Apache Kafka

Apache Kafka is a distributed, open-source, high-throughput, low-latency platform for handling real-time data feeds. It was originally developed at LinkedIn and later open-sourced as an Apache Software Foundation project. Kafka is fundamentally designed as a distributed, partitioned, and replicated commit log service, often referred to as an Event Streaming Platform (ESP).

It serves as a central backbone for real-time data pipelines and event-driven architectures, enabling applications and systems to communicate asynchronously by producing and consuming streams of data records (events).

The Need for Kafka: Managing Data Streams at Scale

Before platforms like Kafka, integrating data between various applications often involved complex, point-to-point connections or traditional Message Queues (MQs) that sometimes struggled with:

1. Scalability: Handling massive volumes of data from numerous producers and delivering it to many consumers reliably.
2. Durability & Fault Tolerance: Ensuring messages weren't lost during failures and providing persistence.
3. Ordering Guarantees: Maintaining the order of messages, especially within specific contexts (like events for a particular user).
4. Decoupling: Allowing producers and consumers to operate independently without direct dependencies or timing constraints.
5. Replayability: Enabling consumers to re-read messages from the past.

Kafka was designed to address these challenges with its log-based architecture.

Core Concepts and How Kafka Works

Kafka's architecture revolves around a few key concepts:

• Event/Record: The fundamental unit of data in Kafka. It consists of a key, a value, a timestamp, and optional metadata headers. Events are immutable once written.
• Topic: A category or feed name to which records are published. Think of a topic like a table in a database or a folder in a filesystem. Topics are partitioned for scalability.
• Partition: Topics are split into multiple partitions. Each partition is an ordered, immutable sequence of records (a commit log). Partitions allow Kafka to parallelize processing and scale horizontally. Records within a single partition are strictly ordered by their offset.
• Offset: A unique sequential ID assigned to each record within a partition. Offsets define the position of a record within its partition's log.
• Broker: A Kafka server that hosts topics or partitions. A Kafka cluster consists of one or more brokers working together. Brokers handle receiving messages from producers, storing them, and serving them to consumers. Partitions are replicated across multiple brokers for fault tolerance.
• Producer: An application that publishes (writes) streams of records to one or more Kafka topics. Producers choose which partition to send a record to (often based on the record's key, ensuring records with the same key land in the same partition).
• Consumer: An application that subscribes to (reads) one or more topics and processes the stream of records produced to them.
• Consumer Group: Consumers typically operate as part of a consumer group. Each record published to a topic partition is delivered to exactly one consumer instance within each subscribing consumer group. This allows for load balancing consumption across multiple consumer instances. Consumers track their progress (which offsets they have processed) within each partition, usually by committing offsets back to Kafka.
• Replication: Each partition can be replicated across multiple brokers. One broker acts as the 'leader' for the partition (handling all reads and writes), while others act as 'followers,' passively replicating the data. If the leader fails, a follower can be elected as the new leader, ensuring high availability and durability.
• ZooKeeper / KRaft: Traditionally, Kafka relied on Apache ZooKeeper for cluster coordination (managing brokers, topic configurations, leader election, access control). Newer Kafka versions are replacing ZooKeeper with an internal quorum controller based on the Raft consensus protocol, known as KRaft, simplifying deployment and management.

Key Features & Benefits

• High Throughput: Capable of handling hundreds of thousands or millions of messages per second per broker.
• Low Latency: Delivers messages with typical end-to-end latencies in milliseconds.
• Scalability: Horizontally scalable by adding more brokers and partitioning topics.
• Durability & Fault Tolerance: Data is persisted to disk and replicated across multiple brokers, preventing data loss.
• Decoupling: Producers and consumers are independent; producers don't need to know about consumers, and vice versa.
• Ordered Delivery (within a Partition): Guarantees that messages within a single partition are delivered to consumers in the order they were produced.
• Data Persistence & Replayability: Records can be stored reliably for configurable retention periods (hours, days, or even indefinitely), allowing consumers to re-read historical data.
• Rich Ecosystem: Supports various client libraries, connectors (Kafka Connect framework), stream processing APIs (Kafka Streams), and integration with numerous data systems.

Common Use Cases

• Messaging: Reliable asynchronous communication between microservices.
• Activity Tracking: Capturing user clicks, logs, IoT sensor data, etc.
• Data Integration / ETL Pipelines: Moving data between different databases, applications, and data warehouses/lakes in real-time.
• Log Aggregation: Collecting logs from multiple servers/applications into a central place.
• Stream Processing: Serving as the input/output buffer for stream processing engines.
• Commit Log: Acting as a source of truth for system state changes.
• Event Sourcing: Storing a sequence of state-changing events as the primary record.

Kafka and RisingWave

Apache Kafka is one of the most common and well-supported external systems integrated with RisingWave. RisingWave acts as a powerful consumer and producer for Kafka:

• Source: RisingWave can efficiently ingest data streams from Kafka topics using its 'CREATE SOURCE' command, supporting various formats like JSON, Avro, and Protobuf (often with a Schema Registry). It handles partitioning and offset management internally.
• Sink: RisingWave can publish the results of its stream processing (e.g., changes from a Materialized View or Table) back into Kafka topics using its 'CREATE SINK' command. This allows downstream applications to consume processed, real-time insights from RisingWave via Kafka.

Kafka often serves as the primary 'event bus' connecting data producers to RisingWave and connecting RisingWave's processed output to downstream consumers, forming the backbone of many real-time analytics pipelines built with RisingWave.

Related Glossary Terms

• Event Streaming Platform (ESP)
• Message Queue (MQ)
• Event-Driven Architecture (EDA)
• Stream Processing
• Producer / Consumer (Concepts)
• Topic / Partition / Offset (Concepts)
• Broker (Concept)
• Replication (Concept)
• Apache Pulsar (Alternative ESP)
• RisingWave Source / Sink
• Schema Registry
• Avro / Protobuf / JSON (Formats)
• Commit Log (Concept)