Low memory/out-of-core index?

Has anyone considered implementing an index for monotonic data that does not require loading all values into main memory?

Motivation: We have data where first dimension can be length ~100,000,000, and coordinates for this dimension are stored as 32-bit integers. Currently if we used a pandas Index this would cast to 64-bit integers, and the index would require ~1GB RAM. This isn't enormous, but isn't negligible for people working on modest computers. Our use cases are simple, typically we only ever need to locate a slice of this dimension from a pair of coordinates, i.e., we only need to do binary search (bisect) on the coordinates. To achieve binary search in fact there is no need at all to load the coordinate values into memory, they could be left on disk (e.g., in HDF5 or Zarr dataset) and still achieve perfectly adequate performance for our needs.

This is of course also relevant to pandas but thought I'd post here as I know there have been some discussions about how to handle indexes when working with larger datasets via dask.

No source file, test, or entry point is named. Start by reviewing xarray's current index handling and the pandas discussions referenced by the issue, then compare the binary-search needs with coordinates stored in HDF5 or Zarr and the larger-dataset use cases involving dask. Done would require an agreed design and implementation scope, not just a prototype.