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The significant size of a PySpark installation is duplicated in several places on your machine.
#HOW TO INSTALL SPARK ON MAC FULL#
The standard method of installing a full PySpark instance into each Python virtual environment has a few drawbacks: There are plenty of other installation guides for the more straightforward approach, which is to install PySpark separately into each Python virtual environment you use for local development. Inspect the execution results in the Run tool window.This guide focuses on a specific method to install PySpark into your local development environment, which may or may not be suitable for your needs. Then select configuration from the list of the created configurations and click. Target directory: the directory on the remote host to upload the executable files.Ĭlick OK to save the configuration. Then click Test Connection to ensure you can connect to the remote server. Specify the URL of the remote host with the Spark cluster and user's credentials to access it. Logging: an option to print debug logging. Kerberos: settings for establishing a secured connection with Kerberos. Spark Monitoring Integration: ability to monitor the execution of your application with Spark Monitoring.
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#HOW TO INSTALL SPARK ON MAC DRIVER#
You can add repositories or exclude some packages from the execution context.ĭriver: Spark Driver settings, such as memory, CPU, local driver libraries, Java options, and a class path.Įxecutor: Executor settings, such as memory, CPU, and archives. Spark Configuration: Spark configuration options available through a properties file or a list of properties.ĭependencies: files and archives (jars) that are required for the application to be executed. You can click the Add options and select an option to add to your configuration: Show this page: select this checkbox to show the run/debug configuration settings prior to actually starting the run/debug configuration.Īctivate tool window: by default this checkbox is selected and the Run tool window opens when you start the run/debug configuration. The tasks are performed in the order they appear in the list. You can also specify environment variables, for example, USER=jetbrains.īefore launch: in this area you can specify tasks that must be performed before starting the selected run/debug configuration. Select the Interactive checkbox if you want to launch the script in the interactive mode. It is recommended to provide an absolute path to the script. Specify Shell options if you want to execute any scripts before the Spark submit.Įnter the path to bash and specify the script to be executed. Proxy user: a username that is enabled for using proxy for the Spark connection. Master: the format of the master URL passed to Spark. See more details in the Cluster Mode Overview. The SparkContext can connect to several types of cluster managers (either Spark’s own standalone cluster manager, Mesos, or YARN). Run arguments: Arguments of your application.Ĭluster manager: select the management method to run an application on a cluster. idea directory, you can save the configuration to any other directory within the project. However, if you do not want to share the. Store as project file: save the file with the run configuration settings to share it with other team members. Name: a name to distinguish between run/debug configurations.Īllow parallel run: select to allow running multiple instances of this run configuration in parallel. Main class: the name of the main class of the jar archive. You can select either jar and py file, or IDEA artifact. Spark home: a path to the Spark installation directory.Īpplication: a path to the executable file.