01b8b45252
This patch refreshes compute_table_stats.py script with the following
changes:
- Limit parallelism to IMPALA_BUILD_THREADS at maximum if --parallelism
argument is not set.
- Change its default connection to hs2, leveraging existing
ImpylaHS2Connection.
- Change OptionParser to ArgumentParser.
- Use impala-python3 to run the script.
- Add --exclude_table_names to skip running COMPUTE STATS on certain
tables/views.
- continue_on_error is False by default.
This patch also improves query handle logging in ImpylaHS2Connection.
collect_profile_and_log argument is added to control whether to pull
logs and runtime profile at the end of __fetch_results(). The default
behavior remains unchanged.
Skip COMPUTE STATS for functional_kudu.alltypesagg and
functional_kudu.manynulls because it is invalid to run COMPUTE STATS
over view.
Customized hive-site.xml to set datanucleus.connectionPool.maxPoolSize
to 30 and hikaricp.connectionTimeout to 60000 ms. Also set hive.log.dir
to ${IMPALA_CLUSTER_LOGS_DIR}/hive.
Testing:
Repeatedly run compute-table-stats.sh from cold state and confirm there
is no error occurs. This is the script to do so from active minicluster:
cd $IMPALA_HOME
./bin/start-impala-cluster.py --kill
./testdata/bin/kill-hive-server.sh
./testdata/bin/run-hive-server.sh
./bin/start-impala-cluster.py
./testdata/bin/compute-table-stats.sh > /tmp/compute-stats.txt 2>&1
grep error /tmp/compute-stats.txt
Core tests ran and passed.
Change-Id: I1ebf02f95b957e7dda3a30622b87e8fca3197699
Reviewed-on: http://gerrit.cloudera.org:8080/22231
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.