Hello,
I am working on a proof of concept to optimize the performance of the join
queries executed through drill. The underlying storage is a NO-SQL based
database - Mongo DB. The time it takes to return the result of the join query
is very high (46 seconds). Upon further analysis, as per the physical plan of
the query it is observed that both the left side (15 lakh records) and right
side table (13 lakh) are fully scanned and it takes 24 seconds and 20 seconds
respectively for the same.
Here is the query -
select ta.[SOME_COLUMN] from mongo.Test.TABLEA ta INNER JOIN mongo.Test.TABLEB
ta ON ta.Id = tb.Id and ta.Id ='123'
Records in table A : 15 Lakhs
Records in table B : 13 Lakhs
Filter condition : Id is indexed field in both the tables (ascending)
Drill Plan shows Hash-join being performed as shown in the plan (as attached
above)
I have the following questions -
1) Why does drill fetch all the records into the memory even though filter
condition on the indexed column was provided for one of the table ?
2) If the Drill planner/optimizer is intelligent then records could have
been filtered on the second table as well based on the join condition (to
reduce the dataset and hence result in faster execution time)
3) Are there any performance tuning options available to bring down the
query response time and generate a better query plan ?
I would appreciate if you can provide me pointers/directions or answers to the
above questions.
Please feel free if you need any additional information.
Thanks,
Mridul
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