Principle:Apache Spark Distribution Packaging
| Field | Value |
|---|---|
| Sources | https://github.com/apache/spark |
| Domains | Build_Systems, Packaging |
| Last Updated | 2026-02-08 14:00 GMT |
Overview
A packaging process that assembles compiled artifacts, configuration templates, scripts, and documentation into a self-contained binary distribution suitable for deployment.
Description
After compilation, software must be packaged into distributable form. Distribution packaging collects compiled JARs, shell scripts, configuration templates, Python packages, and R packages into a structured directory layout. It handles variant generation (different Hadoop versions, optional components) and produces compressed archives for distribution. This decouples the build environment from the deployment environment.
The key responsibilities of a distribution packaging system include:
- Artifact collection -- gathering compiled binaries (JARs, native libraries) from build output directories
- Script bundling -- including launcher scripts, administrative tools, and configuration helpers
- Configuration templating -- providing default configuration files that users can customize
- Variant management -- supporting multiple build profiles (e.g., different Hadoop versions, optional Hive or Kubernetes support)
- Archive generation -- compressing the assembled directory into a portable tarball or zip file
- Language package building -- optionally producing installable packages for Python (pip), R (CRAN), or other language ecosystems
By separating the packaging step from compilation, the same build artifacts can be repackaged into different distribution variants without recompilation.
Usage
Use this when creating official releases, custom deployments, or when you need a portable Spark installation that does not require building from source. This principle applies to any scenario where compiled software must be delivered to environments that lack build tooling.
Theoretical Basis
Distribution packaging follows the assembly pattern, which can be expressed as a pipeline:
artifacts = collect(compiled_jars, scripts, configs)
structured = layout(artifacts, target_dir_structure)
filtered = filter(structured, variant_profiles)
archive = compress(filtered, archive_format)
Each variant is a configuration of included and excluded components. The variant profiles act as a filter over the full set of artifacts, selecting only those that belong to a given distribution configuration.
The directory layout typically follows a convention:
- bin/ -- user-facing launcher scripts
- sbin/ -- administrative scripts
- jars/ -- compiled library JARs
- conf/ -- configuration templates
- python/ -- Python packages
- R/ -- R packages
- data/ -- sample data and resources