How deeply does Apache Kafka in Action cover follower node?

Explains replica counts, leader–follower traffic and ISR inspection, then connects follower fetching and lag to acknowledgments without detailing election internals.

Follower node

As explained in Apache Kafka in Action

A Kafka partition follower is a broker that copies partition data from the leader for resilience and failover.

Suppose an online store keeps price changes in a Kafka topic. If a broker holding a partition fails, another broker needs a copy of its data. A follower is a broker that stores a replica of a partition and continuously fetches new messages from that partition’s leader. If the leader fails, a follower can take over. section 4.2.3

Each partition has one leader. Producers and consumers normally communicate with that leader, not its followers. A replication factor of three means three replicas in the normal state: one leader and two followers. The replication factor cannot exceed the number of available brokers. section 4.2.3

Create and inspect a replicated topic

With three brokers available, run `kafka-topics.sh --create --topic products.prices.replication --partitions 3 --replication-factor 3 --bootstrap-server localhost:9092`. Then use `kafka-topics.sh --describe --topic products.prices.replication --bootstrap-server localhost:9092` to inspect each partition’s leader, replicas and in-sync replicas (ISR). In the shown output, partition 0 has broker 2 as leader and brokers 1 and 3 as its other replicas. section 4.2.3

$ kafka-topics.sh \
    --describe \
    --topic products.prices.replication \
    --bootstrap-server localhost:9092
Topic: products.prices.replication PartitionCount: 3 ReplicationFactor: 3
Configs:    #1
Topic: products.prices.replication Partition: 0 Leader: 2 Replicas: 2,1,3
Isr: 2,1,3 Elr:   #2
Topic: products.prices.replication Partition: 1 Leader: 3 Replicas: 3,2,1
Isr: 3,2,1 Elr:
Topic: products.prices.replication Partition: 2 Leader: 1 Replicas: 1,3,2
Isr: 1,3,2 Elr:

Compare the Leader, Replicas and Isr fields across the three partitions. The leaders are distributed among brokers; each partition has three replicas. section 4.2.3

Check replica health as well as replica count. The ISR field identifies replicas that are up to date; a mismatch between ISR and Replicas can indicate a problem, such as a failed broker. Followers fetch data from the leader, and a leader may wait for replication before acknowledging a producer request. Replication lag can therefore affect availability and performance—not just failover readiness. section 4.2.3 section 8.5

How Apache Kafka in Action covers it

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Also explained in

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MongoDB 8.0 in Action, Third EditionA secondary member is a replica set member that replicates data from the primary and can serve configured read operations.
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Kafka Streams in Action, Second EditionA secondary node is a non-leader Schema Registry node that serves reads and forwards schema write requests to the primary node.

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Related concepts

primary nodecontrasts with · A primary node is a designated authoritative or coordinating member of a distributed system, typically responsible for accepting operations or coordinating replicated state.
availability grouppart of · An availability group is a SQL Server high-availability and disaster-recovery configuration that maintains synchronized database replicas.
replicationused by · Replication is the creation or maintenance of multiple copies of data, state, or functionality across separate locations or components.
read preferenceconfigured by · Read preference is a database-client policy specifying which replica roles or locations may serve read operations.
clientkind of · A client is a person, organization, device, or software component that receives or uses a product, service, resource, or functionality provided by another entity.

This explainer was generated by Manning's knowledge graph from the text of Apache Kafka in Action. The section links point to the parts of the book it draws on.