Kafka # Replication

 Yes — Apache Kafka provides replication to ensure fault tolerance, durability, and high availability of data. Each topic partition can be replicated across multiple brokers, so even if one broker fails, the data remains accessible.

 

🔑 How Kafka Replication Works

• Replication Factor
• Defined at the topic level.
• Specifies how many copies of each partition are maintained across brokers.
• Example: replication factor = 3 → one leader + two followers.
• Leader & Followers
• Each partition has one leader broker and zero or more follower brokers.
• Producers and consumers interact only with the leader.
• Followers replicate the leader’s data to stay in sync.
• Failover
• If the leader broker fails, one of the in-sync followers is automatically promoted to leader.
• This ensures continuity without data loss.
• Committed Messages
• Kafka only considers a message “committed” once it is replicated to all in-sync replicas (ISR).
• This guarantees durability even if a broker crashes immediately after writing.

 

📌 Benefits of Replication

• High Availability → Consumers can continue reading even if a broker goes down.
• Fault Tolerance → Protects against hardware or network failures.
• Durability → Ensures messages are not lost once acknowledged.
• Scalability → Replication allows Kafka clusters to handle massive workloads reliably.

 

⚙️ Example

Suppose you create a topic with replication factor = 3:

bin/kafka-topics.sh --create \

  --topic orders \

  --partitions 3 \

  --replication-factor 3 \

  --bootstrap-server localhost:9092

• Each partition of orders will have 1 leader and 2 followers.
• If the leader fails, a follower takes over seamlessly.

 

🚀 Real-World Note

In microservice architectures (like the ones you’re designing with Spring Boot), replication is critical for exactly-once processing and resilient event-driven systems. For example, in a payment service, replication ensures that transaction events are never lost even if a broker crashes mid-stream.