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Apache Hive Cookbook

Apache Hive Cookbook

By : Hanish Bansal, Saurabh Chauhan, Shrey Mehrotra
3 (4)
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Apache Hive Cookbook

Apache Hive Cookbook

3 (4)
By: Hanish Bansal, Saurabh Chauhan, Shrey Mehrotra
Buy this Book

Overview of this book

Hive was developed by Facebook and later open sourced in Apache community. Hive provides SQL like interface to run queries on Big Data frameworks. Hive provides SQL like syntax also called as HiveQL that includes all SQL capabilities like analytical functions which are the need of the hour in today’s Big Data world. This book provides you easy installation steps with different types of metastores supported by Hive. This book has simple and easy to learn recipes for configuring Hive clients and services. You would also learn different Hive optimizations including Partitions and Bucketing. The book also covers the source code explanation of latest Hive version. Hive Query Language is being used by other frameworks including spark. Towards the end you will cover integration of Hive with these frameworks.
Table of Contents (14 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Sections
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Conventions
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Reader feedback
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Customer support
Free Chapter
1
1. Developing Hive
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1. Developing Hive
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Introduction
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Deploying Hive on a Hadoop cluster
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Deploying Hive Metastore
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Installing Hive
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Configuring HCatalog
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Understanding different components of Hive
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Compiling Hive from source
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Hive packages
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Debugging Hive
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Running Hive
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Changing configurations at runtime
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2. Services in Hive
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2. Services in Hive
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Introducing HiveServer2
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Understanding HiveServer2 properties
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Configuring HiveServer2 high availability
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Using HiveServer2 clients
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Introducing the Hive metastore service
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Configuring high availability of metastore service
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Introducing Hue
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3. Understanding the Hive Data Model
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3. Understanding the Hive Data Model
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Introduction
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Using numeric data types
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Using string data types
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Using Date/Time data types
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Using miscellaneous data types
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Using complex data types
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Using operators
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Partitioning
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Partitioning a managed table
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Partitioning an external table
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Bucketing
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4. Hive Data Definition Language
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4. Hive Data Definition Language
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Introduction
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Creating a database schema
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Dropping a database schema
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Altering a database schema
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Using a database schema
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Showing database schemas
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Describing a database schema
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Creating tables
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Dropping tables
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Truncating tables
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Renaming tables
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Altering table properties
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Creating views
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Dropping views
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Altering the view properties
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Altering the view as select
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Showing tables
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Showing partitions
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Show the table properties
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Showing create table
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HCatalog
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WebHCat
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5. Hive Data Manipulation Language
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5. Hive Data Manipulation Language
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Introduction
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Loading files into tables
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Inserting data into Hive tables from queries
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Inserting data into dynamic partitions
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Writing data into files from queries
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Enabling transactions in Hive
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Inserting values into tables from SQL
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Updating data
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Deleting data
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6. Hive Extensibility Features
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6. Hive Extensibility Features
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Introduction
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Serialization and deserialization formats and data types
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Exploring views
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Exploring indexes
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Hive partitioning
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Creating buckets in Hive
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Analytics functions in Hive
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Windowing in Hive
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File formats
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7. Joins and Join Optimization
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7. Joins and Join Optimization
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Understanding the joins concept
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Using a left/right/full outer join
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Using a left semi join
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Using a cross join
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Using a map-side join
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Using a bucket map join
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Using a bucket sort merge map join
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Using a skew join
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8. Statistics in Hive
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8. Statistics in Hive
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Bringing statistics in to Hive
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Table and partition statistics in Hive
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Column statistics in Hive
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Top K statistics in Hive
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9. Functions in Hive
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9. Functions in Hive
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Using built-in functions
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Using the built-in User-defined Aggregation Function (UDAF)
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Using the built-in User Defined Table Function (UDTF)
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Creating custom User-Defined Functions (UDF)
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10. Hive Tuning
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10. Hive Tuning
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Enabling predicate pushdown optimizations in Hive
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Optimizations to reduce the number of map
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Sampling
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11. Hive Security
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11. Hive Security
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Securing Hadoop
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Authorizing Hive
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Configuring the SQL standards-based authorization
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Authenticating Hive
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12. Hive Integration with Other Frameworks
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12. Hive Integration with Other Frameworks
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Working with Apache Spark
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Working with Accumulo
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Working with HBase
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Working with Google Drill
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Index
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Index

Using a bucket map join

In this recipe, you will learn how to use a bucket map join in Hive.

A bucket map join is used when the tables are large and all the tables used in the join are bucketed on the join columns. In this type of join, one table should have buckets in multiples of the number of buckets in another table. For example, if one table has 2 buckets then the other table must have either 2 buckets or a multiple of 2 buckets (2, 4, 6, and so on). If the preceding condition is satisfied then the joining can be done at the mapper side only, otherwise a normal inner join is performed. This means that only the required buckets are fetched on the mapper side and not the complete table. That is, only the matching buckets of all small tables are replicated onto each mapper. Doing this, the efficiency of the query is improved drastically. In a bucket map join, data is not sorted.

Hive does not support a bucket map join by default. The following property needs to be set to true for the query...

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