Spark Df, Flatten Df
Iterating through nested fields in spark DF Question by Vinit Pandey May 31, 2018 at 02:56 PM Spark spark-sql spark2 sparksql dataframe I have a dataframe with following schema.
I would like to flatten JSON blobs into a Data Frame using Spark/Spark SQl inside Spark-Shell.
val df = spark.sql('select body from test limit 3'); // body is a json encoded blob column
val df2 = df.select(df('body').cast(StringType).as('body'))
when I do
df2.show // shows the 3 rows
body
{'k1': 'v1', 'k2': 'v2' }
{'k3': 'v3'}
{'k4': 'v4', 'k5': 'v5', 'k6': 'v6'}
Now say I have billion of these rows/records but at most there will 5 different json schemas for all billion rows. Now how do I flatten such that I get a data frame in the format below? Should I use df.forEach or df.forEachPartition or df.explode or df.flatMap? How do I make sure I am not creating a billion data frames and trying to union all of them or something more inefficient. It will be great if I could see a code sample. Also since this might have Nil I wonder if they would take up any space? I initially tried it with UDF's and I keep getting some exception saying 'Any' Type is not supported and so on. So an example on how to do this would help!
'K1' 'K2' 'K3' 'K4' 'K5' 'K6'
'V1' 'V2' 'V3' 'V4' 'V5' 'V6'Needless to say, V1, V2, V3..V6 can be any type (String, boolean, integer, Map..). Also open to any new ideas.
Spark Df Flatten Df De
This seem to work
However I wonder if this any inefficiency here ? since I have to apply this function for billion rows.
Michael ArmbrustNov 21 (1 day ago)
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to kant, userIn Spark 2.1 we've added a from_json function that I think will do what you want.