@convex-dev/workpool

Convex Component: Workpool

This Convex component queues actions and mutations as background jobs while limiting how many run in parallel.

• Configure multiple pools with different parallelism.
• Retry failed actions (with backoff and jitter) for idempotent actions, fully configurable (respecting parallelism).
• An onComplete callback so you can build durable, reliable workflows. Called when the work is finished, whether it succeeded, failed, or was canceled.

Separating and throttling async workloads

Suppose you have some important async work, like sending verification emails, and some less important async work, like scraping data from an API. If all of these are scheduled with ctx.scheduler.runAfter, they'll compete with each other for resources. The emails might be delayed if there are too many scraping requests queued ahead of them.

To resolve this problem, you can separate work into different pools.

const emailPool = new Workpool(components.emailWorkpool, {
  maxParallelism: 10,
});
const scrapePool = new Workpool(components.scrapeWorkpool, {
  maxParallelism: 5,
});

export const userSignUp = mutation({
  args: {...},
  handler: async (ctx, args) => {
    const userId = await ctx.db.insert("users", args);
    await emailPool.enqueueAction(ctx, internal.auth.sendEmailVerification, {
      userId,
    });
  },
});

export const downloadLatestWeather = mutation({
  handler: async (ctx, args) => {
    for (const city of allCities) {
      await scrapePool.enqueueAction(ctx, internal.weather.scrape, { city });
    }
  },
});

Durable, reliable workflows

Retry management

Imagine that the payment processor is a 3rd party API, and they temporarily have an outage. Now imagine you implement your own action retrying logic for your busy app. You'll find very quickly that your entire backend is overwhelmed with retrying actions. This could bog down live traffic with background work, and/or cause you to exceed rate limits with the payment provider.

Creating an upper bound on how much work will be done in parallel is a good way to mitigate this risk. Actions that are currently backing off awaiting retry will not tie up a thread in the workpool.

Completion handling

By handing off asynchronous work, it will be guaranteed to run, and with retries you can account for temporary failures, while avoiding a "stampeding herd" during third party outages.

With the onComplete callback, you can define how to proceed after each step, whether that enqueues another job to the workpool, updates the database, etc. It will always be called, whether the work was successful, failed, or was canceled. See below for more info.

Example:

const pool = new Workpool(components.emailWorkpool, {
  retryActionsByDefault: true,
  // Specifies config for actions with retry: true, or if retried by default.
  defaultRetryBehavior: { maxAttempts: 3, initialBackoffMs: 1000, base: 2 },
});

const sendEmailReliablyWithRetries = mutation({
  args: {
    emailType: v.string(),
    userId: v.id("users"),
    title: v.string(),
    body: v.string(),
  },
  handler: async (ctx, args) => {
    // ... do other things in the transaction
    await pool.enqueueAction(ctx, internal.email.send, args, {
      onComplete: internal.email.emailSent,
      context: { emailType: args.emailType, userId: args.userId },
      retry: false, // don't retry this action, as we can't guarantee idempotency.
    });
  },
});

export const emailSent = internalMutation({
  args: vOnCompleteValidator(
    v.object({ emailType: v.string(), userId: v.id("users") }),
  ),
  handler: async (ctx, { workId, context, result }) => {
    if (result.kind === "canceled") return;
    const emailLogId = await ctx.db.insert("userEmailLog", {
      userId: context.userId,
      emailType: context.emailType,
      result: result.kind === "success" ? result.returnValue : null,
      error: result.kind === "failed" ? result.error : null,
    });
    if (result.kind === "failed") {
      await pool.enqueueAction(
        ctx,
        internal.email.checkStatus,
        { userId },
        {
          retry: { maxAttempts: 10, initialBackoffMs: 250, base: 2 }, // custom
          onComplete: internal.email.handleEmailStatus,
          context: { emailLogId },
        },
      );
    }
  },
});

Note: the onComplete handler runs in a different transaction than the job enqueued. If you want to run it in the same transaction, you can do that work at the end of the enqueued function, before returning. This is generally faster and more typesafe when handling the "success" case.

You can also use this equivalent helper to define an onComplete mutation. Note the DataModel type parameter, if you want ctx.db to be type safe.

export const emailSent = pool.defineOnComplete<DataModel>({
  context: v.object({ emailType: v.string(), userId: v.id("users") }),
  handler: async (ctx, { workId, context, result }) => {
    // ...
  },
});

To skip some outcomes, add onCompleteExcludeKinds. For example, the same handler can handle failures and cancellations without being invoked on success:

await pool.enqueueAction(ctx, internal.email.send, args, {
  onComplete: internal.email.emailSent,
  onCompleteExcludeKinds: ["success"],
  context: { emailType: args.emailType, userId: args.userId },
});

The options are "success", "failed", and "canceled".

Filtering applies to the final result. Failed attempts that will be retried do not invoke the callback. A terminal failure, including a NonRetryableError, invokes it unless "failed" is excluded. Cancelation through the workpool yields "canceled" when it lands before the work starts or before a retry is scheduled; an attempt already in progress runs to its own outcome, so a race with cancel can still report "success" or a terminal "failed". Direct scheduler cancelation (for example, from the dashboard) is treated as a failure and can trigger retries instead.

Idempotency

Idempotent actions are actions that can be run multiple times safely. This typically means they don't cause any side effects that would be a problem if executed twice or more.

As an example of an unsafe, non-idempotent action, consider an action that charges a user's credit card without providing a unique transaction id to the payment processor. The first time the action is run, imagine that the API call succeeds to the payment provider, but then the action throws an exception before the transaction is marked finished in our Convex database. If the action is run twice, the user may be double charged for the transaction!

If we alter this action to provide a consistent transaction id to the payment provider, they can simply NOOP the second payment attempt. The this makes the action idempotent, and it can safely be retried.

If you're creating complex workflows with many steps involving 3rd party APIs:

• You should ensure that each step is an idempotent Convex action.
• You should use this component to manage these actions so it all just works!

Reducing database write conflicts (aka OCC errors)

With limited parallelism, you can reduce write conflicts from mutations that read and write the same data.

Consider this action that calls a mutation to increment a singleton counter. By calling the mutation on a workpool with maxParallelism: 1, it will never throw an error due to conflicts with parallel mutations.

const counterPool = new Workpool(components.counterWorkpool, {
  maxParallelism: 1,
});

export const doSomethingAndCount = action({
  handler: async (ctx) => {
    const doSomething = await fetch("https://example.com");
    await counterPool.enqueueMutation(ctx, internal.counter.increment, {});
  },
});

// This mutation is prone to conflicting with itself, because it always reads
// and writes the same data. By running it in a workpool with low parallelism,
// it will run serially.
export const increment = internalMutation({
  handler: async (ctx) => {
    const countDoc = await ctx.db.query("counter").unique();
    await ctx.db.patch(countDoc!._id, { count: countDoc!.count + 1 });
  },
});

Effectively, Workpool runs async functions similar to ctx.scheduler.runAfter(0, ...), but it limits the number of functions that can run in parallel.

Reactive status of asynchronous work

The workpool stores the status of each function in the database, and thanks to Convex's reactive queries, you can read it in a query to power a reactive UI.

You can read the status of a function by calling pool.status(id). After the work finishes, it does not maintain any metadata, and assumes any correctly-formatted workId previously existed, and returns "finished".

import { vWorkIdValidator } from "@convex-dev/workpool";
import { query } from "./_generated/server";

export const getStatus = query({
  args: { id: vWorkIdValidator },
  handler: async (ctx, args) => {
    const status = await pool.status(args.id);
    return status;
  },
});

The status will be one of:

• { kind: "pending"; previousAttempts: number }: The function has not started yet.
• { kind: "running"; previousAttempts: number }: The function is currently running.
• { kind: "finished" }: The function has succeeded, failed, or been canceled.

To get the result of your function, you can either write to the database from within your function, call or schedule another function from there, or use the onComplete handler to respond to the job result.

Get started

Pre-requisite: Convex

You'll need an existing Convex project to use the component. Convex is a hosted backend platform, including a database, serverless functions, and a ton more you can learn about here.

Run npm create convex or follow any of the quickstarts to set one up.

Install the component

See example/ for a working demo.

• Install the Workpool component:

npm install @convex-dev/workpool

• Create a convex.config.ts file in your app's convex/ folder and install the component by calling use:

// convex/convex.config.ts
import { defineApp } from "convex/server";
import workpool from "@convex-dev/workpool/convex.config.js";

const app = defineApp();
app.use(workpool, { name: "emailWorkpool" });
app.use(workpool, { name: "scrapeWorkpool" });
export default app;

See example usage in example.ts.

Configuring the Workpool

Check out the docstrings, but notable options include:

• maxParallelism: How many actions/mutations can run at once within this pool. Avoid exceeding 100 on Pro, 20 on the free plan, across all workpools and workflows.
• retryActionsByDefault: Whether to retry actions that fail by default.
• defaultRetryBehavior: The default retry behavior for enqueued actions.

You can override the retry behavior per-call with the retry option.

If you don't specify a maxParallelism as a Class-level option, you can specify it at runtime by calling ctx.runMutation(components.myWorkpool.config.update, { maxParallelism: 20 }) (you can also call the function with npx convex run or the dashboard) This can be useful for dynamic scaling of the workpool, and also setting it to 0 to pause work. If specified on the Workpool class, it will update on each call to enqueue or cancel. Tip: you can create the Workpool class at runtime based on a dynamic value, such as an environment variable.

Options for enqueueing work

See the docstrings for more details, but notable options include:

• retry: Whether to retry the action if it fails. Overrides defaults. If it's set to true, it will use the defaultRetryBehavior. If it's set to a custom config, it will use that (and do retries).
• onComplete: A mutation to run after the function finishes.
• onCompleteExcludeKinds: Optional list of result kinds to skip: "success", "failed", and "canceled". Defaults to excluding none, so every outcome invokes the callback.
• context: Any data you want to pass to the onComplete mutation.
• runAt and runAfter: Similar to ctx.scheduler.run*, allows you to schedule the work to run later. By default it's immediate.

Retry behavior

The retry options work like this:

• The first request runs as it's scheduled.
• If it fails, it will wait around initialBackoffMs and then try again.
• Each subsequent retry waits initialBackoffMs * base^<retryNumber - 1>.
• The standard base is 2.
• The actual wait time uses "jitter" to avoid all retries happening at once if they all fail at the same time.

You can override the retry behavior per-call with the retry option.

Non-retryable errors

If an action has retries enabled but hits a terminal failure, throw a NonRetryableError. Workpool will treat the attempt as failed, call onComplete with the normal { kind: "failed", error } result unless "failed" is excluded, and skip any remaining retries.

import { NonRetryableError } from "@convex-dev/workpool";

export const sendEmail = action({
  args: { email: v.string() },
  handler: async (ctx, { email }) => {
    if (!email.includes("@")) {
      throw new NonRetryableError("Invalid email address");
    }

    // Throw ordinary errors for failures that should still use retry behavior.
  },
});

Optimizations with and without Workpool

The benefit of Workpool is that it won't fall over if there are many jobs scheduled at once, and it allows you to throttle low-priority jobs.

However, Workpool has some overhead and can slow down your workload compared to using ctx.scheduler directly.

Since each Workpool has some overhead -- each runs several functions to coordinate its work -- don't create too many of them.

If you're running into issues with too many concurrent functions, there are alternatives to Workpool:

• Try combining multiple mutations into a single mutation, with batching or debouncing. See the next section for enqueueing multiple actions at once.
• Call plain TypeScript functions if possible.
  • In particular, an action calling ctx.runAction has more overhead than just calling the action's handler directly.

See best practices for more.

Batching

If you're enqueuing a lot of work, you can use enqueueActionBatch to enqueue a batch of actions at once, or the equivalents for queries or mutations.

This helps in two ways:

• It reduces the number of calls to the component, which reduces overhead as each component call runs in a fresh container (for strong isolation).
• When called from an action, it reduces the number of mutations that might conflict with each other, especially if they were being called in parallel.

await pool.enqueueActionBatch(ctx, internal.weather.scrape, [
  { city: "New York" },
  { city: "Los Angeles" },
  { city: "Chicago" },
]);

Canceling work

You can cancel work by calling pool.cancel(id) or all of them with pool.cancelAll().

export const cancelWork = mutation({
  args: { id: vWorkIdValidator },
  handler: async (ctx, args) => {
    // You can cancel the work, if it hasn't finished yet.
    await pool.cancel(ctx, args.id);
  },
});

This will avoid starting or retrying, but will not stop in-progress work. If an in-progress attempt succeeds, its result is still success. Failures remain failures when retries are disabled or exhausted, or the error is a NonRetryableError. If cancellation prevents work from starting or being retried, the final result is canceled. The callback runs unless that final result is listed in onCompleteExcludeKinds.

Monitoring the workpool

If you want to know the status of your workpool, here are some queries to use for Axiom. Just replace your-dataset with your dataset's name (which is also what you enter in the log streaming configuration in the Convex dashboard).

Note: these are optimized for monitors. For dashboards, you might want to change bin(_time, X) to bin_auto(_time).

Is it backlogged

Reports the current backlog length, where "backlog" is tasks that are past due, not including tasks that have been scheduled for the future. This reports the max for 1 minute intervals (which is roughly how often the report is generated).

['your-dataset']
| extend parsed_message = iff(isnotnull(parse_json(trim("'", tostring(["data.message"])))),
  parse_json(trim("'", tostring(["data.message"]))),
  parse_json('{}') )
| where parsed_message["component"] == "workpool" and parsed_message["event"] == "report"
| summarize max_backlog = max(toint(parsed_message["backlog"]))
  by bin(_time, 1m), workpool = tostring(["data.function.component_path"])

Are functions failing (after retries)

Reports the overall average failure rate per registered workpool in 5 minute intervals.

['your-dataset']
| extend parsed_message = iff(isnotnull(parse_json(trim("'", tostring(["data.message"])))),
  parse_json(trim("'", tostring(["data.message"]))),
  parse_json('{}') )
| where parsed_message["component"] == "workpool" and parsed_message["event"] == "report"
| extend permanentFailureRate = parsed_message["permanentFailureRate"]
| summarize avg(todouble(permanentFailureRate))
  by bin(\_time, 5m), workpool = tostring(["data.function.component_path"])

Are functions retrying a lot

Reports the ratio (0 to 1) of failures per function, in 5 minute intervals. Note: to get this data, set the workpool logLevel to "INFO" (or "DEBUG").

['your-dataset']
| extend parsed_message = iff( 	isnotnull(parse_json(trim("'", tostring(["data.message"])))),
  parse_json(trim("'", tostring(["data.message"]))),
  parse_json('{}') )
| where parsed_message["component"] == "workpool" and (parsed_message["event"] == "completed") and parsed_message["status"] != "canceled"
| summarize failure_ratio = avg(iff(parsed_message["status"] != "success", 1, 0))
  by bin(_time, 5m), function = tostring(parsed_message["fnName"])

Is there a big delay between being enqueued and starting

Reports the average time between enqueueing work and it actually starting. Note: to get this data, set the workpool logLevel to "INFO" (or "DEBUG").

['your-dataset']
| extend parsed_message = iff(isnotnull(parse_json(trim("'", tostring(["data.message"])))),
  parse_json(trim("'", tostring(["data.message"]))),
  parse_json('{}') )
| where parsed_message["component"] == "workpool" and parsed_message["event"] == "started"
| summarize start_lag_seconds = avg(todouble(parsed_message["startLag"])/1000)
  by bin(_time, 1m), function = tostring(parsed_message["fnName"])

While similar to the backlog size, this is a more concrete value, since the events in the backlog may take variable amounts of time. This is a more user- visible metric, though it is a "lagging" indicator - this will be high when the backlog was large enough to delay the processing of an entry. So alerting on the backlog size will give you a faster indicator, while this is a metric of the severity of the incident.

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