Kafka Architecture Diagram Examples

These Kafka examples show how the same producer-broker-consumer model adapts to a single broker, a replicated cluster, multi-topic setups, and Kafka Streams stateful processing.

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Real examples

Single-broker (development only)

Who uses it: Developer running Kafka locally

One broker, no replication

Topic with 1-3 partitions for parallelism

Producer and consumer on the same machine

Zookeeper or KRaft on the same node

Acceptable for development, never for production

Why this works: Single-broker Kafka is for local development — the diagram has no replication or failover. The moment you go to production, this becomes the baseline you upgrade away from.

Replicated production cluster

Who uses it: Team deploying Kafka to production

3+ brokers with replication factor 3

Each partition has a leader and 2 followers across different brokers

Producer acks=all waits for replication before confirming

Broker failure: another replica is elected leader automatically

KRaft (newer) replaces Zookeeper for metadata

Why this works: Production Kafka requires replication — the diagram shows partitions spread across brokers with a leader/follower split, which is what survives broker failure without data loss.

Multi-topic with consumer groups

Who uses it: Team running many event types on one cluster

Multiple topics (orders, payments, inventory)

Each topic has its own partition count

Different consumer groups subscribe to different topics

A service can be a producer for one topic and consumer of another

Group rebalancing happens when consumers join/leave

Why this works: Multi-topic is the typical production shape — the diagram has several topics each with their own subscribers, and consumer-group rebalancing is the mechanism that redistributes partitions when scaling.

Kafka Streams (stateful processing)

Who uses it: Team building stream processing on top of Kafka

Kafka Streams app reads from input topic, writes to output topic

State stores (RocksDB) per partition, backed by changelog topics

Exactly-once processing semantics available

Scales by adding more application instances

No separate processing cluster needed

Why this works: Kafka Streams turns Kafka itself into a stream processor — the diagram adds a streams application between input and output topics, with per-partition state in changelog topics, eliminating the need for a separate Flink or Spark cluster.

Tips for better study mind maps

Always show partitions explicitly — they're the unit of parallelism, replication, and ordering in Kafka.
Distinguish consumer groups from individual consumers; the group is the subscription unit, not the consumer.
Show Zookeeper or KRaft as a separate component; it's coordination metadata, not part of the data path.
Mark replication factor on topics so reviewers can see fault tolerance at a glance.

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