Hadoop: the definitive guide
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing [u.a.]
O'Reilly
2015
|
| Ausgabe: | 4. ed., [rev. & updated] |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | Auf dem Cover: "storage and analysis at internet scale" |
| Beschreibung: | XXV, 727 S. Ill, graph. Darst. |
| ISBN: | 9781491901632 1491901632 |
Internformat
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Datensatz im Suchindex
| _version_ | 1806331441762533376 |
|---|---|
| adam_text |
Table
of
Contents
Foreword
_. xvii
Preface
._»._._. xix
Part I. Hadoop Fundamentals
1.
Meet Hadoop.
._. 3
Data!
3
Data Storage and Analysis
5
Querying All Your Data
6
Beyond Batch
7
Comparison with Other Systems
8
Relational Database Management Systems
8
Grid Computing
10
Volunteer Computing
11
A Brief History of Apache Hadoop
12
What's in This Book?
15
2.
MapReduce
.,.19
A Weather
Dataset
19
Data Format
19
Analyzing the Data with Unix Tools
21
Analyzing the Data with Hadoop
22
Map and Reduce
22
Java MapReduce
24
Scaling Out
30
Data Flow
30
Combiner Functions
34
Running a Distributed MapReduce Job
37
Hadoop Streaming
37
Ruby
37
Python
40
3.
The Hadoop
Distributed
Filesystem
. 43
The Design
of HDFS
43
HDFS
Concepts
45
Blocks
45
Namenodes and
Datanodes
46
Block
Caching
47
HDFS Federation
48
HDFS
High Availability
48
The Command-Line Interface
50
Basic
Filesystem
Operations
51
Hadoop
Filesystems
53
Interfaces
54
The Java Interface
56
Reading Data from a Hadoop URL
57
Reading Data Using the FileSystem API
58
Writing Data
61
Directories
63
Querying the
Filesystem
63
Deleting Data
68
Data Flow
69
Anatomy of a File Read
69
Anatomy of a File Write
72
Coherency Model
74
Parallel Copying with distcp
76
Keeping an HDFS Cluster Balanced
77
4.
YARN.
. 79
Anatomy of a YARN Application Run
80
Resource Requests
81
Application
Lifespan 82
Building YARN Applications
82
YARN Compared to MapReduce
1 83
Scheduling in YARN
85
Scheduler Options
86
Capacity Scheduler Configuration
88
Fair Scheduler Configuration
90
Delay Scheduling
94
Dominant Resource Fairness
95
Further Reading
96
vi
I Table of Contents
Hadoopl/0
. 97
Data Integrity
97
Data Integrity in HDFS
98
LocalFileSystem
99
ChecksumFileSystem
99
Compression
100
Codecs
101
Compression and Input Splits
105
Using Compression in Map Reduce
107
Serialization
109
The Writable Interface
110
Writable Classes
113
Implementing a Custom Writable
121
Serialization Frameworks
126
File-Based Data Structures
127
SequenceFile
127
MapFile
135
Other File Formats and Column-Oriented Formats
136
Parti!. MapReduce
6.
Developing a MapReduce Application.
.
Ί4Ί
The Configuration API
141
Combining Resources
143
Variable Expansion
143
Setting Up the Development Environment
144
Managing Configuration
146
GenericOptionsParser, Tool, and ToolRunner
148
Writing a Unit Test with MRUnit
152
Mapper
153
Reducer
156
Running Locally on Test Data
156
Running a Job in a Local Job Runner
157
Testing the Driver
158
Running on a Cluster
160
Packaging a Job 1
60
Launching a Job
162
The MapReduce Web UI
165
Retrieving the Results
167
Debugging a Job
168
Hadoop Logs
172
fable of Contents I
vii
Remote
Debugging
174
Tuning
a Job
175
Profiling Tasks
175
MapReduce Workflows
177
Decomposing a Problem into MapReduce Jobs
177
JobControl
178
Apache Oozie
179
7.
How MapReduce Works
. 185
Anatomy of a MapReduce Job Run
185
Job Submission
186
Job Initialization
187
Task Assignment
188
Task Execution
189
Progress and Status Updates
190
Job Completion
192
Failures
193
Task Failure
193
Application Master Failure
194
Node Manager Failure
195
Resource Manager Failure
196
Shuffle and Sort
197
The Map Side
197
The Reduce Side
198
Configuration Tuning
201
Task Execution
203
The Task Execution Environment
203
Speculative Execution
204
Output Committers
206
8.
MapReduce Types and Formats.
. 209
MapReduce Types
209
The Default MapReduce Job
214
Input Formats
220
Input Splits and Records
220
Text Input
232
Binary Input
236
Multiple Inputs
237
Database Input (and Output)
238
Output Formats
238
Text Output
239
Binary Output
239
viii
I Table of Contents
Multiple Outputs 240
Lazy
Output 245
Database
Output 245
9. MapReduce Features.,.247
Counters
247
Built-in Counters
247
User-
Defined Java Counters
251
User-Defined Streaming Counters
255
Sorting
255
Preparation
256
Partial Sort
257
Total Sort
259
Secondary Sort
262
Joins
268
Map-Side Joins
269
Reduce-Side Joins
270
Side Data Distribution
273
Using the Job Configuration
273
Distributed Cache
274
Map Reduce Library Classes
279
Part III. Hadoop Operations
10.
Setting lip a Hadoop Cluster.
. —. 283
Cluster Specification
284
Cluster Sizing
285
Network Topology
286
Cluster Setup and Installation
288
Installing Java
288
Creating Unix User Accounts
288
Installing Hadoop
289
Configuring SSH
289
Configuring Hadoop
290
Formatting the HDFS
Filesystem
290
Starting and Stopping the Daemons
290
Creating User Directories
292
OCT.
Hadoop Configuration
Configuration Management
293
Environment Settings
294
Important Hadoop Daemon Properties
296
Table of Contents
] ix
Hadoop
Daemon Addresses and Ports
304
Other Hadoop Properties
307
Security
309
Kerberos and Hadoop
309
Delegation Tokens
312
Other Security Enhancements
313
Benchmarking a Hadoop Cluster
314
Hadoop Benchmarks
314
User Jobs
316
11.
Administering Hadoop
. 317
HDFS
317
Persistent Data Structures
317
Safe Mode
322
Audit Logging
324
Tools
325
Monitoring
330
Logging
330
Metrics and JMX
331
Maintenance
332
Routine Administration Procedures
332
Commissioning and Decommissioning Nodes
334
Upgrades
337
Part IV. Related Projects
12.
Avrò
. 345
Avrò Data
Types and
Schemas 346
In-Memory Serialization and Deserialization
349
The Specific API
351
Avrò
Datafiles 352
Interoperability
354
Python API
354
Avrò
Tools
355
Schema Resolution
355
Sort Order
358
Avrò
Map Reduce
359
Sorting Using
Avrò
Map Reduce
363
Avrò in
Other Languages
365
Table of Contents
13.
Parquet
. 367
Data
Model
368
Nested Encoding
370
Parquet File Format
370
Parquet Configuration
372
Writing and Reading Parquet Files
373
Avrò,
Protocol Buffers, and Thrift
375
Parquet Map Reduce
377
14.
Flume
.
о
. » ». .
о
. «,.« . 381
Installing Flume
381
An Example
382
Transactions and Reliability
384
Batching
385
The HDFS Sink
385
Partitioning and Interceptors
387
File Formats
387
Fan Out
388
Delivery Guarantees
389
Replicating and Multiplexing Selectors
390
Distribution: Agent Tiers
390
Delivery Guarantees
393
Sink Groups
395
Integrating Flume with Applications
398
Component Catalog
399
Further Reading
400
15.
Sqoop
. 401
Getting Sqoop
401
Sqoop Connectors
403
A Sample Import
404
Text and Binary File Formats
406
Generated Code
407
Additional Serialization Systems
408
Imports: A Deeper Look
408
Controlling the Import
410
Imports and Consistency
4.11
Incremental Imports
411
Direct-Mode Imports
411
Wo rkirig with Imported
D ata
412
Imported Data and Hive
413
Importing Large Objects
415
Table of Contents I
xi
Performing an Export
417
Exports: A Deeper Look
419
Exports and Transactionality
420
Exports and SequenceFiles
421
Further Reading
422
16.
Pig
.,. 423
Installing and Running Pig
424
Execution Types
424
Running Pig Programs
426
Grunt
426
Pig Latin Editors
427
An Example
427
Generating Examples
429
Comparison with Databases
430
Pig Latin
432
Structure
432
Statements
433
Expressions
438
Types
439
Schemas 441
Functions
445
Macros
447
User-Defined Functions
448
A Filter
UDF
448
An Eval
UDF
452
A Load
UDF
453
Data Processing Operators
457
Loading and Storing Data
457
Filtering Data
457
Grouping and Joining Data
459
Sorting Data
465
Combining and Splitting Data
466
Pig in Practice
467
Parallelism
467
Anonymous Relations
467
Parameter Substitution
468
Further Reading
469
17.
Hive
. 471
Installing Hive
472
The Hive Shell
473
xii
І
Table of Contents
An Example
474
Running Hive
475
Configuring Hive
475
Hive Services
478
The Metastore
480
Comparison with Traditional Databases
482
Schema on Read Versus Schema on Write
482
Updates, Transactions, and Indexes
483
SQL-on-Hadoop Alternatives
484
HiveQL
485
Data Types
486
Operators and Functions
488
Tables
489
Managed Tables and External Tables
490
Partitions and Buckets
491
Storage Formats
496
Importing Data
500
Altering Tables
502
Dropping Tables
502
Querying Data
503
Sorting and Aggregating
503
MapReduce Scripts
503
Joins
505
Subqueries
508
Views
509
User-Defined Functions
510
Writing
a UDF
511
Writing
a UD AF
513
Further Reading
518
18.
Crunch
. 519
An Example
520
The Core Crunch API
523
Primitive Operations
523
Types
528
Sources and Targets
53
1.
Functions
533
Materialization
53
S
Pipeline Execution
538
Running a Pipeline
538
Stopping a Pipeline
539
Inspecting a Crunch Plan
540
Table of Contents
! xiii
Iterative
Algorithms
543
Checkpointing a Pipeline
545
Crunch Libraries
545
Further Reading
548
19.
Spark
. 549
Installing Spark
550
An Example
550
Spark Applications, Jobs, Stages, and Tasks
552
A Scala
Standalone Application
552
A Java Example
554
A Python Example
555
Resilient Distributed
Datasets
556
Creation
556
Transformations and Actions
557
Persistence
560
Serialization
562
Shared Variables
564
Broadcast Variables
564
Accumulators
564
Anatomy of a Spark Job Run
565
Job Submission
565
DAG Construction
566
Task Scheduling
569
Task Execution
570
Executors and Cluster Managers
570
Spark on YARN
571
Further Reading
574
20.
HBase.
.„. 575
HBasics
575
Backdrop
576
Concepts
576
Whirlwind Tour of the Data Model
576
Implementation
578
Installation
581
Test Drive
582
Clients
584
Java
584
MapReduce
587
REST and Thrift
589
Building an Online Query Application
¡589
xiv
I Table of Contents
Schema Design
590
Loading Data
591
Online
Queries
594
HBase Versus
RDBMS
597
Successful
Service
598
HBase
599
Praxis
600
HDFS
600
UI
601
Metrics
601
Counters
601
Further Reading
601
j
21.
ZooKeeper.
. 603
Installing and Running ZooKeeper
604
An Example
606
Group Membership in ZooKeeper
606
Creating the Group
607
Joining a Group
609
Listing Members in a Group
610
Deleting a Group
612
The ZooKeeper Service
613
Data Model
614
Operations
616
Implementation
620
Consistency
622
Sessions
624
States
625
Building Applications with ZooKeeper
627
A Configuration Service
627
The Resilient ZooKeeper Application
630
A Lock Service
634
More Distributed Data Structures and Protocols
636
ZooKeeper in Production
637
Resilience and Performance
637
Configuration
639
Further Reading
640
Table of Contents
1 xv
Part V.
Casestudies
22.
Composable Data at
Cerner
. 643
From CPUs to Semantic Integration
643
Enter Apache Crunch
644
Building a Complete Picture
644
Integrating Healthcare Data
647
Composability over Frameworks
650
Moving Forward
651
23.
Biological Data Science: Saving Lives with Software
._. 653
The Structure of
DNA 655
The Genetic Code: Turning
DNA
Letters into Proteins
656
Thinking of
DNA
as Source Code
657
The Human Genome Project and Reference Genomes
659
Sequencing and Aligning
DNA 660
ADAM, A Scalable Genome Analysis Platform
661
Literate programming with the
Avrò
interface description language (IDL)
662
Column-oriented access with Parquet
663
A simple example: /c-mer counting using Spark and ADAM
665
From Personalized Ads to Personalized Medicine
667
Join In
668
24.
Cascading
.,. 669
Fields, Tuples, and Pipes
670
Operations
673
Taps, Schemes, and Flows
675
Cascading in Practice
676
Flexibility
679
Hadoop and Cascading at ShareThis
680
Summary
684
A. Installing
Apache
Hadoop
._. 685
B. Cloudera's Distribution Including Apache Hadoop,
. 691
С
Preparing the NCPC Weather Data.
. 693
D. The Old and New Java MapReduce APIs.
. 697
Index
. 701
xvi
I Table of Contents |
| any_adam_object | 1 |
| author | White, Tom |
| author_facet | White, Tom |
| author_role | aut |
| author_sort | White, Tom |
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| ctrlnum | (OCoLC)911245639 (DE-599)DNB1064277527 |
| dewey-full | 005.74 |
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| dewey-ones | 005 - Computer programming, programs, data, security |
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| dewey-search | 005.74 |
| dewey-sort | 15.74 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | 4. ed., [rev. & updated] |
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| id | DE-604.BV042591245 |
| illustrated | Illustrated |
| indexdate | 2024-08-03T02:25:02Z |
| institution | BVB |
| isbn | 9781491901632 1491901632 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028024470 |
| oclc_num | 911245639 |
| open_access_boolean | |
| owner | DE-739 DE-83 DE-11 DE-91G DE-BY-TUM DE-526 DE-573 DE-945 DE-523 DE-384 DE-898 DE-BY-UBR DE-703 |
| owner_facet | DE-739 DE-83 DE-11 DE-91G DE-BY-TUM DE-526 DE-573 DE-945 DE-523 DE-384 DE-898 DE-BY-UBR DE-703 |
| physical | XXV, 727 S. Ill, graph. Darst. |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | O'Reilly |
| record_format | marc |
| spelling | White, Tom Verfasser aut Hadoop the definitive guide Tom White 4. ed., [rev. & updated] Beijing [u.a.] O'Reilly 2015 XXV, 727 S. Ill, graph. Darst. txt rdacontent n rdamedia nc rdacarrier Auf dem Cover: "storage and analysis at internet scale" Apache Hadoop File organization (Computer science) Hadoop (DE-588)1022420135 gnd rswk-swf Cluster-Analyse (DE-588)4070044-6 gnd rswk-swf Open Source (DE-588)4548264-0 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Verteiltes System (DE-588)4238872-7 gnd rswk-swf Dateiorganisation (DE-588)4193494-5 gnd rswk-swf Framework Informatik (DE-588)4464685-9 gnd rswk-swf 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 DE-604 Hadoop (DE-588)1022420135 s X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=5119296&prov=M&dok_var=1&dok_ext=htm Inhaltstext Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028024470&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | White, Tom Hadoop the definitive guide 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 |
| title_auth | Hadoop the definitive guide |
| title_exact_search | Hadoop the definitive guide |
| title_full | Hadoop the definitive guide Tom White |
| title_fullStr | Hadoop the definitive guide Tom White |
| title_full_unstemmed | Hadoop the definitive guide Tom White |
| title_short | Hadoop |
| title_sort | hadoop the definitive guide |
| title_sub | the definitive guide |
| 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://deposit.dnb.de/cgi-bin/dokserv?id=5119296&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028024470&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT whitetom hadoopthedefinitiveguide |