Dialyzer (DIscrepancy AnaLYZer for ERlang programs) is a powerful static analysis tool that helps developers identify potential issues in their Elixir code without executing it. It excels at finding type mismatches, unreachable code, and unnecessary functions through sophisticated flow analysis.

In part one of this two-part series, we’ll first get to grips with the basics of Dialyzer. In part two, we’ll examine more advanced use cases.

An Introduction to Dialyzer for Elixir

In the world of dynamic languages like Elixir, type-related issues traditionally only surface during runtime, making them harder to catch during development. Dialyzer bridges this gap by analyzing your code’s type specifications and usage patterns to detect potential problems before they reach production.

When integrated into your development workflow, Dialyzer provides several key benefits:

1. Enhanced Code Reliability: Catch type-related bugs early in the development cycle.
2. Better Documentation: Type specifications serve as living documentation and provide useful hints with IDE integration.
3. Improved Developer Productivity: Identify issues before they cause runtime errors.
4. Safer Refactoring: Get immediate feedback about type-related changes.

Setting Up Dialyzer for Elixir

While Dialyzer itself comes with Erlang, the recommended way to use it in Elixir projects is through dialyxir, a mix task that makes Dialyzer more convenient to use.

First, add dialyxir to your project’s dependencies in mix.exs:

After adding the dependency, install it by running:

Now you can run Dialyzer with:

On the first run, Dialyzer will build a Persistent Lookup Table (PLT), which contains information about your project and its dependencies. This process might take several minutes, but it’s a one-time operation. The PLT will be cached and reused in subsequent runs.

The output will look something like this:

If Dialyzer finds issues, it will display warnings like:

Although Dialyzer can infer types from your code, adding explicit type specifications helps it perform a more thorough analysis. We’ll explore how to write effective type specifications in the next section.

Types and Type Specifications in Elixir

Type specifications serve multiple purposes in Elixir. Beyond documentation, they provide Dialyzer with the information needed to perform sophisticated flow analysis. As someone who’s worked extensively with Elixir in production, I’ve found that well-designed type specifications:

1. Act as executable documentation
2. Enable early bug detection
3. Make refactoring safer
4. Improve IDE integration

Adding Typespecs to Your Elixir Code

Let’s start with the basics and progress to more complex scenarios. There are two main type attributes you’ll use frequently:

1. @type name :: <<type scpecification>> defines a type with the specified name.
2. @spec function_name(argument_types) :: return_type defines a function specification.

There are several built-in types like any(), none(), atom(), map(), and integer().

It is also possible to compose types using list(type), nonempty_list(type), etc.

You can use types defined in other modules as well. It is quite common for data structures to define their types named t. In fact, several Elixir data structures already define types that can be used in your custom typespecs, like String.t() and Range.t().

Finally, we can use almost any literal as a type that restricts the allowed values to be like the literal. For example, 1, 1..10, {:ok, type}.

We’ll cover advanced attributes to create other special types in the second and final part of this series.

For now, here’s an example of using some typespecs in practice:

In this example:

• We define custom types status and role using union types
• The create_user function specification ensures that arguments are of the correct types
• Dialyzer will catch type mismatches, like passing a number instead of a string for a name

We’ll explore more advanced type attributes like @opaque in the next post.

Understanding Common Dialyzer Warnings

Now that we have Dialyzer set up and running, let’s explore the most common warnings you’ll encounter and learn how to address them effectively.

1. Match Errors

Dialyzer detects unused pattern matches by analyzing your types:

2. Redundant Guard Clauses

This warning shows Dialyzer’s ability to detect impossible conditions:

Here, we’re pattern matching on %{} (a map) but then using an is_binary/1 guard — these conditions can never be true simultaneously since a value cannot be both a map and a binary. Such contradictions often indicate logical errors in your code’s flow control. Remove the contradictory guard or fix the pattern matching based on your intended behavior.

3. Unmatched Returns

This warning is particularly important for maintaining robust error handling:

Dialyzer has detected that the function returns a union type (:ok | {:error, binary()}) that we are ignoring. In Elixir, it’s a best practice to handle all possible return values, especially errors.

To fix this, you should:

1. Pattern match on the result and handle both cases, or
2. Use with expressions for cleaner error handling, or
3. Explicitly discard the result using _ = if that’s truly intended

4. No Local Return

This indicates that your function never returns normally — it either raises an exception or runs indefinitely:

This is common in placeholder code, but in production, you should ensure your functions have proper return paths.

If the function is meant to be raised, specify no_return() in the typespec:

In addition to obvious no-returns, Dialyzer often also raises this as a side-effect of other errors that lead to a guaranteed exception.

For example, the following code tries to pass age as a string when it’s declared type is an integer, leading to a Dialyzer no_return error as a side-effect of the call error.

This can sometimes also happen when a library has incorrect typespecs or complex types that Dialyzer has trouble inferring.

In such cases, it can be desirable to skip Dialyzer warnings. This can be achieved by using a special @dialyzer attribute to disable warnings, specifying a tuple with the warning type and the fuction/arity.

And that’s it for this part of our two-part series!

Wrapping Up

Getting started with Dialyzer might seem daunting at first, but the benefits are worth the initial setup time. As you’ve seen, it can catch many common issues before they reach production.

If you already have a large project without Dialyzer, the trick is to start small. First:

• Add Dialyzer to your development dependencies
• Begin with a single module
• Add type specifications gradually

In our next and final part of this series, we’ll dive deeper into advanced type specifications, explore sophisticated troubleshooting techniques, and learn how to handle complex scenarios that you might encounter as your codebase grows.

We’ll also look at how to effectively configure Dialyzer for larger projects and establish team-wide practices for maintaining type specifications.

Until next time!

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