Segmentation fault during open_dataset() with dask and netcdf4

[enrico-mi]

What happened?

I am writing a snippet of code that reads netCDF files and convert them to a dask dataframe with the method read_nc_to_df().

When opening multiple netCDF files with open_dataset() and dask.delayed, the code fails with segmentation fault error. Some times the segmentation fault error is preceded by more information, like:

• *** Error in 'python3': double free or corruption (fasttop): 0x0000000003448df0 *** (this happened only once)
• [Errno -101] NetCDF: HDF error: 'path/to/file/output_00.nc' There are 28 HDF5 objects open! Report: open objects on 72057594037927944 (this happened multiple times)

Note that I simply re-execute the same code, and different error outputs might appear, the only constant being the segmentation fault line.

What did you expect to happen?

I expected the code to execute regularly, open the netcdf files, and eventually convert the multiple datasets into a dask dataframe.

Minimal Complete Verifiable Example

import xarray as xr
import numpy as np
import dask
import dask.dataframe as dd
import pandas as pd
import threading
##########################################################################
def create_nc():
    # Create data arrays for each variable with different dimensions
    time = np.arange(10)
    lat = np.linspace(-90, 90, 180)
    lon = np.linspace(-180, 180, 360)

    for j in range(3):
        # Variable 1: 1D array (time)
        var1 = xr.DataArray(np.random.rand(len(time)), dims=["time"], coords={"time": time})

        # Variable 2: 2D array (lat, lon)
        var2 = xr.DataArray(np.random.rand(len(lat), len(lon)), dims=["lat", "lon"], coords={"lat": lat, "lon": lon})

        # Variable 3: 3D array (time, lat, lon)
        var3 = xr.DataArray(np.random.rand(len(time), len(lat), len(lon)), dims=["time", "lat", "lon"], coords={"time": time, "lat": lat, "lon": lon})

        # Combine data arrays into a dataset
        ds = xr.Dataset({
            "var1": var1,
            "var2": var2,
            "var3": var3
        })

        # Save the dataset to a NetCDF file
        ds.to_netcdf("output_" + str(j).zfill(2) + ".nc")

    return

##########################################################################
def read_nc_into_df(filename,engine,lock):
    # Read the NetCDF file into an xarray dataset

    if lock is None:
        with xr.open_dataset(filename,cache=False,engine=engine) as ds:
            # Convert the dataset to a pandas dataframe
            df = ds.to_dataframe()
    else:
        with lock:
            with xr.open_dataset(filename,cache=False,engine=engine) as ds:
                # Convert the dataset to a pandas dataframe
                df = ds.to_dataframe()

    return df

############# READING WITHOUT DASK  ######################
def no_dask(filenames,engine):

    # Read each NetCDF file into a pandas dataframe
    dfs = []
    for f in filenames:
        dfs.append( read_nc_into_df(f,engine,None) )
    df = pd.concat(dfs)

    print(df.sample(frac=0.000005, random_state=42))
    return

############# READING WITH DASK.DELAYED  ######################
def with_dask_delayed(filenames, engine, lock):

    df = [ dask.delayed(read_nc_into_df)(f, engine, lock) for f in filenames ]

    df = dd.from_delayed(df)

    print(df.sample(frac=0.000005, random_state=42).compute())

    return

########################################################################
if __name__ == "__main__":

    create_nc()

    filenames = ["output_" + str(j).zfill(2) + ".nc" for j in range(3)]

    engines = [
        "h5netcdf",
        "netcdf4"
    ]

    for engine in engines:
        print("no_dask()")
        print(f"Engine: {engine}")
        try:
            no_dask(filenames,engine)
            print(f"no_dask() + {engine}: ok")
        except Exception as e:
            print(f"Error during no_dask() with engine {engine}: {e}")
            raise
        print()

    for lock in [threading.Lock(),None]:
        for engine in engines:
            print("with_dask_delayed()")
            print(f"Engine: {engine}")
            print(f"Lock: {lock}")
            try:
                with_dask_delayed(filenames,engine,lock)
                print(f"with_dask_delayed() + {engine} + {lock}: ok")
            except Exception as e:
                print(f"Error during with_dask_delayed() with engine {engine} and lock {lock}: {e}")
                raise
            print()

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.
• [ ] Recent environment — the issue occurs with the latest version of xarray and its dependencies.

Relevant log output

no_dask()
Engine: h5netcdf
                                 var1      var2      var3
time lat        lon                                      
5     35.698324  155.933148  0.125790  0.885606  0.719581
4    -43.743017  9.526462    0.733908  0.875954  0.543229
9    -27.653631  165.961003  0.659727  0.687533  0.700775
2     51.787709 -98.774373   0.569830  0.723463  0.108524
4     11.564246  66.685237   0.905972  0.286024  0.552127
7     85.977654  180.000000  0.058067  0.388723  0.834327
3     11.564246 -84.735376   0.896814  0.703934  0.014507
2     42.737430  156.935933  0.728145  0.424194  0.052766
      70.893855  167.966574  0.728145  0.599037  0.101830
4    -20.614525 -33.593315   0.733908  0.540253  0.788079
no_dask() + h5netcdf: ok

no_dask()
Engine: netcdf4
                                 var1      var2      var3
time lat        lon                                      
5     35.698324  155.933148  0.125790  0.885606  0.719581
4    -43.743017  9.526462    0.733908  0.875954  0.543229
9    -27.653631  165.961003  0.659727  0.687533  0.700775
2     51.787709 -98.774373   0.569830  0.723463  0.108524
4     11.564246  66.685237   0.905972  0.286024  0.552127
7     85.977654  180.000000  0.058067  0.388723  0.834327
3     11.564246 -84.735376   0.896814  0.703934  0.014507
2     42.737430  156.935933  0.728145  0.424194  0.052766
      70.893855  167.966574  0.728145  0.599037  0.101830
4    -20.614525 -33.593315   0.733908  0.540253  0.788079
no_dask() + netcdf4: ok

with_dask_delayed()
Engine: h5netcdf
Lock: <unlocked _thread.lock object at 0x2aab12ca6b40>
                                 var1      var2      var3
time lat        lon                                      
8    -9.553073   137.883008  0.285621  0.369071  0.394301
6    -60.837989  78.718663   0.621654  0.588331  0.015968
3     14.581006  45.626741   0.896814  0.346255  0.653513
7     37.709497 -151.922006  0.474633  0.286773  0.913073
9     65.865922  32.590529   0.659727  0.892672  0.478125
4    -48.770950  111.810585  0.752644  0.258328  0.041822
9     59.832402 -43.621170   0.555145  0.361261  0.079000
8     46.759777 -13.537604   0.246128  0.154291  0.980776
6     26.648045  11.532033   0.146838  0.050511  0.163982
with_dask_delayed() + h5netcdf + <unlocked _thread.lock object at 0x2aab12ca6b40>: ok

with_dask_delayed()
Engine: netcdf4
Lock: <unlocked _thread.lock object at 0x2aab12ca6b40>
                                 var1      var2      var3
time lat        lon                                      
8    -9.553073   137.883008  0.285621  0.369071  0.394301
6    -60.837989  78.718663   0.621654  0.588331  0.015968
3     14.581006  45.626741   0.896814  0.346255  0.653513
7     37.709497 -151.922006  0.474633  0.286773  0.913073
9     65.865922  32.590529   0.659727  0.892672  0.478125
4    -48.770950  111.810585  0.752644  0.258328  0.041822
9     59.832402 -43.621170   0.555145  0.361261  0.079000
8     46.759777 -13.537604   0.246128  0.154291  0.980776
6     26.648045  11.532033   0.146838  0.050511  0.163982
with_dask_delayed() + netcdf4 + <unlocked _thread.lock object at 0x2aab12ca6b40>: ok

with_dask_delayed()
Engine: h5netcdf
Lock: None
                                 var1      var2      var3
time lat        lon                                      
8    -9.553073   137.883008  0.285621  0.369071  0.394301
6    -60.837989  78.718663   0.621654  0.588331  0.015968
3     14.581006  45.626741   0.896814  0.346255  0.653513
7     37.709497 -151.922006  0.474633  0.286773  0.913073
9     65.865922  32.590529   0.659727  0.892672  0.478125
4    -48.770950  111.810585  0.752644  0.258328  0.041822
9     59.832402 -43.621170   0.555145  0.361261  0.079000
8     46.759777 -13.537604   0.246128  0.154291  0.980776
6     26.648045  11.532033   0.146838  0.050511  0.163982
with_dask_delayed() + h5netcdf + None: ok

with_dask_delayed()
Engine: netcdf4
Lock: None
Segmentation fault

Anything else we need to know?

The same read_nc_to_df function works regularly when:

• using engine='h5netcdf', or
• if it contained within a with threading.Lock() statement, or
• if executed without dask.

The example above illustrates these situations, too. The behaviour seems to contradict the documentation, which states that "By default, appropriate locks are chosen to safely read and write files with the currently active dask scheduler."

The same read_nc_to_df function does not work regularly when a lock is explicitly passed to open_dataset() through the backend_kwargs argument. This is not in the above example to keep it more coincise.

Environment

INSTALLED VERSIONS ------------------ commit: None python: 3.9.10 (main, Feb 20 2022, 11:57:16) [GCC 8.3.1 20190311 (Red Hat 8.3.1-3)] python-bits: 64 OS: Linux OS-release: 3.10.0-1160.76.1.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: 1.14.2 libnetcdf: 4.9.4-development xarray: 2024.2.0 pandas: 2.2.2 numpy: 1.26.4 scipy: 1.13.1 netCDF4: 1.7.2 pydap: None h5netcdf: 1.4.1 h5py: 3.12.1 Nio: None zarr: None cftime: 1.6.4.post1 nc_time_axis: None iris: None bottleneck: None dask: 2024.7.1 distributed: 2024.7.1 matplotlib: 3.9.2 cartopy: None seaborn: None numbagg: None fsspec: 2023.10.0 cupy: None pint: None sparse: None flox: None numpy_groupies: None setuptools: 69.5.1 pip: 24.3.1 conda: None pytest: None mypy: None IPython: 8.18.1 sphinx: None

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