PySpark “explode” dict in column

Question

I have a column 'true_recoms' in spark dataframe:

-RECORD 17----------------------------------------------------------------- 
item        | 20380109                                                                                                                                                                  
true_recoms | {"5556867":1,"5801144":5,"7397596":21}          

I need to 'explode' this column to get something like this:

item        | 20380109                                                                                                                                                                  
recom_item  | 5556867
recom_cnt   | 1
..............
item        | 20380109                                                                                                                                                                  
recom_item  | 5801144
recom_cnt   | 5
..............
item        | 20380109                                                                                                                                                                  
recom_item  | 7397596
recom_cnt   | 21

I've tried to use from_json but its doesnt work:

    schema_json = StructType(fields=[
        StructField("item", StringType()),
        StructField("recoms", StringType())
    ])
    df.select(col("true_recoms"),from_json(col("true_recoms"), schema_json)).show(5)

+--------+--------------------+------+
|    item|         true_recoms|true_r|
+--------+--------------------+------+
|31746548|{"32731749":3,"31...|   [,]|
|17359322|{"17359392":1,"17...|   [,]|
|31480894|{"31480598":1,"31...|   [,]|
| 7265665|{"7265891":1,"503...|   [,]|
|31350949|{"32218698":1,"31...|   [,]|
+--------+--------------------+------+
only showing top 5 rows

Answer 1

The schema is incorrectly defined. You declare to be as struct with two string fields

• item
• recoms

while neither field is present in the document.

Unfortunately from_json can take return only structs or array of structs so redefining it as

MapType(StringType(), LongType())

is not an option.

Personally I would use an udf

from pyspark.sql.functions import udf, explode
import json

@udf("map<string, bigint>")
def parse(s):
    try:
        return json.loads(s)
    except json.JSONDecodeError:
        pass 

which can be applied like this

df = spark.createDataFrame(
    [(31746548, """{"5556867":1,"5801144":5,"7397596":21}""")],
    ("item", "true_recoms")
)

df.select("item",  explode(parse("true_recoms")).alias("recom_item", "recom_cnt")).show()
# +--------+----------+---------+
# |    item|recom_item|recom_cnt|
# +--------+----------+---------+
# |31746548|   5801144|        5|
# |31746548|   7397596|       21|
# |31746548|   5556867|        1|
# +--------+----------+---------+