Getting a Unique Count over a Particular Time Frame with Spark DataFrames - scala

Question

I'm trying to figure out if what I'm trying to accomplish is even possible in Spark. Let's say I have a CSV that if read in as a DataFrame that looks like so:
+---------------------+-----------+-------+-------------+
| TimeStamp | Customer | User | Application |
+---------------------+-----------+-------+-------------+
| 2017-01-01 00:00:01 | customer1 | user1 | app1 |
| 2017-01-01 12:00:05 | customer1 | user1 | app1 |
| 2017-01-01 14:00:03 | customer1 | user2 | app2 |
| 2017-01-01 23:50:50 | customer1 | user1 | app1 |
| 2017-01-02 00:00:02 | customer1 | user1 | app1 |
+---------------------+-----------+-------+-------------+
I'm trying to produce a dataframe that includes a count of the number of the times a unique user from a certain customer has visited an application in the last 24 hours. So the result would look like so:
+---------------------+-----------+-------+-------------+----------------------+
| TimeStamp | Customer | User | Application | UniqueUserVisitedApp |
+---------------------+-----------+-------+-------------+----------------------+
| 2017-01-01 00:00:01 | customer1 | user1 | app1 | 0 |
| 2017-01-01 12:00:05 | customer1 | user2 | app1 | 1 |
| 2017-01-01 13:00:05 | customer1 | user2 | app1 | 2 |
| 2017-01-01 14:00:03 | customer1 | user1 | app1 | 2 |
| 2017-01-01 23:50:50 | customer1 | user3 | app1 | 2 |
| 2017-01-01 23:50:51 | customer2 | user4 | app2 | 0 |
| 2017-01-02 00:00:02 | customer1 | user1 | app1 | 3 |
+---------------------+-----------+-------+-------------+----------------------+
So I can do a tumbling window with the following code below, but that's not quite what we are looking for.
val data = spark.read.csv('path/to/csv')
val tumblingWindow = data
.groupBy(col("Customer"), col("Application"), window(data.col("TimeStamp"), "24 hours"))
.agg(countDistinct("user")).as("UniqueUsersVisitedApp")
The result is this:
+-----------+-------------+-------------------------+-----------------------+
| Customer | Application | Window | UniqueUsersVisitedApp |
+-----------+-------------+-------------------------+-----------------------+
| customer1 | app1 | [2017-01-01 00:00:00... | 2 |
| customer2 | app2 | [2017-01-01 00:00:00... | 1 |
| customer1 | app1 | [2017-01-02 00:00:00... | 1 |
+-----------+-------------+-------------------------+-----------------------+
Any help would be much appreciated.

Answer 1

If I understand your question correctly, just apply a filter before doing the groupBy:
data = spark.read.csv('path/to/csv')
result = (data
.filter(data['TimeStamp'] > now_minus_24_hours)
.groupBy(["Customer", "Application", "User"])
.count())
Note that users who haven't visited in the last 24 hours will be missing from the DataFrame, instead of having a count of zero.
Edit
If you are trying to get the number of visits in the last 24 hours relative to each timestamp, you can do something similar to my answer here. The basic steps will be:
reduceByKey to get a list of timestamps for each user/app/customer combination (identical to the other example). Each row will now be in the form:
((user, app, customer), list_of_timestamps)
Process each list of timestamps to generate a list of "number of visits in the previous 24 hours" for each timestamp. The data will now be in the form:
((user, app, customer), [(ts_0, num_visits_24hr_before_ts_0), (ts_1, num_visits_24_hr_before ts_2), ...])
flatMap each row back to multiple rows using something like:
lambda row: [(*row[0], *ts_num_visits) for ts_num_visits in row[1]]

Answer 2

I have tried it using pyspark window function, by creating subpartition for each date and apply count on them.Not sure how efficient they are. Here is my code snippet,
>>> from pyspark.sql import functions as F
>>> from pyspark.sql.types import TimestampType
>>> l = [('2017-01-01 00:00:01','customer1','user1','app1'),('2017-01-01 12:00:05','customer1','user1','app1'),('2017-01-01 14:00:03','customer1','user2','app2'),('2017-01-01 23:50:50','customer1','user1','app1'),('2017-01-02 00:00:02','customer1','user1','app1'),('2017-01-02 12:00:02','customer1','user1','app1'),('2017-01-03 14:00:02','customer1','user1','app1'),('2017-01-02 00:00:02','customer1','user2','app2'),('2017-01-01 16:04:01','customer1','user1','app1'),('2017-01-01 23:59:01','customer1','user1','app1'),('2017-01-01 18:00:01','customer1','user2','app2')]
>>> df = spark.createDataFrame(l,['TimeStamp','Customer','User','Application'])
>>> df = df.withColumn('TimeStamp',df['TimeStamp'].cast('timestamp')).withColumn('Date',F.to_date(F.col('TimeStamp')))
>>> df.show()
+-------------------+---------+-----+-----------+----------+
| TimeStamp| Customer| User|Application| Date|
+-------------------+---------+-----+-----------+----------+
|2017-01-01 00:00:01|customer1|user1| app1|2017-01-01|
|2017-01-01 12:00:05|customer1|user1| app1|2017-01-01|
|2017-01-01 14:00:03|customer1|user2| app2|2017-01-01|
|2017-01-01 23:50:50|customer1|user1| app1|2017-01-01|
|2017-01-02 00:00:02|customer1|user1| app1|2017-01-02|
|2017-01-02 12:00:02|customer1|user1| app1|2017-01-02|
|2017-01-03 14:00:02|customer1|user1| app1|2017-01-03|
|2017-01-02 00:00:02|customer1|user2| app2|2017-01-02|
|2017-01-01 16:04:01|customer1|user1| app1|2017-01-01|
|2017-01-01 23:59:01|customer1|user1| app1|2017-01-01|
|2017-01-01 18:00:01|customer1|user2| app2|2017-01-01|
+-------------------+---------+-----+-----------+----------+
>>> df.printSchema()
root
|-- TimeStamp: timestamp (nullable = true)
|-- Customer: string (nullable = true)
|-- User: string (nullable = true)
|-- Application: string (nullable = true)
|-- Date: date (nullable = true)
>>> w = Window.partitionBy('Customer','User','Application','Date').orderBy('Timestamp')
>>> diff = F.coalesce(F.datediff("TimeStamp", F.lag("TimeStamp", 1).over(w)), F.lit(0))
>>> subpartition = F.count(diff<1).over(w)
>>> df.select("*",(subpartition-1).alias('Count')).drop('Date').orderBy('Customer','User','Application','TimeStamp').show()
+-------------------+---------+-----+-----------+-----+
| TimeStamp| Customer| User|Application|Count|
+-------------------+---------+-----+-----------+-----+
|2017-01-01 00:00:01|customer1|user1| app1| 0|
|2017-01-01 12:00:05|customer1|user1| app1| 1|
|2017-01-01 16:04:01|customer1|user1| app1| 2|
|2017-01-01 23:50:50|customer1|user1| app1| 3|
|2017-01-01 23:59:01|customer1|user1| app1| 4|
|2017-01-02 00:00:02|customer1|user1| app1| 0|
|2017-01-02 12:00:02|customer1|user1| app1| 1|
|2017-01-03 14:00:02|customer1|user1| app1| 0|
|2017-01-01 14:00:03|customer1|user2| app2| 0|
|2017-01-01 18:00:01|customer1|user2| app2| 1|
|2017-01-02 00:00:02|customer1|user2| app2| 0|
+-------------------+---------+-----+-----------+-----+