How to explode structs array?

Question

I am working with a JSON object, and want to convert object.hours to relational table, based on Spark SQL dataframe/dataset.

I tried to use "explode", which is not really supporting the "structs array".

The json object is below:

{
  "business_id": "abc",
  "full_address": "random_address",
  "hours": {
    "Monday": {
      "close": "02:00",
      "open": "11:00"
    },
    "Tuesday": {
      "close": "02:00",
      "open": "11:00"
    },
    "Friday": {
      "close": "02:00",
      "open": "11:00"
    },
    "Wednesday": {
      "close": "02:00",
      "open": "11:00"
    },
    "Thursday": {
      "close": "02:00",
      "open": "11:00"
    },
    "Sunday": {
      "close": "00:00",
      "open": "11:00"
    },
    "Saturday": {
      "close": "02:00",
      "open": "11:00"
    }
  }
}

To a relational table like below,

CREATE TABLE "business_hours" (
     "id" integer NOT NULL PRIMARY KEY,
     "business_id" integer NOT NULL FOREIGN KEY REFERENCES "businesses",
     "day" integer NOT NULL,
     "open_time" time,
     "close_time" time
)

Jacek Laskowski · Accepted Answer · 2018-12-20 20:36:02Z

You can do this using this trick:

import org.apache.spark.sql.types.StructType
val days = df.schema 
  .fields
  .filter(_.name=="hours")
  .head
  .dataType
  .asInstanceOf[StructType]
  .fieldNames

val solution = df
  .select(
    $"business_id",
    $"full_address",
    explode(
      array(
        days.map(d => struct(
          lit(d).as("day"),
          col(s"hours.$d.open").as("open_time"),
          col(s"hours.$d.close").as("close_time")
        )):_*
      )
    )
  )
  .select($"business_id",$"full_address",$"col.*")

scala> solution.show
+-----------+--------------+---------+---------+----------+
|business_id|  full_address|      day|open_time|close_time|
+-----------+--------------+---------+---------+----------+
|        abc|random_address|   Friday|    11:00|     02:00|
|        abc|random_address|   Monday|    11:00|     02:00|
|        abc|random_address| Saturday|    11:00|     02:00|
|        abc|random_address|   Sunday|    11:00|     00:00|
|        abc|random_address| Thursday|    11:00|     02:00|
|        abc|random_address|  Tuesday|    11:00|     02:00|
|        abc|random_address|Wednesday|    11:00|     02:00|
+-----------+--------------+---------+---------+----------+

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