Programming Pig : [dataflow scripting with Hadoop

This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop. With Pig, you can batch-process data without having to create a full-fledged application—making it easy for you to experiment with new datasets. __Programming Pig__ introduces new users to Pig, and provides experienced users with comprehensive coverage on key features such as the Pig Latin scripting language, the Grunt shell, and User Defined Functions (UDFs) for extending Pig. If you need to analyze terabytes of data, this book shows you how to do it efficiently with Pig. * Delve into Pig’s data model, including scalar and complex data types * Write Pig Latin scripts to sort, group, join, project, and filter your data * Use Grunt to work with the Hadoop Distributed File System (HDFS) * Build complex data processing pipelines with Pig’s macros and modularity features * Embed Pig Latin in Python for iterative processing and other advanced tasks * Create your own load and store functions to handle data formats and storage mechanisms * Get performance tips for running scripts on Hadoop clusters in less time Content: Table of Contents Preface Data Addiction Who Should Read This Book Conventions Used in This Book Code Examples in This Book Using Code Examples Safari® Books Online How to Contact Us Acknowledgments Chapter 1. Introduction What Is Pig? Pig on Hadoop MapReduce's hello world Pig Latin, a Parallel Dataflow Language Comparing query and dataflow languages How Pig differs from MapReduce What Is Pig Useful For? Pig Philosophy Pig's History Chapter 2. Installing and Running Pig Downloading and Installing Pig Downloading the Pig Package from Apache Downloading Pig from Cloudera. Downloading Pig Artifacts from MavenDownloading the Source Running Pig Running Pig Locally on Your Machine Running Pig on Your Hadoop Cluster Running Pig in the Cloud Command-Line and Configuration Options Return Codes Chapter 3. Grunt Entering Pig Latin Scripts in Grunt HDFS Commands in Grunt Controlling Pig from Grunt Chapter 4. Pig's Data Model Types Scalar Types Complex Types Map Tuple Bag Nulls Schemas Casts Chapter 5. Introduction to Pig Latin Preliminary Matters Case Sensitivity Comments Input and Output Load Store Dump Relational Operations foreach. Expressions in foreachUDFs in foreach Naming fields in foreach Filter Group Order by Distinct Join Limit Sample Parallel User Defined Functions Registering UDFs Registering Python UDFs define and UDFs Calling Static Java Functions Chapter 6. Advanced Pig Latin Advanced Relational Operations Advanced Features of foreach flatten Nested foreach Using Different Join Implementations Joining small to large data Joining skewed data Joining sorted data cogroup union cross Integrating Pig with Legacy Code and MapReduce stream mapreduce Nonlinear Data Flows. Controlling Executionset Setting the Partitioner Pig Latin Preprocessor Parameter Substitution Macros Including Other Pig Latin Scripts Chapter 7. Developing and Testing Pig Latin Scripts Development Tools Syntax Highlighting and Checking describe explain illustrate Pig Statistics MapReduce Job Status Debugging Tips Testing Your Scripts with PigUnit Chapter 8. Making Pig Fly Writing Your Scripts to Perform Well Filter Early and Often Project Early and Often Set Up Your Joins Properly Use Multiquery When Possible Choose the Right Data Type. Select the Right Level of ParallelismWriting Your UDF to Perform Tune Pig and Hadoop for Your Job Using Compression in Intermediate Results Data Layout Optimization Bad Record Handling Chapter 9. Embedding Pig Latin in Python Compile Bind Binding Multiple Sets of Variables Run Running Multiple Bindings Utility Methods Chapter 10. Writing Evaluation and Filter Functions Writing an Evaluation Function in Java Where Your UDF Will Run Evaluation Function Basics Interacting with Pig values Input and Output Schemas Error Handling and Progress Reporting. This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop. With Pig, you can batch-process data without having to create a full-fledged application—making it easy for you to experiment with new datasets.Programming Pig introduces new users to Pig, and provides experienced users with comprehensive coverage on key features such as the Pig Latin scripting language, the Grunt shell, and User Defined Functions (UDFs) for extending Pig. If you need to analyze terabytes of data, this book shows you how to do it efficiently with Pig.Delve into Pig’s data model, including scalar and complex data typesWrite Pig Latin scripts to sort, group, join, project, and filter your dataUse Grunt to work with the Hadoop Distributed File System (HDFS)Build complex data processing pipelines with Pig’s macros and modularity featuresEmbed Pig Latin in Python for iterative processing and other advanced tasksCreate your own load and store functions to handle data formats and storage mechanismsGet performance tips for running scripts on Hadoop clusters in less time This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop. With Pig, you can batch-process data without having to create a full-fledged application{u2014}making it easy for you to experiment with new datasets. Programming Pig introduces new users to Pig, and provides experienced users with comprehensive coverage on key features such as the Pig Latin scripting language, the Grunt shell, and User Defined Functions (UDFs) for extending Pig. If you need to analyze terabytes of data, this book shows you how to do it efficiently with Pig. Delve into Pig{u2019}s data model, including scalar and complex data types Write Pig Latin scripts to sort, group, join, project, and filter your data Use Grunt to work with the Hadoop Distributed File System (HDFS) Build complex data processing pipelines with Pig{u2019}s macros and modularity features Embed Pig Latin in Python for iterative processing and other advanced tasks Create your own load and store functions to handle data formats and storage mechanisms Get performance tips for running scripts on Hadoop clusters in less time Annotation This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop. With Pig, you can batch-process data without having to create a full-fledged applicationmaking it easy for you to experiment with new datasets. Programming Pigintroduces new users to Pig, and provides experienced users with comprehensive coverage on key features such as the Pig Latin scripting language, the Grunt shell, and User Defined Functions (UDFs) for extending Pig. If you need to analyze terabytes of data, this book shows you how to do it efficiently with Pig. Delve into Pigs data model, including scalar and complex data typesWrite Pig Latin scripts to sort, group, join, project, and filter your dataUse Grunt to work with the Hadoop Distributed File System (HDFS)Build complex data processing pipelines with Pigs macros and modularity featuresEmbed Pig Latin in Python for iterative processing and other advanced tasksCreate your own load and store functions to handle data formats and storage mechanismsGet performance tips for running scripts on Hadoop clusters in less time This guide is an ideal learning tool and reference for Apache Pig, the programming language that helps you describe and run large data projects on Hadoop. With Pig, you can analyze data without having to create a full-fledged application - making it easy for you to experiment with new data sets. This book shows you how.