Hadoop: the definitive guide: [storage and analysis at internet scale]
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing [u.a.]
O'Reilly
2011
|
| Ausgabe: | 2. ed., rev. and updated |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXII, 600 S. Ill., graph. Darst. |
| ISBN: | 9781449389734 |
Internformat
MARC
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| 245 | 1 | 0 | |a Hadoop: the definitive guide |b [storage and analysis at internet scale] |c Tom White |
| 250 | |a 2. ed., rev. and updated | ||
| 264 | 1 | |a Beijing [u.a.] |b O'Reilly |c 2011 | |
| 300 | |a XXII, 600 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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Datensatz im Suchindex
| _version_ | 1804143760956719104 |
|---|---|
| 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
|
| any_adam_object | 1 |
| author | White, Tom |
| author_facet | White, Tom |
| author_role | aut |
| author_sort | White, Tom |
| author_variant | t w tw |
| building | Verbundindex |
| bvnumber | BV037180091 |
| classification_rvk | ST 201 ST 230 |
| ctrlnum | (OCoLC)700461100 (DE-599)HBZHT016596024 |
| discipline | Informatik |
| edition | 2. ed., rev. and updated |
| format | Book |
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| id | DE-604.BV037180091 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:52:47Z |
| institution | BVB |
| isbn | 9781449389734 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-021094719 |
| oclc_num | 700461100 |
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| physical | XXII, 600 S. Ill., graph. Darst. |
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| publisher | O'Reilly |
| record_format | marc |
| spelling | White, Tom Verfasser aut Hadoop: the definitive guide [storage and analysis at internet scale] Tom White 2. ed., rev. and updated Beijing [u.a.] O'Reilly 2011 XXII, 600 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Verteiltes System (DE-588)4238872-7 gnd rswk-swf Hadoop (DE-588)1022420135 gnd rswk-swf Dateiorganisation (DE-588)4193494-5 gnd rswk-swf Open Source (DE-588)4548264-0 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Cluster-Analyse (DE-588)4070044-6 gnd rswk-swf Framework Informatik (DE-588)4464685-9 gnd rswk-swf Hadoop (DE-588)1022420135 s DE-604 Verteiltes System (DE-588)4238872-7 s Framework Informatik (DE-588)4464685-9 s Big Data (DE-588)4802620-7 s Open Source (DE-588)4548264-0 s Dateiorganisation (DE-588)4193494-5 s Cluster-Analyse (DE-588)4070044-6 s 1\p DE-604 Digitalisierung UB Bayreuth application/pdf 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 Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | White, Tom Hadoop: the definitive guide [storage and analysis at internet scale] Verteiltes System (DE-588)4238872-7 gnd Hadoop (DE-588)1022420135 gnd Dateiorganisation (DE-588)4193494-5 gnd Open Source (DE-588)4548264-0 gnd Big Data (DE-588)4802620-7 gnd Cluster-Analyse (DE-588)4070044-6 gnd Framework Informatik (DE-588)4464685-9 gnd |
| subject_GND | (DE-588)4238872-7 (DE-588)1022420135 (DE-588)4193494-5 (DE-588)4548264-0 (DE-588)4802620-7 (DE-588)4070044-6 (DE-588)4464685-9 |
| 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 | Verteiltes System (DE-588)4238872-7 gnd Hadoop (DE-588)1022420135 gnd Dateiorganisation (DE-588)4193494-5 gnd Open Source (DE-588)4548264-0 gnd Big Data (DE-588)4802620-7 gnd Cluster-Analyse (DE-588)4070044-6 gnd Framework Informatik (DE-588)4464685-9 gnd |
| topic_facet | Verteiltes System Hadoop Dateiorganisation Open Source Big Data Cluster-Analyse 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 |