Customizing Docker Compose
This section covers how to customize the Docker Compose installation by editing the docker-compose.yml
file.
The Docker Compose file uses Apache Kafka for the messaging middleware and Prometheus for monitoring. If you want to use RabbitMQ or InfluxDB instead, this guide shows you the changes to make in the docker compose file.
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. This guide shows you how to do that as well.
Using RabbitMQ Instead of Kafka
You can use RabbitMQ rather than Kafka for communication. To do so:
-
Delete the following configuration under the
services:
section:kafka: image: confluentinc/cp-kafka:5.2.1 ... zookeeper: image: confluentinc/cp-zookeeper:5.2.1 ....
-
Insert the following:
rabbitmq: image: rabbitmq:3.7 expose: - '5672'
-
In the
dataflow-server
services configuration block, add the followingenvironment
entry:- spring.cloud.dataflow.applicationProperties.stream.spring.rabbitmq.host=rabbitmq
-
Delete the following:
depends_on: - kafka
-
Insert the following:
depends_on: - rabbitmq
- Modify the
app-import
service definitioncommand
attribute to replacehttps://dataflow.spring.io/kafka-maven-latest
withhttps://dataflow.spring.io/rabbitmq-maven-latest
.
Using InfluxDB Instead of Prometheus
You can use InfluxDB rather than Prometheus to monitor time-series database. To do so:
-
Delete the following configuration under the
services
section:prometheus: image: springcloud/spring-cloud-dataflow-prometheus-local:${DATAFLOW_VERSION:?DATAFLOW_VERSION is not set! Use 'export DATAFLOW_VERSION=dataflow-version'} container_name: 'prometheus' volumes: - 'scdf-targets:/etc/prometheus/' ports: - '9090:9090' depends_on: - service-discovery service-discovery: image: springcloud/spring-cloud-dataflow-prometheus-service-discovery:0.0.3 container_name: 'service-discovery' volumes: - 'scdf-targets:/tmp/scdf-targets/' expose: - '8181' ports: - '8181:8181' environment: - metrics.prometheus.target.refresh.cron=0/20 * * * * * - metrics.prometheus.target.discovery.url=http://localhost:9393/runtime/apps - metrics.prometheus.target.file.path=/tmp/targets.json depends_on: - dataflow-server
-
Insert the following:
influxdb: image: influxdb:1.7.4 container_name: 'influxdb' ports: - '8086:8086'
-
In the
dataflow-server
services configuration block, delete the followingenvironment
entries:- spring.cloud.dataflow.applicationProperties.stream.management.metrics.export.prometheus.enabled=true - spring.cloud.dataflow.applicationProperties.stream.spring.cloud.streamapp.security.enabled=false - spring.cloud.dataflow.applicationProperties.stream.management.endpoints.web.exposure.include=prometheus,info,health
-
Insert the following:
- spring.cloud.dataflow.applicationProperties.stream.management.metrics.export.influx.enabled=true - spring.cloud.dataflow.applicationProperties.stream.management.metrics.export.influx.db=myinfluxdb - spring.cloud.dataflow.applicationProperties.stream.management.metrics.export.influx.uri=http://influxdb:8086
- Modify the
grafana
service definitionimage
attribute to replacespring-cloud-dataflow-grafana-prometheus
withspring-cloud-dataflow-grafana-influxdb
.
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 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.