An Apache Spark Join including null keys-CodePudding

My objectifs is to join two dataframes, have infomations from both, despite the fact that I can have nulls in my join keys. These are my two dataframes :

val data1 = Seq(
  (601, null, null, "8121000868-10", "CN88"),
  (3925, null, null, "8121000936-50",   "CN88")
)

val df1 = data1.toDF("id", "work_order_number", "work_order_item_number", "tally_number", "company_code")

val data2 = Seq(
  (null, null, "8121000868-10", "CN88", "popo"),
  (null, null, "8121000936-50", "CN88", "Smith")
)

val df2 = data2.toDF("work_order_number", "work_order_item_number", "tally_number", "company_code", "name")

Actually my objectif is to get the "id" from df1, rename it as "tally_summary_id" and be able to to re-attach some other informations to every single id. This is my code :

val final_df =
  df1.select(col("id").alias("tally_summary_id"), col("work_order_number"), col("work_order_item_number"),
    col("tally_number"), col("company_code"))
  .join(df2, Seq("tally_number", "work_order_number", "work_order_item_number", "company_code"), "full")

A left join give me :

 ------------- ----------------- ---------------------- ------------ ---------------- ---- 
| tally_number|work_order_number|work_order_item_number|company_code|tally_summary_id|name|
 ------------- ----------------- ---------------------- ------------ ---------------- ---- 
|8121000868-10|             null|                  null|        CN88|             601|null|
|8121000936-50|             null|                  null|        CN88|            3925|null|
 ------------- ----------------- ---------------------- ------------ ---------------- ----

A right join give me :

 ------------- ----------------- ---------------------- ------------ ---------------- ----- 
| tally_number|work_order_number|work_order_item_number|company_code|tally_summary_id| name|
 ------------- ----------------- ---------------------- ------------ ---------------- ----- 
|8121000868-10|             null|                  null|        CN88|            null| popo|
|8121000936-50|             null|                  null|        CN88|            null|Smith|
 ------------- ----------------- ---------------------- ------------ ---------------- -----

A full join give me :

 ------------- ----------------- ---------------------- ------------ ---------------- ----- 
| tally_number|work_order_number|work_order_item_number|company_code|tally_summary_id| name|
 ------------- ----------------- ---------------------- ------------ ---------------- ----- 
|8121000868-10|             null|                  null|        CN88|             601| null|
|8121000868-10|             null|                  null|        CN88|            null| popo|
|8121000936-50|             null|                  null|        CN88|            3925| null|
|8121000936-50|             null|                  null|        CN88|            null|Smith|
 ------------- ----------------- ---------------------- ------------ ---------------- -----

What can i do to have something like this :

 ------------- ----------------- ---------------------- ------------ ---------------- ----- 
| tally_number|work_order_number|work_order_item_number|company_code|tally_summary_id| name|
 ------------- ----------------- ---------------------- ------------ ---------------- ----- 
|8121000868-10|             null|                  null|        CN88|             601|popo |
|8121000936-50|             null|                  null|        CN88|            3925|Smith|
 ------------- ----------------- ---------------------- ------------ ---------------- -----

CodePudding user response：

You can use the <=> equality operator which is null safe as shown here.

I added a schema to the dataframe creation as it seemed that without it the auto schema inference didn't give a type to the columns with only nulls and the join failed.

The resulting dataframe is exactly the one you wanted

import scala.collection.JavaConversions._

val data1 = Seq(
  Row(601, null, null, "8121000868-10", "CN88"),
  Row(3925, null, null, "8121000936-50", "CN88")
)

val schema1 = StructType(List(
  StructField("id", IntegerType, false),
  StructField("work_order_number", StringType, true),
  StructField("work_order_item_number", StringType, true),
  StructField("tally_number", StringType, true),
  StructField("company_code", StringType, true)
))

val df1 = sparkSession.createDataFrame(data1, schema1)

val data2 = Seq(
  Row(null, null, "8121000868-10", "CN88", "popo"),
  Row(null, null, "8121000936-50", "CN88", "Smith")
)

val schema2 = StructType(Seq(
  StructField("work_order_number", StringType, true),
  StructField("work_order_item_number", StringType, true),
  StructField("tally_number", StringType, true),
  StructField("company_code", StringType, true),
  StructField("name", StringType, false)
))

val df2 = sparkSession.createDataFrame(data2, schema2)


val final_df =
  df1.join(df2, df1("tally_number") <=> df2("tally_number")
      && df1("work_order_number") <=> df2("work_order_number")
      && df1("work_order_item_number") <=> df2("work_order_item_number")
      && df1("company_code") <=> df2("company_code")
      , "inner")
    .select(df1("tally_number"),
      df1("work_order_number"),
      df1("work_order_item_number"),
      df1("company_code"),
      df1("id").as("tally_summary_id"),
      df2("name"))