Hadoopの上の投稿火花の求人

この例では、平均遅延時間算出航空会社であり、ドロー
コードに直接：

import csv
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.use("TkAgg") # Use TKAgg to show figures
from StringIO import StringIO
from datetime import datetime
from collections import namedtuple
from operator import add, itemgetter
from pyspark import SparkConf, SparkContext

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)

def parse(row):
    # 将每一行解析为一个元祖
    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):
    # 将一行内容作为文件给csv.reader
    reader = csv.reader(StringIO(line))
    return reader.next()

def plot(delays):
    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)

    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=(min+1, idx+0.5), va='center')

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

    plt.grid(axis='x', color = 'white', linestyle='-')

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

def main(sc):
    # 打开查询airline全程的文件，作为一个字典用于查询
    airlines = dict(sc.textFile("flight/airlines.csv").map(split).collect())
    # 将这个字典发送给所有worker节点
    airline_lookup = sc.broadcast(airlines)
    # 打开航班信息
    flights = sc.textFile("flight/flights.csv").map(split).map(parse)
    # 计算延误时间
    delays = flights.map(lambda f:(airline_lookup.value[f.airline],add(f.dep_delay, f.arv_delay)))
    # 对同个航空公司的延误时间相加
    delays = delays.reduceByKey(add).collect()
    # 根据延误时间排序
    delays = sorted(delays, key=itemgetter(1))

    for d in delays:
        print "%0.0f minutes delayed\t%s" % (d[1], d[0])
    # 画图
    plot(delays)




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

    main(sc)

上記のコードは保存：app.pyは
、対応するディレクトリのHDFS上に置かれた2つのcsvファイルを使用するようにしてください

$ spark-submit app.py

実行する準備ができて