Issue 1
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- aws, python
- Domain
- cloud, data, performance
Research direction
Start with Tilebench and the linked morecantile and async-cog-reader tools, then review the GDAL/Rasterio benchmarking references. Build a reproducible comparison across projections using Sentinel 2 and NAIP datasets; done means reporting S3 latency and transfer timings, GET request counts, and transferred bytes.
Written by the indexing model from the issue text.
Description
Internal block size for COG seems like an endless discussion.
Tilebench won't solve everything but with luck and nice reproducible process we will have a better understanding.
Blue - internal blocks
Red - Mercator grid
Methods
Input project has quite an effect so we should make sure to test with several projection
Tools:
- https://github.com/developmentseed/morecantile
- https://github.com/geospatial-jeff/async-cog-reader (compute internal blocks shapes)
- GDAL / Rasterio
Expected results
- Timings (S3 latency and data transfer)
- Number of GET Called
- Transfered byte
Dataset
- Sentinel 2 - Int16 1band
- Naip - 3 bands Byte
Ref:
https://github.com/opendatacube/benchmark-rio-s3/blob/master/report.md
https://github.com/vincentsarago/rio-tiler-bench
https://github.com/vincentsarago/gdal-http2-benchmark
https://github.com/vincentsarago/awspds-benchmark
- Dominant language
- Python
- Stars
- 41
- Forks
- 3
- PR merge metrics
- No merged PRs in 30d
Getting set up
- Ships a Dockerfile or Docker Compose file
- No pull request template
- Read the contributing 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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