While the most common installation setup is Linux and other Unix-based systems, a less-discussed scenario is using Docker.

One of the reasons for this could be a contradiction between what is required from a data pipeline architecture persistence, robustness, security and the ephemeral and distributed nature of Docker.

Having said that, and as demonstrated in the instructions below Docker can be an extremely easy way to set up the stack.

Just a few words on my environment before we begin Im using a recent version of Docker for Mac.

Running our Dockerized ELK There are various ways to install the stack with Docker.

You can pull Elastics individual images and run the containers separately or use Docker Compose to build the stack from a variety of available images on the Docker Hub.

For this tutorial, I am using a Dockerized ELK Stack that results in: three Docker containers running in parallel, for Elasticsearch, Logstash and Kibana, port forwarding set up, and a data volume for persisting Elasticsearch data.

Receiving objects: 100 (11121112), 234.87 KiB 84.00 KiBs, done.

After a few minutes, you can begin to verify that everything is running as expected.

Everything is already pre-configured with a privileged username and password: user: elastic password: changeme You can now query Elasticsearch using: curl.

Shipping data into the Dockerized ELK Stack Our next step is to forward some data into the stack.

By default, the stack will be running Logstash with the default Logstash configuration file.

You can configure that file to suit your purposes and ship any type of data into your Dockerized ELK and then restart the container.

Kubernetes A Win-Win Scenario Container security best practices What Challenges to Avoid When Migrating to Docker Alternatively, you could install Filebeat either on your host machine or as a container and have Filebeat forward logs into the stack.

I highly recommend reading up on using Filebeat on the projects documentation site.

Creating the index pattern, you will now be able to analyze your data on the Kibana Discover page.

Endnotes For a sandbox environment used for development and testing, Docker is one of the easiest and most efficient ways to set up the stack.

Perhaps surprisingly, ELK is being increasingly used on Docker for production environments as well, as reflected in this survey I conducted a while ago: Of course, a production ELK stack entails a whole set of different considerations that involve cluster setups, resource configurations, and various other architectural elements.

Do you want to compare DIY ELK vs Managed ELK Learn More You might also like DevOps Docker Logs with the ELK StackPart I DevOps Docker Logging with the ELK Stack Part Two Learn Docker Monitoring with the ELK Stack: A Step-by-Step Guide Back to Blog Stay updated with us By submitting this form, you are accepting our Terms of Use and our Privacy Policy Thank you for subscribing Internal Platform Log Management Cloud SIEM Infrastructure Monitoring Solutions Infrastructure Monitoring APM Security Analytics Cloud Monitoring DevOps Analytics Container Monitoring Compliance Features Alerts Application Insights Cognitive Insights Data Optimizer ELK Apps Live Tail Log Parsing Security and Compliance Log Patterns Pricing Plans Request Demo Resources Blog ELK Guide Case Studies Logz.io Open Source Community Docs Support About us About us Customers Partners Careers Contact Us Events Newsroom Privacy Policy Terms Of Use All Rights Reserved by Logz.io 2020 Get the latest posts on Cloud Monitoring and DevOps Thank you for subscribing logz.io logo footer Use the field below to search Logz.io.

Search Logz.io This website uses cookies.

By continuing to browse this site, you agree to this use.

Learn more. Okay, thanks.

Docker For Mac Install