aefd1b0920
Currently, the shell tarball maintains its own packaging code
and directory layout. This is very complicated and currently has
several Python packages directly checked into our repository.
To simplify it, this changes the shell tarball to be based on
pip installing the pypi package. Specifically, the new directory
structure for an unpack shell tarball is:
impala-shell-4.5.0-SNAPSHOT/
impala-shell
install_py${PYTHON_VERSION}/
install_py${ANOTHER_PYTHON_VERSION}/
For example, install_py2.7 is the Python 2.7 pip install of impala-shell.
install_py3.8 is a Python 3.8 pip install of impala-shell. This means
that the impala-shell script simply picks the install for the
specified version of python and uses that pip install directory.
To make this more consistent across different Linux distributions, this
upgrades pip in the virtualenv to the latest.
With this, ext-py and pkg_resources.py can be removed.
This requires rearranging the shell build code. Specifically, this splits
out the code that generates impala_build_version.py so that it can run
before generating the pypi package. The shell tarball now has a dependency
on the pypi package and must run after it.
This builds on Michael Smith's work from IMPALA-11399.
Testing:
- Ran shell tests locally
- Built on Centos 7, Redhat 8 & 9, Ubuntu 20 & 22, SLES 15
Change-Id: Ifbb66ab2c5bc7180221f98d9bf5e38d62f4ac036
Reviewed-on: http://gerrit.cloudera.org:8080/20171
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.