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
1083621690,I_kwDOAMm_X85AlsE6,6084,Initialise zarr metadata without computing dask graph,42455466,open,0,,,6,2021-12-17T21:17:42Z,2024-04-03T19:08:26Z,,NONE,,,,"**Is your feature request related to a problem? Please describe.**
On writing large zarr stores, the [xarray docs](https://xarray.pydata.org/en/stable/user-guide/io.html#appending-to-existing-zarr-stores) recommend first creating an initial Zarr store without writing all of its array data. The recommended approach is to first create a dummy dask-backed `Dataset`, and then call `to_zarr` with `compute=False` to write only metadata to Zarr. This works great.

It seems that in one common use case for this approach (including the example in the above docs), the entire dataset to be written to zarr is already represented in a `Dataset` (let's call this `ds`). Thus, rather than creating a dummy `Dataset` with exactly the same metadata as `ds`, it is more convenient to initialise the zarr Store with `ds.to_zarr(..., compute=False)`. See for example:

https://discourse.pangeo.io/t/many-netcdf-to-single-zarr-store-using-concurrent-futures/2029
https://discourse.pangeo.io/t/map-blocks-and-to-zarr-region/2019
https://discourse.pangeo.io/t/netcdf-to-zarr-best-practices/1119/12
https://discourse.pangeo.io/t/best-practice-for-memory-management-to-iteratively-write-a-large-dataset-with-xarray/1989

However, calling `to_zarr` with `compute=False` still computes the dask graph for writing the Zarr store. The graph is never used in this use-case, but computing the graph can take a really long time for large graphs. 

**Describe the solution you'd like**
Is there scope to add an option to `to_zarr` to initialise the store _without_ computing the dask graph? Or perhaps an `initialise_zarr` method would be cleaner?
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