How to explode structs with pyspark explode() - json

Question

How do I convert the following JSON into the relational rows that follow it? The part that I am stuck on is the fact that the pyspark explode() function throws an exception due to a type mismatch. I have not found a way to coerce the data into a suitable format so that I can create rows out of each object within the source key within the sample_json object.
JSON INPUT
sample_json = """
{
"dc_id": "dc-101",
"source": {
"sensor-igauge": {
"id": 10,
"ip": "68.28.91.22",
"description": "Sensor attached to the container ceilings",
"temp":35,
"c02_level": 1475,
"geo": {"lat":38.00, "long":97.00}
},
"sensor-ipad": {
"id": 13,
"ip": "67.185.72.1",
"description": "Sensor ipad attached to carbon cylinders",
"temp": 34,
"c02_level": 1370,
"geo": {"lat":47.41, "long":-122.00}
},
"sensor-inest": {
"id": 8,
"ip": "208.109.163.218",
"description": "Sensor attached to the factory ceilings",
"temp": 40,
"c02_level": 1346,
"geo": {"lat":33.61, "long":-111.89}
},
"sensor-istick": {
"id": 5,
"ip": "204.116.105.67",
"description": "Sensor embedded in exhaust pipes in the ceilings",
"temp": 40,
"c02_level": 1574,
"geo": {"lat":35.93, "long":-85.46}
}
}
}"""
DESIRED OUTPUT
dc_id source_name id description
-------------------------------------------------------------------------------
dc-101 sensor-gauge 10 Sensor attached to the container ceilings
dc-101 sensor-ipad 13 Sensor ipad attached to carbon cylinders
dc-101 sensor-inest 8 Sensor attached to the factory ceilings
dc-101 sensor-istick 5 Sensor embedded in exhaust pipes in the ceilings
PYSPARK CODE
from pyspark.sql.functions import *
df_sample_data = spark.read.json(sc.parallelize([sample_json]))
df_expanded = df_sample_data.withColumn("one_source",explode_outer(col("source")))
display(df_expanded)
ERROR
AnalysisException: cannot resolve 'explode(source)' due to data type
mismatch: input to function explode should be array or map type, not
struct....
I put together this Databricks notebook to further demonstrate the challenge and clearly show the error. I will be able to use this notebook to test any recommendations provided herein.

Answer 1

You can't use explode for structs but you can get the column names in the struct source (with df.select("source.*").columns) and using list comprehension you create an array of the fields you want from each nested struct, then explode to get the desired result :
from pyspark.sql import functions as F
df1 = df.select(
"dc_id",
F.explode(
F.array(*[
F.struct(
F.lit(s).alias("source_name"),
F.col(f"source.{s}.id").alias("id"),
F.col(f"source.{s}.description").alias("description")
)
for s in df.select("source.*").columns
])
).alias("sources")
).select("dc_id", "sources.*")
df1.show(truncate=False)
#+------+-------------+---+------------------------------------------------+
#|dc_id |source_name |id |description |
#+------+-------------+---+------------------------------------------------+
#|dc-101|sensor-igauge|10 |Sensor attached to the container ceilings |
#|dc-101|sensor-inest |8 |Sensor attached to the factory ceilings |
#|dc-101|sensor-ipad |13 |Sensor ipad attached to carbon cylinders |
#|dc-101|sensor-istick|5 |Sensor embedded in exhaust pipes in the ceilings|
#+------+-------------+---+------------------------------------------------+