Whenever a broker stops or crashes, leadership for that broker’s partitions transfers to other replicas. When the broker is restarted it will only be a follower for all its partitions, meaning it will not be used for client reads and writes.
To avoid this imbalance, Kafka has a notion of preferred replicas. If the list of replicas for a partition is 1,5,9 then node 1 is preferred as the leader to either node 5 or 9 because it is earlier in the replica list. By default the Kafka cluster will try to restore leadership to the restored replicas. This behaviour is configured with:
You can also set this to false, but you will then need to manually restore leadership to the restored replicas by running the command:
> bin/kafka-preferred-replica-election.sh --bootstrap-server broker_host:port
The rack awareness feature spreads replicas of the same partition across different racks. This extends the guarantees Kafka provides for broker-failure to cover rack-failure, limiting the risk of data loss should all the brokers on a rack fail at once. The feature can also be applied to other broker groupings such as availability zones in EC2.You can specify that a broker belongs to a particular rack by adding a property to the broker config:
When a topic is created, modified or replicas are redistributed, the rack constraint will be honoured, ensuring replicas span as many racks as they can (a partition will span min(#racks, replication-factor) different racks).The algorithm used to assign replicas to brokers ensures that the number of leaders per broker will be constant, regardless of how brokers are distributed across racks. This ensures balanced throughput.However if racks are assigned different numbers of brokers, the assignment of replicas will not be even. Racks with fewer brokers will get more replicas, meaning they will use more storage and put more resources into replication. Hence it is sensible to configure an equal number of brokers per rack.
We refer to the process of replicating data between Kafka clusters “mirroring” to avoid confusion with the replication that happens amongst the nodes in a single cluster. Kafka comes with a tool for mirroring data between Kafka clusters. The tool consumes from a source cluster and produces to a destination cluster. A common use case for this kind of mirroring is to provide a replica in another datacenter. This scenario will be discussed in more detail in the next section.
You can run many such mirroring processes to increase throughput and for fault-tolerance (if one process dies, the others will take overs the additional load).
Data will be read from topics in the source cluster and written to a topic with the same name in the destination cluster. In fact the mirror maker is little more than a Kafka consumer and producer hooked together.
The source and destination clusters are completely independent entities: they can have different numbers of partitions and the offsets will not be the same. For this reason the mirror cluster is not really intended as a fault-tolerance mechanism (as the consumer position will be different); for that we recommend using normal in-cluster replication. The mirror maker process will, however, retain and use the message key for partitioning so order is preserved on a per-key basis.
Here is an example showing how to mirror a single topic (named my-topic) from an input cluster:
> bin/kafka-mirror-maker.sh --consumer.config consumer.properties --producer.config producer.properties --whitelist my-topic
Note that we specify the list of topics with the
--whitelist option. This option allows any regular expression using Java-style regular expressions. So you could mirror two topics named Aand B using
--whitelist 'A|B'. Or you could mirror all topics using
--whitelist '*'. Make sure to quote any regular expression to ensure the shell doesn’t try to expand it as a file path. For convenience we allow the use of ‘,’ instead of ‘|’ to specify a list of topics. Combining mirroring with the configuration
auto.create.topics.enable=true makes it possible to have a replica cluster that will automatically create and replicate all data in a source cluster even as new topics are added.