Abstract

We have built a novel messaging system for collecting and delivering high volumes of log data with low latency. Our system incorporates design elements from database logs and messaging systems, and is designed to support both offline and online message consumption. We describe the design and implementation of our system, and present performance results that demonstrate the system's efficiency.

Key Design Goals

Requirements

1. High Throughput: Support millions of messages per second
2. Low Latency: Sub-millisecond latency for real-time applications
3. Durability: Persistent storage with configurable retention
4. Scalability: Horizontal scaling across multiple machines
5. Fault Tolerance: Handle machine failures gracefully

Use Cases

• Log Aggregation: Collect logs from multiple services
• Stream Processing: Real-time data processing pipelines
• Event Sourcing: Store event streams for applications
• Metrics Collection: Collect and distribute system metrics

Architecture

Core Components

1. Brokers: Kafka servers that store and serve data
2. Topics: Categories or feeds of messages
3. Partitions: Topics are split into partitions for parallelism
4. Producers: Applications that publish messages
5. Consumers: Applications that read messages
6. Zookeeper: Coordination service for cluster management

Data Model

• Message: Unit of data (key-value pair with timestamp)
• Topic: Named stream of messages
• Partition: Ordered sequence of messages within a topic
• Offset: Unique identifier for each message in a partition

Design Principles

Log-Based Storage

• Append-only: Messages are appended to the end of log files
• Immutable: Once written, messages cannot be modified
• Sequential I/O: Optimized for high throughput
• Retention: Configurable retention period for messages

Partitioning

• Parallelism: Multiple partitions enable parallel processing
• Ordering: Messages within a partition are ordered
• Load Distribution: Partitions distributed across brokers
• Consumer Groups: Multiple consumers can process different partitions

Replication

• Leader-Follower: Each partition has one leader and multiple followers
• In-Sync Replicas (ISR): Replicas that are up-to-date
• Acknowledgment: Configurable acknowledgment requirements
• Failure Handling: Automatic leader election on failures

Performance Characteristics

Throughput

• High Throughput: Millions of messages per second
• Batch Processing: Efficient batching of messages
• Compression: Built-in compression support
• Zero-Copy: Efficient data transfer mechanisms

Latency

• Low Latency: Sub-millisecond latency possible
• Configurable: Trade-off between latency and throughput
• Batching: Batch size affects latency
• Acknowledgment: Sync vs async acknowledgment

Durability

• Persistent Storage: Messages stored on disk
• Replication: Multiple copies for fault tolerance
• Acknowledgment: Configurable durability guarantees
• Retention: Configurable message retention period

Consumer Model

Consumer Groups

• Parallel Processing: Multiple consumers in a group
• Load Balancing: Automatic partition assignment
• Fault Tolerance: Consumer failures handled gracefully
• Rebalancing: Dynamic rebalancing on group changes

Offset Management

• Automatic: Kafka manages offsets automatically
• Manual: Applications can manage offsets manually
• Checkpointing: Periodic offset commits
• Recovery: Resume from last committed offset

Delivery Semantics

• At Most Once: Messages delivered at most once
• At Least Once: Messages delivered at least once
• Exactly Once: Messages delivered exactly once (with transactions)

Use Cases

Log Aggregation

• Centralized Logging: Collect logs from multiple services
• Real-time Monitoring: Monitor system health in real-time
• Audit Trails: Maintain audit trails for compliance
• Debugging: Debug distributed systems

Stream Processing

• Real-time Analytics: Process data streams in real-time
• Event Processing: Process business events
• Data Pipelines: Build data processing pipelines
• Machine Learning: Feed ML models with real-time data

Event Sourcing

• Event Store: Store all events that happened in a system
• State Reconstruction: Reconstruct system state from events
• Audit Trail: Complete history of all changes
• Temporal Queries: Query system state at any point in time

Integration Patterns

Producer Patterns

• Fire and Forget: Send message without waiting for acknowledgment
• Synchronous: Wait for acknowledgment before sending next message
• Asynchronous: Send multiple messages and handle callbacks
• Batching: Batch multiple messages for efficiency

Consumer Patterns

• Polling: Consumers poll for new messages
• Stream Processing: Process messages as they arrive
• Batch Processing: Process messages in batches
• Micro-batching: Process small batches for low latency

Monitoring and Operations

Metrics

• Throughput: Messages per second
• Latency: End-to-end latency
• Lag: Consumer lag behind producers
• Storage: Disk usage and retention

Health Checks

• Broker Health: Monitor broker status
• Topic Health: Monitor topic status
• Consumer Health: Monitor consumer group status
• Replication: Monitor replication status

Why It Matters for Software Engineering

Understanding Kafka is crucial for:

• System Design: Designing real-time data processing systems
• Microservices: Building event-driven architectures
• Data Engineering: Building data pipelines
• Real-time Systems: Understanding streaming architectures

Kafka has become the de facto standard for building real-time data pipelines and event-driven architectures in modern distributed systems.