I have the below spark dataframe/dataset.
column_1 column_2 column_3 column_4
A,B NameA,NameB F NameF
C NameC NULL NULL
NULL NULL D,E NameD,NULL
G NULL H NameH
I NameI J NULL
All the above 4 columns are comma delimited. I have to convert this into a new dataframe/dataset which has only 2 columns and without any comma delimiters. The value in column_1 and its corresponding name in Column_2 should be written to output. Similarly for column_3 and column_4. If both are column_1 and column_2 are null, they are not required in output.
Expected output:
out_column_1 out_column_2
A NameA
B NameB
F NameF
C NameC
D NameD
E NULL
G NULL
H NameH
I NameI
J NULL
Is there a way to achieve this in Java spark without using UDF's?
Scala solution - I think should work in Java. Basically just handle col1, col2 separately from col3, col4, and union the results. Lots of wrangling with arrays.
// maybe replace this with Dataset<Row> result = ... in Java
val result = df.select(
split(col("column_1"), ",").alias("column_1"),
split(col("column_2"), ",").alias("column_2")
).filter(
"column_1 is not null"
).select(
explode(
arrays_zip(
col("column_1"),
coalesce(col("column_2"), array(lit(null)))
)
)
).select(
"col.*"
).union(
df.select(
split(col("column_3"), ",").alias("column_3"),
split(col("column_4"), ",").alias("column_4")
).filter(
"column_3 is not null"
).select(
explode(
arrays_zip(
col("column_3"),
coalesce(col("column_4"), array(lit(null)))
)
)
).select("col.*")
).toDF(
"out_column_1", "out_column_2"
)
result.show
+------------+------------+
|out_column_1|out_column_2|
+------------+------------+
| A| NameA|
| B| NameB|
| C| NameC|
| G| null|
| I| NameI|
| F| NameF|
| D| NameD|
| E| null|
| H| NameH|
| J| null|
+------------+------------+
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