github
html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | issue | performed_via_github_app |
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https://github.com/simonw/datasette/issues/1480#issuecomment-938171377 | https://api.github.com/repos/simonw/datasette/issues/1480 | 938171377 | IC_kwDOBm6k_c4361vx | 110420 | 2021-10-07T21:33:12Z | 2021-10-07T21:33:12Z | CONTRIBUTOR | Thanks for the reply @simonw. What services have you had better success with than Cloud Run for larger database? Also, what about my issue description makes you think there may be a workaround? Is there any instrumentation I could add to see at which point in the deploy the memory usage spikes? Should I be able to see this whether it's running under Docker locally, or do you suspect this is Cloud Run-specific? | { "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
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https://github.com/simonw/datasette/issues/1480#issuecomment-938134038 | https://api.github.com/repos/simonw/datasette/issues/1480 | 938134038 | IC_kwDOBm6k_c436soW | 9599 | 2021-10-07T20:31:46Z | 2021-10-07T20:31:46Z | OWNER | I've had this problem too - my solution was to not use Cloud Run for databases larger than about 2GB, but the way you describe it here makes me think that maybe there is a workaround here which could get it to work. | { "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
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