macOS
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brew install apache-sparkprovider-native install command
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Install apache-spark with Homebrew
Engine for large-scale data processing. Version 4.2.0 via Homebrew; verified 2026-07-15.
install
Additional install commands
overview
Package summary
Engine for large-scale data processing
Project links
Commands and aliases
- docker-image-tool.sh
- find-spark-home
- load-spark-env.sh
- pyspark
- run-example
- spark-beeline
- spark-class
- spark-connect-shell
- spark-pipelines
- spark-shell
- spark-sql
- spark-submit
- sparkR
history
Project history and usage
Apache Spark is a general-purpose engine for large-scale data processing. For package-manager users, it is the canonical install that gives you `spark-submit`, language shells, SQL tooling, example runners, and runtime scripts for local and cluster-oriented workflows.
Project history
Spark originated at the UC Berkeley AMPLab as a faster, more interactive alternative to earlier MapReduce-centered data processing systems. Its project history is closely tied to resilient distributed datasets, in-memory computation, and developer-friendly APIs for Scala, Python, Java, SQL, and R.
Spark became an Apache project and grew into a broad analytics engine rather than a single-purpose batch runner. The official project history notes its Apache Software Foundation path and the release line that made Spark a standard part of the big-data toolchain.
Over time Spark absorbed major adjacent workloads: Spark SQL and DataFrames for structured data, MLlib for machine learning, GraphX for graph processing, Structured Streaming for stream processing, and Spark Connect for client-server connectivity.
Adoption history
Spark's adoption history is unusually deep for a package-manager formula because it crossed from research project to de facto data-platform component. It is used for ETL, interactive analytics, machine learning pipelines, and streaming workloads across local machines, YARN, Mesos-era clusters, Kubernetes, and managed cloud services.
The supplied Homebrew package data shows a CLI-heavy install surface: `spark-submit`, `spark-shell`, `pyspark`, `spark-sql`, `sparkR`, `spark-class`, and helper scripts. That executable set mirrors the way Spark became both an application runtime and a command-line toolbox.
How it is used
The main package workflow is submitting applications with `spark-submit`, opening interactive shells with `spark-shell` or `pyspark`, running SQL through `spark-sql`, and configuring behavior through files in `$SPARK_HOME/conf`.
Spark users often install it locally even when production jobs run elsewhere, because the local CLI is useful for testing jobs, validating dependencies, developing notebooks or scripts, and matching cluster runtime behavior.
Why package nerds care
Spark is a classic heavyweight formula: it is mostly scripts plus a large JVM distribution, but those scripts define the ergonomics of a whole data ecosystem. Packagers care about Java compatibility, Python/R bindings, shell wrappers, classpaths, examples, and config file layout.
It is also one of the packages that turns a laptop into a miniature data platform. A formula install can run local mode, submit to clusters, or serve as a client for remote compute, which makes it more than a simple CLI utility.
Timeline
- 2009: Spark begins at UC Berkeley AMPLab.
- 2010: Spark is open sourced.
- 2013: Spark enters the Apache Incubator.
- 2014: Spark becomes an Apache top-level project.
- 2020s: Spark continues expanding SQL, streaming, Kubernetes, and client-server features.
Related projects
- Apache Hadoop and YARN are central to Spark's early cluster deployment history.
- Apache Hive influenced Spark SQL's data-warehouse compatibility story.
- Delta Lake, Apache Iceberg, and Apache Hudi are common table-format companions in modern Spark deployments.
Sources
security posture
Risk level: yellow
broad file, network, media, or database tool signal. generalized runtime or code generation signal.
Risk classifier
yellow risk · medium confidence · runtime
Why
- broad file, network, media, or database tool signal
- generalized runtime or code generation signal
Signals
- text:shell
- text:sql,image
Install behavior
- No Homebrew bottle metadata was recorded.
Recommended review
Before unattended agent use, check whether the tool reads plaintext credentials, writes remote state, publishes artifacts, or shells out to plugins.
local files
Configuration and credential file locations
These source-backed paths show where this package keeps local settings or durable credentials. Automic Vault can use them as review targets for secret scanning, migration, and command approval.
Configuration files
Config paths the tool may read or write during local use.
- Unix
$SPARK_HOME/conf/spark-defaults.conf$SPARK_HOME/conf/spark-env.sh$SPARK_HOME/conf/log4j2.properties
executables
Installed executables
| Command | Kind | Exposure | Note |
|---|---|---|---|
docker-image-tool.sh | executable | indexed executable | Discovered from the local executable index. |
find-spark-home | executable | indexed executable | Discovered from the local executable index. |
load-spark-env.sh | executable | indexed executable | Discovered from the local executable index. |
pyspark | executable | indexed executable | Discovered from the local executable index. |
run-example | executable | indexed executable | Discovered from the local executable index. |
spark-beeline | executable | indexed executable | Discovered from the local executable index. |
spark-class | executable | indexed executable | Discovered from the local executable index. |
spark-connect-shell | executable | indexed executable | Discovered from the local executable index. |
spark-pipelines | executable | indexed executable | Discovered from the local executable index. |
spark-shell | executable | indexed executable | Discovered from the local executable index. |
spark-sql | executable | indexed executable | Discovered from the local executable index. |
spark-submit | executable | indexed executable | Discovered from the local executable index. |
sparkR | executable | indexed executable | Discovered from the local executable index. |
freshness
Version and freshness
These signals separate page generation age, package-manager activity, and upstream release comparison. Version lag is warned only when an evidence URL and comparable versions are present.
page generated2026-08-03
manager version4.2.0
manager updated2026-07-15
local dataunknown
upstreamnot available
latest detectednot detected
- okNo freshness warnings were generated.
install metadata
Package metadata
| Package key | brew:apache-spark |
|---|---|
| Version | 4.2.0 |
| Package manager | Homebrew |
| Homepage | https://spark.apache.org/ |
| Repository | https://github.com/apache/spark |
| Last updated | 2026-07-15T03:07:47Z |
| Pulse | updated |
| Bottle | not recorded |
| Service | none declared |
source trail
Generated from repository data
This page is generated by av-web from the private package SQLite artifact built by scripts/generate-pkg-sqlite.py.
Combined YAML source
View the package source record on GitHub.
Used sources
- Geiger risk classifier
- Nucleus package database
- curated configuration and credential file locations
- curated package history
- pkgdb category and tag curation