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SPARK: Big Data Cluster Computing in Production

SPARK: Big Data Cluster Computing in Production

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About the Book

Spark: Big Data Cluster Computing in Production goes beyond general Spark overviews to provide targeted guidance toward using lightning-fast big-data clustering in production. Written by an expert team well-known in the big data community, this book walks you through the challenges in moving from proof-of-concept or demo Spark applications to live Spark in production. Real use cases provide deep insight into common problems, limitations, challenges and opportunities, while expert tips and tricks help you get the most out of Spark performance.

About the Author

Ema Iancuta is a Software Engineer with Atiego. She is a Spark Summit speaker and is interested in scaling algorithms and implementing statistical models, and works on bringing big-data analytics to healthcare apps. She is the main commiter on github on a highly scalable and resilient restful interface on top of a managed Spark SQL session Kai Sasaki (Software Engineer, Yahoo!) is an engineer focused on web and data processing platforms. He develops and maintains notification platforms using APNS and GCM, develops data processing platforms such as Hadoop and Storm, contributor to Hadoop, Spark and Storm projects, has operated and scaled thousands of Hadoop clusters and is a committer to the DeepLearning4J project. Kostas Sakellis (Software Engineer, Cloudera) is working on Cloudera's core enterprise team. Anikate Singh (Software Design Engineer- Data Science at Concur) has a versatile background spanning 6 years in Business Intelligence in Oracle Applications on various platforms like Informatica / Oracle Data Integrator / Oracle Business Intelligence Enterprise Edition as well as extensive experience working on enterprise Java projects focusing on application integration and task automation. Brennon York (Data Engineer, Capital One) leads the Data Innovation Lab within Capital One with a focus on applied business projects, contributions to the Open Source landscape and heading vendor evaluations within the data sector.



Table of Contents:
Introduction Chapter 1 Finishing Your Spark Job ·Installation of the Necessary Components ·Native Installation Using a Spark Standalone Cluster ·The History of Distributed Computing That Led to Spark ·Enter the Cloud ·Understanding Resource Management ·Using Various Formats for Storage ·Text Files ·Sequence Files ·Avro Files ·Parquet Files ·Making Sense of Monitoring and Instrumentation ·Spark UI ·Spark Standalone UI ·Metrics REST API ·Metrics System ·External Monitoring Tools Chapter 2 Cluster Management ·Background ·Spark Components ·Driver ·Workers and Executors ·Configuration ·Spark Standalone ·Architecture ·Single-Node Setup Scenario ·Multi-Node Setup ·YARN ·Architecture ·Dynamic Resource Allocation ·Scenario ·Mesos ·Setup ·Architecture ·Dynamic Resource Allocation ·Basic Setup Scenario ·Comparison Chapter 3 Performance Tuning ·Spark Execution Model ·Partitioning ·Controlling Parallelism ·Partitioners ·Shuffling Data ·Shuffling and Data Partitioning ·Operators and Shuffling ·Shuffling Is Not That Bad After All ·Serialization ·Kryo Registrators ·Spark Cache ·Spark SQL Cache ·Memory Management ·Garbage Collection ·Shared Variables ·Broadcast Variables ·Accumulators ·Data Locality Chapter 4 Security ·Architecture ·Security Manager ·Setup Configurations ·ACL ·Configuration ·Job Submission ·Web UI ·Network Security ·Encryption ·Event logging ·Kerberos ·Apache Sentry Chapter 5 Fault Tolerance or Job Execution ·Lifecycle of a Spark Job ·Spark Master ·Spark Driver ·Spark Worker ·Job Lifecycle ·Job Scheduling ·Scheduling within an Application ·Scheduling with External Utilities ·Fault Tolerance ·Internal and External Fault Tolerance ·Service Level Agreements (SLAs) ·Resilient Distributed Datasets (RDDs) ·Batch versus Streaming ·Testing Strategies ·Recommended Configurations Chapter 6 Beyond Spark ·Data Warehousing ·Spark SQL CLI ·Thrift JDBC / ODBC Server ·Hive on Spark ·Machine Learning ·DataFrame ·MLlib and ML ·Mahout on Spark ·Hivemall on Spark ·External Frameworks ·Spark Package ·XGBoost ·spark-jobserver ·Future Works ·Integration with the Parameter Server ·Deep Learning ·Enterprise Usage ·Collecting User Activity Log with Spark and Kafka ·Real-Time Recommendation with Spark ·Real-Time Categorization of Twitter Bots Summary Index


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Product Details
  • ISBN-13: 9788126562480
  • Publisher: Random House
  • Publisher Imprint: Harvill Secker
  • Language: ENGLISH
  • Weight: 450 gr
  • ISBN-10: 812656248X
  • Publisher Date: 26-May-2016
  • Binding: PAPERBACK
  • No of Pages: 216

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