Spark Advanced Window with dynamic last

Question

Problem: Given a time series data which is a clickstream of user activity is stored in hive, ask is to enrich the data with session id using spark.

Session Definition

• Session expires after inactivity of 1 hour
• Session remains active for a total duration of 2 hours

Data:

click_time,user_id
2018-01-01 11:00:00,u1
2018-01-01 12:10:00,u1
2018-01-01 13:00:00,u1
2018-01-01 13:50:00,u1
2018-01-01 14:40:00,u1
2018-01-01 15:30:00,u1
2018-01-01 16:20:00,u1
2018-01-01 16:50:00,u1
2018-01-01 11:00:00,u2
2018-01-02 11:00:00,u2

Below is partial solution considering only 1st point in session definition:

val win1 = Window.partitionBy("user_id").orderBy("click_time")
    val sessionnew = when((unix_timestamp($"click_time") - unix_timestamp(lag($"click_time",1,"2017-01-01 11:00:00.0").over(win1)))/60 >= 60, 1).otherwise(0)
    userActivity
      .withColumn("session_num",sum(sessionnew).over(win1))
      .withColumn("session_id",concat($"user_id", $"session_num"))
      .show(truncate = false)

Actual Output:

+---------------------+-------+-----------+----------+
|click_time           |user_id|session_num|session_id|
+---------------------+-------+-----------+----------+
|2018-01-01 11:00:00.0|u1     |1          |u11       |
|2018-01-01 12:10:00.0|u1     |2          |u12       | -- session u12 starts
|2018-01-01 13:00:00.0|u1     |2          |u12       |
|2018-01-01 13:50:00.0|u1     |2          |u12       |
|2018-01-01 14:40:00.0|u1     |2          |u12       | -- this should be a new session as diff of session start of u12 and this row exceeds 2 hours
|2018-01-01 15:30:00.0|u1     |2          |u12       |
|2018-01-01 16:20:00.0|u1     |2          |u12       |
|2018-01-01 16:50:00.0|u1     |2          |u12       | -- now this has to be compared with row 5 to find difference
|2018-01-01 11:00:00.0|u2     |1          |u21       |
|2018-01-02 11:00:00.0|u2     |2          |u22       |
+---------------------+-------+-----------+----------+

To include the second condition, I tried to find difference between the current time with last session start time to check if that exceeds 2 hours, but however the reference itself changes for the following rows. These are some some use cases which can be achieved through running sum but this doesn’t suit here.

Answer 1

Not a straight forward problem to solve, but here's one approach:

1. Use Window lag timestamp difference to identify sessions (with 0 = start of a session) per user for rule #1
2. Group the dataset to assemble the timestamp diff list per user
3. Process via a UDF the timestamp diff list to identify sessions for rule #2 and create all session ids per user
4. Expand the grouped dataset via Spark's explode

Sample code below:

import org.apache.spark.sql.functions._
import org.apache.spark.sql.expressions.Window
import spark.implicits._

val userActivity = Seq(
  ("2018-01-01 11:00:00", "u1"),
  ("2018-01-01 12:10:00", "u1"),
  ("2018-01-01 13:00:00", "u1"),
  ("2018-01-01 13:50:00", "u1"),
  ("2018-01-01 14:40:00", "u1"),
  ("2018-01-01 15:30:00", "u1"),
  ("2018-01-01 16:20:00", "u1"),
  ("2018-01-01 16:50:00", "u1"),
  ("2018-01-01 11:00:00", "u2"),
  ("2018-01-02 11:00:00", "u2")
).toDF("click_time", "user_id")

def clickSessList(tmo: Long) = udf{ (uid: String, clickList: Seq[String], tsList: Seq[Long]) =>
  def sid(n: Long) = s"$uid-$n"

  val sessList = tsList.foldLeft( (List[String](), 0L, 0L) ){ case ((ls, j, k), i) =>
    if (i == 0 || j + i >= tmo) (sid(k + 1) :: ls, 0L, k + 1) else
       (sid(k) :: ls, j + i, k)
  }._1.reverse

  clickList zip sessList
}

Note that the accumulator for foldLeft in the UDF is a Tuple of (ls, j, k), where:

• ls is the list of formatted session ids to be returned
• j and k are for carrying over the conditionally changing timestamp value and session id number, respectively, to the next iteration

Step 1:

val tmo1: Long = 60 * 60
val tmo2: Long = 2 * 60 * 60

val win1 = Window.partitionBy("user_id").orderBy("click_time")

val df1 = userActivity.
  withColumn("ts_diff", unix_timestamp($"click_time") - unix_timestamp(
    lag($"click_time", 1).over(win1))
  ).
  withColumn("ts_diff", when(row_number.over(win1) === 1 || $"ts_diff" >= tmo1, 0L).
    otherwise($"ts_diff")
  )

df1.show
// +-------------------+-------+-------+
// |         click_time|user_id|ts_diff|
// +-------------------+-------+-------+
// |2018-01-01 11:00:00|     u1|      0|
// |2018-01-01 12:10:00|     u1|      0|
// |2018-01-01 13:00:00|     u1|   3000|
// |2018-01-01 13:50:00|     u1|   3000|
// |2018-01-01 14:40:00|     u1|   3000|
// |2018-01-01 15:30:00|     u1|   3000|
// |2018-01-01 16:20:00|     u1|   3000|
// |2018-01-01 16:50:00|     u1|   1800|
// |2018-01-01 11:00:00|     u2|      0|
// |2018-01-02 11:00:00|     u2|      0|
// +-------------------+-------+-------+

Steps 2-4:

val df2 = df1.
  groupBy("user_id").agg(
    collect_list($"click_time").as("click_list"), collect_list($"ts_diff").as("ts_list")
  ).
  withColumn("click_sess_id",
    explode(clickSessList(tmo2)($"user_id", $"click_list", $"ts_list"))
  ).
  select($"user_id", $"click_sess_id._1".as("click_time"), $"click_sess_id._2".as("sess_id"))

df2.show
// +-------+-------------------+-------+
// |user_id|click_time         |sess_id|
// +-------+-------------------+-------+
// |u1     |2018-01-01 11:00:00|u1-1   |
// |u1     |2018-01-01 12:10:00|u1-2   |
// |u1     |2018-01-01 13:00:00|u1-2   |
// |u1     |2018-01-01 13:50:00|u1-2   |
// |u1     |2018-01-01 14:40:00|u1-3   |
// |u1     |2018-01-01 15:30:00|u1-3   |
// |u1     |2018-01-01 16:20:00|u1-3   |
// |u1     |2018-01-01 16:50:00|u1-4   |
// |u2     |2018-01-01 11:00:00|u2-1   |
// |u2     |2018-01-02 11:00:00|u2-2   |
// +-------+-------------------+-------+

Also note that click_time is "passed thru" in steps 2-4 so as to be included in the final dataset.

Answer 2

Though the answer provided by Leo Works perfectly I feel its a complicated approach to Solve the problem by using Collect and Explode functions.This can be solved using Spark's Way by using UDAF to make it feasible for modifications in the near future as well.Please take a look into a solution on similar lines below

scala> //Importing Packages

scala> import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.expressions.Window

scala> import org.apache.spark.sql.functions._
import org.apache.spark.sql.functions._

scala> import org.apache.spark.sql.types._
import org.apache.spark.sql.types._

scala> // CREATE UDAF To Calculate total session duration Based on SessionIncativeFlag and Current Session Duration

scala> import org.apache.spark.sql.expressions.MutableAggregationBuffer
import org.apache.spark.sql.expressions.MutableAggregationBuffer

scala> import org.apache.spark.sql.expressions.UserDefinedAggregateFunction
import org.apache.spark.sql.expressions.UserDefinedAggregateFunction

scala> import org.apache.spark.sql.Row
import org.apache.spark.sql.Row

scala> import org.apache.spark.sql.types._
import org.apache.spark.sql.types._

scala>

scala> class TotalSessionDuration extends UserDefinedAggregateFunction {
     |   // This is the input fields for your aggregate function.
     |   override def inputSchema: org.apache.spark.sql.types.StructType =
     |     StructType(
     |       StructField("sessiondur", LongType) :: StructField(
     |         "inactivityInd",
     |         IntegerType
     |       ) :: Nil
     |     )
     |
     |   // This is the internal fields you keep for computing your aggregate.
     |   override def bufferSchema: StructType = StructType(
     |     StructField("sessionSum", LongType) :: Nil
     |   )
     |
     |   // This is the output type of your aggregatation function.
     |   override def dataType: DataType = LongType
     |
     |   override def deterministic: Boolean = true
     |
     |   // This is the initial value for your buffer schema.
     |   override def initialize(buffer: MutableAggregationBuffer): Unit = {
     |     buffer(0) = 0L
     |   }
     |
     |   // This is how to update your buffer schema given an input.
     |   override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
     |     if (input.getAs[Int](1) == 1)
     |       buffer(0) = 0L
     |     else if (buffer.getAs[Long](0) >= 7200L)
     |       buffer(0) = input.getAs[Long](0)
     |     else
     |       buffer(0) = buffer.getAs[Long](0) + input.getAs[Long](0)
     |   }
     |
     |   // This is how to merge two objects with the bufferSchema type.
     |   override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
     |     if (buffer2.getAs[Int](1) == 1)
     |       buffer1(0) = 0L
     |     else if (buffer2.getAs[Long](0) >= 7200)
     |       buffer1(0) = buffer2.getAs[Long](0)
     |     else
     |       buffer1(0) = buffer1.getAs[Long](0) + buffer2.getAs[Long](0)
     |   }
     |   // This is where you output the final value, given the final value of your bufferSchema.
     |   override def evaluate(buffer: Row): Any = {
     |     buffer.getLong(0)
     |   }
     | }
defined class TotalSessionDuration

scala> //Create handle for using the UDAD Defined above

scala> val sessionSum=spark.udf.register("sessionSum", new TotalSessionDuration)
sessionSum: org.apache.spark.sql.expressions.UserDefinedAggregateFunction = TotalSessionDuration@64a9719a

scala> //Create Session Dataframe

scala> val clickstream = Seq(
     |   ("2018-01-01T11:00:00Z", "u1"),
     |   ("2018-01-01T12:10:00Z", "u1"),
     |   ("2018-01-01T13:00:00Z", "u1"),
     |   ("2018-01-01T13:50:00Z", "u1"),
     |   ("2018-01-01T14:40:00Z", "u1"),
     |   ("2018-01-01T15:30:00Z", "u1"),
     |   ("2018-01-01T16:20:00Z", "u1"),
     |   ("2018-01-01T16:50:00Z", "u1"),
     |   ("2018-01-01T11:00:00Z", "u2"),
     |   ("2018-01-02T11:00:00Z", "u2")
     | ).toDF("timestamp", "userid").withColumn("curr_timestamp",unix_timestamp($"timestamp", "yyyy-MM-dd'T'HH:mm:ss'Z'").cast(TimestampType)).drop("timestamp")
clickstream: org.apache.spark.sql.DataFrame = [userid: string, curr_timestamp: timestamp]

scala>

scala> clickstream.show(false)
+------+-------------------+
|userid|curr_timestamp     |
+------+-------------------+
|u1    |2018-01-01 11:00:00|
|u1    |2018-01-01 12:10:00|
|u1    |2018-01-01 13:00:00|
|u1    |2018-01-01 13:50:00|
|u1    |2018-01-01 14:40:00|
|u1    |2018-01-01 15:30:00|
|u1    |2018-01-01 16:20:00|
|u1    |2018-01-01 16:50:00|
|u2    |2018-01-01 11:00:00|
|u2    |2018-01-02 11:00:00|
+------+-------------------+


scala> //Generate column SEF with values 0 or 1 depending on whether difference between current and previous activity time is greater than 1 hour=3600 sec

scala>

scala> //Window on Current Timestamp when last activity took place

scala> val windowOnTs = Window.partitionBy("userid").orderBy("curr_timestamp")
windowOnTs: org.apache.spark.sql.expressions.WindowSpec = org.apache.spark.sql.expressions.WindowSpec@41dabe47

scala> //Create Lag Expression to find previous timestamp for the User

scala> val lagOnTS = lag(col("curr_timestamp"), 1).over(windowOnTs)
lagOnTS: org.apache.spark.sql.Column = lag(curr_timestamp, 1, NULL) OVER (PARTITION BY userid ORDER BY curr_timestamp ASC NULLS FIRST unspecifiedframe$())

scala> //Compute Timestamp for previous activity and subtract the same from Timestamp for current activity to get difference between 2 activities

scala> val diff_secs_col = col("curr_timestamp").cast("long") - col("prev_timestamp").cast("long")
diff_secs_col: org.apache.spark.sql.Column = (CAST(curr_timestamp AS BIGINT) - CAST(prev_timestamp AS BIGINT))

scala> val UserActWindowed=clickstream.withColumn("prev_timestamp", lagOnTS).withColumn("last_session_activity_after", diff_secs_col ).na.fill(0, Array("last_session_activity_after"))
UserActWindowed: org.apache.spark.sql.DataFrame = [userid: string, curr_timestamp: timestamp ... 2 more fields]

scala> //Generate Flag Column SEF (Session Expiry Flag) to indicate Session Has Expired due to inactivity for more than 1 hour

scala> val UserSessionFlagWhenInactive=UserActWindowed.withColumn("SEF",when(col("last_session_activity_after")>3600, 1).otherwise(0)).withColumn("tempsessid",sum(col("SEF"))  over windowOnTs)
UserSessionFlagWhenInactive: org.apache.spark.sql.DataFrame = [userid: string, curr_timestamp: timestamp ... 4 more fields]

scala> UserSessionFlagWhenInactive.show(false)
+------+-------------------+-------------------+---------------------------+---+----------+
|userid|curr_timestamp     |prev_timestamp     |last_session_activity_after|SEF|tempsessid|
+------+-------------------+-------------------+---------------------------+---+----------+
|u1    |2018-01-01 11:00:00|null               |0                          |0  |0         |
|u1    |2018-01-01 12:10:00|2018-01-01 11:00:00|4200                       |1  |1         |
|u1    |2018-01-01 13:00:00|2018-01-01 12:10:00|3000                       |0  |1         |
|u1    |2018-01-01 13:50:00|2018-01-01 13:00:00|3000                       |0  |1         |
|u1    |2018-01-01 14:40:00|2018-01-01 13:50:00|3000                       |0  |1         |
|u1    |2018-01-01 15:30:00|2018-01-01 14:40:00|3000                       |0  |1         |
|u1    |2018-01-01 16:20:00|2018-01-01 15:30:00|3000                       |0  |1         |
|u1    |2018-01-01 16:50:00|2018-01-01 16:20:00|1800                       |0  |1         |
|u2    |2018-01-01 11:00:00|null               |0                          |0  |0         |
|u2    |2018-01-02 11:00:00|2018-01-01 11:00:00|86400                      |1  |1         |
+------+-------------------+-------------------+---------------------------+---+----------+


scala> //Compute Total session duration using the UDAF TotalSessionDuration such that :

scala> //(i)counter will be rest to 0 if SEF is set to 1

scala> //(ii)or set it to current session duration if session exceeds 2 hours

scala> //(iii)If both of them are inapplicable accumulate the sum

scala> val UserSessionDur=UserSessionFlagWhenInactive.withColumn("sessionSum",sessionSum(col("last_session_activity_after"),col("SEF"))  over windowOnTs)
UserSessionDur: org.apache.spark.sql.DataFrame = [userid: string, curr_timestamp: timestamp ... 5 more fields]

scala> //Generate Session Marker if SEF is 1 or sessionSum Exceeds 2 hours(7200) seconds

scala> val UserNewSessionMarker=UserSessionDur.withColumn("SessionFlagChangeIndicator",when(col("SEF")===1 || col("sessionSum")>7200, 1).otherwise(0) )
UserNewSessionMarker: org.apache.spark.sql.DataFrame = [userid: string, curr_timestamp: timestamp ... 6 more fields]

scala> //Create New Session ID based on the marker

scala> val computeSessionId=UserNewSessionMarker.drop("SEF","tempsessid","sessionSum").withColumn("sessid",concat(col("userid"),lit("-"),(sum(col("SessionFlagChangeIndicator"))  over windowOnTs)+1.toLong))
computeSessionId: org.apache.spark.sql.DataFrame = [userid: string, curr_timestamp: timestamp ... 4 more fields]

scala> computeSessionId.show(false)
+------+-------------------+-------------------+---------------------------+--------------------------+------+
|userid|curr_timestamp     |prev_timestamp     |last_session_activity_after|SessionFlagChangeIndicator|sessid|
+------+-------------------+-------------------+---------------------------+--------------------------+------+
|u1    |2018-01-01 11:00:00|null               |0                          |0                         |u1-1  |
|u1    |2018-01-01 12:10:00|2018-01-01 11:00:00|4200                       |1                         |u1-2  |
|u1    |2018-01-01 13:00:00|2018-01-01 12:10:00|3000                       |0                         |u1-2  |
|u1    |2018-01-01 13:50:00|2018-01-01 13:00:00|3000                       |0                         |u1-2  |
|u1    |2018-01-01 14:40:00|2018-01-01 13:50:00|3000                       |1                         |u1-3  |
|u1    |2018-01-01 15:30:00|2018-01-01 14:40:00|3000                       |0                         |u1-3  |
|u1    |2018-01-01 16:20:00|2018-01-01 15:30:00|3000                       |0                         |u1-3  |
|u1    |2018-01-01 16:50:00|2018-01-01 16:20:00|1800                       |1                         |u1-4  |
|u2    |2018-01-01 11:00:00|null               |0                          |0                         |u2-1  |
|u2    |2018-01-02 11:00:00|2018-01-01 11:00:00|86400                      |1                         |u2-2  |
+------+-------------------+-------------------+---------------------------+--------------------------+------+

Answer 3

Simple approach :

import spark.implicits._
val userActivity = Seq(
  ("2018-01-01 11:00:00", "u1"),
  ("2018-01-01 12:10:00", "u1"),
  ("2018-01-01 13:00:00", "u1"),
  ("2018-01-01 13:50:00", "u1"),
  ("2018-01-01 14:40:00", "u1"),
  ("2018-01-01 15:30:00", "u1"),
  ("2018-01-01 16:20:00", "u1"),
  ("2018-01-01 16:50:00", "u1"),
  ("2018-01-01 11:00:00", "u2"),
  ("2018-01-02 11:00:00", "u2")
).toDF("click_time", "user_id")

var df1 = userActivity.orderBy(asc("click_time")).
  groupBy("user_id").agg(collect_list("click_time").alias("ts_diff"))

df1=df1.withColumn("test",convert_Case()($"ts_diff")).drop("ts_diff")
df1=df1.withColumn("test",explode(col("test"))).
  withColumn("click_time",$"test._1").withColumn("session_generated",
  concat($"user_id",lit("_"),$"test._2")).drop("test")
df1.show(truncate = false)

def convert_Case()=
  udf {(timeList:Array[String])=>
    import java.text.SimpleDateFormat
    val dateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
    val new_list = ListBuffer[(String, Int)]()
    var session = 1
  new_list.append((timeList(0), session))
  var x = timeList(0)
  var time = 0L
  for(i<-1 until(timeList.length)){
    val d1 = dateFormat.parse(x)
    val d2 = dateFormat.parse(timeList(i))
    val diff = ((d1.getTime - d2.getTime)/1000)*(-1)
    val test = diff + time
  if (diff >= 3600.0 || test >= 7200.0) {
    session = session + 1
    new_list.append((timeList(i), session))
    time = 0L
  }
  else
  {new_list.append((timeList(i), session))
    time = test}
    x = timeList(i)}
    new_list
}

Answer 4

Complete solution

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
import scala.collection.mutable.ListBuffer
import scala.util.control._
import spark.sqlContext.implicits._
import java.sql.Timestamp
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._


val interimSessionThreshold=60
val totalSessionTimeThreshold=120

val sparkSession = SparkSession.builder.master("local").appName("Window Function").getOrCreate()

val clickDF = sparkSession.createDataFrame(Seq(
      ("2018-01-01T11:00:00Z","u1"),
        ("2018-01-01T12:10:00Z","u1"),
        ("2018-01-01T13:00:00Z","u1"),
        ("2018-01-01T13:50:00Z","u1"),
        ("2018-01-01T14:40:00Z","u1"),
        ("2018-01-01T15:30:00Z","u1"),
        ("2018-01-01T16:20:00Z","u1"),
        ("2018-01-01T16:50:00Z","u1"),
        ("2018-01-01T11:00:00Z","u2"),
        ("2018-01-02T11:00:00Z","u2")
    )).toDF("clickTime","user")

val newDF=clickDF.withColumn("clickTimestamp",unix_timestamp($"clickTime", "yyyy-MM-dd'T'HH:mm:ss'Z'").cast(TimestampType).as("timestamp")).drop($"clickTime")  

val partitionWindow = Window.partitionBy($"user").orderBy($"clickTimestamp".asc)

val lagTest = lag($"clickTimestamp", 1, "0000-00-00 00:00:00").over(partitionWindow)
val df_test=newDF.select($"*", ((unix_timestamp($"clickTimestamp")-unix_timestamp(lagTest))/60D cast "int") as "diff_val_with_previous")


val distinctUser=df_test.select($"user").distinct.as[String].collect.toList

val rankTest = rank().over(partitionWindow)
val ddf = df_test.select($"*", rankTest as "rank")

case class finalClick(User:String,clickTime:Timestamp,session:String)

val rowList: ListBuffer[finalClick] = new ListBuffer()


distinctUser.foreach{x =>{
    val tempDf= ddf.filter($"user" === x)
    var cumulDiff:Int=0
    var session_index=1
    var startBatch=true
    var dp=0
    val len = tempDf.count.toInt
    for(i <- 1 until len+1){
      val r = tempDf.filter($"rank" === i).head()
      dp = r.getAs[Int]("diff_val_with_previous")
      cumulDiff += dp
      if(dp <= interimSessionThreshold && cumulDiff <= totalSessionTimeThreshold){
        startBatch=false
        rowList += finalClick(r.getAs[String]("user"),r.getAs[Timestamp]("clickTimestamp"),r.getAs[String]("user")+session_index)
      }
      else{
        session_index+=1
        cumulDiff = 0
        startBatch=true
        dp=0
        rowList += finalClick(r.getAs[String]("user"),r.getAs[Timestamp]("clickTimestamp"),r.getAs[String]("user")+session_index)
      }
    } 
}}


val dataFrame = sc.parallelize(rowList.toList).toDF("user","clickTimestamp","session")

dataFrame.show

+----+-------------------+-------+
|user|     clickTimestamp|session|
+----+-------------------+-------+
|  u1|2018-01-01 11:00:00|    u11|
|  u1|2018-01-01 12:10:00|    u12|
|  u1|2018-01-01 13:00:00|    u12|
|  u1|2018-01-01 13:50:00|    u12|
|  u1|2018-01-01 14:40:00|    u13|
|  u1|2018-01-01 15:30:00|    u13|
|  u1|2018-01-01 16:20:00|    u13|
|  u1|2018-01-01 16:50:00|    u14|
|  u2|2018-01-01 11:00:00|    u21|
|  u2|2018-01-02 11:00:00|    u22|
+----+-------------------+-------+

Answer 5

-----Solution without using explode----.

`In my point of view explode is heavy process and inorder to apply you have taken groupby and collect_list.` 



`

        import pyspark.sql.functions  as f
         from pyspark.sql.window import Window
        streaming_data=[("U1","2019-01-01T11:00:00Z") , 
        ("U1","2019-01-01T11:15:00Z") , 
        ("U1","2019-01-01T12:00:00Z") , 
        ("U1","2019-01-01T12:20:00Z") , 
        ("U1","2019-01-01T15:00:00Z") , 
        ("U2","2019-01-01T11:00:00Z") , 
        ("U2","2019-01-02T11:00:00Z") , 
        ("U2","2019-01-02T11:25:00Z") , 
        ("U2","2019-01-02T11:50:00Z") , 
        ("U2","2019-01-02T12:15:00Z") , 
        ("U2","2019-01-02T12:40:00Z") , 
        ("U2","2019-01-02T13:05:00Z") , 
        ("U2","2019-01-02T13:20:00Z") ]
        schema=("UserId","Click_Time")
        window_spec=Window.partitionBy("UserId").orderBy("Click_Time")
        df_stream=spark.createDataFrame(streaming_data,schema)
        df_stream=df_stream.withColumn("Click_Time",df_stream["Click_Time"].cast("timestamp"))
        
        
        df_stream=df_stream\
        .withColumn("time_diff",
                    (f.unix_timestamp("Click_Time")-f.unix_timestamp(f.lag(f.col("Click_Time"),1).over(window_spec)))/(60*60)).na.fill(0)
        
        df_stream=df_stream\
        .withColumn("cond_",f.when(f.col("time_diff")>1,1).otherwise(0))
        df_stream=df_stream.withColumn("temp_session",f.sum(f.col("cond_")).over(window_spec))
        new_window=Window.partitionBy("UserId","temp_session").orderBy("Click_Time")
new_spec=new_window.rowsBetween(Window.unboundedPreceding,Window.currentRow)
cond_2hr=(f.unix_timestamp("Click_Time")-f.unix_timestamp(f.lag(f.col("Click_Time"),1).over(new_window)))
df_stream=df_stream.withColumn("temp_session_2hr",f.when(f.sum(f.col("2hr_time_diff")).over(new_spec)-(2*60*60)>0,1).otherwise(0))
new_window_2hr=Window.partitionBy(["UserId","temp_session","temp_session_2hr"]).orderBy("Click_Time")
hrs_cond_=(f.when(f.unix_timestamp(f.col("Click_Time"))-f.unix_timestamp(f.first(f.col("Click_Time")).over(new_window_2hr))-(2*60*60)>0,1).otherwise(0))
df_stream=df_stream\
.withColumn("final_session_groups",hrs_cond_)

df_stream=df_stream.withColumn("final_session",df_stream["temp_session_2hr"]+df_stream["temp_session"]+df_stream["final_session_groups"]+1)\
.drop("temp_session","final_session_groups","time_diff","temp_session_2hr","final_session_groups")
df_stream=df_stream.withColumn("session_id",f.concat(f.col("UserId"),f.lit(" session_val----->"),f.col("final_session")))
df_stream.show(20,0) 

`

---Steps taken to solve ---

` 1.first find out those clickstream which are clicked less than one hour and find the continuous groups.

2.then find out the click streams based on the 2hrs condition and make the continuous groups for this condition to be apply i have to create two continuos group based on logic as follows .

3 . One group will be based on the sum of time difference and make one group i.e temp_session_2hr and based on that find the next group final_session_groups .

4.Sum of these above continuous groups and add +1 to populate the final_session column at the end of algo and do concat as per your requirement to show the session_id.`

result will be looking like this

`+------+---------------------+-------------+---------------------+
|UserId|Click_Time           |final_session|session_id           |
+------+---------------------+-------------+---------------------+
|U2    |2019-01-01 11:00:00.0|1            |U2 session_val----->1|
|U2    |2019-01-02 11:00:00.0|2            |U2 session_val----->2|
|U2    |2019-01-02 11:25:00.0|2            |U2 session_val----->2|
|U2    |2019-01-02 11:50:00.0|2            |U2 session_val----->2|
|U2    |2019-01-02 12:15:00.0|2            |U2 session_val----->2|
|U2    |2019-01-02 12:40:00.0|2            |U2 session_val----->2|
|U2    |2019-01-02 13:05:00.0|3            |U2 session_val----->3|
|U2    |2019-01-02 13:20:00.0|3            |U2 session_val----->3|
|U1    |2019-01-01 11:00:00.0|1            |U1 session_val----->1|
|U1    |2019-01-01 11:15:00.0|1            |U1 session_val----->1|
|U1    |2019-01-01 12:00:00.0|2            |U1 session_val----->2|
|U1    |2019-01-01 12:20:00.0|2            |U1 session_val----->2|
|U1    |2019-01-01 15:00:00.0|3            |U1 session_val----->3|
+------+---------------------+-------------+---------------------+  

`