¿Cómo uso Python 3 en PySpark?

How do I use Python 3 in PySpark?

1. edit profile : vim ~/.profile.
2. add the code into the file: export PYSPARK_PYTHON=python3.
3. execute command : source ~/.profile.

Apache Spark is a cluster computing framework, currently one of the most actively developed in the open-source Big Data arena. Since the latest version 1.4 (June 2015), Spark supports R and Python 3 (to complement the previously available support for Java, Scala and Python 2).

To support Python with Spark, Apache Spark community released a tool, PySpark. Using PySpark, you can work with RDDs in Python programming language also. It is because of a library called Py4j that they are able to achieve this.

These steps are for Mac OS X (I am running OS X 10.13 High Sierra), and for Python 3.6.

1. Start a new Conda environment.
2. Install PySpark Package.
3. Install Java 8.
4. Change ‘.
5. Start PySpark.
6. Calculate Pi using PySpark!
7. Next Steps.

I was a bit surprised I can already run pyspark in command line or use it in Jupyter Notebooks and that it does not need a proper Spark installation (e.g. I did not have to do most of the steps in this tutorial https://medium.com/@GalarnykMichael/install-spark-on-windows-pyspark-4498a5d8d66c ).

PySpark is an API written for using Python along with Spark framework. As we all know, Spark is a computational engine, that works with Big Data and Python is a programming language….Python vs PySpark.

PySpark is a great language for performing exploratory data analysis at scale, building machine learning pipelines, and creating ETLs for a data platform.

If we have Apache Spark installed on the machine we don’t need to install the pyspark library into our development environment. We need to install the findspark library which is responsible of locating the pyspark library installed with apache Spark. In each python script file we must add the following lines: 5.3. PySpark example 5.3.1.

Next, you’ll see how you can work with Spark in Python: locally or via the Jupyter Notebook. You’ll learn how to install Spark and how to run Spark applications with Jupyter notebooks, either by adding PySpark as any other library, by working with a kernel or by running PySpark with Jupyter in Docker containers.

You can change python to python3. Change the env to directly use hardcoded the python3 binary. Or execute the binary directly with python3 and omit the shebang line. Yeah, looking into the file helped. Needed to set the PYSPARK_PYTHON environment variable. – tchakravarty May 16 ’15 at 19:34 Thanks for contributing an answer to Stack Overflow!

I built Spark 1.4 from the GH development master, and the build went through fine. But when I do a bin/pyspark I get the Python 2.7.9 version. How can I change this?