Tag: Chef


At VMworld 2015 in San Francisco, support for Flocker data volumes inside a VMware vSphere environment was announced. The announcement was one of the items I was most excited about hearing during the conference. The challenge of data persistence when Dockerizing workloads is prevalent in many organizations today. There are a few projects like Flocker and Rexray from EMC {Code} that are working to address this challenge. As I am working on building my own Cloud Native Application stack for a personal project, being able to maintain persistent data across the stack is key.

For those unfamiliar with Flocker, let me provide a quick overview. In short, it provides data volumes that can be attached to a Docker container that allow the container to be moved across hosts without losing data. Flocker describes the solution in a rather neat graphic.


The way it works is rather simple too. There is a controller node — referred to as the Flocker Control Service — and agents that get installed on the compute nodes running the Docker containers. The vSphere driver for Flocker enables the use of a shared datastore as the place where the provisioning of the Flocker data volumes takes place. Thus allowing you to utilize a familiar virtual storage construct within your environment to provide the data persistence necessary within a Cloud Native Application.

VMware has made a driver for integrating Flocker into a vSphere environment available on GitHub. The page includes basic instructions on how to load the driver into a Flocker Agent node and begin utilizing it within a Docker container. As I looked into Flocker and how to run it within my vSphere environment, my specific use-case called for it to become part of my VMware Big Data Extensions framework, so that it could be tied to an Apache Mesos cluster with Marathon.

This project, with Flocker + Apache Mesos, is the reason I spent a good part of the past weekend working on building out a CentOS 7 template for BDE to use. I needed to be able to support running a newer version of Docker in order to support Flocker. The details on my effort to build a CentOS 7 template VM for BDE are covered in this post. Between that effort and adding Flocker support, the pieces are all starting to come together.

 Flocker Support in VMware Big Data Extensions

When you look at the architecture slide on the ClusterHQ Flocker site, it becomes rather clear that adding Flocker to an Apache Mesos cluster deployment is a natural next step. The controller node could be deployed within the same VM running the Apache Mesos master or as a standalone VM. The agents can be installed and configured on the Apache Mesos slaves with Docker.

Side-Note: A few weeks back, I tried using the Debian 7 template that was released alongside the BDE Fling almost a year ago. Unfortunately it failed with a large number of Chef configuration errors when it was used to deploy an Apache Mesos cluster. Rather than fight it, I went back to using a combination of Photon and CentOS 6 for my deployments within my lab environment until I got the new CentOS 7 template working.

The ClusterHQ Flocker documentation provides directions on how to get Flocker running on an Ubuntu or CentOS 7 node. I used that documentation to help me construct the Chef recipes I needed for BDE. The first decision I made, at least for now, is to install the Flocker Control Service on a dedicated VM. By doing so, it allowed me to create a new cluster definition for a Mesos cluster with Flocker support, while leaving the original cluster specification file untouched.


  1 {
  2   "nodeGroups":[
  3     {
  4       "name": "Zookeeper",
  5       "roles": [
  6         "zookeeper"
  7       ],
  8       "groupType": "zookeeper",
  9       "instanceNum": "[3,3,3]",
 10       "instanceType": "[SMALL]",
 11       "cpuNum": "[1,1,64]",
 12       "memCapacityMB": "[7500,3748,min]",
 13       "storage": {
 14         "type": "[SHARED,LOCAL]",
 15         "sizeGB": "[2,2,min]"
 16       },
 17       "haFlag": "on"
 18     },
 19     {
 20       "name": "Master",
 21       "description": "The Mesos master node",
 22       "roles": [
 23         "mesos_master",
 24         "mesos_chronos",
 25         "mesos_marathon"
 26       ],
 27       "groupType": "master",
 28       "instanceNum": "[2,1,2]",
 29       "instanceType": "[MEDIUM,SMALL,LARGE,EXTRA_LARGE]",
 30       "cpuNum": "[1,1,64]",
 31       "memCapacityMB": "[7500,3748,max]",
 32       "storage": {
 33         "type": "[SHARED,LOCAL]",
 34         "sizeGB": "[1,1,min]"
 35       },
 36       "haFlag": "on"
 37     },
 38     {
 39       "name": "Flocker Control",
 40       "description": "Flocker control node",
 41       "roles": [
 42         "flocker_control"
 43       ],
 44       "groupType": "master",
 45       "instanceNum": "[1,1,1]",
 46       "instanceType": "[SMALL,MEDIUM]",
 47       "cpuNum": "[1,1,16]",
 48       "memCapacityMB": "[3748,3748,max]",
 49       "storage": {
 50         "type": "[SHARED,LOCAL]",
 51         "sizeGB": "[1,1,min]"
 52       },
 53       "haFlag": "on"
 54     },
 55     {
 56       "name": "Slave",
 57       "description": "The Mesos slave node",
 58       "roles": [
 59         "mesos_slave",
 60         "mesos_docker",
 61         "flocker_agent"
 62       ],
 63       "instanceType": "[MEDIUM,SMALL,LARGE,EXTRA_LARGE]",
 64       "groupType": "worker",
 65       "instanceNum": "[3,1,max]",
 66       "cpuNum": "[1,1,64]",
 67       "memCapacityMB": "[7500,3748,max]",
 68       "storage": {
 69         "type": "[SHARED,LOCAL]",
 70         "sizeGB": "[1,1,min]"
 71       },
 72       "haFlag": "off"
 73     }
 74   ]
 75 }

Note: I enabled HA support for the Flocker Control Node within the specification file since it is only going to be deploying a single VM within the cluster.

The corresponding MAP entry included the following lines.


 30   {
 31     "vendor" : "Mesos",
 32     "version" : "^(\\d)+(\\.\\w+)*",
 33     "type" : "Mesos with Flocker Cluster",
 34     "appManager" : "Default",
 35     "path" : "Ironfan/mesos/flocker/spec.json"
 36   },

With the JSON cluster specification file created and the MAP entry added, the next step is to create two new Chef roles — flocker_control and flocker_agent.


 1 name        'flocker_control'
 2 description 'Deploy the Flocker Control Node to support an Apache Mesos cluster.'
 4 run_list *%w[
 5   role[basic]
 6   flocker::default
 7   flocker::control
 8 ]


 1 name        'flocker_agent'
 2 description 'Deploy the Flocker agent on Apache Mesos worker node.'
 4 run_list *%w[
 5   role[basic]
 6   flocker::default
 7   flocker::agent
 8 ]

The flocker-control Chef role will be used to install and configure the standalone VM running the Flocker Control Service. The flocker-agent will be used to configure the Flocker Agent on each of the Apache Mesos worker nodes.

Chef Recipes for Flocker

I broke the Chef recipes into two — one for the Control node and one for the Agents. In addition to the primary recipes for installation and configuration, I created a library recipe, attributes file and several templates. I have included the recipes for the Control and Agent roles below, the remaining files can be downloads from the GitHub repository.


  1 # Cookbook Name:: flocker
  2 # Recipe:: default
  4 include_recipe "java::sun"
  5 include_recipe "hadoop_common::pre_run"
  6 include_recipe "hadoop_common::mount_disk"
  7 include_recipe "hadoop_cluster::update_attributes"
  9 set_bootstrap_action(ACTION_INSTALL_PACKAGE, 'flocker', true)
 11 # Setup the new repositories
 12 template '/etc/yum.repos.d/clusterhq.repo' do
 13   source 'clusterhq.repo.erb'
 14   action :create
 15 end
 17 # Dependency packages
 18 %w{clusterhq-flocker-node clusterhq-flocker-cli}.each do |pkg|
 19   package pkg do
 20     action :install
 21   end
 22 end
 24 clear_bootstrap_action


  1 #
  2 # Cookbook Name:: flocker
  3 # Recipe:: control
  4 #
  5 # Licensed under the Apache License, Version 2.0 (the "License");
  6 # you may not use this file except in compliance with the License.
  7 # You may obtain a copy of the License at
  8 #
  9 #       http://www.apache.org/licenses/LICENSE-2.0
 10 #
 11 # Unless required by applicable law or agreed to in writing, software
 12 # distributed under the License is distributed on an "AS IS" BASIS,
 13 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 14 # See the License for the specific language governing permissions and
 15 # limitations under the License
 17 include_recipe 'flocker::install'
 19 set_bootstrap_action(ACTION_INSTALL_PACKAGE, 'flocker_control', true)
 21 conf_dir = node[:flocker][:conf_dir]
 22 temp_dir = node[:flocker][:temp_dir]
 23 directory conf_dir do
 24   owner 'root'
 25   group 'root'
 26   mode '0700'
 27   action :create
 28 end
 30 template '/etc/flocker/cluster.key' do
 31   source 'cluster.key.erb'
 32   mode '0600'
 33   owner 'root'
 34   group 'root'
 35   action :create
 36 end
 38 template '/etc/flocker/cluster.crt' do
 39   source 'cluster.crt.erb'
 40   mode '0600'
 41   owner 'root'
 42   group 'root'
 43   action :create
 44 end
 46 template '/etc/flocker/control-service.crt' do
 47   source 'control-service.crt.erb'
 48   mode '0600'
 49   owner 'root'
 50   group 'root'
 51   action :create
 52 end
 54 template '/etc/flocker/control-service.key' do
 55   source 'control-service.key.erb'
 56   mode '0600'
 57   owner 'root'
 58   group 'root'
 59   action :create
 60 end
 62 # Generate authentication certificates
 63 #execute 'generate_system_certs' do
 64 #  cwd conf_dir
 65 #  command 'flocker-ca initialize $CLUSTERNAME'
100 # Start the Flocker Control service
101 is_control_running = system("systemctl status #{node[:flocker][:control_service_name]}")
102 service "restart-#{node[:flocker][:control_service_name]}" do
103   service_name node[:flocker][:control_service_name]
104   supports :status => true, :restart => true
106 end if is_control_running
108 service "start-#{node[:flocker][:control_service_name]}" do
109   service_name node[:flocker][:control_service_name]
110   action [ :enable, :start ]
111   supports :status => true, :restart => true
113 end
115 # Register with cluster_service_discovery
116 provide_service(node[:flocker][:control_service_name])
117 clear_bootstrap_action


  1 #
  2 # Cookbook Name:: flocker
  3 # Recipe:: agent
  4 #
  5 # Licensed under the Apache License, Version 2.0 (the "License");
  6 # you may not use this file except in compliance with the License.
  7 # You may obtain a copy of the License at
  8 #
  9 #       http://www.apache.org/licenses/LICENSE-2.0
 10 #
 11 # Unless required by applicable law or agreed to in writing, software
 12 # distributed under the License is distributed on an "AS IS" BASIS,
 13 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 14 # See the License for the specific language governing permissions and
 15 # limitations under the License
 17 include_recipe 'flocker::install'
 19 set_bootstrap_action(ACTION_INSTALL_PACKAGE, 'flocker_agent', true)
 21 # Wait for the Flocker control node to be setup and registered
 22 wait_for_flocker_control
 24 conf_dir = node[:flocker][:conf_dir]
 25 directory conf_dir do
 26   owner 'root'
 27   group 'root'
 28   mode '0700'
 29   action :create
 30 end
 32 controls_ip = flocker_control_ip
 34 template '/etc/flocker/agent.yml' do
 35   source 'agent.yml.erb'
 36   action :create
 37   variables(
 38     control_node: controls_ip
 39   )
 40 end
 42 template '/etc/flocker/node.crt' do
 43   source 'node.crt.erb'
 44   action :create
 45   mode '0600'
 46   owner 'root'
 47   group 'root'
 48 end
 50 template '/etc/flocker/node.key' do
 51   source 'node.key.erb'
 52   action :create
 53   mode '0600'
 54   owner 'root'
 55   group 'root'
 56 end
 58 template '/etc/flocker/cluster.crt' do
 59   source 'cluster.crt.erb'
 60   action :create 61   mode '0600'
 62   owner 'root'
 63   group 'root'
 64 end
 66 # Fix agent.yml hostname string
 67 #   hostname: ["blah.local.domain"]
 68 execute 'fix agent.yml hostname' do
 69   cwd conf_dir
 70   command 'sed -i \'s/\["/"/g\' /etc/flocker/agent.yml && sed -i \'s/"\]/"/g\' /etc/flocker/agent.yml'
 71 end
 73 # Install additional packages
 74 %w{git}.each do |pkg|
 75   package pkg do
 76     action :install
 77   end
 78 end
 80 execute "install-python-pip" do
 81   command 'curl "https://bootstrap.pypa.io/get-pip.py" -o "get-pip.py" && python get-pip.py'
 82 end
 84 # Install the VMware vsphere-flocker-driver
 85 execute 'install vmware-flocker-driver' do
 86   command 'pip install git+https://github.com/vmware/vsphere-flocker-driver.git'
 87 end
 89 # Start the two Flocker agent services
 90 service 'flocker-dataset-agent' do
 91   supports :status => true, :restart => true, :reload => false
 92   action [ :enable, :start ]
 93 end
 95 service 'flocker-container-agent' do
 96   supports :status => true, :restart => true, :reload => false
 97   action [ :enable, :start ]
 98 end
100 provide_service(node[:flocker][:agent_service_name])
101 clear_bootstrap_action

VMware vSphere Flocker Driver

The necessary bits for utilizing the Flocker driver are built into the Chef recipes themselves. However, since the GitHub page does a good job of providing an overview, I would like to highlight the specific bits of the Chef recipes that coincide with that documentation.

I started off my modifying my CentOS 7 VM template for BDE to included the advanced setting in the VMX file.

disk.EnableUUID​ = "TRUE"

Screen Shot 2015-10-28 at 8.49.24 AM

The next step was to mark a datastore for Flocker to use for the shared volumes. Because I already had BDE in my environment and it was currently pointing to three datastores, I went ahead modified my implementation a bit. The three datastores I did have BDE utilizing were actually part of a single Storage DRS cluster. I went ahead and remove the Storage DRS cluster and left the three datastores alone. From there, I selected just a single datastore for BDE to use going forward and created the necessary Flocker folder.

Deploying an Apache Mesos cluster with Flocker support

With all of the pieces in place, the environment is ready for a new Apache Mesos cluster to be deployed through the BDE framework. Using the vSphere Web Client, I deployed a new cluster for testing. Once the cluster was deployed through BDE and fully configured using the new Chef recipes, I verified that the Apache Mesos, Mesosphere Marathon and Chronos interfaces were all online.






The cluster is ready for a workload to be deployed into it.


VMware is making significant strides into the Cloud Native Apps  space with the ability to support Docker, Apache Mesos, Kubernetes and now also Flocker. In my opinion, being able to provide a simple deployment framework for these applications through VMware Big Data Extensions is a key success factor. Tying these pieces all together tells a very compelling story for organizations already using the VMware SDDC within their environments and transitioning to the Cloud Native Apps arena. Future posts will revolve around my own work in the space to build a Cloud Native distributed application running entirely on a vSphere environment.

I am very excited about this project and getting Flocker in place was a critical factor for my success. As always, all of the necessary files to add support for Flocker into VMware Big Data Extensions are available on the Virtual Elephant GitHub repository.

Stay tuned and if you have any questions about what I’ve covered in these posts, please reach out to me over Twitter, LinkedIn or email. Enjoy!



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Let me start off by saying that adding Apache Kafka into the framework of VMware vSphere Big Data Extensions (BDE) has been the most challenging of them all. Not from a framework perspective, but from a Chef cookbook and configuration one. There were a few resources for me to rely on for the overall configuration of Kafka, however many of them had contradicting statements within them. It took a good 8 solid hours of testing and re-testing the recipes before I was able to get a working multi-node Kafka cluster online.

All that being said, it was important for me to get a standardized method for deploying Apache Kafka clusters within the BDE framework. I am aware of several teams that are manually configuring Kafka within an environment today, each with their own insights on how that should be accomplished and few of them are sharing their methods with one another. Frankly, I feel the lack of collaboration between teams is the biggest challenge for any large-scale organization to overcome. Very rarely is a problem too difficult to solve with technology, it is generally difficult to solve because of a lack of knowledge-sharing between teams and/or organizations.

As I hope all of my readers have come to expect, the proceeding will include the JSON files necessary to add Apache Kafka into BDE, the Chef recipes|templates|libraries and a link to the GitHub repository for Virtual Elephant where you can download all of the pieces to add within your own deployments of BDE to further expand your service catalog.


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Apache Cassandra

Apache Cassandra support has been one of the additional clustered software projects I have wanted to add into the vSphere Big Data Extensions framework. In an effort to build out a robust and diverse service catalog for our private cloud environment, adding Cassandra is one more service we can make available to our customers (which is currently in production use).

For those who are unaware of Apache Cassandra, it is a distributed database management system and the Apache Cassandra Project website states that it is:

“…the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data. Cassandra’s support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.”

Below you will find the tutorial for how to implement Apache Cassandra, with the associated JSON files, Chef cookbooks and Chef roles in vSphere Big Data Extensions. All of the files I will be going over are available in GitHub here.


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Last year at VMworld, Andy and I spoke about the data pipeline and all of the different pieces involved and how their interactions lead to congestion. For any organization, how you deal with the data congestion will affect how much data an application can process. Fortunately, much like what Apache Hadoop has done for batch processing, Apache Storm has entered the real-time processing arena to help applications more efficiently process big data.

In case you are unfamiliar with Apache Storm, a basic explanation of its purpose and design can be seen on the Apache Storm site.

Storm is a distributed realtime computation system. Similar to how Hadoop provides a set of general primitives for doing batch processing, Storm provides a set of general primitives for doing realtime computation. Storm is simple, can be used with any programming language, is used by many companies, and is a lot of fun to use!

Hortonworks has provided some great insight into how Apache Storm can be utilized alongside Hadoop to allow organizations to become even more agile and efficient in their data pipeline processing.  (more…)

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After working with EMC over the summer and evaluating the capabilities of utilizing Isilon storage as an HDFS layer, including the NameNode, it got me thinking about how the VMware Big Data Extensions could be utilized to create the exact same functionality. If you’ve read any of the other posts around extending the capabilities of BDE beyond just what it ships with, you’ll know the framework allows an administrator to do nearly anything they can imagine.

As with creating a Zookeeper-only cluster, all of the functionality for a HDFS-only cluster is already built into BDE — it is just a matter of unlocking it. It took less than 10 minutes to set up all the pieces.

I needed to add Cloudera 5.2.1 support into my new BDE 2.1 lab environment, so the first command sets that functionality up. After that, the rest of commands are all that are needed:

# config-distro.rb --name cdh5 --vendor CDH --version 5.2.1 --repos http://archive.cloudera.com/cdh5/redhat/6/x86_64/cdh/cloudera-cdh5.repo
# cd /opt/serengeti/www/specs/Ironfan/hadoop2/
# mkdir -p donly
# cp conly/spec.json donly/spec.json
# vim donly/spec.json

I configured the donly/spec.json file to include the following:

  1 {
  2   "nodeGroups":[
  3     {
  4       "name": "DataMaster",
  5       "description": "It is the VM running the Hadoop NameNode service. It manages HDFS data and assigns tasks to workers. The number of VM can only be one. User can specify     size of VM.",
  6       "roles": [
  7         "hadoop_namenode"
  8       ],
  9       "groupType": "master",
 10       "instanceNum": "[1,1,1]",
 11       "instanceType": "[MEDIUM,SMALL,LARGE,EXTRA_LARGE]",
 12       "cpuNum": "[2,1,64]",
 13       "memCapacityMB": "[7500,3748,max]",
 14       "storage": {
 15         "type": "[SHARED,LOCAL]",
 16         "sizeGB": "[50,10,max]"
 17       },
 18       "haFlag": "on"
 19     },
 20     {
 21       "name": "DataWorker",
 22       "description": "They are VMs running the Hadoop DataNode services. They store HDFS data. User can specify number and size of VMs in this group.",
 23       "roles": [
 24         "hadoop_datanode"
 25       ],
 26       "instanceType": "[SMALL,MEDIUM,LARGE,EXTRA_LARGE]",
 27       "groupType": "worker",
 28       "instanceNum": "[3,1,max]",
 29       "cpuNum": "[1,1,64]",
 30       "memCapacityMB": "[3748,3748,max]",
 31       "storage": {
 32         "type": "[LOCAL,SHARED]",
 33         "sizeGB": "[100,20,max]"
 34       },
 35       "haFlag": "off"
 36     }
 37   ]
 38 }

The final part is to add an entry for a HDFS-Only cluster in the /opt/serengeti/www/specs/map file:

128   {
129     "vendor" : "CDH",
130     "version" : "^\\w+(\\.\\w+)*",
131     "type" : "HDFS Only Cluster",
132     "appManager" : "Default",
133     "path" : "Ironfan/hadoop2/donly/spec.json"
134   },

Restart the Tomcat service on the management server and the option is now available. The configured cluster when it is done looked like this in the VMware vCenter Web Client:


You can view the status of the HDFS layer through the standard interface:


Being able to have this functionality within VMware Big Data Extensions allows an environment to provide a dedicated HDFS data warehouse layer to your applications and other application cells.

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