3. Understanding the Hive Data Model | Apache Hive Cookbook

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Apache Hive Cookbook

By : Hanish Bansal, Saurabh Chauhan, Shrey Mehrotra

3 (4)

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.

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. 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. 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. 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. 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. 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. 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. Joins and Join Optimization

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. 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. 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. Hive Tuning

10. Hive Tuning

Enabling predicate pushdown optimizations in Hive

Optimizations to reduce the number of map

Sampling

11. Hive Security

11. Hive Security

Securing Hadoop

Authorizing Hive

Configuring the SQL standards-based authorization

Authenticating Hive

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

Index

Index

Using complex data types

In addition to primitive data types, Hive also supports a few complex data types: Struct, MAP, and Array. Complex data types are also known as collection data types. Most relational databases don't support such data types.

Complex data types can be built from primitive data types:

STRUCT: The struct data type in Hive is analogous to the STRUCT in C programming language. It is a record type that holds a set of named fields that can be of any primitive data types. Fields in the STRUCT type are accessed using the DOT (.) notation.
Syntax: STRUCT<col_name : data_type [COMMENT col_comment], ...>
For example, if a column address is of the type STRUCT {city STRING; state STRING}, then the city field can be referenced using address.city.
MAP: The map data type contains key-value pairs. In Map, elements are accessed using the keys. For example, if a column name is of type Map: 'firstname' -> 'john' and 'lastname' -> 'roy', then the last name can be accessed using the...

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