c4c9adf592
This patch adds benchmarks to the Byte Stream Split encoding. It
compares different ways to use the decoder.
I added benchmarks for the following comparisons:
* Compile VS Runtime initialized decoder
* Float VS Int VS Double VS Long VS 6 and 11 byte size types
* Repeating VS Sequential VS Random ordered data
* Decoding one by one VS in batch VS with stride (!= byte_size)
* Small VS Medium (10x small) VS Large (100x small) stride
Conclusions:
* Passing the byte size as a template parameter is almost 5 times
as fast as passing it in the constructor.
* The size of the type heavily influences the speed
* The data variation doesn't influence the speed at all
* Reading values in batch is much faster than one-by-one
* The stride sizes have a small influence on the speed
For more details and graphs, go to
https://docs.google.com/spreadsheets/d/129LwvR6gpZInlRhlVWktn6Haugwo_fnloAAYfI0Qp2s
Change-Id: I708af625348b0643aa3f37525b8a6e74f0c47057
Reviewed-on: http://gerrit.cloudera.org:8080/23401
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.