After a few minutes, you can begin to verify that everything is running as expected. It might take a while before the entire stack is pulled, built and initialized. To run the stack, simply use: cd /docker-elk You can tweak the docker-compose.yml file or the Logstash configuration file if you like before running the stack, but for the initial testing, the default settings should suffice. Remote: Total 1112 (delta 0), reused 0 (delta 0), pack-reused 1112 You can pull Elastic’s 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.įor 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. There are various ways to install the stack with Docker. Just a few words on my environment before we begin - I’m using a recent version of Docker for Mac. Having said that, and as demonstrated in the instructions below - Docker can be an extremely easy way to set up the stack. 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. While the most common installation setup is Linux and other Unix-based systems, a less-discussed scenario is using Docker. The ELK Stack ( Elasticsearch, Logstash and Kibana ) can be installed on a variety of different operating systems and in various different setups.
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