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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
1952739368 PR_kwDOAMm_X85dTMKv 8339 Reduce dask tokenization time martindurant 6042212 closed 0     6 2023-10-19T17:22:06Z 2023-10-20T23:13:44Z 2023-10-20T23:13:43Z CONTRIBUTOR   0 pydata/xarray/pulls/8339

When using dask (e.g., chunks={} with a zarr dataset), each dask.array gets a token. Calculating this token currently hits a recursive path within dask and is relatively slow (~10ms), which adds up for many variables. This PR makes a simpler but still unique token.

An example profile of open_dataset before:

and after 

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     xarray 13221727 pull
839823306 MDU6SXNzdWU4Mzk4MjMzMDY= 5070 requires io.IOBase subclass rather than duck file-like martindurant 6042212 closed 0     3 2021-03-24T15:06:39Z 2021-03-29T16:22:35Z 2021-03-29T16:22:35Z CONTRIBUTOR      

If you open_dataset on a file-like object, you will get the error TypeError: cannot read the magic number form [sic!] This is because in xarray.core.utils.read_magic_number, we have isinstance(filename_or_obj, io.IOBase), which is not necessarily the case for a file-like. I recommend changing this to hasattr(filename_or_obj, "read").

Example: ``` In [1]: import fsspec

In [2]: fs = fsspec.filesystem('file')

In [3]: f = fs.open("201704010000.CHRTOUT_DOMAIN1.comp")

In [5]: import xarray as xr

In [6]: xr.open_dataset(f) TypeError `` Wherefis aLocalFileOpener`, made to be lazy and defer file opening, so that it can be pickled.

 
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   completed xarray 13221727 issue
788398518 MDExOlB1bGxSZXF1ZXN0NTU2OTE3MDIx 4823 Allow fsspec URLs in open_(mf)dataset martindurant 6042212 closed 0     20 2021-01-18T16:22:35Z 2021-02-16T21:26:53Z 2021-02-16T21:18:05Z CONTRIBUTOR   0 pydata/xarray/pulls/4823
  • [x] Closes #4461 and related
  • [x] Tests added
  • [x] Passes pre-commit run --all-files
  • [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
  • [x] New functions/methods are listed in api.rst
 
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     xarray 13221727 pull
709187212 MDExOlB1bGxSZXF1ZXN0NDkzMjkyOTIw 4461 Allow fsspec/zarr/mfdataset martindurant 6042212 closed 0     18 2020-09-25T18:14:38Z 2021-02-16T15:36:54Z 2021-02-16T15:36:54Z CONTRIBUTOR   0 pydata/xarray/pulls/4461

Requires https://github.com/zarr-developers/zarr-python/pull/606

  • [ ] ~Closes #xxxx~
  • [x] Tests added
  • [x] Passes isort . && black . && mypy . && flake8
  • [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
  • [x] New functions/methods are listed in api.rst
 
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     xarray 13221727 pull
202260275 MDU6SXNzdWUyMDIyNjAyNzU= 1223 zarr as persistent store for xarray martindurant 6042212 closed 0     12 2017-01-20T22:37:20Z 2017-12-14T02:11:36Z 2017-12-14T02:11:36Z CONTRIBUTOR      

netCDF and HDF are good legacy archival formats handled by xarray and the wider numerical python ecosystem, but they don't play nicely with parallel access across a cluster or from an archive store like s3. zarr is certainly non-standard, but would make a very nice internal store for intermediates.

This gist, below, is a simple motivator that we could use zarr not only for dask but for xarray too without too much expenditure of effort. https://gist.github.com/martindurant/dc27a072da47fab8d63117488f1fd7f1

 
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   completed xarray 13221727 issue

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