ad868b9947
This patch adds the query id to the error messages in both - the result of the `get_log()` RPC, and - the error message in an RPC response before they are returned to the client, so that the users can easily figure out the errored queries on the client side. To achieve this, the query id of the thread debug info is set in the RPC handler method, and is retrieved from the thread debug info each time the error reporting function or `get_log()` gets called. Due to the change of the error message format, some checks in the impala-shell.py are adapted to keep them valid. Testing: - Added helper function `error_msg_expected()` to check whether an error message is expected. It is stricter than only using the `in` operator. - Added helper function `error_msg_equal()` to check if two error messages are equal regardless of the query ids. - Various test cases are adapted to match the new error message format. - `ImpalaBeeswaxException`, which is used in tests only, is simplified so that it has the same error message format as the exceptions for HS2. - Added an assertion to the case of killing and restarting a worker in the custom cluster test to ensure that the query id is in the error message in the client log retrieved with `get_log()`. Change-Id: I67e659681e36162cad1d9684189106f8eedbf092 Reviewed-on: http://gerrit.cloudera.org:8080/21587 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.