Develop benchmarking criteria for consistent comparison across format options

[asteiker]

Candidate criteria:

• Formats / chunking schemes to compare
  • Re-chunked HDF5
  • Cloud-optimized HDF5
  • Geoparquet
  • Zarr
  • Kerchunk json
  • h5coro
• Environment
  • CryoCloud - Small instance
  • Assume we'll store all example files in CryoCloud (i.e. Sync or shared_public)
• Libraries or clients used to open/read data
• For each format option:
  • Dataset(s)
    • Based on community feedback/discussion, initial focus on ATL03
  • Files
    • Single and multiple? Files can vary by several GBs ; optimally produce and test 10 files
  • Variable(s)
  • Spatial subset(s)
  • Temporal subset(s)
  • Aggregation
  • End-to-end wall clock time
    • Time to re-chunk or reformat
    • Time to open/read file
      • Multiple tools/libraries/clients to compare per format option?
        • Geopandas, xarray
        • Should we consider dask data frame
  • Compute cost
  • Do we include a real-world example?
    • Time series of 60 day repeat cycle
    • Real world example tie in: Jacobshavn surface height