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HBase High Performance Cookbook

HBase High Performance Cookbook

By : Ruchir Choudhry
2.5 (2)
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HBase High Performance Cookbook

HBase High Performance Cookbook

2.5 (2)
By: Ruchir Choudhry

Overview of this book

Apache HBase is a non-relational NoSQL database management system that runs on top of HDFS. It is an open source, disturbed, versioned, column-oriented store and is written in Java to provide random real-time access to big Data. We’ll start off by ensuring you have a solid understanding the basics of HBase, followed by giving you a thorough explanation of architecting a HBase cluster as per our project specifications. Next, we will explore the scalable structure of tables and we will be able to communicate with the HBase client. After this, we’ll show you the intricacies of MapReduce and the art of performance tuning with HBase. Following this, we’ll explain the concepts pertaining to scaling with HBase. Finally, you will get an understanding of how to integrate HBase with other tools such as ElasticSearch. By the end of this book, you will have learned enough to exploit HBase for boost system performance.
Table of Contents (13 chapters)
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7
7. Large-Scale MapReduce
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12
Index

Introduction

HBase provides various ways to leverage the potential of MapReduce based on the stack and the architecture you are going to use.

Before we start, let's do a quick revisit to the components, which will be used in MapReduce:

  • Record reader
  • Mapper
  • Combiner
  • Practitioner
  • Shuffle and sort
  • Reduce
  • Output format
    Introduction
  • Record reader: The core responsibility of a record reader is to analyze the data and then parse the data in key-value. The key is the location in the index and the value is the data that is composed of records.
  • Mapper: Mapper executes each key-value pair that we got from the records. The design of the key and values depends on what we are planning to achieve from it. The key is the data we will use to group the values.
  • Combiner: Combiner is an alternative localized reducer; the main advantage is the ability to group data during the mapping process. It gathers all the in-between keys that are parsed from the previous process (Mapper) and invokes a custom method to rearrange values...

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