issue_comments: 1008220270
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| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | issue | performed_via_github_app |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/simonw/sqlite-utils/issues/369#issuecomment-1008220270 | https://api.github.com/repos/simonw/sqlite-utils/issues/369 | 1008220270 | IC_kwDOCGYnMM48GDhu | 9599 | 2022-01-09T03:12:38Z | 2022-01-09T03:13:15Z | OWNER | Basically no difference using this very basic benchmark: ``` analyze % python3 -m timeit '__import__("sqlite3").connect("global-power-plants.db").execute("select country_long, count(*) from [global-power-plants] group by country_long").fetchall()' 100 loops, best of 5: 2.39 msec per loop analyze % python3 -m timeit '__import__("sqlite3").connect("global-power-plants-analyzed.db").execute("select country_long, count(*) from [global-power-plants] group by country_long").fetchall()' 100 loops, best of 5: 2.38 msec per loop ``` I should try this against a much larger database. https://covid-19.datasettes.com/covid.db is 879MB. | {"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | 1097091527 |