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.

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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.

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Use Case

Our workflow will:
  1. Receive an image upload request
  2. Resize the image into different resolutions
  3. Apply various filters to the resized images
  4. Store the processed images in a cloud storage

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Code Example

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: {
            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: {
              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)
        })
      )
    )
  }
)

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Code Breakdown

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1. Retrieving the Image

We begin by getting the URL of the uploaded image based on its ID: 
const imageUrl = await context.run("get-image-url", async () => {
  return await getImageUrl(imageId)
})

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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: 
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: {
        imageUrl,
        width: resolution,
      }
    }
  )
))

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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. 
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)

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4. Storing the Processed Images

We store each processed image in cloud storage and return the URLs of the stored images: 
const storedImageUrls: string[] = await Promise.all(
  processedImages.map(
    processedImage => context.run(`store-image`, async () => {
      return await storeImage(processedImage.body.imageUrl)
    })
  )
)

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Key Features

  1. Batch Processing: This workflow handles batch resizing and filtering of images in parallel to minimize processing time.
  2. 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.
  3. Storage Integration: The workflow integrates with cloud storage to persist processed images for future access.