d4644b0381
When catalogd runs with --start_hms_server=true, it services all the HMS endpoints so that any HMS compatible client would be able to use catalogd as a metadata cache. For all the DDL/DML requests, catalogd just delegates them to HMS APIs without reloading related metadata in the cache. For read requests like get_table_req, catalogd serves them from its cache which could be stale. There is a flag, invalidate_hms_cache_on_ddls, to decide whether to explicitly invalidate the table when catalogd delegates a DDL/DML on the table to HMS. test_cache_valid_on_nontransactional_table_ddls is a test verifying that when invalidate_hms_cache_on_ddls=false, the cache is not updated so should have stale metadata. However, there are HMS events generated from invoking the HMS APIs. Even when invalidate_hms_cache_on_ddls=false, catalogd can still update its cache when processing the corresponding HMS events. The test fails when its check is done after catalogd applies the event (so the cache is up-to-date). If the check is done before that, the test passes. This patch deflakes the test by explicitly disabling event processing. Also updates the description of invalidate_hms_cache_on_ddls to mention the impact of event processing. Tests: - Ran the test locally 100 times. Change-Id: Ib1ffc11a793899a0dbdb009bf2ac311117f2318e Reviewed-on: http://gerrit.cloudera.org:8080/23792 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.