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This patch adds support for the hive.sql.query table property in Hive JDBC tables accessed through Impala. Impala has support for Hive JDBC tables using the hive.sql.table property, which limits users to simple table access. However, many use cases demand the ability to expose complex joins, filters, aggregations, or derived columns as external views. Hive.sql.query leads to a custom SQL query that returns a virtual table(subquery) instead of pointing to a physical table. These use cases cannot be achieved with just the hive.sql.table property. This change allows Impala to: • Interact with views or complex queries defined on external systems without needing schema-level access to base tables. • Expose materialized logic (such as filters, joins, or transformations) via Hive to Impala consumers in a secure, abstracted way. • Better align with data virtualization use cases where physical data location and structure should be hidden from the querying engine. This patch also lays the groundwork for future enhancements such as predicate pushdown and performance optimizations for Hive JDBC tables backed by queries. Testing: End-to-end tests are included in test_ext_data_sources.py. Change-Id: I039fcc1e008233a3eeed8d09554195fdb8c8706b Reviewed-on: http://gerrit.cloudera.org:8080/22865 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.