Customizing Docker Compose

The Docker Compose installation guide explains how to use the docker-compose.yml for installing Data Flow, Skipper, Kafka and MySQL. You can extend this basic configuration with the help of the provided extension docker-compose files. For example if you want to use RabbitMQ or PostgreSQL instead or to enable Data Flow for Monitoring, you can combine some of the provided docker-compose extension files like this:

docker-compose -f ./docker-compose.yml \
               -f ./docker-compose-rabbitmq.yml \
               -f ./docker-compose-postgres.yml \
               -f ./docker-compose-influxdb.yml up

Following section offers a detailed description of the provided extension docker-compose files. Also, when doing development of custom applications, you need to enable the Docker container that runs the Data Flow Server to see your local file system. The Accessing the Host File System chapter below shows how to do that as well.

Docker Compose Extensions

Extension docker-compose files that can be applied on top of the docker-compose.yml. When more than one docker compose file is used, they are applied in the order of definition.

Prometheus & Grafana

docker-compose-prometheus.yml - Extends the default configuration in docker-compose.yml to enable the Stream and Task monitoring with Prometheus and Grafana:

wget https://raw.githubusercontent.com/spring-cloud/spring-cloud-dataflow/v2.3.1.RELEASE/spring-cloud-dataflow-server/docker-compose-prometheus.yml
docker-compose -f ./docker-compose.yml -f ./docker-compose-prometheus.yml up

In addition to the basic services the extended configuration adds Prometheus, Prometheus-RSocket-Proxy for service-discovery, and Grafana with pre-built Stream and Task dashboards.

InfluxDB & Grafana

docker-compose-influxdb.yml - Enables Stream and Task monitoring with InfluxDB and Grafana with pre-built Stream and Task dashboards:

wget https://raw.githubusercontent.com/spring-cloud/spring-cloud-dataflow/v2.3.1.RELEASE/spring-cloud-dataflow-server/docker-compose-influxdb.yml
docker-compose -f ./docker-compose.yml -f ./docker-compose-influxdb.yml up

Postgres Instead of MySQL

docker-compose-postgres.yml - Uses PostgreSQL instead of MySQL for both Spring Cloud Data Flow and SKipper. It disables the default mysql service, adds a new postgres service and overrides the Data Flow and Skipper configurations to use the postgres:

wget https://raw.githubusercontent.com/spring-cloud/spring-cloud-dataflow/v2.3.1.RELEASE/spring-cloud-dataflow-server/docker-compose-postgres.yml
docker-compose -f ./docker-compose.yml -f ./docker-compose-postgres.yml up

RabbitMQ Instead of Kafka

docker-compose-rabbitmq.yml - Use RabbitMQ instead of Kafka as message broker. It disables the default kafka and zookeeper services, add a new rabbitmq service and override the dataflow-server's service binder configuration to RabbitMQ (e.g. spring.cloud.dataflow.applicationProperties.stream.spring.rabbitmq.host=rabbitmq). Finally overrides the app-import service to register the rabbit apps:

wget https://raw.githubusercontent.com/spring-cloud/spring-cloud-dataflow/v2.3.1.RELEASE/spring-cloud-dataflow-server/docker-compose-rabbitmq.yml
docker-compose -f ./docker-compose.yml -f ./docker-compose-rabbitmq.yml up

Multi-platform support

docker-compose-cf.yml Adds a remote Cloud Foundry account as a Data Flow runtime platform under the name cf. You will need to edit the docker-compose-cf.yml to add your CF API URL and access credentials.

wget https://raw.githubusercontent.com/spring-cloud/spring-cloud-dataflow/v2.3.1.RELEASE/spring-cloud-dataflow-server/docker-compose-rabbitmq.yml
wget https://raw.githubusercontent.com/spring-cloud/spring-cloud-dataflow/v2.3.1.RELEASE/spring-cloud-dataflow-server/docker-compose-cf.yml
docker-compose -f ./docker-compose.yml -f ./docker-compose-rabbitmq.yml -f ./docker-compose-cf.yml up

Because Kafka is not supported on CF you, also will need to switch to Rabbit using the docker-compose-rabbitmq.yml. The docker-compose-cf.yml expects a rabbit service configured in the target CF environment.

docker-compose-k8s.yml Adds a remote Kubernetes account as a Data Flow runtime platform under the name k8s. You will need to edit the docker-compose-k8s.yml to add your Kubernetes master URL and access credentials.

wget https://raw.githubusercontent.com/spring-cloud/spring-cloud-dataflow/v2.3.1.RELEASE/spring-cloud-dataflow-server/docker-compose-k8s.yml
STREAM_APPS_URI=https://dataflow.spring.io/kafka-docker-latest docker-compose -f ./docker-compose.yml -f ./docker-compose-k8s.yml up

The default maven based app starters can not be deployed in a Kubernetes environment. Switch to the docker based app distribution using the STREAM_APPS_URI variable.

The docker-compose-k8s.yml expects a kafka-broker service pre-deployed in the target Kubernetes environment. Follow the choose a message broker instructions to deploy kafka-broker service.

Debug Data Flow Server

docker-compose-debug-dataflow.yml enables remote debugging of the Data Flow Server. To enable the debugging run:

wget https://raw.githubusercontent.com/spring-cloud/spring-cloud-dataflow/v2.3.1.RELEASE/spring-cloud-dataflow-server/docker-compose-debug-dataflow.yml
docker-compose -f ./docker-compose.yml -f ./docker-compose-debug-dataflow.yml up

It makes the dataflow-server service wait for a debugger to connect on port 5005 to start debugging. Following snippet shows how to configure remote debug with IntelliJ. Set the Host: with the IP address of you local machine. Do not use localhost as it won't work inside the docker containers.

Often while debugging you will need to build new, local spring-cloud-dataflow-server:latest docker image. You can achieve this running the following commands from the DataFlow root directory:

./mvnw clean install -DskipTests
./mvnw docker:build -pl spring-cloud-dataflow-server

Integration Testing

The self-documented DockerComposeIT.java class demonstrates how to reuse the same docker-compose files to build DataFlow integration and smoke tests.

Accessing the Host File System

If you develop custom applications on your local machine, you need to register them with Spring Cloud Data Flow. Since Spring Cloud Data Flow runs inside of a Docker container, you need to configure the Docker container to access to your local file system to resolve the registration reference. Also in order to deploy those custom applications, the Skipper Server in turn needs to access them from within its own Docker container using exactly the same path definitions configured in the Data Flow server configuration.

You can enable local disk access by mounting the host folders to the dataflow-server and skipper-server containers. For example, if the my-app.jar is in the /thing1/thing2/apps folder on your host machine, add the following volumes block to the dataflow-server and skipper-server service definitions:

dataflow-server:
  image: springcloud/spring-cloud-dataflow-server:${DATAFLOW_VERSION}
  # ...
  volumes:
    - /thing1/thing2/apps:/root/apps

and mount exactly the same volume to the skipper-server service definition:

skipper-server:
  image: springcloud/spring-cloud-skipper-server:${SKIPPER_VERSION:?SKIPPER_VERSION is not set!}
  # ...
  volumes:
    - /thing1/thing2/apps:/root/apps

This configuration provides access to the /thing1/thing2/apps directory that contains your my-app.jar from within dataflow-server and skipper-server containers' /root/apps/ folder. See the compose-file reference for further configuration details.

Volume Mounting

The explicit volume mounting couples docker-compose to your host’s file system, limiting the portability to other machines and operating systems. Unlike docker, docker-compose does not allow volume mounting from the command line (for example, there is no -v parameter). Instead, you can define a placeholder environment variable (such as HOST_APP_FOLDER) in place of the hardcoded path by using - ${HOST_APP_FOLDER}:/root/apps and setting this variable before starting docker-compose.

Once you mount the host folder, you can register the app starters (from /root/apps), with the Data Flow Shell or Dashboard by using the file:// URI schema. The following example shows how to do so:

app register --type source --name my-app --uri file://root/apps/my-app-1.0.0.RELEASE.jar

Metadata URIs

You also need to use --metadata-uri if the metadata jar is available in the /root/apps folder.

Maven Local Repository Mounting

To access the host’s local maven repository from Spring Cloud Data Flow you must mount the host maven local repository to a dataflow-server and skipper-server volume called /root/.m2/. The Maven Local Repository location defaults to ~/.m2 for OSX and Linux and C:\Documents and Settings\{your-username}\.m2 for Windows.

For MacOS or Linux host machines, this looks like the following listing:

dataflow-server:
.........
  volumes:
    - ~/.m2:/root/.m2

 skipper-server:
 ........
   volumes:
    - ~/.m2:/root/.m2

Dataflow Server requires access to the Maven Local repository in order to properly register applications to the Spring Cloud Data Flow server. The Skipper Server manages application runtime deployment directly and thereby also requires access to Maven Local in order to deploy applications created and installed on the host machine.

Mounting this volume allows you to develop applications and install them using mvn install while the server is still running and have immediate access to the applications

Now you can use the maven:// URI schema and Maven coordinates to resolve jars installed in the host’s maven repository, as the following example shows:

app register --type processor --name pose-estimation --uri maven://org.springframework.cloud.stream.app:pose-estimation-processor-rabbit:2.0.2.BUILD-SNAPSHOT --metadata-uri maven://org.springframework.cloud.stream.app:pose-estimation-processor-rabbit:jar:metadata:2.0.2.BUILD-SNAPSHOT

This approach lets you use applications that are built and installed on the host machine (for example, by using mvn clean install) directly with the Spring Cloud Data Flow server.

You can also pre-register the apps directly in the docker-compose instance. For every pre-registered app starer, add an additional wget statement to the app-import block configuration, as the following example shows:

app-import:
  image: alpine:3.7
  command: >
    /bin/sh -c "
      ....
      wget -qO- 'https://dataflow-server:9393/apps/source/my-app' --post-data='uri=file:/root/apps/my-app.jar&metadata-uri=file:/root/apps/my-app-metadata.jar';
      echo 'My custom apps imported'"

See the Data Flow REST API for further details.