cde4bc016c
Impala has an optimization for analytic expressions that have a rank filter on top of the analytic expression. It can add a top-n plan node to reduce the amount of rows examined. This is tested in tpcds query 67. The optimization logic relies on an unassigned rank conjunct within the analyzer while creating the analytic plan node. A slight reorganization of the code was needed to implement this optimization. The SlotRefs for the AnalyticInfo needed to be created a little earlier from where it was done in the previous commit. A small fix was made to normalize binary predicates. A non-normalized binary predicate prevents the optimization from being used. A call to the checkAndApplyLimitPushdown is needed for some of the optimizations to kick in. A new AllProjectInfo internal class was created to hold the relationships between the Calcite RexNode objects and the Impala Analytic expressions. Also, IMPALA-14158 is fixed by this commit. The nullsFirst value was incorrect when the syntax was explicit in the query. A new Calcite planner test was added in the junit tests to ensure the optimization kicks in. The new test file is in the PlannerTest/calcite/limit-pushdown-analytic-calcite.test file. This is a copy of the limit-pushdown-analytic.test file in its parent directory but with some modified results. Most of the differences are trivial, but IMPALA-14469 has been filed to deal with one optimization that did not get fixed, which is when the order by clause has a constant expression. Change-Id: Ie6fa6781db56771b13b0cf49bd236f776016bf8d Reviewed-on: http://gerrit.cloudera.org:8080/23317 Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com> Reviewed-by: Aman Sinha <amsinha@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.