issues: 928533488
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| 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 |
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| 928533488 | MDU6SXNzdWU5Mjg1MzM0ODg= | 5521 | Memory inefficiency when using sortby | 58827984 | open | 0 | 0 | 2021-06-23T18:31:18Z | 2021-06-23T18:31:18Z | NONE | What happened: High memory usage seen when sorting after loading from disk. Loading from disk took about 150MB, where after the sort I saw a usage of about 1.5 GB. I believe this is due to the reindexing that requires the data to be loaded into memory during sort. So I guess I am not surprised, but I wanted to submit this as a possible issue just to make sure that my reasoning is good. For my use case, I will have to abandon sortby and ensure data is sorted prior to writing to disk. I am afraid my MVCE relies on data on disk that I have. If this is an actual issue that needs more looking into, I can provide an example that anyone can run. Otherwise I can close. Minimal Complete Verifiable Example: ```python import xarray as xr from psutil import virtual_memory startmem = virtual_memory().used data = xr.open_zarr(r"D:\falkor\FK181005_processed\em302_105_10_06_2018\attitude.zarr", synchronizer=None, mask_and_scale=False, decode_coords=False, decode_times=False, decode_cf=False, concat_characters=False) afterload_mem = virtual_memory().used - startmem ans = data.sortby('time') aftersort_mem = virtual_memory().used - startmem print('Without sort: {}'.format(afterload_mem)) print('With sort: {}'.format(aftersort_mem)) Out: Without sort: 149241856 Out: With sort: 1657593856 ``` Environment: Output of <tt>xr.show_versions()</tt>INSTALLED VERSIONS ------------------ commit: None python: 3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 15:50:08) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD byteorder: little LC_ALL: None LANG: None LOCALE: English_United States.1252 libhdf5: 1.10.6 libnetcdf: 4.7.4 xarray: 0.17.0 pandas: 1.2.3 numpy: 1.20.3 scipy: 1.6.0 netCDF4: 1.5.6 pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: 2.6.1 cftime: 1.4.1 nc_time_axis: None PseudoNetCDF: None rasterio: 1.2.1 cfgrib: None iris: None bottleneck: 1.3.2 dask: 2021.03.0 distributed: 2021.03.0 matplotlib: 3.3.4 cartopy: 0.18.0 seaborn: 0.11.1 numbagg: None pint: None setuptools: 49.6.0.post20210108 pip: 21.0.1 conda: None pytest: 6.2.2 IPython: 7.21.0 sphinx: 3.5.2 |
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13221727 | issue |