TL;DR

This post shows you how to transform ByteType column in Spark dataframe into complex StructType.

Problem

After running some preprocessing on streamed tweets, I stored the proprocessed results into apache parquet format. Then, somehow, parquet file automatically stores string column into bytearray. Well, it is pretty easy to cast byte array into string using astype function. However, it becomes tricky for colume with structured information,e.g., the bounding_box column, which contains a json string like this:

{
"bounding_box": {
u"coordinates": [
[
[
-74.026675,
40.683935
],
[
-74.026675,
40.877483
],
[
-73.910408,
40.877483
],
[
-73.910408,
40.3935
]
]
],
u"type": "Polygon"
}
}


The ideal scenario would be that spark.read.parquet function can automatically recognize the structure of information in the json string. Obviously, not so fast!

Solution

The extra u have to removed from the decoded result first:

temp_sdf=temp_sdf.withColumn('bbox_str',temp_sdf.bounding_box.astype('string'))
temp_sdf=temp_sdf.withColumn('coords',func.regexp_replace('bbox_str','u',""))


Then, we have to specify the struct type manually in order to let the reader recognize the information in the json string:

schema = StructType([
StructField("type", StringType(), True),
StructField("coordinates", ArrayType(ArrayType(ArrayType(FloatType()))),
True),
])
temp_sdf=temp_sdf.withColumn('bbox',func.from_json('coords',schema))



Check out the jupyter notebook for this post.