June 18, 2021
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Apache Hive
The Apache Hive™ data
warehouse software facilitates reading, writing, and managing large
datasets residing in distributed storage and queried using SQL syntax.
Built on top of Apache Hadoop™, Hive provides the following features:
- Tools to enable easy access to data via SQL, thus enabling data warehousing tasks such as extract/transform/load (ETL), reporting, and data analysis.
- A mechanism to impose structure on a variety of data formats
Access to files stored either directly in Apache HDFS™ or in other data storage systems such as Apache HBase™
- Query execution via Apache Tez™, Apache Spark™, or MapReduce
- Procedural language with HPL-SQL
- Sub-second query retrieval via Hive LLAP, Apache YARN and Apache Slider.
Hive provides standard SQL functionality, including many of the later SQL:2003, SQL:2011, and SQL:2016 features for analytics.
Hive's
SQL can also be extended with user code via user defined functions
(UDFs), user defined aggregates (UDAFs), and user defined table
functions (UDTFs).
There is not a single "Hive format" in which data must be stored. Hive comes with built in connectors for comma and tab-separated values (CSV/TSV) text files, Apache Parquet™, Apache ORC™, and other formats. Users can extend Hive with connectors for other formats. Please see File Formats and Hive SerDe in the Developer Guide for details.
Hive is not designed for online transaction processing (OLTP) workloads. It is best used for traditional data warehousing tasks.
Hive is designed to maximize scalability (scale out with more machines added dynamically to the Hadoop cluster), performance, extensibility, fault-tolerance, and loose-coupling with its input formats.
Components of Hive include HCatalog and WebHCat.
- HCatalog is a table and storage management layer for Hadoop that enables users with different data processing tools — including Pig and MapReduce — to more easily read and write data on the grid.
- WebHCat provides a service that you can use to run Hadoop MapReduce (or YARN), Pig, Hive jobs. You can also perform Hive metadata operations using an HTTP (REST style) interface.
Hive Documentation
The links below provide access to the Apache Hive wiki documents. This list is not complete, but you can navigate through these wiki pages to find additional documents. For more information, please see the official Hive website.
General Information about Hive
- Getting Started
- Books about Hive
- Presentations and Papers about Hive
- Sites and Applications Powered by Hive
- Related Projects
- FAQ
- Hive Users Mailing List
- Hive IRC Channel:
#hiveon irc.freenode.net - About This Wiki
User Documentation
- Hive Tutorial
- Hive SQL Language Manual: Commands, CLIs, Data Types,
DDL (create/drop/alter/truncate/show/describe), Statistics (analyze), Indexes, Archiving,
DML (load/insert/update/delete/merge, import/export, explain plan),
Queries (select), Operators and UDFs, Locks, Authorization - File Formats and Compression: RCFile, Avro, ORC, Parquet; Compression, LZO
- Procedural Language: Hive HPL/SQL
- Hive Configuration Properties
- Hive Clients
- Hive Client (JDBC, ODBC, Thrift)
- HiveServer2: Overview, HiveServer2 Client and Beeline, Hive Metrics
- Hive Web Interface
- Hive SerDes: Avro SerDe, Parquet SerDe, CSV SerDe, JSON SerDe
- Hive Accumulo Integration
- Hive HBase Integration
- Druid Integration
- Kudu Integration
- Hive Transactions, Streaming Data Ingest, and Streaming Mutation API
- Hive Counters
- Using TiDB as the Hive Metastore database
Administrator Documentation
- Installing Hive
- Configuring Hive
- Setting Up Metastore
- Setting Up Hive Web Interface
- Setting Up Hive Server (JDBC, ODBC, Thrift, HiveServer2)
- Hive Replication
- Hive on Amazon Web Services
- Hive on Amazon Elastic MapReduce
- Hive on Spark: Getting Started
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