firstSparkApp - juedaiyuer/researchNote GitHub Wiki

#第一次Spark应用#

使用Spark编写Spark应用的一个基本模板

## Spark Application - execute with spark-submit

## Imports
from pyspark import SparkConf, SparkContext

## Module Constants
APP_NAME = "My Spark Application"

## Closure Functions

## Main functionality

def main(sc):
    pass

if __name__ == "__main__":
    # Configure Spark
    conf = SparkConf().setAppName(APP_NAME)
    conf = conf.setMaster("local[*]")
    sc   = SparkContext(conf=conf)

    # Execute Main functionality
    main(sc)

导入Python库（Imports）
模块常量（Module Constants）

##实际代码##

## Spark Application - execute with spark-submit

## Imports
import csv
import matplotlib.pyplot as plt

from StringIO import StringIO
from datetime import datetime
from collections import namedtuple
from operator import add, itemgetter
from pyspark import SparkConf, SparkContext

## Module Constants
APP_NAME = "Flight Delay Analysis"
DATE_FMT = "%Y-%m-%d"
TIME_FMT = "%H%M"

fields   = ('date', 'airline', 'flightnum', 'origin', 'dest', 'dep',
            'dep_delay', 'arv', 'arv_delay', 'airtime', 'distance')
Flight   = namedtuple('Flight', fields)

## Closure Functions
def parse(row):
    """
    Parses a row and returns a named tuple.
    """

    row[0]  = datetime.strptime(row[0], DATE_FMT).date()
    row[5]  = datetime.strptime(row[5], TIME_FMT).time()
    row[6]  = float(row[6])
    row[7]  = datetime.strptime(row[7], TIME_FMT).time()
    row[8]  = float(row[8])
    row[9]  = float(row[9])
    row[10] = float(row[10])
    return Flight(*row[:11])

def split(line):
    """
    Operator function for splitting a line with csv module
    """
    reader = csv.reader(StringIO(line))
    return reader.next()

def plot(delays):
    """
    Show a bar chart of the total delay per airline
    """
    airlines = [d[0] for d in delays]
    minutes  = [d[1] for d in delays]
    index    = list(xrange(len(airlines)))

    fig, axe = plt.subplots()
    bars = axe.barh(index, minutes)

    # Add the total minutes to the right
    for idx, air, min in zip(index, airlines, minutes):
        if min > 0:
            bars[idx].set_color('#d9230f')
            axe.annotate(" %0.0f min" % min, xy=(min+1, idx+0.5), va='center')
        else:
            bars[idx].set_color('#469408')
            axe.annotate(" %0.0f min" % min, xy=(10, idx+0.5), va='center')

    # Set the ticks
    ticks = plt.yticks([idx+ 0.5 for idx in index], airlines)
    xt = plt.xticks()[0]
    plt.xticks(xt, [' '] * len(xt))

    # minimize chart junk
    plt.grid(axis = 'x', color ='white', linestyle='-')

    plt.title('Total Minutes Delayed per Airline')
    plt.show()

## Main functionality
def main(sc):

    # Load the airlines lookup dictionary
    airlines = dict(sc.textFile("ontime/airlines.csv").map(split).collect())

    # Broadcast the lookup dictionary to the cluster
    airline_lookup = sc.broadcast(airlines)

    # Read the CSV Data into an RDD
    flights = sc.textFile("ontime/flights.csv").map(split).map(parse)

    # Map the total delay to the airline (joined using the broadcast value)
    delays  = flights.map(lambda f: (airline_lookup.value[f.airline],
                                     add(f.dep_delay, f.arv_delay)))

    # Reduce the total delay for the month to the airline
    delays  = delays.reduceByKey(add).collect()
    delays  = sorted(delays, key=itemgetter(1))

    # Provide output from the driver
    for d in delays:
        print "%0.0f minutes delayed\t%s" % (d[1], d[0])

    # Show a bar chart of the delays
    plot(delays)

if __name__ == "__main__":
    # Configure Spark
    conf = SparkConf().setMaster("local[*]")
    conf = conf.setAppName(APP_NAME)
    sc   = SparkContext(conf=conf)

    # Execute Main functionality
    main(sc)

##source##

• Spark入门(Python)