MySQL 8 for Big Data

By : Challawala, Jaydip Lakhatariya, Mehta, Patel

5 (1)

Buy this Book

MySQL 8 for Big Data

5 (1)

By: Challawala, Jaydip Lakhatariya, Mehta, Patel

Buy this Book

Overview of this book

With organizations handling large amounts of data on a regular basis, MySQL has become a popular solution to handle this structured Big Data. In this book, you will see how DBAs can use MySQL 8 to handle billions of records, and load and retrieve data with performance comparable or superior to commercial DB solutions with higher costs. Many organizations today depend on MySQL for their websites and a Big Data solution for their data archiving, storage, and analysis needs. However, integrating them can be challenging. This book will show you how to implement a successful Big Data strategy with Apache Hadoop and MySQL 8. It will cover real-time use case scenario to explain integration and achieve Big Data solutions using technologies such as Apache Hadoop, Apache Sqoop, and MySQL Applier. Also, the book includes case studies on Apache Sqoop and real-time event processing. By the end of this book, you will know how to efficiently use MySQL 8 to manage data for your Big Data applications.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Introduction to Big Data and MySQL 8

The importance of Big Data

The life cycle of Big Data

Structured databases

Basics of MySQL

New features in MySQL 8

Benefits of using MySQL

Installing MySQL 8

Evolution of MySQL for Big Data

Summary

Data Query Techniques in MySQL 8

Overview of SQL

Database storage engines and types

Select statement in MySQL 8

Insert, replace, and update statements in MySQL 8

Transactions in MySQL 8

Aggregating data in MySQL 8

JSON

Summary

Indexing your data for High-Performing Queries

MySQL indexing

MySQL 8 index types

Indexing JSON data

Summary

Using Memcached with MySQL 8

Overview of Memcached

Setting up Memcached

Using of Memcached

Analyzing data stored in Memcached

Memcached replication configuration

Memcached APIs for different technologies

Summary

Partitioning High Volume Data

Partitioning in MySQL 8

Horizontal partitioning in MySQL 8

Vertical partitioning

Pruning partitions in MySQL

Querying on partitioned data

Summary

Replication for building highly available solutions

High availability

Replication with MySQL

Group replication

Summary

MySQL 8 Best Practices

MySQL benchmarks and configurations

Best practices for MySQL queries

Best practices for the Memcached configuration

Best practices for replication

Summary

NoSQL API for Integrating with Big Data Solutions

NoSQL overview

NoSQL versus SQL

Implementing NoSQL APIs

Summary

Case study: Part I - Apache Sqoop for exchanging data between MySQL and Hadoop

Case study for log analysis

Apache Sqoop overview

Integrating Apache Sqoop with MySQL and Hadoop

Importing unstructured data to Hadoop HDFS from MySQL

Loading structured data to MySQL using Apache Sqoop

Summary

Case study: Part II - Real time event processing using MySQL applier

Case study overview

MySQL Applier overview

Real-time integration with MySQL Applier

Organizing and analyzing data in Hadoop

Summary

Customer Reviews

5 (1)

5 star

100%

4 star

3 star

2 star

1 star

MySQL indexing

MySQL supports various indexes on its tables for the faster access of records. Before we define indexes on the tables, it is important to know when we need to index data, and it's also equally important to select the proper field to create indexes.

The following list shows when to use indexing:

When grouping based on a specific column is required
When sorting on a specific column is required
When you need to find the minimum and maximum values from the table
When we have a large dataset and need to find a few records based on certain conditions frequently
When you need to fire a query that has a join between two or more tables