Use exponential scoring and consistent scale values for focus.point - #1209
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exponential scoring and consistent scale values for focus.pointfocus.point
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I think this is great, the only concern I have is potential performance regression, however, the other PRs we discussed today regarding performance will probably more than makeup for it. |
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It turns out from our testing that there is a small performance hit for this change (we saw a query have average times go from around 700 to around 800ms). Since some of our shorter text length autocomplete queries are already quite slow, we're going to look at some performance improvements like #1219 or #1215 before merging this. |
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We have made great improvements in slow autocomplete in #1219, I think this can be merged now. |
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Linear scoring, by design, gives all records the same score past a certain point. This has the disadvantage that identical records that are very far away cannot be sorted by distance. By using exponential scoring, we can achieve decent sorting of even very far away records. This is very helpful for cities and postalcodes. Connects #1206
The `scale` parameter controls how quickly scores decrease from the maximum as the distance from the `center_point` to the record in question increases. Set this to 50km, which is the same as search. Connects #1206
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Passes as of pelias/api#1209
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Our
scalevalues for thecenter_pointquery used by Elasticsearch to score ourfocus.pointqueries was inconsistent between search and autocomplete. (50km for search, 250km for autocomplete).This PR changes autocomplete to 50km, to be consistent with search. It's tough to judge which is the better value, but here's my reasoning:
scalemeans scores drop off faster. So a smaller value means that only very close records would have a high enough distance score to outweigh a far away record with high importance or population.Additionally, this PR changes the decay function from
linearto exponential. I don't recall why we settled on linear, as it was very long ago, but I suspect it might have been an attempt to prevent very far away records from being scored at all. In that case, our understanding of Elasticsearch at the time was incorrect.By using exponential scoring, records of any distance will receive a non-zero score for the distance query. This means we can differentiate between otherwise identically-scoring records that are very far away from the focus point. This is a huge help when searching for administrative areas like localities, as well as postal codes.
Looking at the acceptance tests, I could not find any cases where these changes cause a failure. However, some newly added acceptance tests now pass.
connects #1206