6380a3187c
Currently, we use a std::unordered_set<int64_t> for all numeric types (including DATE type). It's a waste of space for small data types like tinyint, smallint, int, etc. This patch extends the base InListFilter class with native implementations for different data types. For string type in-list filters, this patch uses impala::StringValue instead of std::string. This simplifies the Insert() method, which improves the codegen time. To use impala::StringValue, this patch switches the set implementation to boost::unordered_set. Same as what we use in InPredicate. Another improvement of using impala::StringValue is that we can easily maintain the strings in MemPool. When inserting a new batch of values, the new values are inserted into a temp set. String pointers still reference to the original tuple values. At the end of processing each batch, MaterializeValues() is invoked to copy the strings into the filter's own mem pool. This is more memory-friendly than the original approach since we can allocate the string batch at once. Tests: - Add unit tests for different types of in-list filters Change-Id: Id434a542b2ced64efa3bfc974cb565b94a4193e9 Reviewed-on: http://gerrit.cloudera.org:8080/18433 Reviewed-by: Qifan Chen <qchen@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.
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.