What does “Correlated scalar subqueries must be Aggregated” mean?

Question

I use Spark 2.0.

I'd like to execute the following SQL query:

val sqlText = """
select
  f.ID as TID,
  f.BldgID as TBldgID,
  f.LeaseID as TLeaseID,
  f.Period as TPeriod,
  coalesce(
    (select
       f ChargeAmt
     from
       Fact_CMCharges f
     where
       f.BldgID = Fact_CMCharges.BldgID
     limit 1),
     0) as TChargeAmt1,
  f.ChargeAmt as TChargeAmt2,
  l.EFFDATE as TBreakDate
from
  Fact_CMCharges f
join
  CMRECC l on l.BLDGID = f.BldgID and l.LEASID = f.LeaseID and l.INCCAT = f.IncomeCat and date_format(l.EFFDATE,'D')<>1 and f.Period=EFFDateInt(l.EFFDATE) 
where
  f.ActualProjected = 'Lease'
except(
  select * from TT1 t2 left semi join Fact_CMCharges f2 on t2.TID=f2.ID) 
"""
val query = spark.sql(sqlText)
query.show()

It seems that the inner statement in coalesce gives the following error:

pyspark.sql.utils.AnalysisException: u'Correlated scalar subqueries must be Aggregated: GlobalLimit 1\n+- LocalLimit 1\n

What's wrong with the query?

Answer 1

You have to make sure that your sub-query by definition (and not by data) only returns a single row. Otherwise Spark Analyzer complains while parsing the SQL statement.

So when catalyst can't make 100% sure just by looking at the SQL statement (without looking at your data) that the sub-query only returns a single row, this exception is thrown.

If you are sure that your subquery only gives a single row you can use one of the following aggregation standard functions, so Spark Analyzer is happy:

• first
• avg
• max
• min