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.
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
- Replicate data between Kafka clusters;
- Aggregate record from several topics and put them into one;
- Extend bandwidth for exist topic via repartitioning strategy.
libsasl2-dev
libssl-dev
If you have the Rust toolchain already installed on your local system.
rustup update stable
cargo install kafka-replicator
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
RUST_LOG=info kafka-replicator /path/to/config.yml
sudo docker run -it -v /replication/:/replication/ -e RUST_LOG=info lispython/kafka_replicator:latest kafka-replicator /replication/config.yml
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
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
Any suggestion, feedback or contributing is highly appreciated. Thank you for your support!