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I have data which starts from 1st Jan 2017 to 7th Jan 2017 and it is a week wanted weekly aggregate. I used window function in following manner

val df_v_3 = df_v_2.groupBy(window(col("DateTime"), "7 day"))
      .agg(sum("Value") as "aggregate_sum")
      .select("window.start", "window.end", "aggregate_sum")

I am having data in dataframe as 

    DateTime,value
    2017-01-01T00:00:00.000+05:30,1.2
    2017-01-01T00:15:00.000+05:30,1.30
--
    2017-01-07T23:30:00.000+05:30,1.43
    2017-01-07T23:45:00.000+05:30,1.4

I am getting output as :

2016-12-29T05:30:00.000+05:30,2017-01-05T05:30:00.000+05:30,723.87
2017-01-05T05:30:00.000+05:30,2017-01-12T05:30:00.000+05:30,616.74

It shows that my day is starting from 29th Dec 2016 but in actual data is starting from 1 Jan 2017,why this margin is occuring?

Utkarsh Saraf
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2 Answers2

6

For tumbling windows like this it is possible to set an offset to the starting time, more information can be found in the blog here. A sliding window is used, however, by setting both "window duration" and "sliding duration" to the same value, it will be the same as a tumbling window with starting offset.

The syntax is like follows,

window(column, window duration, sliding duration, starting offset)

With your values I found that an offset of 64 hours would give a starting time of 2017-01-01 00:00:00.

val data = Seq(("2017-01-01 00:00:00",1.0),
               ("2017-01-01 00:15:00",2.0),
               ("2017-01-08 23:30:00",1.43))
val df = data.toDF("DateTime","value")
  .withColumn("DateTime", to_timestamp($"DateTime", "yyyy-MM-dd HH:mm:ss"))

val df2 = df
  .groupBy(window(col("DateTime"), "1 week", "1 week", "64 hours"))
  .agg(sum("value") as "aggregate_sum")
  .select("window.start", "window.end", "aggregate_sum")

Will give this resulting dataframe:

+-------------------+-------------------+-------------+
|              start|                end|aggregate_sum|
+-------------------+-------------------+-------------+
|2017-01-01 00:00:00|2017-01-08 00:00:00|          3.0|
|2017-01-08 00:00:00|2017-01-15 00:00:00|         1.43|
+-------------------+-------------------+-------------+
Shaido
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2

The solution with the python API looks a bit more intuitive since the window function works with the following options: window(timeColumn, windowDuration, slideDuration=None, startTime=None) see: https://spark.apache.org/docs/2.4.0/api/python/_modules/pyspark/sql/functions.html

The startTime is the offset with respect to 1970-01-01 00:00:00 UTC with which to start window intervals. For example, in order to have hourly tumbling windows that start 15 minutes past the hour, e.g. 12:15-13:15, 13:15-14:15... provide startTime as 15 minutes.

No need for a workaround with sliding duration, I used a 3 days "delay" as startTime to match the desired tumbling window:

from datetime import datetime 
from pyspark.sql.functions import sum, window
df_ex = spark.createDataFrame([(datetime(2017,1,1, 0,0) , 1.), \
                               (datetime(2017,1,1,0,15) , 2.), \
                               (datetime(2017,1,8,23,30) , 1.43)], \
                               ["Datetime", "value"])

weekly_ex = df_ex \
            .groupBy(window("Datetime", "1 week", startTime="3 day" )) \
            .agg(sum("value").alias('aggregate_sum'))

weekly_ex.show(truncate=False)

For the same result:

+------------------------------------------+-------------+
|window                                    |aggregate_sum|
+------------------------------------------+-------------+
|[2017-01-01 00:00:00, 2017-01-08 00:00:00]|3.0          |
|[2017-01-08 00:00:00, 2017-01-15 00:00:00]|1.43         |
+------------------------------------------+-------------+
5md
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