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  • Book Overview & Buying Apache Hive Essentials
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Apache Hive Essentials

Apache Hive Essentials

By : Dayong Du
4 (2)
Buy this Book
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Apache Hive Essentials

Apache Hive Essentials

4 (2)
By: Dayong Du
Buy this Book

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.
Table of Contents (12 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Get in touch
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1
Overview of Big Data and Hive
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Overview of Big Data and Hive
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A short history
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Introducing big data
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The relational and NoSQL databases versus Hadoop
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Batch, real-time, and stream processing
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Overview of the Hadoop ecosystem
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Hive overview
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Summary
2
Setting Up the Hive Environment
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Setting Up the Hive Environment
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Installing Hive from Apache
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Installing Hive from vendors
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Using Hive in the cloud
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Using the Hive command
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Using the Hive IDE
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Summary
3
Data Definition and Description
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Data Definition and Description
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Understanding data types
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Data type conversions
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Data Definition Language
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Database
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Tables
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Partitions
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Buckets
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Views
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Summary
4
Data Correlation and Scope
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Data Correlation and Scope
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Project data with SELECT
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Filtering data with conditions
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Linking data with JOIN
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Combining data with UNION
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Summary
5
Data Manipulation
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Data Manipulation
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Data exchanging with LOAD
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Data exchange with INSERT
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Data exchange with [EX|IM]PORT
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Data sorting
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Functions
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Transactions and locks
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Summary
6
Data Aggregation and Sampling
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Data Aggregation and Sampling
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Basic aggregation
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Enhanced aggregation
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Aggregation condition
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Window functions
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Sampling
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Summary
7
Performance Considerations
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Performance Considerations
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Performance utilities
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Design optimization
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Data optimization
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Job optimization
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Summary
8
Extensibility Considerations
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Extensibility Considerations
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User-defined functions
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HPL/SQL
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Streaming
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SerDe
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Summary
9
Security Considerations
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Security Considerations
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Authentication
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Authorization
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Mask and encryption
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Summary
10
Working with Other Tools
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Working with Other Tools
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The JDBC/ODBC connector
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NoSQL
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The Hue/Ambari Hive view
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HCatalog
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Oozie
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Spark
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Hivemall
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Summary
11
Other Books You May Enjoy
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Other Books You May Enjoy
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User-defined functions

Hive defines the following three types of UDF:

  • UDFs: These are regular user-defined functions that operate row-wise and output one result for one row, such as most built-in mathematic and string functions.

  • UDAFs: These are user-defined aggregating functions that operate row-wise or group-wise and output one row or one row for each group as a result, such as the MAX and COUNT built-in functions.

  • UDTFs: These are user-defined table-generating functions that also operate row-wise, but they produce multiple rows/tables as a result, such as the EXPLODE function. UDTF can be used either after SELECT or after the LATERAL VIEW statement.

    Note

    Since Hive is implemented in Java, UDFs should be written in Java as well. Since Java supports running code in other languages through the javax.script API (see http://docs.oracle.com/javase/6/docs/api/javax/script/package-summary.html), UDFs can be written in languages other than Java. In this book, we only focus on Java UDFs.

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Apache Hive Essentials
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