Enforcing an interface on a DataFrame












0















I'm new to Spark and was wondering if the following is possible.



I have 2 Datasets, and they both have fields EventTime and UserId. However, they differ in all other columns.



I want to write a function that takes in these Datasets and spits out the last time I saw each user.



This is easy enough, because we can select the row with the maximum time for each user (groupby)



Let's say I have a function LastSeenTime(events: DataFrame): DataFrame { ... }



My question is how would you organize the code, and potentially define a type/interface such that LastSeenTime can enforce that events has the UserId and EventTime columns it needs to do the processing.



Can Dataset Schema's conform to partial interfaces?



Thanks!






scala apache-spark-dataset







share|improve this question



























    0















    I'm new to Spark and was wondering if the following is possible.



    I have 2 Datasets, and they both have fields EventTime and UserId. However, they differ in all other columns.



    I want to write a function that takes in these Datasets and spits out the last time I saw each user.



    This is easy enough, because we can select the row with the maximum time for each user (groupby)



    Let's say I have a function LastSeenTime(events: DataFrame): DataFrame { ... }



    My question is how would you organize the code, and potentially define a type/interface such that LastSeenTime can enforce that events has the UserId and EventTime columns it needs to do the processing.



    Can Dataset Schema's conform to partial interfaces?



    Thanks!






    scala apache-spark-dataset







    share|improve this question

























      0












      0








      0








      I'm new to Spark and was wondering if the following is possible.



      I have 2 Datasets, and they both have fields EventTime and UserId. However, they differ in all other columns.



      I want to write a function that takes in these Datasets and spits out the last time I saw each user.



      This is easy enough, because we can select the row with the maximum time for each user (groupby)



      Let's say I have a function LastSeenTime(events: DataFrame): DataFrame { ... }



      My question is how would you organize the code, and potentially define a type/interface such that LastSeenTime can enforce that events has the UserId and EventTime columns it needs to do the processing.



      Can Dataset Schema's conform to partial interfaces?



      Thanks!






      scala apache-spark-dataset







      share|improve this question














      I'm new to Spark and was wondering if the following is possible.



      I have 2 Datasets, and they both have fields EventTime and UserId. However, they differ in all other columns.



      I want to write a function that takes in these Datasets and spits out the last time I saw each user.



      This is easy enough, because we can select the row with the maximum time for each user (groupby)



      Let's say I have a function LastSeenTime(events: DataFrame): DataFrame { ... }



      My question is how would you organize the code, and potentially define a type/interface such that LastSeenTime can enforce that events has the UserId and EventTime columns it needs to do the processing.



      Can Dataset Schema's conform to partial interfaces?



      Thanks!






      scala apache-spark-dataset




      scala apache-spark-dataset






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 14 '18 at 23:18









      Vishaal KalwaniVishaal Kalwani

      3821313




      3821313
























          1 Answer
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          2














          You can make something like this:



          sealed trait Event {
          
  def userId: String
          
  def eventTime: String
          
}
          

          
final case class UserEvent(userId: String, eventTime: String, otherField: String) extends Event
          

          
def lastTimeByUser[E <: Event, T](events: Dataset[E]): Dataset[T] = ???


          Edit



          If you're using a Dataframe, you can "cast" it to a Dataset[T] using the .as[T] method. (Where T is the case class you want to use for represent your data - must have the same fields of your Rows).

          Note, you will need a implicit Encoder[T] in the scope for that - the simplest way to provide it is import spark.implicits._, where spark is an instance of SparkSession.






          share|improve this answer


























          • This looks like just what I need. Can you explain what the syntax Dataset[Event] does? Is it some sort of templating ?

            – Vishaal Kalwani
            Nov 15 '18 at 0:23













          • @VishaalKalwani Yes, is just a normal Scala generics. It means is the method accepts as input a Dataset of Events.

            – Luis Miguel Mejía Suárez
            Nov 15 '18 at 0:40






          • 1





            To elaborate on this. Dataframe is a type alias for Dataset[Row] and Row is just a glorified Map[String, Any] and Dataframe basically just has a bunch of additional syntax tacked to it. Personally I'd recomment def lastTimeByUser[T <: Event](events: Dataset[T]): Dataset[T] = ??? because that will not lose you type information when calling the function with a Dataset[UserEvent]

            – Dominic Egger
            Nov 15 '18 at 8:17













          • @DominicEgger, yes you're right - I have updated the answer. I also provided information about transforming a Dataframe into a Dataset for clarification. Please feel free to edit it if you believe you can be more consince than me.

            – Luis Miguel Mejía Suárez
            Nov 15 '18 at 14:57













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          1 Answer
          1






          active

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          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          2














          You can make something like this:



          sealed trait Event {
          
  def userId: String
          
  def eventTime: String
          
}
          

          
final case class UserEvent(userId: String, eventTime: String, otherField: String) extends Event
          

          
def lastTimeByUser[E <: Event, T](events: Dataset[E]): Dataset[T] = ???


          Edit



          If you're using a Dataframe, you can "cast" it to a Dataset[T] using the .as[T] method. (Where T is the case class you want to use for represent your data - must have the same fields of your Rows).

          Note, you will need a implicit Encoder[T] in the scope for that - the simplest way to provide it is import spark.implicits._, where spark is an instance of SparkSession.






          share|improve this answer


























          • This looks like just what I need. Can you explain what the syntax Dataset[Event] does? Is it some sort of templating ?

            – Vishaal Kalwani
            Nov 15 '18 at 0:23













          • @VishaalKalwani Yes, is just a normal Scala generics. It means is the method accepts as input a Dataset of Events.

            – Luis Miguel Mejía Suárez
            Nov 15 '18 at 0:40






          • 1





            To elaborate on this. Dataframe is a type alias for Dataset[Row] and Row is just a glorified Map[String, Any] and Dataframe basically just has a bunch of additional syntax tacked to it. Personally I'd recomment def lastTimeByUser[T <: Event](events: Dataset[T]): Dataset[T] = ??? because that will not lose you type information when calling the function with a Dataset[UserEvent]

            – Dominic Egger
            Nov 15 '18 at 8:17













          • @DominicEgger, yes you're right - I have updated the answer. I also provided information about transforming a Dataframe into a Dataset for clarification. Please feel free to edit it if you believe you can be more consince than me.

            – Luis Miguel Mejía Suárez
            Nov 15 '18 at 14:57


















          2














          You can make something like this:



          sealed trait Event {
          
  def userId: String
          
  def eventTime: String
          
}
          

          
final case class UserEvent(userId: String, eventTime: String, otherField: String) extends Event
          

          
def lastTimeByUser[E <: Event, T](events: Dataset[E]): Dataset[T] = ???


          Edit



          If you're using a Dataframe, you can "cast" it to a Dataset[T] using the .as[T] method. (Where T is the case class you want to use for represent your data - must have the same fields of your Rows).

          Note, you will need a implicit Encoder[T] in the scope for that - the simplest way to provide it is import spark.implicits._, where spark is an instance of SparkSession.






          share|improve this answer


























          • This looks like just what I need. Can you explain what the syntax Dataset[Event] does? Is it some sort of templating ?

            – Vishaal Kalwani
            Nov 15 '18 at 0:23













          • @VishaalKalwani Yes, is just a normal Scala generics. It means is the method accepts as input a Dataset of Events.

            – Luis Miguel Mejía Suárez
            Nov 15 '18 at 0:40






          • 1





            To elaborate on this. Dataframe is a type alias for Dataset[Row] and Row is just a glorified Map[String, Any] and Dataframe basically just has a bunch of additional syntax tacked to it. Personally I'd recomment def lastTimeByUser[T <: Event](events: Dataset[T]): Dataset[T] = ??? because that will not lose you type information when calling the function with a Dataset[UserEvent]

            – Dominic Egger
            Nov 15 '18 at 8:17













          • @DominicEgger, yes you're right - I have updated the answer. I also provided information about transforming a Dataframe into a Dataset for clarification. Please feel free to edit it if you believe you can be more consince than me.

            – Luis Miguel Mejía Suárez
            Nov 15 '18 at 14:57
















          2












          2








          2







          You can make something like this:



          sealed trait Event {
          
  def userId: String
          
  def eventTime: String
          
}
          

          
final case class UserEvent(userId: String, eventTime: String, otherField: String) extends Event
          

          
def lastTimeByUser[E <: Event, T](events: Dataset[E]): Dataset[T] = ???


          Edit



          If you're using a Dataframe, you can "cast" it to a Dataset[T] using the .as[T] method. (Where T is the case class you want to use for represent your data - must have the same fields of your Rows).

          Note, you will need a implicit Encoder[T] in the scope for that - the simplest way to provide it is import spark.implicits._, where spark is an instance of SparkSession.






          share|improve this answer















          You can make something like this:



          sealed trait Event {
          
  def userId: String
          
  def eventTime: String
          
}
          

          
final case class UserEvent(userId: String, eventTime: String, otherField: String) extends Event
          

          
def lastTimeByUser[E <: Event, T](events: Dataset[E]): Dataset[T] = ???


          Edit



          If you're using a Dataframe, you can "cast" it to a Dataset[T] using the .as[T] method. (Where T is the case class you want to use for represent your data - must have the same fields of your Rows).

          Note, you will need a implicit Encoder[T] in the scope for that - the simplest way to provide it is import spark.implicits._, where spark is an instance of SparkSession.







          share|improve this answer














          share|improve this answer



          share|improve this answer








          edited Nov 15 '18 at 14:54

























          answered Nov 15 '18 at 0:16









          Luis Miguel Mejía SuárezLuis Miguel Mejía Suárez

          2,6361822




          2,6361822













          • This looks like just what I need. Can you explain what the syntax Dataset[Event] does? Is it some sort of templating ?

            – Vishaal Kalwani
            Nov 15 '18 at 0:23













          • @VishaalKalwani Yes, is just a normal Scala generics. It means is the method accepts as input a Dataset of Events.

            – Luis Miguel Mejía Suárez
            Nov 15 '18 at 0:40






          • 1





            To elaborate on this. Dataframe is a type alias for Dataset[Row] and Row is just a glorified Map[String, Any] and Dataframe basically just has a bunch of additional syntax tacked to it. Personally I'd recomment def lastTimeByUser[T <: Event](events: Dataset[T]): Dataset[T] = ??? because that will not lose you type information when calling the function with a Dataset[UserEvent]

            – Dominic Egger
            Nov 15 '18 at 8:17













          • @DominicEgger, yes you're right - I have updated the answer. I also provided information about transforming a Dataframe into a Dataset for clarification. Please feel free to edit it if you believe you can be more consince than me.

            – Luis Miguel Mejía Suárez
            Nov 15 '18 at 14:57





















          • This looks like just what I need. Can you explain what the syntax Dataset[Event] does? Is it some sort of templating ?

            – Vishaal Kalwani
            Nov 15 '18 at 0:23













          • @VishaalKalwani Yes, is just a normal Scala generics. It means is the method accepts as input a Dataset of Events.

            – Luis Miguel Mejía Suárez
            Nov 15 '18 at 0:40






          • 1





            To elaborate on this. Dataframe is a type alias for Dataset[Row] and Row is just a glorified Map[String, Any] and Dataframe basically just has a bunch of additional syntax tacked to it. Personally I'd recomment def lastTimeByUser[T <: Event](events: Dataset[T]): Dataset[T] = ??? because that will not lose you type information when calling the function with a Dataset[UserEvent]

            – Dominic Egger
            Nov 15 '18 at 8:17













          • @DominicEgger, yes you're right - I have updated the answer. I also provided information about transforming a Dataframe into a Dataset for clarification. Please feel free to edit it if you believe you can be more consince than me.

            – Luis Miguel Mejía Suárez
            Nov 15 '18 at 14:57



















          This looks like just what I need. Can you explain what the syntax Dataset[Event] does? Is it some sort of templating ?

          – Vishaal Kalwani
          Nov 15 '18 at 0:23







          This looks like just what I need. Can you explain what the syntax Dataset[Event] does? Is it some sort of templating ?

          – Vishaal Kalwani
          Nov 15 '18 at 0:23















          @VishaalKalwani Yes, is just a normal Scala generics. It means is the method accepts as input a Dataset of Events.

          – Luis Miguel Mejía Suárez
          Nov 15 '18 at 0:40





          @VishaalKalwani Yes, is just a normal Scala generics. It means is the method accepts as input a Dataset of Events.

          – Luis Miguel Mejía Suárez
          Nov 15 '18 at 0:40




          1




          1





          To elaborate on this. Dataframe is a type alias for Dataset[Row] and Row is just a glorified Map[String, Any] and Dataframe basically just has a bunch of additional syntax tacked to it. Personally I'd recomment def lastTimeByUser[T <: Event](events: Dataset[T]): Dataset[T] = ??? because that will not lose you type information when calling the function with a Dataset[UserEvent]

          – Dominic Egger
          Nov 15 '18 at 8:17







          To elaborate on this. Dataframe is a type alias for Dataset[Row] and Row is just a glorified Map[String, Any] and Dataframe basically just has a bunch of additional syntax tacked to it. Personally I'd recomment def lastTimeByUser[T <: Event](events: Dataset[T]): Dataset[T] = ??? because that will not lose you type information when calling the function with a Dataset[UserEvent]

          – Dominic Egger
          Nov 15 '18 at 8:17















          @DominicEgger, yes you're right - I have updated the answer. I also provided information about transforming a Dataframe into a Dataset for clarification. Please feel free to edit it if you believe you can be more consince than me.

          – Luis Miguel Mejía Suárez
          Nov 15 '18 at 14:57







          @DominicEgger, yes you're right - I have updated the answer. I also provided information about transforming a Dataframe into a Dataset for clarification. Please feel free to edit it if you believe you can be more consince than me.

          – Luis Miguel Mejía Suárez
          Nov 15 '18 at 14:57






















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