INSERT IF NOT EXISTS ELSE UPDATE in Spark SQL

In regular Spark this could be achieved with a join followed by a map like this:

import spark.implicits._
val df1 = spark.sparkContext.parallelize(List(("id1", "orginal"), ("id2", "original"))).toDF("df1_id", "df1_status")
val df2 = spark.sparkContext.parallelize(List(("id1", "new"), ("id3","new"))).toDF("df2_id", "df2_status")

val df3 = df1
  .join(df2, 'df1_id === 'df2_id, "outer")
  .map(row => {
    if (row.isNullAt(2))
      (row.getString(0), row.getString(1))
    else
      (row.getString(2), row.getString(3))
  })

This yields:

scala> df3.show
+---+--------+
| _1|      _2|
+---+--------+
|id3|     new| 
|id1|     new|
|id2|original|
+---+--------+

You could also use select with udfs instead of map, but in this particular case with null-values, I personally prefer the map variant.

you can use spark sql like this :

select * from (select c.*, row_number() over (partition by tac  order by tag desc) as 
    TAG_NUM from (
    select 
         a.tac
        ,a.name
        ,0 as tag
    from tableA a
    union all
    select 
        b.tac
        ,b.name
         ,1 as tag
    from tableB b) c ) d where TAG_NUM=1

tac is column you want to insert/update by.

I know it's a bit late to share my code, but to add or update my database, i did a fuction that looks like this :

import pandas as pd

#Returns a spark dataframe with added and updated datas
#key parameter is the primary key of the dataframes
#The two parameters dfToUpdate and dfToAddAndUpdate are spark dataframes
def AddOrUpdateDf(dfToUpdate,dfToAddAndUpdate,key):
    #Cast the spark dataframe dfToUpdate to pandas dataframe
    dfToUpdatePandas = dfToUpdate.toPandas()

    #Cast the spark dataframe dfToAddAndUpdate to pandas dataframe
    dfToAddAndUpdatePandas = dfToAddAndUpdate.toPandas()

    #Update the table records with the latest records, and adding new records if there are new records.
    AddOrUpdatePandasDf = pd.concat([dfToUpdatePandas,dfToAddAndUpdatePandas]).drop_duplicates([key], keep = 'last').sort_values(key)

    #Cast back to get a spark dataframe
    AddOrUpdateDf = spark.createDataFrame(AddOrUpdatePandasDf)

    return AddOrUpdateDf

As you can see, we need to cast the spark dataframes to pandas dataframe to be able to do the pd.concat and especially the drop_duplicates with the "keep = 'last'", then we cast back to spark dataframe and return it. I don't think this is the best way to handle the AddOrUpdate, but at least, it works.