How to Understand Kafka Architecture

Let's dive deep into how Kafka handles millions of messages per second. This is the architecture breakdown every software engineer should understand: Core Components of a Kafka Broker: 1. Network Layer:    - Acceptor Thread: Handles incoming connections    - Processor Threads: Manage client requests efficiently    - Request Channel: Central communication highway 2. API Layer:    - API Threads: Process client operations    - Request Purgatory: Manages delayed requests    - Replication Info: Tracks data consistency 3. Log Subsystem:    - Partition Logs: Where your data actually lives    - Log Manager: Orchestrates log operations    - File System Integration: Ensures durability 4. Replication Subsystem:    - Replica Manager: Coordinates data copies    - Replication Threads: Handle data synchronization    - Replication Controller: Maintains consistency    - ZooKeeper Integration: Manages cluster state Why This Matters: - Scalability: Handles massive throughput - Reliability: No message loss - High Availability: Continuous operation - Fault Tolerance: Automatic recovery Real-World Applications: - Real-time analytics - Log aggregation - Stream processing - Event sourcing - Activity tracking Understanding Kafka's internals is crucial for: - Proper configuration - Effective troubleshooting - Performance optimization - System design decisions

If I only had 3 minutes to explain everything about Kafka, this is how I would do it: ➱ What Kafka is and its Main Use Cases  – Kafka, developed by LinkedIn in 2011, is a distributed messaging system designed for scalability and reliability. –Acts as a message queue to enable publishers to send messages to subscribers efficiently. – Use Case 1: Event streaming – streams events like user actions across systems and replays them to sync databases. – Use Case 2: Broadcasting – ensures reliable delivery of updates (e.g., Instagram posts to millions of followers). ➱ Kafka's Architecture and Components  ➥ 1. Producers   - Applications that publish messages to Kafka topics.   - Messages are partitioned to distribute load across Kafka brokers.   - Kafka producers are asynchronous, allowing them to offload message delivery for efficient application performance.  ➥ 2. Topics & Partitions   - Topics: Logical channels where messages are published.   - Partitions: Subdivisions of topics to achieve parallelism and ensure scalability.   - Messages within a partition are ordered, but cross-partition ordering is not guaranteed.   - Kafka can retain messages for a configurable period (e.g., 2 weeks) even after consumption.  ➥ 3. Brokers   - Kafka servers that store and manage topics and partitions.   - A broker can handle thousands of partitions, each supporting replication for fault tolerance.  ➥ 4. Consumers   - Pull messages from Kafka using offsets, which track the last consumed message.   - Part of consumer groups, ensuring distributed processing by assigning each consumer to specific partitions.  ➱ How Kafka Ensures Scalability and Reliability  ➥ 1. Horizontal Scalability   - Kafka scales horizontally by adding more producers, brokers, and consumers.  ➥ 2. Replication   - Partitions have replicas across brokers for fault tolerance.   - A primary replica handles all writes, while other replicas serve reads and maintain data consistency.  ➥ 3. Consistency and Failover   - Kafka ensures consistency through high watermarks, where only fully replicated messages are available for consumption.   - If the primary replica fails, a synchronized replica is promoted as the new leader using Apache Zookeeper.  ➥ 4. Batching Optimization   - Messages are batched (e.g., 50KB) to reduce overhead and increase throughput.  ➥ 5. Zero-Copy Optimization   - Uses Linux’s zero-copy feature to send messages directly from the file system to the network socket, reducing memory usage and improving performance.  ➱ Delivery Guarantees in Kafka  ➥ 1. At Least Once   - Messages are delivered even after retries, ideal for critical tasks like email verifications.  ➥ 2. Exactly Once   - Ensures one-time processing using transactions and consumer groups, preventing duplicates.  ➥ 3. Consumer-Driven Pull Model   - Consumers fetch messages at their pace, simplifying broker operations.

I've been using 𝐀𝐩𝐚𝐜𝐡𝐞 𝐊𝐚𝐟𝐤𝐚 for years now and I absolutely love it. Let me explain the message/event flow in simple terms. Give it a read. 👇 𝐃𝐨 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐰𝐡𝐚𝐭? 𝐀𝐩𝐚𝐜𝐡𝐞 𝐊𝐚𝐟𝐤𝐚 𝐰𝐚𝐬 𝐛𝐨𝐫𝐧 𝐨𝐮𝐭 𝐨𝐟 𝐚 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. 😉 LinkedIn engineers => faced difficulties in tracking website metrics, activity streams and other operational data. A team of engineers => led by Jay Kreps, Neha Narkhede and Jun Rao started developing a distributed publish-subscribe messaging system that could handle high-throughput, low-latency data streams. This system eventually became Apache Kafka. It was open sourced in early 2011. The name 'Kafka' was chosen by Jay Kreps. He named the system after the famous author 'Franz Kafka'. 😊 Kreps was an admirer of Franz Kafka's writing and found the name fitting for a system that dealt with the flow of information. It's written in Java and Scala. Later they founded => 'Confluent' (a company) in 2014 to provide commercial support and additional tools for Kafka users. 📌 𝐋𝐞𝐭'𝐬 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐭𝐡𝐞 𝐛𝐚𝐬𝐢𝐜 𝐟𝐥𝐨𝐰. [1.] Producer sends a message ◾ An application acts as a producer, creating a message with data (payload) and optional key. ◾ The producer connects to a broker in the Kafka cluster and identifies the target topic. ◾ Kafka uses a partitioner to determine which partition within the topic should receive the message. This ensures load balancing and parallel processing. ◾ The message is delivered to the leader replica of the chosen partition. [2.] Message storage and replication ◾ The leader replica appends the message to its log segment. ◾ The message receives a unique offset, serving as its position within the log. ◾ The leader replicates the message to follower replicas for fault tolerance. [3.] Consumer fetches messages ◾ An application acts as a consumer, joining a consumer group. ◾ Consumers within the same group share offsets and coordinate consumption. ◾ Each consumer fetches messages from its assigned partitions based on its committed offset. ◾ The consumer receives batches of messages and processes them. [4.] Acknowledging consumption ◾ Once processing is complete, the consumer commits its new offset. ◾ This tells Kafka which messages have been successfully consumed. ◾ Kafka tracks committed offsets for each consumer in the group. [*.] Flow continues ◾ Producers continue sending messages and consumers keep fetching and processing them based on their latest offsets. ◾ This cycle ensures ordered delivery and reliable consumption even with failures or restarts. Remember, 👉 Message flow is asynchronous. Producers don't wait for consumers to process messages. 👉 Consumers can lag behind producers if processing is slow. 👉 Kafka offers mechanisms for handling failures and ensuring at-least-once or exactly-once delivery semantics. Topics => Partitions =>Log Segments (Data is actually stored in log segments) Source: Mayank Ahuja