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Automatic Data Balancing on Docker

This tutorial runs Confluent Auto Data Balancing (ADB) on Kafka, which allows you to shift data to create an even workload across your cluster. By the end of this tutorial, you will have successfully run Confluent Auto Data Balancing CLI to rebalance data after adding and removing brokers.

Note

In this tutorial, Kafka and ZooKeeper are configured to store data locally in the Docker containers. For production deployments (or generally whenever you care about not losing data), you should use mounted volumes for persisting data in the event that a container stops running or is restarted. This is important when running a system like Kafka on Docker, as it relies heavily on the filesystem for storing and caching messages. For an example of how to add mounted volumes to the host machine, see the documentation on Docker external volumes.

Installing and Running Docker

For this tutorial, Docker is run using the Docker client.

To get started, you’ll need to first install Docker and get it running. The Confluent Platform Docker Images require Docker version 1.11 or greater.

Docker Client: Setting Up a Three Node Kafka Cluster

If you’re running on Windows or Mac OS X, you’ll need to use Docker Machine to start the Docker host. Docker runs natively on Linux, so the Docker host will be your local machine if you go that route. If you are running on Mac or Windows, be sure to allocate at least 4 GB of ram to the Docker Machine.

Now that you have all of the Docker dependencies installed, you can create a Docker machine and begin starting up Confluent Platform.

Note

In the following steps, each Docker container is run in detached mode and you are shown how to access to the logs for a running container. You can also run the containers in the foreground by replacing the -d flags with -it.

  1. Create and configure the Docker machine.

    docker-machine create --driver virtualbox --virtualbox-memory 6000 confluent

    Next, configure your terminal window to attach it to your new Docker Machine:

    eval $(docker-machine env confluent)

  2. Clone the git repository:

    git clone https://github.com/confluentinc/cp-docker-images
cd cp-docker-images

  3. Start the services by using the example Docker Compose file. Navigate to cp-docker-images/examples/enterprise-kafka, where it is located:

    cd cp-docker-images/examples/enterprise-kafka

  4. Start up the services. The Docker Compose file has configuration for one ZooKeeper and 6 Kafka brokers. These brokers are configured to be on 2 racks. We will first start one rack (with 3 brokers) and create a topic with sample data and run the ADB CLI tool to balance the cluster. After this step, we will walk you through a tutorial for adding another rack of brokers and running the ADB CLI tool to rebalance the data across the new added brokers.

    Start ZooKeeper and first rack of brokers using the Docker Compose commands.

    docker-compose create

    You should see the following

    Creating enterprisekafka_zookeeper_1
Creating enterprisekafka_kafka-6_1
Creating enterprisekafka_kafka-4_1
Creating enterprisekafka_kafka-5_1
Creating enterprisekafka_kafka-2_1
Creating enterprisekafka_kafka-3_1
Creating enterprisekafka_kafka-1_1

    Start the services

    docker-compose start zookeeper kafka-1 kafka-2 kafka-3

    You should see the following

    Starting zookeeper ... done
Starting kafka-2 ... done
Starting kafka-3 ... done
Starting kafka-1 ... done

    Make sure the services are up and running:

    docker-compose ps

    You should see the following:

      Name                        Command            State    Ports
------------------------------------------------------------------------
enterprisekafka_kafka-1_1     /etc/confluent/docker/run   Up
enterprisekafka_kafka-2_1     /etc/confluent/docker/run   Up
enterprisekafka_kafka-3_1     /etc/confluent/docker/run   Up
enterprisekafka_kafka-4_1     /etc/confluent/docker/run   Exit 0
enterprisekafka_kafka-5_1     /etc/confluent/docker/run   Exit 0
enterprisekafka_kafka-6_1     /etc/confluent/docker/run   Exit 0
enterprisekafka_zookeeper_1   /etc/confluent/docker/run   Up

    Now check the ZooKeeper logs to verify that ZooKeeper is healthy.

    docker-compose logs zookeeper | grep -i binding

    You should see the following in your terminal window:

    zookeeper_1  | [2016-10-21 22:15:22,494] INFO binding to port 0.0.0.0/0.0.0.0:22181 (org.apache.zookeeper.server.NIOServerCnxnFactory)

    Next, check the Kafka logs for the destination cluster to verify that broker is healthy.

    docker-compose logs kafka-1 | grep -i started

    You should see message a message that looks like the following:

    kafka-1_1    | [2016-10-21 22:19:50,964] INFO [Socket Server on Broker 1], Started 1 acceptor threads (kafka.network.SocketServer)
kafka-1_1    | [2016-10-21 22:19:51,300] INFO [Kafka Server 1], started (kafka.server.KafkaServer)
....

  5. Now that the brokers are up, we will create a test topic called “adb-test”.

    docker run \
  --net=host \
  --rm confluentinc/cp-kafka:5.1.4 \
  kafka-topics --create --topic adb-test --partitions 20 --replication-factor 3 --if-not-exists --zookeeper localhost:22181

    You should see the following output in your terminal window:

    Created topic "adb-test".

    Before moving on, verify that the topic was created successfully:

    docker run \
  --net=host \
  --rm confluentinc/cp-kafka:5.1.4 \
  kafka-topics --describe --topic adb-test --zookeeper localhost:22181

    You should see the following output in your terminal window:

    Topic:adb-test      PartitionCount:20       ReplicationFactor:3     Configs:
Topic: adb-test     Partition: 0    Leader: 2       Replicas: 2,1,3 Isr: 2,1,3
Topic: adb-test     Partition: 1    Leader: 3       Replicas: 3,2,1 Isr: 3,2,1
Topic: adb-test     Partition: 2    Leader: 1       Replicas: 1,3,2 Isr: 1,3,2
Topic: adb-test     Partition: 3    Leader: 2       Replicas: 2,3,1 Isr: 2,3,1
Topic: adb-test     Partition: 4    Leader: 3       Replicas: 3,1,2 Isr: 3,1,2
Topic: adb-test     Partition: 5    Leader: 1       Replicas: 1,2,3 Isr: 1,2,3
Topic: adb-test     Partition: 6    Leader: 2       Replicas: 2,1,3 Isr: 2,1,3
Topic: adb-test     Partition: 7    Leader: 3       Replicas: 3,2,1 Isr: 3,2,1
Topic: adb-test     Partition: 8    Leader: 1       Replicas: 1,3,2 Isr: 1,3,2
Topic: adb-test     Partition: 9    Leader: 2       Replicas: 2,3,1 Isr: 2,3,1
Topic: adb-test     Partition: 10   Leader: 3       Replicas: 3,1,2 Isr: 3,1,2
Topic: adb-test     Partition: 11   Leader: 1       Replicas: 1,2,3 Isr: 1,2,3
Topic: adb-test     Partition: 12   Leader: 2       Replicas: 2,1,3 Isr: 2,1,3
Topic: adb-test     Partition: 13   Leader: 3       Replicas: 3,2,1 Isr: 3,2,1
Topic: adb-test     Partition: 14   Leader: 1       Replicas: 1,3,2 Isr: 1,3,2
Topic: adb-test     Partition: 15   Leader: 2       Replicas: 2,3,1 Isr: 2,3,1
Topic: adb-test     Partition: 16   Leader: 3       Replicas: 3,1,2 Isr: 3,1,2
Topic: adb-test     Partition: 17   Leader: 1       Replicas: 1,2,3 Isr: 1,2,3
Topic: adb-test     Partition: 18   Leader: 2       Replicas: 2,1,3 Isr: 2,1,3
Topic: adb-test     Partition: 19   Leader: 3       Replicas: 3,2,1 Isr: 3,2,1

  6. Next, we’ll try generating some data to our new topic:

    docker run \
  --net=host \
  --rm \
  confluentinc/cp-kafka:5.1.4 \
  bash -c 'kafka-producer-perf-test --topic adb-test --num-records 2000000 --record-size 1000 --throughput 100000 --producer-props bootstrap.servers=localhost:19092'

    This command will use the built-in Kafka Performance Producer to produce 2 GB of sample data to the topic. Upon running it, you should see the following:

    209047 records sent, 41784.3 records/sec (39.85 MB/sec), 91.1 ms avg latency, 520.0 max latency.
325504 records sent, 65100.8 records/sec (62.08 MB/sec), 35.6 ms avg latency, 474.0 max latency.
258023 records sent, 51573.7 records/sec (49.18 MB/sec), 359.6 ms avg latency, 1264.0 max latency.
287934 records sent, 57586.8 records/sec (54.92 MB/sec), 455.1 ms avg latency, 1429.0 max latency.
413091 records sent, 81978.8 records/sec (78.18 MB/sec), 200.6 ms avg latency, 757.0 max latency.
282214 records sent, 56128.5 records/sec (53.53 MB/sec), 495.6 ms avg latency, 1738.0 max latency.
85071 records sent, 16815.8 records/sec (16.04 MB/sec), 468.0 ms avg latency, 3861.0 max latency.
115 records sent, 8.8 records/sec (0.01 MB/sec), 8307.4 ms avg latency, 13127.0 max latency.
13358 records sent, 2671.6 records/sec (2.55 MB/sec), 15408.9 ms avg latency, 23005.0 max latency.
74948 records sent, 14284.0 records/sec (13.62 MB/sec), 6555.0 ms avg latency, 22782.0 max latency.
5052 records sent, 1010.4 records/sec (0.96 MB/sec), 3228.3 ms avg latency, 8508.0 max latency.
2000000 records sent, 30452.988199 records/sec (29.04 MB/sec), 786.61 ms avg latency, 23005.00 ms max latency, 82 ms 50th, 1535 ms 95th, 22539 ms 99th, 22929 ms 99.9th.

  7. Run confluent-rebalancer to balance the data in the cluster.

    docker run \
  --net=host \
  --rm \
  confluentinc/cp-enterprise-kafka:5.1.4 \
  bash -c "confluent-rebalancer execute --zookeeper localhost:22181 --metrics-bootstrap-server localhost:19092 --throttle 100000000 --force --verbose"

    You should see the rebalancing start and should see the following:

    You are about to move 6 replica(s) for 6 partitions to 1 broker(s) with total size 0.9 MB.
The preferred leader for 6 partition(s) will be changed.
In total, the assignment for 7 partitions will be changed.

The following brokers will require more disk space during the rebalance and, in some cases, after the rebalance:
    Broker     Current (MB)    During Rebalance (MB)  After Rebalance (MB)
    2          2,212.8         2,213.8                2,213.8

Min/max stats for brokers (before -> after):
      Type  Leader Count                 Replica Count                Size (MB)
      Min   8 (id: 2) -> 10 (id: 1)      21 (id: 2) -> 27 (id: 1)     2,069.6 (id: 1) -> 2,069.1 (id: 1)
      Max   12 (id: 3) -> 11 (id: 2)     30 (id: 1) -> 27 (id: 1)     2,212.8 (id: 2) -> 2,213.8 (id: 2)

Rack stats (before -> after):
      Rack       Leader Count    Replica Count   Size (MB)
      rack-a     31 -> 31        81 -> 81        6,352 -> 6,352

Broker stats (before -> after):
      Broker     Leader Count    Replica Count   Size (MB)
      1          11 -> 10        30 -> 27        2,069.6 -> 2,069.1
      2          8 -> 11         21 -> 27        2,212.8 -> 2,213.8
      3          12 -> 10        30 -> 27        2,069.6 -> 2,069.1

The rebalance has been started, run `status` to check progress.

Warning: You must run the `status` or `finish` command periodically, until the rebalance completes, to ensure the throttle is removed. You can also alter the throttle by re-running the execute command passing a new value.

    You can check the status of the rebalance operation by running the following command:

    docker run \
  --net=host \
  --rm \
  confluentinc/cp-enterprise-kafka:5.1.4 \
  bash -c "confluent-rebalancer status --zookeeper localhost:22181"

    If you see the a message like 7 partitions are being rebalanced, wait for 15-20 seconds and rerun the above command until you see No rebalance is currently in progress. This means that the rebalance action has completed successfully.

    You can finish the rebalance action by running the following command (this command ensures that the replication throttle is removed):

    docker run \
  --net=host \
  --rm \
  confluentinc/cp-enterprise-kafka:5.1.4 \
  bash -c "confluent-rebalancer finish --zookeeper localhost:22181"

    You should see the following in the logs:

    The rebalance has completed and throttling has been disabled

  8. ADB makes it easy to add new brokers to the cluster. You can now add an entire new rack to your cluster and run the rebalance operation again to balance the data across the cluster.

    Start the new rack by running the following command:

    docker-compose start kafka-4 kafka-5 kafka-6

    You should follow the instructions in step 4 to verify the Kafka brokers are healthy.

    Now start the rebalance operation by following step #. After the rebalance operation has finished, data should be balanced across the cluster. We will verify that by describing the topic metadata as follows.

    docker run \
  --net=host \
  --rm confluentinc/cp-kafka:5.1.4 \
  kafka-topics --describe --topic adb-test --zookeeper localhost:22181

    You should see that partitions are spread across all of the brokers (i.e you should see some replicas and leaders assigned to brokers 4, 5, or 6).

    Topic:adb-test      PartitionCount:20       ReplicationFactor:3     Configs:
Topic: adb-test     Partition: 0    Leader: 1       Replicas: 1,5,6 Isr: 5,1,6
Topic: adb-test     Partition: 1    Leader: 3       Replicas: 3,5,4 Isr: 5,3,4
Topic: adb-test     Partition: 2    Leader: 6       Replicas: 6,4,1 Isr: 1,6,4
Topic: adb-test     Partition: 3    Leader: 6       Replicas: 6,5,3 Isr: 5,6,3
Topic: adb-test     Partition: 4    Leader: 1       Replicas: 1,4,5 Isr: 5,1,4
Topic: adb-test     Partition: 5    Leader: 3       Replicas: 6,4,3 Isr: 6,3,4
Topic: adb-test     Partition: 6    Leader: 1       Replicas: 5,1,6 Isr: 5,1,6
Topic: adb-test     Partition: 7    Leader: 3       Replicas: 3,5,4 Isr: 5,3,4
Topic: adb-test     Partition: 8    Leader: 4       Replicas: 4,6,1 Isr: 1,6,4
Topic: adb-test     Partition: 9    Leader: 5       Replicas: 5,6,3 Isr: 5,6,3
Topic: adb-test     Partition: 10   Leader: 2       Replicas: 2,4,5 Isr: 5,2,4
Topic: adb-test     Partition: 11   Leader: 4       Replicas: 4,2,6 Isr: 6,2,4
Topic: adb-test     Partition: 12   Leader: 5       Replicas: 5,2,6 Isr: 5,6,2
Topic: adb-test     Partition: 13   Leader: 2       Replicas: 2,5,4 Isr: 5,2,4
Topic: adb-test     Partition: 14   Leader: 4       Replicas: 4,6,2 Isr: 6,2,4
Topic: adb-test     Partition: 15   Leader: 1       Replicas: 1,3,2 Isr: 1,2,3
Topic: adb-test     Partition: 16   Leader: 2       Replicas: 3,2,1 Isr: 2,1,3
Topic: adb-test     Partition: 17   Leader: 3       Replicas: 3,2,1 Isr: 3,2,1
Topic: adb-test     Partition: 18   Leader: 1       Replicas: 1,2,3 Isr: 1,2,3
Topic: adb-test     Partition: 19   Leader: 2       Replicas: 2,3,1 Isr: 2,3,1

  9. Now you can try removing a broker and running the rebalance operation again.

    Hint: You must notify the rebalancer to exclude broker from the rebalance plan. For example, to remove broker 1 you must run the following command:

    docker run \
  --net=host \
  --rm \
  confluentinc/cp-enterprise-kafka:5.1.4 \
  bash -c "confluent-rebalancer execute --zookeeper localhost:22181 --metrics-bootstrap-server localhost:19092 --throttle 100000000 --force --verbose --remove-broker-ids 1"

  10. Feel free to experiment with the confluent-rebalance command on your own now. When you are done, use the following commands to shutdown all the components.

    docker-compose stop

    If you want to remove all the containers, run:

    docker-compose rm