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final.py
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final.py
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import sys
import pyspark
from pyspark import SparkContext
from pyspark.sql.session import SparkSession
from pyspark.sql import SQLContext
def centerlineMatch(b, year, county, house_num1, house_num2, street):
for val in b.value:
phys_id = val[0]
L_LOW_HN = val[1]
L_HIGH_HN = val[2]
R_LOW_HN = val[3]
R_HIGH_HN = val[4]
L_LOW_HN_1 = val[8]
L_LOW_HN_2 = val[9]
L_HIGH_HN_1 = val[10]
L_HIGH_HN_2 = val[11]
R_LOW_HN_1 = val[12]
R_LOW_HN_2 = val[13]
R_HIGH_HN_1 = val[14]
R_HIGH_HN_2 = val[15]
BOROCODE = val[5]
ST_LABEL = val[6]
FULL_STREE = val[7]
if house_num2:
if R_LOW_HN_2 and R_HIGH_HN_2 and R_LOW_HN_1 and R_HIGH_HN_1:
if(house_num2%2==0):
if((house_num2 >= R_LOW_HN_2) and (house_num2 <= R_HIGH_HN_2) and (house_num1 >= R_LOW_HN_1) and (house_num1 <= R_HIGH_HN_1)
and (BOROCODE == county) and ((ST_LABEL == street) | (FULL_STREE == street))):
return(phys_id, year)
else:
if L_LOW_HN_2 and L_HIGH_HN_2 and L_LOW_HN_1 and R_HIGH_HN_1:
if((house_num2 >= L_LOW_HN_2) and (house_num2 <= L_HIGH_HN_2) and (house_num1 >= L_LOW_HN_1) and (house_num1 <= R_HIGH_HN_1) and (BOROCODE == county) and ((ST_LABEL == street) | (FULL_STREE == street))):
return(phys_id, year)
else:
if R_LOW_HN and R_HIGH_HN:
if(house_num1%2==0):
if((house_num1 >= R_LOW_HN) and (house_num1 <= R_HIGH_HN) and (BOROCODE == county) and ((ST_LABEL == street) | (FULL_STREE == street))):
return(phys_id, year)
else:
if L_LOW_HN and L_HIGH_HN:
if((house_num1 >= L_LOW_HN) and (house_num1 <= L_HIGH_HN) and (BOROCODE == county) and ((ST_LABEL == street) | (FULL_STREE == street))):
return(phys_id, year)
return(None, year)
def processTrips(pid, records):
import csv
from datetime import datetime
# Skip the header
if pid==0:
next(records)
reader = csv.reader(records)
counts = {}
for row in reader:
try:
year = row[4]
county = row[21]
house = row[23]
street = row[24]
if (county == 'K' or county == 'BK' or county == 'KING' or county == 'KINGS'):
county = 3
elif (county == 'QUEEN' or county == 'Q' or county == 'QN' or county == 'QNS' or county == 'QU'):
county = 4
elif (county == 'MN' or county == 'NY' or county == 'MAN' or county == 'MH' or county == 'NEWY' or county == 'NEW Y'):
county = 1
elif (county == 'BX' or county == 'BRONX'):
county = 2
elif (county == 'ST' or county == 'R' or county == 'RICHMOND'):
county = 5
date_object = datetime.strptime(year, '%m/%d/%Y')
if date_object.year:
if county:
if house:
if street:
## TODO Pass all these values to a function which matches on these and returns physical ID and year
if "-" in house:
house_num1,house_num2 = house.split('-')
else:
house_num1 = house
house_num2 = None
house_num1 = int(house_num1)
if house_num2:
house_num2 = int(house_num2)
county = int(county)
street = street.upper()
phys_id,return_year = centerlineMatch(b, date_object.year, county, house_num1, house_num2, street)
if phys_id:
counts[phys_id,return_year] = counts.get((phys_id,return_year), 0) + 1
except(ValueError, IndexError):
pass
return counts.items()
if __name__=='__main__':
sc = SparkContext.getOrCreate()
from pyspark.sql.functions import *
spark = SparkSession(sc)
centerline = spark.read.csv('hdfs:///tmp/bdm/nyc_cscl.csv', header=True, escape ='"', inferSchema = True, multiLine = True).cache()
centerline.createOrReplaceTempView('centerline')
columns = ['L_LOW_HN','L_HIGH_HN','R_LOW_HN','R_HIGH_HN',
'L_LOW_HN_1','L_LOW_HN_2','L_HIGH_HN_1','L_HIGH_HN_2',
'R_LOW_HN_1','R_LOW_HN_2','R_HIGH_HN_1','R_HIGH_HN_2']
street_columns = ['FULL_STREE', 'ST_LABEL']
split_col = pyspark.sql.functions.split(centerline['L_LOW_HN'], '-')
centerline = centerline.withColumn('L_LOW_HN_1', split_col.getItem(0))
centerline = centerline.withColumn('L_LOW_HN_2', split_col.getItem(1))
split_col = pyspark.sql.functions.split(centerline['L_HIGH_HN'], '-')
centerline = centerline.withColumn('L_HIGH_HN_1', split_col.getItem(0))
centerline = centerline.withColumn('L_HIGH_HN_2', split_col.getItem(1))
split_col = pyspark.sql.functions.split(centerline['R_LOW_HN'], '-')
centerline = centerline.withColumn('R_LOW_HN_1', split_col.getItem(0))
centerline = centerline.withColumn('R_LOW_HN_2', split_col.getItem(1))
split_col = pyspark.sql.functions.split(centerline['R_HIGH_HN'], '-')
centerline = centerline.withColumn('R_HIGH_HN_1', split_col.getItem(0))
centerline = centerline.withColumn('R_HIGH_HN_2', split_col.getItem(1))
for c in columns:
centerline = centerline.withColumn(c, centerline[c].cast('int'))
for s in street_columns:
centerline = centerline.withColumn(s, centerline[s].cast('string'))
centerline = centerline.select('PHYSICALID','L_LOW_HN','L_HIGH_HN','R_LOW_HN','R_HIGH_HN','BOROCODE',
'ST_LABEL','FULL_STREE','L_LOW_HN_1','L_LOW_HN_2','L_HIGH_HN_1','L_HIGH_HN_2','R_LOW_HN_1','R_LOW_HN_2','R_HIGH_HN_1','R_HIGH_HN_2')
b = sc.broadcast(centerline.collect())
rdd = sc.textFile('hdfs:///tmp/bdm/nyc_parking_violation/')
counts = rdd.mapPartitionsWithIndex(processTrips)\
.sortBy(lambda x: x[0], ascending=True)\
.reduceByKey(lambda x,y: x+y) \
.collect()
print(counts)