821c7347d1
Before this change, query plans and profile reported only a single partition even for partitioned Iceberg tables, which was misleading for users. Now we can display the number of scanned partitions correctly for both partitioned and unpartitioned Iceberg tables. This is achieved by extracting the partition values from the file descriptors and storing them in the IcebergContentFileStore. Instead of storing this information redundantly in all file descriptors, we store them in one place and reference the partition metadata in the FDs with an id. This also gives the opportunity to optimize memory consumption in the Catalog and Coordinator as well as reduce network traffic between them in the future. Time travel is handled similarly to oldFileDescMap. In that case we don't know the total number of partitions in the old snapshot, so the output is [Num scanned partitions]/unknown. Testing: - Planner tests - E2E tests - partition transforms - partition evolution - DROP PARTITION - time travel Change-Id: Ifb2f654bc6c9bdf9cfafc27b38b5ca2f7b6b4872 Reviewed-on: http://gerrit.cloudera.org:8080/23113 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.