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/2186#issuecomment-1046665303,https://api.github.com/repos/pydata/xarray/issues/2186,1046665303,IC_kwDOAMm_X84-YthX,691772,2022-02-21T09:41:00Z,2022-02-21T09:41:00Z,CONTRIBUTOR,"I just stumbled across the same issue and created a minimal example similar to @lkilcher. I am using `xr.open_dataarray()` with chunks and do some simple computation. After that 800mb of RAM is used, no matter whether I close the file explicitly, delete the xarray objects or invoke the Python garbage collector.

What seems to work: do not use the `threading` Dask scheduler. The issue does not seem to occur with the single-threaded or processes scheduler. Also setting `MALLOC_MMAP_MAX_=40960` seems to solve the issue as suggested above (disclaimer: I don't fully understand the details here).

If I understand things correctly, this indicates that the issue is a consequence of dask/dask#3530. Not sure if there is anything to be fixed on the xarray side or what would be the best work around. I will try to use the processes scheduler.

I can create a new (xarray) ticket with all details about the minimal example, if anyone thinks that this might be helpful (to collect work-a-rounds or discuss fixes on the xarray side).","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,326533369