|Stable release||2.0.0 / February 15, 2016|
|Type||Database management system|
|License||Apache License 2.0|
Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. While developed by Facebook, Apache Hive is now used and developed by other companies such as Netflix and the Financial Industry Regulatory Authority (FINRA). Amazon maintains a software fork of Apache Hive that is included in Amazon Elastic MapReduce on Amazon Web Services.
Apache Hive supports analysis of large datasets stored in Hadoop's HDFS and compatible file systems such as Amazon S3 filesystem. It provides an SQL-like language called HiveQL with schema on read and transparently converts queries to MapReduce, Apache Tez and Spark jobs. All three execution engines can run in Hadoop YARN. To accelerate queries, it provides indexes, including bitmap indexes. Other features of Hive include:
- Indexing to provide acceleration, index type including compaction and Bitmap index as of 0.10, more index types are planned.
- Different storage types such as plain text, RCFile, HBase, ORC, and others.
- Metadata storage in an RDBMS, significantly reducing the time to perform semantic checks during query execution.
- Operating on compressed data stored into the Hadoop ecosystem using algorithms including DEFLATE, BWT, snappy, etc.
- Built-in user defined functions (UDFs) to manipulate dates, strings, and other data-mining tools. Hive supports extending the UDF set to handle use-cases not supported by built-in functions.
- SQL-like queries (HiveQL), which are implicitly converted into MapReduce or Tez, or Spark jobs.
Four file formats are supported in Hive, which are TEXTFILE, SEQUENCEFILE, ORC and RCFILE. Apache Parquet can be read via plugin in versions later than 0.10 and natively starting at 0.13. Additional Hive plugins support querying of the Bitcoin Blockchain.
While based on SQL, HiveQL does not strictly follow the full SQL-92 standard. HiveQL offers extensions not in SQL, including multitable inserts and create table as select, but only offers basic support for indexes. Also, HiveQL lacks support for transactions and materialized views, and only limited subquery support. Support for insert, update, and delete with full ACID functionality was made available with release 0.14.
Hive unit testing frameworks
- "Apache Hive Download News".
- Venner, Jason (2009). Pro Hadoop. Apress. ISBN 978-1-4302-1942-2.
- Use Case Study of Hive/Hadoop
- on YouTube
- Amazon Elastic MapReduce Developer Guide
- HiveQL Language Manual
- Apache Tez
- Working with Students to Improve Indexing in Apache Hive
- Lam, Chuck (2010). Hadoop in Action. Manning Publications. ISBN 1-935182-19-6.
- Optimising Hadoop and Big Data with Text and HiveOptimising Hadoop and Big Data with Text and Hive
- LanguageManual ORC
- Faster Big Data on Hadoop with Hive and RCFile
- Facebook's Petabyte Scale Data Warehouse using Hive and Hadoop
- Yongqiang He; Rubao Lee; Yin Huai; Zheng Shao; Namit Jain; Xiaodong Zhang; Zhiwei Xu. "RCFile: A Fast and Space-efﬁcient Data Placement Structure in MapReduce-based Warehouse Systems" (PDF).
- "Parquet". 18 Dec 2014. Archived from the original on 2 February 2015. Retrieved 2 February 2015.
- Massie, Matt (21 August 2013). "A Powerful Big Data Trio: Spark, Parquet and Avro". zenfractal.com. Archived from the original on 2 February 2015. Retrieved 2 February 2015.
- Franke, Jörn. "Hive & Bitcoin: Analytics on Blockchain data with SQL".
- White, Tom (2010). Hadoop: The Definitive Guide. O'Reilly Media. ISBN 978-1-4493-8973-4.
- Hive Language Manual
- ACID and Transactions in Hive
- Hive A Warehousing Solution Over a MapReduce Framework
- Official website
- The Free Hive Book (CC by-nc licensed)
- Hive A Warehousing Solution Over a MapReduce Framework - Original paper presented by Facebook at VLDB 2009
- Using Apache Hive With Amazon Elastic MapReduce (Part 1) and on YouTube, presented by an AWS Engineer
- Using hive + cassandra + shark. A hive cassandra cql storage handler.
- Major Technical Advancements in Apache Hive, Yin Huai, Ashutosh Chauhan, Alan Gates, Gunther Hagleitner, Eric N. Hanson, Owen O’Malley, Jitendra Pandey, Yuan Yuan, Rubao Lee and Xiaodong Zhang, SIGMOD 2014
- Apache Hive Wiki