mirror of
https://github.com/apache/impala.git
synced 2026-01-07 00:02:28 -05:00
42ca45e830
Since commit d2d3f4c (on asf-master), TAggregateExpr contains
the logical input types of the Aggregate Expr. The reason they
are included is that merging aggregate expressions will have
input tyes of the intermediate values which aren't necessarily
the same as the input types. For instance, NDV() uses a binary
blob as its intermediate value and it's passed to its merge
aggregate expressions as a StringVal but the input type of NDV()
in the query could be DecimalVal. In this case, we consider
DecimalVal as the logical input type while StringVal is the
intermediate type. The logical input types are accessed by the
BE via GetConstFnAttr() during interpretation and constant
propagation during codegen.
To handle distinct aggregate expressions (e.g. select count(distinct)),
the FE uses 2-phase aggregation by introducing an extra phase of
split/merge aggregation in which the distinct aggregate expressions'
inputs are coverted and added to the group-by expressions in the first
phase while the non-distinct aggregate expressions go through the normal
split/merge treatement.
The bug is that the existing code incorrectly propagates the intermediate
types of the non-grouping aggregate expressions as the logical input types
to the merging aggregate expressions in the second phase of aggregation.
The input aggregate expressions for the non-distinct aggregate expressions
in the second phase aggregation are already merging aggregate expressions
(from phase one) in which case we should not treat its input types as
logical input types.
This change fixes the problem above by checking if the input aggregate
expression passed to FunctionCallExpr.createMergeAggCall() is already
a merging aggregate expression. If so, it will use the logical input
types recorded in its 'mergeAggInputFn_' as references for its logical
input types instead of the aggregate expression input types themselves.
Change-Id: I158303b20d1afdff23c67f3338b9c4af2ad80691
Reviewed-on: http://gerrit.cloudera.org:8080/6724
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
Welcome to Impala
Lightning-fast, distributed SQL queries for petabytes of data stored in Apache Hadoop clusters.
Impala is a modern, massively-distributed, massively-parallel, C++ query engine that lets you analyze, transform and combine data from a variety of data sources:
- Best of breed performance and scalability.
- Support for data stored in HDFS, Apache HBase and Amazon S3.
- Wide analytic SQL support, including window functions and subqueries.
- On-the-fly code generation using LLVM to generate CPU-efficient code tailored specifically to each individual query.
- Support for the most commonly-used Hadoop file formats, including the Apache Parquet (incubating) project.
- Apache-licensed, 100% open source.
More about Impala
To learn more about Impala as a business user, or to try Impala live or in a VM, please visit the Impala homepage.
If you are interested in contributing to Impala as a developer, or learning more about Impala's internals and architecture, visit the Impala wiki.
Supported Platforms
Impala only supports Linux at the moment.
Build Instructions
See bin/bootstrap_build.sh.
Export Control Notice
This distribution uses cryptographic software and may be subject to export controls. Please refer to EXPORT_CONTROL.md for more information.