pyspark hive sql convert array(map(varchar, varchar)) to string by rows - sql

Question

I would like to transform a column of
array(map(varchar, varchar))
to string as rows of a table on presto db by pyspark hive sql programmatically from jupyter notebook python3.
example
user_id sport_ids
'aca' [ {'sport_id': '5818'}, {'sport_id': '6712'}, {'sport_id': '1065'} ]
expected results
user_id. sport_ids
'aca'. '5815'
'aca'. '5712'
'aca'. '1065'
I have tried
sql_q= """
select distinct, user_id, transform(sport_ids, x -> element_at(x, 'sport_id')
from tab """
spark.sql(sql_q)
but got error:
'->' cannot be resolved
I have also tried
sql_q= """
select distinct, user_id, sport_ids
from tab"""
spark.sql(sql_q)
but got error:
org.apache.spark.sql.AnalysisException: Cannot have map type columns in DataFrame which calls set operations(intersect, except, etc.), but the type of column request_features[0] is map<string,string>;;
Did I miss something ?
I have tried this, but helpful
hive convert array<map<string, string>> to string
Extract map(varchar, array(varchar)) - Hive SQL
thanks

Answer 1

Lets try use higher order functions to find map values and explode into individual rows
df.withColumn('sport_ids', explode(expr("transform(sport_ids, x->map_values(x)[0])"))).show()
+-------+---------+
|user_id|sport_ids|
+-------+---------+
| aca| 5818|
| aca| 6712|
| aca| 1065|
+-------+---------+

Answer 2

You can process json data (json_parse, cast to array of json and json_extract_scalar - for more json functions - see here) and flatten (unnest) on presto side:
-- sample data
WITH dataset(user_id, sport_ids) AS (
VALUES
('aca', '[ {"sport_id": "5818"}, {"sport_id": "6712"}, {"sport_id": "1065"} ]')
)
-- query
select user_id,
json_extract_scalar(record, '$.sport_id') sport_id
from dataset,
unnest(cast(json_parse(sport_ids) as array(json))) as t(record)
Output:
user_id
sport_id
aca
5818
aca
6712
aca
1065