Hi @gjoseph92 ,
in the Readme there is this line:
|
- Easier access to `s3://`-style URIs (right now, you'll need to pass in `gdal_env=stackstac.DEFAULT_GDAL_ENV.updated(always=dict(session=rio.session.AWSSession(...)))`) |
I am trying to figure out how to best use stackstac in an AWS instance, for example for reading Sentinel 2 COGs. I realize the pystac_client gives only http: urls and not the s3:. Was wondering if gdal_env solves that or if I would have to manually fiddle with the stac items.
I am aware of the great work you do with coiled, however due to billing issues it is not an option for this project - that is why I am trying to do this directly in AWS. The idea is run an instance that is in the same region as the data and thus get high read speed. Unfortunately I currently just see an increase of 2x compared to running locally, which confuses me.
Do you have any tips?
Hi @gjoseph92 ,
in the Readme there is this line:
stackstac/README.md
Line 93 in 0dff7cf
I am trying to figure out how to best use stackstac in an AWS instance, for example for reading Sentinel 2 COGs. I realize the
pystac_clientgives onlyhttp:urls and not thes3:. Was wondering ifgdal_envsolves that or if I would have to manually fiddle with the stac items.I am aware of the great work you do with coiled, however due to billing issues it is not an option for this project - that is why I am trying to do this directly in AWS. The idea is run an instance that is in the same region as the data and thus get high read speed. Unfortunately I currently just see an increase of 2x compared to running locally, which confuses me.
Do you have any tips?