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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 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:

Getting Started

- Installation Instructions and a quick tutorial:

- A longer tutorial that covers more features of HiveQL:

- The HiveQL Language Manual:


- Java 1.6

- Hadoop 0.20.x (x >= 1)

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

  We have provided upgrade scripts for Derby and MySQL databases. If
  you are using a different database for your MetaStore you will need
  to provide your own upgrade script.

- Please be aware that the Hive 0.8.0 MetaStore upgrade scripts remove
  support for partition-level column information from the MetaStore
  schema. Since this information was not previously exposed by Hive
  the only people potentially impacted by this change are those who
  access the MetaStore directly via the Thrift API. If you fall into
  the latter category please consult HIVE-2246 for more information.

- Hive 0.8.0 ignores the hive-default.xml file, though we continue
  to provide it for reference purposes. Any changes that you
  previously made to hive-default.xml must now be moved to the
  hive-site.xml file.

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