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This page contains details about the Hive design and architecture. A brief technical report about Hive is available at hive.pdf.

Table of Contents

Figure 1

Hive Architecture


  • Parser – Transform a query string to a parse tree representation.
  • Semantic Analyser – Transform the parse tree to an internal query representation, which is still block based and not an operator tree. As part of this step, the column names are verified and expansions like * are performed. Type-checking and any implicit type conversions are also performed at this stage. If the table under consideration is a partitioned table, which is the common scenario, all the expressions for that table are collected so that they can be later used to prune the partitions which are not needed. If the query has specified sampling, that is also collected to be used later on.
  • Logical Plan Generator – Convert the internal query representation to a logical plan, which consists of a tree of operators. Some of the operators are relational algebra operators like 'filter', 'join' etc. But some of the operators are Hive specific and are used later on to convert this plan into a series of map-reduce jobs. One such operator is a reduceSink operator which occurs at the map-reduce boundary. This step also includes the optimizer to transform the plan to improve performance – some of those transformations include: converting a series of joins into a single multi-way join, performing a map-side partial aggregation for a group-by, performing a group-by in 2 stages to avoid the scenario when a single reducer can become a bottleneck in presence of skewed data for the grouping key. Each operator comprises a descriptor which is a serializable object.
  • Query Plan Generator – Convert the logical plan to a series of map-reduce tasks. The operator tree is recursively traversed, to be broken up into a series of map-reduce serializable tasks which can be submitted later on to the map-reduce framework for the Hadoop distributed file system. The reduceSink operator is the map-reduce boundary, whose descriptor contains the reduction keys. The reduction keys in the reduceSink descriptor are used as the reduction keys in the map-reduce boundary. The plan consists of the required samples/partitions if the query specified so. The plan is serialized and written to a file.  


More plan transformations are performed by the optimizer. The optimizer is an evolving component. As of 2011, it was rule-based and performed the following: column pruning and predicate pushdown. However, the infrastructure was in place, and there was work under progress to include other optimizations like map-side join. (Hive 0.11 added several join optimizations.)


The optimizer can be enhanced to be cost-based (see Cost-based optimization in Hive and HIVE-5775). The sorted nature of output tables can also be preserved and used later on to generate better plans. The query can be performed on a small sample of data to guess the data distribution, which can be used to generate a better plan.


A correlation optimizer was added in Hive 0.12.


The plan is a generic operator tree, and can be easily manipulated.




Hive APIs

Hive APIs Overview describes various public-facing APIs that Hive provides.