Switching away from an implementation that everyone is familiar with is the opposite of making something more readable.

While I like the simplicity of the lookup table, I find myself in larger projects to have different backoffs for different upstream services. I'll have shorter ones for services that can degrade without a large impact to service results and longer ones for services that are critical for results. I would prefer to have one algorithmically generated call that is flexible vs multiple individual functions that have different lookup tables, or generating tables and passing them in each time.

Using a lookup table doesn't allow parameter values to easily be changed. Also, some of us do not get paid by the line. Fwiw, I do often note the values in a comment, and this is generally sufficient.

Change of the sake of change?

I recently listened to Marc Brooker's talk [1] on retries. My key takeaway: You don't want to put more load on your system during stress. At AWS, they do "adaptive retries", which I found interesting.

[1] https://www.youtube.com/watch?v=rvHd4Y76-fs

Simple backoff ftw https://github.com/jpillora/backoff

Is this meant as a joke or a serious argument?

I personally find the original code just as if not more readable than the static lookup table and I don't need to count out the elements if I want to know how often it will retry.

But more importantly, changing the max retries is trivial in the original code and tedious in the static lookup table, especially for bigger changes.

Also, this is something you most likely want to make configurable to adapt to certain scenarios which is not possible with a static table

There are more reasons against it but those are the the main ones that jump at me right away

  for range ExponentialBackoff(ctx) {
      err := request(ctx)
      if err == nil {
          return nil
      }
  }

  for range ExponentialBackoff(ctx, MaxAttempts(20), BaseDelay(5)) {
      err := request(ctx)
      if err == nil {
          return nil
      }
  }

Why a lookup table? Why not just have the client remember the last backoff, and multiply it by a constant and apply a ceiling?

def getNewBackoff( oldBackoff: int): int {

newBackoff = oldBackoff * BACKOFF_CONSTANT

if (newBackoff > BACKOFF_CEILING) { newBackoff = BACKOFF_CEILING }

return newBackoff }

if you bit align the ceiling value you can replace the conditional with a bitwise and mask. If you use 2 as your exponent you can replace the multiplication with a bit shift left.

It’s also a good idea to introduce jitter, e.g. don’t set the new backoff to the old backoff * a constant, but add a random value such that the old value gets randomized around the desired new value- this helps prevent pile-ons when a bunch of clients noticed about the same time that the server became unresponsive; if they all have the same backoff algorithm then they’re all going to hit the server again at about the same time. It’s good to spread that out to try to avoid server overloading during restarts/cold starts/whatever.

The best results I ever got were when I used server side hinting to avoid pile-ons when the retries from the client were all clustered around the same time.

When my server got in an overloaded state, the auth path for clients short circuited and delivered an overloaded message with a randomized suggested retry value. The server can’t make the clients honor it (we could in my case because we wrote the clients) but if the clients do honor it you can spread out reconnects over a period instead of make them all point-in-time and dampen or prevent oscillating overloads.

    Retry(5, BackOff(time.Second, Linear(1.4), func(_ context.Context) { // do stuff } )

If the author is here - please turn word wrap off for code. It's unreadable on mobile.

I do this, in a couple of my Watch apps.

Might be worth looking at. The current implementation is slightly awkward, but it also uses a random delay.

Watch/iPhone communication isn't very robust. It's much better, in more recent Watches.

Another approach is to make this a cross-cutting concern using a sidecar like Envoy.

This really just an argument for using iterator generators. Does Go offer those yet?

Offtopic, but huge respect for this[1]:

> Lots of people don’t want to read about AI.

> I respect that.

> But I’m currently steeped in the world of AI, for better or for worse, and I want to blog about it. So I’ve split this blog in half.

> The normal blog, which you are reading, is now AI-free.

> There’s another, at /ai that contains only AI posts. It has its own RSS feed.

Thank you. It (non-AI) definitely goes to my RSS reader.

1: https://commaok.xyz/post/blog-schism/

Am I the only one who wonders how such trivial posts could end up on HN first page?

Here’s a version that’s IMO much simpler still, about half as long as both (after fixing v2’s line count cheating), and fixes a bug: both article versions wait an extra 60 seconds between the last attempt and returning an error.

    func do(ctx context.Context) error {
        for attempt := 1; ; attempt++ {
            if request(ctx) == nil {
                return nil
            }

            if attempt == 10 {
                return fmt.Errorf("failed after %d attempts", attempt)
            }

            select {
            case <-ctx.Done():
                return ctx.Err()
            case <-time.After(time.Second *
                min(60, math.Pow(2, attempt-1)*(0.75+rand.Float64()*0.5))):
            }
        }
    }

I think the problem with the original isn’t using a formula, it’s over-parameterization. Declaring constants right next to where they’re used once isn’t giving you anything but the opportunity for naming issues and rigidity in how you combine them. If they aren’t passed in or repeated, then I wouldn’t pull them out; if they are, then I’d try to look for minimal and natural boundary points, like just min and max, or maybe an attempt → delay function.

    const (
        maxAttempts = 10
        baseDelay   = 1 * time.Second
        maxDelay    = 60 * time.Second
    )

So, this is just about declarative programming style?

I thought based on the title it would have been about different approaches to backoff.

Or a function backoff(attempt) that abstracts away the details and can be re-used in places, perhaps a higher-order function that takes as input the function that does whatever.

"This code, or something like it, probably looks really familiar:"

Better leave that company, to be honest.

On a slightly different note, I am using Fibonacci backoff for a couple of things here and there and (subjectively) finding it a bit nicer than the standard 2x exponential backoff. I have it on a system that waits for a few days to raise an alert on missing data and I find the alerts on a cadence of 1, 2, 3, 5, 8... days are a bit tighter but still not overwhelming.

For Python, I would recommend the stamina package. It provides all mentioned best practices about when and how to retry, in a form of easy to use function decorator. Plus, there is a great video about the problem by the author: https://youtu.be/BxikFuvaT1Y