I have a data set :
+-----+-------------------+---------------------+------------------+
|query|similar_queries |model_score |count |
+-----+-------------------+---------------------+------------------+
|shirt|funny shirt |0.0034038130658784866|189.0 |
|shirt|shirt womens |0.0019435265241921438|136.0 |
|shirt|watch |0.001097496453284101 |212.0 |
|shirt|necklace |6.694577024597908E-4 |151.0 |
|shirt|white shirt |0.0037413097560623485|217.0 |
|shirt|shoes |0.0022062579255572733|575.0 |
|shirt|crop top |9.065831060804897E-4 |173.0 |
|shirt|polo shirts for men|0.007706416273211698 |349.0 |
|shirt|shorts |0.002669621942466027 |200.0 |
|shirt|black shirt |0.03264296242546658 |114.0 |
+-----+-------------------+---------------------+------------------+
I am ranking the data set based on "count" first.
lazy val countWindowByFreq = Window.partitionBy(col(QUERY)).orderBy(col(COUNT).desc)
val ranked_data = data.withColumn("count_rank", row_number over countWindowByFreq)
+-----+-------------------+---------------------+------------------+----------+
|query|similar_queries |model_score |count |count_rank|
+-----+-------------------+---------------------+------------------+----------+
|shirt|shoes |0.0022062579255572733|575.0 |1 |
|shirt|polo shirts for men|0.007706416273211698 |349.0 |2 |
|shirt|white shirt |0.0037413097560623485|217.0 |3 |
|shirt|watch |0.001097496453284101 |212.0 |4 |
|shirt|shorts |0.002669621942466027 |200.0 |5 |
|shirt|funny shirt |0.0034038130658784866|189.0 |6 |
|shirt|crop top |9.065831060804897E-4 |173.0 |7 |
|shirt|necklace |6.694577024597908E-4 |151.0 |8 |
|shirt|shirt womens |0.0019435265241921438|136.0 |9 |
|shirt|black shirt |0.03264296242546658 |114.0 |10 |
+-----+-------------------+---------------------+------------------+----------+
I am now trying to rank the content using a rolling window on row_number(4 rows) and rank within the window based on model_score. For e.g.:
In the first window, row_number 1 to 4, the new rank(new column) will be
1. polo shirts for men
2. white shirt
3. shoes
4. watch
In the first window, row_number 5 to 8, the new rank(new column) will be
5. funny shirt
6. shorts
7. shirt womens
8. crop top
In the first window, row_number 9 to rest, the new rank(new column) will be
9. black shirt
10. shirt womens
Can some one tell me if there how can I achieve with this spark and Scala ? Is there any pre-defined functions I can use ?
I tried :
lazy val MODEL_RANK = Window.partitionBy(col(QUERY)) .orderBy(col(MODEL_SCORE).desc).rowsBetween( 0, 3)
but this gives me :
sql.AnalysisException: Window Frame ROWS BETWEEN CURRENT ROW AND 3 FOLLOWING must match the required frame ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW;
Also, tried with .rowsBetween(-3, 0) but that also gives me error :
org.apache.spark.sql.AnalysisException: Window Frame ROWS BETWEEN 3 PRECEDING AND CURRENT ROW must match the required frame ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW;
Since you have count_rank computed, next step is to find a way to group the row in a set of fours. It can be done as follows:
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val ranked_data_grouped = ranked_data
.withColumn("bucket", (($"count_rank" -1)/4).cast(IntegerType))
ranked_data_grouped will look like:
+-----+-------------------+---------------------+------------------+----------+-------+
|query|similar_queries |model_score |count |count_rank|bucket |
+-----+-------------------+---------------------+------------------+----------+-------+
|shirt|shoes |0.0022062579255572733|575.0 |1 |0 |
|shirt|polo shirts for men|0.007706416273211698 |349.0 |2 |0 |
|shirt|white shirt |0.0037413097560623485|217.0 |3 |0 |
|shirt|watch |0.001097496453284101 |212.0 |4 |0 |
|shirt|shorts |0.002669621942466027 |200.0 |5 |1 |
|shirt|funny shirt |0.0034038130658784866|189.0 |6 |1 |
|shirt|crop top |9.065831060804897E-4 |173.0 |7 |1 |
|shirt|necklace |6.694577024597908E-4 |151.0 |8 |1 |
|shirt|shirt womens |0.0019435265241921438|136.0 |9 |2 |
|shirt|black shirt |0.03264296242546658 |114.0 |10 |2 |
+-----+-------------------+---------------------+------------------+----------+-------+
Now, all you have to do is, partition by bucket & order by model_score:
val output = ranked_data_grouped
.withColumn("finalRank", row_number().over(Window.partitionBy($"bucket").orderBy($"model_score".desc)))