Learn Elixir | Dangers Of Genservers

Start Your Free Preview Today!

Blog Image


The Dangers of GenServers in Elixir


Mika Kalathil

October 29th 2018

This article assumes basic familiarity with GenServers

Common GenServer Mistakes

Overusing GenServers is a common mistake when getting started with Elixir. The common pattern I’ve seen is wrapping most of the code with a potential for failure in a GenServer, because if it does fail, it is then isolated and won’t crash the rest of the application. However, GenServers have the potential to be extremely dangerous to our application because they can become a bottleneck, which could even result in dropped messages from other processes.

One of GenServer’s most important principles is that it can only process one message at a time. This means that in order to handle multiple messages happening in parallel, we need to have multiple instances/processes of that GenServer running.

Elixir’s concurrency and parallelism is achieved through processes.

What are the costs of GenServers?

Let’s say we’re creating a mailer (using Swoosh) and you wrap our code in a GenServer (to stop it from crashing the application if it fails). In this example we will see actual code I’ve seen in a production app.

defmodule MyApp.Mailer do
  use Swoosh.Mailer, otp_app: :sample

defmodule MyApp.MailSender do
  use GenServer

  import Swoosh.Email

  @from_info {"Bill Nye", "[email protected]"}

  # Client

  def start_link(_), do: :ok

  def send_mail(email, name, subject, body) do
    GenServer.call(:mailer, {:send_email, {email, name, subject, body}})

  def send_mail_async(email, name, subject, body) do
    GenServer.cast(:mailer, {:send_email, {email, name, subject, body}})

  # Server (callbacks)

  @impl GenServer
  def init(_), do: :ok

  @impl GenServer
  def handle_cast({:send_mail, {email, name, subect, body}}, _, _) do
      |> create_mail(name, subject, body)
      |> Mailer.deliver

  @impl GenServer
  def handle_call({:send_mail, {email, name, subect, body}}, _, _) do
      |> create_mail(name, subject, body)
      |> Mailer.deliver

  defp create_mail(email, name, subject, body) do
      |> to({name, email})
      |> from(@from_info)
      |> subject(subject)
      |> html_body(body)

Here we’ve created a GenServer with two functions that send mail. The send_mail function uses call, which returns the results of the send request, while the send_mail_async function uses cast, which will return right away and execute the request shortly after. This module allows mail sends to fail such, that the rest of our system does not crash.

Unintended Side Effects

By wrapping our code in a GenServer, a potential unintended side-effect is that we are limited to being able to only process only one message at a time. Our calls for the GenServer are run on a singular thread, meaning that calling GenServer multiple times will result in a queue of messages, which could potentially halt the calling process until the GenServer has finished processing the prior queue to your message. This creates a bottleneck.

While this arrangement can be leveraged as a back-pressure system, there are much better solutions out there, like GenStage. One of the reasons for this is timeouts. By default GenServers have a timeout of 5 seconds, after which your message will be dropped. This timeout can be changed to any number you would like as well as :infinity. However, when :infinity is used there’s a potential to overflow the mailbox with messages and crash the whole VM by running it out of memory.


Say we’ve got a job processing system, and it batches up groups of 100 emails and sends them all at once. In another part of our API, we’ve got an endpoint that sends mail instantly, with the response of the mail passed along to the return of the API.

We’re now waiting for send_mail_async #1 - 100 to run before that endpoint can return the result of sending its one message. In the worst case scenario, 100 messages clog the queue for too long and our API call to send a message times out.

What is the harm

First, usually we don’t want to be losing messages in the queue (as can happen with timeouts). Second, if we’re running many long running functions at once, we want them returning as quickly as possible instead of one after the other, or our application won’t be able to scale to demand.

GenServers have the capability of storing large amounts of state data. But we need to be careful about what we store in the state because memory is not unlimited or cheap. And when it comes to message passing, large messages need to be passed in a different way from small ones, otherwise they will get copied in memory. Making multiple copies of large messages can take up more system resources than expected. Discord created fastglobal as a solution to this problem. Another potential solution is converting the message to binary first, as binaries larger than 64 bytes are stored in a shared area, and only the reference is passed between processes instead of the large binary data.

Parallelism: Registries to the Rescue

If we want to solve some of these issues, particularly around creating bottlenecks or slowdowns, we have the solution: registries! These provide us with a way to store a key-value dictionary of name -> PID. This allows us to ask the registry to lookup the PID and then call the PID directly which can be your GenServer.

Using registries changes the flow to something like this:

Registries provide a great solution to possible bottlenecks with GenServers that come from many messages hitting at once. But this solution assumes we’re running on one machine locally, so what happens when we start to go distributed? This requires a registry that can communicate distributedly, making these libraries some of the possible solutions:

GenServers as Caches

On several occasions, I have seen GenServers being used as caches. Most recently, in a job interview I was asked to create a caching module, so I went ahead and used con_cache. I was told my solution differed from the norm and that most people had chosen to use a GenServer, which might not have been necessary. I see a lot of GenServers that could be implemented as Agents instead. It is better to use the simplest solution, adding complexity if necessary afterwards.

con_cache runs on top of ETS, which for reads and writes can be async and can be called in parallel. This means that we can’t have a queue build up to access and modify the state when there are tons of requests at once, which is a low but non-zero possibility with the GenServer implementation. This limitation is something to be aware of and we should monitor and analyze performance to see if we should use something like ETS instead, which, incidentally, is also how registries work. If you are using ETS, I suggest using it behind con_cache, which abstracts some of the hard parts.


In this article we learned some of the dangers of GenServer, and how using them incorrectly can lead to bottlenecks in your application. We also learned that using GenServers for long running functions requires more thought, especially if the same GenServer has both cast and call as you can lag thecaster due to prior calls . Finally we learned to use ETS or Agents instead of GenServers for storing states if at all possible.

In an upcoming blog post about distributed GenServers we’ll discuss how to use the different tools mentioned to create a distributed registry. We’ll also discuss some of the pains, issues and questions we must ask ourselves around GenServers at a distributed level.

Have you experienced a GenServer bottleneck? Have more questions about GenServers?

Let me know in the comments!