Distribute Design Kafka
Introduction
Apache Kafka, initially developed by LinkedIn, is a distributed messaging system that has become a core component of Apache’s ecosystem. Written in Scala, Kafka is renowned for its scalability and high throughput. It is widely used in big data platforms and integrates seamlessly with distributed processing systems like Cloudera, Apache Storm, and Apache Spark.
Apache Kafka 最初由 LinkedIn 开发，是一个分布式消息系统，已经成为 Apache 生态系统的核心组件。Kafka 使用 Scala 编写，以其可扩展性和高吞吐量而闻名。它广泛应用于大数据平台，并与 Cloudera、Apache Storm 和 Apache Spark 等分布式处理系统无缝集成。
As a commercially viable middleware, Kafka’s message reliability is of utmost importance. How can we ensure the precise transmission, accurate storage, and correct consumption of messages? This article dives into Kafka’s architecture and reliability mechanisms, including its storage structure, replication, and synchronization principles.
作为一个商业可行的中间件，Kafka 的消息可靠性至关重要。我们如何确保消息的精确传输、准确存储和正确消费？本文深入探讨了 Kafka 的架构和可靠性机制，包括其存储结构、复制和同步原理。
Key Terminologies
| Term | Explanation |
|---|---|
| Broker | A Kafka node that handles message processing. Multiple brokers form a Kafka cluster. |
| Topic | Kafka uses topics to categorize messages. Every message published to Kafka needs a topic. |
| Producer | The client that sends messages to Kafka brokers. |
| Consumer | The client that reads messages from Kafka brokers. |
| Consumer Group | A group of consumers, where each message can only be consumed by one consumer within the group. |
| Partition | The physical division of a topic. A topic can have multiple partitions. |
| Segment | A partition is divided into multiple segments. |
| Offset | A unique identifier for messages within a partition. Each message has a sequential offset number. |
| 术语 | 解释 |
|---|---|
| Broker（代理） | 处理消息处理的 Kafka 节点。多个 broker 组成一个 Kafka 集群。 |
| Topic（主题） | Kafka 使用主题对消息进行分类。发布到 Kafka 的每条消息都需要一个主题。 |
| Producer（生产者） | 向 Kafka brokers 发送消息的客户端。 |
| Consumer（消费者） | 从 Kafka brokers 读取消息的客户端。 |
| Consumer Group（消费者组） | 消费者组，组内每条消息只能被一个消费者消费。 |
| Partition（分区） | 主题的物理分区。一个主题可以有多个分区。 |
| Segment（段） | 分区被划分为多个段。 |
| Offset（偏移量） | 分区内消息的唯一标识符。每条消息都有一个顺序的偏移量编号。 |
Kafka’s Storage Mechanism
Kafka’s storage mechanism can be understood from four aspects:
Kafka 的存储机制可以从四个方面理解：
Partition Distribution in Topics
In a Kafka cluster, each partition of a topic is stored across multiple brokers.
For instance, consider a setup where a topic like report_push has four partitions.
Kafka partitions are stored as directories with the naming convention: topic-name-partition-index.
在 Kafka 集群中，主题的每个分区都存储在多个 broker 上。
例如，考虑一个名为 report_push 的主题有四个分区的设置。
Kafka 分区以目录形式存储，命名约定为：主题名称-分区索引。
Partition File Storage
Each partition is stored as a series of segments, which are essentially large files.
Each segment file consists of two parts: an index file (.index) and a data file (.log).
每个分区都以一系列段的形式存储，这些段本质上是大型文件。
每个段文件由两部分组成：索引文件（.index）和数据文件（.log）。
Segment Storage Structure
A segment file includes index and data files. The index file stores metadata, while the data file stores actual messages.
Segment files are named based on the last message’s offset, helping Kafka efficiently locate data.
段文件包括索引文件和数据文件。索引文件存储元数据，而数据文件存储实际消息。
段文件根据最后一条消息的偏移量命名，帮助 Kafka 高效定位数据。
Locating Messages Using Offsets
Kafka uses the offset to locate messages within the partition. Each message has an offset number, which is used to efficiently find and retrieve it.
Kafka 使用偏移量在分区内定位消息。每条消息都有一个偏移量编号，用于高效查找和检索。
Kafka’s Internal Architecture
The internal architecture of Kafka includes the following core components:
- Producer: Sends messages to the Kafka cluster.
- Broker: Handles messages and stores partitions.
- Consumer: Pulls messages from the Kafka cluster.
- Zookeeper: Manages Kafka’s cluster state and coordinates leader election and partition management.
Kafka 的内部架构包括以下核心组件：
- Producer（生产者）： 向 Kafka 集群发送消息。
- Broker（代理）： 处理消息并存储分区。
- Consumer（消费者）： 从 Kafka 集群拉取消息。
- Zookeeper： 管理 Kafka 的集群状态，协调领导者选举和分区管理。
Ensuring High Reliability
Kafka’s high reliability stems from its robust replication mechanism, which ensures message availability even in the event of broker failures.
Kafka 的高可靠性源于其强大的复制机制，即使在代理故障的情况下也能确保消息可用性。
Data Synchronization
Kafka introduced replication in version 0.8 to mitigate data loss during broker failures. Each partition has multiple replicas, with one replica acting as the leader and others as followers.
Kafka 在 0.8 版本中引入了复制机制，以减少代理故障期间的数据丢失。每个分区都有多个副本，其中一个副本作为领导者，其他副本作为跟随者。
Replica Placement Strategy
Kafka distributes replicas across multiple brokers to balance load. It employs a modular arithmetic approach to determine where to place replicas.
Kafka 将副本分布在多个代理上以平衡负载。它采用模运算方法来确定副本的放置位置。
Synchronization Strategy
Producers only send messages to the leader of a partition. After the leader writes the message, followers synchronize with the leader.
生产者只向分区的领导者发送消息。领导者写入消息后，跟随者与领导者同步。
Leader Election
Kafka’s leader election is managed by Zookeeper, which uses a distributed lock mechanism to ensure that only one replica becomes the leader of a partition.
Kafka 的领导者选举由 Zookeeper 管理，它使用分布式锁机制确保只有一个副本成为分区的领导者。
Conclusion
Kafka’s architecture ensures high reliability, scalability, and performance, making it a vital tool in modern data processing. With its sophisticated replication mechanism, partitioning strategies, and efficient storage system, Kafka delivers message guarantees and fault tolerance that are crucial in large-scale distributed systems.
Kafka 的架构确保了高可靠性、可扩展性和性能，使其成为现代数据处理中的重要工具。通过其复杂的复制机制、分区策略和高效的存储系统，Kafka 提供了在大规模分布式系统中至关重要的消息保证和容错能力。
The above content outlines Kafka’s design and operational principles, integrating your provided article with added explanations and visualization using Mermaid diagrams. This should give a clear and comprehensive understanding of Kafka’s message storage, architecture, and reliability features.
上述内容概述了 Kafka 的设计和运行原理，结合了您提供的文章，并通过 Mermaid 图表添加了解释和可视化。这应该能让读者清晰全面地理解 Kafka 的消息存储、架构和可靠性特性。