# Rxify: Retry with Exponential Backoff in RxJava

At Over, we faced an issue wherein the app was running `OutOfMemory`

when trying to export a project with high-resolution images. This made us think of various approaches for retrying our export mechanism.

Whenever we think of retrying, there are quite a few options available to us. Create a custom solution with recursion perhaps or maybe an iterative approach. One could also come up with a solution with co-routines. It all depends upon the implementation of the **crash-site**. In our case, `exportAt(scale)`

is the crash-site.

Fortunately (or unfortunately — whichever way you look at it 😜) our `exportAt(scale)`

function is reactive. Even with Rx, for retrying we have multiple options to choose from depending upon the problem statement. Let us have a look at the problem statement then.

**Problem:**

Given function`exportAt(scale): Single<Result>`

, retry the function whenever Exception occurs. We also need to change the input `scale`

to the function upon each retry. The assumption here is if we were not able to export our project at scale `1.0`

then we can retry by reducing the scale either linearly ( `1.0`

, `0.9`

, `0.8`

, `0.7`

and so on) or exponentially ( `1.0`

, `0.5`

, `0.25`

, `0.0625`

and so on) depending upon the retry mechanism we end up choosing.

What are our options here? With Rx we have an operator called `retry()`

which has a few overloaded options. Our problem demands that we change our original `Single`

`exportAt(scale)`

each time we want to `retry()`

to take a different `scale`

. None of the overloaded variations of the `retry()`

operator would let us change our `scale`

as a function of the number of times we are retrying (At least from what I could make out from the documentation 🤔). So, we need to think of another way.

**Solution:**

The solution I finally came up with makes use of various helpful operators (spells). Let us look at a few of the operators first before jumping to the final solution.

#### Range : Observable.range(0, 5)

It emits values `0, 1, 2, 3, 4 and 5`

.

#### ConcatMap :

ConcatMap operator is like `flatMap()`

with two differences. First, `concatMap()`

preserves order and second, it doesn’t subscribe to the next value until the first one completes.

`TakeUntil : (`Take`um `Until`lum ;)`

`takeUntil(stopPredicateFunction)`

accepts values until the `stopPredicateFunction`

resolves to true.

Here’s the full code-snippet with exponential backoff :

`Observable.range`

emits values 0 to 5, where`5`

is the max. number of times we wish to retry.`map()`

converts our`input`

values into`scale = 1/2^(input)`

i.e.`1.0, 0.5, 0.25 …`

. (Reducing exponentially). You can provide it any function, depending upon the retry mechanism you choose. Linear for example could look like :`scale = 1.0f — 0.1 * input`

producing values`1.0`

,`0.9`

,`0.8`

,`0.7`

and so on.`concatMap()`

subscribes to the next`scale`

value only when one value completes. So, we will not subscribe to`exportAt(0.9)`

until we are done processing`exportAt(1.0)`

.- Then,
`map()`

and`onErrorReturn()`

wrap our`exportAt(scale)`

output inside a`Result`

wrapper which is a sealed class as follows :

sealed classResult {

data classSuccess(val double: Double): Result()

data classFailed(val error: Throwable): Result()

}

5. With `takeUntil(Result.Success)`

: We keep on retrying till our `exportAt(scale)`

successfully exports the Project and thus emits a `Result.Success`

.

6. Finally, with `lastOrError()`

we take the last value which will be `Result.Success`

in case we were successful in 5 attempts. Or we get our `exportAt(scale)`

exception wrapped inside `Result.Error`

in case we couldn’t succeed even in 5 attempts.

7. We can then show User appropriate feedback depending upon whether we were successful in exporting the project or not.

And with this, we are done!

This wraps up our approach to implementing exponential backoff with Rx. I am sure that there are alternative solutions to this problem, let me know if you find a different or better way in the comments below.

Hope it helps. Find me on twitter @ragdroid.