Cloudflare Integration
Cloudflare R2 is a cloud object storage service for data-intensive applications.
Cloudflare's API follows the same schema as any S3 compatible API.
You can link a Windmill workspace to an S3 bucket and use it as source and/or target of your processing steps seamlessly, without any boilerplate.
See Object Storage for Large Data for more details.
Add an S3 Resource
To integrate Cloudflare to Windmill, you need to save the following elements as a resource.
Here is the information on where to find the required details:
| Property | Type | Description | Default | Required | Where to Find | Additional Details |
|---|---|---|---|---|---|---|
| bucket | string | S3 bucket name | true | R2 Dashboard | Name of the S3 bucket to access | |
| region | string | AWS region for the S3 bucket | true | R2 documentation | The region is specific to R2 and is set when creating the bucket | |
| useSSL | boolean | Use SSL for connections | true | false | R2 documentation | SSL/TLS is required for Cloudflare R2 |
| endPoint | string | S3 endpoint | true | R2 documentation | Endpoint URL will be in the format https://[bucket-id].r2.storage.cloud.cloudflare.com | |
| accessKey | string | AWS access key | false | Not applicable for Cloudflare R2 | Access key ID is not required for R2 | |
| pathStyle | boolean | Use path-style addressing | false | false | Not applicable for Cloudflare R2 | Virtual-hosted-style URLs are always used in R2 |
| secretKey | string | AWS secret key | false | Not applicable for Cloudflare R2 | Secret access key is not required for R2 |
Your resource can be used passed as parameters or directly fetched within scripts, flows and apps.
Connect your Windmill workspace to your S3 bucket or your Azure Blob storage
Once you've created an S3 or Azure Blob resource in Windmill, you can use Windmill's native integration with S3 and Azure Blob, making it the recommended storage for large objects like files and binary data.
S3/Azure for Python Cache & Large Logs
For large logs storage (and display) and cache for distributed Python jobs, you can connect your instance to a bucket.