Book Image

MySQL 8 for Big Data

By : Shabbir Challawala, Chintan Mehta, Kandarp Patel, Jaydip Lakhatariya
Book Image

MySQL 8 for Big Data

By: Shabbir Challawala, Chintan Mehta, Kandarp Patel, Jaydip Lakhatariya

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.

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Table of Contents (17 chapters)
Title Page
Title Page
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Introduction to Big Data and MySQL 8
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
2
Data Query Techniques in MySQL 8
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
3
Indexing your data for High-Performing Queries
Indexing your data for High-Performing Queries
MySQL indexing
MySQL 8 index types
Indexing JSON data
Summary
4
Using Memcached with MySQL 8
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
5
Partitioning High Volume Data
Partitioning High Volume Data
Partitioning in MySQL 8
Horizontal partitioning in MySQL 8
Vertical partitioning
Pruning partitions in MySQL
Querying on partitioned data
Summary
6
Replication for building highly available solutions
Replication for building highly available solutions
High availability
Replication with MySQL
Group replication
Summary
7
MySQL 8 Best Practices
MySQL 8 Best Practices
MySQL benchmarks and configurations
Best practices for MySQL queries
Best practices for the Memcached configuration
Best practices for replication
Summary
8
NoSQL API for Integrating with Big Data Solutions
NoSQL API for Integrating with Big Data Solutions
NoSQL overview
NoSQL versus SQL
Implementing NoSQL APIs
Summary
9
Case study: Part I - Apache Sqoop for exchanging data between MySQL and Hadoop
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
10
Case study: Part II - Real time event processing using MySQL applier
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

Pruning partitions in MySQL

Pruning is the selective extraction of data. As we have multiple partitions of big data, it will go through each partition during retrieval, which is time consuming and impacts performance. Some of the partitions will also be included in search while the requested data is not available inside that partition, which is an overhead process. Pruning helps here to search for only those partitions that have the relevant data, which will avoid the unnecessary inclusion of those partitions during retrieval.

This optimization that avoids the scanning of partitions where there can be no matching values is known as the pruning of partitions. In partition pruning, the optimizer analyzes FROM and WHERE clauses in SQL statements to eliminate unneeded partitions, and scans those database partitions that are relevant to the SQL statement. Let's see an example.

Suppose that we have a table with the following structure:

 CREATE TABLE student (
 rollNo INT NOT NULL,
 name VARCHAR(50...
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