Hi Guys,
I am trying to use SparkSQL to convert an RDD to SchemaRDD so that I can
save it in parquet format.
A record in my RDD has the following format:
RDD1
{
field1:5,
field2: 'string',
field3: {'a':1, 'c':2}
}
I am using field3 to represent a "sparse vector" and it can have keys:
'a','b' or 'c' and values any int value
The current approach I am using is :
schemaRDD1 = sqc.jsonRDD(RDD1.map(lambda x: simplejson.dumps(x)))
But when I do this, the dictionary in field 3 also gets converted to a
SparkSQL Row. This converts field3 to be a dense data structure where it
holds value None for every key that is not present in the field 3 for each
record.
When I do
test = RDD1.map(lambda x: simplejson.dumps(x))
test.first()
my output is: {"field1": 5, "field2":"string", "field3" :{"a":1,"c":2}}
But then when I do
schemaRDD = sqc.jsonRDD(test)
schemaRDD.first()
my output is : Row( field1=5, field2='string', field3 = Row(a=1,b=None,c=2))
in realty, I have 1000s of probable keys in field 3 and only 2 to 3 of them
occur per record. So When tic converts to a Row, it generates thousands of
None fields per record.
Is there anyways for me to store "field3" as a dictionary instead of
converting it into a Row in the schemaRDD??
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