Two of the most vibrant communities in the Apache Hadoop ecosystem are now working together to bring users a Hive-on-Spark option that combines the best elements of both.(Editor’s note April 12, 2016: Hive-on-Spark is now.)Apache Hive is a popular SQL interface for batch processing and ETL using Apache Hadoop. Until recently, MapReduce was the only execution engine in the Hadoop ecosystem, and Hive queries could only run on MapReduce. But today, alternative execution engines to MapReduce are available — such as and.Although Spark is relatively new to the Hadoop ecosystem, its. An open-source data analytics cluster computing framework, Spark is built outside of Hadoop’s two-stage MapReduce paradigm but runs on top of HDFS. Because of its successful approach, Spark has quickly gained momentum and become established as an attractive choice for the future of data processing in Hadoop.In this post, you’ll get an overview of the motivations and technical details behind some very exciting news for Spark and Hive users: the fact that the Hive and Spark communities are joining forces to collaboratively introduce Spark as a new execution engine option for Hive, alongside MapReduce and Tez (see ).
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Motivation and ApproachHere are the two main motivations for enabling Hive to run on Spark:. To improve the Hive user experienceHive queries will run faster, thereby improving user experience. Furthermore, users will have access to a robust, non-MR execution engine that has already proven itself to be a leading option for data processing as well as streaming, and which is among the across all of Apache from contributor and commit standpoints. To streamline operational management for Spark shopsHive-on-Spark will be very valuable for users who are already using Spark for other data-processing and machine-learning needs.
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Standardizing on one execution back end is also convenient for operational management, making it easier to debug issues and create enhancements.Superficially, this project’s goals look similar to those of Shark or Spark SQL, which are separate projects that reuse the Hive front end to run queries using Spark. However, this design adds Spark into Hive, parallel to MapReduce and Tez, as another backend execution engine. Thus, existing Hive jobs continue to run as-is transparently on Spark.The key advantage of this approach is to leverage all the existing integration on top of Hive, including ODBC/JDBC, auditing, authorization, and monitoring. Another advantage is that it will have no impact on Hive’s existing code path and thus no functional or performance effects. Users choosing to run Hive on either MapReduce or Tez will have the same functionality and code paths they have today — thus, the Hive user community will be in the great position of being able to choose among MapReduce, Tez, or Spark as a back end. In addition, maintenance costs will be minimized so the Hive community needn’t make specialized investments for Spark.Meanwhile, users opting for Spark as the execution engine will automatically have all the rich functional features that Hive provides.
Future features (such as new data types, UDFs, logical optimization, and so on ) added to Hive should become automatically available to those users, without any customization work to be done in Hive’s Spark execution engine. Overall FunctionalityTo use Spark as an execution engine in Hive, you would set the following:set hive.execution.engine=spark;The default value for this configuration is still “ mr”. Hive will continue to work on MapReduce as-is on clusters that don’t have Spark on them. When Spark is configured as Hive’s execution, a few configuration variables will be introduced, such as the master URL of the Spark cluster.The new execution engine should support all Hive queries without any modification. Query results should be functionally equivalent to those from either MapReduce or Tez.From my point of view we can also add below points as motivations for enabling Hive to run on Spark:1 – Spark user benefits: This feature is very valuable to users who are already using Spark for other data processing and machine learning needs. Standardizing on one execution backend is convenient for operational management, and makes it easier to develop expertise to debug issues and make enhancements.2 – Greater Hive adoption: Following the previous point, this brings Hive into the Spark user base as a SQL on Hadoop option, further increasing Hive’s adoption.3 – Performance: Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does.It is not a goal for the Spark execution backend to replace Tez or MapReduce. It is healthy for the Hive project for multiple backends to coexist.
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Users have a choice whether to use Tez, Spark or MapReduce. Each has different strengths depending on the use case.
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And the success of Hive does not completely depend on the success of either Tez or Spark.