Apache Hive Cookbook

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.

Apache Hive Cookbook

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Preface

Free Chapter

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

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

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

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

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

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

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

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

Statistics in Hive

Bringing statistics in to Hive

Table and partition statistics in Hive

Column statistics in Hive

Top K statistics 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)

Hive Tuning

Enabling predicate pushdown optimizations in Hive

Optimizations to reduce the number of map

Sampling

Hive Security

Securing Hadoop

Authorizing Hive

Configuring the SQL standards-based authorization

Authenticating Hive

Hive Integration with Other Frameworks

Working with Apache Spark

Working with Accumulo

Working with HBase

Working with Google Drill

Index

Customer Reviews

5 star

4 star

3 star

2 star

1 star

About the Authors

Hanish Bansal is a software engineer with over 4 years of experience in developing big data applications. He loves to study emerging solutions and applications mainly related to big data processing, NoSQL, natural language processing, and neural networks. He has worked on various technologies such as Spring Framework, Hibernate, Hadoop, Hive, Flume, Kafka, Storm, and NoSQL databases, which include HBase, Cassandra, MongoDB, and search engines such as Elasticsearch.

In 2012, he completed his graduation in Information Technology stream from Jaipur Engineering College and Research Center, Jaipur, India. He was also the technical reviewer of the book Apache Zookeeper Essentials. In his spare time, he loves to travel and listen to music.

You can read his blog at http://hanishblogger.blogspot.in/ and follow him on Twitter at https://twitter.com/hanishbansal786.

Saurabh Chauhan is a module lead with close to 8 years of experience in data warehousing and big data applications. He has worked on multiple Extract, Transform and Load tools, such as Oracle Data Integrator and Informatica as well as on big data technologies such as Hadoop, Hive, Pig, Sqoop, and Flume.

He completed his bachelor of technology in 2007 from Vishveshwarya Institute of Engineering and Technology. In his spare time, he loves to travel and discover new places. He also has a keen interest in sports.

Shrey Mehrotra has 6 years of IT experience and, since the past 4 years, in designing and architecting cloud and big data solutions for the governance and financial domains.

Having worked with big data R&D Labs and Global Data and Analytical Capabilities, he has gained insights into Hadoop, focusing on HDFS, MapReduce, and YARN. His technical strengths also include Hive, Pig, Spark, Elasticsearch, Sqoop, Flume, Kafka, and Java.

He likes spending time performing R&D on different big data technologies. He is the co-author of the book Learning YARN, a certified Hadoop developer, and has also written various technical papers. In his free time, he listens to music, watches movies, and spending time with friends.

Apache Hive Cookbook

Apache Hive Cookbook

Overview of this book

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Apache Hive Cookbook

About the Authors