Apache Hive

A data warehouse enabling easy data summarization and ad-hoc queries

Apache Hive is data warehouse infrastructure built on top of Apache™ Hadoop® for providing data summarization, ad-hoc query, and analysis of large datasets. It provides a mechanism to project structure onto the data in Hadoop and to query that data using a SQL-like language called HiveQL (HQL). Hive eases integration between Hadoop and tools for business intelligence and visualization.

What Hive Does

Hadoop was built to organize and store massive amounts of data. A Hadoop cluster is a reservoir of heterogeneous data, from multiple sources and in different formats. Hive allows the user to explore and structure that data, analyze it, and then turn it into business insight.

Learn how the Stinger Initiative aims to bring 100x performance improvements and continued SQL compatibility to Hive.

How Hive Works

The tables in Hive are similar to tables in a relational database, and data units are organized in a taxonomy from larger to more granular units. Databases are comprised of tables, which are made up of partitions. Data can be accessed via a simple query language, called HiveQL, which is similar to SQL. Hive supports overwriting or appending data, but not updates and deletes.

Within a particular database, data in the tables is serialized and each table has a corresponding Hadoop Distributed File System (HDFS) directory. Each table can be sub-divided into partitions that determine how data is distributed within sub-directories of the table directory. Data within partitions can be further broken down into buckets.

Hive supports primitive data formats such as TIMESTAMP, STRING, FLOAT, BOOLEAN, DECIMAL, BINARY, DOUBLE, INT, TINYINT, SMALLINT and BIGINT. In addition, primitive data types can be combined to form complex data types, such as structs, maps and arrays.

Here are some advantageous characteristics of Hive:

  • Familiar Hundreds of unique users can simultaneously query the data using a language familiar to SQL users.
  • Fast Response times are typically much faster than other types of queries on the same type of huge datasets.
  • Scalable and extensible As data variety and volume grows, more commodity machines can be added to the cluster, without a corresponding reduction in performance.
  • Informative Familiar JDBC and ODBC drivers allow many applications to pull Hive data for seamless reporting. Hive allows users to read data in arbitrary formats, using SerDes and Input/Output formats.

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Apache Top-Level Project Since
September 2010
Hortonworks Committers & Contributors
Project Page

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