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Architecting HBase Applications [Pehme köide]

3.71/5 (14 hinnangut Goodreads-ist)

Kevin Odell, Jeanmarc Spaggiari

Formaat: Paperback / softback, 252 pages, kõrgus x laius x paksus: 248x170x13 mm, kaal: 438 g
Ilmumisaeg: 06-Sep-2016
Kirjastus: O'Reilly Media
ISBN-10: 1491915811
ISBN-13: 9781491915813

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Formaat: Paperback / softback, 252 pages, kõrgus x laius x paksus: 248x170x13 mm, kaal: 438 g
Ilmumisaeg: 06-Sep-2016
Kirjastus: O'Reilly Media
ISBN-10: 1491915811
ISBN-13: 9781491915813

Teised raamatud teemal:

Püsilink: https://www.kriso.ee/db/9781491915813.html

Märksõnad:

Lots of HBase books, online HBase guides, and HBase mailing lists/forums are available if you need to know how HBase works. But if you want to take a deep dive into use cases, features, and troubleshooting, Architecting HBase Applications is the right source for you.

With this book, you’ll learn a controlled set of APIs that coincide with use-case examples and easily deployed use-case models, as well as sizing/best practices to help jump start your enterprise application development and deployment.

Learn design patterns—and not just components—necessary for a successful HBase deployment
Go in depth into all the HBase shell operations and API calls required to implement documented use cases
Become familiar with the most common issues faced by HBase users, identify the causes, and understand the consequences
Learn document-specific API calls that are tricky or very important for users
Get use-case examples for every topic presented

Foreword

Preface

xiii

Part I. Introduction to HBase

1 What Is HBase?

(4)

Column-Oriented Versus Row-Oriented

(1)

Implementation and Use Cases

(2)

2 HBase Principles

(18)

Table Format

(8)

Table Layout

(1)

Table Storage

(6)

Internal Table Operations

(4)

Compaction

(2)

Splits (Auto-Sharding)

(2)

Balancing

(1)

Dependencies

(1)

HBase Roles

(5)

Master Server

(1)

Region Server

(1)

Thrift Server

(1)

REST Server

(3)

3 HBase Ecosystem

(14)

Monitoring Tools

(8)

Cloudera Manager

(2)

Apache Ambari

(4)

Hannibal

(1)

SQL

(2)

Apache Phoenix

(1)

Apache Trafodion

(1)

Splice Machine

(1)

Honorable Mentions (Kylin, Themis, Tephra, Hive, and Impala)

(1)

Frameworks

(4)

OpenTSDB

(1)

Kite

(1)

HappyBase

(1)

AsyncHBase

(2)

4 HBase Sizing and Tuning Overview

(10)

Hardware

(1)

Storage

(1)

Networking

(1)

OS Tuning

(1)

Hadoop Tuning

(1)

HBase Tuning

(2)

Different Workload Tuning

(3)

5 Environment Setup

(24)

System Requirements

(3)

Operating System

(1)

Virtual Machine

(2)

Resources

(1)

Java

(1)

HBase Standalone Installation

(3)

HBase in a VM

(1)

Local Versus VM

(2)

Local Mode

(1)

Virtual Linux Environment

(1)

QuickStart VM (or Equivalent)

(1)

Troubleshooting

(2)

IP/Name Configuration

(1)

Access to the /tmp Folder

(1)

Environment Variables

(1)

Available Memory

(1)

First Steps

(7)

Basic Operations

(1)

Import Code Examples

(4)

Testing the Examples

(2)

Pseudodistributed and Fully Distributed

(5)

Part II. Use Cases

6 Use Case: HBase as a System of Record

(10)

Ingest/Pre-Processing

(1)

Processing/Serving

(4)

User Experience

(4)

7 Implementation of an Underlying Storage Engine

(24)

Table Design

(5)

Table Schema

(1)

Table Parameters

(2)

Implementation

(1)

Data conversion

(6)

Generate Test Data

(1)

Create Avro Schema

(1)

Implement MapReduce Transformation

(5)

HFile Validation

(1)

Bulk Loading

(1)

Data Validation

(4)

Table Size

(1)

File Content

(2)

Data Indexing

100

(4)

Data Retrieval

104

(1)

Going Further

105

(2)

8 Use Case: Near Real-Time Event Processing

107

(8)

Ingest/Pre-Processing

110

(1)

Near Real-Time Event Processing

111

(1)

Processing/Serving

112

(3)

9 Implementation of Near Real-Time Event Processing

115

(26)

Application Flow

117

(4)

Kafka

117

(1)

Flume

118

(1)

HBase

118

(2)

Lily

120

(1)

Solr

120

(1)

Implementation

121

(19)

Data Generation

121

(1)

Kafka

122

(1)

Flume

123

(7)

Serializer

130

(4)

HBase

134

(2)

Lily

136

(2)

Solr

138

(1)

Testing

139

(1)

Going Further

140

(1)

10 Use Case: HBase as a Master Data Management Tool

141

(6)

Ingest

142

(1)

Processing

143

(4)

11 Implementation of HBase as a Master Data Management Tool

147

(14)

MapReduce Versus Spark

147

(1)

Get Spark Interacting with HBase

148

(1)

Run Spark over an HBase Table

148

(1)

Calling HBase from Spark

148

(1)

Implementing Spark with HBase

149

(11)

Spark and HBase: Puts

150

(4)

Spark on HBase: Bulk Load

154

(2)

Spark Over HBase

156

(4)

Going Further

160

(1)

12 Use Case: Document Store

161

(6)

Serving

163

(1)

Ingest

164

(2)

Clean Up

166

(1)

13 Implementation of Document Store

167

(10)

MOBs

167

(5)

Storage

169

(1)

Usage

170

(1)

Too Big

170

(2)

Consistency

172

(1)

Going Further

173

(4)

Part III. Troubleshooting

14 Too Many Regions

177

(14)

Consequences

177

(1)

Causes

178

(1)

Misconfiguration

178

(1)

Misoperation

179

(1)

Solution

179

(8)

Before 0.98

179

(6)

Starting with 0.98

185

(2)

Prevention

187

(4)

Regions Size

187

(2)

Key and Table Design

189

(2)

15 Too Many Column Families

191

(6)

Consequences

192

(1)

Memory

192

(1)

Compactions

193

(1)

Split

193

(1)

Causes, Solution, and Prevention

193

(4)

Delete a Column Family

194

(1)

Merge a Column Family

194

(2)

Separate a Column Family into a New Table

196

(1)

16 Hotspotting

197

(6)

Consequences

197

(1)

Causes

198

(2)

Monotonically Incrementing Keys

198

(1)

Poorly Distributed Keys

198

(1)

Small Reference Tables

199

(1)

Applications Issues

200

(1)

Meta Region Hotspotting

200

(1)

Prevention and Solution

200

(3)

17 Timeouts and Garbage Collection

203

(10)

Consequences

203

(3)

Causes

206

(1)

Storage Failure

206

(1)

Power-Saving Features

206

(1)

Network Failure

207

(1)

Solutions

207

(1)

Prevention

207

(6)

Reduce Heap Size

208

(1)

Off-Heap BlockCache

208

(1)

Using the GIGC Algorithm

209

(1)

Configure Swappiness to 0 or 1

210

(1)

Disable Environment-Friendly Features

211

(1)

Hardware Duplication

211

(2)

18 HBCK and Inconsistencies

213

(10)

HBase Filesystem Layout

213

(1)

Reading META

214

(1)

Reading HBase on HDFS

215

(2)

General HBCK Overview

217

(1)

Using HBCK

218

(5)

Index

223

Jean-Marc Spaggiari, an HBase contributor since 2012, works as an HBase specialist Solutions Architect for Cloudera to support Hadoop and HBase through technical support and consulting work. He has worked with some of the biggest HBase users in North America. Kevin O'Dell has been an HBase contributor since 2012 where he has been active in the community. Kevin has spoken at numerous Hadoop User Groups, Hadoop Summit, and HBaseCons. Kevin currently works as a Systems Engineer for Cloudera building Big Data applications with a specialization in HBase.

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