- AWS Code Sample

# Copyright, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Purpose Shows how to write a script that calculates pi by using a large number of random numbers run in parallel on an Amazon EMR cluster. This script is intended to be uploaded to an Amazon S3 bucket so it can be run as a job step. """ import argparse import logging from operator import add from random import random from pyspark.sql import SparkSession logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s') def calculate_pi(partitions, output_uri): """ Calculates pi by testing a large number of random numbers against a unit circle inscribed inside a square. The trials are partitioned so they can be run in parallel on cluster instances. :param partitions: The number of partitions to use for the calculation. :param output_uri: The URI where the output is written, typically an Amazon S3 bucket, such as 's3://example-bucket/pi-calc'. """ def calculate_hit(_): x = random() * 2 - 1 y = random() * 2 - 1 return 1 if x ** 2 + y ** 2 < 1 else 0 tries = 100000 * partitions "Calculating pi with a total of %s tries in %s partitions.", tries, partitions) with SparkSession.builder.appName("My PyPi").getOrCreate() as spark: hits = spark.sparkContext.parallelize(range(tries), partitions)\ .map(calculate_hit)\ .reduce(add) pi = 4.0 * hits / tries"%s tries and %s hits gives pi estimate of %s.", tries, hits, pi) if output_uri is not None: df = spark.createDataFrame( [(tries, hits, pi)], ['tries', 'hits', 'pi']) df.write.mode('overwrite').json(output_uri) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( '--partitions', default=2, type=int, help="The number of parallel partitions to use when calculating pi.") parser.add_argument( '--output_uri', help="The URI where output is saved, typically an S3 bucket.") args = parser.parse_args() calculate_pi(args.partitions, args.output_uri)