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Apache Hive Cookbook

Book Image

Apache Hive Cookbook

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.

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Apache Hive Cookbook
Apr, 2016 | 268 pages
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Table of Contents (19 chapters)
Apache Hive Cookbook
Apache Hive Cookbook
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Developing Hive
Developing Hive
Introduction
Deploying Hive on a Hadoop cluster
Deploying Hive Metastore
Installing Hive
Configuring HCatalog
Understanding different components of Hive
Compiling Hive from source
Hive packages
Debugging Hive
Running Hive
Changing configurations at runtime
2
Services in Hive
Services in Hive
Introducing HiveServer2
Understanding HiveServer2 properties
Configuring HiveServer2 high availability
Using HiveServer2 clients
Introducing the Hive metastore service
Configuring high availability of metastore service
Introducing Hue
3
Understanding the Hive Data Model
Understanding the Hive Data Model
Introduction
Using numeric data types
Using string data types
Using Date/Time data types
Using miscellaneous data types
Using complex data types
Using operators
Partitioning
Partitioning a managed table
Partitioning an external table
Bucketing
4
Hive Data Definition Language
Hive Data Definition Language
Introduction
Creating a database schema
Dropping a database schema
Altering a database schema
Using a database schema
Showing database schemas
Describing a database schema
Creating tables
Dropping tables
Truncating tables
Renaming tables
Altering table properties
Creating views
Dropping views
Altering the view properties
Altering the view as select
Showing tables
Showing partitions
Show the table properties
Showing create table
HCatalog
WebHCat
5
Hive Data Manipulation Language
Hive Data Manipulation Language
Introduction
Loading files into tables
Inserting data into Hive tables from queries
Inserting data into dynamic partitions
Writing data into files from queries
Enabling transactions in Hive
Inserting values into tables from SQL
Updating data
Deleting data
6
Hive Extensibility Features
Hive Extensibility Features
Introduction
Serialization and deserialization formats and data types
Exploring views
Exploring indexes
Hive partitioning
Creating buckets in Hive
Analytics functions in Hive
Windowing in Hive
File formats
7
Joins and Join Optimization
Joins and Join Optimization
Understanding the joins concept
Using a left/right/full outer join
Using a left semi join
Using a cross join
Using a map-side join
Using a bucket map join
Using a bucket sort merge map join
Using a skew join
8
Statistics in Hive
Statistics in Hive
Bringing statistics in to Hive
Table and partition statistics in Hive
Column statistics in Hive
Top K statistics in Hive
9
Functions in Hive
Functions in Hive
Using built-in functions
Using the built-in User-defined Aggregation Function (UDAF)
Using the built-in User Defined Table Function (UDTF)
Creating custom User-Defined Functions (UDF)
10
Hive Tuning
Hive Tuning
Enabling predicate pushdown optimizations in Hive
Optimizations to reduce the number of map
Sampling
11
Hive Security
Hive Security
Securing Hadoop
Authorizing Hive
Configuring the SQL standards-based authorization
Authenticating Hive
12
Hive Integration with Other Frameworks
Hive Integration with Other Frameworks
Working with Apache Spark
Working with Accumulo
Working with HBase
Working with Google Drill
Index
Index

Optimizations to reduce the number of map

In this recipe, you will learn how to reduce the number of mappers in Hive.

Getting ready

The number of mappers that is used in a map reduce job depends heavily on the input split. The number of mappers is directly proportional to the number of HDFS blocks, that is, the total number of blocks for the input files. Input split is a logical concept that is used to control the number of mappers. If there is no size defined for an input split in map reduce job, then the number of mappers will be equal to the number of HDFS blocks.

However, if you have defined a particular size for an input split, then the number of mappers will be equal to the number of input splits in the MapReduce job and not to the number of HDFS blocks for that MapReduce job.

Let's suppose that there is a file of 150 MB, and it is broken down into two parts. One part is equal to 128 MB, and the other part is equal to 22 MB. Now consider that the block configuration of HDFS block by default...

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