How do I explode a nested Struct in Spark using Scala - scala

Question

I am creating a dataframe using
val snDump = table_raw
.applyMapping(mappings = Seq(
("event_id", "string", "eventid", "string"),
("lot-number", "string", "lotnumber", "string"),
("serial-number", "string", "serialnumber", "string"),
("event-time", "bigint", "eventtime", "bigint"),
("companyid", "string", "companyid", "string")),
caseSensitive = false, transformationContext = "sn")
.toDF()
.groupBy(col("eventid"), col("lotnumber"), col("companyid"))
.agg(collect_list(struct("serialnumber", "eventtime")).alias("snetlist"))
.createOrReplaceTempView("sn")
I have data like this in the df
eventid | lotnumber | companyid | snetlist
123 | 4q22 | tu56ff | [[12345,67438]]
456 | 4q22 | tu56ff | [[12346,67434]]
258 | 4q22 | tu56ff | [[12347,67455], [12333,67455]]
999 | 4q22 | tu56ff | [[12348,67459]]
I want to explode it put data in 2 columns in my table for that what I am doing is
val serialNumberEvents = snDump.select(col("eventid"), col("lotnumber"), explode(col("snetlist")).alias("serialN"), explode(col("snetlist")).alias("eventT"), col("companyid"))
Also tried
val serialNumberEvents = snDump.select(col("eventid"), col("lotnumber"), col($"snetlist.serialnumber").alias("serialN"), col($"snetlist.eventtime").alias("eventT"), col("companyid"))
but it turns out that explode can be only used once and I get error in the select so how do I use explode/or something else to achieve what I am trying to.
eventid | lotnumber | companyid | serialN | eventT |
123 | 4q22 | tu56ff | 12345 | 67438 |
456 | 4q22 | tu56ff | 12346 | 67434 |
258 | 4q22 | tu56ff | 12347 | 67455 |
258 | 4q22 | tu56ff | 12333 | 67455 |
999 | 4q22 | tu56ff | 12348 | 67459 |
I have looked at a lot of stackoverflow threads but none of it helped me. It is possible that such question is already answered but my understanding of scala is very less which might have made me not understand the answer. If this is a duplicate then someone could direct me to the correct answer. Any help is appreciated.

Answer 1

First, explode the array in a temporary struct-column, then unpack it:
val serialNumberEvents = snDump
.withColumn("tmp",explode((col("snetlist"))))
.select(
col("eventid"),
col("lotnumber"),
col("companyid"),
// unpack struct
col("tmp.serialnumber").as("serialN"),
col("tmp.eventtime").as("serialT")
)

Answer 2

The trick is to pack the columns you want to explode in an array (or struct), use explode on the array and then unpack them.
val col_names = Seq("eventid", "lotnumber", "companyid", "snetlist")
val data = Seq(
(123, "4q22", "tu56ff", Seq(Seq(12345,67438))),
(456, "4q22", "tu56ff", Seq(Seq(12346,67434))),
(258, "4q22", "tu56ff", Seq(Seq(12347,67455), Seq(12333,67455))),
(999, "4q22", "tu56ff", Seq(Seq(12348,67459)))
)
val snDump = spark.createDataFrame(data).toDF(col_names: _*)
val serialNumberEvents = snDump.select(col("eventid"), col("lotnumber"), explode(col("snetlist")).alias("snetlist"), col("companyid"))
val exploded = serialNumberEvents.select($"eventid", $"lotnumber", $"snetlist".getItem(0).alias("serialN"), $"snetlist".getItem(1).alias("eventT"), $"companyid")
exploded.show()
Note that my snetlist has the schema Array(Array) rather then Array(Struct). You can simply get this by also creating an array instead of a struct out of your columns

Answer 3

Another approach, if needing to explode twice, is as follows - for another example, but to demonstrate the point:
val flattened2 = df.select($"director", explode($"films.actors").as("actors_flat"))
val flattened3 = flattened2.select($"director", explode($"actors_flat").as("actors_flattened"))
See Is there an efficient way to join two large Datasets with (deeper) nested array field? for a slightly different context, but same approach applies.
This answer in response to your assertion you can only explode once.