52334ba426
The 'case' function resolver in the original Impala planner has a quirk in it which caused issues in the Calcite planner. The function resolver for the original planner resolves all case statements with the "boolean" version. Later on, in the analysis of the CaseExpr, the proper types are assessed and the necessary casting is added. The Calcite planner follows a similar path. The resolver always returns boolean as well and the coerce nodes module determines the proper return type for the case statement. Two other related issues are also fixed here: Literal strings should be treated as type STRING instead of CHAR(X), but a null should literal should not be changed from a CHAR(x) to a STRING. This broke a 'case' test in the test framework where the columns were non-literals with type char(x), and the return value was a "null" which should not have forced a cast to string. A cast from a varchar to a varchar should be ignored. Testing: Added a test to calcite.test. Ensured the existing cast test in test_chars.py passed. Ran through the Jenkins Calcite testing framework. Change-Id: I82d657f4bfce432c458ee8198188dadf9f23f2ef Reviewed-on: http://gerrit.cloudera.org:8080/23560 Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com> Tested-by: Impala Public Jenkins <impala-public-jenkins@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.