Apache Hive (TM) @[email protected] ====================== 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 via MapReduce Hive defines a simple SQL-like query language, called QL, that enables users familiar with SQL to query the data. At the same time, this language also allows programmers who are familiar with the MapReduce framework to be able 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). Please note that Hadoop is a batch processing system and Hadoop jobs tend to have high latency and incur substantial overheads in job submission and scheduling. Consequently the average latency for Hive queries is generally very high (minutes) even when data sets involved are very small (say a few hundred megabytes). As a result it cannot be compared with systems such as Oracle where analyses are conducted on a significantly smaller amount of data but the analyses proceed much more iteratively with the response times between iterations being less than a few minutes. Hive aims to provide acceptable (but not optimal) latency for interactive data browsing, queries over small data sets or test queries. Hive is not designed for online transaction processing and does not support real-time queries or 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 - Hadoop 0.20.x (x >= 1) Upgrading from older versions of Hive ===================================== - Hive @[email protected] 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 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. [email protected] - To discuss and ask usage questions. Send an empty email to [email protected] in order to subscribe to this mailing list. 2. [email protected] - For discussions about code, design and features. Send an empty email to [email protected] in order to subscribe to this mailing list. 3. [email protected] - In order to monitor commits to the source repository. Send an empty email to [email protected] in order to subscribe to this mailing list.