Cloudera Machine Learning (CML) MCP Server

# # Estimating $\pi$ # # This is the simplest PySpark example. It shows how to estimate $\pi$ in parallel # using Monte Carlo integration. If you're new to PySpark, start here! from __future__ import print_function import sys from random import random from operator import add from pyspark.sql import SparkSession spark = SparkSession\ .builder\ .appName("PythonPi")\ .getOrCreate() # This script is able to receive a command line argument specifying the number of partitions to use. try: partitions = int(sys.argv[-1]) except ValueError: partitions = 2 n = 100000 * partitions def f(_): x = random() * 2 - 1 y = random() * 2 - 1 return 1 if x ** 2 + y ** 2 < 1 else 0 count = spark.sparkContext.parallelize(range(1, n + 1), partitions).map(f).reduce(add) print("Pi is roughly %f" % (4.0 * count / n)) spark.stop()