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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
530800751 https://github.com/pydata/xarray/issues/3200#issuecomment-530800751 https://api.github.com/repos/pydata/xarray/issues/3200 MDEyOklzc3VlQ29tbWVudDUzMDgwMDc1MQ== floschl 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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   open_mfdataset memory leak, very simple case. v0.12 479190812

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