issue_comments: 520136799
This data as json
| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/3200#issuecomment-520136799 | https://api.github.com/repos/pydata/xarray/issues/3200 | 520136799 | MDEyOklzc3VlQ29tbWVudDUyMDEzNjc5OQ== | 6213168 | 2019-08-10T10:10:11Z | 2019-08-10T10:11:18Z | MEMBER | Oh but first and foremost - CPython memory management is designed so that, when PyMem_Free() is invoked, CPython will hold on to it and not invoke the underlying free() syscall, hoping to reuse it on the next PyMem_Alloc(). An increase in RAM usage from 160 to 200MB could very well be explained by this. Try increasing the number of loops in your test 100-fold and see if you get a 100-fold increase in memory usage too (from 160MB to 1.2GB). If yes, it's a real leak; if it remains much more contained, it's normal CPython behaviour. |
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