issues

Table actions

1 row where user = 8431341 sorted by updated_at descending

✖
✖

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: created_at (date), updated_at (date), closed_at (date)

type 1

state 1

repo 1

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" 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

Advanced export

JSON shape: default, array, newline-delimited, object

CSV options: 

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]);