import pymysql
from pyspark.conf import SparkConf
import pytispark.pytispark as pti
from pyspark.sql import SparkSession
import datetime
import pandas as pd
import time
from pyspark import StorageLevel


def ctr(x):
    sum = 0
    for i in x:
        if i['is_cpc'] == 1:
            sum = sum + 1
    return sum

def f(x):
    print(x)



if __name__ == '__main__':
    sparkConf = SparkConf().set("spark.hive.mapred.supports.subdirectories", "true") \
        .set("spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive", "true") \
        .set("spark.tispark.plan.allow_index_double_read", "false") \
        .set("spark.tispark.plan.allow_index_read", "true") \
        .set("spark.sql.extensions", "org.apache.spark.sql.TiExtensions") \
        .set("spark.tispark.pd.addresses", "172.16.40.170:2379").set("spark.io.compression.codec", "lzf") \
        .set("spark.driver.maxResultSize", "8g").set("spark.sql.avro.compression.codec", "snappy")

    spark = SparkSession.builder.config(conf=sparkConf).enableHiveSupport().getOrCreate()
    sql = "select partition_date from online.ml_community_precise_exposure_detail " \
          "where action = 'page_precise_exposure' and page_name = 'search_result_welfare' " \
          "AND partition_date='20190926' limit 20"
    df = spark.sql(sql)
    # df.show(6)
    # params['exposure_cards'],
    df.rdd.map(lambda x:(x[0])).foreach(f)

    # print(rdd.collect())

    # spark.createDataFrame(rdd).show(6)



