| commit | 4be5fd8896dcd445a6379bdcda4bdcf318f24511 | [log] [tgz] |
|---|---|---|
| author | Yida Wu <yida.wu@cloudera.com> | Fri Mar 29 08:41:23 2024 -0700 |
| committer | Yida Wu <wydbaggio000@gmail.com> | Thu Apr 04 15:27:47 2024 +0000 |
| tree | 3a0787b57cdc107c503825e19653b30e3d7adff6 | |
| parent | da8704f90b7a5fde9c17284fa308404d6982433c [diff] |
IMPALA-12960: Fix Incorrect RowsPassedThrough Metric in Streaming Aggregation This patch fixes a bug in the RowsPassedThrough metric within the query profile while using Streaming Aggregation. The issue is from the AddBatchStreaming() function's logic, where the number of rows in the output batch isn't necessarily initialized to 0, while the function uses num_rows() of the output batch directly to be the actual number of rows returned and passed through of this specific aggregator. This discrepancy can significantly impact the accuracy of the returned and passed through numbers, as well as the calculation of reduction rates during hash table expansion in Streaming Aggregation. Huge differences can be observed especially when using the rollup function. The solution is to calculate the actual number of rows added to the output batch within each round of the AddBatchStreaming() function. Tests: Passed exhaustive tests. Added a corresponding case in tpch-passthrough-aggregations.test. Change-Id: I59205a4b06824ee1607a25e906db1f96dc4eda9f Reviewed-on: http://gerrit.cloudera.org:8080/21235 Reviewed-by: Wenzhe Zhou <wzhou@cloudera.com> Reviewed-by: Riza Suminto <riza.suminto@cloudera.com> Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
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:
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.
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.
Impala runs on Linux systems only. The supported distros are
Other systems, e.g. SLES12, may also be supported but are not tested by the community.
This distribution uses cryptographic software and may be subject to export controls. Please refer to EXPORT_CONTROL.md for more information.
See Impala's developer documentation to get started.
Detailed build notes has some detailed information on the project layout and build.