使用来自另一个数据集的值搜索和更新 Spark 数据集列

Question

Java 8 and Spark 2.11:2.3.2 here. Although I would greatly prefer Java API answers, I do speak a wee bit of Scala so I will be able to understand any answers provided in it! But Java if at all possible (please)!

I have two datasets with different schema, with the exception of a common " model_number " (string) column: that exists on both.

For each row in my first Dataset (we'll call that d1 ), I need to scan/search the second Dataset (" d2 ") to see if there is a row with the same model_number , and if so, update another d2 column.

Here are my Dataset schemas:

d1
===========
model_number : string
desc : string
fizz : string
buzz : date

d2
===========
model_number : string
price : double
source : string

So again, if a d1 row has a model_number of, say, 12345, and a d2 row also has the same model_number , I want to update the d2.price by multiplying it by 10.0 .

My best attempt thus far:

// I *think* this would give me a 3rd dataset with all d1 and d2 columns, but only
// containing rows from d1 and d2 that have matching 'model_number' values
Dataset<Row> d3 = d1.join(d2, d1.col("model_number") == d2.col("model_number"));

// now I just need to update d2.price based on matching
Dataset<Row> d4 = d3.withColumn("adjusted_price", d3.col("price") * 10.0);

Can anyone help me cross the finish line here? Thanks in advance!

Answer 1

Some points here, as @VamsiPrabhala mentioned in the comment, the function that you need to use is join on your specific fields. Regarding the " update ", you need to take in mind that df , ds and rdd in spark are immutable, so you can not update them. So, the solution here is, after join your df 's, you need to perform your calculation, in this case multiplication, in a select or using withColumn and then select . In other words, you can not update the column, but you can create the new df with the " new " column.

Example:

Input data:

+------------+------+------+----+
|model_number|  desc|  fizz|buzz|
+------------+------+------+----+
|     model_a|desc_a|fizz_a|null|
|     model_b|desc_b|fizz_b|null|
+------------+------+------+----+

+------------+-----+--------+
|model_number|price|  source|
+------------+-----+--------+
|     model_a| 10.0|source_a|
|     model_b| 20.0|source_b|
+------------+-----+--------+

using join will output:

val joinedDF = d1.join(d2, "model_number")
joinedDF.show()

+------------+------+------+----+-----+--------+
|model_number|  desc|  fizz|buzz|price|  source|
+------------+------+------+----+-----+--------+
|     model_a|desc_a|fizz_a|null| 10.0|source_a|
|     model_b|desc_b|fizz_b|null| 20.0|source_b|
+------------+------+------+----+-----+--------+

applying your calculation:

joinedDF.withColumn("price", col("price") * 10).show()

output:
+------------+------+------+----+-----+--------+
|model_number|  desc|  fizz|buzz|price|  source|
+------------+------+------+----+-----+--------+
|     model_a|desc_a|fizz_a|null| 100.0|source_a|
|     model_b|desc_b|fizz_b|null| 200.0|source_b|
+------------+------+------+----+-----+--------+