e05d92cb3d
Scheduling does not sort scan ranges by modification time. When a new file is added to a table, its order in the list of scan ranges is not based on modification time. Instead, it is based on which partition it belongs to and what its filename is. A new file that is added early in the list of scan ranges can cause cascading differences in scheduling. For tuple caching, this means that multiple runtime cache keys could change due to adding a single file. To minimize that disruption, this adds the ability to sort the scan ranges by modification time and schedule scan ranges oldest to newest. This enables it for scan nodes that feed into tuple cache nodes (similar to deterministic scan range assignment). Testing: - Modified TestTupleCacheFullCluster::test_scan_range_distributed to have stricter checks about how many cache keys change after an insert (only one should change) - Modified TupleCacheTest#testDeterministicScheduling to verify that oldest to newest scheduling is also enabled. Change-Id: Ia4108c7a00c6acf8bbfc036b2b76e7c02ae44d47 Reviewed-on: http://gerrit.cloudera.org:8080/23228 Reviewed-by: Michael Smith <michael.smith@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.