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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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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Sections
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Configuring HBase
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1. Configuring HBase
Introduction
Configuring and deploying HBase
Using the filesystem
Administering clusters
Managing clusters
2
2. Loading Data from Various DBs
2. 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
3. Working with Large Distributed Systems Part I
3. 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
4. Working with Large Distributed Systems Part II
4. Working with Large Distributed Systems Part II
Introduction
Read path
Write Path
Snappy
LZO compression
LZ4 compressor
Replication
5
5. Working with Scalable Structure of tables
5. 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
6. HBase Clients
6. 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
7. Large-Scale MapReduce
7. Large-Scale MapReduce
Introduction
8
8. HBase Performance Tuning
8. HBase Performance Tuning
Introduction
Working with infrastructure/operating systems
Working with Java virtual machines
Changing the configuration of components
Working with HDFS
9
9. Performing Advanced Tasks on HBase
9. Performing Advanced Tasks on HBase
Machine learning using Hbase
Real-time data analysis using Hbase and Mahout
Full text indexing using Hbase
10
10. Optimizing Hbase for Cloud
10. 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
11. Case Study
11. Case Study
Introduction
Configuring Lily Platform
Integrating elastic search with Hbase
Configuring
12
Index
Index

Introduction

HBase is inspired by the Google big table architecture, and is fundamentally a non-relational, open source, and column-oriented distributed NoSQL. Written in Java, it is designed and developed by many engineers under the framework of Apache Software Foundation. Architecturally it sits on Apache Hadoop and runs by using Hadoop Distributed File System (HDFS) as its foundation.

It is a column-oriented database, empowered by a fault-tolerant distributed file structure known as HDFS. In addition to this, it also provides very advanced features, such as auto sharding, load-balancing, in-memory caching, replication, compression, near real-time lookups, strong consistency (using multi-version). It uses the latest concepts of block cache and bloom filter to provide faster response to online/real-time request. It supports multiple clients running on heterogeneous platforms by providing user-friendly APIs.

In this chapter, we will discuss how to effectively set up mid and large size HBase cluster on top of Hadoop/HDFS framework.

This chapter will help you set up HBase on a fully distributed cluster. For cluster setup, we will consider REH (RedHat Enterprise-6.2 Linux 64 bit); for our setup we will be using six nodes.

End of Section 2
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