企业spark案例 —— 出租车轨迹分析(Python)

第1关：SparkSql 数据清洗

# -*- coding: UTF-8 -*-
from pyspark.sql import SparkSession
if __name__ =='__main__':
    spark = SparkSession.builder.appName("demo").master("local").getOrCreate()
    #**********begin**********#
    df = spark.read.option("header",True).option("delimiter","\t").csv("/root/data.csv")
    df.createTempView("data")
    spark.sql("""
    select regexp_replace(TRIP_ID,'\\\W+','') as TRIP_ID ,
        regexp_replace(CALL_TYPE,'\\\W+','') as CALL_TYPE ,
        regexp_replace(ORIGIN_CALL,'\\\W+','') as ORIGIN_CALL ,
        regexp_replace(TAXI_ID,'\\\W+','') as TAXI_ID ,
        regexp_replace(ORIGIN_STAND,'\\\W+','') as ORIGIN_STAND ,
        regexp_replace(TIMESTAMP,'\\\W+','') as TIMESTAMP ,
        regexp_replace(POLYLINE,'\\\W+','') as POLYLINE
    from data
    """).show()
    #**********end**********#
    spark.stop()

第2关：SparkSql数据分析

# -*- coding: UTF-8 -*-
from pyspark.sql import SparkSession
import json
 
if __name__ == '__main__' :
    spark = SparkSession.builder.master("local").appName("demo").getOrCreate()
    #**********begin**********#
    df = spark.read.option("header",True).option("delimiter","\t").csv("/root/data2.csv")
    df.createTempView("data")
    spark.sql("select TRIP_ID,CALL_TYPE,ORIGIN_CALL, TAXI_ID, ORIGIN_STAND, from_unixtime(TIMESTAMP,'yyyy-MM-dd') as TIME ,POLYLINE from data").show()
    spark.udf.register("timeLen", lambda x: {
        (len(json.loads(x)) - 1) * 15 if len(json.loads(x)) > 0 else 8
    })
    spark.udf.register("startLocation", lambda x: {
        str(json.loads(x)[0]) if len(json.loads(x)) > 0 else ""
    })
    spark.udf.register( "endLocation", lambda x: {
        str(json.loads(x)[len(json.loads(x)) - 1]) if len(json.loads(x)) > 0 else ""
    })
    df.createTempView("data2")
    res=spark.sql("select TRIP_ID,CALL_TYPE,ORIGIN_CALL,TAXI_ID,ORIGIN_STAND,from_unixtime(TIMESTAMP,'yyyy-MM-dd') as TIME, POLYLINE, timeLen(POLYLINE) as TIMELEN, startLocation(POLYLINE) as STARTLOCATION, endLocation(POLYLINE) as ENDLOCATION from data2")
    res.createTempView("data3")
    res.show()
    spark.sql("select CALL_TYPE,TIME,count(1) as NUM from data3 group by TIME,CALL_TYPE order by CALL_TYPE,TIME").show()
    #**********end**********#