Run the latest version of the Elastic stack with Docker and Docker Compose.
It gives you the ability to analyze any data set by using the searching/aggregation capabilities of Elasticsearch and the visualization power of Kibana.
Based on the official Docker images from Elastic:
- Elastic stack (ELK) on Docker
- Install Docker version 17.05+
- Install Docker Compose version 1.6.0+
- Clone this repository
By default, the stack exposes the following ports:
- 5000: Logstash TCP input
- 9200: Elasticsearch HTTP
- 9300: Elasticsearch TCP transport
- 5601: Kibana
Start the stack using Docker Compose:
$ docker-compose upYou can also run all services in the background (detached mode) by adding the -d flag to the above command.
ℹ️ You must run
docker-compose buildfirst whenever you switch branch or update a base image.
If you are starting the stack for the very first time, please read the section below attentively.
The stack is pre-configured with the following privileged bootstrap user:
- user: elastic
- password: changeme
Although all stack components work out-of-the-box with this user, we strongly recommend using the unprivileged built-in users instead for increased security. Passwords for these users must be initialized:
$ docker-compose exec -T elasticsearch 'bin/elasticsearch-setup-passwords' auto --batchPasswords for all 6 built-in users will be randomly generated. Take note of them and replace the elastic username with
kibana and logstash_system inside the Kibana and Logstash pipeline configuration files respectively. See the
Configuration section below.
Restart Kibana and Logstash to apply the passwords you just wrote to the configuration files.
$ docker-compose restart kibana logstashGive Kibana a few seconds to initialize, then access the Kibana web UI by hitting http://localhost:5601 with a web browser and use the following default credentials to login:
- user: elastic
- password: <your generated elastic password>
Now that the stack is running, you can go ahead and inject some log entries. The shipped Logstash configuration allows you to send content via TCP:
$ nc localhost 5000 < /path/to/logfile.logWhen Kibana launches for the first time, it is not configured with any index pattern.
ℹ️ You need to inject data into Logstash before being able to configure a Logstash index pattern via the Kibana web UI. Then all you have to do is hit the Create button.
Refer to Connect Kibana with Elasticsearch for detailed instructions about the index pattern configuration.
ℹ️ Configuration is not dynamically reloaded, you will need to restart individual components after any configuration change.
The Elasticsearch configuration is stored in elasticsearch/config/elasticsearch.yml.
The Kibana default configuration is stored in kibana/config/kibana.yml.
It is also possible to map the entire config directory instead of a single file.
The Logstash configuration is stored in logstash/config/logstash.yml.
It is also possible to map the entire config directory instead of a single file, however you must be aware that
Logstash will be expecting a log4j2.properties file for its own logging.
The data stored in Elasticsearch will be persisted after container reboot but not after container removal.
In order to persist Elasticsearch data even after removing the Elasticsearch container, you'll have to mount a volume on
your Docker host. Update the elasticsearch service declaration to:
elasticsearch:
volumes:
- /path/to/storage:/usr/share/elasticsearch/dataThis will store Elasticsearch data inside /path/to/storage.
ℹ️ (Linux users) Beware that the Elasticsearch process runs as the unprivileged
elasticsearchuser is used within the Elasticsearch image, therefore the mounted data directory must be writable by the uid1000.
To add plugins to any ELK component you have to:
- Add a
RUNstatement to the correspondingDockerfile(eg.RUN logstash-plugin install logstash-filter-json) - Add the associated plugin code configuration to the service configuration (eg. Logstash input/output)
- Rebuild the images using the
docker-compose buildcommand
A few extensions are available inside the extensions directory. These extensions provide features which
are not part of the standard Elastic stack, but can be used to enrich it with extra integrations.
The documentation for these extensions is provided inside each individual subdirectory, on a per-extension basis. Some of them require manual changes to the default ELK configuration.
By default, both Elasticsearch and Logstash start with 1/4 of the total host memory allocated to the JVM Heap Size.
The startup scripts for Elasticsearch and Logstash can append extra JVM options from the value of an environment variable, allowing the user to adjust the amount of memory that can be used by each component:
| Service | Environment variable |
|---|---|
| Elasticsearch | ES_JAVA_OPTS |
| Logstash | LS_JAVA_OPTS |
To accomodate environments where memory is scarce (Docker for Mac has only 2 GB available by default), the Heap Size
allocation is capped by default to 256MB per service in the docker-compose.yml file. If you want to override the
default JVM configuration, edit the matching environment variable(s) in the docker-compose.yml file.
For example, to increase the maximum JVM Heap Size for Logstash:
logstash:
environment:
LS_JAVA_OPTS: "-Xmx1g -Xms1g"To use a different Elastic Stack version than the one currently available in the repository, simply change the version
number inside the .env file, and rebuild the stack with:
$ docker-compose build
$ docker-compose upℹ️ Always pay attention to the upgrade instructions for each individual component before performing a stack upgrade.
See the following Wiki pages: