64abca481f
After IMPALA-14074, the passive catalogd can have a warmed up metadata cache during failover (with catalogd_ha_reset_metadata_on_failover=false and a non-empty warmup_tables_config_file). However, it could still use a stale metadata cache when some pending HMS events generated by the previous active catalogd are not applied yet. This patch adds a wait during HA failover to ensure HMS events before the failover happens are all applied on the new active catalogd. The timeout is configured by a new flag which defaults to 300 (5 minutes): catalogd_ha_failover_catchup_timeout_s. When timeout happens, by default catalogd will fallback to resetting all metadata. Users can decide whether to reset or continue using the current cache. This is configured by another flag, catalogd_ha_reset_metadata_on_failover_catchup_timeout. Since the passive catalogd depends on HMS event processing to keep its metadata up-to-date with the active catalogd, this patch adds validation to avoid starting catalogd with catalogd_ha_reset_metadata_on_failover set to false and hms_event_polling_interval_s <= 0. This patch also makes catalogd_ha_reset_metadata_on_failover a non-hidden flag so it's shown in the /varz web page. Tests: - Ran test_warmed_up_metadata_after_failover 200 times. Without the fix, it usually fails in several runs. - Added new tests for the new flags. Change-Id: Icf4fcb0e27c14197f79625749949b47c033a5f31 Reviewed-on: http://gerrit.cloudera.org:8080/23174 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.