cytomining / cytomining/CytoTable
Document system and workflow architecture options
- Dominant language
- Python
- Stars
- 21
- Forks
- 6
- Avg merge
- 2d 3h
- Merged PRs (30d)
- 6
Description
Document system and workflow architecture options in order to improve developer understanding of remote and/or scalable options. For example, see the following reference sketch for a potential future state (from discussions in #10):
```mermaid
flowchart LR
subgraph actor["Actor / Initiator"]
pycytominer-transform
end
subgraph Storage
direction TB
subgraph sourcestorage["Source"]
s3source[S3 or Cloud Source]
end
subgraph deststorage["Destination"]
s3dest[S3 or Cloud Destination]
end
end
subgraph worker["Worker"]
dask[Dask or Ray Cluster]
end
pycytominer-transform --> |initiates work| dask
dask --> |creates data| s3dest
s3source --> |gathers data| dask
```
Contributor guide
Research direction
Start by reading issue #10 and the Mermaid reference sketch to understand the proposed remote and scalable workflow. Review the repository to identify where architecture documentation belongs, then document the relevant system and workflow options and clearly describe the future-state trade-offs.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- aws, python
- Domain
- cloud, distributed-systems, documentation
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100