Apache Kafka

What is Apache Kafka?

Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming applications. Originally developed at LinkedIn and open-sourced in 2011, Kafka has become the industry standard for handling high-volume, real-time data feeds. The platform can process trillions of events per day, making it essential infrastructure for organizations requiring reliable, scalable data streaming at any scale.

What distinguishes Apache Kafka is its unique architecture combining publish-subscribe messaging with distributed commit log storage. Unlike traditional message queues that delete messages after consumption, Kafka retains messages for configurable periods, enabling consumers to replay data and multiple applications to process the same events independently. This design makes Kafka suitable for use cases ranging from messaging to event sourcing to stream processing.

Apache Kafka has become foundational infrastructure for data-driven organizations. Companies like Netflix, Uber, LinkedIn, and thousands of others use Kafka to power real-time analytics, microservices communication, log aggregation, and event-driven architectures. The ecosystem includes Kafka Connect for integration with external systems, Kafka Streams for stream processing, and managed services from Confluent and cloud providers that simplify operations.

Key Features

• High Throughput: Handle millions of messages per second with low latency through sequential disk I/O and zero-copy data transfer.
• Distributed Architecture: Scale horizontally across multiple brokers with automatic partition rebalancing and fault tolerance.
• Durability: Persist messages to disk with configurable replication ensuring data survives broker failures.
• Message Retention: Keep messages for configurable time periods or sizes, enabling replay and multiple consumer patterns.
• Consumer Groups: Allow multiple consumers to work together, automatically distributing partitions among group members.
• Exactly-Once Semantics: Support for exactly-once processing preventing duplicate handling in stream processing.
• Kafka Connect: Framework for building connectors integrating Kafka with databases, cloud services, and other systems.
• Kafka Streams: Client library for building stream processing applications directly in Java or Scala.
• Schema Registry: Manage and enforce schemas for messages ensuring data compatibility across producers and consumers.
• Multi-Tenancy: Support multiple teams and applications on shared clusters with quotas and access controls.

Recent Updates and Improvements

Apache Kafka continues active development with improvements to performance, operations, and the streaming ecosystem.

• KRaft Mode: Removal of ZooKeeper dependency through native Kafka Raft consensus, simplifying operations significantly.
• Tiered Storage: Offload older data to cheaper object storage while maintaining access through Kafka APIs.
• Improved Rebalancing: Faster, more stable consumer group rebalancing reducing disruption during scaling.
• Enhanced Security: Additional authentication mechanisms and fine-grained authorization capabilities.
• Kraft Controller: Production-ready KRaft replacing ZooKeeper for metadata management.
• Performance Improvements: Optimizations reducing latency and increasing throughput for various workloads.
• Connect Improvements: Better error handling, offset management, and connector configuration.
• Streams Enhancements: New DSL operations and improved state store performance.

System Requirements

Production Deployment

• Operating System: Linux recommended (RHEL, Ubuntu, Debian)
• Java: JDK 11 or later (17 recommended)
• RAM: 6 GB minimum per broker (64 GB+ recommended)
• Storage: SSDs recommended for high-throughput workloads
• Network: Low-latency network between brokers

Development/Testing

• Any OS supporting Java (Linux, macOS, Windows)
• JDK 11 or later
• RAM: 2 GB minimum
• Storage: Sufficient for message retention

Managed Services

• Confluent Cloud: Fully managed, any cloud
• Amazon MSK: AWS managed Kafka
• Azure Event Hubs: Kafka-compatible
• Aiven for Kafka: Multi-cloud managed

How to Install Apache Kafka

Local Development Setup

1. Install Java JDK 11 or later
2. Download Kafka from Apache website
3. Extract and configure
4. Start Kafka (KRaft mode)
5. Create topics and start producing/consuming

# Download Kafka
wget https://downloads.apache.org/kafka/3.6.0/kafka_2.13-3.6.0.tgz
tar -xzf kafka_2.13-3.6.0.tgz
cd kafka_2.13-3.6.0

# Generate cluster ID (KRaft mode)
KAFKA_CLUSTER_ID="$(bin/kafka-storage.sh random-uuid)"

# Format storage
bin/kafka-storage.sh format -t $KAFKA_CLUSTER_ID -c config/kraft/server.properties

# Start Kafka
bin/kafka-server-start.sh config/kraft/server.properties

# Create topic
bin/kafka-topics.sh --create --topic my-topic --bootstrap-server localhost:9092

# List topics
bin/kafka-topics.sh --list --bootstrap-server localhost:9092

Docker Installation

# Using Docker Compose (KRaft mode)
version: '3'
services:
  kafka:
    image: apache/kafka:latest
    ports:
      - "9092:9092"
    environment:
      KAFKA_NODE_ID: 1
      KAFKA_PROCESS_ROLES: broker,controller
      KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092,CONTROLLER://0.0.0.0:9093
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092
      KAFKA_CONTROLLER_QUORUM_VOTERS: 1@localhost:9093
      KAFKA_CONTROLLER_LISTENER_NAMES: CONTROLLER

# Start
docker-compose up -d

# Or single container
docker run -d --name kafka -p 9092:9092 apache/kafka:latest

Production Deployment (Linux)

# Install Java
sudo apt update
sudo apt install openjdk-17-jdk

# Create kafka user
sudo useradd -r -s /bin/false kafka

# Download and extract
wget https://downloads.apache.org/kafka/3.6.0/kafka_2.13-3.6.0.tgz
sudo tar -xzf kafka_2.13-3.6.0.tgz -C /opt
sudo mv /opt/kafka_2.13-3.6.0 /opt/kafka
sudo chown -R kafka:kafka /opt/kafka

# Configure (edit server.properties)
sudo nano /opt/kafka/config/kraft/server.properties

# Create systemd service
sudo nano /etc/systemd/system/kafka.service

# Start service
sudo systemctl enable kafka
sudo systemctl start kafka

Pros and Cons

Pros

• Massive Scale: Handle millions of messages per second with horizontal scaling across commodity hardware.
• Durability: Replicated, persistent storage ensures messages survive failures without data loss.
• Ecosystem: Rich ecosystem including Connect, Streams, Schema Registry, and extensive client libraries.
• Flexibility: Use as message queue, event store, stream processor, or integration platform.
• Open Source: Apache licensed with active community and no vendor lock-in for core platform.
• Industry Standard: Wide adoption means available expertise, tooling, and integration options.
• Message Replay: Retention enables replaying events for recovery, debugging, or new consumers.

Cons

• Operational Complexity: Self-managed Kafka requires significant expertise for production operations.
• Resource Intensive: Requires substantial memory, storage, and network resources for production.
• Learning Curve: Concepts like partitions, consumer groups, and offsets take time to master.
• Latency Trade-offs: Batching for throughput can add latency unsuitable for some real-time needs.
• Ordering Guarantees: Message ordering only guaranteed within partitions, requiring careful design.

Apache Kafka vs Alternatives

Who Should Use Apache Kafka?

Apache Kafka is ideal for:

• Event-Driven Architecture: Organizations building systems around events need Kafka’s streaming capabilities.
• High-Volume Data: Companies processing millions of events per second require Kafka’s throughput.
• Real-Time Analytics: Teams building real-time dashboards and analytics benefit from streaming data.
• Microservices: Distributed systems using event-based communication between services.
• Log Aggregation: Centralizing logs from many systems for processing and analysis.
• Data Integration: Building data pipelines connecting diverse systems through Kafka Connect.

Apache Kafka may not be ideal for:

• Simple Queuing: Basic task queues may find RabbitMQ or cloud queues simpler.
• Small Scale: Low-volume messaging doesn’t justify Kafka’s complexity.
• Limited Operations: Teams without capacity for Kafka operations should use managed services.
• Ultra-Low Latency: Sub-millisecond latency requirements may need specialized solutions.

Frequently Asked Questions

Is Apache Kafka free?

Yes, Apache Kafka is open source under the Apache 2.0 license, completely free to use, modify, and distribute. However, running Kafka requires infrastructure costs and operational expertise. Managed services like Confluent Cloud, Amazon MSK, or Aiven provide Kafka as a service with associated costs but reduced operational burden. Many organizations start self-managed then migrate to managed services.

What is the difference between Kafka and traditional message queues?

Traditional queues delete messages after consumption; Kafka retains messages for configurable periods. Queues typically have single consumers per message; Kafka supports multiple independent consumers reading the same messages. Kafka provides higher throughput through sequential I/O and batching. Queues often focus on point-to-point messaging; Kafka excels at publish-subscribe patterns and event streaming.

Do I still need ZooKeeper for Kafka?

No, since Kafka 3.3, KRaft mode provides native consensus without ZooKeeper. KRaft simplifies deployment and operations significantly. New deployments should use KRaft mode. Existing ZooKeeper-based clusters can migrate to KRaft. ZooKeeper support will eventually be removed. For production, ensure your Kafka version fully supports KRaft for your use cases.

How do I choose between Kafka and RabbitMQ?

Choose Kafka for event streaming, high throughput, message replay, and stream processing. Choose RabbitMQ for traditional messaging patterns, routing flexibility, and simpler operations. Kafka excels when events need long retention and multiple consumers. RabbitMQ suits request-reply patterns and complex routing. Kafka requires more resources and expertise; RabbitMQ is operationally simpler.

What is Confluent and how does it relate to Kafka?

Confluent was founded by Kafka’s original creators after leaving LinkedIn. The company offers Confluent Platform (enterprise Kafka distribution) and Confluent Cloud (fully managed Kafka service). Confluent contributes significantly to open-source Kafka development. Additional Confluent features include Schema Registry, ksqlDB, and enterprise tools. You can use Apache Kafka without Confluent, but Confluent provides the most comprehensive commercial support.

Final Verdict

Apache Kafka has earned its position as the industry standard for event streaming and real-time data pipelines. The platform’s ability to handle massive throughput with durability and replay capabilities addresses requirements that traditional messaging systems cannot meet. For organizations building event-driven architectures or processing high-volume data streams, Kafka provides proven, scalable infrastructure.

The ecosystem surrounding Kafka multiplies its value. Kafka Connect simplifies integration with existing systems. Kafka Streams enables sophisticated stream processing without separate frameworks. Schema Registry ensures data compatibility. The transition to KRaft mode removes operational complexity that historically deterred adoption.

For new projects involving event streaming or high-volume messaging, Kafka deserves primary consideration. The learning curve and operational requirements are justified by capabilities that alternatives cannot match at scale. Organizations concerned about operations should evaluate managed services that provide Kafka’s power without infrastructure burden. As event-driven architecture becomes standard, Kafka skills and infrastructure will only increase in importance.