItemsChannel. The Router component considers each OrderItem in turn and places it in the relevant channel, e.g. Book items will be placed in the bookItemsChannel etc. This allows us to consider the different item types separately. The ServiceActivator needs to consider messages within each of the 3 channels and calculate the correct discount based on the channel. After completing the calculation for each OrderItem, it will place the OrderItem in the processedItemsChannel. The Aggregator component will collect all OrderItem instances placed in the processedItemsChannel and reconstruct the original Order. This will subsequently be placed in the deliveriesChannel, which represents the end of the pipeline. The Poller Component is required to configure how often the various endpoints will interrogate their respective input channels for messages. To implement the pipeline shown above using the SI framework, we need to implement the various end points. configure the pipeline in an xml file (Shop.xml) - identifying the various channels and endpoints and how they wire up together. At this point I should mention that SI offers 2 approach to configuring your process pipeline, annotations based and xml. In this article I'll be using the latter. Let's start to look at some code. We'll consider each stage described above and show the java implementation of the endpoint and xml configuration required to wire up the components. Step 1 - Gateway To begin with, we need to implement the Client that will invoke the Gateway component to place the Order. The client (OnlineShop.java) is shown below, public class OnlineShop { public static void main(String[] args) { AbstractApplicationContext context = new ClassPathXmlApplicationContext("/META-INF/com/prodcod/shop.xml", OnlineShop.class); Shop shop = (Shop) context.getBean("shop"); final Order order = createOrder(); shop.placeOrder(order); context.close(); } The logic here is quite simple. The client creates a dummy Order and passes this as an argument when it invokes the placeOrder() method on the gateway component. The gateway component referred here as Shop is injected by Spring. The Gateway component looks like: // Gateway component public interface Shop { @Gateway(requestChannel="ordersChannel") void placeOrder(Order order); } As you can see, this is simply an interface, whose implementation will be provided by Spring when it is injected into the client application. This is achieved by the use of the @Gateway annotation which informs Spring that this is a Gateway component and it needs to provide the implementation. Additionally the annotation accepts an attribute, requestChannel which defines the channel on which the Order instance will be placed. The framework does this by simply wrapping our instance of Order within a Message instance and placing it in the channel, 'ordersChannel'. The Gateway component and the 'ordersChannel' are declared as follows in the file shop.xml Step 2 - Splitter The next end point is the Splitter component. Appropriately named, it's role is to take a single message containing a payload of a collection of items and splitting it into a number of messages, each of which contains a single element from the collection. In our case, we want to decompose the Order into it's constituent OrderItem instances. It does this by taking a Message containing the payload of Order from 'ordersChannel' and then processing it before sending messages (each containing an OrderItem instance) to the 'orderItemsChannel'. Our implementation of the splitter is called OrderSplitter and is defined as below, public class OrderSplitter extends AbstractMessageSplitter{ @Override protected Object splitMessage(Message message) { return ((Order)message.getPayload()).getOrderItems(); } } Implementing a splitter is quite easy and involves extending the AbstractMessageSplitter class and overriding the splitMessage() method. This simply takes a message containing the payload of Order and returns it's collection of OrderItems. Step 3 - Router Having decomposed the Order into it's constituent OrderItems, we now need to separate them into groups of Books, MusicCD, and Software. This is achieved using a router. Our implementation of the Router looks like, public class OrderItemRouter { public String routeOrder(OrderItem orderItem) { String channel = ""; if(isBook(orderItem)) { channel = "bookItemsChannel"; } else if(isMusic(orderItem)) { channel = "musicItemsChannel"; } else if(isSoftware(orderItem)) { channel = "softwareItemsChannel"; } return channel; } ..................... ..................... } Nothing too complicated here. For each OrderItem, the method routeOrder() will determine it's item type and return the name of the channel that this message should be sent to. The channel name is returned by the method. Spring will then ensure that the message containing the OrderItem is relayed to the named channel. The configuration for OrderItemRouter looks like, The config identifies that the class OrderItemRouter is a Router component which will consume messages from the orderItemsChannel. Further Spring needs to invoke the method routeOrder() which contains the logic to perform the routing. The channels for each item type are declared as follows Step 4 - ServiceActivator The next step is to calculate the discounted price for each item type and this is performed by a ServiceActivator component. This is implemented as follows public class Shopkeeper { private static final BigDecimal BOOK_DISCOUNT = new BigDecimal(0.05); private static final BigDecimal MUSIC_DISCOUNT = new BigDecimal(0.10); private static final BigDecimal SOFTWARE_DISCOUNT = new BigDecimal(0.15); /** * Performs discount on books * @param bookOrderItem OrderItem comprising of a book item * @return OrderItem with discount price newly calculated */ public OrderItem processBooks(OrderItem bookOrderItem){ final BigDecimal finalPrice = calculateDiscountedPrice(bookOrderItem, BOOK_DISCOUNT); bookOrderItem.setDiscountedPrice(finalPrice); return bookOrderItem; } /** * Performs discount on music * @param musicOrderItem OrderItem comprising of a music item * @return OrderItem with discount price newly calculated */ public OrderItem processMusic(OrderItem musicOrderItem){ final BigDecimal finalPrice = calculateDiscountedPrice(musicOrderItem, MUSIC_DISCOUNT); musicOrderItem.setDiscountedPrice(finalPrice); return musicOrderItem; } /** * Performs discount on software * @param softwareOrderItem OrderItem comprising of a book item * @return OrderItem with discount price newly calculated */ public OrderItem processSoftware(OrderItem softwareOrderItem){ final BigDecimal finalPrice = calculateDiscountedPrice(softwareOrderItem, SOFTWARE_DISCOUNT); softwareOrderItem.setDiscountedPrice(finalPrice); return softwareOrderItem; } } This class exposes 3 methods to compute the new discounted price for each item type. Each method returns the OrderItem instance with the new price. The ServiceActivator is configured as follows: This tells Spring that the Shopkeeper class is a ServiceActivator and will consume messages from any of the 3 channels defined in the input-channel attribute. When a message appears in one of these channels, Spring will invoke the appropriate method on the ServiceActivator class as specfied by the attribute method. Anything returned from all three methods will be placed in the processedItems channel, ready for the next step of the processing pipeline. Step 5 - Aggregator The final stage is to take the individual OrderItems with their newly computed discounted prices and reconstruct the Order. This is achieved using an aggregator. Our implementation of an aggregator is listed below public class OrderCompleter { public Order prepareDelivery(List orderItems) { final Order order = new Order(); order.setOrderItems(orderItems); return order; } } The aggregator exposes a method that takes a collection of OrderItem objects. These will come from the processedItems channel declared as Recall this is the output channel for the service activator class as discussed above. The aggregator is configured in the xml file as The configuration tells Spring that the aggregator component will consume messages from the processedItems channel. These will be processed by the method prepareDelivery on the class OrderCompleter. Any output from this class will be relayed to the channel-adaptor deliveries, which is declared as The stdout-channel-adapter component writes to the systems STDOUT output stream. Step 6 - Poller To complete the setup we have to configure a poller component. This is required to enable the channels to work correctly. All our channels are of a queue type and so their respective consumers need to know when to query them. This is achieved using a poller mechanism. It is configured in the following way In this case, we have declared a global poller (as indicated by the default attribute). This will be used by the various end points to determine when they should interrogate their respective input-channels for messages. The second attribute fixed-delay is used to configure the polling interval. Running the Application Building and running the app shows the following output: The logging shows that the Customer submitted an Order for 3 items, one of each type. All items cost £100 each. The Order was then split into 3 OrderItems each of which was routed to the correct processing channel based on the item type. The ServiceActivator (Shopkeeper) then calculated the discount for each item and this was set on the OrderItem instance. The OrderItems were then aggregated using the OrderCompleter class which displays the final discounted price of £270 to be paid by the Customer. Note that the messages are logged to be in different stages of the processing pipeline despite starting off in the same order. This completes the tutorial on the Spring Integration Framework. Any comments relating to corrections, omissions, etc are welcome.

You should by now have worked your way through Part 1: Getting Started with Ansible. If you haven't, go and do that now. In this article, I'll be demonstrating a very simple application deployment workflow, deploying an insanely simple node.js application from a github repository, and configuring it to start with supervisord, and be reverse-proxied with Nginx. As with last time, we'll be using Parallax as the starting point for this. I've actually gone through and put the config in there already (if you don't feel like doing it yourself ;) - name: Install all the packages and stuff required for a demobox hosts: demoboxes user: user sudo: yes roles: - redis - nginx - nodejs - zeromq # - deploy_thingy In the 9c818d0b8f version, you'll be able to see that I've created a new role, inventively called "deploy_thingy". **Updated** I've been recommended that my __template__ role be based on the output of ansible-galaxy init $rolename So I've recreated the __template__ role to be based on an ansible-galaxy role template. There's not that many changes, but it does include a new directory 'default/' containing the Galaxy metadata required if you wish to push back to the public galaxy role index. In an attempt to make creating new roles easier, I put a __template__ role into the file tree when I first created Parallax, so that all you do to create a new role is execute: cp -R __template__ new_role_name in the roles/ directory. . ├── files │ ├── .empty │ ├── thingy.nginx.conf │ └── thingy.super.conf ├── handlers │ ├── .empty │ └── main.yml ├── meta │ ├── .empty │ └── main.yml ├── tasks │ └── main.yml └── templates └── .empty In this role, we define some dependencies in meta/main.yml, there's two files in the files/ directory, and there's a set of tasks defined in tasks/main.yml. There's also some handlers defined in handlers/main.yml. Let's have a quick glance at the meta/main.yml file. --- dependencies: - { role: nodejs } - { role: nginx } This basically sets the requirement that this role, deploy_thingy depends on services installed by the roles: nginx and nodejs. Although these roles are explicitly stated to be installed in site.yml, this gives us a level of belt-and-braces configuration, in case the deploy_thingy role were ever included without the other two roles being explicitly stated, or if it were configured to run before its dependencies had explicitly been set to run. tasks/main.yml is simple. --- - name: Create directory under /srv for thingy file: path=/srv/thingy state=directory mode=755 - name: Git checkout from github git: repo=https://github.com/tomoconnor/shiny-octo-computing-machine.git dest=/srv/thingy - name: Drop Config for supervisord into the conf.d directory copy: src=thingy.super.conf dest=/etc/supervisor/conf.d/thingy.conf notify: reread supervisord - name: Drop Reverse Proxy Config for Nginx copy: src=thingy.nginx.conf dest=/etc/nginx/sites-enabled/thingy.conf notify: restart nginx We'll create somewhere for it to live, check the code out of my git repository [1], Then drop two config files in place, one to configure supervisor(d), and one to configure Nginx. Because the command to configure supervisor(d) and nginx change the configuration of those services, there are notify: handlers to reload the configuration, or restart the service. Let's have a quick peek at those handlers now: --- - name: reread supervisord shell: /usr/bin/supervisorctl reread && /usr/bin/supervisorctl update - name: restart nginx service: name=nginx state=restarted When the supervisor config changes (and we add something to /etc/supervisor/conf.d), we need to tell supervisord to re-read it's configuration files, at which point, it will see the new services, and then run supervisorctl update, which will set the state of the newly added items from 'available' to 'started'. When we change the nginx configuration, we'll hit nginx with a restart. It's possible to do softer actions, like reload here, but I've chosen service restart for simplicity. I've also changed the basic Ansible config, and configuration of roles/common/files/insecure_sudoers so that it will still ask you for a sudo password in light of some minor criticism. I've found that if you're developing Ansible playbooks on an isolated system, then there's no great harm in disabling SSH Host Key Checking (in ansible.cfg), similarly how there's no great problems in disabling sudo authentication, so it's effectively like NOPASSWD use. However, Micheil made a very good point that in live environments it's a bit dodgy to say the least. So I've commented those lines out of the playbook in Parallax, so that it should give users a reasonable level of basic security. At the end of the day, it's up to you how you use Parallax, and if you find that disabling security works for you, then fine. It's not like you haven't been warned. But I digress. The next thing to do is to edit site.yml, and ensure that the new role we've created gets mapped to a hostgroup in the play configuration. In the latest version of Parallax this is already done for you, but as long as the role name in the list matches the directory in roles/, it should be ready to go. Now if we run: ansible-playbook -k -K -i playbooks/example/hosts playbooks/example/site.yml It should go through the playbook, installing stuff, then finally do the git clone from github, deploy the configuration files, and trigger a reread of supervisord, and a restart of nginx. If I now test that it's working, with: curl -i http://192.168.20.151/ HTTP/1.1 200 OK Server: nginx/1.4.1 (Ubuntu) Date: Mon, 27 Jan 2014 14:51:29 GMT Content-Type: text/html; charset=utf-8 Content-Length: 170 Connection: keep-alive X-Powered-By: Express ETag: "1827834703" That X-Powered-By: Express line shows that Nginx is indeed working, and that the node.js application is running too. You can get more information about stuff that supervisord is controlling by running: sudo supervisorctl status on the target host. $ sudo supervisorctl status thingy RUNNING pid 19756, uptime 0:00:06 If the Nginx side is configured, but the node.js application isn't running, you'd get a HTTP 502 error, as follows: curl -i http://192.168.20.151/ HTTP/1.1 502 Bad Gateway Server: nginx/1.4.1 (Ubuntu) Date: Mon, 27 Jan 2014 14:59:34 GMT Content-Type: text/html Content-Length: 181 Connection: keep-alive So, that's it. A very simple guide to deploying a very simple application with Ansible. Of course, it should be obvious that you can deploy *anything* from a git repository, it really boils down to the configuration of supervisord. For that matter, it doesn't have to be supervisord. I consider configuring supervisord for process controlling to be outside of the scope of this article, but I might touch on it in future in more detail. Next up, Part 3: Ansible and Amazon Web Services. 1: It's really simple, and I'm not very node-savvy, so I'm sorry if it sucks.

In this blog, we will discuss one particular data grid platform from Redhat namely JBoss Data Grid (JDG). We will firstly cover how to access and install this data grid platform and then we will demonstrate how to develop and deploy a simple remote client/server data grid application which utilises the HotRod protocol. We will be using the latest release JDG 6.2 from Redhat in this article. Installation Overview To start using JDG, firstly log on to the redhat site https://access.redhat.com/home and download the software from the Downloads section of the site. We wish to download JDG 6.2 server by clicking on the appropriate links in the Downloads section. For future reference, it is also useful to download the quickstart and maven repository zip files. To install JDG, we simply unzip the JDG server package into an appropriate directory in your environment. JDG Overview In this section, we will provide a brief overview of the contents of the JDG installation package and the most notable configuration options available to users. Out of the box, users are provided with two runtime options either to run JDG in standalone or clustered mode. We can start JDG in either mode by invoking the stanadalone or clustered start up scripts in the / bin directory. To configure the JDG in either mode we need to configure the files standalone.xml and clustered.xml. In our case we will creating a distributed cache which will run on 3 node JDG cluster so we will be utilizing the clustered startup script. In order to set up and add new cache instances to JDG, we modify the infinispan subsystems in the appropriate xml configuration file above. We should also note the principal difference between the standalone and clustered configuration file is that in the clustered configuration file there is a JGroups subsystem configured element which allows for communication and messaging between configured cache instances running in a JDG cluster. Development Environment Setup and Configuration In this section, we will detail how to develop and configure a simple datagrid application which will be deployed to a 3 node JDG cluster. We will demonstrate how to configure and deploy a distributed cache in JDG and also show how to develop a HotRod Java client application which will be used to insert, update and display entries in the distributed cache. We will firstly discuss setting a new distributed cache on a 3 node JDG cluster. In this example, we will run our JDG cluster on a single machine by running each JDG instance on different ports. Firstly, we will create 3 instances of JDG by creating 3 directories (server1, server2, server3) on our host machine and unzipping each JDG installation into each directory. We will now configure each node in our cluster by copying and renaming the clustered.xml configuration file in the \server1\jboss-datagrid-6.2.0-server\standalone\configuration directory. We will name each of the cluster configuration files as "clustered1.xml", "clustered2.xml" and "clustered3.xml" for the JDG instances denoted by "server1", "server2" and "server3" respectively. We will now set up a new distributed cache on our JDG cluster by modifying the infinispan subsystem element in each clustered.xml file. We will demonstrate this for the node denoted "server1" here by modifying the file "clustered1.xml". The cache configuration shown here will be the same across all 3 nodes. To setup a new distributed cache named "directory-dist-cache", we configure the following elements in the file named "clustered1.xml" ......... ...... .............. ...... ...... /socket-binding-group> We will discuss the key elements and attributes relating to the configuration above. In the infinispan endpoint subsystem, we will configure hotrod clients to connect to the JDG server instance on socket 11222. The name of the cache container to host each of the cache instances will be held in the container named "clusteredcache". We have configured the infinispan core subsystem to the default cache container named "clusteredcacahe" whereby we will allow for jmx statistics to be collected relating the configured cache entries i.e statistics="true" We have created a new distributed cache named "directory-dist-cache" whereby there will be two copies of each cache entry held on two of the 3 cluster nodes. We have also set up an eviction policy whereby should there be more than 20 entries in our cache then cache entries will be removed using the LRU algorithm We should have configured nodes "server2" and "server3" to start up with a port offset of 100 and 200 respectively by configuring the socketing binding group element appropriately. Please view the socket bindings noted below. To set the socket binding element with a port offset of 100 on "server2", we configure "clustered2.xml" with the following entry: ...... ...... /socket-binding-group> To set the socket binding element with a port offset of 200 on "server3", we configure "clustered3.xml" with the following entry: ...... ...... /socket-binding-group> Before discussing the setup and configuration of our Hotrod client which will be used to interact with our JDG clustered HotRod server, we will start up each server instance to ensure our newly configured JDG distributed cache starts up correctly. Open up 3 Windows or Linux consoles and execute the following start up commands: Console 1: 1) Navigate to \server1\jboss-datagrid-6.2.0-server\bin 2) Execute this command to start the first instance of our JDG cluster denoted "server1": clustered -c=clustered1.xml -Djboss.node.name=server1 Console 2: 1) Navigate to \server2\jboss-datagrid-6.2.0-server\bin 2) Execute this command to start the second instance of our JDG cluster denoted "server2": clustered -c=clustered2.xml -Djboss.node.name=server2 Console 3: 1) Navigate to \server3\jboss-datagrid-6.2.0-server\bin 2) Execute this command to start the third instance of our JDG cluster denoted "server3": clustered -c=clustered3.xml -Djboss.node.name=server3 Providing all 3 JDG instances have started up correctly, you should see output in the console window whereby we can see there are 3 JDG instances in the JGroups view: HotRod Client Development Setup Now that the Hotrod server is up and running, we need to develop a Hotrod Java client which will interact with the clustered server application. The development environment consists of the following tools. 1) JDK Hotspot 1.7.0_45 2) IDE - Eclipse Kepler Build id: 20130919-0819 The HotRod client application is a simple application consisting of two Java classes. The application allows users to retrieve a reference to the distributed cache from the JDG server and then perform these actions: a) add new cinema objects. b) add and remove shows to each cinema object. c) print the list of all cinemas and shows stored in our distributed cache. The source code can be downloaded from github @ https://github.com/davewinters/JDG. We could use maven here to build and execute our application by configuring the maven settings.xml to point to the maven repository files we downloaded earlier and set up a maven project file (pom.xml) to build and execute the client application. In this article we will build our application using the Eclipse IDE and run the client application on the command line. To create a HotRod client application and execute the sample application, one should complete the following steps: 1) Create a new Java Project in Eclipse 2) Create a new package named uk.co.c2b2.jdg.hotrod and import the source code that has been downloaded from Github mentioned previously. 3) Now we need to configure the build path in Eclipse to contain the appropriate JDG client jar files which are required to compile the application. You should include all the client jar files in the project build path. These jar files are contained in the JDG installation zip file. For example on my machine these jar files are located in the directory: \server1\jboss-datagrid-6.2.0-server\client\hotrod\java 4. Providing the Eclipse build path has been configured appropriately, the application source should compile without issue. 5. We will need to execute the Hotrod application by opening the console window and executing the following command. Note the path specified here will differ depending on where the JDG client jar files and application class files are located in your environment: java -classpath ".;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\commons-pool-1.6-redhat-4.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\infinispan-client-hotrod-6.0.1.Final-redhat-2.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\infinispan-commons-6.0.1.Final-redhat-2.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\infinispan-query-dsl-6.0.1.Final-redhat-2.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\infinispan-remote-query-client-6.0.1.Final-redhat-2.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\jboss-logging-3.1.2.GA-redhat-1.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\jboss-marshalling-1.4.2.Final-redhat-2.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\jboss-marshalling-river-1.4.2.Final-redhat-2.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\protobuf-java-2.5.0.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\protostream-1.0.0.CR1-redhat-1.jar" uk/co/c2b2/jdg/hotrod/CinemaDirectory 6. The Hotrod client at runtime provides the end user with a number of different options to interact with the distributed cache as we can view from the console window below. Client Application Principal API Details We will not provide a detailed overview of the Hotrod application code however we will describe the principal API and code details briefly. In order to interact with the distributed cache on the JDG cluster using the Hotrod protocol, we will use the RemoteCacheManager Object which will allow us to retrieve a remote reference to the distributed cache. We have initialised a Properties object with the list of JDG instances and the associated with HotRod server port on each instance. We can add Cinema objects into the distributed cache using the RemoteCache.put() method. private RemoteCacheManager cacheManager; private RemoteCache cache; ..... Properties properties = new Properties(); properties.setProperty(ConfigurationProperties.SERVER_LIST, "127.0.0.1:11222;127.0.0.1:11322;127.0.0.1:11422"); cacheManager = new RemoteCacheManager(properties); cache = cacheManager.getCache("directory-dist-cache"); ..... cache.put(cinemaKey, cinemalist); In the webinar below, I describe in further detail how to set up a JDG cluster and how to develop and run the JDG application discussed above. For further details on JDG please visit: http://www.redhat.com/products/jbossenterprisemiddleware/data-grid/ Webinar: Introduction to JBoss Data Grid -- Installation, Configuration and Development In this webinar we will look at the basics of setting up JBoss Data Grid covering installation, configuration and development. We will look at practical examples of storing data, viewing the data in the cache and removing it. We will also take a look at the different clustered modes and what effect these have on the storage of your data:

a powerful multi-format xml schema (xsd) documentation generator and a tool for rapid development of custom xsd documentation generators according to user needs. about docflex/xml "xsddoc" template set template processor template designer integrations generation of xsd diagrams apache ant & maven links about docflex/xml docflex/xml is a java-based software system for development and execution of high performance template-driven documentation generators from any data stored in xml files. the actual doc/report generators are programmed in the form of special templates using a graphic template designer , which represents the templates visually in a form resembling the output they generate. further, the templates are interpreted by a template processor , which takes on input the xml files and produces by them the result documentation. this article describes an application of docflex/xml for the task of generation of high-quality xml schema documentation. that includes the following features of docflex/xml system: " xsddoc " template set that implements the ready-to-use xml schema documentation generator itself. template processor makes the templates works. currently, it provides three interchangeable output generators for html, rtf, txt (plain text) formats. template designer provides a high quality gui to design/modify templates. if you need a special xml schema doc generator, the simplest way to create it is to modify the standard xsddoc templates. the template designer enables you to do that. integrations with altova xmlspy and oxygen xml editor . if you are a user of one of those popular xml editors, you can turn it also into a dynamically linked diagramming engine for docflex, so that to include automatically the xsd diagrams generated by xmlspy/oxygenxml into the xml schema documentation generated by docflex (with the full support of hyperlinks). "xsddoc" template set it is the implementation of xml schema documentation itself, which provides the following functionality: generation of single documentation by any number of xml schema (xsd) files together, in particular: highly navigable framed (javadoc-like) html documentation single-file html documentation rtf documentation (further convertible to pdf) processing of any referenced xml schemas, in particular: correct processing of all , , elements found across all involved xsd files. automatic loading and processing (i.e. inclusion in the documentation scope) all directly/indirectly referenced xsd files. sophisticated documenting of xsd components , including: component diagrams (with hyperlinks to everything depicted on them; see also integrations ) xml representation summary (a textual alternative to diagrams) lists of related components. for elements this includes also the list of possible containing elements . (such a list is never present in the output generated by xslt-based doc generators). list of usage locations support of any xml schema design patterns . this comes down mainly to the following: special treatment of local elements (see below) support and documenting of substitution groups support of importing, inclusion and redefinition of schema files special documenting of local elements . local elements are those components that are declared locally within other xsd components. w3c xml schema spec allows you to declare any number of local elements that may share the same name but have different content. that's because their meaning is local and there will be no collisions with other declarations. that, however, creates a problem for documenting, because in a documentation both global and local elements may appear simultaneously in various lists according to their common properties. if each element component is identified only by its name, you will get the lists with multiple repeating names but little clue what they mean. moreover, some xml schemas may contain lots of identical local element declarations (that is, they have the same both name and content). so, you'll get in those lists a mess of repeating names, some of which referencing to effectively the same entities, whereas others to complete different ones. in xsddoc , those problems are solved in two ways: adding extensions to local element names. the extension provides more information about the element (e.g. where it can be inserted or its global type or where it is defined). that makes the whole string identifying the element unique. here is how it looks. the grey text is the name extension: unifying local elements by type. on the left you can see a documentation generated with such unification. on the right, all local elements are documented straight as they are. click on each screenshot to view the docs: we believe the first documentation (on the left) is easier to understand and use. processing of xhtml markup . you can format your xml schema annotations with xhtml tags, which will be recognized and rendered with the appropriate formatting in both html and rtf output, as shown on the following screenshots (click to see more details): here, on the left you can see the xml source of an xml schema, whose annotations are heavily laden with xhtml markup (including insertion of images). the next is the html documentation generated by that schema. on the right is a page of rtf documentation also generated by that schema. possibility of unlimited customization : xsddoc is controlled by more than 400 parameters, which allow you to adjust the generated documentation within huge range of included details. template parameters serve the same role as options in traditional doc generators. the difference is that docflex template architecture makes the support/implementation of template parameters very cheap (typically, the most of efforts takes writing their descriptions). so, there may be hundreds of parameters controlling a large template application. if parameters are not enough, you can modify the templates themselves using the template designer . in case of html output, you can also apply your own css styles to change how the generated documentation looks. template processor the template processor (also called simply "generator") makes everything work. it consists of two logical parts: 1. template interpreter 2. output generator the output generator actually has three different implementations for each currently supported output format: html, rtf, txt (plain text). the plain-text output can be used to generate documentation in formats not supported directly by docflex. the template processor is started directly from java command line with the following arguments: ● main template ● template parameters ● initial xsd files to be processed (documented) ● xml catalogs (to redirect physical location of input files) ● destination directory/file ● output format (this selects which output generator will be used) ● output format options (specify settings to control the selected output generator) actually, the number of settings may be so large that the template processor provides a special gui to specify everything interactively (click to enlarge): template designer although docflex templates are stored as plain-text files (with an xml-like format), they are not supposed for editing manually. rather, a special graphic template designer must be used, which visualizes the templates in the form of template components they are made of. those components are the actual constructs of the template language (not some textual statements, operators, blocks etc.) the following screenshots show templates open in the template designer (click to see a lot more): that approach has a number of advantages, among them: the processing structures represented by template components may be displayed in a way that visually expresses what a component does (for instance, it may resemble the output it generates). that representation may be both expressive and compact (after all, it is not just a text), which allows you easily to navigate a template, understand what it does and modify anything you need. as template components are visual and interactive, they may have very complex internal structure, for instance, contain lots of properties and nested components. at that, you don't need to scroll and navigate some kind of enormous text, which encodes all of this (as it would be in case of a script). rather, you just need to invoke some property dialogs and expand/collapse some component sections. a template component may be easily copied, pasted and deleted as a whole. at that, you don't need to bother that the template syntax is restored after that. the template designer will also ensure that each component is created, copied or moved only in the allowed place. the highly structured nature of templates eliminates the need for most of various named identifiers. many connections between different template components are also maintained by the template designer (i.e. modified automatically when necessary). as template files are stored and read only programmatically, there is no need to know and understand their syntax. there will be no syntax errors either. the actual syntax of template files may be optimized not for human programmers, but for faster loading and processing of templates by the template processor . there is no need in a compilation phase. the separation of template semantics from the particular structure of template files helps for faster and easier evolution of the template language. the obsolete constructs of older template versions can be automatically converted into new structures. both old and new templates will look and work up-to-date. integrations generation of xsd diagrams docflex/xml is able to work with any kind of diagrams (i.e. inserting them automatically in the generated output). that is supported on the level of templates, along with the generation of hypertext imagemaps, as shown on the following screenshot (click to see a lot more): docflex/xml provides no diagramming engine of its own. instead, it includes integrations with two most popular xml editors that do generate xsd diagrams: ● altova xmlspy ● oxygen xml editor effectively, the third-party software is used as dynamically linked diagramming engine. the advantage of such integrations is that when you are the user of one of those xml editors, you will get in the documentation generated by docflex the same diagrams as you see in your xml editor. here is how such a documentation with diagrams looks (click on a screenshot to view the real html): apache ant & maven as a pure java application, docflex/xml can be run in any environment that runs java itself. the template processor can be easily integrated with ant (that can be specified just in the ant build file). in case of maven, docflex/xml includes a simple maven plugin. it is possible also to use all diagraming integrations with both ant and maven. links docflex/xml (home page): http://www.filigris.com/docflex-xml/ docflex/xml xsddoc: http://www.filigris.com/docflex-xml/xsddoc/ xsddoc examples: http://www.filigris.com/docflex-xml/xsddoc/examples/ xmlspy integration: http://www.filigris.com/docflex-xml/xmlspy/ oxygenxml integration: http://www.filigris.com/docflex-xml/oxygenxml/ free downloads: http://www.filigris.com/downloads/ this original article: http://www.filigris.com/ann/docflex-xsd/

In this post, I will show how to integrate Apache Tiles 3 with Spring MVC.

Recently I’ve been butting heads with some people on the subject of Ownership, Responsibility and Accountability. There seems to be a very unhealthy obsession with these things sometimes, and I think this is indicative of a less-than-ideal culture. I don’t want to say that they’re “anti-agile” because that just sounds a bit weak, and because I also think they’re not just bad for agile, they’re bad for pretty much any system. I’m not sure how familiar most people are with the “RACI matrix” concept, but in my eyes it’s downright evil in the wrong hands, and I’ve been hearing “RACI Matrix” a lot recently (it’s now on my Bullshit Bingo card). I’ll start off by clarifying what I mean. I’ve got nothing against people owning actions or being accountable for certain particular (usually small) things, but I do take offence when pretty much everything has to be given an owner, someone accountable and someone to “take responsibility”. It’s divisive and results in lots of finger pointing, in my experience. I much prefer the concept of shared ownership, and collective accountability. As a software delivery team, we should all feel responsible for the quality of the product, as well as the performance and the feature richness. These things shouldn’t be assigned for ownership to individuals, as it’ll create an attitude of “well it’s not my problem” among the other team members. Here’s an example: I’ve worked in a team where one person was made the “owner” of the build system. They busied themselves making sure all the builds passed and that the system was regularly ticking over. Of course, the builds often failed and nobody cared except this one person, who then had to try to get people to fix their broken builds. It almost seemed as if people didn’t care about the fact that their software wasn’t capable of being compiled, or that the tests were failing, and in truth they didn’t. They cared about writing code and checking it in, because they didn’t “own” the build system. One message that I always try to drive home with software delivery teams is that our objective is to make software that works for our users, not just write code. I know how easy it is for developers to just focus on checking in code, or perhaps just make sure it passes the tests in the CI system, but beyond that, their focus drops off. I know because I was once one of those developers :-) These days I try to encourage everyone to care about things such as: How your code builds How the tests execute How good the tests are How good the code is How easy it is to deploy How easy it is to maintain How easy it is to monitor Because it takes all of these things to produce good software that users can enjoy, which means we get paid. Here’s another example of how “ownership” has hurt a product: A large system I once worked on was deployed into production using a complicated system of bash and perl scripts, which were cobbled together by a sysadmin who did the deployments. He became the de facto “owner” of the deployment system. There were untold issues with the running of the application because of permissions, paths etc and so forth. The deployment process was creaky and relatively untested. Since the “ownership” of this system was assigned to the sysadmin, rather than devolved or collectively shared throughout the delivery team, the “deployability” was seen as a second class citizen within the delivery team, because everybody felt like it was “owned” by one person who just happened to be on the periphery of the team at best. So here’s what I think: The ability to monitor, maintain, deploy, test, build and create software should all be treated as first class citizens and should be the collective responsibility of everyone in the team. They should all own it, and they should all be accountable. I would extend this out further, to include supporting systems such as environments, build systems, testing frameworks and so-on. Sure, each team might have an SME or two who focuses more on one of these things than any other, but that doesn’t make that one person accountable, responsible or the owner any more than any particular developer is the “owner” of any particular class, method or function. If I write some code that depends on a method that someone else has written, and that method is failing, I don’t just down tools, shrug my shoulders and say “well I’m not accountable for that”. That would be hugely unhelpful and I’d make no friends either. In the same way, we shouldn’t treat our supporting functions and systems as someone else’s responsibility. If we need it in order to make our software work for the end user, then it’s our collective responsibility, no matter what “it” is.

In an earlier blog entry I had touched on a fictitious rube goldberg flow for capitalizing a string through a complicated series of steps, the premise of the article was to introduce Spring Integration Java DSL as an alternative to defining integration flows through xml configuration files. I learned a few new things after writing that blog entry, thanks to Artem Bilan and wanted to document those learnings here: So, first my original sample, here I have the following flow(the one's in bold): Take in a message of this type - "hello from spring integ" Split it up into individual words(hello, from, spring, integ) Send each word to a ActiveMQ queue Pick up the word fragments from the queue and capitalize each word Place the response back into a response queue Pick up the message, re-sequence based on the original sequence of the words Aggregate back into a sentence("HELLO FROM SPRING INTEG") and Return the sentence back to the calling application. EchoFlowOutbound.java: @Bean public DirectChannel sequenceChannel() { return new DirectChannel(); } @Bean public DirectChannel requestChannel() { return new DirectChannel(); } @Bean public IntegrationFlow toOutboundQueueFlow() { return IntegrationFlows.from(requestChannel()) .split(s -> s.applySequence(true).get().getT2().setDelimiters("\\s")) .handle(jmsOutboundGateway()) .get(); } @Bean public IntegrationFlow flowOnReturnOfMessage() { return IntegrationFlows.from(sequenceChannel()) .resequence() .aggregate(aggregate -> aggregate.outputProcessor(g -> Joiner.on(" ").join(g.getMessages() .stream() .map(m -> (String) m.getPayload()).collect(toList()))) , null) .get(); } @Bean public JmsOutboundGateway jmsOutboundGateway() { JmsOutboundGateway jmsOutboundGateway = new JmsOutboundGateway(); jmsOutboundGateway.setConnectionFactory(this.connectionFactory); jmsOutboundGateway.setRequestDestinationName("amq.outbound"); jmsOutboundGateway.setReplyChannel(sequenceChannel()); return jmsOutboundGateway; } It turns out, based on Artem Bilan's feedback, that a few things can be optimized here. First notice how I have explicitly defined two direct channels, "requestChannel" for starting the flow that takes in the string message and the "sequenceChannel" to handle the message once it returns back from the jms message queue, these can actually be totally removed and the flow made a little more concise this way: @Bean public IntegrationFlow toOutboundQueueFlow() { return IntegrationFlows.from("requestChannel") .split(s -> s.applySequence(true).get().getT2().setDelimiters("\\s")) .handle(jmsOutboundGateway()) .resequence() .aggregate(aggregate -> aggregate.outputProcessor(g -> Joiner.on(" ").join(g.getMessages() .stream() .map(m -> (String) m.getPayload()).collect(toList()))) , null) .get(); } @Bean public JmsOutboundGateway jmsOutboundGateway() { JmsOutboundGateway jmsOutboundGateway = new JmsOutboundGateway(); jmsOutboundGateway.setConnectionFactory(this.connectionFactory); jmsOutboundGateway.setRequestDestinationName("amq.outbound"); return jmsOutboundGateway; } "requestChannel" is now being implicitly created just by declaring a name for it. The sequence channel is more interesting, quoting Artem Bilan - do not specify outputChannel for AbstractReplyProducingMessageHandler and rely on DSL , what it means is that here jmsOutboundGateway is a AbstractReplyProducingMessageHandler and its reply channel is implicitly derived by the DSL. Further, two methods which were earlier handling the flows for sending out the message to the queue and then continuing once the message is back, is collapsed into one. And IMHO it does read a little better because of this change. The second good change and the topic of this article is the introduction of the Jms namespace factories, when I had written the previous blog article, DSL had support for defining the AMQ inbound/outbound adapter/gateway, now there is support for Jms based inbound/adapter adapter/gateways also, this simplifies the flow even further, the flow now looks like this: @Bean public IntegrationFlow toOutboundQueueFlow() { return IntegrationFlows.from("requestChannel") .split(s -> s.applySequence(true).get().getT2().setDelimiters("\\s")) .handle(Jms.outboundGateway(connectionFactory) .requestDestination("amq.outbound")) .resequence() .aggregate(aggregate -> aggregate.outputProcessor(g -> Joiner.on(" ").join(g.getMessages() .stream() .map(m -> (String) m.getPayload()).collect(toList()))) , null) .get(); } The inbound Jms part of the flow also simplifies to the following: @Bean public IntegrationFlow inboundFlow() { return IntegrationFlows.from(Jms.inboundGateway(connectionFactory) .destination("amq.outbound")) .transform((String s) -> s.toUpperCase()) .get(); } Thus, to conclude, Spring Integration Java DSL is an exciting new way to concisely configure Spring Integration flows. It is already very impressive in how it simplifies the readability of flows, the introduction of the Jms namespace factories takes it even further for JMS based flows.

NOTE: Always Agile Consulting now offers an Introduction To Trunk Based Development training course! Trunk Based Development supports Optimistic and Pessimistic Release Branching Trunk Based Development is a style of software development in which all developers commit their changes to a single shared trunk in source control, and every commit yields a production-ready build. It is a prerequisite for Continuous Delivery as it ensures that all code is continuously integrated into a single workstream, that developers always work against the latest code, and that merge/integration pain is minimised. It is important to note that Trunk Based Development does not prohibit branching. Trunk Based Development is compatible with a Release Branching strategy of short-lived release branches that are used for post-development defect fixes. That Release Branching strategy might be optimistic and defer branch creation until a defect occurs, or be pessimistic and immediately incur branch creation. For example, consider an application developed using Trunk Based Development. The most recent commits to trunk were source revisions a and b which yielded application versions 610 and 611 respectively, and version 610 is intended to be the next production release. With Optimistic Branching, the release of version 610 is immediate as there is no upfront branching. If a defect is subsequently found then a decision must be made where to commit the fix, as trunk has progressed since 610 from a to b. If the risk of pulling forward from a to b is acceptable then the simple solution is to commit the fix to trunk as c, and consequently release version 612. However, if the risk of pulling forward from a to b is unacceptable then a 610.x release branch is created from a, with the fix committed to the branch as c and released as version 610.1. That fix is then merged back into trunk as d to produce the next release candidate 612, and the 610.x branch is earmarked for termination. With Pessimistic Branching, the release of version 610 is accompanied by the upfront creation of a 610.x release branch in anticipation of defect(s). If a defect is found in version 610 then as with Optimistic Branching a decision must be made as to where the defect fix should be committed. If the risk of pulling forward from a to b is deemed insignificant then trunk can be pulled forward from a to b and the fix committed to trunk as c for release as version 612. The 610.x branch is therefore terminated without ever being used. If on the other hand the risk is deemed significant then the fix is committed to the 610.x branch as c and released as version 610.1. The fix is merged back into trunk as d and version 612, which will also receive its own branch upon release. The choice between Optimistic Branching and Pessimistic Branching for Trunk Based Development is dependent upon product quality and lead times. If product quality is poor and lead times are long, then the upfront cost of Pessimistic Branching may be justifiable. Alternatively, if post-development defects are rare and production releases are frequent then Optimistic Branching may be preferable.

This post exists to help with an MSDN Magazine article that I am authoring It provides some of the low-level details for the article How to install Mono and root certificates on a raspberry pi How to create an Azure mobile service How to create a Custom API inside Azure mobile services that the raspberry pi can call into How to create an Azure storage account MONO - HOW TO INSTALL ON A RASPBERRY PI Why Mono? How to install Mono on a raspberry pi Installing trusted root certificates on to the raspberry pi http://www.mono-project.com/Main_Page An open source, cross-platform, implementation of C# and the CLR that is binary compatible with Microsoft.NET Mono is a free and open source project led by Xamarin (formerly by Novell) that provides a .NET Framework-compatible set of tools including, among others, a C# compiler and a Common Language Runtime WHY MONO? Because it lets us write .net code compiled on Windows We can simply copy the binary files from Windows to Linux and run it as is From a raspberry pi device, it is possible to use a .net application to take a photo and upload it to Windows Azure storage HOW TO INSTALL ON A RASPBERRY PI RUNNING LINUX You will issue the following commands: pi@raspberrypi ~ $ sudo apt-get update pi@raspberrypi ~ $ sudo apt-get install mono-complete The first command makes sure all the local package index are up to date with the changes made in repositories. Second command installs the complete Mono tooling and runtime. MAKING SURE THAT YOUR MONO APPLICATIONS CAN MAKE A HTTPS REST-BASED CALLS This command downloads the trusted root certificates from the Mozilla LXR web site into the Mono certificate store. Once complete, the Raspberry PI will be capable of making web requests using HTTPS requests within Mono. pi@raspberrypi ~ $ mozroots --import --ask-remove --machine CREATING A NEW AZURE MOBILE SERVICES ACCOUNT The mobile services account is needed to host a Node.js application that provides shared access signatures to raspberry pi devices The shared access signature is needed by the raspberry pi, so that it can directly and securely upload photos to Azure storage STEPS TO CREATE AN AZURE MOBILE SERVICE The steps below will create an Azure mobile service The service will be used to host a Node.js application interacting with a raspberry pi devices We will provision a SQL database, although it will not be used initially FOLLOW THESE STEPS TO CREATE THE MOBILE SERVICE Login into the Azure Portal Select MOBILE SERVICES from the left menu pane at the Azure Portal. In the lower left corner select "+NEW" to create a new Azure Mobile Service. Make sure you've selected, "COMPUTE / MOBILE SERVICE / CREATE." You will now enter a url. We will call this service raspberrymobileservice. For the DATABASE, we will choose "Create a new SQL database instance." The REGION we chose is "West US." The BACKEND is "JavaScript." Click the "->" arrow to proceed to the next screen. In this screen you will "Specify database settings." The NAME of your database will based on the URL you entered previously. In this case, the database is called "raspberrymobileservice_db." You will need to choose a SERVER. We will choose "New SQL database server" from the drop-down list. You will need to provide a SERVER LOGIN NAME and a SERVER LOGIN PASSWORD. Take note of the login you provided as it will be needed later CREATING A CUSTOM API Azure mobile services allows you to create a custom API written in JavaScript that can be called from a raspberry pi device using REST This custom API is really just a Node.js application running in the server CREATING THE API TO RESPOND TO THE DEVICE TRYING TO UPLOAD PHOTOS Now that the service is established, we will turn our attention to creating an API that the device can call into to upload a photo. Login into the Azure Portal Your mobile service will take a few minutes to complete, and you should see the "Ready" flag as the "Status" for your service. Once it is ready you can drill into your service to customize its behavior. Just to the right of the service name, click the right arrow key "->" to drill into the service details. The top menu bar will offer many options, but we are interested in the one titled "API." The API allows you to create a series of node.JS API calls that a device can call into using rest-based approaches. Click on "API." from there, select "CREATE A CUSTOM API." You will be asked to provide an API name. Type in "photos" for the API name. Below you will see a series of drop-down combo boxes that relate to permission. We will keep the default value of "Anybody with the application key." This might not be the best option for all scenarios. You can read more about this here. http://msdn.microsoft.com/en-us/library/azure/jj193161.aspx. Click the checkmark to complete the process. The name of the AP you just created, "Photos," should be visible on the portal interface. To drill into the photos API click on the right arrow key "->". The right arrow key will be just to the right of the name of the API "Photos". At this point you should see a basic script that has been provided by default. We will overwrite this default script with our own script as described in the MSDN Magazine article. CREATING A STORAGE ACCOUNT TO STORE THE PHOTOS Navigate to the portal and create a storage account Create a container for the photos Obtain the: Storage Account Name (you will provide a name) Storage Account Access key (generated for you) Container Name (you will create) CREATING A STORAGE ACCOUNT We will need a storage account so that we can upload photos to it. The steps are well documented here: http://azure.microsoft.com/en-us/documentation/articles/storage-create-storage-account/ In our case we call the storage account raspberrystorage. This means that the URL that the device will use to upload photos is https://raspberrystorage.blob.core.windows.net/. As you complete these steps make sure that you choose the storage account location to be the same location as was used for your mobile services account. This avoids any unnecessary latency or bandwidth costs between data centers. Once the storage account is created, we will need to create a container within it. Photos or any blob for that matter, are always stored within a container. To create a container drill into your newly created storage account and select CONTAINERS from the top menu. From there, select CREATE A CONTAINER. The new container dialog box will ask for a name for your container. Take note of the name you provide. We are calling our container ?photocontainer.? When the raspberry pi device uploads photos to the storage account, it will target a specific container, such as the one we just created. You will next be asked to indicate ACCESS rights. To keep things simple we will select access rights of Public Blob. ENTERING APP SETTINGS Rather than hard-code storage account information inside your JavaScript/Node.js applications, you should consider using apps settings inside of the Azure mobile services portal This post also discusses it well: http://blogs.msdn.com/b/carlosfigueira/archive/2013/12/09/application-settings-in-azure-mobile-services.aspx ?The idea of application settings is a set of key-value pairs which can be set for the mobile service (either via the portal or via the command-line interface), and those values could be then read in the service runtime.? NAVIGATING TO APP SETTINGS Navigate to the Azure Mobile Services section of the portal. Drill into the specific service by hitting the arrow below Select from the Configure Menu at the top Scroll down to the very bottom to see app settings Note that we need to enter: - We need to get this from Azure Storage - PhotoContainerName - AccountName - AccountKey We get this information from the Azure Storage Section of the Portal. Note that you need to have provisioned a Storage Account to have this information. How to get the AccountKey with Azure Storage Services Now you can get the access keys HOW NODE.JS WILL ACCESS THE APP SETTINGS You will create a Node.js application inside of Azure Mobile Services See previous steps THE NODE.JS APPLICATION READING APP SETTINGS You will starting by going back to Azure Mobile Services and drill down into your newly minted service We called ours raspberrymobileservice Once you click API, you should see: Notice the app settings are being read on lines 12 to 14.

this post will show how to automate the deployment process of a android application using jenkins continuous integration – to build the project, run the unit tests (if any), archive the built artifacts and run the android lint reports. 1. install jenkins as a windows service navigate to jenkins-ci.org website using an internet browser and download the windows native package (the link is underlined for easy identification) as shown from the right side pane of the download jenkins tab. once the download is complete, uncompress the zip file and click on the jenkins-1.xxx.msi file. proceed through the configuration steps to install the jenkins as a windows service. 2. modify default jenkins port by default jenkins runs on the port 8080. in order to avoid conflict with other applications, the default port can be modified by editing the jenkins.xml found under c:\program files (x86)\jenkins location. as shown below, modify the httpport to 8082. jenkins jenkins this service runs jenkins continuous integration system. %base%\jre\bin\java -xrs -xmx256m -dhudson.lifecycle=hudson.lifecycle.windowsservicelifecycle -jar "%base%\jenkins.war" --httpport=8082 rotate once the modification is saved in jenkins.xml file, restart the jenkins service from the windows task manager->services and right clicking on the jenkins service and choose stop service to stop the service as shown below. once the status of the service changes to stopped, restart the service by right clicking on the jenkins service and choose start service to start the service again. navigate to localhost:8082 to verify if the jenkins restart was successful as shown below – jenkins dashboard will be displayed. note that it takes a while before the jenkins service becomes available. 3. install plugins on the jenkins dashboard, navigate to manage jenkins –> manage plugins as shown in the snapshot below. install the following plugins and restart jenkins for the changes to take effect. git plugin (for integrating git with jenkins) gradle plugin (for integrating gradle with jenkins) android lint plugin (for integration lint with jenkins) 4. configure system on the jenkins dashboard, navigate to manage jenkins –> configure system as shown in the snapshot below. navigate to the global properties section and click on add to add an environment variable android_home as shown in the snapshot below. enter the name as android_home and enter the path of the location where the android sdk is stored on windows. navigate to the jdk section and click on “add jdk” to add the jdk installation as shown in the snapshot below. specify a jdk name, choose the jdk version to install and follow the on-screen instructions to save the oracle login credentials. save the changes. next, proceed to the git section and click on “add git” to add the git installation as shown in the snapshot below. specify git name, specify the path to git executable and save the changes. next, proceed to the gradle section and click on “add gradle” to add the gradle installation as shown in the snapshot below. specify gradle name, choose the appropriate version (at the time of writing, i used gradle 1.10) and save the changes. next, proceed to the email notification section and enter the smtp server details as shown below. click on the advanced button to add the further details required and save the changes. click on “test configuration by sending test e-mail”, enter the test e-mail recipient and click on “test configuration” to see if the email is successfully sent. 5. create a new jenkins job from the jenkins dashboard, click on “new job” to create a new job. enter a name for the job and choose “build a free-style software project” as option and click on ok as shown below. from the new job configuration screen, proceed to the source code management section. save the git credentials by clicking on “add” as shown below and entering the details in the following dialog. save the changes by clicking on “add” as shown below. specify the git repository url for the project, choose the saved credentials from the drop-down list as shown in the snapshot below. save the changes. next, from the build triggers section, select the options desired as shown below and save the changes. proceed to the build section, choose “invoke gradle script” from the drop-down list of choices for “add build step”. choose the appropriate gradle version which is configured, enter the tasks to be built and select the options as desired. save the changes. proceed to the post-build actions section, click on “publish android lint results” from the drop-down list of choices for “add post-build action” and specify the location where the lint results should be stored in the jenkins workspace for the job. similarly, click on “archive the artifacts” from the drop-down list of choices for “add post-build action” and the specify the format of apk files to be archived after every build. additionally, options from advanced section such as “discard all but the last successful/stable artifact to save disk space” could be enabled for saving disk space. click on “e-mail notification” from the drop-down list of choices for “add post-build action” and enter the values for the email recipients as shown below. save the changes. 6. build now once the above configuration steps are complete, click on “build now” under the jenkins –> build android application (or the respective job name) to build the project based on the configuration. the console output has the detailed logs of what steps were initiated by the configuration and the outcome of the entire build. clicking on any successful build outcome shows the artifacts that were archived as part of the build, the change that started the build and the lint results as shown below. thus the entire process of building the project an android application project whenever a scm change is triggered or under another condition, running lint reports, archiving the artifacts built, publishing lint reports and triggering emails to the recipients can be automated with a click of a button through jenkins.

Explore different message brokers, and discover how these important web technologies impact a customer's backlog of messages, and cluster/data performance.

A new Java based DSL has now been introduced for Spring Integration which makes it possible to define the Spring Integration message flows using pure java based configuration instead of using the Spring XML based configuration. I tried the DSL for a sample Integration flow that I have - I call it the Rube Goldberg flow, for it follows a convoluted path in trying to capitalize a string passed in as input. The flow looks like this and does some crazy things to perform a simple task: It takes in a message of this type - "hello from spring integ" splits it up into individual words(hello, from, spring, integ) sends each word to a ActiveMQ queue from the queue the word fragments are picked up by a enricher to capitalize each word placing the response back into a response queue It is picked up, resequenced based on the original sequence of the words aggregated back into a sentence("HELLO FROM SPRING INTEG") and returned back to the application. To start with Spring Integration Java DSL, a simple Xml based configuration to capitalize a String would look like this: There is nothing much going on here, a messaging gateway takes in the message passed in from the application, capitalizes it in a transformer and this is returned back to the application. Expressing this in Spring Integration Java DSL: @Configuration @EnableIntegration @IntegrationComponentScan @ComponentScan public class EchoFlow { @Bean public DirectChannel requestChannel() { return new DirectChannel(); } @Bean public IntegrationFlow simpleEchoFlow() { return IntegrationFlows.from(requestChannel()) .transform((String s) -> s.toUpperCase()) .get(); } } @MessagingGateway public interface EchoGateway { @Gateway(requestChannel = "requestChannel") String echo(String message); } Do note that @MessagingGateway annotation is not a part of Spring Integration Java DSL, it is an existing component in Spring Integration and serves the same purpose as the gateway component in XML based configuration. I like the fact that the transformation can be expressed using typesafe Java 8 lambda expressions rather than the Spring-EL expression. Note that the transformation expression could have coded in quite few alternate ways: ??.transform((String s) -> s.toUpperCase()) Or: ??.transform(s -> s.toUpperCase()) Or using method references: ??.transform(String::toUpperCase) Moving onto the more complicated Rube Goldberg flow to accomplish the same task, again starting with XML based configuration. There are two configurations to express this flow: rube-1.xml: This configuration takes care of steps 1, 2, 3, 6, 7, 8 : It takes in a message of this type - "hello from spring integ" splits it up into individual words(hello, from, spring, integ) sends each word to a ActiveMQ queue from the queue the word fragments are picked up by a enricher to capitalize each word placing the response back into a response queue It is picked up, resequenced based on the original sequence of the words aggregated back into a sentence("HELLO FROM SPRING INTEG") and returned back to the application. and rube-2.xml for steps 4, 5: It takes in a message of this type - "hello from spring integ" splits it up into individual words(hello, from, spring, integ) sends each word to a ActiveMQ queue from the queue the word fragments are picked up by a enricher to capitalize each word placing the response back into a response queue It is picked up, resequenced based on the original sequence of the words aggregated back into a sentence("HELLO FROM SPRING INTEG") and returned back to the application. Now, expressing this Rube Goldberg flow using Spring Integration Java DSL, the configuration looks like this, again in two parts: EchoFlowOutbound.java: @Bean public DirectChannel sequenceChannel() { return new DirectChannel(); } @Bean public DirectChannel requestChannel() { return new DirectChannel(); } @Bean public IntegrationFlow toOutboundQueueFlow() { return IntegrationFlows.from(requestChannel()) .split(s -> s.applySequence(true).get().getT2().setDelimiters("\\s")) .handle(jmsOutboundGateway()) .get(); } @Bean public IntegrationFlow flowOnReturnOfMessage() { return IntegrationFlows.from(sequenceChannel()) .resequence() .aggregate(aggregate -> aggregate.outputProcessor(g -> Joiner.on(" ").join(g.getMessages() .stream() .map(m -> (String) m.getPayload()).collect(toList()))) , null) .get(); } and EchoFlowInbound.java: @Bean public JmsMessageDrivenEndpoint jmsInbound() { return new JmsMessageDrivenEndpoint(listenerContainer(), messageListener()); } @Bean public IntegrationFlow inboundFlow() { return IntegrationFlows.from(enhanceMessageChannel()) .transform((String s) -> s.toUpperCase()) .get(); } Again here the code is completely typesafe and is checked for any errors at development time rather than at runtime as with the XML based configuration. Again I like the fact that transformation, aggregation statements can be expressed concisely using Java 8 lamda expressions as opposed to Spring-EL expressions. What I have not displayed here is some of the support code, to set up the activemq test infrastructure, this configuration continues to remain as xml and I have included this code in a sample github project. All in all, I am very excited to see this new way of expressing the Spring Integration messaging flow using pure Java and I am looking forward to seeing its continuing evolution and may be even try and participate in its evolution in small ways. Here is the entire working code in a github repo: https://github.com/bijukunjummen/rg-si References and Acknowledgement: Spring Integration Java DSL introduction blog article by Artem Bilan: https://spring.io/blog/2014/05/08/spring-integration-java-dsl-milestone-1-released Spring Integration Java DSL website and wiki: https://github.com/spring-projects/spring-integration-extensions/wiki/Spring-Integration-Java-DSL-Reference. A lot of code has been shamelessly copied over from this wiki by me :-). Also, a big thanks to Artem for guidance on a question that I had Webinar by Gary Russell on Spring Integration 4.0 in which Spring Integration Java DSL is covered in great detail.

After attending Sam Newman’s microservice talks at Geecon last week I started to think more about what is most likely an essential feature of service-oriented/microservice platforms for monitoring, reporting and diagnostics: correlation ids. Correlation ids allow distributed tracing within complex service oriented platforms, where a single request into the application can often be dealt with by multiple downstream service. Without the ability to correlate downstream service requests it can be very difficult to understand how requests are being handled within your platform. I’ve seen the benefit of correlation ids in several recent SOA projects I have worked on, but as Sam mentioned in his talks, it’s often very easy to think this type of tracing won’t be needed when building the initial version of the application, but then very difficult to retrofit into the application when you do realise the benefits (and the need for!). I’ve not yet found the perfect way to implement correlation ids within a Java/Spring-based application, but after chatting to Sam via email he made several suggestions which I have now turned into a simple project using Spring Boot to demonstrate how this could be implemented. Why? During both of Sam’s Geecon talks he mentioned that in his experience correlation ids were very useful for diagnostic purposes. Correlation ids are essentially an id that is generated and associated with a single (typically user-driven) request into the application that is passed down through the stack and onto dependent services. In SOA or microservice platforms this type of id is very useful, as requests into the application typically are ‘fanned out’ or handled by multiple downstream services, and a correlation id allows all of the downstream requests (from the initial point of request) to be correlated or grouped based on the id. So called ‘distributed tracing’ can then be performed using the correlation ids by combining all the downstream service logs and matching the required id to see the trace of the request throughout your entire application stack (which is very easy if you are using a centralised logging framework such as logstash) The big players in the service-oriented field have been talking about the need for distributed tracing and correlating requests for quite some time, and as such Twitter have created their open source Zipkin framework (which often plugs into their RPC framework Finagle), and Netflix has open-sourced their Karyon web/microservice framework, both of which provide distributed tracing. There are of course commercial offering in this area, one such product being AppDynamics, which is very cool, but has a rather hefty price tag. Creating a proof-of-concept in Spring Boot As great as Zipkin and Karyon are, they are both relatively invasive, in that you have to build your services on top of the (often opinionated) frameworks. This might be fine for some use cases, but no so much for others, especially when you are building microservices. I’ve been enjoying experimenting with Spring Boot of late, and this framework builds on the much known and loved (at least by me :-) ) Spring framework by providing lots of preconfigured sensible defaults. This allows you to build microservices (especially ones that communicate via RESTful interfaces) very rapidly. The remainder of this blog pos explains how I implemented a (hopefully) non-invasive way of implementing correlation ids. Goals Allow a correlation id to be generated for a initial request into the application Enable the correlation id to be passed to downstream services, using as method that is as non-invasive into the code as possible Implementation I have created two projects on GitHub, one containing an implementation where all requests are being handled in a synchronous style (i.e. the traditional Spring approach of handling all request processing on a single thread), and also one for when an asynchronous (non-blocking) style of communication is being used (i.e., using the Servlet 3 asynchronous support combined with Spring’s DeferredResult and Java’s Futures/Callables). The majority of this article describes the asynchronous implementation, as this is more interesting: Spring Boot asynchronous (DeferredResult + Futures) communication correlation id Github repo The main work in both code bases is undertaken by the CorrelationHeaderFilter, which is a standard Java EE Filter that inspects the HttpServletRequest header for the presence of a correlationId. If one is found then we set a ThreadLocal variable in the RequestCorrelation Class (discussed later). If a correlation id is not found then one is generated and added to the RequestCorrelation Class: public class CorrelationHeaderFilter implements Filter { //... @Override public void doFilter(ServletRequest servletRequest, ServletResponse servletResponse, FilterChain filterChain) throws IOException, ServletException { final HttpServletRequest httpServletRequest = (HttpServletRequest) servletRequest; String currentCorrId = httpServletRequest.getHeader(RequestCorrelation.CORRELATION_ID_HEADER); if (!currentRequestIsAsyncDispatcher(httpServletRequest)) { if (currentCorrId == null) { currentCorrId = UUID.randomUUID().toString(); LOGGER.info("No correlationId found in Header. Generated : " + currentCorrId); } else { LOGGER.info("Found correlationId in Header : " + currentCorrId); } RequestCorrelation.setId(currentCorrId); } filterChain.doFilter(httpServletRequest, servletResponse); } //... private boolean currentRequestIsAsyncDispatcher(HttpServletRequest httpServletRequest) { return httpServletRequest.getDispatcherType().equals(DispatcherType.ASYNC); } The only thing is this code that may not instantly be obvious is the conditional checkcurrentRequestIsAsyncDispatcher(httpServletRequest), but this is here to guard against the correlation id code being executed when the Async Dispatcher thread is running to return the results (this is interesting to note, as I initially didn’t expect the Async Dispatcher to trigger the execution of the filter again?) Here is the RequestCorrelation Class, which contains a simple ThreadLocal static variable to hold the correlation id for the current Thread of execution (set via the CorrelationHeaderFilter above) public class RequestCorrelation { public static final String CORRELATION_ID = "correlationId"; private static final ThreadLocal id = new ThreadLocal(); public static String getId() { return id.get(); } public static void setId(String correlationId) { id.set(correlationId); } } Once the correlation id is stored in the RequestCorrelation Class it can be retrieved and added to downstream service requests (or data store access etc) as required by calling the static getId() method within RequestCorrelation. It is probably a good idea to encapsulate this behaviour away from your application services, and you can see an example of how to do this in a RestClient Class I have created, which composes Spring’s RestTemplate and handles the setting of the correlation id within the header transparently from the calling Class. @Component public class CorrelatingRestClient implements RestClient { private RestTemplate restTemplate = new RestTemplate(); @Override public String getForString(String uri) { String correlationId = RequestCorrelation.getId(); HttpHeaders httpHeaders = new HttpHeaders(); httpHeaders.set(RequestCorrelation.CORRELATION_ID, correlationId); LOGGER.info("start REST request to {} with correlationId {}", uri, correlationId); //TODO: error-handling and fault-tolerance in production ResponseEntity response = restTemplate.exchange(uri, HttpMethod.GET, new HttpEntity(httpHeaders), String.class); LOGGER.info("completed REST request to {} with correlationId {}", uri, correlationId); return response.getBody(); } } //... calling Class public String exampleMethod() { RestClient restClient = new CorrelatingRestClient(); return restClient.getForString(URI_LOCATION); //correlation id handling completely abstracted to RestClient impl } Making this work for asynchronous requests… The code included above works fine when you are handling all of your requests synchronously, but it is often a good idea in a SOA/microservice platform to handle requests in a non-blocking asynchronous manner. In Spring this can be achieved by using the DeferredResult Class in combination with the Servlet 3 asynchronous support. The problem with using ThreadLocal variables within the asynchronous approach is that the Thread that initially handles the request (and creates the DeferredResult/Future) will not be the Thread doing the actual processing. Accordingly, a bit of glue code is needed to ensure that the correlation id is propagated across the Threads. This can be achieved by extending Callable with the required functionality: (don’t worry if example Calling Class code doesn’t look intuitive – this adaption between DeferredResults and Futures is a necessary evil within Spring, and the full code including the boilerplate ListenableFutureAdapter is in my GitHub repo): public class CorrelationCallable implements Callable { private String correlationId; private Callable callable; public CorrelationCallable(Callable targetCallable) { correlationId = RequestCorrelation.getId(); callable = targetCallable; } @Override public V call() throws Exception { RequestCorrelation.setId(correlationId); return callable.call(); } } //... Calling Class @RequestMapping("externalNews") public DeferredResult externalNews() { return new ListenableFutureAdapter<>(service.submit(new CorrelationCallable<>(externalNewsService::getNews))); } And there we have it – the propagation of correlation id regardless of the synchronous/asynchronous nature of processing! You can clone the Github report containing my asynchronous example, and execute the application by running mvn spring-boot:run at the command line. If you access http://localhost:8080/externalNewsin your browser (or via curl) you will see something similar to the following in your Spring Boot console, which clearly demonstrates a correlation id being generated on the initial request, and then this being propagated through to a simulated external call (have a look in the ExternalNewsServiceRest Class to see how this has been implemented): [nio-8080-exec-1] u.c.t.e.c.w.f.CorrelationHeaderFilter : No correlationId found in Header. Generated : d205991b-c613-4acd-97b8-97112b2b2ad0 [pool-1-thread-1] u.c.t.e.c.w.c.CorrelatingRestClient : start REST request to http://localhost:8080/news with correlationId d205991b-c613-4acd-97b8-97112b2b2ad0 [nio-8080-exec-2] u.c.t.e.c.w.f.CorrelationHeaderFilter : Found correlationId in Header : d205991b-c613-4acd-97b8-97112b2b2ad0 [pool-1-thread-1] u.c.t.e.c.w.c.CorrelatingRestClient : completed REST request to http://localhost:8080/news with correlationId d205991b-c613-4acd-97b8-97112b2b2ad0 Conclusion I’m quite happy with this simple prototype, and it does meet the two goals I listed above. Future work will include writing some tests for this code (shame on me for not TDDing!), and also extend this functionality to a more realistic example. I would like to say a massive thanks to Sam, not only for sharing his knowledge at the great talks at Geecon, but also for taking time to respond to my emails. If you’re interested in microservices and related work I can highly recommend Sam’s Microservice book which is available in Early Access at O’Reilly. I’ve enjoyed reading the currently available chapters, and having implemented quite a few SOA projects recently I can relate to a lot of the good advice contained within. I’ll be following the development of this book with keen interest! If you have any comments or thoughts then please do share them via the comment below, or feel free to get in touch via the usual mechanisms! References I used Tomasz Nurkiewicz’s excellent blog several times for learning how best to wire up all of the DeferredResult/Future code in Spring: http://www.nurkiewicz.com/2013/03/deferredresult-asynchronous-processing.html

After attending Sam Newman’s microservice talks at Geecon last week I started to think more about what is most likely an essential feature of service-oriented/microservice platforms for monitoring, reporting and diagnostics: correlation ids. Correlation ids allow distributed tracing within complex service oriented platforms, where a single request into the application can often be dealt with by multiple downstream service. Without the ability to correlate downstream service requests it can be very difficult to understand how requests are being handled within your platform. I’ve seen the benefit of correlation ids in several recent SOA projects I have worked on, but as Sam mentioned in his talks, it’s often very easy to think this type of tracing won’t be needed when building the initial version of the application, but then very difficult to retrofit into the application when you do realise the benefits (and the need for!). I’ve not yet found the perfect way to implement correlation ids within a Java/Spring-based application, but after chatting to Sam via email he made several suggestions which I have now turned into a simple project using Spring Boot to demonstrate how this could be implemented. Why? During both of Sam’s Geecon talks he mentioned that in his experience correlation ids were very useful for diagnostic purposes. Correlation ids are essentially an id that is generated and associated with a single (typically user-driven) request into the application that is passed down through the stack and onto dependent services. In SOA or microservice platforms this type of id is very useful, as requests into the application typically are ‘fanned out’ or handled by multiple downstream services, and a correlation id allows all of the downstream requests (from the initial point of request) to be correlated or grouped based on the id. So called ‘distributed tracing’ can then be performed using the correlation ids by combining all the downstream service logs and matching the required id to see the trace of the request throughout your entire application stack (which is very easy if you are using a centralised logging framework such as logstash) The big players in the service-oriented field have been talking about the need for distributed tracing and correlating requests for quite some time, and as such Twitter have created their open source Zipkin framework (which often plugs into their RPC framework Finagle), and Netflix has open-sourced their Karyon web/microservice framework, both of which provide distributed tracing. There are of course commercial offering in this area, one such product being AppDynamics, which is very cool, but has a rather hefty price tag. Creating a proof-of-concept in Spring Boot As great as Zipkin and Karyon are, they are both relatively invasive, in that you have to build your services on top of the (often opinionated) frameworks. This might be fine for some use cases, but no so much for others, especially when you are building microservices. I’ve been enjoying experimenting with Spring Boot of late, and this framework builds on the much known and loved (at least by me :-) ) Spring framework by providing lots of preconfigured sensible defaults. This allows you to build microservices (especially ones that communicate via RESTful interfaces) very rapidly. The remainder of this blog pos explains how I implemented a (hopefully) non-invasive way of implementing correlation ids. Goals Allow a correlation id to be generated for a initial request into the application Enable the correlation id to be passed to downstream services, using as method that is as non-invasive into the code as possible Implementation I have created two projects on GitHub, one containing an implementation where all requests are being handled in a synchronous style (i.e. the traditional Spring approach of handling all request processing on a single thread), and also one for when an asynchronous (non-blocking) style of communication is being used (i.e., using the Servlet 3 asynchronous support combined with Spring’s DeferredResult and Java’s Futures/Callables). The majority of this article describes the asynchronous implementation, as this is more interesting: Spring Boot asynchronous (DeferredResult + Futures) communication correlation id Github repo The main work in both code bases is undertaken by the CorrelationHeaderFilter, which is a standard Java EE Filter that inspects the HttpServletRequest header for the presence of a correlationId. If one is found then we set a ThreadLocal variable in the RequestCorrelation Class (discussed later). If a correlation id is not found then one is generated and added to the RequestCorrelation Class: public class CorrelationHeaderFilter implements Filter { //... @Override public void doFilter(ServletRequest servletRequest, ServletResponse servletResponse, FilterChain filterChain) throws IOException, ServletException { final HttpServletRequest httpServletRequest = (HttpServletRequest) servletRequest; String currentCorrId = httpServletRequest.getHeader(RequestCorrelation.CORRELATION_ID_HEADER); if (!currentRequestIsAsyncDispatcher(httpServletRequest)) { if (currentCorrId == null) { currentCorrId = UUID.randomUUID().toString(); LOGGER.info("No correlationId found in Header. Generated : " + currentCorrId); } else { LOGGER.info("Found correlationId in Header : " + currentCorrId); } RequestCorrelation.setId(currentCorrId); } filterChain.doFilter(httpServletRequest, servletResponse); } //... private boolean currentRequestIsAsyncDispatcher(HttpServletRequest httpServletRequest) { return httpServletRequest.getDispatcherType().equals(DispatcherType.ASYNC); } The only thing is this code that may not instantly be obvious is the conditional check currentRequestIsAsyncDispatcher(httpServletRequest), but this is here to guard against the correlation id code being executed when the Async Dispatcher thread is running to return the results (this is interesting to note, as I initially didn’t expect the Async Dispatcher to trigger the execution of the filter again?) Here is the RequestCorrelation Class, which contains a simple ThreadLocal static variable to hold the correlation id for the current Thread of execution (set via the CorrelationHeaderFilter above) public class RequestCorrelation { public static final String CORRELATION_ID = "correlationId"; private static final ThreadLocal id = new ThreadLocal(); public static String getId() { return id.get(); } public static void setId(String correlationId) { id.set(correlationId); } } Once the correlation id is stored in the RequestCorrelation Class it can be retrieved and added to downstream service requests (or data store access etc) as required by calling the static getId() method within RequestCorrelation. It is probably a good idea to encapsulate this behaviour away from your application services, and you can see an example of how to do this in a RestClient Class I have created, which composes Spring’s RestTemplate and handles the setting of the correlation id within the header transparently from the calling Class. @Component public class CorrelatingRestClient implements RestClient { private RestTemplate restTemplate = new RestTemplate(); @Override public String getForString(String uri) { String correlationId = RequestCorrelation.getId(); HttpHeaders httpHeaders = new HttpHeaders(); httpHeaders.set(RequestCorrelation.CORRELATION_ID, correlationId); LOGGER.info("start REST request to {} with correlationId {}", uri, correlationId); //TODO: error-handling and fault-tolerance in production ResponseEntity response = restTemplate.exchange(uri, HttpMethod.GET, new HttpEntity(httpHeaders), String.class); LOGGER.info("completed REST request to {} with correlationId {}", uri, correlationId); return response.getBody(); } } //... calling Class public String exampleMethod() { RestClient restClient = new CorrelatingRestClient(); return restClient.getForString(URI_LOCATION); //correlation id handling completely abstracted to RestClient impl } Making this work for asynchronous requests… The code included above works fine when you are handling all of your requests synchronously, but it is often a good idea in a SOA/microservice platform to handle requests in a non-blocking asynchronous manner. In Spring this can be achieved by using the DeferredResult Class in combination with the Servlet 3 asynchronous support. The problem with using ThreadLocal variables within the asynchronous approach is that the Thread that initially handles the request (and creates the DeferredResult/Future) will not be the Thread doing the actual processing. Accordingly, a bit of glue code is needed to ensure that the correlation id is propagated across the Threads. This can be achieved by extending Callable with the required functionality: (don’t worry if example Calling Class code doesn’t look intuitive – this adaption between DeferredResults and Futures is a necessary evil within Spring, and the full code including the boilerplate ListenableFutureAdapter is in my GitHub repo): public class CorrelationCallable implements Callable { private String correlationId; private Callable callable; public CorrelationCallable(Callable targetCallable) { correlationId = RequestCorrelation.getId(); callable = targetCallable; } @Override public V call() throws Exception { RequestCorrelation.setId(correlationId); return callable.call(); } } //... Calling Class @RequestMapping("externalNews") public DeferredResult externalNews() { return new ListenableFutureAdapter<>(service.submit(new CorrelationCallable<>(externalNewsService::getNews))); } And there we have it – the propagation of correlation id regardless of the synchronous/asynchronous nature of processing! You can clone the Github report containing my asynchronous example, and execute the application by running mvn spring-boot:run at the command line. If you access http://localhost:8080/externalNews in your browser (or via curl) you will see something similar to the following in your Spring Boot console, which clearly demonstrates a correlation id being generated on the initial request, and then this being propagated through to a simulated external call (have a look in the ExternalNewsServiceRest Class to see how this has been implemented): [nio-8080-exec-1] u.c.t.e.c.w.f.CorrelationHeaderFilter : No correlationId found in Header. Generated : d205991b-c613-4acd-97b8-97112b2b2ad0 [pool-1-thread-1] u.c.t.e.c.w.c.CorrelatingRestClient : start REST request to http://localhost:8080/news with correlationId d205991b-c613-4acd-97b8-97112b2b2ad0 [nio-8080-exec-2] u.c.t.e.c.w.f.CorrelationHeaderFilter : Found correlationId in Header : d205991b-c613-4acd-97b8-97112b2b2ad0 [pool-1-thread-1] u.c.t.e.c.w.c.CorrelatingRestClient : completed REST request to http://localhost:8080/news with correlationId d205991b-c613-4acd-97b8-97112b2b2ad0 Conclusion I’m quite happy with this simple prototype, and it does meet the two goals I listed above. Future work will include writing some tests for this code (shame on me for not TDDing!), and also extend this functionality to a more realistic example. I would like to say a massive thanks to Sam, not only for sharing his knowledge at the great talks at Geecon, but also for taking time to respond to my emails. If you’re interested in microservices and related work I can highly recommend Sam’s Microservice book which is available in Early Access at O’Reilly. I’ve enjoyed reading the currently available chapters, and having implemented quite a few SOA projects recently I can relate to a lot of the good advice contained within. I’ll be following the development of this book with keen interest! If you have any comments or thoughts then please do share them via the comment below, or feel free to get in touch via the usual mechanisms! References I used Tomasz Nurkiewicz’s excellent blog several times for learning how best to wire up all of the DeferredResult/Future code in Spring: http://www.nurkiewicz.com/2013/03/deferredresult-asynchronous-processing.html

Learn more about using the Maven Release plugin on Jenkins, including subversion source control, artifactory, continuous integration, and more.

Cloudfoundry's java buildpack is supporting some popular jvm based applications. This article is oriented to the audiences already with experience of cloudfoundry/heroku buildpack who want to have more understanding of how buildpack and cloudfoundry works internally. cf push app -p app.war -b build-pack-url The above command demonstrates the usage of pushing a war file to cloudfoundry by using a custom buildpack (E.g. https://github.com/cloudfoundry/java-buildpack). However, what exactly happens inside, or how cloudfoundry bootstrap the war file with tomcat? There are three contracts phase that bridge communication between buildpack and cloudfoundry. The three phases are detect, compile and release, which are three ruby shell scripts: Java buildpack has multiple sub components, while each of them has all of these three phases (E.g. tomcat is one of the sub components, while it contained another layer of sub components). Detect Phase: detect phase is to check whether a particular buildpack/component applies to the deployed application. Take the war file example, tomcat applies only when https://github.com/cloudfoundry/java-buildpack/blob/master/lib/java_buildpack/container/tomcat.rb is true: def supports? web_inf? && !JavaBuildpack::Util::JavaMainUtils.main_class(@application) end The above code means, the tomcat applies when the application has a WEB-INF folder andthisisnot a main class bootstrapped application. Compile Phase: Compile phase would be the major/comprehensive work for a customized buildpack, while it is trying to build a file system on a lxc container. Take the example of our war application and tomcat example. In https://github.com/cloudfoundry/java-buildpack/blob/master/lib/java_buildpack/container/tomcat/tomcat_instance.rb def compile download(@version, @uri) { |file| expand file } link_to(@application.root.children, root) @droplet.additional_libraries << tomcat_datasource_jar if tomcat_datasource_jar.exist? @droplet.additional_libraries.link_to web_inf_lib end def expand(file) with_timing "Expanding Tomcat to #{@droplet.sandbox.relative_path_from(@droplet.root)}" do FileUtils.mkdir_p @droplet.sandbox shell "tar xzf #{file.path} -C #{@droplet.sandbox} --strip 1 --exclude webapps 2>&1" @droplet.copy_resources end The above code is all about preparing the tomcat and link the application files, so the application files will be available for the tomcat classpath. Before going to the code, we have to understand the working directory when the above code executes: . => working directory .app => @application, contains the extracted war archive .buildpack/tomcat => @droplet.sandbox .buildpack/jdk .buildpack/other needed components Inside compile method: download method will download tomcat binary file (specified here: https://github.com/cloudfoundry/java-buildpack/blob/master/config/tomcat.yml), and then extract the archive file to @droplet.sandbox directory. Then copy the resources folder's files to https://github.com/cloudfoundry/java-buildpack/tree/master/resources/tomcat/conf to @droplet.sandbox/conf Symlink the @droplet.sandbox/webapps/ROOT to .app/ Symlink additional libraries (comes from other component rather than application) to the WEB-INF/lib Note: All the symlinks use relative path, since when the container deployed to DEA, the absolute paths would be different. RELEASE PHASE: Release phase is to setup instructions of how to start tomcat. Look at the code in :https://github.com/cloudfoundry/java-buildpack/blob/master/lib/java_buildpack/container/tomcat.rb def command @droplet.java_opts.add_system_property 'http.port', '$PORT' [ @droplet.java_home.as_env_var, @droplet.java_opts.as_env_var, "$PWD/#{(@droplet.sandbox + 'bin/catalina.sh').relative_path_from(@droplet.root)}", 'run' ].flatten.compact.join(' ') end The above code does: Add java system properties http.port (referenced in tomcat server.xml) with environment properties ($PORT), this is the port on the DEA bridging to the lxc container already setup when the container was provisioned. instruction of how to run the tomcat Eg. "./bin/catalina.sh run"

This is one annoying problem that happens sometimes to git users: the symptom is: git status command shows you some files as modified (you are sure that you had not modified that files), you revert all changes with a git checkout — . but the files stills are in modified state if you issue another git status. This is a real annoying problem, suppose you want to switch branch with git checkout branchname, you will find that git does not allow you to switch because of uncommitted changes. This problem is likely caused by the end-of-line normalization (I strongly suggest you to read all the details in Pro Git book or read the help of github). I do not want to enter into details of this feature, but I only want to help people to diagnose and avoid this kind of problem. To understand if you really have a Line Ending Issue you should run git diff -w command to verify what is really changed in files that git as modified with git status command. The -w options tells git to ignore whitespace and line endings, if this command shows no differences, you are probably victim of problem in Line Ending Normalization. This is especially true if you are working with git svn, connecting to a subversion repository where developers did not pay attention to line endings and it happens usually when you have files with mixed CRLF / CR / LF. If you work in mixed environment (Unix/Linux, Windows, Macintosh) it is better to find files that are listed as modified and manually (or with some tool) normalize Line Endings. If you do not work in mixed environment you can simply turn off eol normalizationfor the single repository where you experience the problem. To do this you can issue a git config –local core.autocrlf false but it works only for you and not for all the other developers that works to the project. Moreover some people reports that they still have problem even with core.autocrlf to false. Remember that git supports .gitattributes files, used to change settings for a single subdirectory. If you set core.autocrlf to false and still have line ending normalization problem, please search for .gitattribuges files in every subdirectory of your repository, and verify if it has a line where autocrlf is turned on: * text=auto now you can turn off in all .gitattributes files you find in your repository * text=off To be sure that every developer of the team works with autocrlf turned off, you should place a .gitattributes file in repository root with autocrlf turned off. Remember that it is a better option to normalize files and leave autocrlf turned on, but if you are working with legacy code imported from another VCS, or you work with git svn, git-tf or similar tools, probably it is better turn autocrlf to off if you start experiencing that kind of problems.

wanted to go through some of the basic tenets, the technical terminology related to java ee. for many people, java ee/j2ee still mean servlets, jsps or maybe struts at best. no offence or pun intended! this is not a java ee 'bible' by any means. i am not capable enough of writing such a thing! so let us line up the 'keywords' related to java ee and then look at them one by one java ee java ee apis (specifications) containers services multitiered applications components let's try to elaborate on the above mentioned points. ok. so what is java ee? 'ee' stands for enterprise edition. that essentially makes java ee - java enterprise edition. if i had to summarize java ee in a couple of sentences, it would go something like this "java ee is a platform which defines 'standard specifications/apis' which are then implemented by vendors and used for development of enterprise (distributed, 'multi-tired', robust) 'applications'. these applications are composed of modules or 'components' which use java ee 'containers' as their run-time infrastructure." what is this 'standardized platform' based upon? what does it constitute? the platform revolves around 'standard' specifications or apis . think of these as contracts defined by a standard body e.g. enterprise java beans (ejb), java persistence api (jpa), java message service (jms) etc. these contracts/specifications/apis are implemented by different vendors e.g. glassfish, oracle weblogic, apache tomee etc alright. what about containers? containers can be visualized as 'virtual/logical partitions' . each container supports a subset of the apis/specifications defined by the java ee platform they provide run-time 'services' to the 'applications' which they host the java ee specification lists 4 types of containers ejb container web container application client container applet container java ee containers i am not going to dwell into details of these containers in this post. services?? well, 'services' are nothing but a result of the vendor implementations of the standard 'specifications' (mentioned above). examples of specifications are - jersey for jax-rs (restful services), tyrus (web sockets), eclipselink (jpa), weld (cdi) etc. the 'container' is the interface between the deployed application ('service' consumer) and the application server. here is a list of 'services' which are rendered by the 'container' to the underlying 'components' (this is not an exhaustive list) persistence - offered by the java persistence api (jpa) which drives object relational mapping (orm) and an abstraction for the database operations. messaging - the java message service (jms) provides asynchronous messaging between disparate parts of your applications. contexts & dependency injection - cdi provides loosely coupled and type safe injection of resources. web services - jaxrs and jaxws provide support for rest and soap style services respectively transaction - provided by the java transaction api (jta) implementation what is a typical java ee 'application'? what does it comprise of? applications are composed of different ' components ' which in turn are supported by their corresponding ' container ' supported 'component' types are: enterprise applications - make use of the specifications like ejb, jms, jpa etc and are executed within an ejb container web applications - they leverage the servlet api, jsp, jsf etc and are supported by a web container application client - executed in client side. they need an application client container which has a set of supported libraries and executes in a java se environment. applets - these are gui applications which execute in a web browser. how are java ee applications structured? as far as java ee 'application' architecture is concerned, they generally tend follow the n-tier model consisting of client tier, server tier and of course the database (back end) tier client tier - consists of web browsers or gui (swing, java fx) based clients. web browsers tend to talk to the 'web components' on the server tier while the gui clients interact directly with the 'business' layer within the server tier server tier - this tier comprises of the dynamic web components (jsp, jsf, servlets) and the business layer driven by ejbs, jms, jpa, jta specifications. database tier - contains 'enterprise information systems' backed by databases or even legacy data repositories. generic 3-tier java ee application architecture java ee - bare bones, basics.... as quickly and briefly as i possibly could. that's all for now! :-) stay tuned for more java ee content, specifically around the latest and greatest version of the java ee platform --> java ee 7 happy reading!

41% of developers believe they are achieving Continuous Delivery while only 8% actually are. Use the Continuous Delivery Maturity Checklist from DZone's 2014 Guide to Continuous Delivery to determine how close you are to achieving true Continuous Delivery, and be sure to download DZone's 2014 Guide to Continuous Delivery to learn how to improve your Continuous Delivery process. (Download as a PDF)

It is always recommended to use Spring properties with Mule, to externalize any configuration parameters (URLs, ports, user names, passwords, etc.). For example, the Acme APIfrom my previous post connects to an external database. So instead of hard-coding connectivity options inside my application code, I would create a properties file, e.g. acme.properties, as follows: acme.jdbc.host=acmedb acme.jdbc.port=3306 acme.jdbc.database=acmeProducts acme.jdbc.user=WileECoyote acme.jdbc.password=GeeWhizz Obviously, as a developer, I would use a test instance of Acme database to test my application. I’d commit the code to the version control system, including the properties file. Then my application would begin its journey from the automated build system to the Dev environment, to QA, Pre-Prod, and finally Prod – and fail to deploy on production because it wouldn’t be able to connect to the test database! Or even worse, it would connect to the test database and use it and no one would notice the problem until customers placed $0 order for an Acme widget which would normally cost $1000, all because the test database didn’t contain actual prices! Sure, I could just follow the recommendations on our web site and create multiple sets of properties, e.g. acme.dev.properties, acme.qa.properties, acme.prod.properties etc. But instead of solving the problem, it would create a few new ones. First, those properties must still be packaged within the application. Needless to say, IT guys would never give me the credentials for the production database, so I’d have to provide instructions for them on how to modify the properties file AFTER the application is deployed on the prod platform. Second, if (or rather WHEN) any of those properties will need to be changed (for example, the production DB is migrated to a new server), the whole process has to be repeated. And don’t forget about passwords and other sensitive data that should never appear in the code as open text and have to be encrypted. It seems like every single customer I’ve worked with has this problem. And there was no convincing solution until one of our customers told me about an application called Zuul. As the description on the Zuul web site says, “Zuul is a free, open source web application which can be used to centralize and manage configuration for your internal applications. It enables your operations team to control changes and your developers a centralized place to organize settings.” Of course, I couldn’t resist the urge to download it and try it out with Mule. The installation and configuration of the Zuul server was pretty straightforward. After all, Zuul is a standard web application, so I just deployed it to my local Tomcat instance, alongside with MMC which was already deployed on it. I configured the database settings to point to my local MySQL instance. For the LDAP server I used OpenLDAP. I had to download and install the Unlimited Strength JCE Policy Files. Then I started Tomcat and opened the Zuul URL in my browser and logged in as administrator. The first task is to create my environments. Navigating to Administration->Environments menu, I see three environments, prod, qa, and dev, which Zuul creates by default. Just what I need! Moreover, the prod environment is red – which means, only someone with Administrator privileges can mess with it. And while we are in the Administration screen, let’s create a new encryption key for our password values. Administration->Key Management, then click on Create New... button and populate the form: And now we can create our properties. Select Settings->Create New, give it a name, e.g. AcmeProperties. On the next screen, you’re given the option to create a new properties set from scratch, or to upload an existing properties file. Since we already have acme.properties for our dev environment, let’s just use it. Select dev environment on the left tab, then click Upload File button: Upload acme.properties and you’ll see the following screen: Now we can encrypt the database password. Just make sure the correct key is selected, then click Edit and select Encrypt. To finish the server setup, we replicate this set of properties on the qa and prod environments. Select qa tab, then click Copy Existing, then in the Search text box type dev. Your properties set "/dev/AcmeProperties.properties" will be highlighted. Click Copy button and now you have the identical set of properties in qa. Repeat the process for the prod environment. Change properties values on each environment accordingly. This concludes the server setup procedure. In the next post, I will show you how to configure Mule to use Zuul properties management. UPDATE: Zuul can be downloaded at http://www.devnull.org/zuul