I’ve been getting into Elixir lately. I’m still learning, and one thing that helps me learn new languages is to take a problem I’ve solved in another language and solve it in the new language. A few months ago I wrote about storing state in erlang with processes and I thought it would be an interesting exercise to convert that discussion to Elixir.

I’ll start out with a solution that’s too simple to be useful. Then, by adding functionality and refactoring, show that a design pattern emerges that can be abstracted away from our implementation details. In this case, that pattern is a use-case for GenServer or Agent, and I’ll show how we can solve this problem using those tools.

State

State is how we make programs do non-trivial things. A video game’s states might include the position of the player, or a count of the number of bad guys that have been defeated. In most programming languages, we could count dead badguys by assigning the count to a variable and then updating that value each time another bad guy is defeated. Elixir, however, doesn’t allow us to keep around the value of a variable without actually carrying around a reference to that variable, which would get very cumbersome. So how do we keep track of state in Elixir? The answer is by using processes.

State is easy in object-oriented languages

We’ll use an example of a counter throughout the rest of this post. By a counter, I just mean a simple piece of code that’s capable of keeping a count value and provides an API to increment and/or access the value of the count. This is a very simple example of state; the state of the counter is the value of the count.

Object-oriented languages make it really easy and natural to store state in class instance variables. For example, to implement a simple counter in Ruby we could do something like this.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 # a simple object-oriented counter in Ruby class Counter def initialize @count = 0 end def click @count += 1 end end c = Counter . new c . click # => @count is now 1 c . click # => @count is now 2

@count is an instance variable of the Counter class. When we create a new instance of the Counter class by calling Counter.new , @count gets initialized to a value of 0 in the constructor ( initialize ). Each time we call the click method, the value of @count is incremented by 1. @count holds the state of the counter. We can easily create multiple counters, each with its own independent state.

1 2 3 4 5 c1 = Counter . new c2 = Counter . new c1 . click # => 1 c1 . click # => 2 c2 . click # => 1

In Elixir, we COULD do the following.

1 2 3 4 5 6 7 8 9 10 11 12 13 defmodule Counter do def new do 0 end def click ( counter ) do counter + = 1 end end c = Counter . new # c = 0 c = Counter . click ( c ) # c = 1 c = Counter . click ( c ) # c = 2

That is, we could just keep using the count value. This has some obvious limitations. What if we want more than one process to be able to increment the counter? Each process would have to know the counter’s value, and keeping them all consistent would be very cumbersome.

Recursion and state

So how do we keep state in Elixir? We can get part of the way there using recursion by passing state from the intial function call to the next and so on. Here is an example of a counter in Elixir that prints out the value as it increments with each recursive function call.

Recursive counter (loop_counter.ex) download 1 2 3 4 5 6 7 8 9 10 defmodule LoopCounter do def go , do : go ( 0 ) defp go ( n ) do IO . puts ( "n is #{ n } " ) # just to make sure this doesn't blow up our terminal :timer . sleep ( 1000 ) go ( n + 1 ) end end

Running this code from the iex shell will take over the shell because the function LoopCounter.go never returns. You can either exit the shell with the usual Control-g q or kill the job and connect to a new local shell with Control-g followed by k (kill the job), s (start a new shell), c (connect to the new shell).

1 2 3 4 5 6 7 8 9 10 11 12 iex(1)> c ( "loop_counter.ex" ) [LoopCounter] iex(2)> LoopCounter . go n is 0 n is 1 n is 2 n is 3 User switch command --> k --> s --> c

In LoopCounter , we use the input parameter n to the go/1 function to keep track of our state, and we increment it by recursively calling the same function with n + 1 as the parameter for the next call. The fact that we’re able to increment the value means that we are keeping track of the counter’s state. It’s just not very useful at this point because the recursive call loop completely takes over the current Elixir process.

We could use Kernel.spawn/1 to break our counter loop away from the main process (our shell) and Process.exit/2 to stop it.

1 2 3 4 5 6 7 8 9 10 11 12 iex(1)> pid = spawn ( & LoopCounter . go / 0 ) n is 0 #PID<0.55.0> n is 1 n is 2 n is 3 n is 4 n is 5 n is 6 n is 7 iex(2)> Process . exit ( pid , :exit ) true

We’ve got control of our shell back, but we still have no way to control the counter or to get at its current value programmatically.

Controlling and querying state with messages

We use messages to communicate between processes in Elixir. The Elixir Getting Started guide’s section on processes does a good job of explaining message handling in Elixir (and even has a section that’s similar to this post).

Here is an example that adds a click message to our counter. We can use the click message to increment the counter’s value and send the updated value back to the calling process as a return message.

Simple signal-based counter (signal_counter.ex) download 1 2 3 4 5 6 7 8 9 10 11 12 13 defmodule SignalCounter do def go do loop ( 0 ) end defp loop ( n ) do receive do { :click , from } -> send ( from , n + 1 ) loop ( n + 1 ) end end end

We can create a counter using spawn and send messages to it. The counter expects messages to be tuples: {:click, from} where from should be the pid of the caller so we can send back the state as a return message.

1 2 3 4 5 6 7 8 9 10 11 iex(1)> c ( "signal_counter.ex" ) [SignalCounter] iex(2)> pid = spawn ( & SignalCounter . go / 0 ) #PID<0.61.0> iex(3)> send ( pid , { :click , self }) {:click, #PID<0.53.0>} iex(4)> receive do x -> x end 1 iex(5)> send ( pid , { :click , self }) {:click, #PID<0.53.0>} iex(6)> receive do x -> x end

This is still a little messy because the caller (in this case, us via the shell) has to know the message semantics and perform all of the message receiving. We can simplify this quite a bit by including the message handling in the module and providing a simple API to the outside world. Here’s a modified counter that does this, as well as adding a few new messages.

Signal-based counter with API layer (counter.ex) download 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 defmodule Counter do # API methods def new do spawn fn -> loop ( 0 ) end end def set ( pid , value ) do send ( pid , { :set , value , self ()}) receive do x -> x end end def click ( pid ) do send ( pid , { :click , self ()}) receive do x -> x end end def get ( pid ) do send ( pid , { :get , self ()}) receive do x -> x end end # Counter implementation defp loop ( n ) do receive do { :click , from } -> send ( from , n + 1 ) loop ( n + 1 ) { :get , from } -> send ( from , n ) loop ( n ) { :set , value , from } -> send ( from , :ok ) loop ( value ) end end end

This makes the API much more intuitive by adding methods like Counter.click/0 . Note that Counter.new/0 returns the pid of the underlying process and that we pass this pid to subsequent function calls. That’s because we’re using that process to hold the state of the counter! This is a very common Erlang and Elixir idiom.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 iex(1)> c ( "counter.ex" ) [Counter] iex(2)> c = Counter . new #PID<0.62.0> iex(3)> Counter . click ( c ) 1 iex(4)> Counter . set ( c , 42 ) :ok iex(5)> Counter . get ( c ) 42 iex(6)> c2 = Counter . new #PID<0.67.0> iex(7)> Counter . get ( c2 ) 0

Note that the Counter API functions use self/0 to get the PID of the caller. In Elixir, self/0 always returns the pid of the current process (it’s actually shorthand for Kernel.self/0). In the case of Counter.click/0 , self/0 will be the pid of the process that called the function (the shell in the example above), because Counter.click/0 is just a simple function and will therefore be executed by the process that calls it.

Refactoring shared code and logical groups

There’s a lot of repeated code in the counter example above. Also, the implementataion of the counter logic is interspersed with lower-level message handling. You’re a good programmer and this rubs you the wrong way. So let’s refactor the code to share some of the common bits and clean it up quite a lot.

Refactored counter (gen_counter.ex) download 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 defmodule GenCounter do # API def new do spawn ( fn -> loop ( 0 ) end ) end def click ( pid ) do make_call ( pid , :click ) end def get ( pid ) do make_call ( pid , :get ) end def set ( pid , new_value ) do make_call ( pid , { :set , new_value }) end # message handlers # handle_msg(message, current_state) -> {reply, new_state} defp handle_msg ( :click , n ), do : { n + 1 , n + 1 } defp handle_msg ( :get , n ), do : { n , n } defp handle_msg ({ :set , new_value }, _n ), do : { :ok , new_value } # main state loop defp loop ( state ) do receive do { from , msg } -> { reply , new_state } = handle_msg ( msg , state ) send ( from , reply ) loop ( new_state ) end end # call helper defp make_call ( pid , msg ) do send ( pid , { self (), msg }) receive do x -> x end end end

This code has the same functionality as before, but is factored to avoid repeated code and to hopefully group together the important bits of our API and logic. Our API consists of using the make_call/2 helper to send messages and receive the response, which effectively translates our API into message semantics. All of the actual counter logic boils down to defining how the process should react to various messages, which we implement in the handle_msg/2 callbacks.

A pattern is starting to emerge here. We have an API that makes calls by sending messages to an underlying process state loop, and that process state loop handles those messages by delegating to message callbacks. The loop/1 and make_call/2 helpers are low-level and generic. They also don’t handle a ton of edge cases: what happens if the process dies or takes too long to respond to a message? What happens when we send an unexpected message? What if we don’t need to respond to a message (i.e., a broadcast)? How does the state loop handle shutdown? What if we want our process not to respond immediately but instead do some work and then respond when that work is done?

Production-ready with GenServer

GenServer implements the generic part of our counter process, and does it very well. GenServer is an Elixir wrapper around the Erlang gen_server module, which is part of the OTP library.

Rewriting our counter using GenServer is fairly straightforward. Essentially, we add use GenServer to import the GenServer behavior, use GenServer.start_link/3 instead of Kernel.spawn/1 , GenServer.call/3 instead of our make_call/2 helper, and implement init and handle_call callbacks to define our logic.

GenServer-based counter (counter_server.ex) download 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 defmodule CounterServer do use GenServer # API def new do GenServer . start_link ( __MODULE__ , 0 ) end def click ( pid ) do GenServer . call ( pid , :click ) end def set ( pid , new_value ) do GenServer . call ( pid , { :set , new_value }) end def get ( pid ) do GenServer . call ( pid , :get ) end # GenServer callbacks # init(arguments) -> {:ok, state} # see http://elixir-lang.org/docs/v1.0/elixir/GenServer.html def init ( n ) do { :ok , n } end # handle_call(message, from_pid, state) -> {:reply, response, new_state} # see http://elixir-lang.org/docs/v1.0/elixir/GenServer.html def handle_call ( :click , _from , n ) do { :reply , n + 1 , n + 1 } end def handle_call ( :get , _from , n ) do { :reply , n , n } end def handle_call ({ :set , new_value }, _from , _n ) do { :reply , :ok , new_value } end end

By using GenServer, we get a ton of functionality for free. For example, GenServer.call/3 takes an optional timeout argument which we can use to guarantee that the calling process never hangs while waiting for the GenServer to respond. We haven’t implemented it here, but GenServer can also handle termination callbacks, broadcast messages, bare messages (via Kernel.send/2 as opposed to GenServer.call/3 or GenServer.cast/2 ), process registration, idle timeouts, and even hot code swapping.

An Elixir bonus - using Agent

GenServer is essentially a direct adaptation of Erlang’s gen_server. The Elixir developers realized that many times we use a fairly small subset of GenServer’s capabilities, more or less just to store some state and provide an API for accessing that state. The Agent module is designed for just that use case.

Here is how we could rewrite our counter using the Agent module.

Agent-based counter (counter_agent.ex) download 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 defmodule CounterAgent do def new do Agent . start_link ( fn -> 0 end ) end def click ( pid ) do Agent . get_and_update ( pid , fn ( n ) -> { n + 1 , n + 1 } end ) end def set ( pid , new_value ) do Agent . update ( pid , fn ( _n ) -> new_value end ) end def get ( pid ) do Agent . get ( pid , fn ( n ) -> n end ) end end

This reduces our Counter to just four named functions. Much of the work is done by anonymous functions that we supply to the various Agent functions. For example, the initializer is simply fn -> 0 end to set the initial value to zero, and the getter is fn(n) -> n end which just returns the current state.

The end

There it is. We use processes to keep track of state in Elixir, and messages between processes to access and control that state. Elixir is very similar to, and in fact 100% compatible with, Erlang in this way. Elixir provides the Agent module to handle many of the most basic use cases, and the GenServer module to provide more flexibility.

Solving problems by using processes and messaging can be hard to wrap your head around at first. However, once it clicks for you, many problems become much easier to solve in this paradigm.