python - Pyspark：根据两个RDD中两列的条件计算两个对应列的总和

Question

我有两个具有相同列的 RDD：
rdd1 :-

+-----------------+
|中|uid|频率|
+-----------------+
| m1| u1| 1|
| m1| u2| 1|
| 平方米| u1| 2|
+-----------------+

rdd2：-

+-----------------+
|中|uid|频率|
+-----------------+
| m1| u1| 10|
| 平方米| u1| 98|
| 立方米| u2| 21|
+-----------------+

我想根据 和frequencies计算mid总和uid。结果应该是这样的：

+-----------------+
|中|uid|频率|
+-----------------+
| m1| u1| 11|
| 平方米| u1| 100|
| 立方米| u2| 21|
+-----------------+

提前致谢。

编辑：我也以这种方式实现了解决方案（使用 map-reduce）：

from pyspark.sql.functions import col

data1 = [("m1","u1",1),("m1","u2",1),("m2","u1",2)]
data2 = [("m1","u1",10),("m2","u1",98),("m3","u2",21)]
df1 = sqlContext.createDataFrame(data1,['mid','uid','frequency'])
df2 = sqlContext.createDataFrame(data2,['mid','uid','frequency'])

df3 = df1.unionAll(df2)
df4 = df3.map(lambda bbb: ((bbb['mid'], bbb['uid']), int(bbb['frequency'])))\
             .reduceByKey(lambda a, b: a+b)

p = df4.map(lambda p: (p[0][0], p[0][1], p[1])).toDF()

p = p.select(col("_1").alias("mid"), \
             col("_2").alias("uid"), \
             col("_3").alias("frequency"))

p.show()

输出：

+---+----+---------+
|中|uid|频率|
+---+---+---------+
| 平方米| u1| 100|
| m1| u1| 11|
| m1| u2| 1|
| 立方米| u2| 21|
+---+---+---------+

Answer 1

您只需要按 mid 和 uid 执行分组并执行求和操作：

data1 = [("m1","u1",1),("m1","u2",1),("m2","u1",2)]
data2 = [("m1","u1",10),("m2","u1",98),("m3","u2",21)]
df1 = sqlContext.createDataFrame(data1,['mid','uid','frequency'])
df2 = sqlContext.createDataFrame(data2,['mid','uid','frequency'])

df3 = df1.unionAll(df2)

df4 = df3.groupBy(df3.mid,df3.uid).sum() \
         .withColumnRenamed("sum(frequency)","frequency")

df4.show()

# +---+---+---------+
# |mid|uid|frequency|
# +---+---+---------+
# | m1| u1|       11|
# | m1| u2|        1|
# | m2| u1|      100|
# | m3| u2|       21|
# +---+---+---------+

Answer 2

我也以这种方式实现了解决方案（使用 map-reduce）：

from pyspark.sql.functions import col

data1 = [("m1","u1",1),("m1","u2",1),("m2","u1",2)]
data2 = [("m1","u1",10),("m2","u1",98),("m3","u2",21)]
df1 = sqlContext.createDataFrame(data1,['mid','uid','frequency'])
df2 = sqlContext.createDataFrame(data2,['mid','uid','frequency'])

df3 = df1.unionAll(df2)
df4 = df3.map(lambda bbb: ((bbb['mid'], bbb['uid']), int(bbb['frequency'])))\
             .reduceByKey(lambda a, b: a+b)

p = df4.map(lambda p: (p[0][0], p[0][1], p[1])).toDF()

p = p.select(col("_1").alias("mid"), \
             col("_2").alias("uid"), \
             col("_3").alias("frequency"))

p.show()

输出：

+---+----+---------+
|中|uid|频率|
+---+---+---------+
| 平方米| u1| 100|
| m1| u1| 11|
| m1| u2| 1|
| 立方米| u2| 21|
+---+---+---------+