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Kappa 与 Lambda 架构介绍与对比

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Lambda 架构

Lambda架构源自Storm的作者Nathan Marz提出,旨在提供一种满足现代大数据系统关键特性的架构方案,这些关键特性包括高容错性、低延迟性和可扩展性等核心要素.该架构通过整合离线与实时计算过程,融合了不可变性原则以及读写分离原则,同时实现了复杂性的孤立管理.此外,该框架还具备良好的兼容性和扩展能力,能够集成Hadoop生态系统中的多种组件如HDFS和HBase,同时也支持Kafka的消息生产者机制以及Storm的任务调度功能.

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)

Lambda 架构可分解为三层：

  • BATCH LAYER: 该批次使用批处理机制来管理非实时数据。
  • REAL-TIME LAYER: 该实时层采用在线计算引擎来管理不断更新的数据流。
  • SERVING LAYER: 整合层将BATCH LAYER和REAL-TIME LAYE的综合结果集合进行汇总，并支持查询功能。

Kappa 架构

Lambda 架构的一个显著问题是需要管理和维护两套分别部署于批处理计算系统和实时计算系统上的代码，并且这两套代码必须产生完全一致的结果。Kappa架构的核心理念主要围绕三个核心概念展开探讨：

  • 采用类似于Kafka或其他分布式队列系统的架构来存储数据，在这种架构下, 所需的数据量决定了存储的时间
  • 当需要全量重新计算时, 重新启动一个流计算实例, 并从初始状态重新解析数据, 最后将处理结果存入新的结果存储中
  • 当新实例完成任务后立即关闭旧的流计算实例, 并删除不再需要的老结果

两种架构比较

对比项 Lambda 架构 Kappa 架构
数据处理能力 可以处理大规模的历史数据 历史数据处理的能力有限
机器开销 批处理和实时计算需一直运行，机器开销大 必要时进行全量计算，机器开销相对较小
存储开销 只需要保存一份查询结果，开销较小 需要存储新老实例结果，存储开销相对较大
开发、测试难度 实现两套代码，开发、测试难度较大 只需面对一个框架，开发、测试难度相对较小
运维成本 维护两套系统，运维成本大 只需维护一个框架，运维成本小

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