Redis 4.x Cookbook

Redis 4.x Cookbook

Overview of this book

Redis is considered the world's most popular key-value store database. Its versatility and the wide variety of use cases it enables have made it a popular choice of database for many enterprises. Based on the latest version of Redis, this book provides both step-by-step recipes and relevant the background information required to utilize its features to the fullest. It covers everything from a basic understanding of Redis data types to advanced aspects of Redis high availability, clustering, administration, and troubleshooting. This book will be your great companion to master all aspects of Redis. The book starts off by installing and configuring Redis for you to get started with ease. Moving on, all the data types and features of Redis are introduced in detail. Next, you will learn how to develop applications with Redis in Java, Python, and the Spring Boot web framework. You will also learn replication tasks, which will help you to troubleshoot replication issues. Furthermore, you will learn the steps that need to be undertaken to ensure high availability on your cluster and during production deployment. Toward the end of the book, you will learn the topmost tasks that will help you to troubleshoot your ecosystem efficiently, along with extending Redis by using different modules.

Title Page

Dedication

Packt Upsell

Foreword

Contributors

Preface

Free Chapter

Getting Started with Redis

Introduction

Downloading and installing Redis

Starting and shutting down Redis

Connecting to Redis with redis-cli

Getting server information

Understanding the Redis Event Model

Understanding the Redis protocol

Data Types

Introduction

Using the string data type

Using the list data type

Using the hash data type

Using the set data type

Using the sorted set data type

Using the HyperLogLog data type

Using the Geo data type

Managing keys

Data Features

Introduction

Using bitmaps

Setting expiration on keys

Using SORT

Using pipelines

Understanding Redis transactions

Using PubSub

Using Lua

Debugging Lua

Developing with Redis

Introduction

When to use Redis in your application

Using the correct data types

Using the correct Redis APIs

Connecting to Redis with Java

Connecting to Redis with Python

Connecting to Redis with Spring Data Redis

Writing a MapReduce job for Redis

Writing a Spark job for Redis

Replication

Introduction

Setting up Redis replication

Optimizing replication

Troubleshooting replication

Persistence

Combining RDB and AOF

Setting Up High Availability and Cluster

Introduction

Setting up Sentinel

Testing Sentinel

Administrating Sentinel

Setting up Redis Cluster

Testing Redis Cluster

Administrating Redis Cluster

Deploying to a Production Environment

Introduction

Deploying Redis on Linux

Securing Redis

Setting client connection options

Configuring memory policy

Benchmarking

Logging

Administrating Redis

Introduction

Managing Redis Server configurations

Operating Redis using bin/redis-cli

Troubleshooting Redis

Introduction

Health checking in Redis

Identifying slow queries using the SLOWLOG

Troubleshooting latency issues

Troubleshooting memory issues

Troubleshooting crash issues

Extending Redis with Redis Modules

Introduction

Loading a Redis module

Writing a Redis module

The Redis Ecosystem

Introduction

The Redisson client

Twemproxy

Codis – a proxy-based high-performance Redis Cluster solution

The CacheCloud Redis management system

Pika – a Redis-compatible NoSQL database

Windows Environment Setup

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Pika – a Redis-compatible NoSQL database

Through the previous introduction, we have learned that if the dataset size of a Redis instance is too big, it is likely that the replication and persistence can be a big problem. Specifically, it always takes quite a long time to restore the dataset from a persistence file if the dataset of a Redis instance is large. In addition, the bigger is the dataset of a Redis, the longer it takes to perform the forking when the persistence is triggered. Worst of all, if the master Redis has a large dataset and owns multiple slaves, the full synchronization is a disaster. From the perspective of the storage cost, RAM is much more expensive than the Solid State Drive (SSD).

To address these problems, Qihoo 360 developed Pika, which is a large-scale, high-performance, Redis-compatible storage system. Pika stores data on disks instead of memory, and the multithreaded design ensures considerable high performance. It supports multiple data structures and fully supports...

Redis 4.x Cookbook

Redis 4.x Cookbook

Overview of this book

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