Consider omitting unchunked dimensions from Key objects created with DatasetToChunks
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 35/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- python
- Domain
- data-engineering, distributed-systems
Research direction
Start with the read-write documentation example using DatasetToChunks and inspect how its Key objects represent chunk offsets. Determine whether unchunked dimensions should be omitted while preserving usable keys for replacement dimensions; done means reaching and documenting a clear decision, with the relevant behavior updated consistently.
Written by the indexing model from the issue text.
Description
Currently we have (from https://xarray-beam.readthedocs.io/en/latest/read-write.html):
with beam.Pipeline() as p:
p | xbeam.DatasetToChunks(ds, chunks={'time': 1000}) | beam.MapTuple(print_summary)
Key(offsets={'lat': 0, 'lon': 0, 'time': 0}, vars=None)
with <xarray.Dataset data_vars=['air'] dims={'lat': 25, 'time': 1000, 'lon': 53}>
Key(offsets={'lat': 0, 'lon': 0, 'time': 1000}, vars=None)
with <xarray.Dataset data_vars=['air'] dims={'lat': 25, 'time': 1000, 'lon': 53}>
Key(offsets={'lat': 0, 'lon': 0, 'time': 2000}, vars=None)
with <xarray.Dataset data_vars=['air'] dims={'lat': 25, 'time': 920, 'lon': 53}>
Should we instead omit lat and lon from these keys? This is less explicit but also more flexible, e.g,. if replacing these dimensions entirely with different dimensions, you don't need to update the keys.
- Dominant language
- Python
- Stars
- 170
- Forks
- 15
- Avg merge
- 18h 27m
- Merged PRs (30d)
- 1
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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