MR3 is a new execution engine for Hadoop and Kubernetes. Similar in spirit to MapReduce and Tez, it is a new execution engine with simpler design, better performance, and more features. MR3 serves as a framework for running jobs on Hadoop and Kubernetes. MR3 also supports standalone mode which does not require a resource manager such as Hadoop or Kubernetes.
The main application of MR3 is Hive on MR3. With MR3 as the execution engine, the user can run Hive not only on Hadoop but also directly on Kubernetes. By exploiting standalone mode supported by MR3, the user can run Hive virtually in any type of cluster regardless of the availability of Hadoop or Kubernetes and the version of Java installed in the system.
Hive on MR3 is much easier to install and operate than Apache Hive. For performance, it achieves the speed of Hive-LLAP with no additional configuration. For executing typical workloads (such as TPC-DS), Hive on MR3 is indeed faster than Hive-LLAP. We actively maintain Hive on MR3 by backporting critical patches from Apache Hive.
This repository (branch master4.0.1) is a fork of Apache Hive 4.0.1
for running Hive on the execution engine MR3 with Java 17.
For Apache Hive 3.1 on MR3,
check out branch master3.
To discuss the development of this repository in general, join MR3 Slack.
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:
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Tools to enable easy access to data via SQL, thus enabling data warehousing tasks such as extract/transform/load (ETL), reporting, and data analysis
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A mechanism to impose structure on a variety of data formats
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Access to files stored either directly in Apache HDFS (TM) or in other data storage systems such as Apache HBase (TM)
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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.
For the latest information about Hive, please visit out website at:
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Installation Instructions and a quick tutorial: https://cwiki.apache.org/confluence/display/Hive/GettingStarted
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Instructions to build Hive from source: https://cwiki.apache.org/confluence/display/Hive/GettingStarted#GettingStarted-BuildingHivefromSource
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A longer tutorial that covers more features of HiveQL: https://cwiki.apache.org/confluence/display/Hive/Tutorial
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The HiveQL Language Manual: https://cwiki.apache.org/confluence/display/Hive/LanguageManual
| 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 1.x, 2.x
- Hadoop 3.x (Hive 3.x)
- Hadoop 3.3.6 (Hive 4.x)
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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.
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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.
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user@hive.apache.org - To discuss and ask usage questions. Send an empty email to user-subscribe@hive.apache.org in order to subscribe to this mailing list.
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dev@hive.apache.org - For discussions about code, design and features. Send an empty email to dev-subscribe@hive.apache.org in order to subscribe to this mailing list.
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commits@hive.apache.org - In order to monitor commits to the source repository. Send an empty email to commits-subscribe@hive.apache.org in order to subscribe to this mailing list.