[rules-users] Performance scaling with fact insertion

[St. Lawrence, Zachary]

I was running some performance analysis on my rules so I could tune in hopes of scaling up from hundreds of thousands of datapoints to millions of entries and noticed some alarming performance scaling issues when rules are firing and inserting dependent objects.  At first I thought it was one of my more complex rules but when I broke down my examples to the simplest possible test cases I found that the central problem was that performance was not linear with number of facts inserted -- it was polynominal (roughly n^2.5).

My simplest test example was basically each fact inserted initially would trigger a new logical relationship fact to be inserted.  No fancy logic.  Also it could not trigger itself recursively.  I tried three approaches : logical inserts of new facts, manual inserts/retraction, and update of the current fact with new data.

Updates on current fact were roughly linear assuming I prevented re-firing.
Manual Inserts and retracts scaled with O(n^2.5)
LogicalInserts were O(n^3)

The documentation suggest insertion of dependent facts to simplify logic.  But when dealing with tens of thousands of events triggering it did not scale sufficiently.  Can anyone tell me what I may have done wrong?  I have included rules and data below.

This was on 5.3 using fireAllRules.  The time measured was rule execution only and excluded times due to my testing framework.

// SIMPLIFIED RULES FILE
declare DataPoint
anomaly : boolean
entityId : String
average : double
predict : double
predictDev : double
timestamp : long
end

declare AnomalyFact
entityId : String
anomalyType : AnomalyType
ruleName : String
timestamp : long
end

rule "Traffic Below 4 Deviations"
no-loop
    when
$datapoint : DataPoint (
anomaly==false,
average < (predict - (predictDev * 4))
)
    then
// update self only. This line was always active.
    modify($datapoint) {setAnomaly(true)};

// on logicalInsert test I commented in the following line
//     insertLogical( new AnomalyFact ( $datapoint.getEntityId(), AnomalyEnum.LOW, drools.getRule().getName(), $datapoint.getTimestamp()));


// on insert test I commented in the following line and added another rule to retract any anomaly with no datapoint
//     insert( new AnomalyFact ( $datapoint.getEntityId(), AnomalyEnum.LOW, drools.getRule().getName(), $datapoint.getTimestamp()));
end


/* TEST RESULT CONTAINED BELOW
All data inserted triggered the rule.  I was load testing for worst case behavior.

logicalInsert
n run1 run2 run3
5000 1293 1232 1767
10000 9458 8210 10079
15000 24389 31050 24311
30000 134936

manual insert/retract
n run1 run2 run3
5000 1137 1094 1169
10000 8090 5862 5616
15000 16741 16728 15874
30000 76034

update on current object only
n run1 run2 run3
5000 417 444 413
10000 600
15000 712 697 688
30000 999 970 1041 1016 1163
60000 1420
100000 1930

Thanks for any help!
*/
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