4. Optimizing the HBase/Hadoop Cluster | Learning Hbase

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Learning Hbase

By : Shashwat Shriparv

4.1 (8)

Learning Hbase

4.1 (8)

By: Shashwat Shriparv

Overview of this book

If you are an administrator or developer who wants to enter the world of Big Data and BigTables and would like to learn about HBase, this is the book for you.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Understanding the HBase Ecosystem

1. Understanding the HBase Ecosystem

HBase layout on top of Hadoop

Comparing architectural differences between RDBMs and HBase

HBase features

HBase in the Hadoop ecosystem

Comparing functional differences between RDBMs and HBase

About the internal storage architecture of HBase

Getting started with HBase

Applications of HBase

HBase pros and cons

Summary

2. Let's Begin with HBase

2. Let's Begin with HBase

Understanding HBase components in detail

Reading and writing cycle

HBase housekeeping

The HBase delete request

List of available HBase distributions

Prerequisites and capacity planning for HBase

Summary

3. Let's Start Building It

3. Let's Start Building It

Downloading Java on Ubuntu

Considering host configurations

Installing and configuring SSH

Installing and configuring NTP

Performing capacity planning

Installing and configuring Hadoop

Hadoop start up steps

Configuring Apache HBase

Installing and configuring ZooKeeper

Installing Cloudera Hadoop and HBase

Installing the Hadoop and MapReduce packages

Installing Hadoop on Windows

Summary

4. Optimizing the HBase/Hadoop Cluster

4. Optimizing the HBase/Hadoop Cluster

Setup types for Hadoop and HBase clusters

Recommendations for CDH cluster configuration

Capacity planning

Hadoop optimization

Optimizing HBase

Optimizing ZooKeeper

Important files in Hadoop

Important files in HBase

Summary

5. The Storage, Structure Layout, and Data Model of HBase

5. The Storage, Structure Layout, and Data Model of HBase

Data types in HBase

Storing data in HBase – logical view versus actual physical view

Services of HBase

Data model operations

Versioning and why

Deciding the number of the version

Schema designing

Calculating the data size stored in HBase

Summary

6. HBase Cluster Maintenance and Troubleshooting

6. HBase Cluster Maintenance and Troubleshooting

Hadoop shell commands

HBase shell commands

HBase administration tools

Writing HBase shell scripts

Using the Hadoop tool or JARs for HBase

Connecting HBase with Hive

HBase region management

HBase node management

Implementing security

Troubleshooting the most frequent HBase errors and their explanations

Summary

7. Scripting in HBase

7. Scripting in HBase

HBase backup and restore techniques

HBase on Windows

Scripting in HBase

Contributing to HBase

Summary

8. Coding HBase in Java

8. Coding HBase in Java

Setting up the environment for development

Data types

Data model Java operations

HBase filters

Client APIs

Summary

9. Advance Coding in Java for HBase

9. Advance Coding in Java for HBase

Interfaces, classes, and exceptions

Code related to administrative tasks

Data operation code

MapReduce and HBase

RESTful services and Thrift services interface

Coding for HDFS operations

Some advance topics in brief

Summary

10. HBase Use Cases

10. HBase Use Cases

HBase in industry today

The future of HBase against relational databases

Some real-world project examples' use cases

Useful links and references

Summary

Index

Index

Setup types for Hadoop and HBase clusters

Now, let's see the files and their parameters in Hadoop, using which optimization can be performed. Mentioned next are examples of Hadoop/HBase cluster types. When we configure a Hadoop/HBase cluster, we can have the following types of clusters, according to their usability:

Standalone: This cluster type is suitable for development work where one machine can host all the daemon processes or we have a single machine with many virtual machines on a single system. This type of cluster is good for evaluation and testing purposes.
Small: We can have less than or equal to 20 nodes with different processes running of different machines. It is good for small productions with less data and processing requirements.
Medium: This cluster type can have 20 to 1000 nodes with HA, three to five ZooKeepers, and DataNodes, which is better for full-fledged production clusters.
Large: This cluster type can have 1000 or more nodes with huge storage capacity and several...

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