# apache-spark を Homebrew でインストール

apache-spark のインストール経路、実行ファイル、メタデータ、AI エージェント向けセキュリティノートを確認します。

## インストール

```sh
sudo av install brew:apache-spark
```

追加のインストールコマンド:

### macOS

- Homebrew (100%):

```sh
brew install apache-spark
```

  証拠: local Homebrew formula metadata

## パッケージ情報

- **パッケージキー:** brew:apache-spark
- **パッケージマネージャ:** Homebrew
- **バージョン:** 4.2.0
- **ソース概要:** Engine for large-scale data processing
- **ホームページ:** <https://spark.apache.org/>
- **リポジトリ:** <https://github.com/apache/spark>
- **最終更新:** 2026-07-15T03:07:47Z
- **生成日時:** 2026-08-03T19:37:03+00:00

## 実行可能ファイル

- docker-image-tool.sh (エイリアス)
- find-spark-home (エイリアス)
- load-spark-env.sh (エイリアス)
- pyspark (エイリアス)
- run-example (エイリアス)
- spark-beeline (エイリアス)
- spark-class (エイリアス)
- spark-connect-shell (エイリアス)
- spark-pipelines (エイリアス)
- spark-shell (エイリアス)
- spark-sql (エイリアス)
- spark-submit (エイリアス)
- sparkR (エイリアス)

## インストール挙動

- Bottle: 利用不可

## バージョンと鮮度

- ページ生成日: 2026-08-03
- マネージャ版: 4.2.0
## プロジェクトの歴史と使われ方

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.

### プロジェクトの歴史

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.

### 採用の歴史

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.

### 使われ方

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.

### パッケージ好きにとっての重要性

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.

### タイムライン

- 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.

### ソース

- <https://github.com/apache/spark>
- <https://news.apache.org/foundation/entry/the_apache_software_foundation_announces50>
- <https://spark.apache.org/docs/latest>
- <https://spark.apache.org/docs/latest/configuration.html>
- <https://spark.apache.org/history.html>
- source_facts.executables


## セキュリティノート

broad file, network, media, or database tool signal. generalized runtime or code generation signal.

- **Geiger リスク:** yellow / 中
- broad file, network, media, or database tool signal
- generalized runtime or code generation signal


## 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

- Unix: $SPARK_HOME/conf/spark-defaults.conf, $SPARK_HOME/conf/spark-env.sh, $SPARK_HOME/conf/log4j2.properties

## Combined YAML source

View the package source record on GitHub. [combined/apache-spark.yml](https://github.com/mxcl/pkgdb/blob/main/combined/apache-spark.yml)


## ソース

- pkg.so package database
- Geiger risk classifier
- curated configuration and credential file locations
- curated package history
- pkgdb category and tag curation
- cross-ecosystem install command graph