Question

我有一个由＆＃34;事件＆＃34;，＆＃34;时间＆＃34;，＆＃34; UserId＆＃34;组成的日志文件。

+------------+----------------+---------+
|   Events   |      Time      | UserId  |
+------------+----------------+---------+
| ClickA     | 7/6/16 10:00am | userA   |
+------------+----------------+---------+
| ClickB     | 7/6/16 12:00am | userA   |
+------------+----------------+---------+

我想为每个用户计算事件之间的平均时间。你们是如何解决这个问题的？在传统的编程环境中，我会为用户浏览每个事件，并计算事件 n 和 n-1 之间的时间差，将此值添加到数组A.然后计算A中每个值的平均值。我怎么能用Spark做到这一点？

Answer 1

忽略解析它的日期看起来像一个窗口函数的作业，接着是一个简单的聚合，所以大概你需要这样的东西：

import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions.{lag, avg}

val df = Seq(
  ("ClickA", "2016-06-07 10:00:00", "UserA"),
  ("ClickB", "2016-06-07 12:00:00", "UserA")
).toDF("events", "time", "userid").withColumn("time", $"time".cast("timestamp"))

val w = Window.partitionBy("userid").orderBy("time")

// Difference between consecutive events in seconds
val diff = $"time".cast("long") - lag($"time", 1).over(w).cast("long")

df.withColumn("diff", diff).groupBy("userid").agg(avg($"diff"))

用pySpark计算用户事件之间的平均时间

1 个答案: