Apache Kafka

Distributed streaming platform for high-throughput, fault-tolerant messaging

🔥 What is Apache Kafka?

Apache Kafka is a distributed streaming platform designed for building real-time data pipelines and streaming applications. Originally developed by LinkedIn, Kafka excels at handling high-throughput, fault-tolerant messaging with the ability to process millions of messages per second.

Unlike traditional message brokers, Kafka is built around the concept of a distributed commit log, where messages are persisted to disk and replicated across multiple brokers. This architecture enables Kafka to provide both messaging and storage capabilities, making it ideal for event streaming, log aggregation, and real-time analytics.

Kafka's publish-subscribe model, combined with its distributed nature and horizontal scalability, makes it the backbone of many modern data architectures, enabling organizations to build resilient, event-driven systems that can handle massive scale.

⭐ Key Features

High Throughput

Handle millions of messages per second with low latency, optimized for high-volume data streams.

Fault Tolerance

Built-in replication and partitioning ensure data durability and system resilience.

Horizontal Scalability

Scale out by adding more brokers to the cluster without downtime.

Message Persistence

Messages are persisted to disk and retained for configurable periods, enabling replay and recovery.

Stream Processing

Native stream processing capabilities with Kafka Streams for real-time data transformation.

Multi-tenancy

Support for multiple applications and teams on the same cluster with proper isolation.

🧩 Core Concepts

Topics

Named feeds of messages that categorize data streams. Producers publish to topics, consumers subscribe to topics.

Producer → Topic → Consumer

Partitions

Topics are divided into partitions for parallelism and scalability. Messages within a partition are ordered.

Topic: [P0] [P1] [P2]

Brokers

Individual Kafka servers that store data and serve clients. Multiple brokers form a cluster.

Cluster: Broker1, Broker2, Broker3

Producers

Applications that publish messages to Kafka topics. Can specify partitioning strategy and delivery guarantees.

App → Producer → Topic

Consumers

Applications that read messages from topics. Can work individually or as part of consumer groups.

Topic → Consumer → App

Consumer Groups

Groups of consumers that coordinate to consume partitions, enabling load balancing and fault tolerance.

Group: [C1] [C2] [C3]

🔌 Protocol and APIs

📡 Kafka Protocol

Kafka uses a custom binary protocol over TCP for communication between clients and brokers. The protocol is designed for high performance, supporting batching, compression, and efficient serialization.

Protocol Features:

  • • Binary format for efficiency
  • • Request/response model
  • • Message batching support
  • • Built-in compression (gzip, snappy, lz4, zstd)
  • • Stateless broker design

Key Operations:

  • • Produce: Send messages to topics
  • • Fetch: Read messages from topics
  • • Metadata: Get cluster information
  • • Offset: Manage consumer positions

🛠️ Client APIs

Kafka provides several APIs for different use cases, from simple messaging to complex stream processing applications.

Core APIs:

  • • Producer API: Publish messages to topics
  • • Consumer API: Subscribe and read from topics
  • • Admin API: Manage topics, configs, ACLs
  • • Streams API: Build stream processing apps

Language Support:

  • • Java (official)
  • • Python (kafka-python)
  • • Go (sarama, confluent-kafka-go)
  • • C/C++ (librdkafka)
  • • .NET, Node.js, and more

🌊 Stream Processing with Kafka Streams

Kafka Streams is a client library for building real-time streaming applications that transform and analyze data stored in Kafka. It provides a simple yet powerful API for processing data streams with features like windowing, joins, and aggregations.

Stream Processing Features

  • •
    Stateful Processing: Aggregations, joins, windowing
  • •
    Exactly-Once Semantics: Guaranteed message processing
  • •
    Fault Tolerance: Automatic recovery and rebalancing
  • •
    No External Dependencies: Pure Java library

Common Patterns

  • •
    Filtering: Select specific events
  • •
    Transformation: Map, flatten, enrich data
  • •
    Aggregation: Count, sum, average over windows
  • •
    Joins: Combine streams and tables

🎯 Common Use Cases

Data Integration

  • •
    Event Streaming: Real-time data pipelines between systems
  • •
    Log Aggregation: Centralized logging from distributed applications
  • •
    CDC (Change Data Capture): Track database changes in real-time
  • •
    ETL Pipelines: Extract, transform, and load data streams

Real-time Analytics

  • •
    Metrics & Monitoring: Real-time operational dashboards
  • •
    Fraud Detection: Real-time transaction analysis
  • •
    Recommendation Engines: Live user behavior analysis
  • •
    IoT Data Processing: Sensor data ingestion and analysis

📊 Performance & Deployment

Performance Characteristics

Throughput: 1M+ messages/second per broker
Latency: 2-5ms end-to-end (typical)
Storage: Configurable retention (hours to years)
Durability: Replication factor and acks configuration

Deployment Options

Self-Managed: Full control over cluster configuration
Confluent Cloud: Fully managed Kafka service
Amazon MSK: AWS managed Kafka service
Containers: Docker and Kubernetes deployments

🌐 Kafka Ecosystem

Kafka Connect

Framework for connecting Kafka with external systems

  • • Source connectors (import data)
  • • Sink connectors (export data)
  • • Distributed and scalable

Schema Registry

Centralized repository for managing Avro, JSON, and Protobuf schemas

  • • Schema evolution
  • • Compatibility checking
  • • Version management

KSQL/ksqlDB

SQL-like interface for stream processing

  • • Real-time queries
  • • Stream-table joins
  • • Materialized views

🔗 Related Technologies

Kafka is part of a broader ecosystem of messaging and streaming technologies:

Alternative Brokers

  • • RabbitMQ - Traditional AMQP message broker
  • • Apache Pulsar - Cloud-native messaging platform
  • • Redis - In-memory pub/sub messaging

Stream Processing

  • • Apache Flink - Low-latency stream processing
  • • Apache Storm - Real-time computation
  • • Apache Spark Streaming - Micro-batch processing
← Back to Glossary