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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

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
Preface
What this book covers
What you need for this book
Who this book is for
Sections
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Developing Hive
1. 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
2. Services in Hive
2. 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
3. Understanding the Hive Data Model
3. 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
4. Hive Data Definition Language
4. 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
5. Hive Data Manipulation Language
5. 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
6. Hive Extensibility Features
6. 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
7. Joins and Join Optimization
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7. 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
8. Statistics in Hive
8. Statistics in Hive
Bringing statistics in to Hive
Table and partition statistics in Hive
Column statistics in Hive
Top K statistics in Hive
9
9. Functions in Hive
9. 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
10. Hive Tuning
10. Hive Tuning
Enabling predicate pushdown optimizations in Hive
Optimizations to reduce the number of map
Sampling
11
11. Hive Security
11. Hive Security
Securing Hadoop
Authorizing Hive
Configuring the SQL standards-based authorization
Authenticating Hive
12
12. Hive Integration with Other Frameworks
12. Hive Integration with Other Frameworks
Working with Apache Spark
Working with Accumulo
Working with HBase
Working with Google Drill
13
Index
Index

Using a cross join

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

Cross join, also known as Cartesian product, is a way of joining multiple tables in which all the rows or tuples from one table are paired with the rows and tuples from another table. For example, if the left-hand side table has 10 rows and the right-hand side table has 13 rows then the result set after joining the two tables will be 130 rows. That means all the rows from the left-hand side table (having 10 rows) are paired with all the tables from the right-hand side table (having 13 rows).

If there is a WHERE clause in the SQL statement that includes a cross join, then first the cross join takes place and then the result set is filtered out with the help of the WHERE clause. This means cross joins are not an efficient and optimized way of joining the tables.

The general syntax of a cross join is as follows:

join_condition
  | table_reference [CROSS] JOIN table_reference join_condition

Where:

  • table_reference: Is...

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