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Apache Hive (TM) @VERSION@ ====================== The Apache Hive (TM) data warehouse software facilitates querying and managing large datasets residing in distributed storage. Built on top of Apache Hadoop (TM), it provides: * Tools to enable easy data extract/transform/load (ETL) * A mechanism to impose structure on a variety of data formats * Access to files stored either directly in Apache HDFS (TM) or in other data storage systems such as Apache HBase (TM) * Query execution using Apache Hadoop MapReduce or Apache Tez frameworks. Hive implements a dialect of SQL (Hive QL) that focuses on analytics and presents a rich set of SQL semantics including OLAP functions, subqueries, common table expressions and more. Hive allows SQL developers or users with SQL tools to easily query, analyze and process data stored in Hadoop. Hive also allows programmers familiar with the MapReduce framework to plug in their custom mappers and reducers to perform more sophisticated analysis that may not be supported by the built-in capabilities of the language. QL can also be extended with custom scalar functions (UDF's), aggregations (UDAF's), and table functions (UDTF's). Hive users have a choice of 2 runtimes when executing SQL queries. Users can choose to use the Apache Hadoop MapReduce framework, which is mature and proven at large scales. MapReduce is a purely batch framework, and queries run using the MapReduce framework may experience higher latencies (tens of seconds), even over small datasets. Alternatively, users can choose to use the newer Apache Tez framework to process SQL queries. Tez is designed for interactive query and has substantially reduced overheads versus MapReduce. Users are free to switch back and forth between these frameworks at any time. In either case, Hive is best suited for use cases where the amount of data processed is large enough to require a distributed system. Hive is not designed for online transaction processing and does not support row level insert/updates. It is best used for batch jobs over large sets of immutable data (like web logs). What Hive values most are scalability (scale out with more machines added dynamically to the Hadoop cluster), extensibility (with MapReduce framework and UDF/UDAF/UDTF), fault-tolerance, and loose-coupling with its input formats. General Info ============ For the latest information about Hive, please visit out website at: http://hive.apache.org/ Getting Started =============== - Installation Instructions and a quick tutorial: https://cwiki.apache.org/confluence/display/Hive/GettingStarted - A longer tutorial that covers more features of HiveQL: https://cwiki.apache.org/confluence/display/Hive/Tutorial - The HiveQL Language Manual: https://cwiki.apache.org/confluence/display/Hive/LanguageManual Requirements ============ - Java 1.6, 1.7 - Hadoop 1.x, 2.x Upgrading from older versions of Hive ===================================== - Hive @VERSION@ includes changes to the MetaStore schema. If you are upgrading from an earlier version of Hive it is imperative that you upgrade the MetaStore schema by running the appropriate schema upgrade scripts located in the scripts/metastore/upgrade directory. - We have provided upgrade scripts for MySQL, PostgreSQL, Oracle, Microsoft SQL Server, and Derby databases. If you are using a different database for your MetaStore you will need to provide your own upgrade script. Useful mailing lists ==================== 1. firstname.lastname@example.org - To discuss and ask usage questions. Send an empty email to email@example.com in order to subscribe to this mailing list. 2. firstname.lastname@example.org - For discussions about code, design and features. Send an empty email to email@example.com in order to subscribe to this mailing list. 3. firstname.lastname@example.org - In order to monitor commits to the source repository. Send an empty email to email@example.com in order to subscribe to this mailing list.