6. Data Aggregation and Sampling | Apache Hive Essentials

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Table Of Contents

Apache Hive Essentials

By : Dayong Du

4.5 (4)

Apache Hive Essentials

4.5 (4)

By: Dayong Du

Overview of this book

If you are a data analyst, developer, or simply someone who wants to use Hive to explore and analyze data in Hadoop, this is the book for you. Whether you are new to big data or an expert, with this book, you will be able to master both the basic and the advanced features of Hive. Since Hive is an SQL-like language, some previous experience with the SQL language and databases is useful to have a better understanding of this book.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Overview of Big Data and Hive

1. Overview of Big Data and Hive

A short history

Introducing big data

Relational and NoSQL database versus Hadoop

Batch, real-time, and stream processing

Overview of the Hadoop ecosystem

Hive overview

Summary

2. Setting Up the Hive Environment

2. Setting Up the Hive Environment

Installing Hive from Apache

Installing Hive from vendor packages

Starting Hive in the cloud

Using the Hive command line and Beeline

The Hive-integrated development environment

Summary

3. Data Definition and Description

3. Data Definition and Description

Understanding Hive data types

Data type conversions

Hive Data Definition Language

Hive database

Hive internal and external tables

Hive partitions

Hive buckets

Hive views

Summary

4. Data Selection and Scope

4. Data Selection and Scope

The SELECT statement

The INNER JOIN statement

The OUTER JOIN and CROSS JOIN statements

Special JOIN – MAPJOIN

Set operation – UNION ALL

Summary

5. Data Manipulation

5. Data Manipulation

Data exchange – LOAD

Data exchange – INSERT

Data exchange – EXPORT and IMPORT

ORDER and SORT

Operators and functions

Transactions

Summary

6. Data Aggregation and Sampling

6. Data Aggregation and Sampling

Basic aggregation – GROUP BY

Advanced aggregation – GROUPING SETS

Advanced aggregation – ROLLUP and CUBE

Aggregation condition – HAVING

Analytic functions

Sampling

Summary

7. Performance Considerations

7. Performance Considerations

Performance utilities

Design optimization

Data file optimization

Job and query optimization

Summary

8. Extensibility Considerations

8. Extensibility Considerations

User-defined functions

Streaming

SerDe

Summary

9. Security Considerations

9. Security Considerations

Authentication

Authorization

Encryption

Summary

10. Working with Other Tools

10. Working with Other Tools

JDBC / ODBC connector

HBase

Hue

HCatalog

ZooKeeper

Oozie

Hive roadmap

Summary

Index

Index

Sampling

When data volume is extra large, we may need to find a subset of data to speed up data analysis. Here it comes to a technique used to select and analyze a subset of data in order to identify patterns and trends. In Hive, there are three ways of sampling data: random sampling, bucket table sampling, and block sampling.

Random sampling uses the RAND() function and LIMIT keyword to get the sampling of data as shown in the following example. The DISTRIBUTE and SORT keywords are used here to make sure the data is also randomly distributed among mappers and reducers efficiently. The ORDER BY RAND() statement can also achieve the same purpose, but the performance is not good:

SELECT * FROM <Table_Name> DISTRIBUTE BY RAND() SORT BY RAND()
LIMIT <N rows to sample>;

Bucket table sampling is a special sampling optimized for bucket tables as shown in the following syntax and example. The colname value specifies the column where to sample the data. The RAND() function can also be...

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