5bdd9c7f39
This adds more tests in test_catalogd_ha.py for warm failover. Refactored _test_metadata_after_failover to run in the following way: - Run DDL/DML in the active catalogd. - Kill the active catalogd and wait until the failover finishes. - Verify the DDL/DML results in the new active catalogd. - Restart the killed catalogd It accepts two methods in parameters to perform the DDL/DML and the verifier. In the last step, the killed catalogd is started so we keep having 2 catalogd and can merge these into a single test by invoking _test_metadata_after_failover for different method pairs. This saves some test time. The following DDL/DML statements are tested: - CreateTable - AddPartition - REFRESH - DropPartition - INSERT - DropTable After each failover, the table is verified to be warmed up (i.e. loaded). Also validate flags in startup to make sure enable_insert_events and enable_reload_events are both set to true when warm failover is enabled, i.e. --catalogd_ha_reset_metadata_on_failover=false. Change-Id: I6b20adeb0bd175592b425e521138c41196347600 Reviewed-on: http://gerrit.cloudera.org:8080/23206 Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com> Reviewed-by: Wenzhe Zhou <wzhou@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.