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

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Bibliographic Details
Main Author:	White, Tom (Author)
Format:	Book
Language:	English
Published:	Beijing [u.a.] O'Reilly 2011
Edition:	2. ed., rev. and updated
Subjects:	Hadoop Cluster-Analyse Open Source Big Data Verteiltes System Dateiorganisation Framework > Informatik
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021094719&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Physical Description:	XXII, 600 S. Ill., graph. Darst.
ISBN:	9781449389734

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MARC


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Record in the Search Index

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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 RDBMS 4 Grid Computing 6 Volunteer Computing 8 A Brief History of Hadoop 9 Apache Hadoop and the Hadoop Ecosystem 12 2. MapReduce .15 A Weather Dataset 15 Data Format 15 Analyzing the Data with Unix Tools 17 Analyzing the Data with Hadoop 18 Map and Reduce 18 Java MapReduce 20 Scaling Out 27 Data Flow 28 Combiner Functions 30 Running a Distributed MapReduce Job 33 Hadoop Streaming 33 Ruby 33 Python 36 Hadoop Pipes 37 Compiling and Running 38 3. The Hadoop Distributed Filesystem . 41 The Design of HDFS 41 HDFS Concepts 43 Blocks 43 Namenodes and Datanodes 44 The Command-Line Interface 45 Basic Filesystem Operations 46 Hadoop Filesystems 47 Interfaces 49 The Java Interface -51 Reading Data from a Hadoop URL 51 Reading Data Using the FileSystem API 52 Writing Data 55 Directories 57 Querying the Filesystem 57 Deleting Data 62 Data Flow 62 Anatomy of a File Read 62 Anatomy of a File Write 65 Coherency Model 68 Parallel Copying with distcp 70 Keeping an HDFS Cluster Balanced 71 Hadoop Archives 71 Using Hadoop Archives 72 Limitations 73 4. Hadoop I/O . 75 Data Integrity 75 Data Integrity in HDFS 75 LocalFileSystem 76 ChecksumFileSystem 77 Compression 77 Codecs 78 Compression and Input Splits 83 Using Compression in MapReduce 84 Serialization 86 The Writable Interface 87 Writable Classes 89 Implementing a Custom Writable 96 Serialization Frameworks 101 Avrò ЮЗ File-Based Data Structures 116 SequenceFile Ц6 vi I Table of Contents MapFile 123 5. Developing a MapReduce Application .129 The Configuration API 130 Combining Resources 131 Variable Expansion 132 Configuring the Development Environment 132 Managing Configuration 132 GenericOptionsParser, Tool, and ToolRunner 135 Writing a Unit Test 138 Mapper 138 Reducer 140 Running Locally on Test Data 141 Running a Job in a Local Job Runner 141 Testing the Driver 145 Running on a Cluster 146 Packaging 146 Launching a Job 146 The MapReduce Web UI 148 Retrieving the Results 151 Debugging a Job 153 Using a Remote Debugger 158 Tuning a Job 160 Profiling Tasks 160 MapReduce Workflows 163 Decomposing a Problem into MapReduce Jobs 163 Running Dependent Jobs 165 6. How MapReduce Works .167 Anatomy of a MapReduce Job Run 167 Job Submission 167 Job Initialization 169 Task Assignment 169 Task Execution 170 Progress and Status Updates 170 Job Completion 172 Failures 173 Task Failure 173 Tasktracker Failure 175 Jobtracker Failure 175 Job Scheduling 175 The Fair Scheduler 176 The Capacity Scheduler 177 Table of Contents I vii Shuffle and Sort 177 The Map Side 177 The Reduce Side 179 Configuration Tuning 180 Task Execution 183 Speculative Execution 183 Task JVM Reuse 184 Skipping Bad Records 185 The Task Execution Environment 186 7. MapReduce Types and Formats .189 MapReduce Types 189 The Default MapReduce Job 191 Input Formats 198 Input Splits and Records 198 Text Input 209 Binary Input 213 Multiple Inputs 214 Database Input (and Output) 215 Output Formats 215 Text Output 216 Binary Output 216 Multiple Outputs 217 Lazy Output 224 Database Output 224 8. MapReduce Features .225 Counters 225 Built-in Counters 225 User-Defined Java Counters 227 User-Defined Streaming Counters 232 Sorting 232 Preparation 232 Partial Sort 233 Total Sort 237 Secondary Sort 241 Joins 247 Map-Side Joins 247 Reduce-Side Joins 249 Side Data Distribution 252 Using the Job Configuration 252 TV~*~L" " ' ~ ' 253 257 Distributed Cache MapReduce Library Classes viii I Table of Contents 9. Setting Up a Hadoop Cluster .259 Cluster Specification 259 Network Topology 261 Cluster Setup and Installation 263 Installing Java 264 Creating a Hadoop User 264 Installing Hadoop 264 Testing the Installation 265 SSH Configuration 265 Hadoop Configuration 266 Configuration Management 267 Environment Settings 269 Important Hadoop Daemon Properties 273 Hadoop Daemon Addresses and Ports 278 Other Hadoop Properties 279 User Account Creation 280 Security 281 Kerberos and Hadoop 282 Delegation Tokens 284 Other Security Enhancements 285 Benchmarking a Hadoop Cluster 286 Hadoop Benchmarks 287 User Jobs 289 Hadoop in the Cloud 289 Hadoop on Amazon EC2 290 10. Administering Hadoop .293 HDFS 293 Persistent Data Structures 293 Safe Mode 298 Audit Logging 300 Tools 300 Monitoring 305 Logging 305 Metrics 306 Java Management Extensions 309 Maintenance 312 Routine Administration Procedures 312 Commissioning and Decommissioning Nodes 313 Upgrades 316 n. Pig .321 Installing and Running Pig 322 Table of Contents \| ix Execution Types 322 Running Pig Programs 324 Grunt 324 Pig Latin Editors 325 An Example 32^ Generating Examples 327 Comparison with Databases 328 Pig Latin 330 Structure 330 Statements 331 Expressions 335 Types 336 Schemas 338 Functions 342 User-Defined Functions 343 A Filter UDF 343 An Eval UDF 347 A Load UDF 348 Data Processing Operators 351 Loading and Storing Data 351 Filtering Data 352 Grouping and Joining Data 354 Sorting Data 359 Combining and Solitrinp Пягя Pig in Practice Combining and Splitting Data 360 g in Practice 361 Parallelism 361 rarameter substitution 362 12. Hive . 365 Installing Hive 366 The Hive Shell 367 An Example 368 Running Hive 369 Configuring Hive 369 Hive Services 371 The Metastore 373 Comparison with Traditional Databases Schema on Read Versus Schema on Write 376 Updates, Transactions, and Indexes 375 . .,_ .«».vuuiij, auu UlUCXtS 376 HiveQL 377 Data Types 378 Operators and Functions 380 Tables 381 χ \| Table of Contents Managed Tables and External Tables 381 Partitions and Buckets 383 Storage Formats 387 Importing Data 392 Altering Tables 394 Dropping Tables 395 Querying Data 395 Sorting and Aggregating 395 MapReduce Scripts 396 Joins 397 Subqueries 400 Views 401 User-Defined Functions 402 Writing a UDF 403 Writing a UDAF 405 13. HBase .411 HBasics 411 Backdrop 412 Concepts 412 Whirlwind Tour of the Data Model 412 Implementation 413 Installation 416 Test Drive 417 Clients 419 Java 419 Avrò, REST, and Thrift 422 Example 423 Schemas 424 Loading Data 425 Web Queries 428 HBase Versus RDBMS 431 Successful Service 432 HBase 433 Use Case: HBase at Streamy.com 433 Praxis 435 Versions 435 HDFS 436 UI 437 Metrics 437 Schema Design 438 Counters 438 Bulk Load 439 Table of Contents \| xi 14. ZooKeeper .441 Installing and Running ZooKeeper 442 An Example 443 Group Membership in ZooKeeper 444 Creating the Group 444 Joining a Group 447 Listing Members in a Group 448 Deleting a Group 450 The ZooKeeper Service 451 Data Model 451 Operations 453 Implementation 457 Consistency 458 Sessions 460 States 462 Building Applications with ZooKeeper 463 A Configuration Service 463 The Resilient ZooKeeper Application 466 A Lock Service 470 More Distributed Data Structures and Protocols 472 ZooKeeper in Production 473 Resilience and Performance 473 Configuration 474 15. Sqoop .477 Getting Sqoop 477 A Sample Import 479 Generated Code 482 Additional Serialization Systems 482 Database Imports: A Deeper Look 483 Controlling the Import 485 Imports and Consistency 485 Direct-mode Imports 485 Working with Imported Data 486 Imported Data and Hive 487 Importing Large Objects 489 Performing an Export 491 Exports: A Deeper Look 493 Exports and Transactionality 494 Exports and SequenceFiles 494 16. Case Studies . 497 Hadoop Usage at Last.fm 497 xii I Table of Contents Last.fm: The Social Music Revolution 497 Hadoop at Last.fm 497 Generating Charts with Hadoop 498 The Track Statistics Program 499 Summary 506 Hadoop and Hive at Facebook 506 Introduction 506 Hadoop at Facebook 506 Hypothetical Use Case Studies 509 Hive 512 Problems and Future Work 516 Nutch Search Engine 517 Background 517 Data Structures 518 Selected Examples of Hadoop Data Processing in Nutch 521 Summary 530 Log Processing at Rackspace 531 Requirements/The Problem 531 Brief History 532 Choosing Hadoop 532 Collection and Storage 532 MapReduce for Logs 533 Cascading 539 Fields, Tuples, and Pipes 540 Operations 542 Taps, Schemes, and Flows 544 Cascading in Practice 545 Flexibility 548 Hadoop and Cascading at ShareThis 549 Summary 552 TeraByte Sort on Apache Hadoop 553 Using Pig and Wukong to Explore Billion-edge Network Graphs 556 Measuring Community 558 Everybody's Talkin' at Me: The Twitter Reply Graph 558 Symmetric Links 561 Community Extraction 562 A. Installing Apache Hadoop .565 B. Cloudera's Distribution for Hadoop . 571 С Preparing the NCDC Weather Data .573 Table of Contents I xiii Index . 577 xiv I Table of Contents
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spellingShingle	White, Tom Hadoop: the definitive guide [storage and analysis at internet scale] 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
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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: the definitive guide
title_sort	hadoop the definitive guide storage and analysis at internet scale
title_sub	[storage and analysis at internet scale]
topic	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	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=021094719&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT whitetom hadoopthedefinitiveguidestorageandanalysisatinternetscale

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