Supabase is an open-source Firebase alternative for building apps. It offers a real-time database that allows developers to store and sync data across multiple devices in real-time.

Many application workflows are triggered to run in the background due (with Defer) to their long-running times. As many of these are triggered by users, they require notifications to indicate progress.

You can use Defer with Supabase to execute long-running background workflows, leveraging Supabase’s Realtime Broadcast within Defer to send realtime end-user notifications (ex: progress).

Use case

Supabase Realtime Broadcast is a WebSocket messaging solution. Below is a NextJS application that shows how to connect an end-user’s Social accounts to set up alerts on selected topics. We use OpenAI to extract topics from the imported verbatim (e.g., “Nodejs performance”).

1. Emit updates from a long-running Workflow

The initial import, performed by importVerbatim(), of users’ posts and related searches’ posts might take time (e.g., 5 - 15 minutes); for this reason, importVerbatim() sends regular updates to the front end by leveraging Supabase’s Broadcast feature:

import { defer } '@defer/client'

import supabaseClient from '../supabaseClient'
import { importersForUser } from '../importers'
import { classifierForUser } from '../classifier'

async importVerbatim(userID: string) {
   const channelName = `import-socials-${userID}`
   const channel =

   const importers = importersForUser(userID)
   for (const importer of importers) {
     // notify the user that we start importing data
		   type: 'broadcast',
		   event: 'progress',
		   payload: { message: `Importing ${}...` },
     await importer.perform()

     // retrieve a classifier that will index the imported data
     //   into a Vector database
     const classifier = classifierForUser(, userID)
		   type: 'broadcast',
		   event: 'progress',
		   payload: { message: `Extracting topics for ${}...` },
     await classifier.perform()

   // always close the channel once finished

export default defer(importVerbatim, {
   concurrency: 5 // ensure we don't exceed our 3rd Party API rate limits

The above code does the following:

  1. For a given userID, it retrieves the importers for each connected social account
  2. Then, sequentially, it runs the importer and, once the data is imported, runs an OpenAI classifier that creates embedding to later extract major topics (e.g., “NodeJS performance”)
  3. Finally, once all the data is imported and stored in a Supabase Vector database, it closes the broadcast channel

Note: this function could be broken down easily into multiple parallel background functions, sharing the same channel to broadcast updates.

2.Display real-time updates to the users

Finally, let’s add the real-time updates to our front end:

"use client";
import importVerbatim from "@/defer/importVerbatim";
import { useDeferRoute } from "@defer/client/next";
import { useState, useRef, useEffect } from "react";

import supabaseClient from "../../supabaseClient";
import useUser from "../hooks/useUser";

export default function Setup() {
  const { id: userID } = useUser();
  const subscribed = useRef(false);
  const channelName = `import-socials-${userID}`;

  const [progress, updateProgress] = useState<string>();

  const [startImport, { loading, result }] = useDeferRoute<
    typeof importVerbatim

  useEffect(() => {
    if (loading && !subscribed.current) {
      subscribed.current = true;
        .supabaseClient.on("broadcast", { event: "progress" }, (payload) =>
  }, []);

  return (
      {/* JSX UI that will trigger `startImport(userID)` and display `progress`*/}

The above code does the following:

  1. It creates a progress state that will store the received live updates from the workflows
  2. It instantiates the useDeferRoute() hook from @defer/client/next to get the startImport() function to trigger the workflow
  3. The useEffect() block will start listening for the importVerbatim execution once started and store the received updates in the progress local state