Docker on Elastic Beanstalk Tips

AWS Elastic Beanstalk is one of the most used PaaS today, it allows you to deploy your application without provisioning the underlying infrastructure while maintaining the high availability of your application. However, it’s painful to use due to the lack of documentation and real-world scenarios. In this post, I will walk you through how to use Elastic Beanstalk to deploy Docker containers from scratch. Followed by how to automate your deployment process with a Continuous Integration pipeline. At the end of this post, you should be familiar with advanced topics like debugging and monitoring of your applications in EB.

1 – Environment Setup

To get started, create a new Application using the following AWS CLI command:

Create a new environment. Let’s call it “staging” :

Head back to AWS Elastic Beanstalk Console, your new environment should be created:

Point your browser to the environment URL, a sample Docker application should be displayed:

Let’s deploy our application. I wrote a small web application in Go to return a list of Marvel Avengers (I see you Thanos 😉 )

Next, we will create a Dockerfile to build the Docker image. Go is a compiled language, therefore we can use the Docker multi-stage feature to build a lightweight Docker image:

Next, we create a Dockerrun.aws.json that describes how the container will be deployed in Elastic Beanstalk:

Now the application is defined, create an application bundle by creating a ZIP package:

Then, create a S3 bucket to store the different versions of your application bundles:

Issue the following command in order to copy the application into the bucket:

And create a new application version from the application bundle:

Finally, deploy the version to the staging environment:

Give it a few seconds while it’s deploying the new version:

Then, repoint your browser to the environment URL, a list of Avengers will be returned in a JSON format as follows:

Now that our Docker application is deployed, let’s automate this process by setting up a CI/CD pipeline.

2 – CI/CD Pipeline

I opt for CircleCI, but you’re free to use whatever CI server you’re familiar with. The same steps can be applied.

Create a circle.yml file with the following content:

The pipeline will firstly prepare the environment, installing the AWS CLI. Then run unit tests. Next, a Docker image will be built, then pushed to DockerHub. Last step is creating a new application bundle and deploying the bundle to Elastic Beanstalk.

In order to grant Circle CI permissions to call AWS operations, we need to create a new IAM user with following IAM policy:

Generate AWS access & secret keys. Then, head back to Circle CI and click on the project settings and paste the credentials :

Now, everytime you push a change to your code repository, a build will be triggered:

And a new version will be deployed automatically to Elastic Beanstalk:

3 – Monitoring

Monitoring your applications is mandatory. Unfortunately, CloudWatch doesn’t expose useful metrics like Memory usage of your applications in Elastic Beanstalk. Hence, in this part, we will solve this issue by creating our custom metrics.

I will install a data collector agent on the instance. The agent will collect metrics and push them to a time-series database.

To install the agent, we will use .ebextensions folder, on which we will create 3 configuration files:

  • 01-install-telegraf.config: install Telegraf on the instance

  • 02-config-file.config: create a Telegraf configuration file to collect system usage & docker containers metrics.

  • 03-start-telegraf.config: start Telegraf agent.

Once the application version is deployed to Elastic Beanstalk, metrics will be pushed to your timeseries database. In this example, I used InfluxDB as data storage and I created some dynamic Dashboards in Grafana to visualize metrics in real-time:

Containers:

Hosts:

Note: for in-depth explaination on how to configure Telegraf, InfluxDB & Grafana read my previous article.

Full code can be found on my GitHub. Make sure to drop your comments, feedback, or suggestions below — or connect with me directly on Twitter @mlabouardy

MySQL Monitoring with Telegraf, InfluxDB & Grafana

This post will walk you through each step of creating interactive, real-time & dynamic dashboard to monitor your MySQL instances using Telegraf, InfluxDB & Grafana.

Start by enabling the MySQL input plugin in /etc/telegraf/telegraf.conf :

Once Telegraf is up and running it’ll start collecting data and writing them to the InfluxDB database:

Finally, point your browser to your Grafana URL, then login as the admin user. Choose ‘Data Sources‘ from the menu. Then, click ‘Add new‘ in the top bar.

Fill in the configuration details for the InfluxDB data source:

You can now import the dashboard.json file by opening the dashboard dropdown menu and click ‘Import‘ :

Note: Check my Github for more interactive & beautiful Grafana dashboards.

Continuous Monitoring with TICK stack

Monitoring your system is required. It helps you detect any issues before they cause any major downtime that effect your customers and damage your business reputation. It helps you also to plan growth based on the real usage of your system. But collecting metrics from different data sources isn’t enough, you need to personalize your monitoring to meet your own business needs and define the right alerts so that any abnormal changes in the system will reported.

In this post, I will show you how to setup a resilient continuous monitoring platform with only open source projects & how to define an event alert to report changes in the system.

Clone the following Github repository:

1 – Terraform & AWS

In the tick-stack/terraform directory, update the variables.tfvars file with your own AWS credentials (make sure you have the right IAM policies) :

Issue the following command to download the AWS provider plugin:

Issue the following command to provision the infrastructure:

2 – Ansible & Docker

Update the inventory file with your instance DNS name:

Then, install the Ansible custom role:

Execute the Ansible Playbook:

Point your browser to http://DNS_NAME:8083, you should see InfluxDB Admin Dashboard:

Now, create an InfluxDB Data Source in Chronograf (http://DNS_NAME:8888):

Create a new Dashboard as follow:

You can create multiple graphs to visualize different types of metrics:

Note: For in depth details on how to create interactive & dynamic dashboards in Chronograf check my previous tutorial.

You need to elaborate on the data collected to do something like alerting. So make sure to enable Kapacitor:

Define a new alert to send a Slack notification if the CPU utilization is higher than 70%.

To test it out, we need to generate some workload. For this case, I used stress:

Stressing the CPU:

After few seconds, you should receive a Slack notification.

Exploring Swarm & Container Overview Dashboard in Grafana

In my previous post, your learnt how to monitor your Swarm Cluster with TICK Stack. In this part, I will show you how to use the same Stack but instead of using Chronograf as our visualization and exploration tool we will use Grafana.

Connect to your manager node via SSH, and clone the following Github repository:

Use the docker-compose.yml below to setup the monitoring stack:

Then, issue the following command to deploy the stack:

Once deployed, you should see the list of services running on the cluster:

Point your browser to http://IP:3000, you should be able to reach the Grafana Dashboard:

The default username & password are admin. Go ahead and log in.

Go to “Data Sources” and create 2 InfluxDB data sources:

  • Vms: pointing to your Cluster Nodes metrics database.
  • Docker: pointing to your Docker Services metrics database.

Finally, import the dashboard by hitting the “import” button:

From here, you can upload the dashboard.json, then pick the data sources you created earlier:

You will end up with an interactive and dynamic dashboard:

Monitor Swarm cluster with TICK stack & Slack

In this article, I will show you how to setup an Open Source time series platform to monitor your Docker Swarm cluster & send notification on Slack in case of anomaly detection.

Components of our monitoring Stack:

Plugin driven server agent for collecting and reporting metrics.

Scalable time series database for metrics, events and real-time analytics.

Real time visualization tool for building graphs on top of data.

Framework for processing, monitoring, and alerting on time series data.

Real-time team messaging application.

Note: all the code used in this post is available on my Github.

1 – Swarm Setup

If you already have an existing Swarm cluster, you can skip this part, if not use the following script to setup a Swarm with 3 nodes (1 manager & 2 workers):

Issue the following commands:

The output of the above command is as follows:

2 – Stack Setup

Once created, connect to your manager node via SSH, and clone the following repository:

To start all of these containers I’m using docker-compose:

Issue the following command to deploy the stack:

Wait for nodes to pull the images from DockerHub:

Once pulled you should see the services running:

Open your browser on http://IP:8888 (Chronograf Dashboard) and properly configure the data source:

3 – System usage Dashboard

Click on “create dashboard“, and assign a name to the dashboard:

Before adding graphs, we will use a concept called Dashboard Template Variable, to create dynamic & interactive graphs. Instead of hard-coding things like node name and container name in our metric queries we will use variables in their place. So click on “Templates Varibles” in top of the dashboard created earlier. And, create a variable called :host: as follows:

Note: currently, there’s no solution to set hostname for services created with swarm global mode (Github). Thats why we got list of IDs instead of names

You can now use the dropdown at the top of the dashboard to select the different options for the :host: template variable:

Now it’s time to create our first graph, so click on “Add Graph” button.

3.1 – Memory usage per Node

To create a query, you can either use the Query Builder or, if you’re already familar with InfluxQL, you can manually enter the query in the text input:

Our query calculates the average of the field keys freeused, and total in the measurement mem_vm, and it groups them by the time and node name.

You can change the graph type, X, and Y axes format by clicking on “Options” tab:

One visualization on a dashboard isn’t spectacularly interesting, so I added a couple more graphs to show you more possibilities:

3.2 – CPU usage per Node

3.3 – Disk usage per Node

We end up with a beautiful dashboard like this:

Let’s create another dashboard to monitor Docker Containers running on the Cluster.

4 – Swarm Services Dashboard

Create a second dashboard called “Services“, and create a template variable to store list of services running on cluster:

You can filter now metrics by service name:

4.1 – Memory usage per Service

4.2 – CPU usage per Service

4.3 – Network Transmit/Receive

4.4 – IO Read/Write per Service

Result:

Note: you can take this further, and filter metrics by the node on which the service is running on by creating another template variable:

Let’s see what happen if we create another service on Swarm:

If you go back to Chronograf, you should see the service has been added automatically to the container dropdown list:

And that’s it! You now have the foundation for building beautiful data visualizations and dashboards with Chronograf.

Kapacitor is the last piece of the puzzle. We now know how to store, get and read metrics, and now you need to elaborate on them to do something like alerting or proactive monitoring.

So on the “Configuration” tab, click on “Add config“:

Add new Kapacitor instance as below, and enable Slack:

Note: update the Slack channel & Webhook URL in case you didn’t update the kapacitor.conf file in the beginning of this tutorial. You can get a Webhook URL by going to this page:

5 – Alerts definition

5.1 – High Memory Utilization Alert

Navigate to the “Rule Configuration” page by visiting the “Alerting” page and click on the “Build Rule” button in the top right corner:

We will trigger an alert if the memory usage is over 60%:

Next, we select Slack as the event handler and configure the alert message:

Note: there’s no need to include a Slack channel in the Alert Message section if you specified a default channel in the initial Slack configuration.

5.2 – High CPU Utilization Alert

Create a second rule to trigger an alert if CPU usage is over 40%:

Alert endpoint:

Save the rule, and you’re all set !

Now our alert rules are defined, lets test them out by creating some load on our cluster.

6 – Stress Testing

I used stress, a tool for generating workload. It can produce CPU, memory, I/O, and disk stress.

6.1 – Stressing the CPU

Note: depending on the type of your CPU, make sure to replace ‘4‘ accordingly.

After few seconds, you should receive a Slack notification:

Kapacitor trigger an alert and also recovered them (Status OK) if the alert is resolved.

6.2 – Stressing the Memory

It will stress memory using three processes, with each about 256 Mb (override with the option –vm-bytes).

Let it run for a couple seconds :

That’s it! You’ve successfully setup a highly scalable, distributed monitoring platform for your Swarm cluster with only Open Source projects.

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