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2 rows where "created_at" is on date 2019-10-27 and "updated_at" is on date 2019-10-27 sorted by updated_at descending
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| id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | issue | performed_via_github_app |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 546723302 | https://github.com/simonw/datasette/issues/607#issuecomment-546723302 | https://api.github.com/repos/simonw/datasette/issues/607 | MDEyOklzc3VlQ29tbWVudDU0NjcyMzMwMg== | zeluspudding 8431341 | 2019-10-27T18:59:55Z | 2019-10-27T19:00:48Z | NONE | Ultimately, I'm needing to serve searches like this to multiple users (at times concurrently). Given the size of the database I'm working with, can anyone comment as to whether I should be storing this in something like MySQL or Postgres rather than SQLite. I know there's been much defense of sqlite being performant but I wonder if those arguments break down as the database size increases. For example, if I scroll to the bottom of that linked page, where it says Checklist For Choosing The Right Database Engine, here's how I answer those questions:
So is sqlite still a good idea here? |
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Ways to improve fuzzy search speed on larger data sets? 512996469 | |
| 546722281 | https://github.com/simonw/datasette/issues/607#issuecomment-546722281 | https://api.github.com/repos/simonw/datasette/issues/607 | MDEyOklzc3VlQ29tbWVudDU0NjcyMjI4MQ== | zeluspudding 8431341 | 2019-10-27T18:46:29Z | 2019-10-27T19:00:40Z | NONE | Update: I've created a table of only unique names. This reduces the search space from over 16 million, to just about 640,000. Interestingly, it takes less than 2 seconds to create this table using Python. Performing the same search that we did earlier for Any ideas for slashing the search speed nearly 10 fold? |
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Ways to improve fuzzy search speed on larger data sets? 512996469 |
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