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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| Sprache: | English |
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O'Reilly
2012
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| Ausgabe: | 3. ed., [rev. & updated] |
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| 100 | 1 | |a White, Tom |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Hadoop |b the definitive guide ; [storage and analysis at Internet scale] |c Tom White |
| 250 | |a 3. ed., [rev. & updated] | ||
| 264 | 1 | |a Beijing [u.a.] |b O'Reilly |c 2012 | |
| 300 | |a XXIII, 657 S. |b Ill., graph. Darst. | ||
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| 520 | |a 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. | ||
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| 650 | 4 | |a File organization (Computer science) | |
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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 |
| any_adam_object | 1 |
| author | White, Tom |
| author_facet | White, Tom |
| author_role | aut |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | 3. ed., [rev. & updated] |
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| id | DE-604.BV040292213 |
| illustrated | Illustrated |
| indexdate | 2024-11-25T17:37:10Z |
| institution | BVB |
| isbn | 9781449311520 1449311520 |
| language | English |
| lccn | 2012418418 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025147414 |
| oclc_num | 812195948 |
| open_access_boolean | |
| owner | DE-11 DE-473 DE-BY-UBG DE-945 DE-739 DE-863 DE-BY-FWS DE-706 DE-384 DE-91G DE-BY-TUM DE-83 DE-634 DE-526 DE-20 DE-19 DE-BY-UBM DE-573 |
| owner_facet | DE-11 DE-473 DE-BY-UBG DE-945 DE-739 DE-863 DE-BY-FWS DE-706 DE-384 DE-91G DE-BY-TUM DE-83 DE-634 DE-526 DE-20 DE-19 DE-BY-UBM DE-573 |
| physical | XXIII, 657 S. Ill., graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | O'Reilly |
| record_format | marc |
| spellingShingle | White, Tom Hadoop the definitive guide ; [storage and analysis at Internet scale] 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 |
| subject_GND | (DE-588)1022420135 (DE-588)4070044-6 (DE-588)4548264-0 (DE-588)4802620-7 (DE-588)4238872-7 (DE-588)4193494-5 (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 |
| 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 |