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-530800751,https://api.github.com/repos/pydata/xarray/issues/3200,530800751,MDEyOklzc3VlQ29tbWVudDUzMDgwMDc1MQ==,1262767,2019-09-12T12:24:12Z,2019-09-12T12:36:02Z,NONE,"I have observed a similar memleak (config see below).
It occurs for both parameters engine=netcdf4 and engine=h5netcdf.
Example for loading a 1.2GB netCDF file:
In contrast, the memory is just released with a `del ds` on the object, this is the large memory (2.6GB) - a `ds.close()` has no effect. There is still a ""minor"" memleak remaining (4MB), when a `open_dataset` is called. See the output using the `memory_profiler` package:
```python
Line # Mem usage Increment Line Contents
================================================
31 168.9 MiB 168.9 MiB @profile
32 def load_and_unload_ds():
33 173.0 MiB 4.2 MiB ds = xr.open_dataset(LFS_DATA_DIR + '/dist2coast_1deg_merged.nc')
34 2645.4 MiB 2472.4 MiB ds.load()
35 2645.4 MiB 0.0 MiB ds.close()
36 173.5 MiB 0.0 MiB del ds
```
- there is **no** difference using `open_dataset(file, engine='h5netcdf')`, the minor memleak is even larger (~9MB).
- memory leak persists, if an additional chunks parameter is used for `open_dataset`
**Output of `xr.show_versions()`**
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7 | packaged by conda-forge | (default, Jul 2 2019, 02:18:42)
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 4.15.0-62-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.5
libnetcdf: 4.6.2
xarray: 0.12.3
pandas: 0.25.1
numpy: 1.16.4
scipy: 1.2.1
netCDF4: 1.5.1.2
pydap: None
h5netcdf: 0.7.4
h5py: 2.9.0
Nio: None
zarr: None
cftime: 1.0.3.4
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: 2.3.0
distributed: 2.3.2
matplotlib: 3.1.1
cartopy: 0.17.0
seaborn: None
numbagg: None
setuptools: 41.0.1
pip: 19.2.3
conda: None
pytest: 5.0.1
IPython: 7.7.0
sphinx: None
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