Examples
Image Processing
Since parallel steps are not yet available in workflow-py, Python example is implemented in a sequential manner. See our Roadmap for feature parity plans and Changelog for updates.
Introduction
This example demonstrates how to process images using Upstash Workflow. The following workflow will upload an image, resize it into multiple resolutions, apply filters, and store the processed versions for later retrieval.
Use Case
Our workflow will:
- Receive an image upload request
- Resize the image into different resolutions
- Apply various filters to the resized images
- Store the processed images in a cloud storage
Code Example
api/workflow/route.ts
import { serve } from "@upstash/workflow/nextjs"import { resizeImage, applyFilters, storeImage, getImageUrl,} from "./utils"type ImageResult = { imageUrl: string}export const { POST } = serve<{ imageId: string; userId: string }>( async (context) => { const { imageId, userId } = context.requestPayload // Step 1: Retrieve the uploaded image const imageUrl = await context.run("get-image-url", async () => { return await getImageUrl(imageId) }) // Step 2: Resize the image to multiple resolutions const resolutions = [640, 1280, 1920] const resizedImages: { body: ImageResult }[] = await Promise.all(resolutions.map( resolution => context.call<ImageResult>( `resize-image-${resolution}`, { // endpoint which returns ImageResult type in response url: "https://image-processing-service.com/resize", method: "POST", body: JSON.stringify({ imageUrl, width: resolution, }) } ) )) // Step 3: Apply filters to each resized image const filters = ["grayscale", "sepia", "contrast"] const processedImagePromises: Promise<string>[] = [] for (const resizedImage of resizedImages) { for (const filter of filters) { const processedImagePromise = context.call<ImageResult>( `apply-filter-${filter}`, { // endpoint which returns ImageResult type in response url: "https://image-processing-service.com/filter", method: "POST", body: JSON.stringify({ imageUrl: resizedImage.body.imageUrl, filter, }) } ) processedImagePromises.push(processedImagePromise) } } const processedImages: { body: ImageResult }[] = await Promise.all(processedImagePromises) // Step 4: Store processed images in cloud storage const storedImageUrls: string[] = await Promise.all( processedImages.map( processedImage => context.run(`store-image`, async () => { return await storeImage(processedImage.body.imageUrl) }) ) ) })main.py
from fastapi import FastAPIfrom typing import List, TypedDictfrom upstash_workflow.fastapi import Servefrom upstash_workflow import AsyncWorkflowContext, CallResponsefrom utils import store_image, get_image_urlapp = FastAPI()serve = Serve(app)class ImageResult(TypedDict): image_url: strclass ImageProcessingPayload(TypedDict): image_id: str user_id: str@serve.post("/process-image")async def process_image(context: AsyncWorkflowContext[ImageProcessingPayload]) -> None: payload = context.request_payload image_id = payload["image_id"] user_id = payload["user_id"] # Step 1: Retrieve the uploaded image async def _get_image_url() -> str: return await get_image_url(image_id) image_url: str = await context.run("get-image-url", _get_image_url) # Step 2: Resize the image to multiple resolutions resolutions = [640, 1280, 1920] resize_responses = [] for resolution in resolutions: response: CallResponse[ImageResult] = await context.call( f"resize-image-{resolution}", # endpoint which returns ImageResult type in response url="https://image-processing-service.com/resize", method="POST", body={"imageUrl": image_url, "width": resolution}, ) resize_responses.append(response) resized_images = [response.body for response in resize_responses] # Step 3: Apply filters to each resized image filters = ["grayscale", "sepia", "contrast"] filter_responses = [] for resized_image in resized_images: for filter in filters: response: CallResponse[ImageResult] = await context.call( f"apply-filter-{filter}", # endpoint which returns ImageResult type in response url="https://image-processing-service.com/filter", method="POST", body={"imageUrl": resized_image["imageUrl"], "filter": filter}, ) filter_responses.append(response) processed_images = [response.body for response in filter_responses] # Step 4: Store processed images in cloud storage async def _store_image() -> str: return await store_image(processed_image["imageUrl"]) stored_image_urls: List[str] = [] for processed_image in processed_images: stored_image_url = await context.run("store-image", _store_image) stored_image_urls.append(stored_image_url)Code Breakdown
1. Retrieving the Image
We begin by getting the URL of the uploaded image based on its ID:
TypeScript
const imageUrl = await context.run("get-image-url", async () => { return await getImageUrl(imageId)})Python
async def _get_image_url() -> str: return await get_image_url(image_id)image_url: str = await context.run("get-image-url", _get_image_url)2. Resizing the Image
We resize the image into three different resolutions (640, 1280, 1920) using an external image processing service.
We call context.call for each resolution and use Promise.all to run them parallel:
TypeScript
const resolutions = [640, 1280, 1920]const resizedImages: { body: ImageResult }[] = await Promise.all(resolutions.map( resolution => context.call<ImageResult>( `resize-image-${resolution}`, { // endpoint which returns ImageResult type in response url: "https://image-processing-service.com/resize", method: "POST", body: JSON.stringify({ imageUrl, width: resolution, }) } )))Python
resolutions = [640, 1280, 1920]resize_responses = []for resolution in resolutions: response: CallResponse[ImageResult] = await context.call( f"resize-image-{resolution}", # endpoint which returns ImageResult type in response url="https://image-processing-service.com/resize", method="POST", body={"imageUrl": image_url, "width": resolution}, ) resize_responses.append(response)resized_images = [response.body for response in resize_responses]3. Applying Filters
After resizing, we apply filters such as grayscale, sepia, and contrast to the resized images.
Again, we call context.call for each filter & image pair. We collect the promises of these requests in an array processedImagePromise. Then, we call Promise.all again to run them all parallel.
TypeScript
const filters = ["grayscale", "sepia", "contrast"]const processedImagePromises: Promise<string>[] = []for (const resizedImage of resizedImages) { for (const filter of filters) { const processedImagePromise = context.call<ImageResult>( `apply-filter-${filter}`, { // endpoint which returns ImageResult type in response url: "https://image-processing-service.com/filter", method: "POST", body: { imageUrl: resizedImage.body.imageUrl, filter, } } ) processedImagePromises.push(processedImagePromise) }}const processedImages: { body: ImageResult }[] = await Promise.all(processedImagePromises)Python
filters = ["grayscale", "sepia", "contrast"]filter_responses = []for resized_image in resized_images: for filter in filters: response: CallResponse[ImageResult] = await context.call( f"apply-filter-{filter}", # endpoint which returns ImageResult type in response url="https://image-processing-service.com/filter", method="POST", body={"imageUrl": resized_image["imageUrl"], "filter": filter}, ) filter_responses.append(response)processed_images = [response.body for response in filter_responses]4. Storing the Processed Images
We store each processed image in cloud storage and return the URLs of the stored images:
TypeScript
const storedImageUrls: string[] = await Promise.all( processedImages.map( processedImage => context.run(`store-image`, async () => { return await storeImage(processedImage.body.imageUrl) }) ))Python
async def _store_image() -> str: return await store_image(processed_image["imageUrl"])stored_image_urls: List[str] = []for processed_image in processed_images: stored_image_url = await context.run("store-image", _store_image) stored_image_urls.append(stored_image_url)Key Features
- Batch Processing: This workflow handles batch resizing and filtering of images in parallel to minimize processing time.
- Scalability: Image resizing and filtering are handled through external services, making it easy to scale. Also, the requests to these services are handled by QStash, not by the compute where the workflow is hosted.
- Storage Integration: The workflow integrates with cloud storage to persist processed images for future access.