fa5400268a
IMPALA-10482: SELECT * query on unrelative collection column of transactional ORC table will hit IllegalStateException. The AcidRewriter will rewrite queries like "select item from my_complex_orc.int_array" to "select item from my_complex_orc t, t.int_array" This cause troubles in star expansion. Because the original query "select * from my_complex_orc.int_array" is analyzed as "select item from my_complex_orc.int_array" But the rewritten query "select * from my_complex_orc t, t.int_array" is analyzed as "select id, item from my_complex_orc t, t.int_array". Hidden table refs can also cause issues during regular column resolution. E.g. when the table has top-level 'pos'/'item'/'key'/'value' columns. The workaround is to keep track of the automatically added table refs during query rewrite. So when we analyze the rewritten query we can ignore these auxiliary table refs. IMPALA-10493: Using JOIN ON syntax to join two full ACID collections produces wrong results. When AcidRewriter.splitCollectionRef() creates a new collection ref it doesn't copy every information needed to correctly execute the query. E.g. it dropped the ON clause, turning INNER joins to CROSS joins. Testing: * added e2e tests Change-Id: I8fc758d3c1e75c7066936d590aec8bff8d2b00b0 Reviewed-on: http://gerrit.cloudera.org:8080/17038 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 Apache Hadoop clusters.
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 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.
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.