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MySQL 8 for Big Data

MySQL 8 for Big Data

By : Challawala, Jaydip Lakhatariya, Mehta, Patel
5 (1)
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MySQL 8 for Big Data

MySQL 8 for Big Data

5 (1)
By: Challawala, Jaydip Lakhatariya, Mehta, Patel

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.
Table of Contents (11 chapters)
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Overview of Memcached


While talking about handling Big Data with MySQL, we would have to obviously think about the performance--how do we handle the performance when storing and retrieving data frequently? One of the prominent answers is Memcached, which boosts the performance of frequent data retrieval and storage because it skips the query parser, SQL optimizer, and other parts of the engine that are unnecessary and allows us to store or retrieve data directly with InnoDB. Using Memcached, data management is much faster and convenient for handling Big Data.

MySQL 8 provides you with the InnoDB Memcached plugin named daemon_memcached, which can help us in managing data easily. It will automatically store and retrieve data from InnoDB tables and provide get, set, and incr operations that remove performance overhead by skipping SQL parsing, which speeds up data operations.

The following diagram will help you understand better how queries are parsed when using Memcache:

As shown in the previous...

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