id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
479190812,MDU6SXNzdWU0NzkxOTA4MTI=,3200,"open_mfdataset memory leak, very simple case. v0.12",19933988,open,0,,,7,2019-08-09T22:38:39Z,2023-02-03T22:58:32Z,,NONE,,,,"#### MCVE Code Sample
<!-- In order for the maintainers to efficiently understand and prioritize issues, we ask you post a ""Minimal, Complete and Verifiable Example"" (MCVE): http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports -->

```python
import glob
import xarray as xr
import numpy as np
from memory_profiler import profile

def CreateTestFiles():
        # create a bunch of files
        xlen = int(1e2)
        ylen = int(1e2)
        xdim = np.arange(xlen)
        ydim = np.arange(ylen)

        nfiles = 100
        for i in range(nfiles):
                data = np.random.rand(xlen, ylen, 1)
                datafile = xr.DataArray(data, coords=[xdim, ydim, i], dims=['x', 'y', 'time'])
                datafile.to_netcdf('testfiles/datafile_{}.nc'.format(i))

@profile
def ReadFiles():
        xr.open_mfdataset(glob.glob('testfiles/*'), concat_dim='time')

if __name__ == '__main__':
        # write out files for testing 
        CreateTestFiles()

        # loop thru file read step
        for i in range(100):
                ReadFiles()
~
~
```

#### usage: 
mprof run simplest_case.py
mprof plot

(mprof is a python memory profiling library) 

#### Problem Description
dask version 1.1.4
xarray version 0.12 
python 3.7.3

There appears to be a persistent memory leak in open_mfdataset. I'm creating a model calibration script that runs for ~1000 iterations, opening and closing the same set of files (dimensions are the same, but the data is different) with each iteration. I eventually run out of memory because of the leak. This simple case captures the same behavior. Closing the files with .close() does not fix the problem.

Is there a work around for this? I've perused some of the issues but cannot tell if this has been resolved. 

![Figure_1](https://user-images.githubusercontent.com/19933988/62812349-8b008680-bac2-11e9-9896-f089f7ba2deb.png)


#### Output of ``xr.show_versions()``
<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3 (default, Mar 27 2019, 22:11:17)
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 3.10.0-693.17.1.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.4
libnetcdf: 4.6.2

xarray: 0.12.0
pandas: 0.24.2
numpy: 1.16.2
scipy: 1.2.1
netCDF4: 1.4.2
pydap: None
h5netcdf: None
h5py: None
Nio: 1.5.5
zarr: None
cftime: 1.0.3.4
nc_time_axis: None
PseudonetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.2.1
dask: 1.1.4
distributed: 1.26.0
matplotlib: 3.0.2
cartopy: 0.17.0
seaborn: None
setuptools: 41.0.1
pip: 19.1.1
conda: None
pytest: None
IPython: 7.3.0
sphinx: None
</details>
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