This can be fixed by adding a dummy Hadoop installation that tricks Windows to believe that Hadoop is actually installed.ĭownload Hadoop 2.7 winutils.exe.
Even if you are not working with Hadoop (or only using Spark for local development), Windows still needs Hadoop to initialize “Hive” context, otherwise Java will throw java.io.IOException. Spark uses Hadoop internally for file system access. To achieve this, open log4j.properties in an editor and replace ‘INFO’ by ‘ERROR’ on line number 19. It is advised to change log level for log4j from ‘INFO’ to ‘ERROR’ to avoid unnecessary console clutter in spark-shell. (If you have pre-installed Python 2.7 version, it may conflict with the new installations by the development environment for python 3).įollow the installation wizard to complete the installation. )ĭownload your system compatible version 2.1.9 for Windows from Enthought Canopy. ( You can also go by installing Python 3 manually and setting up environment variables for your installation if you do not prefer using a development environment. If you are already using one, as long as it is Python 3 or higher development environment, you are covered. Install Python Development EnvironmentĮnthought canopy is one of the Python Development Environments just like Anaconda. – Ensure Python 2.7 is not pre-installed independently if you are using a Python 3 Development Environment. – Apache Spark version 2.4.0 has a reported inherent bug that makes Spark incompatible for Windows as it breaks worker.py. Please ensure that you install JAVA 8 to avoid encountering installation errors. Pointers for smooth installation: – As of writing of this blog, Spark is not compatible with Java version>=9.
In this tutorial, we will set up Spark with Python Development Environment by making use of Spark Python API (PySpark) which exposes the Spark programming model to Python. Spark supports a number of programming languages including Java, Python, Scala, and R.