The current KNN implementation has two areas that can be improved:
- The current behavior is somewhat incorrect. When performing a kNN
query, the current code fetches k items from the index, and then sorts
these items according to Haversine distance. The problem with this
approach is that since the items fetched from the index are ordered by
a Euclidean metric, there is no guarantee that item k + 1 is not closer
than item k in great circle distance, and hence incorrect results can be
returned when closer items beyond k exist.
- The secondary sort is a performance killer. This requires buffering
all k items (again...they were already run through a priority queue in)
the index, and then a sort. Since the items are mostly sorted, and
Go's sort implementation is a quickSort this is the worst case for the
sort algorithm.
Both of these can be fixed by applying a proper distance metric in
the index nearby operation. In addition, this cleans up the code
considerably, removing a number of special cases that applied only
to NEARBY operations.
This change implements a geodetic distance metric that ensures that
the order from the index is correct, eliminating the need for the
secondary sort and special filtering cases in the ScanWriter code.
This commit fixes a case where a roaming geofence will not fire
a "faraway" event when it's supposed to.
The fix required rewriting the nearby/faraway detection logic. It
is now much more accurate and takes overall less memory, but it's
also a little slower per operation because each object proximity
is checked twice per update. Once to compare the old object's
surrounding, and once to evaulated the new object. The two lists
are then used to generate accurate "nearby" and "faraway" results.
tidwall