src-kafka/connect/mirror

MirrorMaker 2.0

MM2 leverages the Connect framework to replicate topics between Kafka clusters. MM2 includes several new features, including:

• both topics and consumer groups are replicated
• topic configuration and ACLs are replicated
• cross-cluster offsets are synchronized
• partitioning is preserved

Replication flows

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:

• fan-out, e.g. K->A, K->B, K->C
• aggregation, e.g. A->K, B->K, C->K
• active/active, e.g. A->B, B->A

Each 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 blacklisted ones), across all enabled replication flows. Each replication flow must be explicitly enabled to begin replication:

A->B.enabled = true
B->A.enabled = true

Starting an MM2 process

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

Multicluster environments

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.

Example

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.

Shared configuration

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.

Remote topics

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).

Monitoring an MM2 process

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-latecny-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.