How to pick latest record in spark structured streaming join

Question

I am using spark-sql 2.4.x version , datastax-spark-cassandra-connector for Cassandra-3.x version. Along with kafka.

I have rates meta data of currenc

Answer 1

Replace your last code part with below code. This code will do left join and calculate date difference calc_date & rate_date. Next Window function we will pick nearest date and calculate prev_sales by using same your calculation.

Please note I have added one filter condition filter(col("diff") >=0), which will handle a case of calc_date < rate_date. I have added few more records for better understanding of this case.

scala> ratesMetaDataDf.show
+---------+----------+----------+-----------+
|base_code| rate_date|rate_value|target_code|
+---------+----------+----------+-----------+
|      EUR|2019-05-10|  1.130657|        USD|
|      EUR|2019-05-09|   1.12088|        USD|
|      EUR|2019-12-20|    1.1584|        USD|
+---------+----------+----------+-----------+


scala> kafkaDf.show
+---------+----+-------+-----+----+----------+------+----------+
|companyId|year|quarter|sales|code| calc_date|c_code|prev_sales|
+---------+----+-------+-----+----+----------+------+----------+
|       15|2016|      4|100.5| USD|2021-01-20|   EUR|     221.4|
|       15|2016|      4|100.5| USD|2019-06-20|   EUR|     221.4|
+---------+----+-------+-----+----+----------+------+----------+


scala>  val W = Window.partitionBy("companyId","year","quarter","sales","code","calc_date","c_code","prev_sales").orderBy(col("diff"))

scala>   val rateJoinResultDf= kafkaDf.alias("k").join(ratesMetaDataDf.alias("r"), col("k.c_code") === col("r.base_code"), "left")
                                         .withColumn("diff",datediff(col("calc_date"), col("rate_date")))
                                         .filter(col("diff") >= 0)
                                         .withColumn("closedate", row_number.over(W))
                                         .filter(col("closedate") === 1)
                                         .drop("diff", "closedate")
                                         .withColumn("prev_sales", (col("prev_sales") * col("rate_value")).cast("Decimal(14,5)"))
                                         .select("companyId", "year","quarter","sales","code","calc_date","prev_sales")

scala> rateJoinResultDf.show
+---------+----+-------+-----+----+----------+----------+
|companyId|year|quarter|sales|code| calc_date|prev_sales|
+---------+----+-------+-----+----+----------+----------+
|       15|2016|      4|100.5| USD|2021-01-20| 256.46976|
|       15|2016|      4|100.5| USD|2019-06-20| 250.32746|
+---------+----+-------+-----+----+----------+----------+