Kafka replicator

Kafka Replicator is an easy to use tool for copying data between two Apache Kafka clusters with configurable re-partitionning strategy.

Data will be read from topics in the origin cluster and written to a topic/topics in the destination cluster according config rules.

Features

Lets start with an overview of features that exist in kafka-replicator:

• Data replication: Real-time event streaming between Kafka clusters and data centers;
• Schema replication: Copy schema from source cluster to destination;
• Flexible topic selection: Select topics with configurable config;
• Auto-create topics: Destination topics are automatically created for strict_p2p strategy;
• Stats: The tool shows replication status;
• Monitoring: Kafka replicator exports stats via prometheus.
• Cycle detection

Use cases

• Replicate data between Kafka clusters;
• Aggregate record from several topics and put them into one;
• Extend bandwidth for exist topic via repartitioning strategy.

Installation

System dependencies

libsasl2-dev
libssl-dev

Install from crates.io

If you have the Rust toolchain already installed on your local system.

rustup update stable
cargo install kafka-replicator

Compile and run it from sources

Clone the repository and change it to your working directory.

git clone https://github.com/lispython/kafka-replicator.git
cd kafka-replicator
rustup override set stable
rustup update stable
cargo install

Usage

RUST_LOG=info kafka-replicator /path/to/config.yml

Run it using Docker

sudo docker run -it -v /replication/:/replication/ -e RUST_LOG=info lispython/kafka_replicator:latest kafka-replicator /replication/config.yml

Example config

clusters:
  - name: cluster_1
    hosts:
      - replicator-kafka-1:9092
      - replicator-kafka-1:9092
  - name: cluster_2
    hosts:
      - replicator-kafka-2:9092
clients:
  - client: cl_1_client_1
    cluster: cluster_1
    config: # optional
       message.timeout.ms: 5000
       auto.offset.reset: earliest
  - client: cl_2_client_1
    cluster: cluster_2
routes:
  - upstream_client: cl_1_client_1
    downstream_client: cl_1_client_1
    upstream_topics:
      - 'topic1'
    downstream_topic: 'topic2'
    repartitioning_strategy: random # strict_p2p | random
    upstream_group_id: group_22
    show_progress_interval_secs: 10
    limits:
      messages_per_sec: 10000
      number_of_messages:
  - upstream_client: cl_1_client_1
    downstream_client: cl_2_client_1
    upstream_topics:
      - 'topic2'
    downstream_topic: 'topic2'
    repartitioning_strategy: strict_p2p
    upstream_group_id: group_22
    show_progress_interval_secs: 10
  - upstream_client: cl_2_client_1
    downstream_client: cl_1_client_1
    upstream_topics:
      - 'topic2'
    downstream_topic: 'topic3'
    repartitioning_strategy: strict_p2p # strict_p2p | random
    default_begin_offset: earliest # optional
    upstream_group_id: group_2
    show_progress_interval_secs: 10
observers:
  - client: cl_1_client_1
    name: "my name"
    group_id: group_name # used for remaining metrics
    topics: # filter by topics
      - 'topic1'
      - 'topic2'
    fetch_timeout_secs: 5 # default: 5
    fetch_interval_secs: 5 # default: 60
    show_progress_interval_secs: 10 # default: 60
  - client: cl_2_client_1
    topic: 'topic3'
    topics:
      - 'topic2'
    show_progress_interval_secs: 5
  - client: cl_1_client_1
    topic: 'topic1'
    topics: [] # fetch all topics

Options describing

Root config options:

• clusters - are a list of Kafka Clusters
• clients - are a list of configurations for consumers
• routes - are a list of replication rules
• observers - are a list of observers

Contributing

Any suggestion, feedback or contributing is highly appreciated. Thank you for your support!