Elastic stack (ELK) on Docker

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.

ℹ️ The Docker images backing this stack include Stack Features (formerly X-Pack) with paid features enabled by default (see How to disable paid features to disable them). The trial license is valid for 30 days.

Based on the official Docker images from Elastic:

Other available stack variants:

searchguard: Search Guard support

Contents

Requirements
Usage
- Bringing up the stack
- Initial setup
  - Setting up user authentication
  - Default Kibana index pattern creation
Configuration
Storage
- How to persist Elasticsearch data
Extensibility
- How to add plugins
- How to enable the provided extensions
JVM tuning
- How to specify the amount of memory used by a service
- How to enable a remote JMX connection to a service
Going further

Requirements

Host setup

Docker version 17.05+
Docker Compose version 1.6.0+
1 GB of RAM

By default, the stack exposes the following ports:

5000: Logstash TCP input
9200: Elasticsearch HTTP
9300: Elasticsearch TCP transport
5601: Kibana

SELinux

On distributions which have SELinux enabled out-of-the-box you will need to either re-context the files or set SELinux into Permissive mode in order for docker-elk to start properly. For example on Redhat and CentOS, the following will apply the proper context:

$ chcon -R system_u:object_r:admin_home_t:s0 docker-elk/

Docker for Desktop

Windows

Ensure the Shared Drives feature is enabled for the C: drive.

macOS

The default Docker for Mac configuration allows mounting files from /Users/, /Volumes/, /private/, and /tmp exclusively. Make sure the repository is cloned in one of those locations or follow the instructions from the documentation to add more locations.

Usage

Bringing up the stack

Clone this repository, then start the stack using Docker Compose:

$ docker-compose up

You 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 build first 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.

Initial setup

Setting up user authentication

ℹ️ Refer to How to disable paid features to disable authentication.

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 --batch

Passwords 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 configuration files respectively. See the Configuration section below.

ℹ️ Do not use the logstash_system user inside the Logstash pipeline file, it does not have sufficient permissions to create indices. Follow the instructions from the Built-in roles page to create a user with suitable roles.

Restart Kibana and Logstash to apply the passwords you just wrote to the configuration files.

$ docker-compose restart kibana logstash

Give Kibana about a minute to initialize, then access the Kibana web UI by hitting http://localhost:5601 with a web browser and use the following default credentials to log in:

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.log

Default Kibana index pattern creation

When Kibana launches for the first time, it is not configured with any index pattern.

Via the Kibana web UI

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.

On the command line

Create an index pattern via the Kibana API:

$ curl -XPOST -D- 'http://localhost:5601/api/saved_objects/index-pattern' \
    -H 'Content-Type: application/json' \
    -H 'kbn-version: 7.2.0' \
    -u elastic:<your generated elastic password> \
    -d '{"attributes":{"title":"logstash-*","timeFieldName":"@timestamp"}}'

The created pattern will automatically be marked as the default index pattern as soon as the Kibana UI is opened for the first time.

Configuration

ℹ️ Configuration is not dynamically reloaded, you will need to restart individual components after any configuration change.

How to configure Elasticsearch

The Elasticsearch configuration is stored in elasticsearch/config/elasticsearch.yml.

You can also specify the options you want to override by setting environment variables inside the Compose file:

elasticsearch:

  environment:
    network.host: _non_loopback_
    cluster.name: my-cluster

Please refer to the following documentation page for more details about how to configure Elasticsearch inside Docker containers: Install Elasticsearch with Docker.

How to configure Kibana

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.

Please refer to the following documentation page for more details about how to configure Kibana inside Docker containers: Running Kibana on Docker.

How to configure Logstash

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.

Please refer to the following documentation page for more details about how to configure Logstash inside Docker containers: Configuring Logstash for Docker.

How to disable paid features

Switch the value of Elasticsearch's xpack.license.self_generated.type option from trial to basic (see License settings).

How to scale out the Elasticsearch cluster

Follow the instructions from the Wiki: Scaling out Elasticsearch

Storage

How to persist Elasticsearch data

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/data

This will store Elasticsearch data inside /path/to/storage.

ℹ️ (Linux users) Beware that the Elasticsearch process runs as the unprivileged elasticsearch user is used within the Elasticsearch image, therefore the mounted data directory must be writable by the uid 1000.

Extensibility

How to add plugins

To add plugins to any ELK component you have to:

Add a RUN statement to the corresponding Dockerfile (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 build command

How to enable the provided extensions

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.

JVM tuning

How to specify the amount of memory used by a service

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

How to enable a remote JMX connection to a service

As for the Java Heap memory (see above), you can specify JVM options to enable JMX and map the JMX port on the Docker host.

Update the {ES,LS}_JAVA_OPTS environment variable with the following content (I've mapped the JMX service on the port 18080, you can change that). Do not forget to update the -Djava.rmi.server.hostname option with the IP address of your Docker host (replace DOCKER_HOST_IP):

logstash:

  environment:
    LS_JAVA_OPTS: -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.port=18080 -Dcom.sun.management.jmxremote.rmi.port=18080 -Djava.rmi.server.hostname=DOCKER_HOST_IP -Dcom.sun.management.jmxremote.local.only=false

Going further

Using a newer stack version

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 up

ℹ️ Always pay attention to the upgrade instructions for each individual component before performing a stack upgrade.

Plugins and integrations

See the following Wiki pages:

Swarm mode

Experimental support for Docker Swarm mode is provided in the form of a docker-stack.yml file, which can be deployed in an existing Swarm cluster using the following command:

$ docker stack deploy -c docker-stack.yml elk

If all components get deployed without any error, the following command will show 3 running services:

$ docker stack services elk

ℹ️ To scale Elasticsearch in Swarm mode, configure zen to use the DNS name tasks.elasticsearch instead of elasticsearch.

License

Mudboyzh/docker-elk

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