How can AIOps help you prevent the next major incident.

What is it?

AIOps is a term that has been used in the last few years to describe the ability to drive intelligence from the day-to-day data that IT operations generate. The data source could vary from monitoring tools like SolarWinds to service desk tools like ServiceNow to automation tools like configuration management ( chef, puppet … ), or log search platforms like Splunk

Untitled Diagram (5)

One area where AIOps can be an asset to operation teams is incident predictability and remediation, there are others like storage and capacity management, resources utilization …

How can AIOPS help prevent the next outage :

the footprint of digital systems and businesses is increasing every day and so is the speed at which the data is produced.

For example, a Palo Alto firewall can produce up to 12 million events in one day, the manual correlation of data is nearly impossible, and that’s why we need an overview of the entire landscape of data produced by IT operations,  transformation of data to be able to serve as training and test sets for machine learning.

Starting from the promise that an incident is a result of a change ( voluntary or involuntary) to a configuration, a device, a network, or an application, all these changes if monitored and reported on correctly can help create a good context to understand the root-cause analysis of the incident.

You can create an ML model that will help you predict the next outage, notify operation teams, and help reduce the downtime.

Suppose that you transformed the input data that you gathered from all your sources, organized it into dataset like the one below and used a supervised learning process to create an ML model :

Screen Shot 2018-09-18 at 10.20.52 PM

your model will be able to make predictions of future incidents when fed with real-time input coming from your tools and logs :

Untitled Diagram (7)

over time, with more data, your model will get better at detecting future anomalies, with much more accuracy.

in conclusion

There is a lot of writing out there about AIOps, but the application, in my opinion, is a bit harder.

For different reasons, one being the spectrum of toolset in IT operations is very wide, and two being that the data structures are different from one organization to another, which means that trying to put a generic machine learning process to produce insights, will be at worst impossible and at best will lack accuracy.

For an organization to be able to get intelligent insights from  AIOps, there has to be an internal effort to train your models, because the quality of your future prediction of major incidents will essentially depend on the quality of your training and test sets.




Links :

AIOps Platforms

Deploying Apps and ML models on mesosphere DC/OS

Have you ever thought of your data centers and cloud infrastructure ( private and public ) as one big computer? where you can deploy your applications with a click of a button, without worrying too much about the underlying infrastructure? well … DCOS allows you to manage your infrastructure from a single point, offering you the possibility to run distributed applications, containers, services, jobs while maintaining a certain abstraction from the infrastructure layer, as long as it provides computing, storage, and networking capabilities.

After deploying my ML model on a kubernates Cluster, a lambda function, I will deploy it on a DCOS cluster.

what is DCOS:

DCOS is a datacenter operating system, DC/OS is itself a distributed system, a cluster manager, a container platform, and an operating system.

DC/OS Architecture Layers

DCOS manages the 3 layers of software, platform, and infrastructure.

the dashboard :

Screen Shot 2018-09-03 at 7.43.36 PM

the catalog:

DCOS UI offers a catalog of certified and community packages that the users can install in seconds , like kafka, spark, hadoop, MySQL ..



Deploying Apps and ML models on DCOS :

the application I’m deploying is a web server running the model I created in my previous posts to make predictions.

DCOS relies on an application definition file that looks like this :

app.json :

“volumes”: null,
“id”: “mlpregv3”,
“cmd”: “python”,
“instances”: 1,
“cpus”: 1,
“mem”: 128,
“disk”: 0,
“gpus”: 0,

“container”: {
“type”: “DOCKER”,
“docker”: {
“image”: “mbenachour/dcos-mlpreg:1”,
“forcePullImage”: false,
“privileged”: false,
“network”: “HOST”,
“portMappings”: [
{ “containerPort”: 8088, “hostPort”: 8088 }


the rest of the code can be found in my GitHub repo

after you configure your DCOS CLI and log in, you can run this command :

Screen Shot 2018-09-03 at 8.01.37 PM

if we take a look at the UI we can see that app/web server has been deployed :

Screen Shot 2018-09-03 at 8.03.35 PM

Deploy machine learning models on AWS lambda and serverless

in the last post, we talked about how to deploy a Machine learning trained model on Kubernates.

Here is another way of deploying ML models: AWS lambda + API gateway

Untitled Diagram (3)

Basically, your model (mlpreg.pkl) will be stored in S3, your lambda function will download the model use it to make predictions, another call will allow you to get the model hyperparameters, and sent it back to the user.

Screen Shot 2018-08-07 at 9.11.00 AM

to deploy AWS services, we will use a framework called Serverless

serverless allow you with a single configuration file to define functions, create resources, declare permissions, configure endpoints …

serverless uses one main config file and one or multiple code files :

  • : the lambda function
  • serverless.yml : serverless configuration file

here is what the serverless configuration file for this example would look like :


service: deploy-ml-service
– serverless-python-requirements
name: aws
runtime: python2.7
– Effect: Allow
# Note: just for the demo, we are giving full access to s3
– s3:*
Resource: “*”
handler: handler.predict
– http:
path: predict
method: post
cors: true
integration: lambda
handler: handler.getModelInfo
– http:
path: params
method: post
cors: true
integration: lambda[/code]

as described in the example we will create two functions one will make a prediction using the model we built in the last post, the other one will display the model hyperparameters :

  • predict
  • getModelInfo

to load the model we have  :

  • load_model : loading the stored model from S3

from sklearn.externals import joblib
import boto3

BUCKET_NAME = ‘asset-s3-uploader-02141’

def predict(event,context):
input = event[“body”][“input”]
modelName = event[“body”][“model_name”]
data = float(input)
return loadModel(modelName).predict(data)[0]

def loadModel(model):
s3_client = boto3.client(‘s3’)
download_path = ‘/tmp/model.pkl’
s3_client.download_file(BUCKET_NAME, model, ‘/tmp/model.pkl’)
return joblib.load(download_path)

def getModelInfo(event,context):
model = event[“body”][“model_name”]
return loadModel(model).get_params()

$Serverless Deploy ! 

yep that’s all it takes, and your services will be deployed in seconds:

Screen Shot 2018-08-06 at 9.25.59 PM

Run the tests:

getting the model Hyperparameters :


root@58920085f9af:/tmp/deploy# curl -s -d “model_name=mlpreg.pkl” | python -m json.tool
“activation”: “relu”,
“alpha”: 0.001,
“batch_size”: “auto”,
“beta_1”: 0.9,
“beta_2”: 0.999,
“early_stopping”: false,
“epsilon”: 1e-08,
“hidden_layer_sizes”: [
“learning_rate”: “constant”,
“learning_rate_init”: 0.01,
“max_iter”: 1000,
“momentum”: 0.9,
“nesterovs_momentum”: true,
“power_t”: 0.5,
“random_state”: 9,
“shuffle”: true,
“solver”: “adam”,
“tol”: 0.0001,
“validation_fraction”: 0.1,
“verbose”: false,
“warm_start”: false


Making Predictions :


root@58920085f9af:/tmp/deploy# curl -s -d “input=1&model_name=mlpreg.pkl” | python -m json.tool


Automating the training and deployment of ML models on Kubernates

With the rise of Machine Learning and models, the need for automating and streamlining model deployment become a necessity. Pushed mostly by the fact that ML models as a new way of programming, are no longer an experimental concept but rather a day to day artifacts that can also follow a release and versioning process.

here is a link to the code used below: github

throughout this example I will :

  • train a model.
  • serialize it and save it.
  • build a docker image with front-end web server.
  • make a deployment on a kubernates cluster.




minikube & Kubernates

Building The Model

Training  data : 

our training data is going to be generated with a math function y= sin(2*π*tan(x)), where is between 0 and 1 with an increment of 0.001.

x = np.arange(0.0, 1, 0.001).reshape(-1, 1)

x = [[ 0. ]
[ 0.001]
[ 0.002]


[ 0.997]
[ 0.998]
[ 0.999]]

y = np.sin(2 * np.pi * np.tan(x).ravel()) #with max/min values of 1,-1

Screen Shot 2018-06-20 at 2.40.48 PM

Fitting the Model : 

In this example, I will use  a MultiLayer Perceptron implemented by SCIKIT python library

This is what the regressor function will all the parameters ( already tuned ) :

reg = MLPRegressor(hidden_layer_sizes=(500,), activation='relu', solver='adam', alpha=0.001,batch_size='auto',

learning_rate='constant', learning_rate_init=0.01, power_t=0.5, max_iter=1000, shuffle=True,

random_state=9, tol=0.0001, verbose=False, warm_start=False, momentum=0.9,

nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999,

Test Data :
for testing we will use a generated set of data as well :
test_x = np.arange(0.0, 1, 0.05).reshape(-1, 1)
Prediction :
test_y = reg.predict(test_x)
Results :
continuous blue is the real output
dotted red is the predicted output
Screen Shot 2018-06-20 at 4.27.17 PM
Saving the model :
I used Python object serialization framework Pickle :
joblib.dump(reg, 'mlpreg.pkl')
this will save your model to a file named: mlpreg.pkl

Deploying the model

building an image :
I have created a docker image for deploying the model on a web server :

[code] FROM python:2.7.15-stretch



RUN python -m pip install –user numpy scipy matplotlib ipython jupyter pandas sympy nose

RUN python -m pip install -U scikit-learn

RUN python


CMD python


to build the image you can run this :
docker build -t mbenachour/mlpreg:latest .
kubernates deployment :
this the kubernates yaml file that describes the deployment :

[code ]

apiVersion: apps/v1beta2
kind: Deployment
name: mlpreg-deployment
app: mlpreg
replicas: 3
app: mlpreg
app: mlpreg
terminationGracePeriodSeconds: 30
– name: mlpreg
image: mbenachour/mlpreg:latest
imagePullPolicy: “Always”
– containerPort: 8088

apiVersion: v1
kind: Service
name: mlpreg-svc
app: mlpreg
#tier: frontend
type: NodePort
– port: 8088
app: mlpreg
#tier: frontend


you can deploy the kubernates model :
kubectl apply -f mlp.yml
to check on the status of your kubernates services :
kubectl get services
you should see something similar to this :
Screen Shot 2018-06-21 at 4.31.45 PM.png

Making predictions

to get the kubernates cluster IP address, in my case I’m using minikube the command line :

$minikube service mlpreg-svc --url
to make a prediction using the API for an input of 0.1
Screen Shot 2018-06-21 at 5.33.13 PM

a lot of products have been introduced to help solve this problem, one of them is Chris Fregly project:
the project gives you the possibility to create-train-deploy models using different frameworks :
– tensorflow
– scikit
– pytorch
implementing a lot of ML most used algorithms like linear regression.

Using AWS GuardDuty to stop compromised instances and send notifications.

GuardDuty  (announced in the 2017 edition of AWS Re:Invent) , is a managed threat detection service that continuously monitors for malicious or unauthorized behavior to help you protect your AWS accounts and workloads. It monitors for activity such as unusual API calls or potentially unauthorized deployments that indicate a possible account compromise. GuardDuty also detects potentially compromised instances or reconnaissance by attackers.

with a minimal amount of code, and few clicks in the AWS console we can set up guardduty to scan EC2 fleets for eventual threats, notify a lambda function to stop the compromised instances and send an SMS notification using AWS SNS service:

Screen Shot 2018-01-04 at 9.43.11 AM

1- Testing few threats :

1-a – Bitcoin mining : one of the possible threats is using your EC2 instances for bitcoin mining , I started a bitcoind container on my EC2 instance to :

Screen Shot 2018-01-04 at 9.53.26 AM

1-b SSH brute-force : I’m not using any username and passwords dictionaries

Screen Shot 2018-01-04 at 9.55.03 AM

2- SNS topic : create an SNS topic called guardduty_alerts, with an SMS subscription

3- Lambda: for stopping instances and sending notifications

import boto3
import json

def lambda_handler(event, context):
print(‘loading handler’)# print(event)
sns = boto3.client(service_name = “sns”)
topicArn = ‘arn:aws:sns:us-east-1:9999999999:guardduty_alerts’

result = json.loads(event)# result is now a dict
instanceId = event[‘detail’][‘resource’][‘instanceDetails’][‘instanceId’]
type = event[‘detail’][‘description’]
message = “your EC2 instance ” + instanceId + “has been compromised by attack of ” + type + “, it will be stopped”
TopicArn = topicArn,
Message = message

ec2 = boto3.client(‘ec2’, region_name = ‘us-east-1’)
ec2.stop_instances(InstanceIds = [instanceId])

4- CloudWatch rule: create a cloudwatch rule that triggers the lambda function we created previosly


et voila , all the threats that we did earlier shows in the GuardDuty findings :

Screen Shot 2018-01-04 at 10.36.08 AM

Stoping the compromised instances :

Screen Shot 2018-01-04 at 10.42.33 AM

sending notifications:

Screen Shot 2018-01-04 at 10.43.29 AM


Local (and S3) cloud storage server using Minio

Minio is a local cloud object storage server, it’s open source, released under Apache License V2.0, allowing developers and devops to have a local and a public cloud storage to:

  • backup VMs
  • backup containers
  • store unstructured Data ( photos, files, …)
  • store objects in AWS S3
  • store objects using SDKs (GO, Javascripts, Java )

to start a server you can use the container image of MiniO available on Docker hub here :


you can run this cmd :

docker pull minio/minio

to start the server run :

 docker run -p 9000:9000 minio/minio server /export

you can access the web UI at http://localhost:9000


the access key and secret key for the local server are generated at the start of the server

create a bucket :


accessible also through the web UI:


using your AWS S3 storage :

we need to add AWS S3 end point to the list of hosts :

mc config host add my-aws YOUR_ACCESS_KEY  YOUR_SECRET_KEY

create a bucket in S3 :


and it’s created  :


CI and code promotion for Chef cookbooks with Jenkins – POC


I have been browsing the internet for blogs or articles to help Chef developers have a way of promoting the code of their cookbooks, a way of vetting code, and avoiding that code goes from Operations guys straight to production.

I have found a lot of theoretical articles on building a CI pipeline for chef cookbooks, but not a lot of practical ones,  so I decided to make a proof of concept for  the public and my team as well.

when it comes to integration tools, I like Jenkins, it’s open source and the community is very active in adding  and updating plugins .

In this example I will use a Java cookbook as a code base, and I will be running 4 test :

  • Foodcritic : a helpful lint tool you can use to check your Chef cookbooks for common problems. It comes with 61 built-in rules that identify problems ranging from simple style inconsistencies to difficult to diagnose issues that will hurt in production.
  • ChefSpec : a unit testing framework for testing Chef cookbooks. ChefSpec makes it easy to write examples and get fast feedback on cookbook changes without the need for virtual machines or cloud servers.
  • Rubocop :  a Ruby static code analyzer. Out of the box it will enforce many of the guidelines outlined in the community Ruby Style Guide.
  • Test Kitchen : a test harness tool to execute your configured code on one or more platforms in isolation. A driver plugin architecture is used which lets you run your code on various cloud providers and virtualization technologies such as Amazon EC2,  Vagrant, Docker, LXC containers, and more. Many testing frameworks are already supported out of the box including Bats, shUnit2, RSpec, Serverspec, with others being created weekly.

of course you can have all these tools in one package…. the famous ChefDK

Code Promotion :

The concept of code promotion helps the CI process distinguish between good and bad builds, I like to define a good build as a build where ALL the tests are successful .

Jenkins helps you implement this concept with a community plugin : Promoted Build Plugin

based of the status of your build ( promoted of not)  you can control the code that goes into your repository (github or gitlab), you can use set up hooks to deny merge requests from builds that are not promoted.


let’s setup our jobs, we will too categories of jobs :

  • Build Jobs
  • Test Jobs



whenever a build job is successful will trigger all the test jobs to start.


Build-java-cookbook : will clone the code repo and create a temporary artifact, this is the config section for this job


Rubocop Test : will copy the temporary artifact decompress it to have all code repo and run rubocop test on the code :


ChefSpec Test :


FoodCritic : 


Test Kitchen : 


Test Kitchen will spin a vagrant box (ubuntu-14.04) and run the cookbook on it and test the results.

First Run : 

with the configurations above we run the build and test jobs :

Result :


Rubocop test failed, by looking at the execution log we can see why :

+ rubocop
Inspecting 44 files


providers/alternatives.rb:34:38: C: Use shell_out("#{alternatives_cmd} --display #{cmd} | grep #{alt_path} | grep 'priority #{priority}$'") instead of shell_out("#{alternatives_cmd} --display #{cmd} | grep #{alt_path} | grep 'priority #{priority}$'").exitstatus == 0.
      alternative_exists_same_prio = shell_out("#{alternatives_cmd} --display #{cmd} | grep #{alt_path} | grep 'priority #{priority}$'").exitstatus == 0
providers/alternatives.rb:35:28: C: Use shell_out("#{alternatives_cmd} --display #{cmd} | grep #{alt_path}") instead of shell_out("#{alternatives_cmd} --display #{cmd} | grep #{alt_path}").exitstatus == 0.
      alternative_exists = shell_out("#{alternatives_cmd} --display #{cmd} | grep #{alt_path}").exitstatus == 0
providers/alternatives.rb:43:18: C: Use instead of remove_cmd.exitstatus == 0.
          unless remove_cmd.exitstatus == 0
providers/alternatives.rb:57:18: C: Use instead of install_cmd.exitstatus == 0.
          unless install_cmd.exitstatus == 0
providers/alternatives.rb:66:28: C: Use shell_out("#{alternatives_cmd} --display #{cmd} | grep \"link currently points to #{alt_path}\"") instead of shell_out("#{alternatives_cmd} --display #{cmd} | grep \"link currently points to #{alt_path}\"").exitstatus == 0.
      alternative_is_set = shell_out("#{alternatives_cmd} --display #{cmd} | grep \"link currently points to #{alt_path}\"").exitstatus == 0
providers/alternatives.rb:72:16: C: Use instead of set_cmd.exitstatus == 0.
        unless set_cmd.exitstatus == 0
providers/alternatives.rb:87:50: C: Use instead of cmd.exitstatus == 0.
    new_resource.updated_by_last_action(true) if cmd.exitstatus == 0
providers/ark.rb:39:20: C: Omit parentheses for ternary conditions.
    package_name = (file_name =~ /^server-jre.*$/) ? 'jdk' : file_name.scan(/[a-z]+/)[0]
providers/ark.rb:134:14: C: Use 0o for octal literals.
        mode 0755
providers/ark.rb:149:14: C: Closing method call brace must be on the line after the last argument when opening brace is on a separate line from the first argument.
providers/ark.rb:150:16: C: Use instead of cmd.exitstatus == 0.
        unless cmd.exitstatus == 0
providers/ark.rb:157:16: C: Use instead of cmd.exitstatus == 0.
        unless cmd.exitstatus == 0
providers/ark.rb:164:16: C: Use instead of cmd.exitstatus == 0.
        unless cmd.exitstatus == 0
providers/ark.rb:172:14: C: Use instead of cmd.exitstatus == 0.
      unless cmd.exitstatus == 0
recipes/ibm.rb:44:8: C: Use 0o for octal literals.
  mode 00755
recipes/ibm_tar.rb:36:8: C: Use 0o for octal literals.
  mode 00755
recipes/ibm_tar.rb:49:8: C: Use 0o for octal literals.
  mode 00755
recipes/set_java_home.rb:27:8: C: Use 0o for octal literals.
  mode 00755
recipes/set_java_home.rb:32:8: C: Use 0o for octal literals.
  mode 00755
resources/ark.rb:39:54: C: Use 0o for octal literals.
attribute :app_home_mode, kind_of: Integer, default: 0755

44 files inspected, 20 offenses detected
Build step 'Execute shell' marked build as failure
Finished: FAILURE



let’s go ahead and fix these offenses and commit the code :


we restart the build :

and here everything is green this time :


from this point on you can create do two things :

  • save your cookbook in a private supermarket with a corresponding version number
  • upload this cookbook to chef server


Promotion status :

After the completion of all tests, this build can now be promoted.



Running ContainerVMs on ESXI vmware host


until today I knew that running containers was always dependent on the existence of a host and an OS of some kind, but I came across this project : vSphere Integrated containers, it’s a runtime environment allowing developer to run containers as VMs, instead of running containers in  VMs .

there is a good read to understand the contrast between traditional containers and containerVMs

vic-machine :
is CLI tool allowing for the creating of containerVMs in the following setups :
  • vCenter Server with a cluster
  • vCenter Server with one or more standalone ESXi hosts
  • A standalone ESXi host

this architectures relies on a Virtual Container Host, the VHC is an end point to start, stop, delete containers across the datacenter .

“The Virtual Container Host (VCH) is the means of controlling, as well as consuming, container services – a Docker API endpoint is exposed for developers to access, and desired ports for client connections are mapped to running containers as required. Each VCH is backed by a vSphere resource pool, delivering compute resources far beyond that of a single VM or even a dedicated physical host. Multiple VCHs can be deployed in an environment, depending on business requirements. For example, to separate resources for development, testing, and production.”

the binaries can be downloaded from here :

untar the compressed file :

$ tar xvzf vic_3711.tar.gz

this is the content of the tar file :

Screen Shot 2016-08-06 at 11.54.13 PM

Setting up ESXI host : 

  • download the ISO file from vmware wbesite :
  • use virtualbox or vmware fusion to create a host using esxi host ( Shot 2016-08-06 at 11.50.51 PM

Creating a Virtual Container Host :

$ vic-machine-darwin create –target –user root –image-datastore datastore1
INFO[2016-08-06T14:05:48-05:00] Please enter ESX or vCenter password:
INFO[2016-08-06T14:05:50-05:00] ### Installing VCH ####
INFO[2016-08-06T14:05:50-05:00] Generating certificate/key pair – private key in ./virtual-container-host-key.pem
INFO[2016-08-06T14:05:50-05:00] Validating supplied configuration
INFO[2016-08-06T14:05:51-05:00] Firewall status: ENABLED on “/ha-datacenter/host/localhost.localdomain/localhost.localdomain”
INFO[2016-08-06T14:05:51-05:00] Firewall configuration OK on hosts:
INFO[2016-08-06T14:05:51-05:00] “/ha-datacenter/host/localhost.localdomain/localhost.localdomain”
WARN[2016-08-06T14:05:51-05:00] Evaluation license detected. VIC may not function if evaluation expires or insufficient license is later assigned.
INFO[2016-08-06T14:05:51-05:00] License check OK
INFO[2016-08-06T14:05:51-05:00] DRS check SKIPPED – target is standalone host
INFO[2016-08-06T14:05:51-05:00] Creating Resource Pool “virtual-container-host”
INFO[2016-08-06T14:05:51-05:00] Creating VirtualSwitch
INFO[2016-08-06T14:05:51-05:00] Creating Portgroup
INFO[2016-08-06T14:05:51-05:00] Creating appliance on target
INFO[2016-08-06T14:05:51-05:00] Network role “client” is sharing NIC with “external”
INFO[2016-08-06T14:05:51-05:00] Network role “management” is sharing NIC with “external”
INFO[2016-08-06T14:05:52-05:00] Uploading images for container
INFO[2016-08-06T14:05:52-05:00] “bootstrap.iso”
INFO[2016-08-06T14:05:52-05:00] “appliance.iso”
INFO[2016-08-06T14:06:00-05:00] Waiting for IP information
INFO[2016-08-06T14:06:18-05:00] Waiting for major appliance components to launch
INFO[2016-08-06T14:06:18-05:00] Initialization of appliance successful
INFO[2016-08-06T14:06:18-05:00] vic-admin portal:
INFO[2016-08-06T14:06:18-05:00] DOCKER_HOST=
INFO[2016-08-06T14:06:18-05:00] Connect to docker:
INFO[2016-08-06T14:06:18-05:00] docker -H –tls info
INFO[2016-08-06T14:06:18-05:00] Installer completed successfully

you can use vSphere or ESXI web client to take a look :

Screen Shot 2016-08-07 at 12.20.56 AM

creating ContainerVM :

$ docker –tls run  –name container1 ubuntu

the container has been created :

Screen Shot 2016-08-07 at 12.25.32 AM


Conclusion :

ContainerVMs seem to have the following distinctive characteristics over the traditional containers  :

  1. There is no default shared filesystem between the container and its host
    • Volumes are attached to the container as disks and are completely isolated from each other
    • A shared filesystem could be provided by something like an NFS volume driver
  2. The way that you do low-level management and monitoring of a container is different. There is no VCH shell.
    • Any API-level control plane query, such as docker ps, works as expected
    • Low-level management and monitoring uses exactly the same tools and processes as for a VM
  3. The kernel running in the container is not shared with any other container
    • This means that there is no such thing as an optional privileged mode. Every container is privileged and fully isolated.
    • When a containerVM kernel is forked rather than booted, much of its immutable memory is shared with a parenttemplate
  4. There is no such thing as unspecified memory or CPU limits
    • A Linux container will have access to all of the CPU and memory resource available in its host if not specified
    • A containerVM must have memory and CPU limits defined, either derived from a default or specified explicitly

but the traditional containers like Docker are definitely  a more mature solution, offers more tools for orchestration and scaling.

Riak Cluster Using Docker Compose

Riak is some hot stuff lately with the increasing need for clusterzitation in the world of NoSQL data stores .

Riak is a solution to big data problem, it was based on Amazon Dynamo design, to respond to request at a very large scale,

Basho introduced Riak as fault tolerant, simple, scalable, high availability friendly .

it’s fairly easy to create/add nodes with riak-admin to a riak cluster, but if this is combined with docker-compose capabilities it will give you the possibility to have an easily deployable/scalable cluster of riak nodes.

I created a docker-compose yaml to specify all the components of the cluster, basically a seed and a set of nodes

I used  Hectcastro Riak docker image, because :  that’s the beauty of containers, they are reusable !

$git clone
$cd riak-cluster-compose


  • start compose as a daemon :
$docker-compose up -d
  • you will have one seed and one node running :

Screen Shot 2015-12-15 at 5.00.52 PM

  • list all running containers

Screen Shot 2015-12-15 at 5.02.33 PM

  • let’s create an “Artists” buckets and add an object using node1 (port : 32812)
$curl -i -d '{"name":"Bruce"}' -H "Content-Type: application/json" \
  • check the bucket was created :

Screen Shot 2015-12-15 at 5.30.05 PM

Scale time ! : 

  • to scale you cluster you can add 3 nodes
    $docker scale riak_node=4

Screen Shot 2015-12-15 at 5.32.32 PM

  •  let’s query node4 for the list of buckets (port: 32818)

Screen Shot 2015-12-15 at 5.36.08 PM

works !

if you are more interested to scale on multiple hosts, you can combine this with docker swarm.