大规模Apache Cassandra部署优化实战

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"Cassandra High Performance Cookbook 是一本关于优化大规模 Apache Cassandra 部署的书籍，包含超过150个设计和优化策略。作者是 Edward Capriolo，由 Packt Publishing 出版。" Apache Cassandra 是一个分布式、容错的数据存储系统，以其线性可扩展性而闻名，使其成为处理大型高流量网站的理想存储平台。本书"Cassandra High Performance Cookbook"旨在帮助读者设计和优化大型 Cassandra 部署，以实现最佳性能。在 NoSQL 数据库领域，Cassandra 是一个关键的参与者，因为它提供了一种灵活、高性能的解决方案来处理非结构化数据。以下是书中可能涵盖的一些关键知识点： 1. **分布式架构**：Cassandra 的分布式特性允许数据在多台服务器之间分布，确保高可用性和故障恢复能力。它采用一致性哈希算法进行数据分片，使得数据均匀分布并支持动态添加和删除节点。 2. **数据模型**：Cassandra 使用列族（Column Family）作为其数据模型，类似于键值对和宽列存储。这使得快速读取和写入大量列成为可能，适合时间序列数据或社交网络数据等场景。 3. **一致性与可用性**：Cassandra 提供了不同的数据一致性级别，如 QUORUM、LOCAL_QUORUM、EACH_QUORUM 等，以平衡读写速度和数据一致性需求。 4. **读写优化**：书中可能探讨如何通过调整 tombstones、批处理、缓存策略以及预读取来优化读写性能。 5. **分区策略**：选择正确的分区键对于优化查询性能至关重要。书中可能会详细介绍如何根据业务需求选择合适的分区策略。 6. **索引管理**：Cassandra 支持创建Secondary Indexes，但它们可能会影响性能。书中的菜谱可能会指导读者如何有效地利用索引，并在需要时避免它们。 7. **监控与调优**：了解如何监控系统的性能指标（如 CPU 使用率、内存消耗、磁盘 I/O），以及如何通过调整 JVM 参数和系统配置进行性能调优。 8. **备份与恢复**：Cassandra 提供了快照和 SSTable 导出/导入功能，用于数据备份和灾难恢复。书中会介绍如何实施这些策略。 9. **安全与权限控制**：Cassandra 的认证和授权机制，包括 SSL 加密通信和角色基础的访问控制，对于企业级部署非常重要。 10. **操作与维护**：如故障检测、自动修复、节点的加入与离开，以及如何进行有效的数据迁移和集群扩展。这本书通过实践性的“菜谱”形式，帮助读者解决在实际操作中遇到的问题，从而提升 Cassandra 集群的性能和稳定性。无论你是刚接触 Cassandra 还是经验丰富的管理员，都能从中受益。

Preface

Apache Cassandra is a fault-tolerant, distributed data store which offers linear scalability

allowing it to be a storage platform for large high volume websites.

This book provides detailed recipes that describe how to use the features of Cassandra and

improve its performance. Recipes cover topics ranging from setting up Cassandra for the rst

time to complex multiple data center installations. The recipe format presents the information

in a concise actionable form.

The book describes in detail how features of Cassandra can be tuned and what the possible

effects of tuning can be. Recipes include how to access data stored in Cassandra and use

third party tools to help you out. The book also describes how to monitor and do capacity

planning to ensure it is performing at a high level. Towards the end, it takes you through the

use of libraries and third-party applications with Cassandra and Cassandra integration

with Hadoop.

What this book covers

Chapter 1, Getting Started: The recipes in this chapter provide a whirlwind tour of Cassandra.

Setup recipes demonstrate how to download and install Cassandra as a single instance or

simulating multiple instance clusters. Trouble-shooting recipes show how to run Cassandra

with more debugging information and how to use management tools. Also included

are recipes for end users which connect with the command like interface and setup an

environment to build code to access Cassandra.

Chapter 2, Command-line Interface: This chapter provides recipes on using Cassandra's

command line interface. Recipes cover how the CLI is used to make changes to the metadata

such as key spaces, column families, and cache settings. Additionally recipes show how to

use the CLI to set, get and scan data.

Chapter 3, Application Programmer Interface: Cassandra provides an application programmer

interface for programs to insert and access data. The chapter has recipes for doing common

operations like inserting fetching, deleting, and range scanning data. Also covered in this

chapter are recipes for batch mutate and multi-get which are useful in batch programs.

Preface

Chapter 4, Performance Tuning: Many conguration knobs and tunable settings exist

for Cassandra. Additionally hardware choices and operating system level tuning effect

performance. The recipes in this chapter show conguration options and how changing

them optimizes performance.

Chapter 5, Consistency, Availability, and Partition Tolerance with Cassandra: Cassandra is

designed from the ground up to store and replicate data across multiple nodes. This chapter

has recipes that utilize tunable consistency levels and congure features like read repair.

These recipes demonstrate how to use features of Cassandra that make available even in

the case of failures or network partitions.

Chapter 6, Schema Design: The Cassandra data model is designed for storing large amounts

of data across many nodes. This chapter has recipes showing how common storage

challenges can be satised using Cassandra. Recipes include techniques for serializing data,

storing large objects, time series, normalized, and de-normalized data.

Chapter 7, Administration and Cluster Management: Cassandra allows nodes to be added and

remove from the cluster without downtime. This chapter contains recipes for adding, moving,

and removing nodes as well as administrative techniques for backing up and restoring data.

Also covered administrative techniques such as backing up or restoring data.

Chapter 8, Multiple Datacenter Deployments: Cassandra is designed to work both when

nodes are deployed in a local area network and when nodes are separated by larger

geographical distances such as a wide area network. The recipes in this chapter show how

to congure and use features that control and optimize how Cassandra works in multiple

datacenter environments.

Chapter 9, Coding and Internals: This chapter covers programming recipes that go beyond the

typical application programmer interface, including building Cassandra from source, creating

custom types for use with Cassandra, and modifying tools like the JSON export tools.

Chapter 10, Third-party Libraries and Applications: A variety of libraries and applications exist

for Cassandra. This chapter introduces tools that make coding easier such as the high-level

client Hector, ot the object mapping tool Kundera. Recipes also show how to setup and use

applications built on top of Cassandra such as the full text search engine solandra.

Chapter 11, Hadoop and Cassanda: Hadoop is a distributed le system, HDFS that provides

high throughput and redundant storage and MapReduce, a software framework for distributed

processing of large data sets on compute clusters. This chapter provides recipes with tips on

setting up Hadoop and Cassandra both individually and on shared hardware. Recipes show

how to use Cassandra as the input or output of map reduce jobs, as well as common tasks

like counting or joining data that can be done with Cassandra data inside Hadoop.

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