The Resource Hadoop MapReduce cookbook : recipes for analyzing large and complex datasets with Hadoop MapReduce, Srinath Perera, Thilina Gunarathne

Hadoop MapReduce cookbook : recipes for analyzing large and complex datasets with Hadoop MapReduce, Srinath Perera, Thilina Gunarathne

Label
Hadoop MapReduce cookbook : recipes for analyzing large and complex datasets with Hadoop MapReduce
Title
Hadoop MapReduce cookbook
Title remainder
recipes for analyzing large and complex datasets with Hadoop MapReduce
Statement of responsibility
Srinath Perera, Thilina Gunarathne
Creator
Contributor
Subject
Genre
Language
eng
Summary
Individual self-contained code recipes. Solve specific problems using individual recipes, or work through the book to develop your capabilities. If you are a big data enthusiast and striving to use Hadoop to solve your problems, this book is for you. Aimed at Java programmers with some knowledge of Hadoop MapReduce, this is also a comprehensive reference for developers and system admins who want to get up to speed using Hadoop
Member of
Cataloging source
E7B
http://library.link/vocab/creatorName
Perera, Srinath
Dewey number
005.7565
Illustrations
illustrations
Index
index present
LC call number
QA76.9.D5
LC item number
P47 2013eb
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
Gunarathne, Thilina
Series statement
Community experience distilled
http://library.link/vocab/subjectName
  • Electronic data processing
  • File organization (Computer science)
  • Cloud computing
  • Open source software
Label
Hadoop MapReduce cookbook : recipes for analyzing large and complex datasets with Hadoop MapReduce, Srinath Perera, Thilina Gunarathne
Link
http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=533226
Instantiates
Publication
Note
Includes index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Hadoop Up and Running in a Cluster; Introduction; Setting up Hadoop in your machine; Writing a WordCount MapReduce sample, bundling it, and running it using standalone; Hadoop; Adding the combiner step to the WordCount MapReduce program; Setting up HDFS; Using HDFS monitoring UI; HDFS basic command-line file operations; Setting Hadoop in a distributed cluster environment; Running WordCount program in a distributed cluster environment
  • Using MapReduce monitoring UIChapter 2: Advanced HDFS; Introduction; Benchmarking HDFS; Adding a new DataNode; Decommissioning DataNodes; Using multiple disks/volumes and limiting HDFS disk usage; Setting HDFS block size; Setting the file replication factor; Using HDFS Java API; Using HDFS C API (libhdfs); Mounting HDFS (Fuse-DFS); Merging files in HDFS; Chapter 3: Advanced Hadoop MapReduce Administration; Introduction; Tuning Hadoop configurations for cluster deployments; Running benchmarks to verify the Hadoop installation; Reusing Java VMs to improve the performance
  • Fault tolerance and speculative executionDebug scripts -- analyzing task failures; Setting failure percentages and skipping bad records; Shared-user Hadoop clusters -- using fair and other schedulers; Hadoop security -- integrating with Kerberos; Using the Hadoop Tool interface; Chapter 4: Developing Complex Hadoop MapReduce Applications; Introduction; Choosing appropriate Hadoop data types; Implementing a custom Hadoop Writable data type; Implementing a custom Hadoop key type; Emitting data of different value types from a mapper; Choosing a suitable Hadoop InputFormat for your input data format
  • Adding support for new input data formats -- implementing a custom InputFormatFormatting the results of MapReduce computations -- using Hadoop; OutputFormats; Hadoop intermediate (map to reduce) data partitioning; Broadcasting and distributing shared resources to tasks in a MapReduce; job -- Hadoop DistributedCache; Using Hadoop with legacy applications -- Hadoop Streaming; Adding dependencies between MapReduce jobs; Hadoop counters for reporting custom metrics; Chapter 5: Hadoop Ecosystem; Introduction; Installing HBase; Data random access using Java client APIs
  • Running MapReduce jobs on HBase (table input/output)Installing Pig; Running your first Pig command; Set operations (join, union) and sorting with Pig; Installing Hive; Running SQL-style query with Hive; Performing a join with Hive; Installing Mahout; Running K-means with Mahout; Visualizing K-means results; Chapter 6: Analytics; Introduction; Simple analytics using MapReduce; Performing Group-By using MapReduce; Calculating frequency distributions and sorting using MapReduce; Plotting the Hadoop results using GNU Plot; Calculating histograms using MapReduce
Dimensions
unknown
Extent
1 online resource (iv, 284 pages)
Form of item
online
Isbn
9781849517294
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
eBooks on EBSCOhost
Other physical details
illustrations
Specific material designation
remote
System control number
  • (OCoLC)834589785
  • (OCoLC)ocn834589785
Label
Hadoop MapReduce cookbook : recipes for analyzing large and complex datasets with Hadoop MapReduce, Srinath Perera, Thilina Gunarathne
Link
http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=533226
Publication
Note
Includes index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Hadoop Up and Running in a Cluster; Introduction; Setting up Hadoop in your machine; Writing a WordCount MapReduce sample, bundling it, and running it using standalone; Hadoop; Adding the combiner step to the WordCount MapReduce program; Setting up HDFS; Using HDFS monitoring UI; HDFS basic command-line file operations; Setting Hadoop in a distributed cluster environment; Running WordCount program in a distributed cluster environment
  • Using MapReduce monitoring UIChapter 2: Advanced HDFS; Introduction; Benchmarking HDFS; Adding a new DataNode; Decommissioning DataNodes; Using multiple disks/volumes and limiting HDFS disk usage; Setting HDFS block size; Setting the file replication factor; Using HDFS Java API; Using HDFS C API (libhdfs); Mounting HDFS (Fuse-DFS); Merging files in HDFS; Chapter 3: Advanced Hadoop MapReduce Administration; Introduction; Tuning Hadoop configurations for cluster deployments; Running benchmarks to verify the Hadoop installation; Reusing Java VMs to improve the performance
  • Fault tolerance and speculative executionDebug scripts -- analyzing task failures; Setting failure percentages and skipping bad records; Shared-user Hadoop clusters -- using fair and other schedulers; Hadoop security -- integrating with Kerberos; Using the Hadoop Tool interface; Chapter 4: Developing Complex Hadoop MapReduce Applications; Introduction; Choosing appropriate Hadoop data types; Implementing a custom Hadoop Writable data type; Implementing a custom Hadoop key type; Emitting data of different value types from a mapper; Choosing a suitable Hadoop InputFormat for your input data format
  • Adding support for new input data formats -- implementing a custom InputFormatFormatting the results of MapReduce computations -- using Hadoop; OutputFormats; Hadoop intermediate (map to reduce) data partitioning; Broadcasting and distributing shared resources to tasks in a MapReduce; job -- Hadoop DistributedCache; Using Hadoop with legacy applications -- Hadoop Streaming; Adding dependencies between MapReduce jobs; Hadoop counters for reporting custom metrics; Chapter 5: Hadoop Ecosystem; Introduction; Installing HBase; Data random access using Java client APIs
  • Running MapReduce jobs on HBase (table input/output)Installing Pig; Running your first Pig command; Set operations (join, union) and sorting with Pig; Installing Hive; Running SQL-style query with Hive; Performing a join with Hive; Installing Mahout; Running K-means with Mahout; Visualizing K-means results; Chapter 6: Analytics; Introduction; Simple analytics using MapReduce; Performing Group-By using MapReduce; Calculating frequency distributions and sorting using MapReduce; Plotting the Hadoop results using GNU Plot; Calculating histograms using MapReduce
Dimensions
unknown
Extent
1 online resource (iv, 284 pages)
Form of item
online
Isbn
9781849517294
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
eBooks on EBSCOhost
Other physical details
illustrations
Specific material designation
remote
System control number
  • (OCoLC)834589785
  • (OCoLC)ocn834589785

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