The way JSON Facet's simple refinement "re-sorts" buckets after refinement can cause refined buckets to be "bumped out" of the topN based on the refined counts/stats depending on the sort - causing unrefined buckets originally discounted in phase#2 to bubble up into the topN and be returned to clients with inaccurate counts/stats
The simplest way to demonstrate this bug (in some data sets) is with a sort: 'count asc' facet:
- assume shard1 returns termX & termY in phase#1 because they have very low shard1 counts
- but not returned at all by shard2, because these terms both have very high shard2 counts.
- Assume termX has a slightly lower shard1 count then termY, such that:
- termX "makes the cut" off for the limit=N topN buckets
- termY does not make the cut, and is the "N+1" known bucket at the end of phase#1
- termX then gets included in the phase#2 refinement request against shard2
- termX now has a much higher known total count then termY
- the coordinator now sorts termX "worse" in the sorted list of buckets then termY
- which causes termY to bubble up into the topN
- termY is ultimately included in the final result with incomplete count/stat/sub-facet data instead of termX
- this is all indepenent of the possibility that termY may actually have a significantly higher total count then termX across the entire collection
- the key problem is that all/most of the other terms returned to the client have counts/stats that are the cumulation of all shards, but termY only has the contributions from shard1
- This scenerio can happen regardless of the amount of overrequest used. Additional overrequest just increases the number of "extra" terms needed in the index with "better" sort values then termX & termY in shard2
- sort: 'count asc' is not just an exceptional/pathelogical case:
- any function sort where additional data provided shards during refinement can cause a bucket to "sort worse" can also cause this problem.
- Examples: sum(price_i) asc , min(price_i) desc , avg(price_i) asc|desc , etc...