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| id ▼ | node_id | number | title | user | state | locked | assignee | milestone | comments | created_at | updated_at | closed_at | author_association | pull_request | body | repo | type | active_lock_reason | performed_via_github_app | reactions | draft | state_reason |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 512996469 | MDU6SXNzdWU1MTI5OTY0Njk= | 607 | Ways to improve fuzzy search speed on larger data sets? | zeluspudding 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: >  | datasette 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 |
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CREATE TABLE [issues] (
[id] INTEGER PRIMARY KEY,
[node_id] TEXT,
[number] INTEGER,
[title] TEXT,
[user] INTEGER REFERENCES [users]([id]),
[state] TEXT,
[locked] INTEGER,
[assignee] INTEGER REFERENCES [users]([id]),
[milestone] INTEGER REFERENCES [milestones]([id]),
[comments] INTEGER,
[created_at] TEXT,
[updated_at] TEXT,
[closed_at] TEXT,
[author_association] TEXT,
[pull_request] TEXT,
[body] TEXT,
[repo] INTEGER REFERENCES [repos]([id]),
[type] TEXT
, [active_lock_reason] TEXT, [performed_via_github_app] TEXT, [reactions] TEXT, [draft] INTEGER, [state_reason] TEXT);
CREATE INDEX [idx_issues_repo]
ON [issues] ([repo]);
CREATE INDEX [idx_issues_milestone]
ON [issues] ([milestone]);
CREATE INDEX [idx_issues_assignee]
ON [issues] ([assignee]);
CREATE INDEX [idx_issues_user]
ON [issues] ([user]);