issue_comments: 1560559426

Thanks @tomwhite for the PR. I've only quickly checked the approach, which looks reasonable. But those changes have implications on several locations of the backend code, which we would have to sort out.

Considering this example:

```python import numpy as np import xarray as xr print(f"creating dataset with empty string array") print("-----------------------------------------") dtype = xr.coding.strings.create_vlen_dtype(str) ds = xr.Dataset({"a": np.array([], dtype=dtype)}) print(f"dtype: {ds['a'].dtype}") print(f"metadata: {ds['a'].dtype.metadata}") ds.to_netcdf("a.nc", engine="netcdf4")

print("\nncdump") print("-------") !ncdump a.nc

engines = ["netcdf4", "h5netcdf"] for engine in engines: with xr.open_dataset("a.nc", engine=engine) as ds: print(f"\nloading with {engine}") print("-------------------") print(f"dtype: {ds['a'].dtype}") print(f"metadata: {ds['a'].dtype.metadata}") ```

```python creating dataset with empty string array

---

dtype: object metadata: {'element_type': <class 'str'>}

ncdump

netcdf a { dimensions: a = UNLIMITED ; // (0 currently) variables: string a(a) ; data: }

loading with netcdf4

dtype: object metadata: None

loading with h5netcdf

dtype: object metadata: {'vlen': <class 'str'>} ```

Engine netcdf4 does not roundtrip here, losing the dtype metadata information. There is special casing for h5netcdf backend, though.

The source is actually located in open_store_variable of netcdf4 backend, when the underlying data is converted to Variable (which does some object dtype twiddling).

Unfortunately I do not have an immediate solution here.