pydata / pydata/xarray

Missing Blocks when loading zarr file

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bug
Dominant language
Python
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Description

What happened?

Under load blocks of Zarr objects go missing. This happens on our minio server (see example) and on the hpc file system. This happens under load, when the filesystem gets slow, so I guess there must be a timeout somewhere.

What did you expect to happen?

A complete map.

Incomplete map:

image

Complete map, when the filesystem is not under load:

temperature_trends

Minimal Complete Verifiable Example
# Calculate global temperature trends

from dask.distributed import Client
import xarray as xr
from scipy import stats
from datetime import datetime
import matplotlib.pyplot as plt


def slope(y):
    x = list(range(0, len(y)))
    l = stats.linregress(x, y)
    return l.slope


def main():
    print(datetime.now(), "startup", flush = True)
    print(datetime.now(), "starting dask workers", flush = True)
    Client(n_workers=1, threads_per_worker=32, memory_limit='64GB')

    print(datetime.now(), "opening esdc", flush = True)
    c = xr.open_zarr("http://data.rsc4earth.de:9000/earthsystemdatacube/v3.0.1/esdc-8d-0.25deg-256x128x128-3.0.1.zarr/")

    print(datetime.now(), "getting air teperature data", flush = True)
    ct = c.air_temperature_2m

    print(datetime.now(), "setup calculations", flush = True)
    cs = xr.apply_ufunc( 
            slope, 
            ct, 
            input_core_dims=[['time']], 
            vectorize=True, 
            dask='parallelized', 
            dask_gufunc_kwargs=dict(allow_rechunk=True))

    print(datetime.now(), "saving data", flush = True)
    csset = xr.Dataset(dict(tslope = cs))
    csset.to_zarr(store="temp_slopes.zarr", mode="w")
    print(datetime.now(), "plotting", flush = True)
    cssetcalc = xr.open_zarr("temp_slopes.zarr")
    cssetcalc.tslope.plot()
    plt.savefig("temperature_trends.png")
    print(datetime.now(), "done", flush = True)



if __name__ == '__main__':
    main()
MVCE confirmation
  • Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
  • Complete example — the example is self-contained, including all data and the text of any traceback.
  • Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
  • New issue — a search of GitHub Issues suggests this is not a duplicate.
Relevant log output

No response

Anything else we need to know?

This only seems to happen under load, so you will need to stress the server a bit to reproduce it.

Environment

INSTALLED VERSIONS

commit: None
python: 3.8.15 (default, Nov 24 2022, 15:19:38)
[GCC 11.2.0]
python-bits: 64
OS: Linux
OS-release: 4.18.0-372.26.1.el8_6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: None
libnetcdf: None

xarray: 2022.11.0
pandas: 1.5.2
numpy: 1.23.5
scipy: 1.9.3
netCDF4: None
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: 2.13.3
cftime: None
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.3.5
dask: 2022.02.1
distributed: 2022.2.1
matplotlib: 3.6.2
cartopy: None
seaborn: None
numbagg: None
fsspec: 2022.11.0
cupy: None
pint: None
sparse: None
flox: None
numpy_groupies: None
setuptools: 65.5.0
pip: 22.3.1
conda: None
pytest: None
IPython: None
sphinx: None

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by running the supplied MVCE against the example MinIO Zarr URL while stressing the filesystem, since the issue reports missing blocks only under load. Compare the resulting map with the complete map and capture logs or a traceback; done means identifying and preventing incomplete Zarr reads under slow-storage conditions.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
data
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
35/100

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