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

MySQL Applier overview

MySQL Applier provides real-time data processing between MySQL and Hadoop. This is quite an efficient way to load data because of its performance and real-time processing. This will load the data which has been changed rather than loading whole data. Hence, No More Bulk Transfer needed!

MySQL Applier replicates rows inserted into MySQL to the HDFS with help of MySQL binlog. This will use binary log and insert data in real-time; based on events in MySQL. All the events occurred in MySQL Server is available in binlog and Applier takes these changes from the events and applies same in Hadoop. This way we can rapidly acquire new data from the MySQL.

MySQL Applier uses API provided by libhdfs which is C library. This library is pre-compiled with Hadoop distributions for connecting MySQL master or read a binlog file of MySQL. It is responsible for various operations as listed in the following points:

  • Get the events (insert, update, delete) occurred on the MySQL Server.
  • Decodes...
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