Replicator for Multi-Datacenter Replication

Confluent Replicator can be deployed across clusters and in multiple datacenters. Multi-datacenter deployments enable use-cases such as:

  • Active-active geo-localized deployments: allows users to access a near-by data center to optimize their architecture for low latency and high performance
  • Active-passive disaster recover (DR) deployments: in an event of a partial or complete datacenter disaster, allow failing over applications to use Confluent Platform in a different datacenter.
  • Centralized analytics: Aggregate data from multiple Apache Kafka® clusters into one location for organization-wide analytics
  • Cloud migration: Use Kafka to synchronize data between on-prem applications and cloud deployments

Replication of events in Kafka topics from one cluster to another is the foundation of Confluent’s multi datacenter architecture.

Replication can be done with Confluent Replicator or using the open source Kafka MirrorMaker. Confluent Replicator can be used for replication of topic data as well as migrating schemas in Schema Registry.

This documentation focuses on Confluent Replicator, including architecture, quick start tutorial, how to configure and run Replicator in different contexts, tuning and monitoring, cross-cluster failover, and more. A section on how to migrate from MirrorMaker to Replicator is also included.

Some of the general thinking on deployment strategies can also apply to MirrorMaker, but if you are primarily interested in MirrorMaker, see Mirroring data between clusters in the Kafka documentation.

Replicator

Replicator allows you to easily and reliably replicate topics from one Kafka cluster to another. In addition to copying the messages, Replicator will create topics as needed preserving the topic configuration in the source cluster. This includes preserving the number of partitions, the replication factor, and any configuration overrides specified for individual topics.

Architecture

The diagram below shows the Replicator architecture. Replicator uses the Kafka Connect APIs and Workers to provide high availability, load-balancing and centralized management.

../../_images/replicator_components.png

Replicator Architecture

Tip

You can deploy Replicator near the destination cluster or the origin cluster, and it will work either way. However, a best practice is to deploy Replicator closer to the destination cluster for reliability and performance over networks. Therefore, if the destination cluster is Confluent Cloud, we recommend that you deploy Replicator on an instance in the same region as your Confluent Cloud cluster. However, if the origin cluster does not permit external connections, you may deploy Replicator in the origin cluster. (See also Migrate Topics on Confluent Cloud Clusters.)

Example Deployment

In a typical multi-datacenter deployment, data from two geographically distributed Kafka clusters located in separate datacenters is aggregated in a separate cluster located in another datacenter. The origin of the copied data is referred to as the “source” cluster while the target of the copied data is referred to as the “destination.”

Each source cluster requires a separate instance of Replicator. For convenience you can run them in the same Connect cluster, located in the aggregate datacenter.

../../_images/replicator.png

Replication to an Aggregate Cluster

Guidelines for Getting Started

Follow these guidelines to configure a multi-datacenter deployment using Replicator:

  1. Use the Replicator quick start to set up replication between two Kafka clusters.
  2. Learn how to install and configure Replicator and other Confluent Platform components in multi datacenter environments.
  3. Before running Replicator in production, make sure you read the monitoring and tuning guide.
  4. For a practical guide to designing and configuring multiple Kafka clusters to be resilient in case of a disaster scenario, see the Disaster Recovery white paper. This white paper provides a plan for failover, failback, and ultimately successful recovery.

Demos and Examples

After completing the Replicator quick start, explore these hands-on working examples of Replicator in multi-datacenter deployments, for which you can download the demo from GitHub and run yourself. Refer to the diagram below to determine the Replicator examples that correspond to your deployment scenario.

../../_images/replicator-demos.png
  1. Kafka on-premises to Kafka on-premises
    • Replicator Demo on Docker: fully-automated example of an active-active multi-datacenter design with two instances of Replicator copying data bidirectionally between the datacenters
    • Schema translation: showcases the transfer of schemas stored in Schema Registry from one cluster to another using Replicator
    • Confluent Platform demo: deploy a Kafka streaming ETL, along with Replicator to replicate data
  2. Kafka on-premises to Confluent Cloud
  3. Kafka in GKE to Confluent Cloud
  4. Confluent Cloud to Confluent Cloud

Topic Renaming

By default, the replicator is configured to use the same topic name in both the source and destination clusters. This works fine if you are only replicating from a single cluster. When copying data from multiple clusters to a single destination (i.e. the aggregate use case), you should use a separate topic for each source cluster in case there are any configuration differences between the topics in the source clusters.

It is possible to use the same Kafka cluster as the source and destination as long as you ensure that the replicated topic name is different. This is not a recommended pattern since generally you should prefer Kafka’s built-in replication within the same cluster, but it may be useful in some cases (e.g. testing).

Starting with Confluent Platform 5.0, Replicator protects against circular replication through the use of provenance headers. This guarantees that if two Replicator instances are configured to run, one replicating from DC1 to DC2 and the second instance configured to replicate from DC2 to DC1, Replicator will ensure that messages replicated to DC2 are not replicated back to DC1, and vice versa. As a result, Replicator safely runs in each direction.

Although Replicator can enable applications in different datacenters to access topics with the same names, you should design client applications with a topic naming strategy that takes into consideration a number of factors.

If you plan to have the same topic name span datacenters, be aware that in this configuration:

  • Producers do not wait for commit acknowledgment from the remote cluster, and Replicator asynchronously copies the data between datacenters after it has been committed locally.
  • If there are producers in each datacenter writing to topics of the same name, there is no “global ordering”. This means there are no message ordering guarantees for data that originated from producers in different datacenters.
  • If there are consumer groups in each datacenter with the same group ID reading from topics of the same name, in steady state, they will be reprocessing the same messages in each datacenter.

In some cases, you may not want to use the same topic name in each datacenter. For example, in cases where:

  • Replicator is running a version less than 5.0.1
  • Kafka brokers are running a version prior to Kafka 0.11 that does not yet support message headers
  • Kafka brokers are running Kafka version 0.11 or later but have less than the minimum required log.message.format.version=2.0 for using headers
  • Client applications are not designed to handle topics with the same name across datacenters

In these cases, refer to the appendix on “Topic Naming Strategies to Prevent Cyclic Repetition” in the Disaster Recovery white paper.

Periodic Metadata Updates

The replicator periodically checks topics in the source cluster to tell whether there are any new topics which need to be replicated, and whether there are any configuration changes (e.g. increases in the number of partitions). The frequency of this checking is controlled with the metadata.max.age.ms setting in the connector configuration. The default is set to 2 minutes, which is intended to provide reasonable responsiveness to configuration changes while ensuring that the connector does not add any unnecessary load on the source cluster. You can lower this setting to detect changes quicker, but it’s probably not advisable as long as topic creation/reconfiguration is relatively rare (as is most common).

Security

Important

A ZooKeeper quorum enabled with TLS is not supported with Replicator. To run Replicator with a TLS-enabled ZooKeeper, remove any ZooKeeper-related connection details for your Replicator.

Replicator supports communication with secure Kafka over SSL for both the source and destination clusters. Replicator also supports SSL or SASL for authentication. Differing security configurations can be used on the source and destination clusters.

All properties documented here are additive (i.e. you can apply both SSL Encryption and SASL Plain authentication properties) except for security.protocol. The following table can be used to determine the correct value for this:

Encryption Authentication security.protocol
SSL None SSL
SSL SSL SSL
SSL SASL SASL_SSL
Plaintext SASL SASL_PLAINTEXT

You can configure Replicator connections to source and destination Kafka with:

You can configure ZooKeeper by passing the name of its JAAS file as a JVM parameter when starting:

export KAFKA_OPTS="-Djava.security.auth.login.config=etc/kafka/zookeeper_jaas.conf"
bin/zookeeper-server-start etc/kafka/zookeeper.properties

Important

The source and destination ZooKeeper must be secured with the same credentials.

To configure security on the source cluster, see the connector configurations for Source Kafka: Security. To configure security on the destination cluster, see the connector configurations here and the general security configuration for Connect workers here.

See also

To see the required security configuration parameters for Replicator consolidated in one place, try out the docker-compose environments in GitHub confluentinc/examples.

When using SASL or SSL authentication and ACL is enabled on source or destination or both, Replicator requires the following ACLs:

For license management:

Cluster Resource Operation
Destination (or other cluster configured with confluent.topic.bootstrap.servers) TOPIC - _confluent-command All

Commands to execute to configure the above ACLs:

kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf --add --allow-principal User:<destination principal> --operation ALL --topic _confluent-command

To read from the source cluster:

Cluster Resource Operation
Source CLUSTER Describe
Source TOPIC - all topics Replicator will replicate Describe
Source TOPIC - all topics Replicator will replicate Read

Commands to execute to configure the above ACLs:

kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf --add --allow-principal User:<source principal> --operation DESCRIBE --cluster
kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf --add --allow-principal User:<source principal> --operation DESCRIBE --topic <source topic>
kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf --add --allow-principal User:<source principal> --operation READ --topic <source topic>

To write to the destination cluster:

Cluster Resource Operation
Destination CLUSTER Describe
Destination TOPIC - all topics Replicator will replicate Describe
Destination TOPIC - all topics Replicator will replicate Write
Destination TOPIC - all topics Replicator will replicate Read

Commands to execute to configure the above ACLs:

kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf --add --allow-principal User:<destination principal> --operation DESCRIBE --cluster
kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf --add --allow-principal User:<destination principal> --operation DESCRIBE --topic <destination topic>
kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf --add --allow-principal User:<destination principal> --operation WRITE --topic <destination topic>

If using the topic creation and config sync features of Replicator (enabled by default):

Cluster Resource Operation
Source TOPIC - all topics Replicator will replicate DescribeConfigs
Destination TOPIC - all topics Replicator will replicate Create
Destination TOPIC - all topics Replicator will replicate DescribeConfigs
Destination TOPIC - all topics Replicator will replicate AlterConfigs

for configuration options relating to topic creation and config sync see Destination Topics.

Commands to execute to configure the above ACLs:

kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf --add --allow-principal User:<source principal> --operation DESCRIBECONFIGS --topic <source topic>
kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf --add --allow-principal User:<destination principal> --operation DESCRIBECONFIGS --topic <destination topic>
kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf --add --allow-principal User:<destination principal> --operation CREATE --cluster

If using the offset translation feature of Replicator (enabled by default):

Cluster Resource Operation
Source TOPIC - __consumer_timestamps All
Destination GROUP - All consumer groups that will be translated All

for configuration options relating to offset translation see Consumer Offset Translation.

Commands to execute to configure the above ACLs:

kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf --add --allow-principal User:<source principal> --operation ALL --topic {__consumer_timestamps}

Important

Any clients instrumented with the Replicator timestamp interceptor must also have the following ACLs provided:

Cluster Resource Operation
Source TOPIC - __consumer_timestamps Write
Source TOPIC - __consumer_timestamps Describe

Commands to execute to configure the above ACLs:

kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf --add --allow-principal User:<source principal> --operation WRITE --topic __consumer_timestamps
kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf --add --allow-principal User:<source principal> --operation DESCRIBE --topic __consumer_timestamps

If using the source offset management feature of Replicator (enabled by default):

Cluster Resource Operation
Source GROUP - The consumer group name is determined by the Replicator name or by the src.consumer.group.id property All

For configuration options relating to offset management see Offset Management.

Commands to execute to configure the above ACLs:

kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf --add --allow-principal User:<source principal> --operation ALL --group <group name>

For more information on configuring ACLs, see Authorization using ACLs.

Replicating messages with schemas

Important

This section covers replication of schemas between 2 active Schema Registry instances. If there is not a requirement for the destination Schema Registry instance to be active, then it’s best to follow the process described in Migrate Schemas (Confluent Cloud and self-managed) instead of this one.

When you replicate messages that have schemas associated with them, you must replicate not only the message data but also the associated schemas. Replicator enables the replication of schemas by utilising Connect converters to fetch the schema associated with the replicated message and register it on a destination Schema Registry during replication.

First, Replicator must fetch the schema for incoming messages. This is achieved using the src.key.converter and src.value.converter connector configurations. These should be configured to access the Schema Registry instance associated with the source cluster:

src.key.converter=io.confluent.connect.avro.AvroConverter
src.key.converter.schema.registry.url=http://source:8081
src.value.converter=io.confluent.connect.avro.AvroConverter
src.value.converter.schema.registry.url=http://source:8081

The schema must then be registered in the destination Schema Registry. This is achieved with the key.converter and value.converter connector configurations. These should be configured to access the Schema Registry instance associated with the destination cluster:

key.converter=io.confluent.connect.avro.AvroConverter
key.converter.schema.registry.url=http://destination:8081
value.converter=io.confluent.connect.avro.AvroConverter
value.converter.schema.registry.url=http://destination:8081

for more information on converters see Converters

Important

In this approach, schemas are registered in the order seen by Replicator and not necessarily the order they were registered in the source Schema Registry. This can cause compatibility problems and we recommend that destination subjects are placed into a mode of FULL or NONE to tolerate ordering differences.

Requirements

From a high level, Replicator works like a consumer group with the partitions of the replicated topics from the source cluster divided between the connector’s tasks. Replicator periodically polls the source cluster for changes to the configuration of replicated topics and the number of partitions, and updates the destination cluster accordingly by creating topics or updating configuration. For this to work correctly, the following is required:

  • The Origin and destination clusters must be Apache Kafka® or Confluent Platform. For version compatibility see connector interoperability
  • The Replicator version must match the Kafka Connect version it is deployed on. For instance Replicator 6.0 should only be deployed to Kafka Connect 6.0.
  • The Replicator principal must have permission to create and modify topics in the destination cluster. In version 4.0 or lower this requires write access to the corresponding ZooKeeper. In later versions this requires the Acls mentioned in here
  • The default topic configurations in the source and destination clusters must match. In general, aside from any broker-specific settings (such as broker.id), you should use the same broker configuration in both clusters.
  • The destination Kafka cluster must have a similar capacity as the source cluster. In particular, since Replicator will preserve the replication factor of topics in the source cluster, which means that there must be at least as many brokers as the maximum replication factor used. If not, topic creation will fail until the destination cluster has the capacity to support the same replication factor. Note in this case, that topic creation will be retried automatically by the connector, so replication will begin as soon as the destination cluster has enough brokers.
  • The dest.kafka.bootstrap.servers destination connection setting in the Replicator properties file must be configured to use a single destination cluster, even when using multiple source clusters. For example, the figure shown at the start of this section shows two source clusters in different datacenters targeting a single aggregate destination cluster. Note that the aggregate destination cluster must have a similar capacity as the total of all associated source clusters.
  • On Confluent Platform versions 5.3.0 and later, Confluent Replicator requires the enterprise edition of Kafka Connect. Starting with Confluent Platform 5.3.0, Replicator does not support the community edition of Connect. You can install the enterprise edition of Connect as part of the Confluent Platform on-premises bundle, as described in Production Environments and in the Quick Start for Apache Kafka using Confluent Platform (Local) (choose Self-managed Confluent Platform). Demos of enterprise Connect are available at Quick Start for Apache Kafka using Confluent Platform (Docker) and on Docker Hub at confluentinc/cp-server-connect.

Tip

For best performance, run Replicator as close to the destination cluster as possible to provide a low latency connection for Kafka Connect operations within Replicator.

Compatibility

For data transfer Replicator maintains the same compatibility matrix as Java clients, detailed in Kafka Java Clients. However some Replicator features have different compatibility requirements:

  • Schema Translation requires that both source and destination clusters are running Confluent 5.2.0 or later.
  • Offset Translation requires that both source and destination clusters are running Confluent 5.1.0 or later.
  • Automatic topic creation and config sync requires that the destination cluster is at a later version than the source cluster.

Note

Newer versions of Replicator cannot be used to replicate data from early version Kafka clusters to Confluent Cloud. Specifically, Replicator version 5.4.0 or later cannot be used to replicate data from clusters Apache Kafka® v0.10.2 or earlier nor from Confluent Platform v3.2.0 or earlier, to Confluent Cloud. If you have clusters on these earlier versions, use Replicator 5.0.x to replicate to Confluent Cloud until you can upgrade. Keep in mind the following, and plan your upgrades accordingly:

Replicator Connector

Replicator is implemented as a Kafka connector. For basic information on the connector and additional use cases beyond multi-datacenter, see Confluent Replicator in Supported Connectors.

Important

This connector is bundled natively with Confluent Platform. If you have Confluent Platform installed and running, there are no additional steps required to install.

If you are using Confluent Platform using only Confluent Community components, you can install the connector using the Confluent Hub client (recommended) or you can manually download the ZIP file.

MirrorMaker

MirrorMaker is a stand-alone tool for copying data between two Kafka clusters. To learn more, see Mirroring data between clusters in the Kafka documentation

Confluent Replicator is a more complete solution that handles topic configuration and data, and integrates with Kafka Connect and Confluent Control Center to improve availability, scalability and ease of use. To learn more, try out the Quick Start Tutorial: Replicating Data Between Clusters and see Migrate from MirrorMaker to Replicator.

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