How to Install Latest Version Apache Spark on Mac OS

Apache Spark is a powerful, open-source processing engine for data analytics on large scale data-sets. It provides high-level APIs in Java, Scala, Python, and R, and an optimized engine that supports general execution graphs. Install Apache Spark on Mac can be a straightforward process if the steps are followed carefully. This guide will cover all necessary steps to install Apache Spark on your Mac, including prerequisites, the actual installation, and testing to ensure it’s been installed correctly.

Prerequisites

Before installing Apache Spark on Mac OS (macOS), you need to have the following prerequisites in place:

• Homebrew: Homebrew is an excellent package manager for macOS. If you don’t have Homebrew installed, you can install it by running the installation script. Paste the following command into the Terminal and press Enter:
  /bin/bash -c “$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)” Follow the on-screen instructions to complete the installation of Homebrew.
• Java: Apache Spark requires Java to be installed. You can check if Java is already installed by typing `java -version` in your terminal. If it’s not installed, you can install Java by entering the following command in the Terminal: brew install openjdk
• Scala: Although Spark comes with an embedded Scala interpreter, it is recommended to have Scala installed on your system. You can install Scala by using Homebrew with brew install scala.

With these prerequisites in place, you’re now ready to install Apache Spark on your Mac.

Step 1: Install Apache Spark

Using Homebrew

The easiest way to install Apache Spark on a Mac is by using Homebrew. Follow these simple steps:

1. Open your Terminal on your Mac.
2. Update Homebrew’s package database with the command: brew update.
3. Once updated, install Apache Spark by running the command: brew install apache-spark.

This will download and install Apache Spark and its dependencies. Homebrew will also take care of linking the necessary files.

Manual Install

If you prefer to install Apache Spark manually or want to use a specific version, you can follow these steps:

1. Download a pre-built version of Apache Spark from the official Spark website. Ensure you download the correct version based on your use case.
2. Unzip the downloaded file in your preferred directory, for example: `tar -xzf spark-x.x.x-bin-hadoopx.x.tgz` – in a terminal window.
3. Move the unzipped directory to a more permanent location with the `mv` command, such as `/usr/local/spark` for ease of access.
4. Edit your shell’s profile file (for example, `.bash_profile`, `.zshrc`, etc.) to add Spark to your `PATH`. Add the line: `export PATH=/usr/local/spark/bin:$PATH`.
5. Source your profile to update your current terminal session with the command: `source ~/.bash_profile` (or the respective file you edited).

Step 2: Verify the Installation

Regardless of your installation method, you should verify the installation to ensure that Apache Spark is ready to use. Perform the following steps to check the installation.

1. Open your Terminal.
2. Type spark-shell and press Enter to launch the Spark interactive Scala shell.

If the installation was successful, you should see output that ends with a welcome message and the Spark context Web UI available message. The Spark shell allows you to run Scala code interactively.

  Welcome to
    ____              __
   / __/__  ___ _____/ /__
  _\ \/ _ \/ _ `/ __/  '_/
 /___/ .__/\_,_/_/ /_/\_\   version 3.5.0
    /_/

Using Scala version 2.12.18 (Java HotSpot(TM) 64-Bit Server VM, Java 17.0.9)
Type in expressions to have them evaluated.
Type :help for more information.

Spark context Web UI available at http://localhost:4040
Spark context available as 'sc' (master = local[*], app id = local-x).
Spark session available as 'spark'.

To test Apache Spark is working, run a simple count operation in the Spark shell:

scala> sc.parallelize(1 to 100).count()

You should get the following output:

res0: Long = 100

This confirms that Spark has been installed correctly and is able to execute operations.

Step 3: Configuring Apache Spark (Optional)

If you wish to configure Apache Spark further, like setting up a specific logging level or allocating more memory, you can edit the `spark-defaults.conf` and `log4j.properties` files that are located within the `conf` directory of your Spark installation.

Before editing, it’s a good practice to create copies of the original configuration files. Make the copies with the following commands:

cd /usr/local/spark/conf
cp spark-defaults.conf.template spark-defaults.conf
cp log4j.properties.template log4j.properties

You can then edit these files with your preferred text editor. For instance, to set the Spark driver memory to 4GB, add the following line to your `spark-defaults.conf`:

spark.driver.memory 4g

Likewise, to reduce the verbosity of Spark’s logs, you can change the `log4j` entry in `log4j.properties` to:

log4j.logger.org.apache.spark=ERROR

After making your changes, save the files and start Spark to have the new configurations taken effect.

Conclusion: Install Apache Spark on Mac

You should now have a fully functioning Apache Spark installation on your Mac. By walking through this guide, you now have the tools you need to begin developing and running your Spark applications locally. If you plan to use Spark with a third-party service or cluster manager like Mesos or YARN, additional configuration will be required. However, for local development and testing, your current setup should be more than sufficient.

Always remember to check the official Apache Spark documentation for the most up-to-date information on configuration options and best practices.

About Editorial Team

Our Editorial Team is made up of tech enthusiasts deeply skilled in Apache Spark, PySpark, and Machine Learning, alongside proficiency in Pandas, R, Hive, PostgreSQL, Snowflake, and Databricks. They're not just experts; they're passionate educators, dedicated to demystifying complex data concepts through engaging and easy-to-understand tutorials.