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# MirrorMaker 2.0
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MM2 leverages the Connect framework to replicate topics between Kafka
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clusters. MM2 includes several new features, including:
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- both topics and consumer groups are replicated
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- topic configuration and ACLs are replicated
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- cross-cluster offsets are synchronized
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- partitioning is preserved
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## Replication flows
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MM2 replicates topics and consumer groups from upstream source clusters
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to downstream target clusters. These directional flows are notated
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`A->B`.
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It's possible to create complex replication topologies based on these
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`source->target` flows, including:
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- *fan-out*, e.g. `K->A, K->B, K->C`
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- *aggregation*, e.g. `A->K, B->K, C->K`
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- *active/active*, e.g. `A->B, B->A`
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Each replication flow can be configured independently, e.g. to replicate
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specific topics or groups:
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A->B.topics = topic-1, topic-2
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A->B.groups = group-1, group-2
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By default, all topics and consumer groups are replicated (except
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blacklisted ones), across all enabled replication flows. Each
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replication flow must be explicitly enabled to begin replication:
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A->B.enabled = true
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B->A.enabled = true
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## Starting an MM2 process
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You can run any number of MM2 processes as needed. Any MM2 processes
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which are configured to replicate the same Kafka clusters will find each
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other, share configuration, load balance, etc.
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To start an MM2 process, first specify Kafka cluster information in a
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configuration file as follows:
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# mm2.properties
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clusters = us-west, us-east
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us-west.bootstrap.servers = host1:9092
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us-east.bootstrap.servers = host2:9092
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You can list any number of clusters this way.
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Optionally, you can override default MirrorMaker properties:
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topics = .*
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groups = group1, group2
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emit.checkpoints.interval.seconds = 10
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These will apply to all replication flows. You can also override default
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properties for specific clusters or replication flows:
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# configure a specific cluster
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us-west.offset.storage.topic = mm2-offsets
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# configure a specific source->target replication flow
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us-west->us-east.emit.heartbeats = false
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Next, enable individual replication flows as follows:
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us-west->us-east.enabled = true # disabled by default
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Finally, launch one or more MirrorMaker processes with the `connect-mirror-maker.sh`
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script:
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$ ./bin/connect-mirror-maker.sh mm2.properties
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## Multicluster environments
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MM2 supports replication between multiple Kafka clusters, whether in the
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same data center or across multiple data centers. A single MM2 cluster
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can span multiple data centers, but it is recommended to keep MM2's producers
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as close as possible to their target clusters. To do so, specify a subset
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of clusters for each MM2 node as follows:
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# in west DC:
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$ ./bin/connect-mirror-maker.sh mm2.properties --clusters west-1 west-2
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This signals to the node that the given clusters are nearby, and prevents the
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node from sending records or configuration to clusters in other data centers.
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### Example
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Say there are three data centers (west, east, north) with two Kafka
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clusters in each data center (west-1, west-2 etc). We can configure MM2
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for active/active replication within each data center, as well as cross data
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center replication (XDCR) as follows:
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# mm2.properties
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clusters: west-1, west-2, east-1, east-2, north-1, north-2
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west-1.bootstrap.servers = ...
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---%<---
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# active/active in west
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west-1->west-2.enabled = true
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west-2->west-1.enabled = true
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# active/active in east
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east-1->east-2.enabled = true
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east-2->east-1.enabled = true
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# active/active in north
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north-1->north-2.enabled = true
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north-2->north-1.enabled = true
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# XDCR via west-1, east-1, north-1
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west-1->east-1.enabled = true
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west-1->north-1.enabled = true
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east-1->west-1.enabled = true
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east-1->north-1.enabled = true
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north-1->west-1.enabled = true
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north-1->east-1.enabled = true
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Then, launch MM2 in each data center as follows:
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# in west:
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$ ./bin/connect-mirror-maker.sh mm2.properties --clusters west-1 west-2
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# in east:
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$ ./bin/connect-mirror-maker.sh mm2.properties --clusters east-1 east-2
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# in north:
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$ ./bin/connect-mirror-maker.sh mm2.properties --clusters north-1 north-2
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With this configuration, records produced to any cluster will be replicated
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within the data center, as well as across to other data centers. By providing
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the `--clusters` parameter, we ensure that each node only produces records to
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nearby clusters.
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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
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data centers, and you may incur unnecessary data transfer costs.
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## Shared configuration
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MM2 processes share configuration via their target Kafka clusters.
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For example, the following two processes would be racy:
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# process1:
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A->B.enabled = true
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A->B.topics = foo
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# process2:
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A->B.enabled = true
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A->B.topics = bar
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In this case, the two processes will share configuration via cluster `B`.
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Depending on which processes is elected "leader", the result will be
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that either `foo` or `bar` is replicated -- but not both. For this reason,
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it is important to keep configuration consistent across flows to the same
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target cluster. In most cases, your entire organization should use a single
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MM2 configuration file.
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## Remote topics
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MM2 employs a naming convention to ensure that records from different
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clusters are not written to the same partition. By default, replicated
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topics are renamed based on "source cluster aliases":
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topic-1 --> source.topic-1
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This can be customized by overriding the `replication.policy.separator`
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property (default is a period). If you need more control over how
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remote topics are defined, you can implement a custom `ReplicationPolicy`
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and override `replication.policy.class` (default is
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`DefaultReplicationPolicy`).
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## Monitoring an MM2 process
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MM2 is built on the Connect framework and inherits all of Connect's metrics, e.g.
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`source-record-poll-rate`. In addition, MM2 produces its own metrics under the
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`kafka.connect.mirror` metric group. Metrics are tagged with the following properties:
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- *target*: alias of target cluster
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- *source*: alias of source cluster
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- *topic*: remote topic on target cluster
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- *partition*: partition being replicated
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Metrics are tracked for each *remote* topic. The source cluster can be inferred
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from the topic name. For example, replicating `topic1` from `A->B` will yield metrics
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like:
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- `target=B`
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- `topic=A.topic1`
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- `partition=1`
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The following metrics are emitted:
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# MBean: kafka.connect.mirror:type=MirrorSourceConnector,target=([-.w]+),topic=([-.w]+),partition=([0-9]+)
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record-count # number of records replicated source -> target
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record-age-ms # age of records when they are replicated
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record-age-ms-min
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record-age-ms-max
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record-age-ms-avg
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replication-latecny-ms # time it takes records to propagate source->target
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replication-latency-ms-min
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replication-latency-ms-max
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replication-latency-ms-avg
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byte-rate # average number of bytes/sec in replicated records
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# MBean: kafka.connect.mirror:type=MirrorCheckpointConnector,source=([-.w]+),target=([-.w]+)
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checkpoint-latency-ms # time it takes to replicate consumer offsets
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checkpoint-latency-ms-min
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checkpoint-latency-ms-max
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checkpoint-latency-ms-avg
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These metrics do not discern between created-at and log-append timestamps.
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