98b6c0208f
This commit enables the Calcite planner join optimization rule to make use
of table and column statistics in Impala.
The ImpalaRelMetadataProvider class provides the metadata classes to the
rule optimizer.
All the ImpalaRelMd* classes are extensions of Calcite Metadata classes. The
ones overridden are:
ImpalaRelMdRowCount:
This provides the cardinality of a given type of RelNode.
The default implementation in the RelMdRowCount is used for some of the
RelNodes. The ones overridden are:
TableScan: Gets the row count from the Table object.
Filter: Calls the FilterSelectivityEstimator and adjusts the number of
rows based on the selectivity of the filter condition.
Join: Uses our own algorithm to determine the number of rows that will
be created by the join condition using the JoinRelationInfo (more on this
below).
ImpalaRelMdDistinctRowCount:
This provides the number of distinct rows returned by the RelNode.
The default implementation in the RelMdDistinct RowCount is used for
some of the RelNodes. The ones overridden are:
TableScan: Uses the stats. If stats are not defined, all rows will
be marked as distinct.
Aggregate: For some reason, Calcite sometimes returns a number of
distinct rows greater than the number of rows, which doesn't make
sense. So this ensures the number of distinct rows never exceeds
the number of rows.
Filter: The number of distinct rows is reduced by the calculated
selectivity.
Join: same as aggregate.
ImpalaRelMdRowSize:
Provides the Impala interpreted size of the Calcite datatypes.
ImpalaRelMdSelectivity:
The selectivity is calculated within the RowCount. An initial attempt
was done to use this class for selectivity, but it was seemed rather clunky
since the row counts and selectivity are very closely intertwined and
the pruned row counts (a future commit) made this even more complicated.
So the selectivity metadata is overridden or all our RelNodes as full
selectivity (1.0).
As mentioned above, the FilterSelectivityEstimator class tries to approximate
the number of rows filtered out with the given condition. Some work still
needs to be done to make this more in line with the Expr seletivities, a Jira
will be filed for this.
The JoinRelationInfo is the helper class that estimates the number of rows
that will be output of the Join RelNode. The join condition is split up into
multiple conditions broken up by the AND keyword. This first pass has some major
flaws which need to be corrected, including:
- Only equality conditions limit the number of rows. Non-equality conditions
will be ignored. If there are only non-equality conditions, the cardinality
will be the equivalent of a cross join.
- Left joins take the maximum of the calculated join and the total number
of rows on the left side. This can probably be improved upon if we find
the matching rows provide a cardinality that is greater than one for each
row. (Of course, right joins and outer joins have this same logic).
Change-Id: I9d5bb50eb562c28e4b7c7a6529d140f98e77295c
Reviewed-on: http://gerrit.cloudera.org:8080/23122
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Reviewed-by: Steve Carlin <scarlin@cloudera.com>
Welcome to Impala
Lightning-fast, distributed SQL queries for petabytes of data stored in open data and table formats.
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 Apache Iceberg, HDFS, Apache HBase, Apache Kudu, Amazon S3, Azure Data Lake Storage, Apache Hadoop Ozone and more!
- Wide analytic SQL support, including window functions and subqueries.
- On-the-fly code generation using LLVM to generate lightning-fast code tailored specifically to each individual query.
- Support for the most commonly-used Hadoop file formats, including Apache Parquet and Apache ORC.
- Support for industry-standard security protocols, including Kerberos, LDAP and TLS.
- Apache-licensed, 100% open source.
More about Impala
The fastest way to try out Impala is a quickstart Docker container. You can try out running queries and processing data sets in Impala on a single machine without installing dependencies. It can automatically load test data sets into Apache Kudu and Apache Parquet formats and you can start playing around with Apache Impala SQL within minutes.
To learn more about Impala as a user or administrator, or to try Impala, please visit the Impala homepage. Detailed documentation for administrators and users is available at Apache Impala documentation.
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. Impala supports x86_64 and has experimental support for arm64 (as of Impala 4.0). Impala Requirements contains more detailed information on the minimum CPU requirements.
Supported OS Distributions
Impala runs on Linux systems only. The supported distros are
- Ubuntu 16.04/18.04
- CentOS/RHEL 7/8
Other systems, e.g. SLES12, may also be supported but are not tested by the community.
Export Control Notice
This distribution uses cryptographic software and may be subject to export controls. Please refer to EXPORT_CONTROL.md for more information.
Build Instructions
See Impala's developer documentation to get started.
Detailed build notes has some detailed information on the project layout and build.