{ "info": { "author": "Databricks", "author_email": "feedback@databricks.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "License :: Other/Proprietary License", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy" ], "description": "Databricks Connect is a Spark client library that lets you connect your \nfavorite IDE (IntelliJ, Eclipse, PyCharm, and so on), notebook server\n(Zeppelin, Jupyter, RStudio), and other custom applications to Databricks\nclusters and run Spark code.\n \n \nTo get started, run ``databricks-connect configure`` after installation.\n\n- AWS docs: https://docs.databricks.com/dev-tools/databricks-connect.html\n\n- Azure docs: https://docs.microsoft.com/azure/databricks/dev-tools/databricks-connect\n\n- GCP docs: https://docs.gcp.databricks.com/dev-tools/databricks-connect.html\n\n\nOverview\n---------\nDatabricks Connect allows you to write \njobs using Spark native APIs and have them execute remotely on a Databricks \ncluster instead of in the local Spark session.\n\nFor example, when you run the DataFrame command ``spark.read.parquet(...).\ngroupBy(...).agg(...).show()`` using Databricks Connect, the parsing and \nplanning of the job runs on your local machine. Then, the logical \nrepresentation of the job is sent to the Spark server running in Databricks \nfor execution in the cluster.\n\nWith Databricks Connect, you can:\n- Run large-scale Spark jobs from any Python, Java, Scala, or R application. \nAnywhere you can ``import pyspark``, ``import org.apache.spark``, or \n``require(SparkR)``, you can now run Spark jobs directly from your \napplication, without needing to install any IDE plugins or use Spark \nsubmission scripts.\n- Step through and debug code in your IDE even when working with a remote \ncluster.\n- Iterate quickly when developing libraries. You do not need to restart the \ncluster after changing Python or Java library dependencies in Databricks \nConnect, because each client session is isolated from each other in the \ncluster.\n- Shut down idle clusters without losing work. Because the client session is \ndecoupled from the cluster, it is unaffected by cluster restarts or upgrades, \nwhich would normally cause you to lose all the variables, RDDs, and DataFrame \nobjects defined in a notebook.\n\nLICENSE\n---------------------------------\nCopyright (2018) Databricks, Inc.\nThis library (the \"Software\") may not be used except in connection with the\nLicensees use of the Databricks Platform Services pursuant to an Agreement\n(defined below) between Licensee (defined below) and Databricks, Inc.\n(\"Databricks\"). 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