issues: 512996469
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| 512996469 | MDU6SXNzdWU1MTI5OTY0Njk= | 607 | Ways to improve fuzzy search speed on larger data sets? | 8431341 | closed | 0 | 6 | 2019-10-27T17:31:37Z | 2019-11-07T03:38:10Z | 2019-11-07T03:38:10Z | NONE | I have an sqlite table with 16 million rows in it. Having read @simonw article "[Fast Autocomplete Search for Your Website](https://24ways.org/2018/fast-autocomplete-search-for-your-website/)" I was curious to try datasette to see what kind of query performance I could get out of it. In truth I don't need to do full text search since all I would like to do is give my users a way to search for the names of investors such as "Warren Buffet", or "Tim Cook" (who's names are in a single column). On the first search, Datasette takes over 20 seconds to return all records associated with `elon musk`: >  >  If I rerun the same search, it then takes almost 9 seconds: >  That's far to slow to implement an autocomplete feature. I could reduce the latency by making a special table of only unique investor names, thereby reducing the search space to less than a million rows (then I'd need to implement a way to add only new investor names to the table as I received new data.. about 4,000 rows a day). If I did that, I'm still concerned the new table wouldn't be lean enough to lookup investor names quickly. Plus, even if I can implement the autocomplete feature, I would still finally have to lookup records for that investors which would take between 8 - 20 seconds. Are there any tricks for speeding this up? Here's my hardware: >  | 107914493 | issue | {"url": "https://api.github.com/repos/simonw/datasette/issues/607/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | completed |