用下面的代码替换你最后的代码部分.此代码将执行 left join 并计算日期差 calc_date &rate_date.下一个 Window 函数，我们将选择最近的日期并使用您的计算方法计算 prev_sales.

请注意，我添加了一个过滤条件 filter(col("diff") >=0),它将处理 calc_date .我加了几个更多记录以更好地了解此案例.

scala>rateMetaDataDf.show+---------+----------+----------+-----------+|基本代码|rate_date|rate_value|target_code|+---------+----------+----------+-----------+|欧元|2019-05-10|1.130657|美元||欧元|2019-05-09|1.12088|美元||欧元|2019-12-20|1.1584|美元|+---------+----------+----------+-----------+标度>kafkaDf.show+---------+----+-------+-----+----+----------+-------+-----------+|companyId|年|季度|销售额|代码|calc_date|c_code|prev_sales|+---------+----+-------+-----+----+----------+-------+-----------+|15|2016|4|100.5|美元|2021-01-20|欧元|221.4||15|2016|4|100.5|美元|2019-06-20|欧元|221.4|+---------+----+-------+-----+----+----------+-------+-----------+标度>val W = Window.partitionBy("companyId","year","quarter","sales","code","calc_date","c_code","prev_sales").orderBy(col(差异"))标度>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(关闭日期", 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", 季度", sales", code", calc_date", prev_sales")标度>rateJoinResultDf.show+---------+----+-------+-----+----+----------+----------+|companyId|年|季度|销售额|代码|calc_date|prev_sales|+---------+----+-------+-----+----+----------+----------+|15|2016|4|100.5|美元|2021-01-20|256.46976||15|2016|4|100.5|美元|2019-06-20|250.32746|+---------+----+-------+-----+----+----------+----------+

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 currency sample as below :

val ratesMetaDataDf = Seq(
     ("EUR","5/10/2019","1.130657","USD"),
     ("EUR","5/9/2019","1.13088","USD")
     ).toDF("base_code", "rate_date","rate_value","target_code")
.withColumn("rate_date", to_date($"rate_date" ,"MM/dd/yyyy").cast(DateType))
.withColumn("rate_value", $"rate_value".cast(DoubleType))

Sales records which i received from kafka topic is , as (sample) below :

val kafkaDf = Seq((15,2016, 4, 100.5,"USD","2021-01-20","EUR",221.4)
                                ).toDF("companyId", "year","quarter","sales","code","calc_date","c_code","prev_sales")

To calculate "prev_sales" , I need get its "c_code" 's respective "rate_value" which is nearest to the "calc_date" i.e. rate_date"

Which i am doing as following

val w2 = Window.orderBy(col("rate_date") desc)
val rateJoinResultDf = kafkaDf.as("k").join(ratesMetaDataDf.as("e"))
                                   .where( ($"k.c_code" === $"e.base_code") &&
                                           ($"rate_date" < $"calc_date")
                                         ).orderBy($"rate_date" desc)
                                  .withColumn("row",row_number.over(w2))
                                  .where($"row" === 1).drop("row")
                                  .withColumn("prev_sales", (col("prev_sales") * col("rate_value")).cast(DoubleType))
                                  .select("companyId", "year","quarter","sales","code","calc_date","prev_sales")

In the above to get nearest record (i.e. "5/10/2019" from ratesMetaDataDf ) for given "rate_date" I am using window and row_number function and sorting the records by "desc".

But in the spark-sql streaming it is causing the error as below

"
Sorting is not supported on streaming DataFrames/Datasets, unless it is on aggregated DataFrame/Dataset in Complete output mode;;"

So how to fetch first record to join in the above.

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|
+---------+----+-------+-----+----+----------+----------+