Pro Apache Hadoop: [analyze large volumes of data in amazingly short wall-clock intervals]
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Apress
2014
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | The expert's voice in big data
Books for professionals by professionals |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes index |
| Beschreibung: | XXVI, 413 S. Ill. |
| ISBN: | 9781430248637 |
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| 245 | 1 | 0 | |a Pro Apache Hadoop |b [analyze large volumes of data in amazingly short wall-clock intervals] |c Sameer Wadkar ; Madhu Siddalingaiah |
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| 300 | |a XXVI, 413 S. |b Ill. | ||
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| 490 | 0 | |a The expert's voice in big data | |
| 490 | 0 | |a Books for professionals by professionals | |
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| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Electronic data processing / Distributed processing | |
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Datensatz im Suchindex
| _version_ | 1804152712636399616 |
|---|---|
| adam_text | Titel: Pro Apache Hadoop
Autor: Wadkar, Sameer
Jahr: 2014
Contents
_J
About the Authors..............................................................................................................xix
About the Technical Reviewer...........................................................................................xxi
Acknowledgments...........................................................................................................xxiii
Introduction......................................................................................................................xxv
?Chapter 1: Motivation for Big Data....................................................................................1
What Is Big Data?..........................................................................................................................1
Key Idea Behind Big Data Techniques...........................................................................................2
Data Is Distributed Across Several Nodes.............................................................................................................2
Applications Are Moved to the Data.......................................................................................................................3
Data Is Processed Local to a Node........................................................................................................................3
Sequential Reads Preferred Over Random Reads.................................................................................................3
An Example............................................................................................................................................................4
Big Data Programming Models.....................................................................................................4
Massively Parallel Processing (MPP) Database Systems......................................................................................4
In-Memory Database Systems..............................................................................................................................5
MapReduce Systems.............................................................................................................................................5
Bulk Synchronous Parallel (BSP) Systems............................................................................................................6
Big Data and Transactional Systems.............................................................................................7
How Much Can We Scale?.............................................................................................................8
A Compute-Intensive Example...............................................................................................................................8
Amdhal s Law........................................................................................................................................................9
Business Use-Cases for Big Data..................................................................................................9
Summary.....................................................................................................................................10
vii
CONTENTS
ESChapter 2: Hadoop Concepts...........................................................................................11
Introducing Hadoop.....................................................................................................................11
Introducing the MapReduce Model.............................................................................................12
Components of Hadoop...............................................................................................................16
Hadoop Distributed File System (HDFS)..............................................................................................................17
Secondary NameNode.........................................................................................................................................22
TaskTracker.........................................................................................................................................................23
JobTracker...........................................................................................................................................................23
Hadoop 2.0..................................................................................................................................24
Components of YARN...........................................................................................................................................26
HDFS High Availability................................................................................................................29
Summary.....................................................................................................................................30
^Chapter 3: Getting Started with the Hadoop Framework.................................................31
Types of Installation....................................................................................................................31
Stand-Alone Mode...............................................................................................................................................31
Pseudo-Distributed Cluster..................................................................................................................................32
Multinode Node Cluster Installation.....................................................................................................................32
Preinstalled Using Amazon Elastic MapReduce..................................................................................................32
Setting up a Development Environment with a Cloudera Virtual Machine..................................33
Components of a MapReduce program.......................................................................................34
Your First Hadoop Program.........................................................................................................34
Prerequisites to Run Programs in Local Mode....................................................................................................35
WordCount Using the Old API...............................................................................................................................36
Building the Application.......................................................................................................................................38
Running WordCount in Cluster Mode...................................................................................................................39
WordCount Using the New API.............................................................................................................................39
Building the Application.......................................................................................................................................41
Running WordCount in Cluster Mode...................................................................................................................41
Third-Party Libraries in Hadoop Jobs..........................................................................................41
Summary.....................................................................................................................................46
viìi
CJ CONTENTS
^Chapter 4: Hadoop Administration..................................................................................47
Hadoop Configuration Files.........................................................................................................47
Configuring Hadoop Daemons.....................................................................................................48
Precedence of Hadoop Configuration Files.................................................................................49
Diving into Hadoop Configuration Files.......................................................................................49
core-site.xml........................................................................................................................................................50
hdfs-*.xml............................................................................................................................................................51
mapred-site.xml..................................................................................................................................................52
yarn-site.xml........................................................................................................................................................54
Memory Allocations in YARN................................................................................................................................55
Scheduler....................................................................................................................................56
Capacity Scheduler..............................................................................................................................................57
Fair Scheduler.....................................................................................................................................................59
Fair Scheduler Configuration...............................................................................................................................60
yarn-site.xml Configurations...............................................................................................................................61
Allocation File Format and Configurations...........................................................................................................62
Determine Dominant Resource Share in drf Policy.............................................................................................63
Slaves File...................................................................................................................................64
Rack Awareness..........................................................................................................................64
Providing Hadoop with Network Topology...........................................................................................................64
Cluster Administration Utilities....................................................................................................65
Check the HDFS..................................................................................................................................................66
Command-Line HDFS Administration..................................................................................................................68
Rebalancing HDFS Data.......................................................................................................................................70
Copying Large Amounts of Data from the HDFS..................................................................................................71
Summary.....................................................................................................................................72
?Chapter 5: Basics of MapReduce Development...............................................................73
Hadoop and Data Processing......................................................................................................73
Reviewing the Airline Dataset....................................................................................................73
Preparing the Development Environment............................................................................................................75
Preparing the Hadoop System.............................................................................................................................75
ix
CONTENTS
MapReduce Programming Patterns............................................................................................76
Map-Only Jobs (SELECT and WHERE Queries).....................................................................................................76
Problem Definition: SELECT Clause.....................................................................................................................76
Problem Definition: WHERE Clause......................................................................................................................84
Map and Reduce Jobs (Aggregation Queries).....................................................................................................87
Problem Definition: GROUP BY and SUM Clauses................................................................................................88
Improving Aggregation Performance Using the Combiner..................................................................................94
Problem Definition: Optimized Aggregators.........................................................................................................95
Role of the Partitioner........................................................................................................................................100
Problem Definition: Split Airline Data by Month.................................................................................................100
Bringing it All Together..............................................................................................................103
Summary...................................................................................................................................106
?Chapter 6: Advanced MapReduce Development............................................................107
MapReduce Programming Patterns..........................................................................................107
Introduction to Hadoop I/O.................................................................................................................................107
Problem Definition: Sorting...............................................................................................................................109
Problem Definition: Analyzing Consecutive Records.........................................................................................124
Problem Definition: Join Using MapReduce......................................................................................................134
Problem Definition: Join Using Map-Only jobs.................................................................................................140
Writing to Multiple Output Files in a Single MR Job.........................................................................................145
Collecting Statistics Using Counters..................................................................................................................147
Summary...................................................................................................................................150
?Chapter 7: Hadoop Input/Output....................................................................................151
Compression Schemes..............................................................................................................151
What Can Be Compressed?...............................................................................................................................152
Compression Schemes......................................................................................................................................152
Enabling Compression.......................................................................................................................................153
Inside the Hadoop I/O processes...............................................................................................154
InputFormat.......................................................................................................................................................155
OutputFormat.....................................................................................................................................................156
Custom OutputFormat: Conversion from Text to XML........................................................................................157
X
r CONTENTS
Custom InputFormat: Consuming a Custom XML file........................................................................................161
Hadoop Files..............................................................................................................................170
SequenceFile.....................................................................................................................................................171
MapFiles............................................................................................................................................................176
Avrò Files...........................................................................................................................................................177
Summary...................................................................................................................................183
^Chapter 8: Testing Hadoop Programs............................................................................185
Revisiting the Word Counter......................................................................................................185
Introducing MRUnit....................................................................................................................187
Installing MRUnit...............................................................................................................................................187
MRUnit Core Classes.........................................................................................................................................187
Writing an MRUnit Test Case.............................................................................................................................188
Testing Counters................................................................................................................................................190
Features of MRUnit............................................................................................................................................193
Limitations of MRUnit........................................................................................................................................194
Testing with LocalJobRunner....................................................................................................194
Limitations of LocalJobRunner..........................................................................................................................197
Testing with MiniMRCIuster.......................................................................................................197
Setting up the Development Environment.........................................................................................................197
Example for MiniMRCIuster...............................................................................................................................199
Limitations of MiniMRCIuster............................................................................................................................201
Testing MR Jobs with Access Network Resources....................................................................201
Summary...................................................................................................................................202
OChapter 9: Monitoring Hadoop.......................................................................................203
Writing Log Messages in Hadoop MapReduce Jobs.................................................................203
Viewing Log Messages in Hadoop MapReduce Jobs................................................................206
User Log Management in Hadoop 2.x.......................................................................................209
Log Storage In Hadoop 2.x.................................................................................................................................209
Log Management Improvements.......................................................................................................................211
Viewing Logs Using Web-Based Ul...................................................................................................................211
xi
CONTENTS
Command-Line Interface...................................................................................................................................211
Log Retention....................................................................................................................................................212
Hadoop Cluster Performance Monitoring..................................................................................212
Using YARN REST APIs...............................................................................................................213
Managing the Hadoop Cluster Using Vendor Tools....................................................................213
Ambari Architecture...........................................................................................................................................214
Summary...................................................................................................................................215
üChapter 10: Data Warehousing Using Hadoop...............................................................217
Apache Hive..............................................................................................................................217
Installing Hive...................................................................................................................................................218
Hive Architecture...............................................................................................................................................218
Metastore..........................................................................................................................................................219
Compiler Basics.................................................................................................................................................219
Hive Concepts....................................................................................................................................................219
HiveQL Compiler Details....................................................................................................................................223
Data Definition Language.................................................................................................................................227
Data Manipulation Language............................................................................................................................228
External Interfaces............................................................................................................................................229
Hive Scripts.......................................................................................................................................................231
Performance......................................................................................................................................................232
MapReduce Integration.....................................................................................................................................232
Creating Partitions.............................................................................................................................................233
User-Defined Functions.....................................................................................................................................234
Impala.......................................................................................................................................236
Impala Architecture...........................................................................................................................................237
impala Features.................................................................................................................................................237
Impala Limitations.............................................................................................................................................237
Shark.........................................................................................................................................238
Shark/Spark Architecture..................................................................................................................................238
Summary...................................................................................................................................239
xii
CONTENTS
?Chapter 11: Data Processing Using Pig.........................................................................241
An Introduction to Pig................................................................................................................241
Running Pig...............................................................................................................................243
Executing in the Grunt Shell..............................................................................................................................244
Executing a Pig Script........................................................................................................................................244
Embedded Java Program...................................................................................................................................245
Pig Latin....................................................................................................................................246
Comments in a Pig Script..................................................................................................................................246
Execution of Pig Statements..............................................................................................................................247
Pig Commands...................................................................................................................................................247
User-Defined Functions............................................................................................................252
Eval Functions Invoked in the Mapper...............................................................................................................253
Eval Functions Invoked in the Reducer..............................................................................................................253
Writing and Using a Custom FilterFunc.............................................................................................................260
Comparison of PIG versus Hive................................................................................................262
Crunch API.................................................................................................................................263
How Crunch Differs from Pig............................................................................................................................263
Sample Crunch Pipeline....................................................................................................................................264
Summary...................................................................................................................................269
^Chapter 12: HCatalog and Hadoop in the Enterprise.....................................................271
HCatalog and Enterprise Data Warehouse Users.......................................................................271
HCatalog: A Brief Technical Background...................................................................................272
HCatalog Command-Line Interface....................................................................................................................274
WebHCat............................................................................................................................................................274
HCatalog Interface for MapReduce....................................................................................................................275
HCatalog Interface for Pig..................................................................................................................................278
HCatalog Notification Interface..........................................................................................................................279
Security and Authorization in HCatalog.....................................................................................279
Bringing It All Together..............................................................................................................280
Summary...................................................................................................................................281
xiii
CONTENTS
SChapter 13: Log Analysis Using Hadoop........................................................................283
Log File Analysis Applications...................................................................................................283
Web Analytics....................................................................................................................................................283
Security Compliance and Forensics..................................................................................................................284
Monitoring and Alerts........................................................................................................................................284
Internet of Things...............................................................................................................................................285
Analysis Steps...........................................................................................................................286
Load...................................................................................................................................................................286
Refine................................................................................................................................................................286
Visualize............................................................................................................................................................287
Apache Flume............................................................................................................................287
Core Concepts..................................................................................................................................................288
Netflix Suro................................................................................................................................290
Cloud Solutions.........................................................................................................................291
Summary...................................................................................................................................291
OChapter 14: Building Real-Time Systems Using HBase.................................................293
What Is HBase?.........................................................................................................................293
Typical HBase Use-Case Scenarios...........................................................................................294
HBase Data Model.....................................................................................................................295
HBase Logical or Client-Side View.....................................................................................................................295
Differences Between HBase and RDBMSs........................................................................................................296
HBase Tables.....................................................................................................................................................297
HBase Cells........................................................................................................................................................297
HBase Column Family........................................................................................................................................297
HBase Commands and APIs......................................................................................................298
Getting a Command List: help Command..........................................................................................................299
Creating a Table: create Command....................................................................................................................300
Adding Rows to a Table: put Command.............................................................................................................300
Retrieving Rows from the Table: get Command.................................................................................................300
Reading Multiple Rows: scan Command...........................................................................................................300
xiv
: : CONTENTS
Counting the Rows in the Table: count Command...............
Deleting Rows: delete Command.........................................
Truncating a Table: truncate Command...............................
Dropping a Table: drop Command........................................
Altering a Table: alter Command..........................................
HBase Architecture....................................................
HBase Components.............................................................
Compaction and Splits in HBase..........................................
Compaction..........................................................................
HBase Configuration: An Overview............................
hbase-default.xml and hbase-site.xml................................
HBase Application Design........................................
Tall vs. Wide vs. Narrow Table Design.................................
Row Key Design...................................................................
HBase Operations Using Java API.............................
HBase Treats Everything as Bytes.......................................
Create an HBase Table...............................................................
Administrative Functions Using HBaseAdmin............................
Accessing Data Using the Java API...........................................
HBase MapReduce Integration.......................................
A MapReduce Job to Read an HBase Table....................
HBase and MapReduce Clusters....................................
Scenario I: Frequent MapReduce Jobs Against HBase Tables...
Scenario II: HBase and MapReduce have Independent SLAs....
Summary.......................................................................
?Chapter 15: Data Science with Hadoop...................
Hadoop Data Science Methods......................................
Apache Hama................................................................
Bulk Synchronous Parallel Model..............................................
Hama Hello World!.....................................................................
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XV
CONTENTS
Monte Carlo Methods........................................................................................................................................329
K-Means Clustering...........................................................................................................................................333
Apache Spark............................................................................................................................336
Resilient Distributed Datasets (RDDs)...............................................................................................................336
Monte Carlo with Spark.....................................................................................................................................337
KMeans with Spark...........................................................................................................................................339
RHadoop....................................................................................................................................341
Summary...................................................................................................................................342
?Chapter 16: Hadoop in the Cloud...................................................................................343
Economics.................................................................................................................................343
Self-Hosted Cluster............................................................................................................................................343
Cloud-Hosted Cluster.........................................................................................................................................344
Elasticity............................................................................................................................................................344
On Demand........................................................................................................................................................344
Bid Pricing.........................................................................................................................................................345
Hybrid Cloud......................................................................................................................................................345
Logistics....................................................................................................................................345
Ingress/Egress...................................................................................................................................................345
Data Retention...................................................................................................................................................345
Security.....................................................................................................................................346
Cloud Usage Models..................................................................................................................346
Cloud Providers.........................................................................................................................347
Amazon Web Services.......................................................................................................................................347
Google Cloud Platform.......................................................................................................................................349
Microsoft Azure..................................................................................................................................................350
Choosing a Cloud Vendor...................................................................................................................................350
Case Study: Amazon Web Services...........................................................................................351
Elastic MapReduce............................................................................................................................................351
Elastic Compute Cloud.......................................................................................................................................354
Summary...................................................................................................................................356
xvi
CONTENTS
?Chapter 17: Building a YARN Application......................................................................357
YARN: A General-Purpose Distributed System...........................................................................357
YARN: A Quick Review...............................................................................................................359
Creating a YARN Application......................................................................................................361
POM Configuration.............................................................................................................................................362
DownloadService.java Class......................................................................................................362
Client.java..................................................................................................................................365
Steps to Launch the Application Master from the Client...................................................................................365
ApplicationMaster.java..............................................................................................................373
Communication Protocol between Application Master and Resource Manager:
Application Master Protocol..............................................................................................................................373
Node Manager Communication Protocol: Container Management Protocol......................................................373
Steps to Launch the Worker Tasks.....................................................................................................................373
Executing the Application Master..............................................................................................378
Launch the Application in Un-Managed Mode...................................................................................................379
Launch the Application in Managed Mode.........................................................................................................379
Summary...................................................................................................................................379
ElAppendix A: Installing Hadoop.....................................................................................381
Installing Hadoop 2.2.0 on Windows........................................................................................381
Preparing the Installation Environment.............................................................................................................381
Building Hadoop 2.2.0 for Windows...................................................................................................................383
Installing Hadoop 2.2.0 for Windows.................................................................................................................383
Configuring Hadoop 2.2.0..................................................................................................................................383
Preparing the Hadoop Cluster............................................................................................................................386
Starting HDFS...................................................................................................................................................387
Starting MapReduce (YARN)..............................................................................................................................387
Verifying that the Cluster Is Running.................................................................................................................387
Testing the Cluster.............................................................................................................................................387
Installing Hadoop 2.2.0 on Linux...............................................................................................388
xvii
CONTENTS
^Appendix B: Using Maven with Eclipse.........................................................................391
A Quick Introduction to Maven.................................................................................................391
Creating a Maven Project..................................................................................................................................391
Using Maven with Eclipse........................................................................................................393
Installing the m2e Maven Eclipse Plug-in.........................................................................................................393
Creating a Maven Project from Eclipse..............................................................................................................393
Building a Maven Project from Eclipse..............................................................................................................396
^Appendix C: Apache Ambari..........................................................................................399
Hadoop Components Supported by Apache Ambari..................................................................399
Installing Apache Ambari...........................................................................................................401
Trying the Ambari Sandbox on Your OS.....................................................................................401
Index.................................................................................................................................403
xviii
|
| any_adam_object | 1 |
| author | Wadkar, Sameer Siddalingaiah, Madhu 1965- |
| author_GND | (DE-588)1059371499 (DE-588)121303411 |
| author_facet | Wadkar, Sameer Siddalingaiah, Madhu 1965- |
| author_role | aut aut |
| author_sort | Wadkar, Sameer |
| author_variant | s w sw m s ms |
| building | Verbundindex |
| bvnumber | BV042197590 |
| classification_rvk | ST 201 |
| ctrlnum | (OCoLC)915510121 (DE-599)BVBBV042197590 |
| discipline | Informatik |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV042197590 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T01:15:04Z |
| institution | BVB |
| isbn | 9781430248637 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027636544 |
| oclc_num | 915510121 |
| open_access_boolean | |
| owner | DE-11 |
| owner_facet | DE-11 |
| physical | XXVI, 413 S. Ill. |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Apress |
| record_format | marc |
| series2 | The expert's voice in big data Books for professionals by professionals |
| spelling | Wadkar, Sameer Verfasser (DE-588)1059371499 aut Pro Apache Hadoop [analyze large volumes of data in amazingly short wall-clock intervals] Sameer Wadkar ; Madhu Siddalingaiah 2. ed. New York, NY Apress 2014 XXVI, 413 S. Ill. txt rdacontent n rdamedia nc rdacarrier The expert's voice in big data Books for professionals by professionals Includes index Apache Hadoop Datenverarbeitung Electronic data processing / Distributed processing Open source software Big data Data Mining (DE-588)4428654-5 gnd rswk-swf Hadoop (DE-588)1022420135 gnd rswk-swf Electronic books Hadoop (DE-588)1022420135 s Data Mining (DE-588)4428654-5 s 1\p DE-604 Siddalingaiah, Madhu 1965- Verfasser (DE-588)121303411 aut Erscheint auch als Online-Ausgabe 978-1-4302-4864-4 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027636544&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 | Wadkar, Sameer Siddalingaiah, Madhu 1965- Pro Apache Hadoop [analyze large volumes of data in amazingly short wall-clock intervals] Apache Hadoop Datenverarbeitung Electronic data processing / Distributed processing Open source software Big data Data Mining (DE-588)4428654-5 gnd Hadoop (DE-588)1022420135 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)1022420135 |
| title | Pro Apache Hadoop [analyze large volumes of data in amazingly short wall-clock intervals] |
| title_auth | Pro Apache Hadoop [analyze large volumes of data in amazingly short wall-clock intervals] |
| title_exact_search | Pro Apache Hadoop [analyze large volumes of data in amazingly short wall-clock intervals] |
| title_full | Pro Apache Hadoop [analyze large volumes of data in amazingly short wall-clock intervals] Sameer Wadkar ; Madhu Siddalingaiah |
| title_fullStr | Pro Apache Hadoop [analyze large volumes of data in amazingly short wall-clock intervals] Sameer Wadkar ; Madhu Siddalingaiah |
| title_full_unstemmed | Pro Apache Hadoop [analyze large volumes of data in amazingly short wall-clock intervals] Sameer Wadkar ; Madhu Siddalingaiah |
| title_short | Pro Apache Hadoop |
| title_sort | pro apache hadoop analyze large volumes of data in amazingly short wall clock intervals |
| title_sub | [analyze large volumes of data in amazingly short wall-clock intervals] |
| topic | Apache Hadoop Datenverarbeitung Electronic data processing / Distributed processing Open source software Big data Data Mining (DE-588)4428654-5 gnd Hadoop (DE-588)1022420135 gnd |
| topic_facet | Apache Hadoop Datenverarbeitung Electronic data processing / Distributed processing Open source software Big data Data Mining Hadoop |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027636544&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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