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Hadoop: the definitive guide ; [storage and analysis at Internet scale]

Contents: Meet Hadoop -- MapReduce -- The Hadoop distributed filesystem -- Hadoop I/O -- Developing a MapReduce application -- How MapReduce works -- MapReduce types and formats -- MapReduce features -- Setting up a Hadoop cluster -- Administering Hadoop -- Pig -- Hive -- HBase -- ZooKepper -- Sqoop...

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Bibliographic Details
Main Author:	White, Tom (Author)
Format:	Book
Language:	English
Published:	Beijing [u.a.] O'Reilly 2012
Edition:	3. ed., [rev. & updated]
Subjects:	Apache Hadoop File organization (Computer science) Hadoop Cluster-Analyse Open Source Big Data Verteiltes System Dateiorganisation Framework > Informatik
Online Access:	Inhaltsverzeichnis
Summary:	Contents: Meet Hadoop -- MapReduce -- The Hadoop distributed filesystem -- Hadoop I/O -- Developing a MapReduce application -- How MapReduce works -- MapReduce types and formats -- MapReduce features -- Setting up a Hadoop cluster -- Administering Hadoop -- Pig -- Hive -- HBase -- ZooKepper -- Sqoop -- Case studies -- Installing Apache Hadoop -- Cloudera's distribution including Apache Hadoop -- Preparing the NCDC weather data.
Physical Description:	XXIII, 657 S. Ill., graph. Darst.
ISBN:	9781449311520 1449311520

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MARC


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adam_text	Table of Contents Foreword .xv Preface .xvii 1. Meet Hadoop .1 Data! 1 Data Storage and Analysis 3 Comparison with Other Systems 4 Rational Database Management System 4 Grid Computing 6 Volunteer Computing 8 A Brief History of Hadoop 9 Apache Hadoop and the Hadoop Ecosystem 12 Hadoop Releases 13 What's Covered in This Book 15 Compatibility 15 2. MapReduce .17 A Weather Dataset 17 Data Format 17 Analyzing the Data with Unix Tools 19 Analyzing the Data with Hadoop 20 Map and Reduce 20 Java MapReduce 22 Scaling Out 30 Data Flow 30 Combiner Functions 33 Running a Distributed MapReduce Job 36 Hadoop Streaming 36 Ruby 36 Python 39 Hadoop Pipes 40 Compiling and Running 41 3. The Hadoop Distributed Filesystem .43 The Design of HDFS 43 HDFS Concepts 45 Blocks 45 Namenodes and Datanodes 46 HDFS Federation 47 HDFS High-Availability 48 The Command-Line Interface 49 Basic Filesystem Operations 50 Hadoop Filesystems 52 Interfaces 53 The Java Interface 55 Reading Data from a Hadoop URL 55 Reading Data Using the FileSystem API 57 Writing Data 60 Directories 62 Querying the Filesystem 62 Deleting Data 67 Data Flow 67 Anatomy of a File Read 67 Anatomy of a File Write 70 Coherency Model 72 Data Ingest with Flume and Sqoop 74 Parallel Copying with distcp 75 Keeping an HDFS Cluster Balanced 76 Hadoop Archives 77 Using Hadoop Archives 77 Limitations 79 4. Hadoop I/O . 81 Data Integrity 81 Data Integrity in HDFS 81 LocalFileSystem 82 ChecksumFileSystem 83 Compression 83 Codecs 85 Compression and Input Splits 89 Using Compression in MapReduce 90 Serialization 93 The Writable Interface 94 vi I Table of Contents Writable Classes Implementing a Custom Writable 103 Serialization Frameworks 108 Avrò 110 Avrò Data Types and Schemas 111 In-Memory Serialization and Deserialization 114 Avrò Datafiles 117 Interoperability 118 Schema Resolution 121 Sort Order 123 Avrò MapReduce 124 Sorting Using Avrò MapReduce 128 Avrò MapReduce in Other Languages 130 File-Based Data Structures 130 SequenceFile 130 MapFile 137 5. Developing a MapReduce Application . . 143 The Configuration API 144 Combining Resources 145 Variable Expansion 146 Setting Up the Development Environment 146 Managing Configuration 148 GenericOptionsParser, Tool, and ToolRunner 150 Writing a Unit Test with MRUnit 154 Mapper 154 Reducer 156 Running Locally on Test Data 157 Running a Job in a Local Job Runner 157 Testing the Driver 160 Running on a Cluster 161 Packaging a Job 162 Launching a Job 163 The MapReduce Web UI 165 Retrieving the Results 168 Debugging a Job 170 Hadoop Logs 175 Remote Debugging 177 Tuning a Job 178 Profiling Tasks 179 MapReduce Workflows 181 Decomposing a Problem into MapReduce Jobs 181 JobControl 183 Table of Contents ! vii Apache Oozie 183 6. How MapReduce Works .189 Anatomy of a MapReduce Job Run 189 Classic MapReduce (MapReduce 1) 190 YARN (MapReduce 2) 196 Failures 202 Failures in Classic MapReduce 202 Failures in YARN 204 Job Scheduling 206 The Fair Scheduler 207 The Capacity Scheduler 207 Shuffle and Sort 208 The Map Side 208 The Reduce Side 210 Configuration Tuning 211 Task Execution 214 The Task Execution Environment 215 Speculative Execution 215 Output Committers 217 Task JVM Reuse 219 Skipping Bad Records 220 7. MapReduce Types and Formats .223 MapReduce Types 223 The Default MapReduce Job 227 Input Formats 234 Input Splits and Records 234 Text Input 245 Binary Input 249 Multiple Inputs 250 Database Input (and Output) 251 Output Formats 251 Text Output 252 Binary Output 253 Multiple Outputs 253 Lazy Output 257 Database Output 258 8. MapReduce Features .259 Counters 259 Built-in Counters 259 User-Defined Java Counters 264 viii I Table of Contents User-Defined Streaming Counters 268 Sorting 268 Preparation 269 Partial Sort 270 Total Sort 274 Secondary Sort 277 Joins 283 Map-Side Joins 284 Reduce-Side Joins 285 Side Data Distribution 288 Using the Job Configuration 288 Distributed Cache 289 MapReduce Library Classes 295 9. Setting Up a Hadoop Cluster .297 Cluster Specification 297 Network Topology 299 Cluster Setup and Installation 301 Installing Java 302 Creating a Hadoop User 302 Installing Hadoop 302 Testing the Installation 303 SSH Configuration 303 Hadoop Configuration 304 Configuration Management 305 Environment Settings 307 Important Hadoop Daemon Properties 311 Hadoop Daemon Addresses and Ports 316 Other Hadoop Properties 317 User Account Creation 320 YARN Configuration 320 Important YARN Daemon Properties 321 YARN Daemon Addresses and Ports 324 Security 325 Kerberos and Hadoop 326 Delegation Tokens 328 Other Security Enhancements 329 Benchmarking a Hadoop Cluster 331 Hadoop Benchmarks 331 User Jobs 333 Hadoop in the Cloud 334 Apache Whirr 334 Table of Contents I ¡x 10. Administering Hadoop .339 HDFS 339 Persistent Data Structures 339 Safe Mode 344 Audit Logging 346 Tools 347 Monitoring 351 Logging 352 Metrics 352 Java Management Extensions 355 Maintenance 358 Routine Administration Procedures 358 Commissioning and Decommissioning Nodes 359 Upgrades 362 11. Pig . 367 Installing and Running Pig 368 Execution Types 368 Running Pig Programs 370 Grunt 370 Pig Latin Editors 371 An Example 371 Generating Examples 373 Comparison with Databases 374 Pig Latin 375 Structure 376 Statements 377 Expressions 381 Types 382 Schemas 384 Functions 388 Macros 390 User-Defined Functions 391 A Filter UDF 391 An Eval UDF 394 A Load UDF 396 Data Processing Operators 399 Loading and Storing Data 399 Filtering Data 400 Grouping and Joining Data 402 Sorting Data 407 Combining and Splitting Data 408 Pig in Practice 409 Table of Contents Parallelism 409 Parameter Substitution 410 12. Hive . 413 Installing Hive 414 The Hive Shell 415 An Example 416 Running Hive 417 Configuring Hive 417 Hive Services 419 The Metastore 421 Comparison with Traditional Databases 423 Schema on Read Versus Schema on Write 423 Updates, Transactions, and Indexes 424 HiveQL 425 Data Types 426 Operators and Functions 428 Tables 429 Managed Tables and External Tables 429 Partitions and Buckets 431 Storage Formats 435 Importing Data 441 Altering Tables 443 Dropping Tables 443 Querying Data 444 Sorting and Aggregating 444 MapReduce Scripts 445 Joins 446 Subqueries 449 Views 450 User-Defined Functions 451 Writing a UDF 452 Writing a UDAF 454 13. HBase .459 HBasics 459 Backdrop 460 Concepts 460 Whirlwind Tour of the Data Model 460 Implementation 461 Installation 464 Test Drive 465 Clients 467 Table of Contents I xi Java 467 Avrò, REST, and Thrift 470 Example 472 Schemas 472 Loading Data 473 Web Queries 476 HBase Versus RDBMS 479 Successful Service 480 HBase 481 Use Case: HBase at Streamy.com 481 Praxis 483 Versions 483 HDFS 484 UI 485 Metrics 485 Schema Design 486 Counters 486 Bulk Load 487 14. ZooKeeper .489 Installing and Running ZooKeeper 490 An Example 492 Group Membership in ZooKeeper 492 Creating the Group 493 Joining a Group 495 Listing Members in a Group 496 Deleting a Group 498 The ZooKeeper Service 499 Data Model 499 Operations 501 Implementation 506 Consistency 507 Sessions 509 States 511 Building Applications with ZooKeeper 512 A Configuration Service 512 The Resilient ZooKeeper Application 515 A Lock Service 519 More Distributed Data Structures and Protocols 521 ZooKeeper in Production 522 Resilience and Performance 523 Configuration 524 xii I Table of Contents 15 SaooD . .527 Getting Sqoop 527 Sqoop Connectors 529 A Sample Import 529 Text and Binary File Formats 532 Generated Code 532 Additional Serialization Systems 533 Imports: A Deeper Look 533 Controlling the Import 535 Imports and Consistency 536 Direct-mode Imports 536 Working with Imported Data 536 Imported Data and Hive 537 Importing Large Objects 540 Performing an Export 542 Exports: A Deeper Look 543 Exports and Transactionality 545 Exports and SequenceFiles 545 16. Case Studies . . 547 Hadoop Usage at Last.fm 547 Last.fm: The Social Music Revolution 547 Hadoop at Last.fm 547 Generating Charts with Hadoop 548 The Track Statistics Program 549 Summary 556 Hadoop and Hive at Facebook 556 Hadoop at Facebook 556 Hypothetical Use Case Studies 559 Hive 562 Problems and Future Work 566 Nutch Search Engine 567 Data Structures 568 Selected Examples of Hadoop Data Processing in Nutch 571 Summary 580 Log Processing at Rackspace 581 Requirements/The Problem 581 Brief History 582 Choosing Hadoop 582 Collection and Storage 582 MapReduce for Logs 583 Cascading 589 Fields, Tuples, and Pipes 590 Table of Contents 1 xiii Operations 593 Taps, Schemes, and Flows 594 Cascading in Practice 595 Flexibility 598 Hadoop and Cascading at ShareThis 599 Summary 603 Terabyte Sort on Apache Hadoop 603 Using Pig and Wukong to Explore Billion-edge Network Graphs 607 Measuring Community 609 Everybody's Talkin' at Me: The Twitter Reply Graph 609 Symmetric Links 612 Community Extraction 613 A. Installing Apache Hadoop .617 B. Cloudera's Distribution Including Apache Hadoop .623 С Preparingthe NCDC Weather Data .625 Index .629 xiv I Table of Contents
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title	Hadoop the definitive guide ; [storage and analysis at Internet scale]
title_auth	Hadoop the definitive guide ; [storage and analysis at Internet scale]
title_exact_search	Hadoop the definitive guide ; [storage and analysis at Internet scale]
title_full	Hadoop the definitive guide ; [storage and analysis at Internet scale] Tom White
title_fullStr	Hadoop the definitive guide ; [storage and analysis at Internet scale] Tom White
title_full_unstemmed	Hadoop the definitive guide ; [storage and analysis at Internet scale] Tom White
title_short	Hadoop
title_sort	hadoop the definitive guide storage and analysis at internet scale
title_sub	the definitive guide ; [storage and analysis at Internet scale]
topic	Apache Hadoop File organization (Computer science) Hadoop (DE-588)1022420135 gnd Cluster-Analyse (DE-588)4070044-6 gnd Open Source (DE-588)4548264-0 gnd Big Data (DE-588)4802620-7 gnd Verteiltes System (DE-588)4238872-7 gnd Dateiorganisation (DE-588)4193494-5 gnd Framework Informatik (DE-588)4464685-9 gnd
topic_facet	Apache Hadoop File organization (Computer science) Hadoop Cluster-Analyse Open Source Big Data Verteiltes System Dateiorganisation Framework Informatik
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025147414&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT whitetom hadoopthedefinitiveguidestorageandanalysisatinternetscale

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