Docker Developer Guide for Confluent Platform

This document assumes knowledge of Docker and Dockerfiles. To review best practices for writing Dockerfiles, see Docker’s best practices guide.

If you want to contribute back to the project, review the contributing guidelines.

Confluent Platform image bootup process

Upon startup of Docker, the entry point /etc/confluent/docker/run runs three executable scripts found in the /etc/confluent/docker. They are run in the following sequence:

  1. Configure script

    The /etc/confluent/docker/configure script does all the necessary configuration for each image. This includes the following:

    • Create all configuration files and copying them to their proper location.
    • Ensure that mandatory configuration properties are present.
    • If required, handle service discovery.

  2. Ensure script

    The /etc/confluent/docker/ensure scripts makes sure that all the prerequisites for launching the service are in place. This includes:

    • Ensure the configuration files are present and readable.
    • Ensure that you can write/read to the data directory. The directories must be world writable.
    • Ensure that supporting services are in the READY state. For example, ensure that ZooKeeper is ready before launching a Kafka broker.
    • Ensure supporting systems are configured properly. For example, make sure all topics required for Confluent Control Center are created with proper replication, security and partition settings.

  3. Launch

    The /etc/confluent/docker/launch script runs the actual process. The script should ensure that:

    • The process is run with process id 1. Your script should use exec so the program takes over the shell process rather than running as a child process. This is so that your program will receive signals like SIGTERM directly rather than its parent shell process receiving them.
    • Log to stdout.

Prerequisites

  1. Install Docker:

    • Docker version 1.11 or later is installed and running.
    • Docker Compose is installed. Docker Compose is installed by default with Docker for Mac.
    • Docker memory is allocated minimally at 8 GB. When using Docker Desktop for Mac, the default Docker memory allocation is 2 GB. You can change the default allocation to 8 GB in Docker. Navigate to Preferences > Resources > Advanced.

  2. Install Maven:

    brew install maven

Build the Confluent Platform images

Refer to Docker Image Reference for Confluent Platform for a list of GitHub repos for the Confluent Platform components.

For each Confluent Platform image you want to build:

  1. Clone the repo.

  2. Checkout the release branch.

  3. Get the values for the required and optional arguments for the build command.

    For the list of supported arguments, see this README file.

    The following are required arguments:

    • CONFLUENT_PACKAGES_REPO: Specify the location of the Confluent Platform packages repository. Depending on the type of OS for the image you are building, you might need to provide a Debian or RPM repository.
    • CONFLUENT_VERSION: Specify the full Confluent Platform release version, e.g., 7.4.7.
    • docker.upstream-registry: Registry to pull base images from. Trailing / is required. Used as DOCKER_UPSTREAM_REGISTRY during docker build.
    • docker.upstream-tag: Use the given tag when pulling base images. Used as DOCKER_UPSTREAM_TAG during docker build.

  4. Optionally, you can choose an operating system you want your docker image based on, specifically Debian or RHEL UBI. To build a RHEL UBI image pass the following argument to the Maven command:

    -Ddocker.os_type=ubi8

  5. From the root folder of the repo, build the Confluent Platform images using Maven.

    For example:

    mvn clean package \
-DskipTests -Pdocker \
-DCONFLUENT_PACKAGES_REPO='https://packages.confluent.io/rpm/7.4' \
-DCONFLUENT_VERSION=7.4.7 \
-Ddocker.upstream-registry=docker.io/ \
-Ddocker.upstream-tag=7.4.7

Extend Confluent Platform images

You can extend the images to add or customize connectors, add new software, change the configuration management, and set up external service discovery.

The following sections provide examples of extending Confluent Platform images.

Note

You can apply the similar steps to extend custom Docker images that you built.

However, if you run into an issue with a custom Docker image, you need to work with Confluent Support to narrow down the issue. Confluent Support can only assist with the issues involving Confluent Platform software.

The issues with Docker or any 3rd party software cannot be further debugged by Confluent Support.

Add Connectors or Software

The Kafka Connect Base image contains Kafka Connect and all of its dependencies. When started, it will run the Connect framework in distributed mode. To add new connectors to this image, you need to build a new Docker image that has the new connectors installed. The following examples show how to add connectors.

Note

Starting with Confluent Platform version 6.0 release, many connectors previously bundled with Confluent Platform are now available for download from Confluent Hub. For more information, see the 6.0 Connector Release Notes.

Create a Docker Image containing Confluent Hub Connectors

This example shows how to use the Confluent Hub Client to create a Docker image that extends from one of Confluent’s Kafka Connect images but which contains a custom set of connectors. This may be useful if you’d like to use a connector that isn’t contained in the cp-server-connect image, or if you’d like to keep the custom image lightweight and not include any connectors that you don’t plan to use.

  1. Add connectors from Confluent Hub.

  2. Choose an image to extend.

  3. Choose the connectors from Confluent Hub that you’d like to include in your custom image. Note that the remaining steps result in a custom image containing a MongoDB connector, a Microsoft Azure IoT Hub connector, and a Google BigQuery connector.

  4. Write a Dockerfile.

    FROM confluentinc/cp-server-connect-base:7.4.7

RUN   confluent-hub install --no-prompt hpgrahsl/kafka-connect-mongodb:1.1.0 \
   && confluent-hub install --no-prompt microsoft/kafka-connect-iothub:0.6 \
   && confluent-hub install --no-prompt wepay/kafka-connect-bigquery:1.1.0

  5. Build the Dockerfile.

    docker build . -t my-custom-image:1.0.0

    The output from that command should resemble:

    Step 1/2 : FROM confluentinc/cp-server-connect-base
---> e0d92da57dc3
...
Running in a "--no-prompt" mode
Implicit acceptance of the license below:
Apache 2.0
https://github.com/wepay/kafka-connect-bigquery/blob/master/LICENSE.md
Implicit confirmation of the question: You are about to install 'kafka-connect-bigquery' from WePay, as published on Confluent Hub.
Downloading component BigQuery Sink Connector 1.1.0, provided by WePay from Confluent Hub and installing into /usr/share/confluent-hub-components
Adding installation directory to plugin path in the following files:
  /etc/kafka/connect-distributed.properties
  /etc/kafka/connect-standalone.properties
  /etc/schema-registry/connect-avro-distributed.properties
  /etc/schema-registry/connect-avro-standalone.properties

Completed
Removing intermediate container 48d4506b8a83
 ---> 496befc3d3f7
Successfully built 496befc3d3f7
Successfully tagged my-custom-image:1.0.0

    This results in an image named my-custom-image that contains the MongoDB, Azure IoT Hub, and BigQuery connectors, and which is capable of running any or all of the connectors using the Kafka Connect framework.

If you are using a docker-compose.yml file and the Confluent Hub Client to build your Kafka environment, use the following properties to enable a connector. (The example shows the Datagen development and testing connector.)

connect:
  image: confluentinc/kafka-connect-datagen:latest
  build:
    context: .
    dockerfile: Dockerfile-confluenthub

Create a Docker Image containing Local Connectors

This example shows how to create a Docker image that extends the cp-server-connect-base image to contain one or more local connectors. This is useful if you want to use your connectors instead of pulling connectors from Confluent Hub.

  1. Package your local connector in a zip file.

  2. Set up the Dockerfile as shown in the example below.

    FROM confluentinc/cp-server-connect-base:7.4.7

COPY target/components/packages/my-connector-7.4.7.zip /tmp/my-connector-7.4.7.zip

RUN confluent-hub install --no-prompt /tmp/my-connector-7.4.7.zip

  3. Build the Dockerfile.

    docker build . -t my-custom-image:1.0.0

Add Additional Software

This example shows how to add new software to an image. For example, you might want to extend the Kafka Connect client to include the MySQL JDBC driver. If this approach is used to add new connectors to an image, the connector JARs must be on the plugin.path or the CLASSPATH for the Connect framework.

  1. Write the Dockerfile.

    FROM confluentinc/cp-server-connect

ENV MYSQL_DRIVER_VERSION 5.1.39

RUN curl -k -SL "https://dev.mysql.com/get/Downloads/Connector-J/mysql-connector-java-${MYSQL_DRIVER_VERSION}.tar.gz" \
     | tar -xzf - -C /usr/share/java/kafka/ --strip-components=1 mysql-connector-java-5.1.39/mysql-connector-java-${MYSQL_DRIVER_VERSION}-bin.jar

  2. Build the image.

    docker build -t foo/mysql-connect:latest .

Note

This approach can also be used to create images with your own Kafka Connect Plugins.

Change configuration management

This example describes how to change the configuration management. To accomplish this, you override the configure script to download the scripts from a URL.

For example, with the ZooKeeper image, you need the following Dockerfile and configure script. This example assumes that each property file has a URL.

Dockerfile:

FROM confluentinc/cp-zookeeper

COPY include/etc/confluent/docker/configure /etc/confluent/docker/configure

Example Configure Script:

Location: include/etc/confluent/docker/configure

. /etc/confluent/docker/bash-config

# Ensure that URL locations are available.
dub ensure ZOOKEEPER_SERVER_CONFIG_URL
dub ensure ZOOKEEPER_SERVER_ID_URL
dub ensure ZOOKEEPER_LOG_CONFIG_URL

# Ensure that the config location is writable.
dub path /etc/kafka/ writable

curl -XGET ZOOKEEPER_SERVER_CONFIG_URL > /etc/kafka/zookeeper.properties
curl -XGET ZOOKEEPER_SERVER_ID_URL > /var/lib/zookeeper/data/myid
curl -XGET ZOOKEEPER_LOG_CONFIG_URL > /etc/kafka/log4j.properties

Build the image:

    docker build -t foo/zookeeper:latest .

Enter the command.

docker run \
     -e ZOOKEEPER_SERVER_CONFIG_URL=http://foo.com/zk1/server.properties \
     -e ZOOKEEPER_SERVER_ID_URL =http://foo.com/zk1/myid \
     -e ZOOKEEPER_LOG_CONFIG_URL =http://foo.com/zk1/log4j.properties \
     foo/zookeeper:latest

Log to external volumes

The images only expose volumes for data and security configuration. But you might want to write to external storage for some use cases. The following example shows how to write the Kafka authorizer logs to a volume for auditing.

Dockerfile:

FROM confluentinc/cp-server

# Make sure the log directory is world-writable
RUN echo "===> Creating authorizer logs dir ..." \\
     && mkdir -p /var/log/kafka-auth-logs \\
     && chmod -R ag+w /var/log/kafka-auth-logs

VOLUME \["/var/lib/$\{COMPONENT}/data", "/etc/$\{COMPONENT}/secrets", "/var/log/kafka-auth-logs"]

COPY include/etc/confluent/log4j.properties.template /etc/confluent/docker/log4j.properties.template

log4j.properties.template:

Location: include/etc/confluent/log4j.properties.template

log4j.rootLogger={{ env["KAFKA_LOG4J_ROOT_LOGLEVEL"] | default('INFO') }}, stdout

log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=[%d] %p %m (%c)%n

log4j.appender.authorizerAppender=org.apache.log4j.DailyRollingFileAppender
log4j.appender.authorizerAppender.DatePattern='.'yyyy-MM-dd-HH
log4j.appender.authorizerAppender.File=/var/log/kafka-auth-logs/kafka-authorizer.log
log4j.appender.authorizerAppender.layout=org.apache.log4j.PatternLayout
log4j.appender.authorizerAppender.layout.ConversionPattern=[%d] %p %m (%c)%n

log4j.additivity.kafka.authorizer.logger=false

{% set loggers = {
 'kafka': 'INFO',
 'kafka.network.RequestChannel$': 'WARN',
 'kafka.producer.async.DefaultEventHandler': 'DEBUG',
 'kafka.request.logger': 'WARN',
 'kafka.controller': 'TRACE',
 'kafka.log.LogCleaner': 'INFO',
 'state.change.logger': 'TRACE',
 'kafka.authorizer.logger': 'WARN, authorizerAppender'
 } -%}


{% if env['KAFKA_LOG4J_LOGGERS'] %}
{% set loggers = parse_log4j_loggers(env['KAFKA_LOG4J_LOGGERS'], loggers) %}
{% endif %}

{% for logger,loglevel in loggers.iteritems() %}
log4j.logger.\{\{logger}}=\{\{loglevel}}
{% endfor %}

Build the image.

docker build -t foo/kafka-auditable:latest .

Write garbage collection logs to an external volume

The following example shows how to log heap dumps and GC logs to an external volume. This is useful for debugging the Kafka image.

Dockerfile:

FROM confluentinc/cp-server

# Make sure the jvm log directory is world-writable
RUN echo "===> Creating jvm logs dir ..." \
     && mkdir -p /var/log/jvm-logs
     && chmod -R ag+w /var/log/jvm-logs

VOLUME ["/var/lib/${COMPONENT}/data", "/etc/${COMPONENT}/secrets", "/var/log/jvm-logs"]

  1. Build the image.

    docker build -t foo/kafka-verbose-jvm:latest .

  2. Enter the command.

    docker run \
    -e KAFKA_HEAP_OPTS="-Xmx256M -Xloggc:/var/log/jvm-logs/verbose-gc.log -verbose:gc -XX:+PrintGCDateStamps -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/jvm-logs" \
    foo/kafka-verbose-jvm:latest

Use external service discovery

You can extend the images to support for any service discovery mechanism either by overriding relevant properties or by overriding the configure script as shown in Change Configuration Management.

The Docker images provide Mesos support by overriding relevant properties for Mesos service discovery. See debian/kafka-connect/includes/etc/confluent/docker/mesos-overrides for examples.

Use the Oracle JDK

The Confluent Platform images ship with Azul Zulu OpenJDK. If you are required to use Oracle’s version, follow the steps below to modify the cp-base-new image to include Oracle JDK instead of Zulu OpenJDK.

  1. Obtain a download URL from Oracle’s website.

  2. In the base directory of the Confluent Base image repo, edit the Dockerfile.ubi8 file in the correct version branch, 7.4.7-post.

  3. Replace the following lines for Zulu OpenJDK:

    ENV ZULU_OPENJDK="zulu-11-11.990-1"

RUN microdnf install yum \
    && rpm --import https://www.azul.com/files/0xB1998361219BD9C9.txt \
    && yum -y install https://cdn.azul.com/zulu/bin/zulu-repo-1.0.0-1.noarch.rpm \
    && yum -y install ${ZULU_OPENJDK} \

    With the following lines for Oracle JDK:

    RUN microdnf install yum \
    && curl -L <URL you obtained in Step #1> --output /tmp/jre1.8-latest.rpm \
    && yum -y install /tmp/jre1.8-latest.rpm \
    && rm -f /tmp/jre1.8-latest.rpm \

  4. Rebuild the cp-base-new image. Refer to Build the Confluent Platform images for the steps.

Utility scripts

Given the dependencies between the various Confluent Platform components (e.g. ZooKeeper required for Kafka, Kafka and ZooKeeper required for Schema Registry, etc.), it is sometimes necessary to be able to check the status of different services. The following utilities are used during the bootup sequence of the images and in the testing framework.

Docker Utility Belt (dub)

  1. Template

    usage: dub template [-h] input output

Generate template from env vars.

positional arguments:
  input       Path to template file.
  output      Path of output file.

  2. ensure

    usage: dub ensure [-h] name

Check if env var exists.

positional arguments:
  name        Name of env var.

  3. wait

    usage: dub wait [-h] host port timeout

wait for network service to appear.

positional arguments:
  host        Host.
  port        Host.
  timeout     timeout in secs.

  4. path

    usage: dub path [-h] path {writable,readable,executable,exists}

Check for path permissions and existence.

positional arguments:
  path                  Full path.
  {writable,readable,executable,exists} One of [writable, readable, executable, exists].

  5. path-wait

    usage: dub path-wait [-h] path timeout

Wait for a path to exist.

positional arguments:
  path        Full path.
  timeout     Time in secs to wait for the path to exist.

optional arguments:
  -h, --help  show this help message and exit

Confluent Platform Utility Belt (cub)

  1. zk-ready

    Used for checking if ZooKeeper is ready.

    usage: cub zk-ready [-h] connect_string timeout retries wait

Check if ZK is ready.

positional arguments:
  connect_string  ZooKeeper connect string.
  timeout         Time in secs to wait for service to be ready.
  retries         No of retries to check if leader election is complete.
  wait            Time in secs between retries

  2. kafka-ready

    Used for checking if Kafka is ready.

    usage: cub kafka-ready [-h] (-b BOOTSTRAP_BROKER_LIST | -z ZOOKEEPER_CONNECT)
                 [-c CONFIG] [-s SECURITY_PROTOCOL]
                 expected_brokers timeout

Check if Kafka is ready.

positional arguments:
expected_brokers      Minimum number of brokers to wait for
timeout               Time in secs to wait for service to be ready.

optional arguments:
-h, --help                     show this help message and exit
-b BOOTSTRAP_BROKER_LIST,      --bootstrap_broker_list BOOTSTRAP_BROKER_LIST
                               List of bootstrap brokers.
-z ZOOKEEPER_CONNECT,          This option is deprecated in Confluent Platform 5.5.0 and later.
                               --zookeeper_connect ZOOKEEPER_CONNECT
                               ZooKeeper connect string.
-c CONFIG,                     --config CONFIG
                               Path to config properties file (required when security
                               is enabled).
-s SECURITY_PROTOCOL,          --security-protocol SECURITY_PROTOCOL
                               Security protocol to use when multiple listeners are
                               enabled.

  3. sr-ready

    Used for checking if Schema Registry is ready. If you have multiple Schema Registry nodes, you may need to check their availability individually.

    usage: cub sr-ready [-h] host port timeout

positional arguments:
  host     Hostname for Schema Registry.
  port     Port for Schema Registry.
  timeout  Time in secs to wait for service to be ready.

  1. kr-ready

    Used for checking if the REST Proxy is ready. If you have multiple REST Proxy nodes, you may need to check their availability individually.

    usage: cub kr-ready [-h] host port timeout

positional arguments:
  host     Hostname for REST Proxy.
  port     Port for REST Proxy.
  timeout  Time in secs to wait for service to be ready.

  2. connect-ready

    Used for checking if Kafka Connect is ready.

    usage: cub connect-ready [-h] host port timeout

positional arguments:
  host     Hostname for Connect worker.
  port     Port for Connect worker.
  timeout  Time in secs to wait for service to be ready.

  3. ksql-server-ready

    Used for checking if ksqlDB is ready.

    usage: cub ksql-server-ready [-h] host port timeout

positional arguments:
  host     Hostname for KSQL server.
  port     Port for KSQL server.
  timeout  Time in secs to wait for service to be ready.

  4. control-center-ready

    Used for checking if Confluent Control Center is ready.

    usage: cub control-center-ready [-h] host port timeout

positional arguments:
  host     Hostname for Control Center.
  port     Port for Control Center.
  timeout  Time in secs to wait for service to be ready.

Client properties

The following properties may be configured when using the kafka-ready utility described above.

bootstrap.servers

A list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping - this list only impacts the initial hosts used to discover the full set of servers. This list should be in the form host1:port1,host2:port2,.... Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down).

  • Type: list
  • Default:
  • Importance: high
ssl.key.password

The password of the private key in the key store file. This is optional for client.

  • Type: password
  • Importance: high
ssl.keystore.location

The location of the key store file. This is optional for client and can be used for two-way authentication for client.

  • Type: string
  • Importance: high
ssl.keystore.password

The store password for the key store file.This is optional for client and only needed if ssl.keystore.location is configured.

  • Type: password
  • Importance: high
ssl.truststore.location

The location of the trust store file.

  • Type: string
  • Importance: high
ssl.truststore.password

The password for the trust store file.

  • Type: password
  • Importance: high
sasl.kerberos.service.name

The Kerberos principal name that Kafka runs as. This can be defined either in Kafka’s JAAS config or in Kafka’s config.

  • Type: string
  • Importance: medium
sasl.mechanism

SASL mechanism used for client connections. This may be any mechanism for which a security provider is available. GSSAPI is the default mechanism.

  • Type: string
  • Default: “GSSAPI”
  • Importance: medium
security.protocol

Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.

  • Type: string
  • Default: “PLAINTEXT”
  • Importance: medium
ssl.enabled.protocols

The list of protocols enabled for SSL connections.

  • Type: list
  • Default: [TLSv1.2, TLSv1.1, TLSv1]
  • Importance: medium
ssl.keystore.type

The file format of the key store file. This is optional for client.

  • Type: string
  • Default: “JKS”
  • Importance: medium
ssl.protocol

The SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.

  • Type: string
  • Default: “TLS”
  • Importance: medium
ssl.provider

The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.

  • Type: string
  • Importance: medium
ssl.truststore.type

The file format of the trust store file.

  • Type: string
  • Default: “JKS”
  • Importance: medium
sasl.kerberos.kinit.cmd

Kerberos kinit command path.

  • Type: string
  • Default: “/usr/bin/kinit”
  • Importance: low
sasl.kerberos.min.time.before.relogin

Login thread sleep time between refresh attempts.

  • Type: long
  • Default: 60000
  • Importance: low
sasl.kerberos.ticket.renew.jitter

Percentage of random jitter added to the renewal time.

  • Type: double
  • Default: 0.05
  • Importance: low
sasl.kerberos.ticket.renew.window.factor

Login thread will sleep until the specified window factor of time from last refresh to ticket’s expiry has been reached, at which time it will try to renew the ticket.

  • Type: double
  • Default: 0.8
  • Importance: low
ssl.cipher.suites

A list of cipher suites. This is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol.By default all the available cipher suites are supported.

  • Type: list
  • Importance: low
ssl.endpoint.identification.algorithm

The endpoint identification algorithm to validate server hostname using server certificate.

  • Type: string
  • Importance: low
ssl.keymanager.algorithm

The algorithm used by key manager factory for SSL connections. Default value is the key manager factory algorithm configured for the Java Virtual Machine.

  • Type: string
  • Default: “SunX509”
  • Importance: low
ssl.trustmanager.algorithm

The algorithm used by trust manager factory for SSL connections. Default value is the trust manager factory algorithm configured for the Java Virtual Machine.

  • Type: string
  • Default: “PKIX”
  • Importance: low

Reference

See this document for an example for setting up a Dockerized AWS EC2 instance.