c0174bb438
When enable_insert_events is set to true (default), Impala will fire HMS
INSERT events for each INSERT statement. Preparing data of the
InsertEvents actually takes time since it fetches checksums of all the
new files. This patch adds a catalog timeline item to reveal this step.
Before this patch, the duration of "Got Metastore client" before "Fired
Metastore events" could be long:
Catalog Server Operation: 65.762ms
- Got catalog version read lock: 12.724us (12.724us)
- Got catalog version write lock and table write lock: 224.572us (211.848us)
- Got Metastore client: 418.346us (193.774us)
- Got Metastore client: 29.001ms (28.583ms) <---- Unexpected long
- Fired Metastore events: 52.665ms (23.663ms)
After this patch, we shows what actually takes the time is "Prepared
InsertEvent data":
Catalog Server Operation: 61.597ms
- Got catalog version read lock: 7.129us (7.129us)
- Got catalog version write lock and table write lock: 114.476us (107.347us)
- Got Metastore client: 200.040us (85.564us)
- Prepared InsertEvent data: 25.335ms (25.135ms)
- Got Metastore client: 25.342ms (7.009us)
- Fired Metastore events: 46.625ms (21.283ms)
Tests:
- Added e2e test
Change-Id: Iaef1cae7e8ca1c350faae8666ab1369717736978
Reviewed-on: http://gerrit.cloudera.org:8080/22778
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.