Spark 2 Coalesce Multiple Columns at once
0
I'm trying to upsert one dataframe into another.
scala> addressOrigRenamed.show
+--------------+----------------------+-----------+-----------+
|orig_contactid|orig_contactaddresskey|orig_valueA|orig_valueB|
+--------------+----------------------+-----------+-----------+
| 1| 1| 54| 3|
| 1| 2| 55| 7|
+--------------+----------------------+-----------+-----------+
scala> dfNew.show
+---------+-----------------+------+------+
|contactId|contactaddresskey|valueA|valueB|
+---------+-----------------+------+------+
| 1| 2| 10| 9|
+---------+-----------------+------+------+
scala> val endDF = addressOrigRenamed.join(dfNew, $"orig_contactid" === $"contactid" && $"orig_contactaddresskey" === "$contactaddresskey", "fullouter").select(coalesce($"contactid", $"orig_contactid").alias("contactid"), coalesce($"contactaddresskey", $"orig_contactaddresskey").alias("contactaddresskey"), coalesce($"valueA", $"orig_valueA").alias("valueA"), coalesce($"valueB", $"orig_valueB").alias("valueB"))
scala> endDF.show
+---------+-----------------+------+------+
|contactid|contactaddresskey|valueA|valueB|
+---------+-----------------+------+------+
| 1| 1| 54| 3|
| 1| 2| 10| 9|
+---------+-----------------+------+------+
As you can see, this works. But the syntax is horrible. This is only a test, and I'll need to coalesce 15-20 columns. Writing coalesce(....).alias(...) 15-20 is really a terrible option. How can I write this better?
scala apache-spark
asked Nov 13 '18 at 16:45
haadukenhaaduken
47749
add a comment |
0
I'm trying to upsert one dataframe into another.
scala> addressOrigRenamed.show
+--------------+----------------------+-----------+-----------+
|orig_contactid|orig_contactaddresskey|orig_valueA|orig_valueB|
+--------------+----------------------+-----------+-----------+
| 1| 1| 54| 3|
| 1| 2| 55| 7|
+--------------+----------------------+-----------+-----------+
scala> dfNew.show
+---------+-----------------+------+------+
|contactId|contactaddresskey|valueA|valueB|
+---------+-----------------+------+------+
| 1| 2| 10| 9|
+---------+-----------------+------+------+
scala> val endDF = addressOrigRenamed.join(dfNew, $"orig_contactid" === $"contactid" && $"orig_contactaddresskey" === "$contactaddresskey", "fullouter").select(coalesce($"contactid", $"orig_contactid").alias("contactid"), coalesce($"contactaddresskey", $"orig_contactaddresskey").alias("contactaddresskey"), coalesce($"valueA", $"orig_valueA").alias("valueA"), coalesce($"valueB", $"orig_valueB").alias("valueB"))
scala> endDF.show
+---------+-----------------+------+------+
|contactid|contactaddresskey|valueA|valueB|
+---------+-----------------+------+------+
| 1| 1| 54| 3|
| 1| 2| 10| 9|
+---------+-----------------+------+------+
As you can see, this works. But the syntax is horrible. This is only a test, and I'll need to coalesce 15-20 columns. Writing coalesce(....).alias(...) 15-20 is really a terrible option. How can I write this better?
scala apache-spark
asked Nov 13 '18 at 16:45
haadukenhaaduken
47749
add a comment |
0
0
0
I'm trying to upsert one dataframe into another.
scala> addressOrigRenamed.show
+--------------+----------------------+-----------+-----------+
|orig_contactid|orig_contactaddresskey|orig_valueA|orig_valueB|
+--------------+----------------------+-----------+-----------+
| 1| 1| 54| 3|
| 1| 2| 55| 7|
+--------------+----------------------+-----------+-----------+
scala> dfNew.show
+---------+-----------------+------+------+
|contactId|contactaddresskey|valueA|valueB|
+---------+-----------------+------+------+
| 1| 2| 10| 9|
+---------+-----------------+------+------+
scala> val endDF = addressOrigRenamed.join(dfNew, $"orig_contactid" === $"contactid" && $"orig_contactaddresskey" === "$contactaddresskey", "fullouter").select(coalesce($"contactid", $"orig_contactid").alias("contactid"), coalesce($"contactaddresskey", $"orig_contactaddresskey").alias("contactaddresskey"), coalesce($"valueA", $"orig_valueA").alias("valueA"), coalesce($"valueB", $"orig_valueB").alias("valueB"))
scala> endDF.show
+---------+-----------------+------+------+
|contactid|contactaddresskey|valueA|valueB|
+---------+-----------------+------+------+
| 1| 1| 54| 3|
| 1| 2| 10| 9|
+---------+-----------------+------+------+
As you can see, this works. But the syntax is horrible. This is only a test, and I'll need to coalesce 15-20 columns. Writing coalesce(....).alias(...) 15-20 is really a terrible option. How can I write this better?
scala apache-spark
asked Nov 13 '18 at 16:45
haadukenhaaduken
47749
I'm trying to upsert one dataframe into another.
scala> addressOrigRenamed.show
+--------------+----------------------+-----------+-----------+
|orig_contactid|orig_contactaddresskey|orig_valueA|orig_valueB|
+--------------+----------------------+-----------+-----------+
| 1| 1| 54| 3|
| 1| 2| 55| 7|
+--------------+----------------------+-----------+-----------+
scala> dfNew.show
+---------+-----------------+------+------+
|contactId|contactaddresskey|valueA|valueB|
+---------+-----------------+------+------+
| 1| 2| 10| 9|
+---------+-----------------+------+------+
scala> val endDF = addressOrigRenamed.join(dfNew, $"orig_contactid" === $"contactid" && $"orig_contactaddresskey" === "$contactaddresskey", "fullouter").select(coalesce($"contactid", $"orig_contactid").alias("contactid"), coalesce($"contactaddresskey", $"orig_contactaddresskey").alias("contactaddresskey"), coalesce($"valueA", $"orig_valueA").alias("valueA"), coalesce($"valueB", $"orig_valueB").alias("valueB"))
scala> endDF.show
+---------+-----------------+------+------+
|contactid|contactaddresskey|valueA|valueB|
+---------+-----------------+------+------+
| 1| 1| 54| 3|
| 1| 2| 10| 9|
+---------+-----------------+------+------+
As you can see, this works. But the syntax is horrible. This is only a test, and I'll need to coalesce 15-20 columns. Writing coalesce(....).alias(...) 15-20 is really a terrible option. How can I write this better?
scala apache-spark
scala apache-spark
asked Nov 13 '18 at 16:45
haadukenhaaduken
47749
asked Nov 13 '18 at 16:45
haadukenhaaduken
47749
asked Nov 13 '18 at 16:45
haadukenhaaduken
47749
asked Nov 13 '18 at 16:45
haadukenhaaduken
47749
asked Nov 13 '18 at 16:45
haadukenhaaduken
47749
47749
add a comment |
add a comment |
1 Answer
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It is possible to create an array of coalesce functions:
scala> val joinedDF = addressOrigRenamed.join(dfNew, $"orig_contactid" === $"contactid" && $"orig_contactaddresskey" === "$contactaddresskey", "fullouter")
scala> val arr = dfNew.columns.map(x => {
val y = "orig_" + x
coalesce(joinedDF.col(x), joinedDF.col(y)).alias(x)
})
And then you can select using this arr, keeping in mind to spread the arr's elements:
scala> joinedDF.select(arr:_*).show
+---------+-----------------+------+------+
|contactId|contactaddresskey|valueA|valueB|
+---------+-----------------+------+------+
| 1| 1| 54| 3|
| 1| 2| 10| 9|
+---------+-----------------+------+------+
answered Nov 13 '18 at 18:56
haadukenhaaduken
47749
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
It is possible to create an array of coalesce functions:
scala> val joinedDF = addressOrigRenamed.join(dfNew, $"orig_contactid" === $"contactid" && $"orig_contactaddresskey" === "$contactaddresskey", "fullouter")
scala> val arr = dfNew.columns.map(x => {
val y = "orig_" + x
coalesce(joinedDF.col(x), joinedDF.col(y)).alias(x)
})
And then you can select using this arr, keeping in mind to spread the arr's elements:
scala> joinedDF.select(arr:_*).show
+---------+-----------------+------+------+
|contactId|contactaddresskey|valueA|valueB|
+---------+-----------------+------+------+
| 1| 1| 54| 3|
| 1| 2| 10| 9|
+---------+-----------------+------+------+
answered Nov 13 '18 at 18:56
haadukenhaaduken
47749
add a comment |
0
It is possible to create an array of coalesce functions:
scala> val joinedDF = addressOrigRenamed.join(dfNew, $"orig_contactid" === $"contactid" && $"orig_contactaddresskey" === "$contactaddresskey", "fullouter")
scala> val arr = dfNew.columns.map(x => {
val y = "orig_" + x
coalesce(joinedDF.col(x), joinedDF.col(y)).alias(x)
})
And then you can select using this arr, keeping in mind to spread the arr's elements:
scala> joinedDF.select(arr:_*).show
+---------+-----------------+------+------+
|contactId|contactaddresskey|valueA|valueB|
+---------+-----------------+------+------+
| 1| 1| 54| 3|
| 1| 2| 10| 9|
+---------+-----------------+------+------+
answered Nov 13 '18 at 18:56
haadukenhaaduken
47749
add a comment |
0
0
0
It is possible to create an array of coalesce functions:
scala> val joinedDF = addressOrigRenamed.join(dfNew, $"orig_contactid" === $"contactid" && $"orig_contactaddresskey" === "$contactaddresskey", "fullouter")
scala> val arr = dfNew.columns.map(x => {
val y = "orig_" + x
coalesce(joinedDF.col(x), joinedDF.col(y)).alias(x)
})
And then you can select using this arr, keeping in mind to spread the arr's elements:
scala> joinedDF.select(arr:_*).show
+---------+-----------------+------+------+
|contactId|contactaddresskey|valueA|valueB|
+---------+-----------------+------+------+
| 1| 1| 54| 3|
| 1| 2| 10| 9|
+---------+-----------------+------+------+
answered Nov 13 '18 at 18:56
haadukenhaaduken
47749
It is possible to create an array of coalesce functions:
scala> val joinedDF = addressOrigRenamed.join(dfNew, $"orig_contactid" === $"contactid" && $"orig_contactaddresskey" === "$contactaddresskey", "fullouter")
scala> val arr = dfNew.columns.map(x => {
val y = "orig_" + x
coalesce(joinedDF.col(x), joinedDF.col(y)).alias(x)
})
And then you can select using this arr, keeping in mind to spread the arr's elements:
scala> joinedDF.select(arr:_*).show
+---------+-----------------+------+------+
|contactId|contactaddresskey|valueA|valueB|
+---------+-----------------+------+------+
| 1| 1| 54| 3|
| 1| 2| 10| 9|
+---------+-----------------+------+------+
answered Nov 13 '18 at 18:56
haadukenhaaduken
47749
answered Nov 13 '18 at 18:56
haadukenhaaduken
47749
answered Nov 13 '18 at 18:56
haadukenhaaduken
47749
answered Nov 13 '18 at 18:56
haadukenhaaduken
47749
47749
add a comment |
add a comment |
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