hadoop is best known for map reduce and it's distributed file system (hdfs). recently other productivity tools developed on top of these will form a complete ecosystem of hadoop. most of the projects are hosted under apache software foundation . hadoop ecosystem projects are listed below. hadoop common a set of components and interfaces for distributed file system and i/o (serialization, java rpc, persistent data structures) http://hadoop.apache.org/ hadoop ecosystem hdfs a distributed file system that runs on large clusters of commodity hardware. hadoop distributed file system, hdfs renamed form ndfs. scalable data store that stores semi-structured, un-structured and structured data. http://hadoop.apache.org/docs/r2.3.0/hadoop-project-dist/hadoop-hdfs/hdfsuserguide.html http://wiki.apache.org/hadoop/hdfs map reduce map reduce is the distributed, parallel computing programming model for hadoop. inspired from google map reduce research paper . hadoop includes implementation of map reduce programming model. in map reduce there are two phases, not surprisingly map and reduce. to be precise in between map and reduce phase, there is another phase called sort and shuffle. job tracker in name node machine manages other cluster nodes. map reduce programming can be written in java. if you like sql or other non- java languages, you are still in luck. you can use utility called hadoop streaming. http://wiki.apache.org/hadoop/hadoopmapreduce hadoop streaming a utility to enable map reduce code in many languages like c, perl, python, c++, bash etc., examples include a python mapper and awk reducer. http://hadoop.apache.org/docs/r1.2.1/streaming.html avro a serialization system for efficient, cross-language rpc and persistent data storage. avro is a framework for performing remote procedure calls and data serialization. in the context of hadoop, it can be used to pass data from one program or language to another, e.g. from c to pig. it is particularly suited for use with scripting languages such as pig, because data is always stored with its schema in avro. http://avro.apache.org/ apache thrift apache thrift allows you to define data types and service interfaces in a simple definition file. taking that file as input, the compiler generates code to be used to easily build rpc clients and servers that communicate seamlessly across programming languages. instead of writing a load of boilerplate code to serialize and transport your objects and invoke remote methods, you can get right down to business. http://thrift.apache.org/ hive and hue if you like sql, you would be delighted to hear that you can write sql and hive convert it to a map reduce job. but, you don't get a full ansi-sql environment. hue gives you a browser based graphical interface to do your hive work. hue features a file browser for hdfs, a job browser for map reduce/yarn, an hbase browser, query editors for hive, pig, cloudera impala and sqoop2.it also ships with an oozie application for creating and monitoring workflows, a zookeeper browser and an sdk. pig a high-level programming data flow language and execution environment to do map reduce coding the pig language is called pig latin. you may find naming conventions some what un-conventional, but you get incredible price-performance and high availability. https://pig.apache.org/ jaql jaql is a functional, declarative programming language designed especially for working with large volumes of structured, semi-structured and unstructured data. as its name implies, a primary use of jaql is to handle data stored as json documents, but jaql can work on various types of data. for example, it can support xml, comma-separated values (csv) data and flat files. a "sql within jaql" capability lets programmers work with structured sql data while employing a json data model that's less restrictive than its structured query language counterparts. 1. jaql in google code 2. what is jaql? by ibm sqoop sqoop provides a bi-directional data transfer between hadoop -hdfs and your favorite relational database. for example you might be storing your app data in relational store such as oracle, now you want to scale your application with hadoop so you can migrate oracle database data to hadoop hdfs using sqoop. http://sqoop.apache.org/ oozie manages hadoop workflow. this doesn't replace your scheduler or BPM tooling, but it will provide if-then-else branching and control with hadoop jobs. https://oozie.apache.org/ zookeeper a distributed, highly available coordination service. zookeeper provides primitives such as distributed locks that can be used for building the highly scalable applications. it is used to manage synchronization for cluster. http://zookeeper.apache.org/ hbase based on google's bigtable , hbase "is an open-source, distributed, version, column-oriented store" that sits on top of hdfs. a super scalable key-value store. it works very much like a persistent hash-map (for python developers think like a dictionary). it is not a conventional relational database. it is a distributed, column oriented database. hbase uses hdfs for it's underlying. supports both batch-style computations using map reduce and point queries for random reads. https://hbase.apache.org/ cassandra a column oriented nosql data store which offers scalability, high availability with out compromising on performance. it perfect platform for commodity hardware and cloud infrastructure.cassandra's data model offers the convenience of column indexes with the performance of log-structured updates, strong support for de-normalization and materialized views , and powerful built-in caching. http://cassandra.apache.org/ flume a real time loader for streaming your data into hadoop. it stores data in hdfs and hbase.flume "channels" data between "sources" and "sinks" and its data harvesting can either be scheduled or event-driven. possible sources for flume include avro, files, and system logs, and possible sinks include hdfs and hbase. http://flume.apache.org/ mahout machine learning for hadoop, used for predictive analytics and other advanced analysis. there are currently four main groups of algorithms in mahout: recommendations, a.k.a. collective filtering classification, a.k.a categorization clustering frequent item set mining, a.k.a parallel frequent pattern mining mahout is not simply a collection of pre-existing algorithms; many machine learning algorithms are intrinsically non-scalable; that is, given the types of operations they perform, they cannot be executed as a set of parallel processes. algorithms in the mahout library belong to the subset that can be executed in a distributed fashion. http://en.wikipedia.org/wiki/list_of_machine_learning_algorithms https://www.coursera.org/course/machlearning https://mahout.apache.org/ fuse makes the hdfs system to look like a regular file system so that you can use ls, rm, cd etc., directly on hdfs data. whirr apache whirr is a set of libraries for running cloud services. whirr provides a cloud-neutral way to run services. you don't have to worry about the idiosyncrasies of each provider.a common service api. the details of provisioning are particular to the service. smart defaults for services. you can get a properly configured system running quickly, while still being able to override settings as needed. you can also use whirr as a command line tool for deploying clusters. https://whirr.apache.org/ giraph an open source graph processing api like pregel from google https://giraph.apache.org/ chukwa chukwa, an incubator project on apache, is a data collection and analysis system built on top of hdfs and map reduce. tailored for collecting logs and other data from distributed monitoring systems, chukwa provides a workflow that allows for incremental data collection, processing and storage in hadoop. it is included in the apache hadoop distribution as an independent module. https://chukwa.apache.org/ drill apache drill, an incubator project on apache, is an open-source software framework that supports data-intensive distributed applications for interactive analysis of large-scale datasets. drill is the open source version of google's dremel system which is available as an iaas service called google big query. one explicitly stated design goal is that drill is able to scale to 10,000 servers or more and to be able to process petabytes of data and trillions of records in seconds. http://incubator.apache.org/drill/ impala (cloudera) released by cloudera, impala is an open-source project which, like apache drill, was inspired by google's paper on dremel; the purpose of both is to facilitate real-time querying of data in hdfs or hbase. impala uses an sql-like language that, though similar to hiveql, is currently more limited than hiveql. because impala relies on the hive meta store, hive must be installed on a cluster in order for impala to work. the secret behind impala's speed is that it "circumvents map reduce to directly access the data through a specialized distributed query engine that is very similar to those found in commercial parallel rdbmss." (source: cloudera) http://www.cloudera.com/content/cloudera/en/products-and-services/cdh/impala.html http://training.cloudera.com/elearning/impala/

The best way to write to a Cassandra cluster are concurrent asynchronous writes. In cases where data exhibits strong temporal locality, speed can be improved.

In this article I will go through a common set-up for a small production environment. A single tier, load balanced application server cluster. Overview A high level overview of what we will be doing. Downloading and installing Apache HTTP server and mod_jk Downloading Tomcat Downloading Java Configuring two local Tomcat servers Clustering the two Tomcat servers Configuring Apache to use mod_jk to forward request to Tomcat Deploying application to Tomcat server that tests our set-up Introduction What is Apache? Apache is an HTTP server. What is mod_jk? It is an Apache module that allows AJP communication between Apache and a back end application server like Tomcat.I am running this on Ubuntu 14.04LTS installed on a dual boot PC with Windows 7. Download Apache2 We are going to use Ubuntu's APT package maintenance system to obtain and install Apache2. sudo apt-get install apache2 This will install in /etc/apache2 Download and install mod_jk The mod_jk module is not included in the Apache2 download so must be obtained and installed separately. The installation requires that the mod_jk module is visible to Apache and configured to ensure that Apache knows where to look for it and what to do with the requests you want to proxy. sudo apt-get install libapache2-mod-jk This will install in /etc/libapache2-mod-jk also two files have been added to the /etc/apache2/mods-available folder. Downloading and installing Tomcat 8 At the time of writing this Tomcat 8 does not have a package in APT so you must download the binaries from the tomcat website.http://tomcat.apache.org/download-80.cgi select the appropriate binary distribution and extract it as follows. tar xvzf apache-tomcat-8.0.5.tar.gz We need two copies of the Tomcat server to be load balanced. I created two directories in the /opt/ location: /opt/tomcat-server1/ and /opt/tomcat-server2/ and copied tomcat into each one. Download and install Java Download Java from APT as follows: apt-get install openjdk-7-jdk and set JAVA_HOME in .bashrc vim ~/.bashrc export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64 Configure two local Tomcat servers We will edit only the server.xml of the server2 installation of tomcat. We need to change port numbers to avoid conflicts.We change the following: and comment out the HTTP Connector as we only want the web application to be accessible through the load balancer.Here is my server2 Tomcat server.xml configuration. Configure mod_jk Load balancing is configured in the workers.properties file, located /etc/libapache2-mod-jk/ where workers represent actual or virtual workers.We will define two actual workers and two virtual workers which map to the Tomcat servers. In the worker.list property I have defined two virtual workers: status and loadbalancer, I will refer to these later in the Apache configuration.Workers for each server have been defined using values for the server.xml configuration files. I used the port values for the AJP connectors and I have included an lbfactor that sets the preference that the load balancer will show for that server.Finally we define the virtual workers. The loadbalancer worker is set to type lb and set the workers that represent the Tomcat servers in the balancer_workers properties. The status only needs to be set to type status. worker.list=loadbalancer,status worker.server1.port=8009worker.server1.host=localhostworker.server1.type=ajp13 worker.server2.port=9009worker.server2.host=localhostworker.server2.type=ajp13 worker.server1.lbfactor=1worker.server2.lbfactor=1 worker.loadbalancer.type=lbworker.loadbalancer.balance_workers=server1,server2 worker.status.type=status Ensure that you remove any other worker configuration that are not being used. Configure Apache Web Server to forward requests You will need to add the following to the Apache configurations located in etc/apache2/sites-enabled/000-default.conf JkMount /status status JkMount /* loadbalancer Verify the installation To test that all has been configured correctly we need to deploy an application. A sample application that has been used for years to test such configurations is called the ClusterJSP sample application. You can find it by googling in or from the JBoss site.Now deploy the war to the webapps folder on both servers and start each server using the start-up script /opt/tomcat-server1/bin/startup.sh.Go to http://localhost/clusterjsp/HaJsp.jsp and you should see the page show HttpSession information. Now lets look at the mod_jk status page: http://localhost/status. You will see that this page shows information about the load balancer workers and the workers it is balancing. If everything is working you will see the worker error state show OK or OK/IDLE if they are not currently balancing load. Things to try out Enable sticky sessions: Configure jvmRoute in the server.xml configuration. Further reading Loadbalancing with mod_jk and ApacheWorking with mod_jk Connecting Apache's Web Server to Multiple Instances of Tomcat

I've been playing around with Docker a lot lately. Many reasons for that, one for sure is, that I love to play around with latest technology and even help out to build a demo or two or a lab. The main difference, between what everybody else of my coworkers is doing is, that I run my setup on Windows. Like most of the middleware developers out there. So, If you followed Arun's blog about "Docker Machine to Setup Docker Host" you might have tried to make this work on windows already. Here is the ultimate short how-to guide on using Docker Machine to administrate and spin up your Docker hosts. Docker Machine Machine lets you create Docker hosts on your computer, on cloud providers, and inside your own data center. It creates servers, installs Docker on them, then configures the Docker client to talk to them. You basically don't have to have anything installed on your machine prior to this. Which is a hell lot easier, than having to manually install boot2docker before. So, let's try this out. You want to have at least one thing in place before starting with anything Docker or Machine. Go and get Git for Windows (aka msysgit). It has all kinds of helpful unix tools in his belly, which you need anyway. Prerequisites - The One For All Solution The first is to install the windows boot2docker distribution which I showed in an earlier blog. It contains the following bits configured and ready for you to use: - VirtualBox - Docker Windows Client Prerequisites- The Bits And Pieces I dislike the boot2docker installer for a variety of reasons. Mostly, because I want to know what exactly is going on on my machine. So I played around a bit and here is the bits and pieces installer if you decide against the one-for-all solution. Start with the virtualization solution. We need something like that on Windows, because it just can't run Linux and this is what Docker is based on. At least for now. So, get VirtualBox and ensure that version 4.3.18 is correctly installed on your system (VirtualBox-4.3.18-96516-Win.exe, 105 MB). WARNING: There is a strange issue, when you run Windows itself in Virtualbox. You might run into an issue with starting the host. And while you're at it, go and get the Docker Windows Client. The other is to grab the final from the test servers as a direct download (docker-1.6.0.exe, x86_64, 7.5MB). Rename to "docker" and put it into a folder of your choice (I assume it will be c:\docker\. Now you also need to download Docker Machine, which is another single executable (docker-machine_windows-amd64.exe, 11.5MB). Rename to "docker-machine" and put it into the same folder. Now add this folder to your PATH: set PATH=%PATH%;C:\docker If you change your standard PATH environment variable, this might safe your from a lot of typing. That's it. Now you're ready to create your first Machine managed Docker Host. Create Your Docker Host With Machine All you need is a simple command: docker-machine create --driver virtualbox dev And the output should state: ←[34mINFO←[0m[0000] Creating SSH key... ←[34mINFO←[0m[0001] Creating VirtualBox VM... ←[34mINFO←[0m[0016] Starting VirtualBox VM... ←[34mINFO←[0m[0022] Waiting for VM to start... ←[34mINFO←[0m[0076] "dev" has been created and is now the active machine. ←[34mINFO←[0m[0076] To point your Docker client at it, run this in your shell: eval "$(docker-machine.exe env dev)" This means, you just created a Docker Host using the VirtualBox provider and the name “dev”. Now you need to find out on which IP address the host is running. docker-machine ip 192.168.99.102 If you want to configure your environment variables, needed by the client more easy, just use the following command: docker-machine env dev export DOCKER_TLS_VERIFY=1 export DOCKER_CERT_PATH="C:\\Users\\markus\\.docker\\machine\\machines\\dev" export DOCKER_HOST=tcp://192.168.99.102:2376 Which outputs the Linux version of environment variable definition. All you have to do is to change the "export" keyword to "set", remove the " and the double back-slashes and you are ready to go. C:\Users\markus\Downloads>set DOCKER_TLS_VERIFY=1 C:\Users\markus\Downloads>set DOCKER_CERT_PATH=C:\Users\markus\.docker\machine\machines\dev C:\Users\markus\Downloads>set DOCKER_HOST=tcp://192.168.99.102:2376 Time to test our Docker Client And here we go now run WildFly on your freshly created host: docker run -it -p 8080:8080 jboss/wildfly Watch the container being downloaded and check, that it is running by redirecting your browser to http://192.168.99.102:8080/. Congratulations on having setup your very first docker host with Maschine on Windows.

In containers and microservices, we’re facing the greatest potential change in how we deliver and run software services since the arrival of virtual machines.

[This article was written by Stephane Combaudon] State Snapshot Transfer (SST) is used in Percona XtraDB Cluster (PXC) when a new node joins the cluster or to resync a failed node if Incremental State Transfer (IST) is no longer available. SST is triggered automatically but there is no magic: If it is not configured properly, it will not work and new nodes will never be able to join the cluster. Let’s have a look at a few classic issues. Port for SST is not open The donor and the joiner communicate on port 4444, and if the port is closed on one side, SST will always fail. You will see in the error log of the donor that SST is started: [...] 141223 16:08:48 [Note] WSREP: Node 2 (node1) requested state transfer from '*any*'. Selected 0 (node3)(SYNCED) as donor. 141223 16:08:48 [Note] WSREP: Shifting SYNCED -> DONOR/DESYNCED (TO: 6) 141223 16:08:48 [Note] WSREP: wsrep_notify_cmd is not defined, skipping notification. 141223 16:08:48 [Note] WSREP: Running: 'wsrep_sst_xtrabackup-v2 --role 'donor' --address '192.168.234.101:4444/xtrabackup_sst' --auth 'sstuser:s3cret' --socket '/var/lib/mysql/mysql.sock' --datadir '/var/lib/mysql/' --defaults-file '/etc/my.cnf' --gtid '04c085a1-89ca-11e4-b1b6-6b692803109b:6'' [...] But then nothing happens, and some time later you will see a bunch of errors: [...] 2014/12/23 16:09:52 socat[2965] E connect(3, AF=2 192.168.234.101:4444, 16): Connection timed out WSREP_SST: [ERROR] Error while getting data from donor node: exit codes: 0 1 (20141223 16:09:52.057) WSREP_SST: [ERROR] Cleanup after exit with status:32 (20141223 16:09:52.064) WSREP_SST: [INFO] Cleaning up temporary directories (20141223 16:09:52.068) 141223 16:09:52 [ERROR] WSREP: Failed to read from: wsrep_sst_xtrabackup-v2 --role 'donor' --address '192.168.234.101:4444/xtrabackup_sst' --auth 'sstuser:s3cret' --socket '/var/lib/mysql/mysql.sock' --datadir '/var/lib/mysql/' --defaults-file '/etc/my.cnf' --gtid '04c085a1-89ca-11e4-b1b6-6b692803109b:6' [...] On the joiner side, you will see a similar sequence: SST is started, then hangs and is finally aborted: [...] 141223 16:08:48 [Note] WSREP: Shifting PRIMARY -> JOINER (TO: 6) 141223 16:08:48 [Note] WSREP: Requesting state transfer: success, donor: 0 141223 16:08:49 [Note] WSREP: (f9560d0d, 'tcp://0.0.0.0:4567') turning message relay requesting off 141223 16:09:52 [Warning] WSREP: 0 (node3): State transfer to 2 (node1) failed: -32 (Broken pipe) 141223 16:09:52 [ERROR] WSREP: gcs/src/gcs_group.cpp:long int gcs_group_handle_join_msg(gcs_group_t*, const gcs_recv_msg_t*)():717: Will never receive state. Need to abort. The solution is of course to make sure that the ports are open on both sides. SST is not correctly configured Sometimes you will see an error like this on the donor: 141223 21:03:15 [Note] WSREP: Running: 'wsrep_sst_xtrabackup-v2 --role 'donor' --address '192.168.234.102:4444/xtrabackup_sst' --auth 'sstuser:s3cretzzz' --socket '/var/lib/mysql/mysql.sock' --datadir '/var/lib/mysql/' --defaults-file '/etc/my.cnf' --gtid 'e63f38f2-8ae6-11e4-a383-46557c71f368:0'' [...] WSREP_SST: [ERROR] innobackupex finished with error: 1. Check /var/lib/mysql//innobackup.backup.log (20141223 21:03:26.973) And if you look at innobackup.backup.log: 41223 21:03:26 innobackupex: Connecting to MySQL server with DSN 'dbi:mysql:;mysql_read_default_file=/etc/my.cnf;mysql_read_default_group=xtrabackup;mysql_socket=/var/lib/mysql/mysql.sock' as 'sstuser' (using password: YES). innobackupex: got a fatal error with the following stacktrace: at /usr//bin/innobackupex line 2995 main::mysql_connect('abort_on_error', 1) called at /usr//bin/innobackupex line 1530 innobackupex: Error: Failed to connect to MySQL server: DBI connect(';mysql_read_default_file=/etc/my.cnf;mysql_read_default_group=xtrabackup;mysql_socket=/var/lib/mysql/mysql.sock','sstuser',...) failed: Access denied for user 'sstuser'@'localhost' (using password: YES) at /usr//bin/innobackupex line 2979 What happened? The default SST method is xtrabackup-v2 and for it to work, you need to specify a username/password in the my.cnf file: [mysqld] wsrep_sst_auth=sstuser:s3cret And you also need to create the corresponding MySQL user: mysql> GRANT RELOAD, LOCK TABLES, REPLICATION CLIENT ON *.* TO 'sstuser'@'localhost' IDENTIFIED BY 's3cret'; So you should check that the user has been correctly created in MySQL and that wsrep_sst_auth is correctly set. Galera versions do not match Here is another set of errors you may see in the error log of the donor: 141223 21:14:27 [Warning] WSREP: unserialize error invalid flags 2: 71 (Protocol error) at gcomm/src/gcomm/datagram.hpp:unserialize():101 141223 21:14:30 [Warning] WSREP: unserialize error invalid flags 2: 71 (Protocol error) at gcomm/src/gcomm/datagram.hpp:unserialize():101 141223 21:14:33 [Warning] WSREP: unserialize error invalid flags 2: 71 (Protocol error) at gcomm/src/gcomm/datagram.hpp:unserialize():101 Here the issue is that you try to connect a node using Galera 2.x and a node running Galera 3.x. This can happen if you try to use a PXC 5.5 node and a PXC 5.6 node. The right solution is probably to understand why you ended up with such inconsistent versions and make sure all nodes are using the same Percona XtraDB Cluster version and Galera version. But if you know what you are doing, you can also instruct the node using Galera 3.x that it will communicate with Galera 2.x nodes by specifying in the my.cnf file: [mysqld] wsrep_provider_options="socket.checksum=1" Conclusion SST errors can have multiple reasons for occurring, and the best way to diagnose the issue is to have a look at the error log of the donor and the joiner. Galera is in general quite verbose so you can follow the progress of SST on both nodes and see where it fails. Then it is mostly about being able to interpret the error messages.

written by josh long on the spring blog applications generated more and more data than ever before and a huge part of the challenge - before it can even be analyzed - is accommodating the load in the first place. apache’s kafka meets this challenge. it was originally designed by linkedin and subsequently open-sourced in 2011. the project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. the design is heavily influenced by transaction logs. it is a messaging system, similar to traditional messaging systems like rabbitmq, activemq, mqseries, but it’s ideal for log aggregation, persistent messaging, fast (_hundreds_ of megabytes per second!) reads and writes, and can accommodate numerous clients. naturally, this makes it perfect for cloud-scale architectures! kafka powers many large production systems . linkedin uses it for activity data and operational metrics to power the linkedin news feed, and linkedin today, as well as offline analytics going into hadoop. twitter uses it as part of their stream-processing infrastructure. kafka powers online-to-online and online-to-offline messaging at foursquare. it is used to integrate foursquare monitoring and production systems with hadoop-based offline infrastructures. square uses kafka as a bus to move all system events through square’s various data centers. this includes metrics, logs, custom events, and so on. on the consumer side, it outputs into splunk, graphite, or esper-like real-time alerting. netflix uses it for 300-600bn messages per day. it’s also used by airbnb, mozilla, goldman sachs, tumblr, yahoo, paypal, coursera, urban airship, hotels.com, and a seemingly endless list of other big-web stars. clearly, it’s earning its keep in some powerful systems! installing apache kafka there are many different ways to get apache kafka installed. if you’re on osx, and you’re using homebrew, it can be as simple as brew install kafka . you can also download the latest distribution from apache . i downloaded kafka_2.10-0.8.2.1.tgz , unzipped it, and then within you’ll find there’s a distribution of apache zookeeper as well as kafka, so nothing else is required. i installed apache kafka in my $home directory, under another directory, bin , then i created an environment variable, kafka_home , that points to $home/bin/kafka . start apache zookeeper first, specifying where the configuration properties file it requires is: $kafka_home/bin/zookeeper-server-start.sh $kafka_home/config/zookeeper.properties the apache kafka distribution comes with default configuration files for both zookeeper and kafka, which makes getting started easy. you will in more advanced use cases need to customize these files. then start apache kafka. it too requires a configuration file, like this: $kafka_home/bin/kafka-server-start.sh $kafka_home/config/server.properties the server.properties file contains, among other things, default values for where to connect to apache zookeeper ( zookeeper.connect ), how much data should be sent across sockets, how many partitions there are by default, and the broker id ( broker.id - which must be unique across a cluster). there are other scripts in the same directory that can be used to send and receive dummy data, very handy in establishing that everything’s up and running! now that apache kafka is up and running, let’s look at working with apache kafka from our application. some high level concepts.. a kafka broker cluster consists of one or more servers where each may have one or more broker processes running. apache kafka is designed to be highly available; there are no master nodes. all nodes are interchangeable. data is replicated from one node to another to ensure that it is still available in the event of a failure. in kafka, a topic is a category, similar to a jms destination or both an amqp exchange and queue. topics are partitioned, and the choice of which of a topic’s partition a message should be sent to is made by the message producer. each message in the partition is assigned a unique sequenced id, its offset . more partitions allow greater parallelism for consumption, but this will also result in more files across the brokers. producers send messages to apache kafka broker topics and specify the partition to use for every message they produce. message production may be synchronous or asynchronous. producers also specify what sort of replication guarantees they want. consumers listen for messages on topics and process the feed of published messages. as you’d expect if you’ve used other messaging systems, this is usually (and usefully!) asynchronous. like spring xd and numerous other distributed system, apache kafka uses apache zookeeper to coordinate cluster information. apache zookeeper provides a shared hierarchical namespace (called znodes ) that nodes can share to understand cluster topology and availability (yet another reason that spring cloud has forthcoming support for it..). zookeeper is very present in your interactions with apache kafka. apache kafka has, for example, two different apis for acting as a consumer. the higher level api is simpler to get started with and it handles all the nuances of handling partitioning and so on. it will need a reference to a zookeeper instance to keep the coordination state. let’s turn now turn to using apache kafka with spring. using apache kafka with spring integration the recently released apache kafka 1.1 spring integration adapter is very powerful, and provides inbound adapters for working with both the lower level apache kafka api as well as the higher level api. the adapter, currently, is xml-configuration first, though work is already underway on a spring integration java configuration dsl for the adapter and milestones are available. we’ll look at both here, now. to make all these examples work, i added the libs-milestone-local maven repository and used the following dependencies: org.apache.kafka:kafka_2.10:0.8.1.1 org.springframework.boot:spring-boot-starter-integration:1.2.3.release org.springframework.boot:spring-boot-starter:1.2.3.release org.springframework.integration:spring-integration-kafka:1.1.1.release org.springframework.integration:spring-integration-java-dsl:1.1.0.m1 using the spring integration apache kafka with the spring integration xml dsl first, let’s look at how to use the spring integration outbound adapter to send message instances from a spring integration flow to an external apache kafka instance. the example is fairly straightforward: a spring integration channel named inputtokafka acts as a conduit that forwards message messages to the outbound adapter, kafkaoutboundchanneladapter . the adapter itself can take its configuration from the defaults specified in the kafka:producer-context element or it from the adapter-local configuration overrides. there may be one or many configurations in a given kafka:producer-context element. here’s the java code from a spring boot application to trigger message sends using the outbound adapter by sending messages into the incoming inputtokafka messagechannel . package xml; import org.apache.commons.logging.log; import org.apache.commons.logging.logfactory; import org.springframework.beans.factory.annotation.qualifier; import org.springframework.boot.commandlinerunner; import org.springframework.boot.springapplication; import org.springframework.boot.autoconfigure.springbootapplication; import org.springframework.context.annotation.bean; import org.springframework.context.annotation.dependson; import org.springframework.context.annotation.importresource; import org.springframework.integration.config.enableintegration; import org.springframework.messaging.messagechannel; import org.springframework.messaging.support.genericmessage; @springbootapplication @enableintegration @importresource("/xml/outbound-kafka-integration.xml") public class demoapplication { private log log = logfactory.getlog(getclass()); @bean @dependson("kafkaoutboundchanneladapter") commandlinerunner kickoff(@qualifier("inputtokafka") messagechannel in) { return args -> { for (int i = 0; i < 1000; i++) { in.send(new genericmessage<>("#" + i)); log.info("sending message #" + i); } }; } public static void main(string args[]) { springapplication.run(demoapplication.class, args); } } using the new apache kafka spring integration java configuration dsl shortly after the spring integration 1.1 release, spring integration rockstar artem bilan got to work on adding a spring integration java configuration dsl analog and the result is a thing of beauty! it’s not yet ga (you need to add the libs-milestone repository for now), but i encourage you to try it out and kick the tires. it’s working well for me and the spring integration team are always keen on getting early feedback whenever possible! here’s an example that demonstrates both sending messages and consuming them from two different integrationflow s. the producer is similar to the example xml above. new in this example is the polling consumer. it is batch-centric, and will pull down all the messages it sees at a fixed interval. in our code, the message received will be a map that contains as its keys the topic and as its value another map with the partition id and the batch (in this case, of 10 records), of records read. there is a messagelistenercontainer -based alternative that processes messages as they come. package jc; import org.apache.commons.logging.log; import org.apache.commons.logging.logfactory; import org.springframework.beans.factory.annotation.autowired; import org.springframework.beans.factory.annotation.qualifier; import org.springframework.beans.factory.annotation.value; import org.springframework.boot.commandlinerunner; import org.springframework.boot.springapplication; import org.springframework.boot.autoconfigure.springbootapplication; import org.springframework.context.annotation.bean; import org.springframework.context.annotation.configuration; import org.springframework.context.annotation.dependson; import org.springframework.integration.integrationmessageheaderaccessor; import org.springframework.integration.config.enableintegration; import org.springframework.integration.dsl.integrationflow; import org.springframework.integration.dsl.integrationflows; import org.springframework.integration.dsl.sourcepollingchanneladapterspec; import org.springframework.integration.dsl.kafka.kafka; import org.springframework.integration.dsl.kafka.kafkahighlevelconsumermessagesourcespec; import org.springframework.integration.dsl.kafka.kafkaproducermessagehandlerspec; import org.springframework.integration.dsl.support.consumer; import org.springframework.integration.kafka.support.zookeeperconnect; import org.springframework.messaging.messagechannel; import org.springframework.messaging.support.genericmessage; import org.springframework.stereotype.component; import java.util.list; import java.util.map; /** * demonstrates using the spring integration apache kafka java configuration dsl. * thanks to spring integration ninja artem bilan * for getting the java configuration dsl working so quickly! * * @author josh long */ @enableintegration @springbootapplication public class demoapplication { public static final string test_topic_id = "event-stream"; @component public static class kafkaconfig { @value("${kafka.topic:" + test_topic_id + "}") private string topic; @value("${kafka.address:localhost:9092}") private string brokeraddress; @value("${zookeeper.address:localhost:2181}") private string zookeeperaddress; kafkaconfig() { } public kafkaconfig(string t, string b, string zk) { this.topic = t; this.brokeraddress = b; this.zookeeperaddress = zk; } public string gettopic() { return topic; } public string getbrokeraddress() { return brokeraddress; } public string getzookeeperaddress() { return zookeeperaddress; } } @configuration public static class producerconfiguration { @autowired private kafkaconfig kafkaconfig; private static final string outbound_id = "outbound"; private log log = logfactory.getlog(getclass()); @bean @dependson(outbound_id) commandlinerunner kickoff( @qualifier(outbound_id + ".input") messagechannel in) { return args -> { for (int i = 0; i < 1000; i++) { in.send(new genericmessage<>("#" + i)); log.info("sending message #" + i); } }; } @bean(name = outbound_id) integrationflow producer() { log.info("starting producer flow.."); return flowdefinition -> { consumer spec = (kafkaproducermessagehandlerspec.producermetadataspec metadata)-> metadata.async(true) .batchnummessages(10) .valueclasstype(string.class) .valueencoder(string::getbytes); kafkaproducermessagehandlerspec messagehandlerspec = kafka.outboundchanneladapter( props -> props.put("queue.buffering.max.ms", "15000")) .messagekey(m -> m.getheaders().get(integrationmessageheaderaccessor.sequence_number)) .addproducer(this.kafkaconfig.gettopic(), this.kafkaconfig.getbrokeraddress(), spec); flowdefinition .handle(messagehandlerspec); }; } } @configuration public static class consumerconfiguration { @autowired private kafkaconfig kafkaconfig; private log log = logfactory.getlog(getclass()); @bean integrationflow consumer() { log.info("starting consumer.."); kafkahighlevelconsumermessagesourcespec messagesourcespec = kafka.inboundchanneladapter( new zookeeperconnect(this.kafkaconfig.getzookeeperaddress())) .consumerproperties(props -> props.put("auto.offset.reset", "smallest") .put("auto.commit.interval.ms", "100")) .addconsumer("mygroup", metadata -> metadata.consumertimeout(100) .topicstreammap(m -> m.put(this.kafkaconfig.gettopic(), 1)) .maxmessages(10) .valuedecoder(string::new)); consumer endpointconfigurer = e -> e.poller(p -> p.fixeddelay(100)); return integrationflows .from(messagesourcespec, endpointconfigurer) .>>handle((payload, headers) -> { payload.entryset().foreach(e -> log.info(e.getkey() + '=' + e.getvalue())); return null; }) .get(); } } public static void main(string[] args) { springapplication.run(demoapplication.class, args); } } the example makes heavy use of java 8 lambdas. the producer spends a bit of time establishing how many messages will be sent in a single send operation, how keys and values are encoded (kafka only knows about byte[] arrays, after all) and whether messages should be sent synchronously or asynchronously. in the next line, we configure the outbound adapter itself and then define an integrationflow such that all messages get sent out via the kafka outbound adapter. the consumer spends a bit of time establishing which zookeeper instance to connect to, how many messages to receive (10) in a batch, etc. once the message batches are recieved, they’re handed to the handle method where i’ve passed in a lambda that’ll enumerate the payload’s body and print it out. nothing fancy. using apache kafka with spring xd apache kafka is a message bus and it can be very powerful when used as an integration bus. however, it really comes into its own because it’s fast enough and scalable enough that it can be used to route big-data through processing pipelines. and if you’re doing data processing, you really want spring xd ! spring xd makes it dead simple to use apache kafka (as the support is built on the apache kafka spring integration adapter!) in complex stream-processing pipelines. apache kafka is exposed as a spring xd source - where data comes from - and a sink - where data goes to. spring xd exposes a super convenient dsl for creating bash -like pipes-and-filter flows. spring xd is a centralized runtime that manages, scales, and monitors data processing jobs. it builds on top of spring integration, spring batch, spring data and spring for hadoop to be a one-stop data-processing shop. spring xd jobs read data from sources , run them through processing components that may count, filter, enrich or transform the data, and then write them to sinks. spring integration and spring xd ninja marius bogoevici , who did a lot of the recent work in the spring integration and spring xd implementation of apache kafka, put together a really nice example demonstrating how to get a full working spring xd and kafka flow working . the readme walks you through getting apache kafka, spring xd and the requisite topics all setup. the essence, however, is when you use the spring xd shell and the shell dsl to compose a stream. spring xd components are named components that are pre-configured but have lots of parameters that you can override with --.. arguments via the xd shell and dsl. (that dsl, by the way, is written by the amazing andy clement of spring expression language fame!) here’s an example that configures a stream to read data from an apache kafka source and then write the message a component called log , which is a sink. log , in this case, could be syslogd, splunk, hdfs, etc. xd> stream create kafka-source-test --definition "kafka --zkconnect=localhost:2181 --topic=event-stream | log"--deploy and that’s it! naturally, this is just a tase of spring xd, but hopefully you’ll agree the possibilities are tantalizing. deploying a kafka server with lattice and docker it’s easy to get an example kafka installation all setup using lattice , a distributed runtime that supports, among other container formats, the very popular docker image format. there’s a docker image provided by spotify that sets up a collocated zookeeper and kafka image . you can easily deploy this to a lattice cluster, as follows: ltc create --run-as-root m-kafka spotify/kafka from there, you can easily scale the apache kafka instances and even more easily still consume apache kafka from your cloud-based services. next steps you can find the code for this blog on my github account . we’ve only scratched the surface! if you want to learn more (and why wouldn’t you?), then be sure to check out marius bogoevici and dr. mark pollack’s upcoming webinar on reactive data-pipelines using spring xd and apache kafka where they’ll demonstrate how easy it can be to use rxjava, spring xd and apache kafka!

What is Helix? It is used for the automatic management of partitioned, replicated and distributed resources hosted on a cluster of nodes. Helix automates reassignment of resources in the face of node failure and recovery, cluster expansion, and reconfiguration. Modeling a distributed system as a state machine with constraints on states and transitions. Terminologies Node : A single machine Cluster: Set of Nodes Resource : A logical entry (e.g. database, index, task) Partition: Subset of the resource (Each subtask is referred to as a partition) Replica: Copy of a Partition State (e.g Master, Slave). It increase the availability of the system State: Describes the role of a replica (Each node in the cluster has its own Current State) State Machine and Transitions: An action that allows a replica to move from one state to another, thus changing its role. ( e.g Slave --> Master ) spectators: the external clients. Helix provides an External View that is an aggregated view of the current state across all nodes. Current State: represents resource's actual state at a participating node. - INSTANCE_NAME: Unique name representing the process - SESSION_ID: ID that is automatically assigned every time a process joins the cluster Rebalancer: The core component of Helix is the Controller which runs the Rebalance algorithm on every cluster event. Dynamic Ideal State: Helix powerful is that Ideal State can be changed dynamically. It is adjusting the ideal state. Whenever a cluster event occurs, Helix can operate in one of three modes FULL_AUTO SEMI_AUTO CUSTOMIZED Cluster events can be one of the following: Nodes start and/or stop Nodes experience soft and/or hard failures New nodes are added/removed [1] http://helix.apache.org/Concepts.html

Some key concepts for Apache's popular Cassandra Architecture include partitioning, replication, consistency, bootstrapping, and write paths.

Clustering is a very important thing to master for any serious user of an application server. Clustering allows for high availability by making your application available on secondary servers when the primary instance is down or it lets you scale up or out by increasing the server density on the host, or by adding servers on other hosts. It can even help to increase performance with effective load balancing between servers based on their respective hardware. Andy Overton has already covered how to set up a cluster of servers in standalone mode fronted by mod_cluster for load balancing, so in this post I'll cover clustering in domain mode. I won't rehash mod_cluster settings, so this will just cover the set up of a doman controller on one host, and the host controller and server instances on another host. To follow along with this blog, you'll need to download either JBoss EAP 6.x or WildFly. I'll be using WildFly 8.2 on Xubuntu 14.04. I'll be using $WF_HOME to refer to your WildFly home directory. Configuring the Domain Controller The domain controller needs both the domain.xml and host.xml configured. In the $WF_HOME/domain/configuration directory, you'll see that those two files are joined by a host-master.xml and a host-slave.xml. These are preconfigured host.xml files which you can use to give you a head start in making a host.xml for the domain controller (master) and host controller (slave) to use. You can either change the name of the file to be host.xml, so it will get picked up and used by default, or you can specify the host configuration you want to use on the command line by adding the --host-config argument: domain.sh --host-config=host-master.xml Whether you choose to modify the host.xml or the host-master.xml, you need to make sure that the empty element has been added to the section. This is so that when WildFly looks to see which server is the domain controller, it knows to become the domain controller itself. The other change is optional, but recommended. We need to tell the domain controller to bind its management interface to the correct IP address because, by default, it will bind to localhost, so the management communication it needs to do with the remote hosts won't be able to reach the domain controller at all! We can set this address permanently in the host.xml by making sure the inet-address value is set to the right IP, by changing the 127.0.0.1 in the example below to the correct IP: The result of that is that the default bind IP of the management interface is no longer localhost, although you can still override this value by starting JBoss with the variable left of the colon as a -D argument: domain.sh -Djboss.bind.address.management=10.0.0.1 Next, we need to modify the domain.xml file, where we need to define our server groups; essentially just defining the cluster. Each server group is named, so we can reference it later, and references a particular profile which needs to be one of the profiles named and defined in the same XML file. As I mentioned in my previous blog, domain mode has several profiles in the same file (domain.xml) rather than multiple files for each, like standalone mode (standalone.xml, standalone-ha.xml etc.). In the screenshot, there are two server groups defined - "main-server-group" which references the "full" profile, and "other-server-group" which references the "full-ha" profile. These are just the defaults which come with WildFly, so you're free to use them and modify the settings or create your own from scratch. Whichever you choose, it's a good idea to rename your server group to something meaningful, like a description of the workload, or the application name. Configuring the Host Controllers Every host server which you want to be part of the cluster must have the host.xml file configured. We've already configured the host.xml on the domain controller, so now we'll focus on the host controller. Remember, this process can be repeated on any number of hosts, depending on how many servers you want in your server group and their topology. First, we need to make sure that the domain controller and the host controller can communicate, and to do that we need a valid management user. On the domain controller, run the add-user.sh or add-user.bat script. You will need to make sure to: Choose a management user Make sure the user is different than the one you would use to log in to the web console Confirm that the new user will connect one AS process to another AS process Make a note of the secret value (this is very important!) You will find that you get prompts similar to the following: mike@mike-C2B2:~$ /opt/wildfly/wildfly-8.2.0.Final/bin/add-user.sh What type of user do you wish to add? a) Management User (mgmt-users.properties) b) Application User (application-users.properties) (a): a Enter the details of the new user to add. Using realm 'ManagementRealm' as discovered from the existing property files. Username : mgmt Password recommendations are listed below. To modify these restrictions edit the add-user.properties configuration file. - The password should not be one of the following restricted values {root, admin, administrator} - The password should contain at least 8 characters, 1 alphabetic character(s), 1 digit(s), 1 non-alphanumeric symbol(s) - The password should be different from the username Password : Re-enter Password : What groups do you want this user to belong to? (Please enter a comma separated list, or leave blank for none)[ ]: About to add user 'mgmt' for realm 'ManagementRealm' Is this correct yes/no? yes Added user 'mgmt' to file '/opt/wildfly/wildfly-8.2.0.Final/standalone/configuration/mgmt-users.properties' Added user 'mgmt' to file '/opt/wildfly/wildfly-8.2.0.Final/domain/configuration/mgmt-users.properties' Added user 'mgmt' with groups to file '/opt/wildfly/wildfly-8.2.0.Final/standalone/configuration/mgmt-groups.properties' Added user 'mgmt' with groups to file '/opt/wildfly/wildfly-8.2.0.Final/domain/configuration/mgmt-groups.properties' Is this new user going to be used for one AS process to connect to another AS process? e.g. for a slave host controller connecting to the master or for a Remoting connection for server to server EJB calls. yes/no? yes To represent the user add the following to the server-identities definition Once we have the secret value for our management user, we can add it to the host.xml file. I'm choosing to modify the host-slave.xml file, since much of the configuration I need is done for me: Next, we need to tell the host controller where to look for the domain controller. We set this to for the domain controller's host.xml file, but in the host-slave.xml we have an example tag filled out for us. All we need to do is add the domain controller's IP or hostname exactly as we did for the management bind address earlier. So our host-slave.xml should go from this: to this: This way, like with the management interface on the domain controller, the default address will be 10.0.0.1, but it can also be overridden on the command line if needed. Once we've sorted the communication out, we need to tell the host controller to actually start some server instances! At the bottom of the host-slave.xml file, there are two predefined servers to use: These are already configured to become members of the two server groups configured in the domain.xml. Note that the second server has to have a port offset. Despite it being in a different server group, it's still going to run on the same host and will attempt to bind to the same ports as the first server unless we tell it not to! We would also need to do the same thing if we added other server instances. Optionally, we can make things a little easier for ourselves when managing a lot of servers on a lot of hosts. We can give each server instance its own unique name, but we can also name the host by adding a name attribute to the parent tag, changing it from: to So both in the logs and in the admin console, you should see this host controller referred to as "host1". Now, if you wanted to name your server instances the same across hosts, you'll be able to tell which is which! If all you wanted was to configure a single domain controller and a single host controller, then that's all we need to do to get them speaking to each other. You can then carry on and configure mod_cluster and Apache to forward requests on to the right server, or just deploy your applications and connect to them directly.

Problem definition Imagine that you’ve been using some RDBMS for last ten years, and there’s millions of records inside, moreover the information keeps flowing every minute. And one day you needed to organize some complex information retrieval process on this data with a full-fledge enterprise search engine like Apache Solr. In order to do this you need to develop some ETL process that will be able to convert your data to internal Solr documents. You obviously want to distribute it and make fault-tolerant (you don’t want to lose even a row of information). And the last requirement, that makes everything more interesting - you want zero support burden. Everything should come out of the box! Too difficult? Absolutely not! Let’s see how to nail it with Solr, Spark and Zookeeper binded together. Why Spark? It is distributed, it doesn’t force you to use MapReduce programming model (like Hadoop does), and finally, it has a failover mechanism backed by Zookeeper. So, we introduced our toolbox, now it’s time to make it work. Let’s start with Solr and Zookeeper. Historically, Solr was just an universal search engine built on top of Apache Lucene, but starting from 4.x version it comes with distributed search support aka SolrCloud, which provides additional failure resilience, delegating cluster management to Zookeeper. Spark can also use Zookeeper for failure recovery in cluster mode. Preparation First, let’s prepare a simple application that will emulate our long running data import task. We’ll submit this application to Spark and try to simulate node/process failure, while it sends simple documents to Solr. Our goal is to ensure that there’s no document loss due to any failures. Spark applications are just Java archives containing all project dependencies. Documentation recommends to use eithersbt orMaven project management tools with appropriate plugins: sbt-assembly Apache Maven Shade Plugin We’ll make our application with Maven. In order to do this we need to add a dependency to SolrJ (http://mvnrepository.com/artifact/org.apache.solr/solr-solrj) and put a dummy class with a simple code like this: package com.example; import org.apache.solr.client.solrj.SolrServer; import org.apache.solr.client.solrj.impl.HttpSolrServer; import org.apache.solr.common.SolrInputDocument; import java.util.ArrayList; import java.util.List; public class Executor { public static void main(String[] args) { String solrServerURL = "http://localhost:8983/solr"; int offset = 0; int delay = 1; for (String arg : args) { if (arg.startsWith("offset=")) { // This offset is needed to avoid sending several documents with the // same id. In spite of the fact that Solr can handle such documents, // we don't want to have any intersections. If we run 4 tasks like this // one, then we need check if all 4000 documents are indexed. offset = Integer.parseInt(arg.split("=")[1]); } else if (arg.startsWith("delay")) { delay = Integer.parseInt(arg.split("=")[1]); } else if (arg.startsWith("solr_server")) { solrServerURL = arg.split("=")[1]; } } SolrServer solrServer = new HttpSolrServer(solrServerURL); List docs = new ArrayList(); for (int i = 1; i <= offset + 1000; i ++) { SolrInputDocument document = new SolrInputDocument(); document.addField("docid", String.valueOf(i)); docs.add(document); // Add documents to Solr by batches with a size equal to 100 try { if (i % 100 == 0) { solrServer.add(docs); docs = new ArrayList(); } } catch (Exception e) { // TODO: Proper exception handling } // Artificial delay to prolongate total time try { Thread.sleep(delay); } catch (InterruptedException e) { // Nothing to do } } // Finally commit documents and free resources try { solrServer.commit(); } catch (Exception e) { // TODO: Proper exception handling } finally { solrServer.shutdown(); } } } This code generates Solr input documents with the only field “id”, and sends them to server using Java client called SolrJ. The “id” field must be unique in the context of Solr index, and nevertheless it is not exceptional situation for Solr to handle two documents with the same id, we need to count every document sent to server to ensure that no document is lost. Fine, let’s build it, and now we have our “uber” JAR with all dependencies. SolrCloud deployment Next step. Now we are going to download and unpack SolrCloud distributive. Downloads are located on the pagehttp://lucene.apache.org/solr/downloads.html. I took 4.10.2 version. We’ll be using embedded example in our how-to, let’s clone its folder to make four independent workspaces: my_device:~ Root$ cd ~/solr-4.10.2/ my_device:solr-4.10.2 Root$ cp -r example example2 my_device:solr-4.10.2 Root$ cp -r example exampleB my_device:solr-4.10.2 Root$ cp -r example example2B Ok, now deploy first instance: my_device:example Root$ export SOLR_INSTANCE=~/solr-4.10.2/example my_device:example Root$ java -Djetty.port=8983 -Djetty.home=$SOLR_INSTANCE -Dsolr.solr.home=$SOLR_INSTANCE/solr -Dbootstrap_confdir=$SOLR_INSTANCE/solr/collection1/conf -Dcollection.configName=myconf -DzkRun -DzkHost=localhost:9983,localhost:8574,localhost:9900,localhost:9500 -DnumShards=2 -jar $SOLR_INSTANCE/start.jar And continue deploying, changing jetty.port (8983, 7574, 8900, 8500) and SOLR_INSTANCE (example, example2, exampleB, example2B) parameters accordingly. You can send all these commands to background using “&” appendix or run them separately in four consoles. Here: jetty.port, jetty.home - Solr from example uses Jetty embedded server under the hood, so we just need to specify right port to bind on and working folder solr.solr.home - home directory for all Solr files (properties, index files and so on) bootstrap_confdir - initial boot configuration, that will be uploaded to ZooKeeper collection.configName - sets a name for ZooKeeper configuration zkRun - tells to run embedded ZooKeeper zkHost - specifies all nodes eligible to host ZooKeeper When everything is done, we’ll get two shards and two replicas SolrCloud. Also we told Solr to run embedded ZooKeeper and upload its configuration there. You can check Solr state, cloud details and ZooKeeper data by going to Solr admin page located by the link http://localhost:port/solr (pic. 1 and 2). Pic. 1 – SolrCloud graph Pic. 2 – Zookeeper tree Spark deployment Now, it’s Spark turn. Spark can be downloaded fromhttps://spark.apache.org/downloads.html. There are several pre-built versions available for download for your convenience. When it’s done, unpack an archive and clone it - one instance will became a leader or master node, second one will became spare or standby node. Let’s name these folders accordingly: my_device:~ Root$ tar -xzvf spark-1.1.1-bin-hadoop2.4.tgz my_device:~ Root$ mv spark-1.1.1-bin-hadoop2.4 spark-leader my_device:~ Root$ cp spark-leader spark-standby Alright, then we need to configure our Spark nodes. In order to do this, copy or rename existing template: my_device:~ Root$ cd spark-leader/conf my_device:~ Root$ cp spark-env.sh.template spark-env.sh And reduce it’s content to: export SPARK_MASTER_IP="localhost" export SPARK_MASTER_PORT=1101 export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=localhost:9983,localhost:8574,localhost:9900,localhost:9500" export SPARK_PID_DIR="/tmp/spark-leader" export SPARK_MASTER_WEBUI_PORT=6661 export SPARK_WORKER_PORT=1102 export SPARK_WORKER_MEMORY=512m export SPARK_LOCAL_DIRS="./data" export SPARK_WORKER_DIR="./work" What is this all about? Well, every option is described in the original template, we’ll only pay an attention to the following variables: SPARK_DAEMON_JAVA_OPTS - we told Spark to use Zookeeper for recovery and pointed to Zookeeper cluster SPARK_PID_DIR - there’s a dir where Spark will keep process ids, we will need it in order to simulate node failure Do the same for the spare node: export SPARK_MASTER_IP="localhost" export SPARK_MASTER_PORT=2101 export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=localhost:9983,localhost:8574,localhost:9900,localhost:9500" export SPARK_PID_DIR="/tmp/spark-standby" export SPARK_MASTER_WEBUI_PORT=7661 export SPARK_WORKER_PORT=2102 export SPARK_WORKER_MEMORY=512m export SPARK_LOCAL_DIRS="./data" export SPARK_WORKER_DIR="./work" That’s all, now it’s time to deploy Spark cluster. I wrote a simple bash script to achieve this goal: #!/bin/bash rm -rf /tmp/spark-leader rm -rf /tmp/spark-standby bash spark-leader/sbin/start-master.sh bash spark-standby/sbin/start-master.sh SPARK_WORKER_INSTANCES=4 SPARK_WORKER_WEBUI_PORT=8661 for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do bash spark-leader/sbin/start-slave.sh $(( $i + 1 )) spark://localhost:1101 --webui-port $(( $SPARK_WORKER_WEBUI_PORT + $i )) done This script first cleans tmp directories, then runs two master nodes and finally starts four worker nodes. What are these master and worker nodes? Worker nodes are executors, they can only run assigned tasks, and master nodes are Spark coordinators, they orchestrate Spark cluster and assign tasks to available workers. If everything is fine, in a minute after the script is run we’ll be able to see following page by addressing to http://locahost:6661: Pic. 3 – Spark master web UI In the picture 3 we can see Spark master with four connected worker nodes. If we go to another node (http://localhost:7661), we’ll see similar screen, but without any worker nodes connected (that's because we sent them to spark://localhost:1101). Testing So, we deployed Solr, Zookeeper and Spark clusters, and we have an assembled application, let’s submit it and experiment with failover! my_device:~ Root$ spark-leader/bin/spark-submit --class com.example.Executor --master spark://localhost:1101 --deploy-mode cluster --supervise --executor-memory 512m --total-executor-cores 4 path-to-application.jar offset=0 solr_server=http://localhost:8983/solr & What do these command mean? First of all, we use spark-submit script to submit our tasks. Second, we point to an executable class within our jar. Further we tell Spark how to submit the tasks (or drivers in terminology of Spark): --deploy-mode cluster – from documentation “Whether to deploy your driver on the worker nodes (cluster) or locally as an external client (client) (default: client)” --supervise – specify this flag to make sure that the driver is automatically restarted if it fails with non-zero exit code. This’s exactly what we’re looking for – in case if Spark worker node is terminated, Spark cluster will just restart it on any free worker node. What about the rest of the arguments? Well, one of them is obviously just path to our jar. And the others two are arguments that are sent to our executable class. Solr server url is an endpoint where to send Solr documents. And the last one – offset – we need it, as it was told, to avoid sending several documents with the same id. Run this command four times increasing offset by thousand. If everything is fine, we’ll get four completed tasks and four thousand documents in Solr (see pictures 4 and 5). Pic. 4 – finished drivers in Spark master web UI Pic. 5 – Match all query in Solr web UI What next? Remember we introduced “delay” command line argument in our code? We need it now to manipulate total execution time causing some artificial processing delay. During the first run we minimized it to default 1 ms, because we needed this run only to collect our control data. During the second run we’ll extend this value up to 180 ms (total execution time will be at least 3 minutes) - this will give us enough time to simulate node failure. In the end we’ll compare control data with test data. But first we need to find out leader node PID: my_device:~ Root$ cd /tmp/spark-leader my_device:~ Root$ cat spark-Root-org.apache.spark.deploy.master.Master-1.pid 38073 my_device:~ Root$ Ok, this number – 38073 – is our leader node PID. And let’s find out our worker nodes ids: my_device:~ Root$ jps 38145 Master 38073 Master 38016 start.jar 38014 start.jar 38015 start.jar 38865 Jps 38013 start.jar 38385 Worker 38325 Worker 38265 Worker 38205 Worker my_device:~ Root$ We’ll choose one of the workers as well as leader node and terminate them, but first we need to purge Solr and run our modified submit commands (again, four times): my_device:~ Root$ curl http://localhost:8983/solr/update --data '*:*' -H 'Content-type:text/xml; charset=utf-8' my_device:~ Root$ curl http://localhost:8983/solr/update --data '' -H 'Content-type:text/xml; charset=utf-8' my_device:~ Root$ spark-leader/bin/spark-submit --class com.example.Executor --master spark://localhost:1101 --deploy-mode cluster --supervise --executor-memory 512m --total-executor-cores 4 path-to-application.jar offset=0 delay=180 solr_server=http://localhost:8983/solr & Ready? Lets simulate nodes failure: my_device:~ Root$ kill -9 38385 my_device:~ Root$ kill -9 38073 If we’re fast enough, we can see how Spark failover mechanism works – all we need is to track changes on our standby node web UI (http://localhost:7661). Usually it takes about a half of a minute to see any updates. Expected behaviour is that no submitted task will lost if the worker is down (pic. 6), spare node will change its status from STANDBY to ALIVE (it becomes leader node) if current leader node is terminated, and no orphan worker will left – they all become connected to new leader (pic. 7): Pic. 6 – Driver relaunching Pic. 7 – Spark recovery process on spare master node Finally, when all drivers become completed, we need to go back to Solr and check that all match query returns the same result as we seen (pic. 5) – 4000 documents. Okay, seems our problem is solved - we simulated long-run distributed task, we randomly terminated executing applications and we saw entire recovery process which ensured that we haven’t lost any data. We didn’t have to write any special code to achieve this level of resiliency and job recovery. So, Spark can be used as a resilient job manager for long running data importing tasks.

In this article I will attempt to run the Mule ESB community edition in Docker in order to see whether it is feasible without any greater inconvenience. My goal is to be able to use Docker both when testing as well as in a production environment in order to gain better control over the environment and to separate different types of environments. I imagine that most of the Docker-related information can be applied to other applications – I have used Mule since it is what I usually work with. The conclusion I have made after having completed my experiments is that it is possible to run Mule ESB in Docker without any inconvenience. In addition, Docker will indeed allow me to have better control over the different environments and also allow me to separate them as I find appropriate. Finally, I just want to mention that I have used Docker in an Ubuntu environment. I have not attempted any of the exercises in Docker running on Windows or Mac OS X. Docker Briefly In short, Docker allows for creating of images that serve as blueprints for containers. A Docker container is an instance of a Docker image in the same way a Java object is an instance of a Java class. FROM codingtony/java MAINTAINER tony(dot)bussieres(at)ticksmith(dot)com RUN wget https://repository.mulesoft.org/nexus/content/repositories/releases/org/mule/distributions/mule-standalone/3.5.0/mule-standalone-3.5.0.tar.gz RUN cd /opt && tar xvzf ~/mule-standalone-3.5.0.tar.gz RUN echo "4a94356f7401ac8be30a992a414ca9b9 /mule-standalone-3.5.0.tar.gz" | md5sum -c RUN rm ~/mule-standalone-3.5.0.tar.gz RUN ln -s /opt/mule-standalone-3.5.0 /opt/mule CMD [ "/opt/mule/bin/mule" ] The resource isolation features of Linux are used to create Docker containers, which are more lightweight than virtual machines and are separated from the environment in which Docker runs, the host. Using Docker an image can be created that, every time it is started has a known state. In order to remove any doubts about whether the environment has been altered in any way, the container can be stopped and a new container started. I can even run multiple Docker containers on one and the same computer to simulate a multi-server production environment. Applications can also be run in their own Docker containers, as shown in this figure. Three Docker containers, each containing a specific application, running in one host. A more detailed introduction to Docker is available here. The main entry point to the Docker documentation can be found here. Motivation Some of the motivations I have for using Docker in both testing and production environments are: The environment in which I test my application should be as similar as the final deployment environment as possible, if not identical. Making the deployment environment easy to scale up and down. If it is easy to start a new processing node when need arise and stop it if it is no longer used, I will be able to adapt to changes rather quickly and thus reduce errors caused by, for instance, load peaks. Maintain an increased number of nodes to which applications can be deployed. Instead of running one instance of some kind of application server, Mule ESB in my case, on a computer, I want multiple instances that are partitioned, for instance, according to importance. High-priority applications run on one separate instance, which have higher priority both as far as resources (CPU, memory, disk etc) are concerned but also as far as support is concerned. Applications which are less critical run on another instance. Enable quick replacement of instances in the deployment environment. Reasons for having to replace instances may be hardware failure etc. Better control over the contents of the different environments. The concept of an environment that, at any time, may be disposed (and restarted) discourages hacks in the environment, which are usually poorly documented and sometimes difficult to trace. Using Docker, I need to change the appropriate Docker image if I want to make changes to some application environment. The Docker image file, commonly known as Dockerfile, can be checked into any ordinary revision control system, such as Git, Subversion etc, making changes reversible and traceable. Automate the creation of a testing environment. An example could be a nightly job that runs on my build server which creates a test environment, deploys one or more applications to it and then performs tests, such as load-testing. Prerequisites To get the best possible experience when running Docker, I run it under Ubuntu. According to the current documentation, Docker is supported under the following versions of Ubuntu: 12.04 LTS (64-bit) 13.04 (64-bit) 13.10 (64-bit) 14.04 (64-bit) Against my usual conservative self, I chose Ubuntu 14.10, which at the time of writing this article is the latest version. While I haven’t run into any issues, I cannot promise anything regarding compatibility with Docker as far as this version of Ubuntu is concerned. Installing Docker Before we install anything, those who have the Docker version from the Ubuntu repository should remove this version before installing a newer version of Docker, since the Ubuntu repository does not contain the most recent version and the package does not have the same name as the Docker package we will install: sudo apt-get remove docker.io The simplest way to install Docker is to use an installation script made available at the Docker website: curl -sSL https://get.docker.com/ubuntu/ | sudo sh If you are not running Ubuntu or if you do not want to use the above way of installing Docker, please refer to this page containing instructions on how to install Docker on various platforms. To verify the Docker installation, open a terminal window and enter: sudo docker version Output similar to the following should appear: Client version: 1.4.1 Client API version: 1.16 Go version (client): go1.3.3 Git commit (client): 5bc2ff8 OS/Arch (client): linux/amd64 Server version: 1.4.1 Server API version: 1.16 Go version (server): go1.3.3 Git commit (server): 5bc2ff8 We are now ready to start a Mule instance in Docker. Running Mule in Docker One of the advantages with Docker is that there is a large repository of Docker images that are ready to be used, and even extended if one so wishes. ThisDocker image is the one that I will use in this article. It is well documented, there is a source repository and it contains a recent version of the Mule ESB Community Edition. Some additional details on the Docker image: Ubuntu 14.04. Oracle JavaSE 1.7.0_65. This version will change as the PPA containing the package is updated. Mule ESB CE 3.5.0 Note that the image may change at any time and the specifications above may have changed. If you intend to use Docker in your organization, I would suspect that the best alternative is to create your own Docker images that are totally under your control. The Docker image repository is an excellent source of inspiration and aid even in this case. Starting a Docker Container To start a Docker container using this image, open a terminal window and write: sudo docker run codingtony/mule The first time an image is used it needs to be downloaded and created. This usually takes quite some time, so I suggest a short break here – perhaps for a cup of coffee or tea. If you just want to download an image without starting it, exchange the Docker command “run” with “pull”. Once the container is started, you will see some output to the console. If you are familiar with Mule, you will recognize the log output: MULE_HOME is set to /opt/mule-standalone-3.5.0 Running in console (foreground) mode by default, use Ctrl-C to exit... MULE_HOME is set to /opt/mule-standalone-3.5.0 Running Mule... --> Wrapper Started as Console Launching a JVM... Starting the Mule Container... Wrapper (Version 3.2.3) http://wrapper.tanukisoftware.org Copyright 1999-2006 Tanuki Software, Inc. All Rights Reserved. INFO 2015-01-05 04:41:42,302 [WrapperListener_start_runner] org.mule.module.launcher.MuleContainer: ********************************************************************** * Mule ESB and Integration Platform * * Version: 3.5.0 Build: ff1df1f3 * * MuleSoft, Inc. * * For more information go to http://www.mulesoft.org * * * * Server started: 1/5/15 4:41 AM * * JDK: 1.7.0_65 (mixed mode) * * OS: Linux (3.16.0-28-generic, amd64) * * Host: f95698cfb796 (172.17.0.2) * ********************************************************************** Note that: In the text-box containing information about the Mule ESB and Integration Platform, there is a row which starts with “Host:”. The hexadecimal digit that follows is the Docker container id and the IP-address is the external IP-address of the Docker container in which Mule is running. Before we do anything with the Mule instance running in Docker, let’s take a look at Docker containers. Docker Containers We can verify that there is a Docker container running by opening another terminal window, or a tab in the first terminal window, and running the command: sudo docker ps As a result, you will see output similar to the following (I have edited the output in order for the columns to be aligned with the column titles): CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES f95698cfb796 codingtony/mule:latest "/opt/mule/bin/mule" 7 min ago Up 7 min jolly_hopper From this output we can see that: The ID of the container is f95698cfb796. This ID can be used when performing operations on the container, such as stopping it, restarting it etc. The name of the image used to created the container. The command that is currently executing. If we look at the Dockerfile for the image, we can see that the last line in this file is: CMD [ “/opt/mule/bin/mule” ] This is the command that is executed whenever an instance of the Docker image is launched and it matches what we see in the COMMAND column for the Docker container. The CREATED column shows how much time has passed since the container was created. The STATUS column shows the current status of the image. When you have used Docker for a while, you can view all the containers using: sudo docker ps -a This will show you containers that are not running, in addition to the running ones. Containers that are not running can be restarted. The PORTS column shows any port mappings for the container. More about port mappings later. Finally, the NAMES column contain a more human-friendly container name. This container name can be used in the same way as the container id. Docker containers will consume disk-space and if you want to determine how much disk-space each of the containers on your computer use, issue the following command: sudo docker ps -a -s An additional column, SIZE, will be shown and in this column I see that my Mule container consumes 41,76kB. Note that this is in addition to the disk-space consumed by the Docker image. This number will grow if you use the container under a longer period of time, as the container retains any files written to disk. To completely remove a stopped Docker container, find the id or name of the container and use the command: sudo docker rm [container id or name here] Before going further, let’s stop the running container and remove it: sudo docker stop [container id or name here] sudo docker rm [container id or name here] Files and Docker Containers So far we have managed to start a Mule instance running inside a Docker container, but there were no Mule applications deployed to it and the logs that were generated were only visible in the terminal window. I want to be able to deploy my applications to the Mule instance and examine the logs in a convenient way. In this section I will show how to: Share one or more directories in the host file-system with a Docker container. Access the files in a Docker container from the host. As the first step in looking at sharing directories between the host operating system and a Docker container, we are going to look at Mule logs. As part of this exercise we also set up the directories in the host operating system that are going to be shared with the Docker container. In your home directory, create a directory named “mule-root”. In the “mule-root” directory, create three directories named “apps”, “conf” and “logs”. Download the Mule CE 3.5.0 standalone distribution from this link. From the Mule CE 3.5.0 distribution, copy the files in the “apps” directory to the “mule-root/apps” directory you just created. From the Mule CE 3.5.0 distribution, copy the files in the “conf” directory to the “mule-root/conf” directory you created. The resulting file- and directory-structure should look like this (shown using the tree command): ~/mule-root/ ├── apps │ └── default │ └── mule-config.xml ├── conf │ ├── log4j.properties │ ├── tls-default.conf │ ├── tls-fips140-2.conf │ ├── wrapper-additional.conf │ └── wrapper.conf └── logs Edit the log4j.properties file in the “mule-root/conf” directory and set the log-level on the last line in the file to “DEBUG”. This modification has nothing to do with sharing directories, but is in order for us to be able to see some more output from Mule when we run it later. The last two lines should now look like this: # Mule classes log4j.logger.org.mule=DEBUG Binding Volumes We are now ready to launch a new Docker container and when we do, we will tell Docker to map three directories in the Docker container to three directories in the host operating system. Three directories in a Docker container bound to three directories in the host. Launch the Docker container with the command below. The -v option tells Docker that we want to make the contents of a directory in the host available at a certain path in the Docker container file-system. The -d option runs the container in the background and the terminal prompt will be available as soon as the id of the newly launched Docker container has been printed. sudo docker run -d -v ~/mule-root/apps:/opt/mule/apps -v ~/mule-root/conf:/opt/mule/conf -v ~/mule-root/logs:/opt/mule/logs codingtony/mule Examine the “mule-root” directory and its subdirectories in the host, which should now look like below. The files on the highlighted rows have been created by Mule. mule-root/ ├── apps │ ├── default │ │ └── mule-config.xml │ └── default-anchor.txt ├── conf │ ├── log4j.properties │ ├── tls-default.conf │ ├── tls-fips140-2.conf │ ├── wrapper-additional.conf │ └── wrapper.conf └── logs ├── mule-app-default.log ├── mule-domain-default.log └── mule.log Examine the “mule.log” file using the command “tail -f ~/mule-root/logs/mule.log”. There should be periodic output written to the log file similar to the following: DEBUG 2015-01-05 12:05:37,216 [Mule.app.deployer.monitor.1.thread.1] org.mule.module.launcher.DeploymentDirectoryWatcher: Checking for changes... DEBUG 2015-01-05 12:05:37,216 [Mule.app.deployer.monitor.1.thread.1] org.mule.module.launcher.DeploymentDirectoryWatcher: Current anchors: default-anchor.txt DEBUG 2015-01-05 12:05:37,216 [Mule.app.deployer.monitor.1.thread.1] org.mule.module.launcher.DeploymentDirectoryWatcher: Deleted anchors: Stop and remove the container: sudo docker stop [container id or name here] sudo docker rm [container id or name here] Direct Access to Docker Container Files When running Docker under the Ubuntu OS it is also possible to access the file-system of a Docker container from the host file-system. It may be possible to do this under other operating systems too, but I haven’t had the opportunity to test this. This technique may come in handy during development or testing with Docker containers for which you haven’t bound any volumes. Note! If given the choice to use either volume binding, as seen above, or direct access to container files as we will look at in this section for something more than a temporary file access, I would chose to use volume binding. Direct access to Docker container files relies on implementation details that I suspect may change in future versions of Docker if the developers find it suitable. With all that said, lets get the action started: Start a new Docker container: sudo docker run -d codingtony/mule Find the id of the newly launched Docker container: sudo docker ps Examine low-level information about the newly launched Docker container: sudo docker inspect [container id or name here] Output similar to this will be printed to the console (portions removed to conserve space): [{ "AppArmorProfile": "", "Args": [], "Config": { ... }, "Created": "2015-01-12T07:58:47.913905369Z", "Driver": "aufs", "ExecDriver": "native-0.2", "HostConfig": { ... }, "HostnamePath": "/var/lib/docker/containers/68b40def7ad6a7f819bd654d5627ad1c3a0f40c84e0fb0f875760f1bd6790eef/hostname", "HostsPath": "/var/lib/docker/containers/68b40def7ad6a7f819bd654d5627ad1c3a0f40c84e0fb0f875760f1bd6790eef/hosts", "Id": "68b40def7ad6a7f819bd654d5627ad1c3a0f40c84e0fb0f875760f1bd6790eef", "Image": "bcd0f37d48d4501ad64bae941d95446b157a6f15e31251e26918dbac542d731f", "MountLabel": "", "Name": "/thirsty_darwin", "NetworkSettings": { ... }, "Path": "/opt/mule/bin/mule", "ProcessLabel": "", "ResolvConfPath": "/var/lib/docker/containers/68b40def7ad6a7f819bd654d5627ad1c3a0f40c84e0fb0f875760f1bd6790eef/resolv.conf", "State": { ... }, "Volumes": {}, "VolumesRW": {} }] Locate the “Driver” node (highlighted in the above output) and ensure that its value is “aufs”. If it is not, you may need to modify the directory paths below replacing “aufs” with the value of this node. Personally I have only seen the “aufs” value at this node so anything else is uncharted territory to me. Copy the long hexadecimal value that can be found at the “Id” node (also highlighted in the above output). This is the long id of the Docker container. In a terminal window, issue the following command, inserting the long id of your container where noted: sudo ls -al /var/lib/docker/aufs/mnt/[long container id here] You are now looking at the root of the volume used by the Docker container you just launched. In the same terminal window, issue the following command: sudo ls -al /var/lib/docker/aufs/mnt/[long container id here]/opt The output from this command should look like this: total 12 drwxr-xr-x 4 root root 4096 jan 12 15:58 . drwxr-xr-x 75 root root 4096 jan 12 15:58 .. lrwxrwxrwx 1 root root 26 aug 10 04:19 mule -> /opt/mule-standalone-3.5.0 drwxr-xr-x 17 409 409 4096 jan 12 15:58 mule-standalone-3.5.0 Examine this line in the Dockerfile:RUN ln -s /opt/mule-standalone-3.5.0 /opt/muleWe see that a symbolic link is created and that the directory name and the name of the symbolic link matches the output we saw earlier. This matches the directory output in the previous step. To examine the Mule log file that we looked at when binding volumes earlier, use the following command: sudo cat /var/lib/docker/aufs/mnt/[long container id here]/opt/mule-standalone-3.5.0/logs/mule.log Next we create a new file in the Docker container using vi: sudo vi /var/lib/docker/aufs/mnt/[long container id here]/opt/mule-standalone-3.5.0/test.txt Enter some text into the new file by first pressing i and the type the text. When you are finished entering the text, press the Escape key and write the file to disk by typing the characters “:wq” without quotes. This writes the new contents of the file to disk and quits the editor. Leave the Docker container running after you are finished. In the next section, we are going to look at the file we just created from inside the Docker container. We have seen that we can examine the file system of a Docker container without binding volumes. It is also possible to copy or move files from the host file-system to the container’s file system using the regular commands. Root privileges are required both when examining and writing to the Docker container’s file system. Entering a Docker Container In order to verify that the file we just created in the host was indeed written to the Docker container, we are going to start a bash shell in the running Docker container and examine the location where the new file is expected to be located and the contents of the file. In the process we will see how we can execute commands in a Docker container from the host. Issue the command below in a terminal window. The exec Docker command is used to run a command, bash in this case, in a running Docker container. The -i flags tell Docker to keep the input stream open while the command is being executed. In this example, it allows us to enter commands into the bash shell running inside the Docker container. The -t flag cause Docker to allocate a text terminal to which the output from the command execution is printed. sudo docker exec -i -t [container id or name here] bash Note the prompt, which should change to [user]@[Docker container id]. In my case it looks like this: root@3ea374a280da:/# Go to the Mule installation directory using this command: cd /opt/mule-standalone-3.5.0/ Examine the contents of the directory: ls -al Among the other files, you should see the “test.txt” file: -rw-r--r-- 1 root root 53 Jan 14 03:19 test.txt Examine the contents of the “text.txt” file. The contents of the file should match what you entered earlier. cat text.txt Exit to the host OS: exit Stop and remove the container: sudo docker stop [container id or name here] sudo docker rm [container id or name here] We have seen that we can execute commands in a running Docker container. In this particular example, we used it to execute the bash shell and examine a file. I draw the conclusion that I should be able to set up a Docker image that contains a very controlled environment for some type of test and then create a container from that image and start the test from the host. Deploying a Mule Application In this section we will look at deploying a Mule application to an instance of the Mule ESB running in a Docker container. We will use volume binding, that we looked at in the section on files and Docker containers, to share directories in the host with the Docker container in order to make it easy to deploy applications, modify running applications, examine logs etc. Preparations Before deploying the application, we need to make some preparations: First of all, we restore the original log-level that we changed earlier. In this example, there will be log output when the applications we will deploy is run and we can limit the log generated by Mule. Edit the log4j.properties file in the “mule-root/conf” directory in the host and set the log-level on the last line in the file back to “INFO” and add one line, as in the listing below. The last three lines should now look like this: # Mule classes log4j.logger.org.mule=INFO log4j.logger.org.mule.tck.functional=DEBUG Next, we create the Mule application which we will deploy to the Mule ESB running in Docker: In some directory, create a file named “mule-deploy.properties” with the following contents: redeployment.enabled=true encoding=UTF-8 domain=default config.resources=HelloWorld.xml In the same directory create a file named “HelloWorld.xml”. This file contains the Mule configuration for our example application: Create a zip-archive named “mule-hello.zip” containing the two files created above: zip mule-hello.zip mule-deploy.properties HelloWorld.xml Deploy the Mule Application Before you start the Docker container in which the Mule EBS will run, make sure that you have created and prepared the directories in the host as described in the section Files and Docker Containers above. Start a new Mule Docker container using the command that we used when binding volumes: sudo docker run -d -v ~/mule-root/apps:/opt/mule/apps -v ~/mule-root/conf:/opt/mule/conf -v ~/mule-root/logs:/opt/mule/logs codingtony/mule As before, the -v option tells Docker to bind three directories in the host to three locations in the Docker container’s file system. Find the IP-address of the Docker container: sudo docker inspect [container id or name here] | grep IPAddress In my case, I see the following line which reveals the IP-address of the Docker container: “IPAddress”: “172.0.17.2”, Open a terminal window or tab and examine the Mule log. Leave this window or tab open during the exercise, in order to be able to verify the output from Mule. tail -f ~/mule-root/logs/mule.log Copy the zip-archive “mule-hello.zip” created earlier to the host directory ~/mule-root/apps/. Verify that the application has been deployed without errors in the Mule log: ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + Started app 'mule-hello' + ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Leave the Docker container running after you are finished. In the next section we will look at how to access endpoints exposed by applications running in Docker containers. By binding directories in the host thus making them available in the Docker container, it becomes very simple to deploy Mule applications to an instance of Mule ESB running in a Docker container. I am considering this setup for a production environment as well, since it will enable me to perform backups of the directories containing Mule applications and configuration without having to access the Docker container’s file system. It is also in accord with the idea that a Docker container should be able to be quickly and easily restarted, which I feel it would not be if I had to deploy a number of Mule applications to it in order to recreate its previous state. Accessing Endpoints We now know that we can run the Mule ESB in a Docker container, we can deploy applications and examine the logs quite easily but one final, very important question remains to be answered; how to access endpoints exposed by applications running in a Docker container. This section assumes that the Mule application we deployed to Mule in the previous section is still running. In the host, open a web-browser and issue a request to the Docker container’s IP-address at port 8181. In my case, the URL is http://172.17.0.2:8181 Alternatively use the curl command in a terminal window. In my case I would write: curl 172.17.0.2:8181 The result should be a greeting in the following format: Hello World! It is now: 2015-01-14T07:39:03.942Z In addition, you should be able to see that a message was received in the Mule log. Now try the URL http://localhost:8181 You will get a message saying that the connection was refused, provided that you do not already have a service listening at that port. If you have another computer available that is connected to the same network as the host computer running Ubuntu, do the following: – Find the IP-address of the Ubuntu host computer using the ifconfigcommand. – In a web-browser on the other computer, try accessing port 8181 at the IP-address of the Ubuntu host computer. Again you will get a message saying that the connection was refused. Stop and remove the container: sudo docker stop [container id or name here] sudo docker rm [container id or name here] Without any particular measures taken, we see that we can access a service exposed in a Docker container from the Docker host but we did not succeed in accessing the service from another computer. To make a service exposed in a Docker container reachable from outside of the host, we need to tell Docker to publish a port from the Docker container to a port in the host using the -p flag: Launch a new Docker container using the following command: sudo docker run -d -p 8181:8181 -v ~/mule-root/apps:/opt/mule/apps -v ~/mule-root/conf:/opt/mule/conf -v ~/mule-root/logs:/opt/mule/logs codingtony/mule The added flag -p 8181:8181 makes the service exposed at port 8181 in the Docker container available at port 8181 in the host. Try accessing the URL http://localhost:8181 from a web-browser on the host computer.The result should be a greeting of the form we have seen earlier. Try accessing port 8181 at the IP-address of the Ubuntu host computer from another computer.This should also result in a greeting message. Stop and remove the container: sudo docker stop [container id or name here] sudo docker rm [container id or name here] Using the -p flag, we have seen that we can expose a service in a Docker container so that it becomes accessible from outside of the host computer. However, we also see that this information need to be supplied at the time of launching the Docker container. The conclusions that I draw from this is that: I can test and develop against a Mule ESB instance running in a Docker container without having to publish any ports, provided that my development computer is the Docker host computer. In a production environment or any other environment that need to expose services running in a Docker container to “the outside world” and where services will be added over time, I would consider deploying an Apache HTTP Server or NGINX on the Docker host computer and use it to proxy the services that are to be exposed. This way I can avoid re-launching the Docker container each time a new service is added and I can even (temporarily) redirect the proxy to some other computer if I need to perform some maintenance. Is There More? Of course! This article should only be considered an introduction and I am just a beginner with Docker. I hope I will have the time and inspiration to write more about Docker as I learn more.

[This article was written by Yaron Parasol.] Docker containers were created to help enable the fast, and reliable deployment of application components or tiers, by creating a container that holds a self-contained ready to deploy parts of applications, with the middleware and the app business logic needed to run them successfully. For example, a Spring application within a Tomcat container. By design, Docker is purposely an isolated self-contained part of the application, typically one tier or even one node in a tier. However, an application is typically multi-tier in its architecture and that means you have tiers with dependencies between them, where the nature of the dependencies can be anything from network connections and remote API invocations, to exchange of messages between application tiers. And hence an app is a set of different containers with specific configurations. This is why you need a way to glue the pieces of your app together. While, Docker has a basic solution for connecting containers using a Docker bridge, this solution is not always the preferred one, especially when deploying the container across different hosts and you need to take care of real network settings. Docker orchestration with TOSCA + Cloudify. Check it out. Go So, what role does the orchestrator play? The orchestrator will take care of two things: The timing of container creation - as containers need to be created by order of dependencies and Container configuration in order to allow containers to communicate with one another - and for that the orchestrator needs to pass runtime properties between containers. As a side note here: With Docker you need a special tweak here, as you typically don’t touch config files inside a container, you keep the container intact, so there is an interesting workaround for cases that this is required. One method to do this is by using a YAML-based orchestration plan to orchestrate the deployment of apps and post-deployment automation processes, which is the approach Cloudify employs. Based on TOSCA (topology and orchestration standard of cloud apps), this orchestration plan describes the components and their lifecycle, and the relationships between components, especially when it comes to complex topologies. This includes, what’s connected to what, what’s hosted on what, and other such considerations. TOSCA is able to describe the infrastructure, as well as, the middleware tier, and app layers on top of these. Cloudify basically takes this TOSCA orchestration plan (dubbed blueprints in Cloudify speak) and materializes these using workflows that traverse the graph of components, or this plan of components and issues commands to agents. These then create the app components and glue them together. The agents use extensions called plugins that are adaptors between the Cloudify configuration and the various infrastructure as a service (IaaS) and automation tools’ APIs. In our case, we created a plugin to interface with the Docker API. Introducing the Docker Cloudify Plugin The Cloudify-Docker plugin is quite straightforward, it installs the Docker API endpoint/server on the machine and then uses the Docker-Py binding to create, configure, and remove containers. TOSCA lifecycle events are: Create - installation of the app components Configure - configuration of the component Start - startup/running the component There is also stop & delete - for shutdown and removal We started by using the create - to create the container, we did not implement configure at the beginning, and start to run the application. But then we realized that for containers with dependencies we need to have runtime properties, such as IP import of the counterpart container in order to create the container for example. When we create an app server container, we need the port and IP of the database container. So, we pushed the creation of the container to the configure event, and used a TOSCA relationship pre-configure hook, to get the dependent container’s info at runtime. The way to expose the runtime info to the container with the dependencies is by setting them as environment variables. 01.interfaces: 02. cloudify.interfaces.lifecycle: 03. configure: 04. implementation: docker.docker_plugin.tasks.configure 05. inputs: 06. container_config: 07. command: mongod--rest--httpinterface --smallfiles 08. image: dockerfile/mongodb 09. start: 10. implementation: docker.docker_plugin.tasks.run 11. inputs: 12. container_start: 13. port_bindings: 14. 27017: 27017 15. 28017: 28017 Nodecellar Example I’d like to explain how this works by using our Nodecellar app as an example. The Nodecellar app is composed of two hosts that, in this case, Cloudify didn’t create but just SSHed into and then installed agents on. On one we have the MongoD container, with a MongoD process. On the other we have the Nodecellar container with NodeJS and the Nodecellar app within it. The Nodecellar container needs a connection to the MongoD container to run the app queries when the app starts. Ultimately, an orchestrator should not be limited to software deployment, the whole idea behind Docker Is to allow for agility, so we’d also like to use Docker in situations of auto-scale out and auto-heal, CD. In our next post we’ll show exactly that - how Cloudify can be used with Docker for post-deployment scenarios.

I’m a huge Vaadin fan and I’ve created a Github workshop I can demo at conferences. A common issue with such kind of workshops is that attendees have to prepare their workstations in advance… and there’s always a significant part of them that comes with not everything ready. At this point, two options are available to the speaker: either wait for each of the attendee to finish the preparation – too bad for the people who took the time at home to do that, or start anyway – and lose the not-ready part. Given the current buzz around Docker, I thought that could be a very good way to make the workshop preparation quicker – only one step, and hasslefree – no problem regarding the quirks of your operation system. The required steps I ask the attendees are the following: Install Git Install Java, Maven and Tomcat Clone the git repo Build the project (to prepare the Maven repository) Deploy the built webapp Start Tomcat These should directly be automated into Docker. As I wasted much time getting this to work, here’s the tale of my journey in achieving this (be warned, it’s quite long). If you’ve got similar use-cases, I hope it will be useful in you getting things done faster. Starting with Docker The first step was to get to know the basics about Docker. Fortunately, I had the chance to attend a Docker workshop by David Gageot at Duchess Swiss. This included both Docker installation and basics of Dockerfile. I assume readers have likewise a basic understanding of Docker. For those who don’t, I guess browsing the Docker’s official documentation is a nice idea: Installation Dockerfile reference Building my first Dockerfile The Docker image can be built with the following command ran into the directory of the Dockerfile: $ docker build -t vaadinworkshop . The first issues one can encounter when playing with Docker the first time, is to get the following error message: Get http:///var/run/docker.sock/v1.14/containers/json: dial unix /var/run/docker.sock: no such file or directory The reason is because one didn’t export the required environment variables displayed by the boot2docker information message. If you lost the exact data, no worry, just use the shellinit boot2docker parameter: $ boot2docker shellinit Writing /Users/i303869/.docker/boot2docker-vm/ca.pem: Writing /Users/i303869/.docker/boot2docker-vm/cert.pem: Writing /Users/i303869/.docker/boot2docker-vm/key.pem: export DOCKER_HOST=tcp://192.168.59.103:2376 export DOCKER_CERT_PATH=/Users/i303869/.docker/boot2docker-vm Copy-paste the export lines above will solve the issue. These can also be set in one’s .bashrc script as it seems these values seldom change. Next in line is the following error: Get http://192.168.59.103:2376/v1.14/containers/json: malformed HTTP response "x15x03x01x00x02x02" This error message seems to be because of a mismatch between versions of the client and the server. It seems it is because of a bug on Mac OSX when upgrading. For a long term solution, reinstall Docker from scratch; for a quick fix, use the --tls flag with the docker command. As it is quite cumbersome to type it everything, one can alias it: $ alias docker="docker --tls" My last mistake when building the image comes from building the Dockerfile from a not empty directory. Docker sends every file it finds in the directory of the Dockerfile to the Docker container for build: $ docker --tls build -t vaadinworkshop . Sending build context to Docker daemon Too many kB Fix: do not try this at home and start from a directory container the Dockerfile only. Starting from scratch Dockerfiles describe images – images are built as a layered list of instructions. Docker images are designed around single inheritance: one image has to be set a single parent. An image requiring no parent starts from scratch, but Docker provides 4 base official distributions: busybox, debian, ubuntu and centos (operating systems are generally a good start). Whatever you want to achieve, it is necessary to choose the right parent. Given the requirements I set for myself (Java, Maven, Tomcat and Git), I tried to find the right starting image. Many Dockerfiles are already available online on the Docker hub. The browsing app is quite good, but to be really honest, the search can really be improved. My intention was to use the image that matched the most of my requirements, then fill the gap. I could find no image providing Git, but I thought the dgageot/maven Dockerfile would be a nice starting point. The problem is that the base image is a busybox and provides no installer out-of-the-box (apt-get, yum, whatever). For this reason, David uses a lot of curl to get Java 8 and Maven in his Dockerfiles. I foolishly thought I could use a different flavor of busybox that provides the opkg installer. After a while, I accumulated many problems, resolving one heading to another. In the end, I finally decided to use the OS I was most comfortable with and to install everything myself: FROM ubuntu:utopic Scripting Java installation Installing git, maven and tomcat packages is very straightforward (if you don’t forget to use the non-interactive options) with RUN and apt-get: RUN apt-get update && \ apt-get install -y --force-yes git maven tomcat8 Java doesn’t fall into this nice pattern, as Oracle wants you to accept the license. Nice people did however publish it to a third-party repo. Steps are the following: Add the needed package repository Configure the system to automatically accept the license Configure the system to add un-certified packages Update the list of repositories At last, install the package Also add a package for Java 8 system configuration. RUN echo "deb http://ppa.launchpad.net/webupd8team/java/ubuntu precise main" | tee -a /etc/apt/sources.list && \ echo oracle-java8-installer shared/accepted-oracle-license-v1-1 select true | /usr/bin/debconf-set-selections && \ apt-key adv --keyserver keyserver.ubuntu.com --recv-keys EEA14886 RUN apt-get update && \ apt-get install -y --force-yes oracle-java8-installer oracle-java8-set-default Building the sources Getting the workshop’s sources and building them is quite straightforward with the following instructions: RUN git clone https://github.com/nfrankel/vaadin7-workshop.git WORKDIR /vaadin7-workshop RUN mvn package The drawback of this approach is that Maven will start from a fresh repository, and thus download the Internet the first time it is launched. At first, I wanted to mount a volume from the host to the container to share the ~/.m2/repository folder to avoid this, but I noticed this could only be done at runtime through the -v option as the VOLUME instruction cannot point to a host directory. Starting the image The simplest command to start the created Docker image is the following: $ docker run -p 8080:8080 Do not forget the port forwarding from the container to the host, 8080 for the standard HTTP port. Also, note that it’s not necessary to run the container as a daemon (with the -d option). The added value of that is that the standard output of the CMD (see below) will be redirected to the host. When running as a daemon and wanting to check the logs, one has to execute bash in the container, which requires a sequence of cumbersome manipulations. Configuring and launching Tomcat Tomcat can be launched when starting the container by just adding the following instruction to the Dockerfile: CMD ["catalina.sh", "run"] However, trying to start the container at this point will result in the following error: Nov 15, 2014 9:24:18 PM org.apache.catalina.startup.ClassLoaderFactory validateFile WARNING: Problem with directory [/usr/share/tomcat8/common/classes], exists: [false], isDirectory: [false], canRead: [false] Nov 15, 2014 9:24:18 PM org.apache.catalina.startup.ClassLoaderFactory validateFile WARNING: Problem with directory [/usr/share/tomcat8/common], exists: [false], isDirectory: [false], canRead: [false] Nov 15, 2014 9:24:18 PM org.apache.catalina.startup.ClassLoaderFactory validateFile WARNING: Problem with directory [/usr/share/tomcat8/server/classes], exists: [false], isDirectory: [false], canRead: [false] Nov 15, 2014 9:24:18 PM org.apache.catalina.startup.ClassLoaderFactory validateFile WARNING: Problem with directory [/usr/share/tomcat8/server], exists: [false], isDirectory: [false], canRead: [false] Nov 15, 2014 9:24:18 PM org.apache.catalina.startup.ClassLoaderFactory validateFile WARNING: Problem with directory [/usr/share/tomcat8/shared/classes], exists: [false], isDirectory: [false], canRead: [false] Nov 15, 2014 9:24:18 PM org.apache.catalina.startup.ClassLoaderFactory validateFile WARNING: Problem with directory [/usr/share/tomcat8/shared], exists: [false], isDirectory: [false], canRead: [false] Nov 15, 2014 9:24:18 PM org.apache.catalina.startup.Catalina initDirs SEVERE: Cannot find specified temporary folder at /usr/share/tomcat8/temp Nov 15, 2014 9:24:18 PM org.apache.catalina.startup.Catalina load WARNING: Unable to load server configuration from [/usr/share/tomcat8/conf/server.xml] Nov 15, 2014 9:24:18 PM org.apache.catalina.startup.Catalina initDirs SEVERE: Cannot find specified temporary folder at /usr/share/tomcat8/temp Nov 15, 2014 9:24:18 PM org.apache.catalina.startup.Catalina load WARNING: Unable to load server configuration from [/usr/share/tomcat8/conf/server.xml] Nov 15, 2014 9:24:18 PM org.apache.catalina.startup.Catalina start SEVERE: Cannot start server. Server instance is not configured. I have no idea why, but it seems Tomcat 8 on Ubuntu is not configured in any meaningful way. Everything is available but we need some symbolic links here and there as well as creating the temp directory. This translates into the following instruction in the Dockerfile: RUN ln -s /var/lib/tomcat8/common $CATALINA_HOME/common && \ ln -s /var/lib/tomcat8/server $CATALINA_HOME/server && \ ln -s /var/lib/tomcat8/shared $CATALINA_HOME/shared && \ ln -s /etc/tomcat8 $CATALINA_HOME/conf && \ mkdir $CATALINA_HOME/temp The final trick is to connect the exploded webapp folder created by Maven to Tomcat’s webapps folder, which it looks for deployments: RUN mkdir $CATALINA_HOME/webapps && \ ln -s /vaadin7-workshop/target/workshop-7.2-1.0-SNAPSHOT/ $CATALINA_HOME/webapps/vaadinworkshop At this point, the Holy Grail is not far away, you just have to browse the URL… if only we knew what the IP was. Since running on Mac, there’s an additional VM beside the host and the container that’s involved. To get this IP, type: $ boot2docker ip The VM's Host only interface IP address is: 192.168.59.103 Now, browsing http://192.168.59.103:8080/vaadinworkshop/ will bring us to the familiar workshop screen: Developing from there Everything works fine but didn’t we just forget about one important thing, like how workshop attendees are supposed to work on the sources? Easy enough, just mount the volume when starting the container: docker run -v /Users//vaadin7-workshop:/vaadin7-workshop -p 8080:8080 vaadinworkshop Note that the host volume must be part of /Users and if on OSX, it must use boot2docker v. 1.3+. Unfortunately, it seems now is the showstopper, as mounting an empty directory from the host to the container will not make the container’s directory available from the host. On the contrary, it will empty the container’s directory given that the host’s directory doesn’t exist… It seems there’s an issue in Docker on Mac. The installation of JHipster runs into the same problem, and proposes to use the Samba Docker folder sharing project. I’m afraid I was too lazy to go further at this point. However, this taught me much about Docker, its usages and use-cases (as well as OSX integration limitations). For those who are interested, you’ll find below the Docker file. Happy Docker! FROM ubuntu:utopic MAINTAINER Nicolas Frankel # Config to get to install Java 8 w/o interaction RUN echo "deb http://ppa.launchpad.net/webupd8team/java/ubuntu precise main" | tee -a /etc/apt/sources.list && echo oracle-java8-installer shared/accepted-oracle-license-v1-1 select true | /usr/bin/debconf-set-selections && apt-key adv --keyserver keyserver.ubuntu.com --recv-keys EEA14886 RUN apt-get update && apt-get install -y --force-yes git oracle-java8-installer oracle-java8-set-default maven tomcat8 RUN git clone https://github.com/nfrankel/vaadin7-workshop.git WORKDIR /vaadin7-workshop RUN git checkout v7.2-1 RUN mvn package ENV JAVA_HOME /usr/lib/jvm/java-8-oracle ENV CATALINA_HOME /usr/share/tomcat8 ENV PATH $PATH:$CATALINA_HOME/bin # Configure Tomcat 8 directories RUN ln -s /var/lib/tomcat8/common $CATALINA_HOME/common && ln -s /var/lib/tomcat8/server $CATALINA_HOME/server && ln -s /var/lib/tomcat8/shared $CATALINA_HOME/shared && ln -s /etc/tomcat8 $CATALINA_HOME/conf && mkdir $CATALINA_HOME/temp && mkdir $CATALINA_HOME/webapps && ln -s /vaadin7-workshop/target/workshop-7.2-1.0-SNAPSHOT/ $CATALINA_HOME/webapps/vaadinworkshop VOLUME ["/vaadin7-workshop"] CMD ["catalina.sh", "run"] # docker build -t vaadinworkshop . # docker run -v ~/vaadin7-workshop training/webapp -p 8080:8080 vaadinworkshop

The last couple of weeks I've been playing around with docker and kubernetes. If you are not familiar with kubernetes let's just say for now that its an open source container cluster management implementation, which I find really really awesome. One of the first things I wanted to try out was running an Apache ZooKeeper ensemble inside kubernetes and I thought that it would be nice to share the experience. For my experiments I used Docker v. 1.3.0 and Openshift V3, which I built from source and includes Kubernetes. ZooKeeper on Docker Managing a ZooKeeper ensemble is definitely not a trivial task. You usually need to configure an odd number of servers and all of the servers need to be aware of each other. This is a PITA on its own, but it gets even more painful when you are working with something as static as docker images. The main difficulty could be expressed as: "How can you create multiple containers out of the same image and have them point to each other?" One approach would be to use docker volumes and provide the configuration externally. This would mean that you have created the configuration for each container, stored it somewhere in the docker host and then pass the configuration to each container as a volume at creation time. I've never tried that myself, I can't tell if its a good or bad practice, I can see some benefits, but I can also see that this is something I am not really excited about. It could look like this: docker run -p 2181:2181 -v /path/to/my/conf:/opt/zookeeper/conf my/zookeeper An other approach would be to pass all the required information as environment variables to the container at creation time and then create a wrapper script which will read the environment variables, modify the configuration files accordingly, launch zookeeper. This is definitely easier to use, but its not that flexible to perform other types of tuning without rebuilding the image itself. Last but not least one could combine the two approaches into one and do something like: Make it possible to provide the base configuration externally using volumes. Use env and scripting to just configure the ensemble. There are plenty of images out there that take one or the other approach. I am more fond of the environment variables approach and since I needed something that would follow some of the kubernetes conventions in terms of naming, I decided to hack an image of my own using the env variables way. Creating a custom image for ZooKeeper I will just focus on the configuration that is required for the ensemble. In order to configure a ZooKeeper ensemble, for each server one has to assign a numeric id and then add in its configuration an entry per zookeeper server, that contains the ip of the server, the peer port of the server and the election port. The server id is added in a file called myid under the dataDir. The rest of the configuration looks like: server.1=server1.example.com:2888:3888 server.2=server2.example.com:2888:3888 server.3=server3.example.com:2888:3888 ... server.current=[bind address]:[peer binding port]:[election biding port]Note that if the server id is X the server.X entry needs to contain the bind ip and ports and not the connection ip and ports. So what we actually need to pass to the container as environment variables are the following: The server id. For each server in the ensemble: The hostname or ip The peer port The election port If these are set, then the script that updates the configuration could look like: if [ ! -z "$SERVER_ID" ]; then echo "$SERVER_ID" > /opt/zookeeper/data/myid #Find the servers exposed in env. for i in `echo {1..15}`;do HOST=`envValue ZK_PEER_${i}_SERVICE_HOST` PEER=`envValue ZK_PEER_${i}_SERVICE_PORT` ELECTION=`envValue ZK_ELECTION_${i}_SERVICE_PORT` if [ "$SERVER_ID" = "$i" ];then echo "server.$i=0.0.0.0:2888:3888" >> conf/zoo.cfg elif [ -z "$HOST" ] || [ -z "$PEER" ] || [ -z "$ELECTION" ] ; then #if a server is not fully defined stop the loop here. break else echo "server.$i=$HOST:$PEER:$ELECTION" >> conf/zoo.cfg fi done fi For simplicity the function that read the keys and values from env are excluded. The complete image and helping scripts to launch zookeeper ensembles of variables size can be found in the fabric8io repository. ZooKeeper on Kubernetes The docker image above, can be used directly with docker, provided that you take care of the environment variables. Now I am going to describe how this image can be used with kubernetes. But first a little rambling... What I really like about using kubernetes with ZooKeeper, is that kubernetes will recreate the container, if it dies or the health check fails. For ZooKeeper this also means that if a container that hosts an ensemble server dies, it will get replaced by a new one. This guarantees that there will be constantly a quorum of ZooKeeper servers. I also like that you don't need to worry about the connection string that the clients will use, if containers come and go. You can use kubernetes services to load balance across all the available servers and you can even expose that outside of kubernetes. Creating a Kubernetes confing for ZooKeeper I'll try to explain how you can create 3 ZooKeeper Server Ensemble in Kubernetes. What we need is 3 docker containers all running ZooKeeper with the right environment variables: { "image": "fabric8/zookeeper", "name": "zookeeper-server-1", "env": [ { "name": "ZK_SERVER_ID", "value": "1" } ], "ports": [ { "name": "zookeeper-client-port", "containerPort": 2181, "protocol": "TCP" }, { "name": "zookeeper-peer-port", "containerPort": 2888, "protocol": "TCP" }, { "name": "zookeeper-election-port", "containerPort": 3888, "protocol": "TCP" } ] } The env needs to specify all the parameters discussed previously. So we need to add along with the ZK_SERVER_ID, the following: ZK_PEER_1_SERVICE_HOST ZK_PEER_1_SERVICE_PORT ZK_ELECTION_1_SERVICE_PORT ZK_PEER_2_SERVICE_HOST ZK_PEER_2_SERVICE_PORT ZK_ELECTION_2_SERVICE_PORT ZK_PEER_3_SERVICE_HOST ZK_PEER_3_SERVICE_PORT ZK_ELECTION_3_SERVICE_PORT An alternative approach could be instead of adding all these manual configuration, to expose peer and election as kubernetes services. I tend to favor the later approach as it can make things simpler when working with multiple hosts. It's also a nice exercise for learning kubernetes. So how do we configure those services? To configure them we need to know: the name of the port the kubernetes pod the provide the service The name of the port is already defined in the previous snippet. So we just need to find out how to select the pod. For this use case, it make sense to have a different pod for each zookeeper server container. So we just need to have a label for each pod, the designates that its a zookeeper server pod and also a label that designates the zookeeper server id. "labels": { "name": "zookeeper-pod", "server": 1 } Something like the above could work. Now we are ready to define the service. I will just show how we can expose the peer port of server with id 1, as a service. The rest can be done in a similar fashion: { "apiVersion": "v1beta1", "creationTimestamp": null, "id": "zk-peer-1", "kind": "Service", "port": 2888, "containerPort": "zookeeper-peer-port", "selector": { "name": "zookeeper-pod", "server": 1 } } The basic idea is that in the service definition, you create a selector which can be used to query/filter pods. Then you define the name of the port to expose and this is pretty much it. Just to clarify, we need a service definition just like the one above per zookeeper server container. And of course we need to do the same for the election port. Finally, we can define an other kind of service, for the client connection port. This time we are not going to specify the sever id, in the selector, which means that all 3 servers will be selected. In this case kubernetes will load balance across all ZooKeeper servers. Since ZooKeeper provides a single system image (it doesn't matter on which server you are connected) then this is pretty handy. { "apiVersion": "v1beta1", "creationTimestamp": null, "id": "zk-client", "kind": "Service", "port": 2181, "createExternalLoadBalancer": "true", "containerPort": "zookeeper-client-port", "selector": { "name": "zookeeper-pod" } } The basic idea is that in the service definition, you create a selector which can be used to query/filter pods. Then you define the name of the port to expose and this is pretty much it. Just to clarify, we need a service definition just like the one above per zookeeper server container. And of course we need to do the same for the election port. Finally, we can define an other kind of service, for the client connection port. This time we are not going to specify the sever id, in the selector, which means that all 3 servers will be selected. In this case kubernetes will load balance across all ZooKeeper servers. Since ZooKeeper provides a single system image (it doesn't matter on which server you are connected) then this is pretty handy. { "apiVersion": "v1beta1", "creationTimestamp": null, "id": "zk-client", "kind": "Service", "port": 2181, "createExternalLoadBalancer": "true", "containerPort": "zookeeper-client-port", "selector": { "name": "zookeeper-pod" } } I hope you found it useful. There is definitely room for improvement so feel free to leave comments.

In Parts 1 and 2 we have covered a number of common issues people run into when managing a sharded MongoDB cluster. In this final post of the series we will cover a subtle, but important distinction in terms of balancing a sharded cluster as well as an interesting limitation that can be worked around relatively easily, but is nonetheless surprising when it comes up. 6. Chunk balancing != data balancing != traffic balancing The balancer in a sharded cluster cares about just one thing: Are chunks for a given collection evenly balanced across all shards? If they are not, then it will take steps to rectify that imbalance. This all sounds perfectly logical, and even with extra complexity like tagging involved the logic is pretty straight forward. If we assume that all chunks are equal, then we can rest assured that our data is being evenly balanced across all the shards in our cluster and rest easy at night. Although that is sometimes, perhaps even frequently, the case it is not always true - chunks are not always equal. There can be massive “jumbo” chunks that exceed the maximum chunk size (64MiB), completely empty chunks and everything in between. Let’s use an example from our first pitfall, the monotonically increasing shard key. For our example, we have picked just such a key to shard on (date), and up until this point we have had just one shard and had not sharded the collection. We are about to add a second shard to our cluster and so we enable sharding on the collection and do the necessary admin work to add the new shard into the cluster. Once the collection is enabled for sharding, the first shard contains all the newly minted chunks. Let’s represent them in a simplified table of 10 chunks. This is not representative of a real data set, but it will do for illustrative purposes: Table 1 - Initial Chunk Layout Now we add our second shard. The balancer will kick in and attempt to distribute the chunks evenly. It will do this by moving the lowest range chunks to the new shard until the counts are identical. Once it is finished balancing, our table now looks like this: Table 2 - Balanced Chunk Layout That looks pretty good at the moment, but lets imagine that more recent chunks are more likely to have more activity (updates say) than older chunks. Adding the traffic share estimates for each chunk shows that shard1 is taking far more traffic (72%) than shard2 (28%) despite the chunks seeming balanced overall based on the approximate size. Hence, chunk balancing is not equal to traffic balancing. Using that same example, let’s add another wrinkle - periodic deletion of old data. Every 3 months we run a job to delete any data older than 12 months. Let’s look at the impact of that on our table after we run it for the first time (assuming the first run happens on July 1st 2015). Table 3 - Post-Delete Chunk Layout The distribution of data is now completely skewed toward shard1 - shard2 is in fact empty! However, the balancer is completely unaware of this imbalance - the chunk count has remained the same the entire time, and as far as it is concerned the system is in a steady state. With no data on shard2, our traffic imbalance as seen above will be even worse, and we have essentially negated the benefit of having a second shard for this collection. Possible Mitigation Strategies If data and traffic balance are important, select an appropriate shard key Move chunks manually to address the imbalances - swap “hot” chunks for “cool” chunks, empty chunks for larger chunks 7. Waiting too long to shard a collection (collection too large) This is not very common, but when it falls on your shoulders, it can be quite challenging to solve. There is a maximum data size for a collection when when it is initially split which is a function of the chunk size and data size as noted on the limits page. If your collection contains less than 256GiB of data, then there will be no issue. If the collection size exceeds 256GiB but is less than 400GiB, then MongoDB may be able to do an initial split without any special measures being taken. Otherwise, with larger initial data sizes and the default settings, the initial split will fail. It is worth noting that once split the collection may grow as needed and without any real limitations as long as you can continue to add shards as data size grows. Possible Mitigation Strategies Since the limit is dictated by the chunk size and the data size, and assuming there is not much to be done about the data size, then the remaining variable is the chunk size. This is adjustable (default is 64MiB) and can be raised in order to let a large collection split initially and then reduced once that has been completed. The required chunk size increase will depend on the actual data size. However, this is relatively easy to work out - simply divide your data size by 256GB and then multiply that figure by 64MiB (and round up if it is not a nice even number). As an example, let’s consider a 4TiB collection: 4TiB divided by 256GiB = 16 64MiB x 16 = 1024MiB Hence, set the max chunk size to 1024MiB, then perform the initial sharding of the collection, and then finally reduce the chunk size back to 64MiB using the same procedure. . Thanks for reading through the Sharding Pitfall series! If you want to learn more about managing MongoDB deployments at scale, sign up for my online education course, MongoDB Advanced Deployment and Operations. Planning for scale? No problem: MongoDB is here to help. Get a preview of what it’s like to work with MongoDB’s Technical Services Team. Give us some details on your deployment and we can set you up with an expert who can provide detailed guidance on all aspects of scaling with MongoDB, based on our experience with hundreds of deployments.

By Adam Comerford, Senior Solutions Engineer In Part I we discussed important considerations when picking a shard key. In this post we will go through some recommendations when running a sharded cluster at scale. Scalability is one of the core benefits of sharding in MongoDB but this can give you a false sense of security; even with that flexibility, you still have to make smart decisions about how and when you deploy resources. In this post, we will cover a couple of common mistakes that people tend to make when it comes to running a sharded cluster. 3. Waiting too long to add a new shard (overloaded) You sharded your database and scaled horizontally for a reason, perhaps it was to add more memory or disk capacity. Whatever the reason, if your application usage grows over time so (generally) does your database utilization. Eventually, your current sharded cluster will pass a certain point, let’s call it 80% utilized (as a nice round estimate), such that it becomes problematic to add another shard. Why? Well, adding a new shard to a cluster is not free, and it is not instantaneous. It consumes resources and (initially) accepts very little traffic. Essentially, at the start of its existence, a newly added shard costs you capacity instead of adding capacity. The length of time it will stay in this state will depend on the balancer and how long it takes for a significant portion of “busy/active” chunks to move onto the new shard. It can often be easier to visualize this process, so let’s make up some hypothetical numbers and set the bar relatively low. Our imaginary existing cluster will be a set of 2 shards, with 2000 chunks (500 considered “active”) and to that we need to add a 3rd shard. This 3rd shard will eventually store one third of the active chunks (and total chunks). The question is, when does this shard stop adding overhead overall and instead become an asset? In reality, this will vary from cluster to cluster and have a lot of dependencies and variables - in other words you need to have good metrics about your cluster, particularly your load bottleneck. Therefore we will once again use our imaginations and go with a relatively low bar: when 5% of active chunks—that is, those chunks seeing most traffic—have migrated to the new shard, you should expect a net gain in performance. In our imaginary system we have evaluated our load levels, the expected impact of migrations and have determine that once that 5% threshold of active chunks has been migrated to the new shard it can be considered a net gain for the overall system. Once all chunks have been balanced, then the migration overhead disappears, but initially this will be an expected trade off. This chart shows how long it would take for new shards to reach net positive contribution in your cluster (the dotted line implies net gain): In this fabricated example, it takes almost 2 hours for the new shard to attain a viable level of active chunks and be considered a net gain for the overall system. Although these numbers are fictional, these numbers are based on setups we have seen in real systems with moderate load. From there it is relatively easy to imagine this set of migrations taking even longer on an overloaded set of shards, and taking far longer for our newly added shard to cross the threshold and become a net gain. As such it is best to be proactive and add capacity before it becomes a necessity. Possible Mitigation Strategies Manual balancing of targeted “hot” chunks (chunk that is being accessed more than others) to move activity to the new shard more quickly Add the shard at low traffic time so that there is less competition for resources Disable balancing on some collections, prioritise balancing busy collections first 4. Under-provisioning Config Servers Provisioning enough resources without being wasteful is always tricky, and all the more so in a complicated distributed system like a MongoDB sharded cluster. Everyone wants to use their hardware, virtual instances, virtual machines, containers and the like in the most efficient way possible, and get the best bang for their buck. Hence it is only natural to take a look at the various pieces of a distributed cluster and look for lower utilized pieces that could be put on less expensive resources. The most common pitfall here with MongoDB are the config servers, which are often neglected when stress testing a cluster. In testing environments and smaller deployments (unless specific measures are taken to stress them) they are relatively lightly loaded and usually identified as candidates for lesser instances/hardware. The problem is that these are critical pieces of infrastructure. They may not be heavily loaded all the time, but when they do see load and struggle to service requests, that can impact all queries (reads, writes, authentication) and add latency to all requests made of the cluster in question. In particular, the first config server in the list supplied to your mongos processes is vital. This is the config server that all mongos processes will default to read from when fetching or refreshing their view of the data distribution in your cluster. Similarly, this is the server that will be hit when attempting to authenticate a user. If it is under-provisioned and cannot service queries, or if it has problems with networking (packet loss, congestion), then the effects will be significant. Possible Mitigation Strategies Ensure the config servers are load tested, slightly over-provisioned (the first config server in particular) If using virtual machines or cloud based instances, investigate increasing available resources Turning off the balancer, disabling chunk splitting will reduce the chances of high read traffic to the config servers (no migrations, no meta data refresh) but this is only a temporary fix unless you have a perfect write distribution and may not eliminate issues completely. 5. Using the count() command on sharded collections This pitfall is very common, and it seems to hit somewhat randomly in terms of how long someone has been running a sharded environment. At some point, a question will arise along the lines of: “How are we tracking/verifying/checking how many documents we have in each collection on each shard, how balanced are they and do they agree with ?” Hopefully no one is actually constructing questions this way in your organization, but you get the basic idea. The most obvious way to do a quick check on this type of thing is to count the documents and see if the numbers make sense and/or agree with counts elsewhere. That thinking naturally leads people to the count command and they proceed to use it to gather figures for their documents and collections. Unfortunately, on a busy, mature sharded cluster, the results will very rarely be what is expected. The reason for this is that the count command as implemented today has several optimizations in place to make it faster to run in general and those speed optimizations essentially bypass a key piece of the sharding functionality needed to return accurate results in this case. This is a known bug and is being tracked in SERVER-3645, but does not stop people from consistently hitting this issue. The nature of the issue means that count will report documents in the results that it should not, for example: Documents that are being deleted as part of a chunk migrations Documents that have been left behind from previous chunk migrations (also known as orphans) Documents currently being copied as part of an in-flight chunk migration A regular query (rather than a count) will have its results filtered by the respective primary and not suffer from the same problem. Hence, if you were to manually count the results from a query client-side you would get an accurate result. This quirk of sharded environments will eventually be fixed, but for now it will inevitably crop up from time to time in all active sharded clusters used by a large team. Possible Mitigation Strategies Do counts on the client side, or use targeted, range based queries (with a primary read preference) to count instead Use cleanUpOrphaned and disable the balancer (make sure it has finished current round) when performing counts across the cluster If you want tolearn more about managing MongoDB deployments at scale, sign up for my online education course, MongoDB Advanced Deployment and Operations. Planning for scale? No problem: MongoDB is here to help. Get a preview of what it’s like to work with MongoDB’s Technical Services Team. Give us some details on your deployment and we can set you up with an expert who can provide detailed guidance on all aspects of scaling with MongoDB, based on our experience with hundreds of deployments.

Last week I was tasked with developing a quick prototype that used AngularJS for its client and Spring MVC for its server. A colleague developed the same application using Backbone.js and Spring MVC. At first, I considered using my boot-ionic project as a starting point. Then I realized I didn't need to develop a native mobile app, but rather a responsive web app. My colleague mentioned he was going to use RESThub as his starting point, so I figured I'd use JHipster as mine. We allocated a day to get our environments setup with the tools we needed, then timeboxed our first feature spike to four hours. My first experience with JHipster failed the 10-minute test. I spent a lot of time flailing about with various "npm" and "yo" commands, getting permissions issues along the way. After getting thinks to work with some sudo action, I figured I'd try its Docker development environment. This experience was no better. JHipster seems like a nice project, so I figured I'd try to find the causes of my issues. This article is designed to save you the pain I had. If you'd rather just see the steps to get up and running quickly, skip to the summary. The "npm" and "yo" issues I had seemed to be caused by a bad node/npm installation. To fix this, I removed node and installed nvm. Here's the commands I needed to remove node and npm: sudo rm -rf /usr/local/lib/node_modules sudo rm -rf /usr/local/include/node sudo rm /usr/local/bin/node sudo rm -rf /usr/local/bin/npm sudo rm /usr/local/share/man/man1/node.1 sudo rm -rf /usr/local/lib/dtrace/node.d sudo rm -rf ~/.npm Next, I ran "brew doctor" to make sure Homebrew was still happy. It told me some things were broken: $ brew doctor Warning: Broken symlinks were found. Remove them with `brew prune`: /usr/local/bin/yo /usr/local/bin/ionic /usr/local/bin/grunt /usr/local/bin/bower I ran brew update && brew prune, followed by brew install nvm. Next, I added the following to my ~/.profile: source $(brew --prefix nvm)/nvm.sh To install the latest version of node, I ran the commands below and set the latest version as the default: nvm ls-remote nvm install v0.11.13 nvm alias default v0.11.13 Once I had a fresh version of Node.js, I was able to run JHipster's local installation instructions. npm install -g yo npm install -g generator-jhipster Then I created my project: yo jhipster I was disappointed to find this created all the project files in my current directory, rather than in a subdirectory. I'd recommend you do the following instead: mkdir ~/projectname && cd ~/projectname && yo jhipster Before creating your project, JHipster asks you a number of questions. To see what they are, see its documentation on creating an application. Two things to be aware of: Hot reloading Java code doesn't work well (yet) with Java 8 Its OAuth2 implementation doesn't work with WebSockets In other words, I'd recommend using Java 7 + (cookie-based authentication with websockets) or (oauth2 authentication w/o websockets). After creating my project, I was able to run it using "mvn spring-boot:run" and view it at http://localhost:8080. To get hot-reloading for the client, I ran "grunt server" and opened my browser to http://localhost:9000. JHipster + Docker on OS X I had no luck getting the Docker instructions to work initially. I spent a couple hours on it, then gave up. A couple of days ago, I decided to give it another good ol' college-try. To make sure I figured out everything from scratch, I started by removing Docker. I re-installed Docker and pulled the JHipster image using the following: sudo docker pull jdubois/jhipster-docker The error I got from this was the following: 2014/09/05 19:43:38 Post http:///var/run/docker.sock/images/create?fromImage=jdubois%2Fjhipster-docker&tag=: dial unix /var/run/docker.sock: no such file or directory After doing some research, I learned I needed to run boot2docker init first. Next I ran boot2docker up to start the Docker daemon. Then I copied/pasted "export DOCKER_HOST=tcp://192.168.59.103:2375" into my console and tried to run docker pull again. It failed with the same error. The solution was simpler than you might think: don't use sudo. $ docker pull jdubois/jhipster-docker Pulling repository jdubois/jhipster-docker 01bdc74025db: Pulling dependent layers 511136ea3c5a: Download complete ... The next command that JHipster's documentation recommends is to run the Docker image, forward ports and share folders. When you run it, the terminal seems to hang and trying to ssh into it doesn't work. Others have recently reported a similar issue. I discovered the hanging is caused by a missing "-d" parameter and ssh doesn't work because you need to add a portmap to the VM to expose the port to your host. You can fix this by running the following: boot2docker down VBoxManage modifyvm "boot2docker-vm" --natpf1 "containerssh,tcp,,4022,,4022" VBoxManage modifyvm "boot2docker-vm" --natpf1 "containertomcat,tcp,,8080,,8080" VBoxManage modifyvm "boot2docker-vm" --natpf1 "containergruntserver,tcp,,9000,,9000" VBoxManage modifyvm "boot2docker-vm" --natpf1 "containergruntreload,tcp,,35729,,35729" boot2docker start After making these changes, I was able to start the image and ssh into it. docker run -d -v ~/jhipster:/jhipster -p 8080:8080 -p 9000:9000 -p 35729:35729 -p 4022:22 -t jdubois/jhipster-docker ssh -p 4022 jhipster@localhost I tried creating a new project within the VM (cd /jhipster && yo jhipster), but it failed with the following error: /usr/lib/node_modules/generator-jhipster/node_modules/yeoman-generator/node_modules/mkdirp/index.js:89 throw err0; ^ Error: EACCES, permission denied '/jhipster/src' The fix was giving the "jhipster" user ownership of the directory. sudo chown jhipster /jhipster After doing this, I was able to generate an app and run it using "mvn spring-boot:run" and access it from my Mac at http://localhost:8080. I was also able to run "grunt server" and see it at http://localhost:9000 However, I was puzzled to see that there was nothing in my ~/jhipster directory. After doing some searching, I found that the docker run -v /host/path:/container/path doesn't work on OS X. David Gageot's A Better Boot2Docker on OSX led me to svendowideit/samba, which solved this problem. The specifics are documented in boot2docker's folder sharing section. I shutdown my docker container by running "docker ps", grabbing the first two characters of the id and then running: docker stop [2chars] I started the JHipster container without the -v parameter, used "docker ps" to find its name (backstabbing_galileo in this case), then used that to add samba support. docker run -d -p 8080:8080 -p 9000:9000 -p 35729:35729 -p 4022:22 -t jdubois/jhipster-docker docker run --rm -v /usr/local/bin/docker:/docker -v /var/run/docker.sock:/docker.sock svendowideit/samba backstabbing_galileo Then I was able to connect using Finder > Go > Connect to Server, using the following for the server address: cifs://192.168.59.103/jhipster To make this volume appear in my regular development area, I created a symlink: ln -s /Volumes/jhipster ~/dev/jhipster After doing this, all the files were marked as read-only. To fix, I ran "chmod -R 777 ." in the directory on the server. I noticed that this also worked if I ran it from my Mac's terminal, but it took quite a while to traverse all the files. I noticed a similar delay when loading the project into IntelliJ. Summary Phew! That's a lot of information that can be condensed down into four JHipster + Docker on OS X tips. Make sure your npm installation doesn't require sudo rights. If it does, reinstall using nvm. Add portmaps to your VM to expose ports 4022, 8080, 9000 and 35729 to your host. Change ownership on the /jhipster in the Docker image: sudo chown jhipster /jhipster. Use svendowideit/samba to share your VM's directories with OS X.

This is a post in a series discussing using spring-boot and docker for deployment. Refer to the end of the first post for a table of contents. Shortly after you start building docker containers you will realize that you need some place to publish your images. You could push to the central docker registry. However, the central registry is public. Not a great idea if you are working on a private project. If this is your case, you can simply run a local docker registry. To install and run your private registry run $ docker run -p 5000:5000 -d registry Surprise!!! It is ran in a docker container. You can now start pushing to your local repository. As an example, I will pull the latest postgres image and push version 9.4 to my local registry. $ docker pull postgres $ docker tag postgres:9.4 localhost:5000/postgres:9.4 $ docker push localhost:5000/postgres Outputs: The push refers to a repository [localhost:5000/postgres] (len: 1) Sending image list Pushing repository localhost:5000/postgres (1 tags) 511136ea3c5a: Image successfully pushed ec3443b7b068: Image successfully pushed 06af7ad6cff1: Image successfully pushed 37eae31ff4e9: Image successfully pushed 83e30bf01299: Image successfully pushed 499da968a652: Image successfully pushed bf09bd07d760: Image successfully pushed 1eee820e762b: Image successfully pushed 7bf9287ccfce: Image successfully pushed 288b8d534217: Image successfully pushed f20dbf0acb45: Image successfully pushed bd511e81a5ed: Image successfully pushed 8fe7eb38aea1: Image successfully pushed 464263a50f65: Image successfully pushed 1f58a67adecd: Image successfully pushed a99fb4ee814d: Image successfully pushed 6112f975feab: Image successfully pushed 6dff1b5c2259: Image successfully pushed Pushing tag for rev [6dff1b5c2259] on {http://localhost:5000/v1/repositories/postgres/tags/9.4} Looking at the current images, you will notice that the version tagged with localhost and the official images have the same information. Notice that I had to retag the image with the location of the repository. I thought the requirement to put the location address as part of the image name was a little odd. However, after using docker longer, it makes sense. It ensures you know where the image was originally pulled. $ docker images postgres 9.4 6dff1b5c2259 5 days ago 244.4 MB localhost:5000/postgres 9.4 6dff1b5c2259 5 days ago 244.4 MB Since docker tags are not permanent, and newer version of the postgres:9.4 image could be pushed to the public registry. When you self-host images, you are in control of when updates are pushed to any base image that you have extended. Someday I intend to learn how to build an image completely from scratch. Docker-ize All the Things!

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: