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| @@ -0,0 +1,63 @@ | ||
| from pyspark import SparkContext, SparkConf,SQLContext | ||
| from pyspark.sql.functions import count, lit, col, udf, expr, collect_list, explode | ||
| from pyspark.sql.types import IntegerType, StringType, MapType, ArrayType, BooleanType,FloatType | ||
| from pyspark.sql import HiveContext | ||
| from datetime import datetime, timedelta | ||
| from pyspark.sql.functions import broadcast | ||
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| def _list_to_map(count_array): | ||
| count_map = {} | ||
| for item in count_array: | ||
| key_value = item.split(':') | ||
| count_map[key_value[0]] = key_value[1] | ||
| return count_map | ||
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| def add_count_map(df): | ||
| # Convert count_array to count_map | ||
| list_to_map_udf = udf(_list_to_map, MapType( | ||
| StringType(), StringType(), False)) | ||
| df = df.withColumn('count_map', list_to_map_udf(df.count_array)) | ||
| return df | ||
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| def variance(plist): | ||
| l=len(plist) | ||
| ex=sum(plist)/l | ||
| ex2=sum([i*i for i in plist])/l | ||
| return ex2-ex*ex | ||
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| query="select count_array,day,uckey from factdata where day in ('2020-05-15','2020-05-14','2020-05-13','2020-05-12','2020-05-11','2020-05-10','2020-05-09')" | ||
| sc = SparkContext() | ||
| hive_context = HiveContext(sc) | ||
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| df = hive_context.sql(query) | ||
| df = add_count_map(df) | ||
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| df = df.select('uckey', 'day', explode(df.count_map)).withColumnRenamed("value", "impr_count") | ||
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| df = df.withColumn('impr_count', udf(lambda x: int(x), IntegerType())(df.impr_count)) | ||
| df = df.groupBy('uckey', 'day').sum('impr_count').withColumnRenamed("sum(impr_count)", 'impr_count') | ||
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| split_uckey_udf = udf(lambda x: x.split(","), ArrayType(StringType())) | ||
| df = df.withColumn('col', split_uckey_udf(df.uckey)) | ||
| df = df.select('uckey','impr_count', 'day', df.col[1]).withColumnRenamed("col[1]", 'slot_id') | ||
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| df_slot=df.select('slot_id','impr_count', 'day') | ||
| df_slot=df_slot.groupBy('slot_id','day').sum('impr_count').withColumnRenamed("sum(impr_count)", "impr_total") | ||
| bc_df_slot = broadcast(df_slot) | ||
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| df_new = df.join(bc_df_slot, on=["slot_id",'day'],how="inner") | ||
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| df_new = df_new.withColumn('percent', udf(lambda x,y: (x*100)/y, FloatType())(df_new.impr_count,df_new.impr_total)) | ||
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| df2=df_new.groupBy("uckey").agg(collect_list('percent').alias('percent')) | ||
| df2 = df2.withColumn('var', udf(lambda x: variance(x), FloatType())(df2.percent)) | ||
| df2.select("uckey","var").orderBy(["var"],ascending=False).show(300,truncate=False) | ||
| df2.cache() | ||
| print("% uckeys having varience > 0.01 ",df2.filter((df2.var <= 0.01)).count()*100/df2.count()) | ||
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