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-392235000,https://api.github.com/repos/pydata/xarray/issues/2186,392235000,MDEyOklzc3VlQ29tbWVudDM5MjIzNTAwMA==,12929327,2018-05-26T04:11:18Z,2018-05-26T04:11:18Z,NONE,"Using ```autoclose=True``` doesn't seem to make a difference. My test only uses 4 files anyway.

Thanks for the explanation of ```open_dataset()``` - that makes sense.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,326533369
https://github.com/pydata/xarray/issues/2186#issuecomment-392226004,https://api.github.com/repos/pydata/xarray/issues/2186,392226004,MDEyOklzc3VlQ29tbWVudDM5MjIyNjAwNA==,12929327,2018-05-26T01:35:36Z,2018-05-26T01:35:36Z,NONE,"I've discovered that setting the environment variable MALLOC_MMAP_MAX_ to a reasonably small value can partially mitigate this memory fragmentation.

Performing 4 iterations over dataset slices of shape ~(5424, 5424) without this tweak was yielding >800MB of memory usage (an increase of ~400MB over the first iteration). 

Setting MALLOC_MMAP_MAX_=40960 yielded ~410 MB of memory usage (an increase of only ~130MB over the first iteration).

This level of fragmentation is still offensive, but this does suggest the problem may lie deeper within the entire unix, glibc, Python, xarray, dask ecosystem.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,326533369
https://github.com/pydata/xarray/issues/2186#issuecomment-392217441,https://api.github.com/repos/pydata/xarray/issues/2186,392217441,MDEyOklzc3VlQ29tbWVudDM5MjIxNzQ0MQ==,12929327,2018-05-26T00:03:59Z,2018-05-26T00:03:59Z,NONE,"I'm now wondering if this issue is in dask land, based on this issue: https://github.com/dask/dask/issues/3247

It has been suggested in other places to get around the memory accumulation by running each loop iteration in a forked process:

```python
def worker(ds, k):
    print('accessing data')
    data = ds.datavar[k,:,:].values
    print('data acquired')

for k in range(ds.dims['t']):
    p = multiprocessing.Process(target=worker, args=(ds, k))
    p.start()
    p.join()
```
But apparently one can't access dask-wrapped xarray datasets in subprocesses without a deadlock. I don't know enough about the internals to understand why.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,326533369
https://github.com/pydata/xarray/issues/2186#issuecomment-392110253,https://api.github.com/repos/pydata/xarray/issues/2186,392110253,MDEyOklzc3VlQ29tbWVudDM5MjExMDI1Mw==,12929327,2018-05-25T16:23:55Z,2018-05-25T16:24:33Z,NONE,"Yes, I understand the garbage collection. The problem I'm struggling with here is that normally when working with arrays, maintaining only one reference to an array and replacing the data that reference points to within a loop does not result in memory accumulation because GC is triggered on the prior, now dereferenced array from the previous iteration.

Here, it seems that under the hood, references to arrays have been created other than my ""data"" variable that are not being dereferenced when I reassign to ""data,"" so stuff is accumulating in memory. ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,326533369