MM2 leverages the Connect framework to replicate topics between Kafka clusters. MM2 includes several new features, including:
MM2 replicates topics and consumer groups from upstream source clusters to downstream target clusters. These directional flows are notated A->B.
It's possible to create complex replication topologies based on these source->target flows, including:
K->A, K->B, K->CA->K, B->K, C->KA->B, B->AEach replication flow can be configured independently, e.g. to replicate specific topics or groups:
A->B.topics = topic-1, topic-2 A->B.groups = group-1, group-2
By default, all topics and consumer groups are replicated (except excluded ones), across all enabled replication flows. Each replication flow must be explicitly enabled to begin replication:
A->B.enabled = true B->A.enabled = true
You can run any number of MM2 processes as needed. Any MM2 processes which are configured to replicate the same Kafka clusters will find each other, share configuration, load balance, etc.
To start an MM2 process, first specify Kafka cluster information in a configuration file as follows:
# mm2.properties clusters = us-west, us-east us-west.bootstrap.servers = host1:9092 us-east.bootstrap.servers = host2:9092
You can list any number of clusters this way.
Optionally, you can override default MirrorMaker properties:
topics = .* groups = group1, group2 emit.checkpoints.interval.seconds = 10
These will apply to all replication flows. You can also override default properties for specific clusters or replication flows:
# configure a specific cluster us-west.offset.storage.topic = mm2-offsets # configure a specific source->target replication flow us-west->us-east.emit.heartbeats = false
Next, enable individual replication flows as follows:
us-west->us-east.enabled = true # disabled by default
Finally, launch one or more MirrorMaker processes with the connect-mirror-maker.sh script:
$ ./bin/connect-mirror-maker.sh mm2.properties
MM2 supports replication between multiple Kafka clusters, whether in the same data center or across multiple data centers. A single MM2 cluster can span multiple data centers, but it is recommended to keep MM2's producers as close as possible to their target clusters. To do so, specify a subset of clusters for each MM2 node as follows:
# in west DC: $ ./bin/connect-mirror-maker.sh mm2.properties --clusters west-1 west-2
This signals to the node that the given clusters are nearby, and prevents the node from sending records or configuration to clusters in other data centers.
Say there are three data centers (west, east, north) with two Kafka clusters in each data center (west-1, west-2 etc). We can configure MM2 for active/active replication within each data center, as well as cross data center replication (XDCR) as follows:
# mm2.properties clusters: west-1, west-2, east-1, east-2, north-1, north-2 west-1.bootstrap.servers = ... ---%<--- # active/active in west west-1->west-2.enabled = true west-2->west-1.enabled = true # active/active in east east-1->east-2.enabled = true east-2->east-1.enabled = true # active/active in north north-1->north-2.enabled = true north-2->north-1.enabled = true # XDCR via west-1, east-1, north-1 west-1->east-1.enabled = true west-1->north-1.enabled = true east-1->west-1.enabled = true east-1->north-1.enabled = true north-1->west-1.enabled = true north-1->east-1.enabled = true
Then, launch MM2 in each data center as follows:
# in west: $ ./bin/connect-mirror-maker.sh mm2.properties --clusters west-1 west-2 # in east: $ ./bin/connect-mirror-maker.sh mm2.properties --clusters east-1 east-2 # in north: $ ./bin/connect-mirror-maker.sh mm2.properties --clusters north-1 north-2
With this configuration, records produced to any cluster will be replicated within the data center, as well as across to other data centers. By providing the --clusters parameter, we ensure that each node only produces records to nearby clusters.
N.B. that the --clusters parameter is not technically required here. MM2 will work fine without it; however, throughput may suffer from “producer lag” between data centers, and you may incur unnecessary data transfer costs.
The following sections target for dedicated MM2 cluster. If running MM2 in a Connect cluster, please refer to KIP-382: MirrorMaker 2.0 for guidance.
All Kafka Connect, Source Connector, Sink Connector configs, as defined in Kafka official doc, can be directly used in MM2 configuration without prefix in the configuration name. As the starting point, most of these default configs may work well with the exception of tasks.max.
In order to evenly distribute the workload across more than one MM2 instance, it is advised to set tasks.max at least to 2 or even larger depending on the hardware resources and the total number partitions to be replicated.
If needed, Kafka Connect worker-level configs could be even specified “per connector”, which needs to follow the format of cluster_alias.config_name in MM2 configuration. For example,
backup.ssl.truststore.location = /usr/lib/jvm/zulu-8-amd64/jre/lib/security/cacerts // SSL cert location backup.security.protocol = SSL // if target cluster needs SSL to send message
MM2 itself has many configs to control how it behaves. To override those default values, add the config name by the format of source_cluster_alias->target_cluster_alias.config_name in MM2 configuration. For example,
backup->primary.enabled = false // set to false if one-way replication is desired primary->backup.topics.blacklist = topics_to_blacklist primary->backup.emit.heartbeats.enabled = false primary->backup.sync.group.offsets = true
In many cases, customized values for producer or consumer configurations are needed. In order to override the default values of producer or consumer used by MM2, target_cluster_alias.producer.producer_config_name, source_cluster_alias.consumer.consumer_config_name or cluster_alias.admin.admin_config_name are the formats to use in MM2 configuration. For example,
backup.producer.compression.type = gzip backup.producer.buffer.memory = 32768 primary.consumer.isolation.level = read_committed primary.admin.bootstrap.servers = localhost:9092
MM2 processes share configuration via their target Kafka clusters. For example, the following two processes would be racy:
# process1: A->B.enabled = true A->B.topics = foo # process2: A->B.enabled = true A->B.topics = bar
In this case, the two processes will share configuration via cluster B. Depending on which processes is elected “leader”, the result will be that either foo or bar is replicated -- but not both. For this reason, it is important to keep configuration consistent across flows to the same target cluster. In most cases, your entire organization should use a single MM2 configuration file.
Exactly-once semantics are supported for dedicated MM2 clusters as of version 3.5.0.
For new MM2 clusters, set the exactly.once.source.support property to enabled for all targeted Kafka clusters that should be written to with exactly-once semantics. For example, to enable exactly-once for writes to cluster B, add the following to your MM2 config file:
B.exactly.once.source.support = enabled
For existing MM2 clusters, a two-step upgrade is necessary. Instead of immediately setting the exactly.once.source.support property to enabled, first set it to preparing on all nodes in the cluster. Once this is complete, it can be set to enabled on all nodes in the cluster, in a second round of restarts.
In either case, it is also necessary to enable intra-cluster communication between your MM2 nodes, as described in KIP-710. To do this, the dedicated.mode.enable.internal.rest property must be set to true in your MM2 config. In addition, many of the REST-related configuration properties available for Kafka Connect can be specified in your MM2 config. For example, to enable intra-cluster communication in your MM2 cluster with each node listening on port 8080 of their local machine, add this to your config file:
dedicated.mode.enable.internal.rest = true listeners = http://localhost:8080
Note that, if intra-cluster communication is enabled in production environments, it is highly recommended to secure the REST servers brought up by each MM2 node. See the configuration properties for Kafka Connect for information on how this can be accomplished.
It is also recommended to filter records from aborted transactions out from replicated data when running MM2. To do this, ensure that the consumer used to read from source clusters is configured with isolation.level set to read_committed. If replicating data from cluster A, this can be done for all replication flows that read from that cluster by adding the following to your MM2 config:
A.consumer.isolation.level = read_committed
As a final note, under the hood, MM2 uses Kafka Connect source connectors to replicate data. For more information on exactly-once support for these kinds of connectors, see the relevant docs page.
MM2 employs a naming convention to ensure that records from different clusters are not written to the same partition. By default, replicated topics are renamed based on “source cluster aliases”:
topic-1 --> source.topic-1
This can be customized by overriding the replication.policy.separator property (default is a period). If you need more control over how remote topics are defined, you can implement a custom ReplicationPolicy and override replication.policy.class (default is DefaultReplicationPolicy).
MM2 is built on the Connect framework and inherits all of Connect's metrics, e.g. source-record-poll-rate. In addition, MM2 produces its own metrics under the kafka.connect.mirror metric group. Metrics are tagged with the following properties:
- *target*: alias of target cluster - *source*: alias of source cluster - *topic*: remote topic on target cluster - *partition*: partition being replicated
Metrics are tracked for each remote topic. The source cluster can be inferred from the topic name. For example, replicating topic1 from A->B will yield metrics like:
- `target=B` - `topic=A.topic1` - `partition=1`
The following metrics are emitted:
# MBean: kafka.connect.mirror:type=MirrorSourceConnector,target=([-.w]+),topic=([-.w]+),partition=([0-9]+) record-count # number of records replicated source -> target record-age-ms # age of records when they are replicated record-age-ms-min record-age-ms-max record-age-ms-avg replication-latency-ms # time it takes records to propagate source->target replication-latency-ms-min replication-latency-ms-max replication-latency-ms-avg byte-rate # average number of bytes/sec in replicated records # MBean: kafka.connect.mirror:type=MirrorCheckpointConnector,source=([-.w]+),target=([-.w]+) checkpoint-latency-ms # time it takes to replicate consumer offsets checkpoint-latency-ms-min checkpoint-latency-ms-max checkpoint-latency-ms-avg
These metrics do not discern between created-at and log-append timestamps.