Book Image

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.
Table of Contents (19 chapters)
Apache Hive Cookbook
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Exploring indexes


Indexes are useful for increasing the performance of frequent queries based on certain columns. But Hive has limited a capability to index data as indexing large datasets requires sufficient additional storage space and processing overheads. Hive can index the columns to speed up some operations. It stores the indexed data in another table.

How to do it…

Indexes could be created on the tables in Hive. Let us create a sales table in Hive on which we are going to create indexes:

Create table sales(id int, fname string, state string, zip string, ip string, pid string) Row format delimited fields terminated by '\t';

Let us create an index on the state column of this table:

CREATE INDEX index_ip ON TABLE sales(ip) AS 'org.apache.hadoop.hive.ql.index.compact.CompactIndexHandler' WITH DEFERRED REBUILD;

In the metastore, it is stored in the IDXS table as shown in the following screenshot: