Spark: DataFrame Aggregation (Scala) - apache-spark

Question

I have a below requirement to aggregate the data on Spark dataframe in scala.
And, I have two datasets.
Dataset 1 contains values (val1, val2..) for each "t" types distributed on several different columns like (t1,t2...) .
val data1 = Seq(
("1","111",200,"221",100,"331",1000),
("2","112",400,"222",500,"332",1000),
("3","113",600,"223",1000,"333",1000)
).toDF("id1","t1","val1","t2","val2","t3","val3")
data1.show()
+---+---+----+---+----+---+----+
|id1| t1|val1| t2|val2| t3|val3|
+---+---+----+---+----+---+----+
| 1|111| 200|221| 100|331|1000|
| 2|112| 400|222| 500|332|1000|
| 3|113| 600|223|1000|333|1000|
+---+---+----+---+----+---+----+
Dataset 2 represent the same thing by having a separate row for each "t" type.
val data2 = Seq(("1","111",200),("1","221",100),("1","331",1000),
("2","112",400),("2","222",500),("2","332",1000),
("3","113",600),("3","223",1000), ("3","333",1000)
).toDF("id*","t*","val*")
data2.show()
+---+---+----+
|id*| t*|val*|
+---+---+----+
| 1|111| 200|
| 1|221| 100|
| 1|331|1000|
| 2|112| 400|
| 2|222| 500|
| 2|332|1000|
| 3|113| 600|
| 3|223|1000|
| 3|333|1000|
+---+---+----+
Now,I need to groupBY(id,t,t*) fields and print the balances for sum(val) and sum(val*) as a separate record.
And both balances should be equal.
My output should look like below:
+---+---+--------+---+---------+
|id1| t |sum(val)| t*|sum(val*)|
+---+---+--------+---+---------+
| 1|111| 200|111| 200|
| 1|221| 100|221| 100|
| 1|331| 1000|331| 1000|
| 2|112| 400|112| 400|
| 2|222| 500|222| 500|
| 2|332| 1000|332| 1000|
| 3|113| 600|113| 600|
| 3|223| 1000|223| 1000|
| 3|333| 1000|333| 1000|
+---+---+--------+---+---------+
I'm thinking of exploding the dataset1 into mupliple records for each "t" type and then join with dataset2.
But could you please suggest me a better approach which wouldn't affect the performance if datasets become bigger?

Answer 1

The simplest solution is to do sub-selects and then union datasets:
val ts = Seq(1, 2, 3)
val dfs = ts.map (t => data1.select("t" + t as "t", "v" + t as "v"))
val unioned = dfs.drop(1).foldLeft(dfs(0))((l, r) => l.union(r))
val ds = unioned.join(df2, 't === col("t*")
here aggregation
You can also try array with explode:
val df1 = data1.withColumn("colList", array('t1, 't2, 't3))
.withColumn("t", explode(colList))
.select('t, 'id1 as "id")
val ds = df2.withColumn("val",
when('t === 't1, 'val1)
.when('t === 't2, 'val2)
.when('t === 't3, 'val3)
.otherwise(0))
The last step is to join this Dataset with data2:
ds.join(data2, 't === col("t*"))
.groupBy("t", "t*")
.agg(first("id1") as "id1", sum(val), sum("val*"))