The example below creates a Conda environment to use on both the driver and executor and packs it into an archive file. In Apache Spark 3.0 or lower versions, it can be used only with YARN. It is supported in all types of clusters in the upcoming Apache Spark 3.1.

PySpark users can directly use a Conda environment to ship their third-party Python packages by leveraging conda-pack which is a command line tool creating relocatable Conda environments. Using CondaĬonda is one of the most widely-used Python package management systems. Please refer to An Update on Project Zen: Improving Apache Spark for Python Users for more details. Most of the content will be also documented in the upcoming Apache Spark 3.1 as part of Project Zen. This blog post introduces how to control Python dependencies in Apache Spark comprehensively. One straightforward method is to use script options such as -py-files or the, but this functionality cannot cover many cases, such as installing wheel files or when the Python libraries are dependent on C and C++ libraries such as pyarrow and NumPy. ModuleNotFoundError: No module named 'pyarrow' If they do not have required dependencies installed in all other nodes, it fails and complains that PyArrow and pandas have to be installed. One simple example that illustrates the dependency management scenario is when users run pandas UDFs.įrom import pandas_plus_one(v: pd.Series) -> pd.Series: In contrast, PySpark users often ask how to do it with Python dependencies – there have been multiple issues filed such as SPARK-13587, SPARK-16367, SPARK-20001 and SPARK-25433. Controlling the environment of an application is often challenging in a distributed computing environment – it is difficult to ensure all nodes have the desired environment to execute, it may be tricky to know where the user’s code is actually running, and so on.Īpache Spark™ provides several standard ways to manage dependencies across the nodes in a cluster via script options such as -jars, -packages, and configurations such as spark.jars.* to make users seamlessly manage the dependencies in their clusters.

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