ff8bb33b91
Logs from Java threads running in ExecutorService are missing the query id which is stored in the C++ thread-local ThreadDebugInfo variable. This patch adds JNI calls for Java threads to manage the ThreadDebugInfo variable. Currently two thread pools are changed: - MissingTable loading pool in StmtMetadataLoader.parallelTableLoad(). - Table loading pool in TableLoadingMgr. MissingTable loading pool only lives within the parallelTableLoad() method. So we initialize ThreadDebugInfo with the queryId at the beginning of the thread and delete it at the end of the thread. Note that a thread might be reused to load different tables, but they all belong to the same query. Table loading pool is a long running pool in catalogd that never shut down. Threads in it is used to load tables triggered by different queries. We initialize ThreadDebugInfo as the above but update it when the thread starts loading table for a different query id, and reset it when the loading is done. The query id is passed down from the catalogd RPC request headers. Tests: - Added e2e test to verify the logs. - Ran existing CORE tests. Change-Id: I83cca55edc72de35f5e8c5422efc104e6aa894c1 Reviewed-on: http://gerrit.cloudera.org:8080/23558 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.