Short autocomplete inputs are very difficult to serve in a performant
and low-latency way. With shorter inputs, many more documents match for
just about any input string.
In our testing, one to three character input texts generally match up to
100 million documents out of a 560 million document full planet build.
There's really no way to make scoring 100 million documents fast,
so in order to achieve acceptable performance (ideally, <100ms P99
latency), it's worth looking at ways to either avoid querying
Elasticsearch or reducing the scope of autocomplete queries.
Short autocomplete queries without a focus.point parameter can be
cached. There are only 47,000 possible 1-3 character alphanumerical
inputs. At this time, caching is outside the scope of Pelias itself but
can easily be implemented with Varnish, Nginx, Fastly, Cloudfront, and
lots of other tools and services.
Queries with a `focus.point` are effectively uncachable however, since
the coordinate chosen will often be unique.
This PR uses the `focus.point` coordinate to build a
hard filter limiting the search to documents only within a certain
radius of the coordinate. This can reduce the number of documents
searched and improve performance, while still returning results that are
useful.
It takes two parameters, driven by `pelias-config`:
- `api.autocomplete.focusHardLimitTextLength': the maximum length of text
for which a hard distance filter will be constructed
- `api.autocomplete.focusHardLimitMultiplier`: the length of the input
text will be multiplied by this number to get the total hard filter
radius in kilometers.
For example, with `focusHardLimitTextLength` 4, and
`focusHardLimitMultiplier` 50, the following hard filters would be
constructed:
| text length | max distance |
| ---- | ----|
| 1 | 50 |
| 2 | 100 |
| 3 | 150 |
| 4+ | unlimited |
Julian Simioni
df8f1bb332