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Hadoop MapReduce cookbook : recipes for analyzing large and complex datasets with Hadoop MapReduce /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Pub.,
2013.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Perera, Srinath. | |
| 245 | 1 | 0 | |a Hadoop MapReduce cookbook : |b recipes for analyzing large and complex datasets with Hadoop MapReduce / |c Srinath Perera, Thilina Gunarathne. |
| 260 | |a Birmingham : |b Packt Pub., |c 2013. | ||
| 300 | |a 1 online resource (iv, 284 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Community experience distilled | |
| 500 | |a Includes index. | ||
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 520 | |a 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. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
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| 650 | 0 | |a File organization (Computer science) | |
| 650 | 0 | |a Cloud computing. | |
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| 650 | 6 | |a Traitement réparti. | |
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| 830 | 0 | |a Community experience distilled. | |
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