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https://github.com/apache/hive

Apache Hive
https://github.com/apache/hive

apache big-data database hadoop hive java sql

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Apache Hive

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README 

        

Apache Hive (TM)

================

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The Apache Hive (TM) data warehouse software facilitates reading,

writing, and managing large datasets residing in distributed storage

using SQL. Built on top of Apache Hadoop (TM), it provides:



* 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 (TM) or in other

  data storage systems such as Apache HBase (TM)



* Query execution using Apache Hadoop MapReduce or Apache Tez frameworks.



Hive provides standard SQL functionality, including many of the later

2003 and 2011 features for analytics.  These include OLAP functions,

subqueries, common table expressions, and more.  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).



Hive users can choose between Apache Hadoop MapReduce or Apache Tez

frameworks as their execution backend. Note that MapReduce framework

has been deprecated since Hive 2, and Apache Tez is recommended. MapReduce

is a mature framework that is proven at large scales. However, MapReduce

is a purely batch framework, and queries using it may experience

higher latencies (tens of seconds), even over small datasets. Apache

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 each 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. 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.



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



- Instructions to build Hive from source:

  https://cwiki.apache.org/confluence/display/Hive/GettingStarted#GettingStarted-BuildingHivefromSource



- 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

------



| Hive Version  | Java Version  |

| ------------- |:-------------:|

| Hive 1.0      | Java 6        |

| Hive 1.1      | Java 6        |

| Hive 1.2      | Java 7        |

| Hive 2.x      | Java 7        |

| Hive 3.x      | Java 8        |

| Hive 4.x      | Java 8        |



Hadoop

------



- Hadoop 1.x, 2.x

- Hadoop 3.x (Hive 3.x)



Upgrading from older versions of Hive

=====================================



- Hive 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

====================



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