CODEWITHSUNDEEP
mongodbapache-sparkpyspark
2713
2713

Reputation: 205

Creating hierarchical JSON in Spark

I have a spark dataframe which I need to write to MongoDB. I wanted to know how can I write some of the columns of the dataframe as nested/hierarchical JSON in mongoDB. Lets say the dataframe has 6 columns, col1, col2,.....col5, col6 I would want col1, col2, col3 as 1st hierarchy and the rest columns col4 to col6 as the 2nd hierarchy. something like this,

{
    "col1": 123,
    "col2": "abc",
    "col3": 45,
    "fields": {
        "col4": "ert",
        "col5": 45,
        "col6": 56
    }
}

how do I achieve this in pyspark?

Upvotes: 2

Views: 619

Answers (1)

notNull
notNull

Reputation: 31540

Use to_json + struct in built function for this case.

Example:

df.show()                                                                                                         
#+----+----+----+----+----+----+
#|col1|col2|col3|col4|col5|col6|
#+----+----+----+----+----+----+
#| 123| abc|  45| ert|  45|  56|
#+----+----+----+----+----+----+

from pyspark.sql.functions import *
df.withColumn("jsn",to_json(struct("col1","col2","col3",struct("col4","col5","col6").alias("fields")))).show(10,False)
#+----+----+----+----+----+----+---------------------------------------------------------------------------------------+
#|col1|col2|col3|col4|col5|col6|jsn                                                                                    |
#+----+----+----+----+----+----+---------------------------------------------------------------------------------------+
#|123 |abc |45  |ert |45  |56  |{"col1":"123","col2":"abc","col3":"45","fields":{"col4":"ert","col5":"45","col6":"56"}}|
#+----+----+----+----+----+----+---------------------------------------------------------------------------------------+

cols=df.columns

df.withColumn("jsn",to_json(struct("col1","col2","col3",struct("col4","col5","col6").alias("fields")))).drop(*cols).show(10,False)
#+---------------------------------------------------------------------------------------+
#|jsn                                                                                    |
#+---------------------------------------------------------------------------------------+
#|{"col1":"123","col2":"abc","col3":"45","fields":{"col4":"ert","col5":"45","col6":"56"}}|
#+---------------------------------------------------------------------------------------+

#using toJSON
df.withColumn("jsn",struct("col1","col2","col3",struct("col4","col5","col6").alias("fields"))).drop(*cols).toJSON().collect()
#[u'{"jsn":{"col1":"123","col2":"abc","col3":"45","fields":{"col4":"ert","col5":"45","col6":"56"}}}']

#to write as json file
df.withColumn("jsn",struct("col1","col2","col3",struct("col4","col5","col6").alias("fields"))).\
drop(*cols).\
write.\
format("json").\
save("<path>")

Update:

jsn column Represented as json struct

df.withColumn("jsn",struct("col1","col2","col3",struct("col4","col5","col6").alias("fields"))).drop(*cols).printSchema()
#root
# |-- jsn: struct (nullable = false)
# |    |-- col1: string (nullable = true)
# |    |-- col2: string (nullable = true)
# |    |-- col3: string (nullable = true)
# |    |-- fields: struct (nullable = false)
# |    |    |-- col4: string (nullable = true)
# |    |    |-- col5: string (nullable = true)
# |    |    |-- col6: string (nullable = true)

Upvotes: 1

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