Running PySpark with Conda Env

Controlling the environment of an application is vital for it’s functionality and stability. Especially in a distributed environment it is important for developers to have control over the version of dependencies. In such an scenario it’s a critical task to ensure possible conflicting requirements of multiple applications are not disturbing each other.

That is why frameworks like YARN ensure that each application is executed in a self-contained environment – typically in a Linux Container or Docker Container – that is controlled by the developer. In this post we show what this means for Python environments being used by Spark. Continue reading “Running PySpark with Conda Env”