demo-temporal

README

demo-temporal

Demo application built with Temporal in Golang

Description

The demo uses Temporal to manage an image processing workflow.

The image processing workflow takes an uploaded image, ensures it's of a valid image type, converts it to grayscale, and makes the resulting image available for download.

To access this workflow, an API is provided on localhost port 8081. This API has an /upload path to which an image may be uploaded. This starts the workflow running. The /status/:workflowID/run:runID queries the status of the workflow given information returned from the upload. Once complete, the status will read "processing complete". At that point, the result image can be downloaded from the /download/:imageID API path.

Running

1. Clone github.com/temporalio/docker-compose in a separate directory.
2. Change to the directory with the above repository and run docker compose up -d.
3. Change back to this repository's directory and run docker compose up -d.
4. Check that the API is up and running by curling its health check. It should return successfully with a status of "ok".

   $ curl http://localhost:8081/health
   {"status":"ok"}
   

Usage

The following contains examples. The imageId, workflowId, and runId is randomly generated for each image uploaded.

Upload Image

$ curl -X POST -F "file=@test1.webp" http://localhost:8081/upload
{"imageId":"79839d04-5dd1-47a9-a2c6-ba91bb7edbb1","message":"file uploaded","runId":"6c2a3179-6dc8-4ddc-919a-3eb1fa6c58a6","workflowId":"79839d04-5dd1-47a9-a2c6-ba91bb7edbb1"}

Get Image Processing Status

$ curl http://localhost:8081/status/79839d04-5dd1-47a9-a2c6-ba91bb7edbb1/run/6c2a3179-6dc8-4ddc-919a-3eb1fa6c58a6
{"error":"","runId":"6c2a3179-6dc8-4ddc-919a-3eb1fa6c58a6","status":"converting image to grayscale","workflowId":"79839d04-5dd1-47a9-a2c6-ba91bb7edbb1"}

Download Processed Image

Open http://localhost:8081/download/<imageId> with your browser, replacing the <imageId> with your imageId returned from the upload.

Notes

1. Using a managed service like S3 to handle image uploading would move the network bandwidth for images to that service so that the api does not need to bear the burden. The api could generate a signed S3 URL for to POST the image and add an upload to the database. The frontend would take the signed URL from the backend to upload the image directly to S3. The backend could have some kind of process that handles S3 notifications and start the image processing workflow once the image is fully uploaded to S3.
2. Everything needs unit tests badly. Definitely needs error path testing. :)
3. API needs standardization. Same for the workflow status that gets returned from the API status endpoint.
4. There's no auth so please don't run this publicly. The image ID probably isn't even really large enough to make it hard to guess.
5. There are no neat Grafana dashboards for service status.
6. The worker process does not provide an endpoint for Prometheus to scrape.