087b715a2b
tables This patch implements partition filtering support for the SHOW FILES statement on Iceberg tables, based on the functionality added in IMPALA-12243. Prior to this change, the syntax resulted in a NullPointerException. Key changes: - Added ShowFilesStmt.analyzeIceberg() to validate and transform partition expressions using IcebergPartitionExpressionRewriter and IcebergPartitionPredicateConverter. After that, it collects matching file paths using IcebergUtil.planFiles(). - Added FeIcebergTable.Utils.getIcebergTableFilesFromPaths() to accept pre-filtered file lists from the analysis phase. - Enhanced TShowFilesParams thrift struct with optional selected_files field to pass pre-filtered file paths from frontend to backend. Testing: - Analyzer tests for negative cases: non-existent partitions, invalid expressions, non-partition columns, unsupported transforms. - Analyzer tests for positive cases: all transform types, complex expressions. - Authorization tests for non-filtered and filtered syntaxes. - E2E tests covering every partition transform type with various predicates. - Schema evolution and rollback scenarios. The implementation follows AlterTableDropPartition's pattern where the analysis phase performs validation/metadata retrieval and the execution phase handles result formatting and display. Change-Id: Ibb9913e078e6842861bdbb004ed5d67286bd3152 Reviewed-on: http://gerrit.cloudera.org:8080/23455 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.