Look, I'm not going to sugarcoat it. Elixir is an incredible language with powerful abstractions, but it's also a minefield if you walk in blindly. After years of building production systems and fixing what others broke, I've seen the same mistakes repeated over and over. The worst part? Most of these pitfalls are completely avoidable if you know what to look for.
This isn't another tutorial promising that Elixir will solve all your problems. Instead, this is a brutally honest field guide written by someone who has debugged crashes at 3 AM and watched applications crumble under load. If you're an architect responsible for designing Elixir systems, or a developer who wants to avoid career-limiting mistakes, keep reading.
The Overconfidence Trap: When Experience Becomes Your Worst Enemy
Here's something nobody wants to admit: being a senior developer in another language can actually make you worse at Elixir when you start. According to research from DockYard's analysis of common mistakes, overconfidence driven by deep experience in other programming languages is the root cause of most fraught Elixir software development lifecycles. You come in thinking "I've been coding for 15 years, how hard can this be?" and then proceed to write Java in Elixir syntax.
The fundamental issue is that Elixir requires a paradigm shift that goes far beyond syntax. You're not just learning new keywords—you're learning to think in processes, immutability, pattern matching, and fault tolerance. When you treat Elixir as just another language with different punctuation, you build systems that fight against the grain of what makes Elixir powerful. Your object-oriented instincts scream to create classes and inheritance hierarchies, but Elixir wants you to think in lightweight processes and message passing. Your imperative programming background tells you to mutate state in place, but Elixir demands immutability and transformation pipelines. The result is code that technically runs but misses the entire point of why you chose Elixir in the first place.
What makes this particularly insidious is that these anti-patterns often work fine in development. You build your prototype, it passes tests, stakeholders are happy, and you ship to production feeling confident. Then reality hits. Your application can't scale horizontally because you've built implicit coupling between components. Your error handling is brittle because you relied on exceptions instead of supervision trees. Your database queries slow to a crawl because you're treating Ecto like ActiveRecord. By the time you realize the architecture is fundamentally flawed, you've got months of technical debt and a team that's learned all the wrong patterns.
The solution isn't to throw away your experience—it's to actively unlearn certain habits while preserving the valuable parts. Start by reading "Designing for Scalability with Erlang/OTP" even before you write production code. Use static analysis tools like Credo from day one; according to DockYard, Credo goes beyond linting to teach invaluable lessons about consistency using your own code. Most importantly, pair program with experienced Elixir developers who can catch your object-oriented reflexes before they become permanent architecture. Your 15 years of experience are valuable, but only if you're willing to question every assumption you bring to the table.
# WRONG: Treating GenServer like a class with instance variables
defmodule UserManager do
use GenServer
def start_link(_) do
GenServer.start_link(__MODULE__, %{users: [], cache: %{}}, name: __MODULE__)
end
# Storing everything in one giant state map
def handle_call({:add_user, user}, _from, state) do
new_users = [user | state.users]
new_cache = update_complex_cache(state.cache, user)
{:reply, :ok, %{state | users: new_users, cache: new_cache}}
end
end
# RIGHT: Separate concerns, use supervision, keep state minimal
defmodule UserRegistry do
use GenServer
def start_link(_) do
GenServer.start_link(__MODULE__, :ets.new(:users, [:named_table, :public]),
name: __MODULE__)
end
def handle_call({:register, user_id, pid}, _from, table) do
:ets.insert(table, {user_id, pid})
{:reply, :ok, table}
end
end
Supervision Tree Dysfunction: Building Houses of Cards Instead of Fortresses
The OTP supervision tree is Elixir's killer feature, but I've seen more supervision trees that are actively harmful than helpful. The issue isn't that developers don't use supervisors—it's that they use them wrong, creating intricate structures that look impressive but crumble when stressed.
One of the most common anti-patterns, identified in multiple community discussions, is the "inception supervisor" problem. You create a GenServer that spawns other GenServers in its init callback, storing their PIDs in state and hoping for the best. This feels natural if you're coming from languages where you manually manage object lifecycles, but it's architectural suicide in Elixir. When that parent GenServer crashes, those child processes become orphaned. When you try to shut down your application, you get dangling processes that refuse to terminate cleanly. The supervisor tree that's supposed to make your system resilient instead makes it unpredictable.
The correct approach is to let supervisors do what they're designed to do: supervise. According to Elixir's official documentation, when you define children in a supervisor, it handles the entire lifecycle—starting processes in order, linking them properly, restarting them according to strategy, and shutting them down in reverse order. But here's what the documentation doesn't emphasize enough: choosing the right supervision strategy is critical and context-dependent. The :one_for_one strategy means only the crashed child restarts, which works when children are independent. The :one_for_all strategy restarts all children when any one crashes, suitable when processes are interdependent. The :rest_for_one strategy restarts the crashed child and any started after it, useful for processes with dependencies in order.
What kills production systems is using :one_for_one when children are actually dependent on each other. Imagine you have a process that maintains a connection to an external service and another process that uses that connection. If the connection process crashes and restarts with :one_for_one, the dependent process keeps trying to use the old, dead connection. The supervisor restarts the crashed process but leaves the dependent one in a broken state. You end up with a system that looks running from the outside but is actually frozen internally. The solution requires understanding the actual dependencies between your processes and choosing supervision strategies that match reality, not convenience.
# WRONG: Supervisor inception anti-pattern
defmodule BadSupervisor do
use GenServer
def init(:ok) do
# Spawning children in init - supervision tree knows nothing about them
{:ok, worker1} = Worker.start_link()
{:ok, worker2} = Worker.start_link()
{:ok, %{worker1: worker1, worker2: worker2}}
end
end
# RIGHT: Proper supervision with explicit strategies
defmodule GoodSupervisor do
use Supervisor
def start_link(init_arg) do
Supervisor.start_link(__MODULE__, init_arg, name: __MODULE__)
end
def init(_init_arg) do
children = [
{ConnectionPool, []},
{ApiClient, []}, # Depends on ConnectionPool
{DataProcessor, []} # Depends on ApiClient
]
# rest_for_one ensures if ConnectionPool crashes,
# both ApiClient and DataProcessor restart
Supervisor.init(children, strategy: :rest_for_one)
end
end
Another critical mistake is ignoring the child_spec/1 function. When using use GenServer, you get a default implementation, but relying solely on defaults means you're accepting restart behaviors that might be wrong for your use case. Should a temporary job processor automatically restart when it crashes? Probably not—it should stay dead and let the supervisor move on. Should your database connection pool restart immediately with :permanent? Absolutely. According to community best practices from multiple Elixir experts, you need to explicitly think through these decisions rather than accepting whatever defaults the framework provides. The difference between a system that gracefully handles failures and one that creates cascading crashes often comes down to these seemingly minor configuration choices.
The N+1 Database Apocalypse: When Ecto Becomes Your Performance Bottleneck
Ecto is phenomenal, but it's also easy to shoot yourself in the foot if you treat it like an ORM from another ecosystem. The N+1 query problem is database performance 101, yet I consistently see it in production Elixir applications. What's particularly frustrating is that Ecto gives you all the tools to avoid it, but developers either don't know they exist or don't understand when to use them.
According to AppSignal's analysis of Ecto performance issues, the N+1 problem occurs when loading a parent record and its associated child records using separate queries instead of a single join. The classic example: loading all users and then, for each user, loading their posts. If you have 100 users, that's 1 query to load users plus 100 queries to load posts—101 queries total when you could have used one. In development with fake data this feels instant. In production with real data volumes, your application crawls to a halt and your database server catches fire.
The brutal truth is that Ecto deliberately doesn't lazy-load associations. The Ecto documentation explicitly states this decision was intentional: lazy loading may sound convenient initially, but in the long run it becomes a source of confusion and performance issues. When you access user.posts and nothing happens, that's not a bug—that's Ecto forcing you to be explicit about your data fetching strategy. You have to consciously decide to preload those posts, and that conscious decision is what prevents accidental N+1 queries. But here's the problem: many developers coming from Rails or Django where lazy loading is default don't understand why their associations are nil, so they work around it by manually loading data in loops. This creates exactly the N+1 problem Ecto was designed to prevent.
# WRONG: The classic N+1 disaster
def get_users_with_posts do
users = Repo.all(User)
Enum.map(users, fn user ->
# This runs a separate query for EACH user
posts = Repo.all(from p in Post, where: p.user_id == ^user.id)
Map.put(user, :posts, posts)
end)
end
# Result: 1 query for users + N queries for posts = N+1 queries
# RIGHT: Use preload for simple cases
def get_users_with_posts do
Repo.all(User)
|> Repo.preload(:posts)
end
# Result: 2 queries total (users, then posts)
# EVEN BETTER: Use join preload for filtering
def get_active_users_with_recent_posts do
from(u in User,
join: p in assoc(u, :posts),
where: u.active == true and p.inserted_at > ago(7, "day"),
preload: [posts: p]
)
|> Repo.all()
end
# Result: 1 query with join
Beyond N+1 queries, there's the related problem of loading too much data from the database, which becomes a memory issue in LiveView applications especially. A case study from Liefery-IT shows how database query performance can degrade dramatically based on how you structure your queries and indexes. They found a query that took longer the fewer elements it affected, which seemed counterintuitive until they discovered PostgreSQL was using the wrong index. The fix required understanding the query planner and creating a composite index with the correct column order. These aren't Elixir problems or Ecto problems—they're database problems that Elixir developers must understand because Ecto won't save you from poor query design.
The solution requires treating database access as a first-class architectural concern. Use Ecto's query composition features to build queries incrementally rather than writing raw SQL fragments. Leverage Telemetry to monitor query performance in production; according to AppSignal's recommendations, instrumenting Ecto queries lets you catch performance regressions before they become critical. Most importantly, regularly review your query patterns in code reviews and reject any PR that doesn't explicitly justify why preloading isn't used for associations. Your database is likely your biggest performance bottleneck—treat it with the respect it deserves.
LiveView Memory Leaks: When Real-Time Becomes Real-Time Disaster
Phoenix LiveView is magical when used correctly and catastrophic when used incorrectly. The fundamental architecture—stateful server processes tied to browser sessions—means every mistake you make gets multiplied by the number of concurrent users. What works fine with 10 test users becomes a memory leak crisis with 1,000 production users.
According to a comprehensive analysis by Hex Shift on common LiveView mistakes, keeping too much data in the socket is the mistake that underpins many others. Every piece of data you put in assigns stays in memory for the entire session. A LiveView process is created for each connected browser, holding all assigns in memory, and every assign change triggers a diff sent to the browser. Load a list of 10,000 items into assigns? That's fine for one user. Load it for 1,000 concurrent users? You've just consumed gigabytes of RAM on things that probably didn't need to be there.
The issue compounds because developers treat LiveView assigns like a convenient data store. You fetch data from the database once, stuff it in assigns, and access it whenever needed. This feels efficient—you're avoiding repeated database queries. But you're trading database load for memory consumption, and memory is far more constrained than most people realize. A typical Elixir process starts at about 300 bytes but can grow significantly based on what you store in it. Multiply that by thousands of concurrent LiveView sessions and you'll hit memory limits long before you hit CPU limits.
# WRONG: Storing entire collections in assigns
defmodule ProductListLive do
use Phoenix.LiveView
def mount(_params, _session, socket) do
# Loading ALL products into memory for this session
products = Repo.all(Product) |> Repo.preload([:category, :reviews, :images])
{:ok, assign(socket, products: products, selected: nil)}
end
def handle_event("select", %{"id" => id}, socket) do
# Filtering in memory instead of querying
product = Enum.find(socket.assigns.products, &(&1.id == id))
{:noreply, assign(socket, selected: product)}
end
end
# RIGHT: Load only what you need, when you need it
defmodule ProductListLive do
use Phoenix.LiveView
def mount(_params, _session, socket) do
# Load only IDs and essential display data
products = from(p in Product, select: %{id: p.id, name: p.name, price: p.price})
|> Repo.all()
{:ok, assign(socket, products: products, selected_id: nil)}
end
def handle_event("select", %{"id" => id}, socket) do
# Query the full product only when actually needed
product = Repo.get!(Product, id) |> Repo.preload([:category, :reviews])
{:noreply, assign(socket, :selected, product)}
end
end
The second major LiveView pitfall is blocking the LiveView process with long-running operations. As documented in community post-mortems, when you have blocking work in a LiveView callback, the entire user experience stalls. Your LiveView process can only handle one message at a time. If you make a slow API call or run a complex computation in handle_event, the user's browser can't receive any updates until that operation completes. The interface freezes, and users assume something broke.
The solution is start_async/3 for truly asynchronous operations, introduced in LiveView 0.18 according to official documentation. This spawns work in a separate process and lets your LiveView continue handling other events. When the async work completes, you handle the result in handle_async/3. The pattern is simple but requires discipline: identify any operation that might take more than a few milliseconds and make it async. Database queries, HTTP requests, file operations, complex calculations—all candidates for async handling. The performance difference between blocking and async operations in LiveView is the difference between an application that scales and one that falls over.
Another critical issue identified by AppSignal is passing the entire assigns map to helper functions. This completely ruins change tracking because LiveView can't determine which specific assigns actually changed. Every assign modification triggers a full re-render instead of a targeted diff. The fix is simple but requires vigilance: pass only the assigns you actually need to each function, and collapse multiple arguments into a keyword list if needed. Code reviews should specifically flag any function that receives assigns as a parameter.
Context and Boundary Confusion: When Your Code Becomes Unmaintainable Spaghetti
Phoenix contexts were introduced to solve organizational problems, but I've seen more projects where contexts made things worse instead of better. The issue isn't the concept—it's that developers either ignore contexts entirely or create so many tiny contexts that navigation becomes impossible.
According to Phoenix documentation and community best practices, contexts are modules that expose and group related functionality. They should define the public API for a domain of your system. The classic example is a "Accounts" context that handles user registration, authentication, and profile management. All the complex details about password hashing, database queries, and validation are hidden behind a clean interface like Accounts.create_user(attrs) or Accounts.authenticate_user(email, password).
But here's where it goes wrong. Some developers create contexts for every single database table, ending up with Users, Posts, Comments, Likes, Notifications—each with their own folder, schema, and context module. You want to create a post with tags? You're now calling into multiple contexts from your controller, manually coordinating transactions and error handling. The controllers become fat again, filled with the coordination logic that contexts were supposed to encapsulate. Other developers go the opposite direction: one giant App context that contains everything, negating any organizational benefit.
The real solution, as described in community discussions about service modules and Ecto Multi, is to think about bounded contexts from Domain-Driven Design. Group functionality by business capability, not by database table. A "Content" context might handle posts, comments, and tags together. A "Billing" context might handle subscriptions, invoices, and payment methods. These contexts can use Ecto Multi to coordinate complex transactions that span multiple tables while still providing a clean, atomic interface to the rest of the application.
# WRONG: Controllers coordinating between too many tiny contexts
def create_post(conn, params) do
user = Users.get_user!(params["user_id"])
{:ok, post} = Posts.create_post(params["post"])
Enum.each(params["tag_ids"], fn tag_id ->
tag = Tags.get_tag!(tag_id)
PostTags.create_post_tag(post, tag)
end)
Notifications.notify_followers(user, post)
# Controller is doing business logic coordination
end
# RIGHT: Single context handles the entire operation
defmodule Content do
alias Ecto.Multi
def create_post(user, attrs) do
Multi.new()
|> Multi.insert(:post, Post.changeset(%Post{user_id: user.id}, attrs))
|> Multi.run(:tags, fn repo, %{post: post} ->
attach_tags(repo, post, attrs["tag_ids"])
end)
|> Multi.run(:notify, fn _repo, %{post: post} ->
notify_followers(user, post)
end)
|> Repo.transaction()
|> case do
{:ok, %{post: post}} -> {:ok, post}
{:error, _failed_operation, changeset, _changes} -> {:error, changeset}
end
end
end
Another boundary issue is schema coupling. According to identified anti-patterns in Elixir code, using Ecto schemas directly in migrations is dangerous because schema changes over time can break old migrations. Migrations should be explicit about their database structure instead of relying on schemas that might change. Similarly, exposing Ecto schemas directly through your context API couples your internal representation to your external interface. When you need to change your database structure, you end up breaking every place that consumes your context.
The solution is to treat schemas as internal implementation details and use plain maps or custom structs for your context boundaries. This is more verbose upfront but provides flexibility later. You can change your database schema without breaking your API. You can return different data structures from different functions based on what callers actually need instead of always returning the full schema with all associations.
The 80/20 Rule: 20% of Knowledge That Prevents 80% of Problems
After seeing hundreds of Elixir projects succeed and fail, certain patterns emerge as high-leverage knowledge. These are the insights that, if understood early, prevent the majority of serious architectural mistakes:
Understand Process Fundamentals Before Abstractions. GenServer, Supervisor, and Agent are all built on processes, message passing, and links. If you understand these primitives from the Erlang VM, the higher abstractions make sense. If you don't, you'll cargo-cult patterns without understanding when and why they work. Spend time with the Process module, understand linking and monitoring, and grasp what happens when processes crash. This 20% of knowledge prevents 80% of OTP-related mistakes.
Database Access Is Architecture, Not Implementation. How you query your database determines your application's scalability ceiling. The decision to use preload versus joins, how you structure your indexes, whether you batch queries—these aren't minor optimizations you can fix later. They're architectural decisions that get harder to change as your codebase grows. Invest in understanding Ecto's query composition, learn to read EXPLAIN ANALYZE output, and establish query review as part of your code review process. Getting database patterns right early prevents massive rewrites later.
State Management Is Your Primary Design Challenge. Every bug, every performance issue, and every scaling problem ultimately traces back to how you manage state. Where state lives (process memory, ETS, database), how it flows through your system (messages, function calls, database queries), and when it gets updated (synchronously, asynchronously, eventual consistency) defines your entire architecture. OTP provides tools for state management, but you have to consciously design state flow. Poor state management causes 80% of production issues even though it represents maybe 20% of your conceptual focus during initial development.
Supervision Strategies Must Match Reality. The supervision tree is only as good as the strategies you configure. :one_for_one when you need :rest_for_one creates subtle bugs that appear under load. Understanding the actual dependencies between your processes and configuring supervision to match those dependencies prevents cascading failures. This requires honest assessment of your system's actual behavior, not what you wish it behaved like.
Explicit Is Better Than Clever. Elixir provides powerful metaprogramming, macros, and protocols. The temptation to be clever is strong. Resist it. According to multiple community sources on anti-patterns, excessive metaprogramming and macro usage creates code that's hard to understand, difficult to debug, and nearly impossible to maintain. The 20% of cases where metaprogramming is truly justified are obvious—you're building a framework or DSL that will be used extensively. For the 80% of normal business logic, explicit, boring code is superior.
Five Key Actions Every Elixir Architect Should Take
Action 1: Establish Code Review Standards Focused on Architectural Patterns. Create a checklist specifically for Elixir patterns. Every pull request should be evaluated for proper supervision tree structure, appropriate use of contexts, database query efficiency, and state management decisions. According to DockYard's recommendations, getting architectural reviews from experienced Elixir developers can prevent months of technical debt. Make this a required step, not an optional consultation.
Action 2: Implement Monitoring and Observability From Day One. Don't wait until production to discover performance problems. Use Telemetry to instrument database queries, process message queue lengths, and memory usage. According to AppSignal's analysis, instrumenting Ecto and LiveView through Telemetry catches issues early. Set up alerts for concerning patterns—query times over 100ms, process mailbox sizes growing beyond threshold, memory usage trending upward. What you don't measure, you can't improve.
Action 3: Run Credo in Your CI/CD Pipeline. Based on recommendations from multiple Elixir experts, Credo should be a required check before merging code. Configure it strictly—don't just run with default settings. Customize the rules to match your team's architectural decisions. Create custom checks for your specific anti-patterns. Credo is free education on Elixir best practices that runs automatically; there's no excuse not to use it.
Action 4: Build Architectural Decision Records (ADRs) for Key Choices. Document why you chose certain supervision strategies, context boundaries, and state management patterns. According to software engineering best practices, these decisions seem obvious when you make them but become mysterious six months later. When someone asks "why is this structured this way," you should have a written explanation that captures the tradeoffs you considered. This prevents architectural drift where each new developer makes different decisions based on different assumptions.
Action 5: Schedule Regular Architecture Review Sessions. Set aside time monthly or quarterly to review the actual architecture that emerged versus the architecture you intended. Identify areas where the codebase deviated from principles and either fix the code or update the principles. Software architecture is not a one-time design document; it's an ongoing practice of alignment between intention and implementation. These review sessions prevent the slow degradation that turns clean architectures into unmaintainable messes.
Memory Aids: Analogies That Make Elixir Patterns Stick
Processes as Actors in a Play. Each Elixir process is like an actor on stage. They can't directly interact—they can only send messages to each other. The director (supervisor) watches them and replaces any actor who forgets their lines (crashes). When you think about spawning a process, imagine adding another actor to your production. Do they need to be on stage? Are you tracking who they are and how to replace them? This analogy makes supervision and process isolation intuitive.
Immutability as Copy-On-Write Documents. Think of Elixir data structures like word documents where "save as" is the only option—there's no "save" button. Every modification creates a new version of the document. The original stays untouched. This is why you must assign the return value of assign(socket, :key, value) to a variable—you're getting a new socket, not modifying the old one. Once this clicks, immutability stops feeling restrictive and starts feeling safe.
Contexts as Building Facades. A building's facade hides complex internal structure—electrical, plumbing, structural support—behind a clean exterior. Phoenix contexts work the same way. The public functions are the facade; the schemas, database queries, and business logic are the hidden internals. When you're tempted to expose a schema directly, ask yourself: would an architect put exposed wiring on the outside of a building? The facade metaphor makes proper encapsulation obvious.
N+1 Queries as Nested Loops. Experienced developers know nested loops can create performance disasters. The N+1 query problem is a nested loop in disguise: loop over users (one query), and for each user, loop over their posts (N queries). When you wouldn't write a nested loop in code, don't write its database equivalent. This connection makes N+1 queries feel as obviously wrong as they actually are.
LiveView State as Shared Apartment Space. Imagine you're sharing an apartment with roommates (concurrent users). If everyone dumps all their belongings in the common living room (LiveView assigns), the space becomes unusable. Smart roommates keep most stuff in private bedrooms (database) and only bring to common areas what they're actively using (minimal assigns). This makes the memory management principles of LiveView immediately intuitive.
Conclusion: Survival Requires Honesty About Complexity
Elixir is not magic, and it won't automatically make your systems better. It provides exceptional tools for building concurrent, fault-tolerant applications, but those tools require understanding and discipline to use correctly. The pitfalls described in this guide aren't theoretical—they're patterns extracted from real production failures and architectural post-mortems shared by the community.
What separates successful Elixir projects from failed ones isn't the technical capability of the team. It's the willingness to learn Elixir's paradigms instead of forcing familiar patterns from other languages. It's the discipline to establish architectural standards and enforce them through code review and automated tooling. It's the humility to recognize when you're building something that works but isn't idiomatic, and to refactor before it becomes permanent.
The most important lesson is this: Elixir rewards thoughtful architecture and punishes shortcuts. You can't hack your way through poor supervision tree design or compensate for N+1 queries with faster hardware. The language's design pushes you toward patterns that scale—isolated processes, immutable data, explicit state flow—but only if you actually learn and apply those patterns. Treat this guide as a checklist of anti-patterns to avoid, not just interesting theory to acknowledge.
If you're starting a new Elixir project, read this article again in three months and see which mistakes you made anyway. If you're maintaining an existing Elixir codebase, use this as a framework for identifying architectural debt that needs addressing. Either way, remember that every mistake described here was made by competent developers who simply didn't know what they didn't know. Learn from their mistakes so you can make new, more interesting ones instead.
The Elixir community has built tremendous knowledge over the past decade. Use it. Read the documentation thoroughly, not just to understand syntax but to grasp the design philosophy. Engage with community resources, follow experienced developers, and ask questions when patterns don't make sense. Most importantly, build systems that embrace Elixir's strengths—concurrency, fault tolerance, and scalability—instead of working around them. That's how you move from writing Elixir code to architecting Elixir systems.