Book Image

HBase High Performance Cookbook

By : Ruchir Choudhry
Book Image

HBase High Performance Cookbook

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.

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Table of Contents (19 chapters)
HBase High Performance Cookbook
HBase High Performance Cookbook
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Configuring HBase
Configuring HBase
Introduction
Configuring and deploying HBase
Using the filesystem
Administering clusters
Managing clusters
2
Loading Data from Various DBs
Loading Data from Various DBs
Introduction
Extracting data from Oracle
Loading data using Oracle Big data connector
Bulk utilities
Using Hive with Apache HBase
Using Sqoop
3
Working with Large Distributed Systems Part I
Working with Large Distributed Systems Part I
Introduction
Scaling elastically or Auto Scaling with built-in fault tolerance
Auto Scaling HBase using AWS
Works on different VM/physical, cloud hardware
4
Working with Large Distributed Systems Part II
Working with Large Distributed Systems Part II
Introduction
Read path
Write Path
Snappy
LZO compression
LZ4 compressor
Replication
5
Working with Scalable Structure of tables
Working with Scalable Structure of tables
Introduction
HBase data model part 1
HBase data model part 2
How HBase truly scales on key and schema design
6
HBase Clients
HBase Clients
Introduction
HBase REST and Java Client
Working with Apache Thrift
Working with Apache Avro
Working with Protocol buffer
Working with Pig and using Shell
7
Large-Scale MapReduce
Large-Scale MapReduce
Introduction
8
HBase Performance Tuning
HBase Performance Tuning
Introduction
Working with infrastructure/operating systems
Working with Java virtual machines
Changing the configuration of components
Working with HDFS
9
Performing Advanced Tasks on HBase
Performing Advanced Tasks on HBase
Machine learning using Hbase
Real-time data analysis using Hbase and Mahout
Full text indexing using Hbase
10
Optimizing Hbase for Cloud
Optimizing Hbase for Cloud
Introduction
Configuring Hbase for the Cloud
Connecting to an Hbase cluster using the command line
Backing up and restoring Hbase
Terminating an HBase cluster
Accessing HBase data with hive
Viewing the Hbase user interface
Monitoring HBase with CloudWatch
Monitoring Hbase with Ganglia
11
Case Study
Case Study
Introduction
Configuring Lily Platform
Integrating elastic search with Hbase
Configuring
Index
Index

Introduction

Hadoop/Hbase was designed to crunch a huge amount of data in a batch mode and provide meaningful results to this data. However, as the technology evolved over the years, the original architecture was fine tuned to move from the world of big-iron to the choice of cloud infrastructure:

  1. It provides optimum pricing for the provisioning of new hardware, storage, and monitoring the infrastructure.

  2. One-click setup of additional nodes and storage.

  3. Elastic load-balancing to different clusters within the Hbase ecosystem.

  4. Ability to resize the cluster on-demand.

  5. Share capacity with different time-zones, for example, doing batch jobs in different data centers to and real-time analytics near to the customer.

  6. Easy integration with other Cloud-based services.

  7. HBase on Amazon EMR provides the ability to back up your HBase data directly to Amazon Simple Storage Service (Amazon S3). You can also restore from a previously created backup when launching an HBase cluster.

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