Bind Docker Container for vPod Lab

I am currently working on building out a vPod nested ESXi lab environment that will be deployed through OpenStack’s Heat orchestration service. As I worked out the vPod application components, I realized that I wanted to include a single Linux VM that would run various services inside Docker containers.

I needed a Bind Docker container!

It seems like everything in a VMware SDDC environment needs both the forward and reverse records working properly — so I started here. The Docker container is completely self-contained — all external zone data is stored in S3 and downloaded when the container is built.

https://hub.docker.com/r/chrismutchler/vpod-bind/

The Dockerfile for the container contains the following code:

# Designed to be used in conjunction with a nested ESXi
# virtual lab environment deployed through an OpenStack
# Heat template.

FROM ubuntu:latest

MAINTAINER chris@virtualelephant.com

RUN apt-get -y update && apt-get -y install bind9 dnsutils curl

RUN curl https://s3-us-west-1.amazonaws.com/virtualelephant-vpod-bind/db.192.168 -o /etc/bind/db.192.168 && curl https://s3-us-west-1.amazonaws.com/virtualelephant-vpod-bind/db.vsphere.local -o /etc/bind/db.vsphere.local && curl https://s3-us-west-1.amazonaws.com/virtualelephant-vpod-bind/named.conf.options -o /etc/bind/named.conf.options && curl https://s3-us-west-1.amazonaws.com/virtualelephant-vpod-bind/named.conf.local -o /etc/bind/named.conf.local

EXPOSE 53

CMD ["/usr/sbin/named", "-g", "-c", "/etc/bind/named.conf", "-u", "bind"]

To start the container, I setup the Ubuntu VM to execute the following code when it is deployed inside OpenStack.

# docker run -d -p 53:53 -p 53:53/udp chrismutchler/vpod-bind

Once running, it is now able to provide the critical DNS service inside the vPod ESXi environment. From here it is onto building out the Heat template that will leverage the container.

Enjoy!

Updating Apache Mesos Chef Recipes

 

Apache Mesos Chef Recipes

The recent update to v2.3.1 of VMware Big Data Extensions required a few updates on the management server to enable the proper deployment of Apache Mesos, Mesosphere Marathon and Docker. In order to have BDE deploy the latest code, the Apache Mesos Chef recipes needed a few updates. I prefer to use the Mesosphere CentOS repo for installing the packages, as it allows for keeping the clusters up-to-date with the proper packages. In order to do so, the following file should be created in /opt/serengeti/www/yum.

mesos.repo

[mesosphere]
name=Mesosphere Packages for EL 6 - $basearch
baseurl=http://repos.mesosphere.io/el/6/$basearch/
enabled=1
gpgcheck=1
gpgkey=https://bde.local.domain/yum/RPM-GPG-KEY-mesosphere

[mesosphere-noarch]
name=Mesosphere Packages for EL 6 - noarch
baseurl=http://repos.mesosphere.io/el/6/noarch/
enabled=1
gpgcheck=1
gpgkey=https://bde.local.domain/yum/RPM-GPG-KEY-mesosphere

[mesosphere-source]
name=Mesosphere Packages for EL 6 - $basearch - Source
baseurl=http://repos.mesosphere.io/el/6/SRPMS/
enabled=0
gpgcheck=1
gpgkey=https://bde.local.domain/yum/RPM-GPG-KEY-mesosphere

[cloudera-cdh4]
name=Cloudera's Distribution for Hadoop, Version 4
baseurl=http://archive.cloudera.com/cdh4/redhat/6/x86_64/cdh/4/
gpgkey =  http://archive.cloudera.com/cdh4/redhat/6/x86_64/cdh/RPM-GPG-KEY-cloudera
gpgcheck = 1

The file adds an extra repo for Zookeeper that Cloudier provides. These repos were necessary in order for the updated Chef recipes to properly install those packages. The remaining changes are strictly within the Chef recipes on the BDE management server.

/opt/serengeti/chef/cookbooks/mesos/recipes/install.rb

 69 when 'rhel', 'centos', 'amazon', 'scientific'
 70   %w( unzip libcurl ).each do |pkg|
 71     yum_package pkg do
 72       action :install
 73     end
 74   end
 75 
 76   template "/etc/pki/rpm-gpg/RPM-GPG-KEY-mesosphere" do
 77     source "RPM-GPG-KEY-mesosphere.erb"
 78   end
 79 
 80   package 'mesos'
 81   package 'chronos' if node.role?('mesos_chronos')
 82   package 'marathon' if node.role?('mesos_marathon')
 83 end

100 if distro == 'debian'
101   bash 'reload-configuration-debian' do
102     user 'root'
103     code <<-EOH
104     update-rc.d -f mesos-master remove
105     EOH
106     not_if { ::File.exist? '/usr/sbin/mesos-master' }
107   end
108 else
109   bash 'reload-configuration' do
110     user 'root'
111     code <<-EOH
112     initctl reload-configuration
113     EOH
114     not_if { ::File.exist? '/usr/sbin/mesos-master' }
115   end
116 end

Update:

A colleague pointed out the addition for the RPM-GPG-KEY-mesosphere.erb file was not as clear as it should have been. Make sure you add lines 76-78 AND copy the GPG key into /opt/serengeti/chef/cookbooks/mesos/templates/default with a .erb file extension.

/opt/serengeti/chef/cookbooks/mesos/templates/default/chronos.conf.erb

Several of the template files have an old path for executables that need to be updated. The Chronos configuration file needs the following change.

  9 exec /usr/bin/chronos

/opt/serengeti/chef/cookbooks/mesos/templates/default/mesos-slave.conf.erb

  6 exec /usr/bin/mesos-init-wrapper slave

/opt/serengeti/chef/cookbooks/mesos/templates/default/marathon.conf.erb

  9 exec /usr/bin/marathon

The final step was to update the Chef server so that the changes will take effect by executing the knife cookbook upload -a command. When all of the outlined steps have been completed, the Apache Mesos Chef recipes on the Big Data Extensions management server will be all set to deploy the latest code for Apache Mesos, Mesosphere Marathon, Chronos and Docker on CentOS.

There is a series of posts coming to show how to tie VMware NSX together with Apache Mesos, Apache Hadoop and VMware Big Data Extensions.

Enjoy!

Flocker Data Volumes for Docker in VMware vSphere

flocker

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.

flocker

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.

/opt/serengeti/www/specs/Ironfan/mesos/flocker/spec.json

  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.

/opt/serengeti/www/specs/map

 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.

/opt/serengeti/chef/roles/flocker_control.rb

 1 name        'flocker_control'
 2 description 'Deploy the Flocker Control Node to support an Apache Mesos cluster.'
 3 
 4 run_list *%w[
 5   role[basic]
 6   flocker::default
 7   flocker::control
 8 ]
 9

/opt/serengeti/chef/roles/flocker_agent.rb

 1 name        'flocker_agent'
 2 description 'Deploy the Flocker agent on Apache Mesos worker node.'
 3 
 4 run_list *%w[
 5   role[basic]
 6   flocker::default
 7   flocker::agent
 8 ]
 9

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.

/opt/serengeti/chef/cookbooks/flocker/reciptes/default.rb

  1 # Cookbook Name:: flocker
  2 # Recipe:: default
  3 
  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"
  8 
  9 set_bootstrap_action(ACTION_INSTALL_PACKAGE, 'flocker', true)
 10 
 11 # Setup the new repositories
 12 template '/etc/yum.repos.d/clusterhq.repo' do
 13   source 'clusterhq.repo.erb'
 14   action :create
 15 end
 16 
 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
 23 
 24 clear_bootstrap_action

/opt/serengeti/chef/cookbooks/flocker/recipes/control.rb

  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
 16
 17 include_recipe 'flocker::install'
 18
 19 set_bootstrap_action(ACTION_INSTALL_PACKAGE, 'flocker_control', true)
 20
 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
 29
 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
 37
 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
 45
 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
 53
 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
 61
 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
105
106 end if is_control_running
107
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
112
113 end
114
115 # Register with cluster_service_discovery
116 provide_service(node[:flocker][:control_service_name])
117 clear_bootstrap_action

/opt/serengeti/chef/cookbooks/flocker/recipes/agent.rb

  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
 16
 17 include_recipe 'flocker::install'
 18
 19 set_bootstrap_action(ACTION_INSTALL_PACKAGE, 'flocker_agent', true)
 20
 21 # Wait for the Flocker control node to be setup and registered
 22 wait_for_flocker_control
 23
 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
 31
 32 controls_ip = flocker_control_ip
 33
 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
 41
 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
 49
 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
 57
 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
 65
 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
 72
 73 # Install additional packages
 74 %w{git}.each do |pkg|
 75   package pkg do
 76     action :install
 77   end
 78 end
 79
 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
 83
 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
 88
 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
 94
 95 service 'flocker-container-agent' do
 96   supports :status => true, :restart => true, :reload => false
 97   action [ :enable, :start ]
 98 end
 99
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.

flocker

flocker

 

mesos-flocker-bde-1

marathon-flocker-1

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

Conclusion

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!

 

 

Docker Minecraft Containers to the Rescue!

docker

My 11-year old son has been after me to get a Minecraft server setup locally that he could connect to and play off of any of the computers in the house. Fortunately for him, my home lab environment is running a few different Docker container hosts, and I figured loading up a couple servers via Docker would be the simplest solution. A quick search on Docker Hub yielded several results for Minecraft and after looking through the most popular options, I settled on the itzg/minecraft-server image.

I deployed a new Photon VM to act as the container host and after a slow 180 second install, I was pulling the image down to the host. I wanted to run a couple different options for him, so that he could choose what type of server to log into. I created a couple simple shell scripts to load the different server types.

cre-minecraft.sh

  1 docker run -d -it \
  2 -e EULA=TRUE -e DIFFICULTY=normal -e VERSION=LATEST \
  3 -e MODE=creative -e PVP=false -e LEVEL_TYPE=LARGEBIOMES \
  4 -e 'JVM_OPTS=-Xmx4096M -Xms4096M' \
  5 -p 25566:25565 --name cre-minecraft \
  6 -v /opt/minecraft2:/data itzg/minecraft-server

adv-minecraft.sh

  1 docker run -d -it \
  2 -e EULA=TRUE -e DIFFICULTY=normal -e VERSION=LATEST \
  3 -e MODE=adventure -e PVP=false -e LEVEL_TYPE=AMPLIFIED \
  4 -e 'JVM_OPTS=-Xmx8192M -Xms8192M' \
  5 -p 25565:25565 --name adv-minecraft \
  6 -v /opt/minecraft:/data itzg/minecraft-server

After that it was just a matter of starting both containers and checking that they were listening on the correct host ports.

docker-minecraft-1

docker-minecraft-2

The Docker Minecraft containers worked wonderfully this evening. He was excited to see that I was able to spin up as many servers as he wanted to run and that he could easily hop between them in his Minecraft client. It was a fun little experiment with Docker volumes for me as well — I had not used them yet — resulting in a very happy kid!

Docker Container for IO Benchmarking

docker

I had a need this week for a quick-and-easy IO benchmarking tool and decided to create a Docker container to achieve my goals. The Docker container itself is rather simple and is available for use at your leisure. The Docker container is based off of Ubuntu latest, installs the FIO package and grabs a simple test file from my website for generating load and benchmarking results.

Dockerfile

  1 # FIO benchmark on Ubuntu:latest
  2 
  3 FROM ubuntu:latest
  4 MAINTAINER Chris Mutchler <chris@virtualelephant.com>
  5 
  6 RUN apt-get update
  7 RUN apt-get -y install fio wget
  8 RUN wget http://virtualelephant.com/wp-content/uploads/2015/10/threads.txt
  9 
 10 CMD [ "/bin/bash" ]

Once I had the Docker image uploaded to my repo, I created a simple JSON file for deploying the container into my Mesos cluster.

iobench.json

{
  "container": {
    "type": "DOCKER",
    "docker": {
      "image": "chrismutchler/iobench"
    }    
  },
  "id": "iobench",
  "instances": 1,
  "cpus": 0.25,
  "mem": 256,
  "uris": [],
  "cmd": "while true; do date; /usr/bin/fio /threads.txt; sleep 10; done"
}

As noted, the Docker container will continue to run the FIO test infinitely until the app is destroyed in Marathon. The app can also be scaled to run across multiple Apache Mesos nodes, each running the FIO test independently. Be careful when running any sort of load or benchmarking test in your environments, it may have adverse effects.

Launching the application in Marathon was simple enough from the command line.

# curl -A POST http://mesos.local.domain:8080/v2/apps -d @iobench.json -H "Content-Type: application/json"

I used the test to create a noisy neighbor issue in my environment to test out VMware Storage IO Control (SIOC) settings and it worked adequately in this role. Once the application had been launched, the results are available for downloading or viewing in the Mesos UI, by selecting ‘Sandbox’ and then the STDOUT log file. To understand what FIO is performing for the IO benchmark, please read the blog post by Ben Martin where I copied the FIO test file from.

stdout

--container="mesos-20151009-234723-2583865536-5050-2901-S1.c0052620-2322-4d12-aa49-1e3b24402f50" --docker="docker" --help="false" --initialize_driver_logging="true" --logbufsecs="0" --logging_level="INFO" --mapped_directory="/mnt/mesos/sandbox" --quiet="false" --sandbox_directory="/tmp/mesos/slaves/20151009-234723-2583865536-5050-2901-S1/frameworks/20151009-234723-2583865536-5050-2901-0001/executors/iobench.cb93c618-6eea-11e5-9209-0050569a4da8/runs/c0052620-2322-4d12-aa49-1e3b24402f50" --stop_timeout="0ns"
--container="mesos-20151009-234723-2583865536-5050-2901-S1.c0052620-2322-4d12-aa49-1e3b24402f50" --docker="docker" --help="false" --initialize_driver_logging="true" --logbufsecs="0" --logging_level="INFO" --mapped_directory="/mnt/mesos/sandbox" --quiet="false" --sandbox_directory="/tmp/mesos/slaves/20151009-234723-2583865536-5050-2901-S1/frameworks/20151009-234723-2583865536-5050-2901-0001/executors/iobench.cb93c618-6eea-11e5-9209-0050569a4da8/runs/c0052620-2322-4d12-aa49-1e3b24402f50" --stop_timeout="0ns"
Registered docker executor on dhcp2-157.local.domain
Starting task iobench.cb93c618-6eea-11e5-9209-0050569a4da8
Sat Oct 10 01:04:40 UTC 2015
bgwriter: (g=0): rw=randwrite, bs=4K-4K/4K-4K/4K-4K, ioengine=libaio, iodepth=32
queryA: (g=0): rw=randread, bs=4K-4K/4K-4K/4K-4K, ioengine=mmap, iodepth=1
queryB: (g=0): rw=randread, bs=4K-4K/4K-4K/4K-4K, ioengine=mmap, iodepth=1
bgupdater: (g=0): rw=randrw, bs=4K-4K/4K-4K/4K-4K, ioengine=libaio, iodepth=16
fio-2.1.3
Starting 4 processes
bgwriter: Laying out IO file(s) (1 file(s) / 256MB)
queryA: Laying out IO file(s) (1 file(s) / 256MB)
queryB: Laying out IO file(s) (1 file(s) / 256MB)
bgupdater: Laying out IO file(s) (1 file(s) / 32MB)

bgwriter: (groupid=0, jobs=1): err= 0: pid=8: Sat Oct 10 01:05:32 2015
  write: io=262144KB, bw=11578KB/s, iops=2894, runt= 22641msec
    slat (usec): min=16, max=4713.1K, avg=334.93, stdev=20011.69
    clat (usec): min=9, max=4750.2K, avg=10711.15, stdev=112034.10
     lat (usec): min=75, max=4750.6K, avg=11048.59, stdev=113823.09
    clat percentiles (msec):
     |  1.00th=[    3],  5.00th=[    3], 10.00th=[    3], 20.00th=[    6],
     | 30.00th=[    6], 40.00th=[    7], 50.00th=[    7], 60.00th=[    7],
     | 70.00th=[    8], 80.00th=[    8], 90.00th=[    9], 95.00th=[    9],
     | 99.00th=[   16], 99.50th=[   31], 99.90th=[  947], 99.95th=[ 1582],
     | 99.99th=[ 4752]
    bw (KB  /s): min=    5, max=39208, per=100.00%, avg=15073.13, stdev=7536.85
    lat (usec) : 10=0.01%, 100=0.01%, 250=0.01%, 500=0.01%, 750=0.01%
    lat (usec) : 1000=0.01%
    lat (msec) : 2=0.05%, 4=12.47%, 10=85.23%, 20=1.46%, 50=0.34%
    lat (msec) : 250=0.05%, 500=0.19%, 750=0.05%, 1000=0.05%, 2000=0.05%
    lat (msec) : >=2000=0.05%
  cpu          : usr=2.60%, sys=8.83%, ctx=126969, majf=0, minf=26
  IO depths    : 1=0.1%, 2=0.1%, 4=0.1%, 8=0.1%, 16=0.1%, 32=100.0%, >=64=0.0%
     submit    : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     complete  : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.1%, 64=0.0%, >=64=0.0%
     issued    : total=r=0/w=65536/d=0, short=r=0/w=0/d=0
queryA: (groupid=0, jobs=1): err= 0: pid=9: Sat Oct 10 01:05:32 2015
  read : io=262144KB, bw=12106KB/s, iops=3026, runt= 21654msec
    clat (usec): min=26, max=4713.2K, avg=322.55, stdev=20018.65
     lat (usec): min=26, max=4713.2K, avg=322.77, stdev=20018.66
    clat percentiles (usec):
     |  1.00th=[   31],  5.00th=[   71], 10.00th=[   77], 20.00th=[  115],
     | 30.00th=[  126], 40.00th=[  155], 50.00th=[  159], 60.00th=[  165],
     | 70.00th=[  183], 80.00th=[  207], 90.00th=[  253], 95.00th=[  314],
     | 99.00th=[  724], 99.50th=[ 1012], 99.90th=[ 2576], 99.95th=[ 4384],
     | 99.99th=[272384]
    bw (KB  /s): min=    7, max=24840, per=67.92%, avg=16686.43, stdev=7338.70
    lat (usec) : 50=4.04%, 100=11.27%, 250=74.04%, 500=8.29%, 750=1.42%
    lat (usec) : 1000=0.45%
    lat (msec) : 2=0.36%, 4=0.09%, 10=0.03%, 20=0.01%, 50=0.01%
    lat (msec) : 100=0.01%, 250=0.01%, 500=0.01%, 750=0.01%, 1000=0.01%
    lat (msec) : 2000=0.01%, >=2000=0.01%
  cpu          : usr=2.93%, sys=7.36%, ctx=131487, majf=65536, minf=31
  IO depths    : 1=100.0%, 2=0.0%, 4=0.0%, 8=0.0%, 16=0.0%, 32=0.0%, >=64=0.0%
     submit    : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     complete  : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     issued    : total=r=65536/w=0/d=0, short=r=0/w=0/d=0
queryB: (groupid=0, jobs=1): err= 0: pid=10: Sat Oct 10 01:05:32 2015
  read : io=262144KB, bw=11910KB/s, iops=2977, runt= 22010msec
    clat (usec): min=27, max=4714.3K, avg=325.85, stdev=20020.01
     lat (usec): min=27, max=4714.3K, avg=326.11, stdev=20020.01
    clat percentiles (usec):
     |  1.00th=[   32],  5.00th=[   72], 10.00th=[   82], 20.00th=[  117],
     | 30.00th=[  139], 40.00th=[  155], 50.00th=[  159], 60.00th=[  169],
     | 70.00th=[  187], 80.00th=[  213], 90.00th=[  262], 95.00th=[  318],
     | 99.00th=[  700], 99.50th=[  980], 99.90th=[ 2608], 99.95th=[ 4768],
     | 99.99th=[272384]
    bw (KB  /s): min=    2, max=23384, per=65.75%, avg=16152.90, stdev=6882.55
    lat (usec) : 50=2.90%, 100=9.19%, 250=76.15%, 500=9.50%, 750=1.34%
    lat (usec) : 1000=0.43%
    lat (msec) : 2=0.32%, 4=0.09%, 10=0.03%, 20=0.01%, 50=0.01%
    lat (msec) : 250=0.01%, 500=0.01%, 750=0.01%, 1000=0.01%, 2000=0.01%
    lat (msec) : >=2000=0.01%
  cpu          : usr=3.81%, sys=6.74%, ctx=131493, majf=65536, minf=30
  IO depths    : 1=100.0%, 2=0.0%, 4=0.0%, 8=0.0%, 16=0.0%, 32=0.0%, >=64=0.0%
     submit    : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     complete  : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     issued    : total=r=65536/w=0/d=0, short=r=0/w=0/d=0
bgupdater: (groupid=0, jobs=1): err= 0: pid=11: Sat Oct 10 01:05:32 2015
  read : io=16416KB, bw=3611.1KB/s, iops=902, runt=  4545msec
    slat (usec): min=81, max=285023, avg=520.41, stdev=5536.65
    clat (usec): min=1, max=2841, avg= 5.40, stdev=80.88
     lat (usec): min=115, max=285031, avg=528.39, stdev=5537.20
    clat percentiles (usec):
     |  1.00th=[    2],  5.00th=[    2], 10.00th=[    2], 20.00th=[    2],
     | 30.00th=[    2], 40.00th=[    2], 50.00th=[    2], 60.00th=[    2],
     | 70.00th=[    2], 80.00th=[    3], 90.00th=[    3], 95.00th=[    3],
     | 99.00th=[    3], 99.50th=[    6], 99.90th=[ 1448], 99.95th=[ 2384],
     | 99.99th=[ 2832]
    bw (KB  /s): min= 2096, max= 4472, per=14.60%, avg=3587.00, stdev=1065.00
  write: io=16352KB, bw=3597.9KB/s, iops=899, runt=  4545msec
    slat (usec): min=74, max=254089, avg=489.47, stdev=3971.19
    clat (usec): min=1, max=2233, avg= 5.13, stdev=66.52
     lat (usec): min=77, max=254096, avg=497.09, stdev=3971.57
    clat percentiles (usec):
     |  1.00th=[    2],  5.00th=[    2], 10.00th=[    2], 20.00th=[    2],
     | 30.00th=[    2], 40.00th=[    2], 50.00th=[    2], 60.00th=[    2],
     | 70.00th=[    2], 80.00th=[    3], 90.00th=[    3], 95.00th=[    3],
     | 99.00th=[    3], 99.50th=[    5], 99.90th=[ 1256], 99.95th=[ 1816],
     | 99.99th=[ 2224]
    bw (KB  /s): min= 2024, max= 4504, per=28.58%, avg=3514.88, stdev=1085.81
    lat (usec) : 2=0.04%, 4=99.26%, 10=0.24%, 50=0.18%, 100=0.04%
    lat (usec) : 250=0.06%, 500=0.02%, 750=0.01%, 1000=0.01%
    lat (msec) : 2=0.07%, 4=0.06%
  cpu          : usr=8.30%, sys=4.71%, ctx=16460, majf=0, minf=25
  IO depths    : 1=99.8%, 2=0.1%, 4=0.1%, 8=0.1%, 16=0.1%, 32=0.0%, >=64=0.0%
     submit    : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     complete  : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     issued    : total=r=4104/w=4088/d=0, short=r=0/w=0/d=0

Run status group 0 (all jobs):
   READ: io=540704KB, aggrb=24566KB/s, minb=3611KB/s, maxb=12106KB/s, mint=4545msec, maxt=22010msec
  WRITE: io=278496KB, aggrb=12300KB/s, minb=3597KB/s, maxb=11578KB/s, mint=4545msec, maxt=22641msec

Disk stats (read/write):
    dm-2: ios=135188/67188, merge=0/0, ticks=39356/28285, in_queue=67640, util=97.96%, aggrios=135217/70245, aggrmerge=0/0, aggrticks=1492/4673, aggrin_queue=6169, aggrutil=15.06%
    dm-0: ios=135217/70245, merge=0/0, ticks=1492/4673, in_queue=6169, util=15.06%, aggrios=0/0, aggrmerge=0/0, aggrticks=0/0, aggrin_queue=0, aggrutil=0.00%
  loop1: ios=0/0, merge=0/0, ticks=0/0, in_queue=0, util=0.00%
  loop0: ios=0/0, merge=0/0, ticks=0/0, in_queue=0, util=0.00%

If you would like to grab the Docker image and use it for your own needs, you can pull it from Docker Hub:

# docker pull chrismutchler/iobench

One of the next little Docker containers I will be building is a web page traffic generator — I’ve used all sorts of traffic testing applications in the past, but being able to spin up a test inside a Docker container and then scale it across a large Mesos cluster will simplify things even more!

Enjoy!