HBase Administration Cookbook

HBase Administration Cookbook

By : Yifeng Jiang

Buy this Book

HBase Administration Cookbook

By: Yifeng Jiang

Buy this Book

Overview of this book

As an Open Source distributed big data store, HBase scales to billions of rows, with millions of columns and sits on top of the clusters of commodity machines. If you are looking for a way to store and access a huge amount of data in real-time, then look no further than HBase.HBase Administration Cookbook provides practical examples and simple step-by-step instructions for you to administrate HBase with ease. The recipes cover a wide range of processes for managing a fully distributed, highly available HBase cluster on the cloud. Working with such a huge amount of data means that an organized and manageable process is key and this book will help you to achieve that.The recipes in this practical cookbook start from setting up a fully distributed HBase cluster and moving data into it. You will learn how to use all of the tools for day-to-day administration tasks as well as for efficiently managing and monitoring the cluster to achieve the best performance possible. Understanding the relationship between Hadoop and HBase will allow you to get the best out of HBase so the book will show you how to set up Hadoop clusters, configure Hadoop to cooperate with HBase, and tune its performance.

HBase Administration Cookbook

Credits

About the Author

Acknowledgement

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

Setting Up HBase Cluster

Introduction

Quick start

Getting ready on Amazon EC2

Setting up Hadoop

Setting up ZooKeeper

Changing the kernel settings

Setting up HBase

Basic Hadoop/ZooKeeper/HBase configurations

Setting up multiple High Availability (HA) masters

Data Migration

Introduction

Importing data from MySQL via single client

Importing data from TSV files using the bulk load tool

Writing your own MapReduce job to import data

Precreating regions before moving data into HBase

Using Administration Tools

Introduction

HBase Master web UI

Using HBase Shell to manage tables

Using HBase Shell to access data in HBase

Using HBase Shell to manage the cluster

Executing Java methods from HBase Shell

Row counter

WAL tool—manually splitting and dumping WALs

HFile tool—viewing textualized HFile content

HBase hbck—checking the consistency of an HBase cluster

Hive on HBase—querying HBase using a SQL-like language

Backing Up and Restoring HBase Data

Introduction

Full shutdown backup using distcp

Using CopyTable to copy data from one table to another

Exporting an HBase table to dump files on HDFS

Restoring HBase data by importing dump files from HDFS

Backing up NameNode metadata

Backing up region starting keys

Cluster replication

Monitoring and Diagnosis

Introduction

Showing the disk utilization of HBase tables

Setting up Ganglia to monitor an HBase cluster

OpenTSDB—using HBase to monitor an HBase cluster

Setting up Nagios to monitor HBase processes

Using Nagios to check Hadoop/HBase logs

Simple scripts to report the status of the cluster

Hot region—write diagnosis

Maintenance and Security

Introduction

Enabling HBase RPC DEBUG-level logging

Graceful node decommissioning

Adding nodes to the cluster

Rolling restart

Simple script for managing HBase processes

Simple script for making deployment easier

Kerberos authentication for Hadoop and HBase

Configuring HDFS security with Kerberos

HBase security configuration

Troubleshooting

Introduction

Troubleshooting tools

Handling the XceiverCount error

Handling the "too many open files" error

Handling the "unable to create new native thread" error

Handling the "HBase ignores HDFS client configuration" issue

Handling the ZooKeeper client connection error

Handling the ZooKeeper session expired error

Handling the HBase startup error on EC2

Basic Performance Tuning

Introduction

Setting up Hadoop to spread disk I/O

Using network topology script to make Hadoop rack-aware

Mounting disks with noatime and nodiratime

Setting vm.swappiness to 0 to avoid swap

Java GC and HBase heap settings

Using compression

Managing compactions

Managing a region split

Advanced Configurations and Tuning

Introduction

Benchmarking HBase cluster with YCSB

Increasing region server handler count

Precreating regions using your own algorithm

Avoiding update blocking on write-heavy clusters

Tuning memory size for MemStores

Client-side tuning for low latency systems

Configuring block cache for column families

Client side scanner setting

Tuning block size to improve seek performance

Enabling Bloom Filter to improve the overall throughput

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Introduction

This is another chapter about performance tuning. In Chapter 8, Basic Performance Tuning, we described some recipes to tune Hadoop, OS settings, Java, and HBase itself to improve the overall performance of the HBase cluster. Those are general improvements for many use cases. In this chapter, we will describe more "specific" recipes; some of them are for write-heavy clusters, while some are aimed to improve read performance of the cluster.

Before tuning a HBase cluster, you will need to know how its performance is. Therefore, we will start by introducing how to use Yahoo! Cloud Serving Benchmark (YCSB) to measure (benchmark) performance of a HBase cluster.

In the recipe Precreating regions before moving data into HBase in Chapter 2, we introduced how to use HBase's RegionSplitter utility to create a table with precreated regions to improve data loading speed. While RegionSplitter by default precreate regions with MD5 number boundaries, for situations where row keys cannot be represented...

HBase Administration Cookbook

By : Yifeng Jiang

HBase Administration Cookbook

By: Yifeng Jiang

Overview of this book

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HBase Administration Cookbook

Introduction