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Common metadata layer for Hadoop's Map Reduce, Pig, and Hive
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Apache Hive 0.6.0 ================= Hive is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc querying and analysis of large datasets stored in Hadoop compatible file systems. Hive provides a mechanism to put structure on this data and query the data using a SQL-like language called HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL. 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/ and our wiki at: http://wiki.apache.org/hadoop/Hive/ Getting Started =============== - Installation Instructions and a quick tutorial: http://wiki.apache.org/hadoop/Hive/GettingStarted - A longer tutorial that covers more features of HiveQL: http://wiki.apache.org/hadoop/Hive/Tutorial - The HiveQL Language Manual: http://wiki.apache.org/hadoop/Hive/LanguageManual Requirements ============ - Java 1.6 - Hadoop 0.17, 0.18, 0.19, or 0.20. *NOTE*: We strongly recommend that you use Hadoop 0.20 since the majority of our testing is done against this version and because support for pre-0.20 versions of Hadoop will be dropped in Hive 0.7. Upgrading from older versions of Hive ===================================== - Hive 0.6.0 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 script located in the scripts/metastore/upgrade directory. We have provided upgrade scripts for Derby, MySQL, and PostgreSQL databases. If you are using a different database for your MetaStore you will need to provide your own upgrade script. - Hive 0.6.0 includes new configuration properties. If you are upgrading from an earlier version of Hive it is imperative that you replace all of the old copies of the hive-default.xml configuration file with the new version located in the conf/ directory. 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.