In part one of this series, we managed distributed state using GenServers. This provided a foundation for understanding some core concepts in distributed Phoenix applications.
Now, we turn our focus to deployment and scaling strategies. As your application evolves to meet growing demands, knowing how to scale horizontally, maintain high availability, and monitor distributed components becomes crucial.
This post will walk you through best practices for deploying and scaling Phoenix applications in a distributed setup, ensuring they are performant and resilient.
Distributed Deployment Strategies
Deploying a Phoenix application in a distributed environment introduces some unique challenges and opportunities that are less common to traditional web stacks. Thanks to the underlying power of the BEAM VM, Elixir applications offer native clustering capabilities.
When deployed correctly, a distributed Phoenix app becomes more than a collection of stateless servers — it turns into a unified system where instances can communicate directly, share state, and respond gracefully to network failures.
Let’s explore two common deployment strategies for Phoenix apps: single-node and multi-node deployment.
Single-Node Deployment
In smaller setups, it’s possible to run your Phoenix app as a single instance. You can package the app either as a mix release (recommended for all deployments, as it packages the app into a single directory that includes everything you need to run the app), or simply run it with mix phx.server with the correct environment variables.
This is common for small-scale deployments where high availability isn’t critical and the load can be handled by a single server. The pros are that it's a simple deployment with minimal overhead and is easy to manage. But your web server is also now a single point of failure. Though unsuitable for large-scale distributed systems, single-node deployments are a good starting point before introducing more complexity.
Deployment Across Multiple Nodes
A node in the context of a Phoenix (or Elixir) application refers to a single instance of the BEAM VM, capable of running the Phoenix app and connecting with other nodes to form a cluster.
Each node can share its state and manage processes independently, but can also interact with other nodes when deployed together.
To deploy a Phoenix application across multiple nodes:
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Create Multiple Instances: Use tools like Docker, Kubernetes, or your preferred cloud provider (like AWS EC2 or DigitalOcean Droplets) to create multiple instances of your Phoenix application. Each instance will act as an independent node.
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Set Up Clustering: Configure the nodes to communicate with each other by setting the
RELEASE_DISTRIBUTIONandRELEASE_NODEenvironment variables. For example:ShellRELEASE_DISTRIBUTION=name RELEASE_NODE=app_name@host
Each node should be configured with a unique name (like app_name@host1, app_name@host2, etc.), allowing them to recognize each other within the cluster.
Load Balancing: Place a load balancer in front of nodes to distribute incoming requests evenly. On AWS, for example, you can use an Elastic Load Balancer (ELB) and configure it to forward requests to nodes. In the load balancer setup, specify the DNS or IP of each node and configure health checks to detect if any node goes down.
Testing the Cluster: Once the nodes are up, test that they recognize each other.
You can use :observer.start() (or connect via iex shell) to inspect the network status of nodes and verify they’re communicating as expected.
This can be done by running and checking the return value.
The command will return :pong if the node is reachable and :pang otherwise.
iex> Node.ping(:"app_name@host2") :pong
With clustering, Phoenix applications can benefit from shared state and inter-node communication. Scenarios like real-time messaging, shared PubSub broadcasts, and distributed task management (e.g., using Horde) are enabled in a clustered setup.
Pros:
- Native support for process distribution across nodes.
- Enables the use of libraries like Horde for distributed task management.
- Stateful GenServers and ETS tables can be shared or replicated between nodes.
Cons:
- Requires attention to network partitions (nodes becoming temporarily unreachable).
- Inter-node communication adds complexity — issues like split-brain scenarios must be handled.
Scaling a Distributed Phoenix Application
Horizontal scaling is often the go-to strategy for distributed applications. An external user's first point of contact with your app is usually the web layer. Scaling this layer is easy — add more instances of your Phoenix app behind a load balancer to handle increased traffic. To further distribute load, use CDNs to offload static asset delivery.
Before adding more web servers, scaling processes allows an application to handle more load within a single instance by distributing work across lightweight processes. The most common way to achieve this is using background job processing. Using libraries like Oban, an application can spawn lightweight processes to handle background tasks. These processes are isolated and managed independently by the BEAM scheduler, improving concurrency without increasing web layer load.
Finally, for apps that significantly scale, your database needs to keep up with new connections from the scaled-up web and background job processes. The easiest way to scale a database is through vertical scaling — keep adding more CPU power and memory to the database server.
An alternative to this is to have read replicas to distribute read-heavy workloads from the primary database server.
Your setup on the database side will depend a lot on the database (Postgres, MySQL, etc.) and the cloud provider. On the app side, Ecto can be configured with multiple repositories to leverage replicas for reading queries.
Considerations for High Availability
High Availability (HA) ensures that your application remains operational even in the face of failure, minimizing downtime and disruptions. Phoenix applications, powered by the BEAM VM, are particularly well-suited for building HA systems due to native support for fault tolerance, process isolation, and clustering.
Let’s explore strategies for ensuring high availability in Phoenix apps, along with specific tools and techniques tailored for Elixir-based architectures.
Clustering and Node Redundancy
A core feature of the BEAM ecosystem is clustering, where multiple nodes work together to act as a single, logical system.
In a Phoenix app, clustering ensures that instances (nodes) share state and responsibilities dynamically. When clustered, nodes can pass messages between processes on different instances, ensuring tasks or state can failover seamlessly if a node goes down.
Phoenix.PubSub benefits from clustering, enabling nodes to broadcast real-time updates (like notifications) across the cluster without additional complexity.
Load Balancing and Failover
Load balancing ensures that traffic is evenly distributed across all available nodes, while failover mechanisms detect unhealthy nodes and redirect requests to healthy instances. Use NGINX or HAProxy to route HTTP traffic between multiple Phoenix nodes. If using WebSockets, enable sticky sessions to maintain persistent connections between users and the same server. In a Kubernetes environment, use Service objects or Ingress controllers to manage traffic between pod instances.
Set health checks to monitor node availability, removing unresponsive nodes from the load balancer pool.
Use Erlang’s node monitoring features to detect when nodes leave or join the cluster and adjust load distribution accordingly.
Fault Tolerance with Process Isolation
The BEAM VM’s design philosophy revolves around process isolation, meaning that failure in one part of the system does not bring down the entire app. This makes it easier to build systems that gracefully recover from failures.
Every Phoenix app is backed by supervision trees, where supervisors restart crashed processes automatically. In complex apps (e.g., background job workers or live dashboards), GenServers can isolate logic into independent processes that recover individually when something goes wrong.
Zero-Downtime Deployments
In highly available systems, every deployment needs to occur without any downtime, especially when serving global users around the clock.
Here are a few strategies for zero-downtime deployments:
- Blue-Green Deployments: Run two identical environments (blue and green) and switch traffic between them seamlessly after the deployment.
- Rolling Deployments: Update nodes incrementally, keeping some instances online while others are upgraded.
- Hot Code Upgrades: With Distillery or Releases, you can deploy changes without restarting your application (though this requires some additional planning).
Monitoring and Maintenance
No distributed system is truly complete without robust monitoring and proactive maintenance. Ensuring high performance and quick resolution of issues requires tracking key metrics, receiving alerts on failures, and understanding application behavior in real time.
Phoenix applications, with their modular design and reliance on the BEAM VM, offer a wealth of telemetry options.
This section explores monitoring techniques and best practices for keeping a distributed Phoenix app healthy and resilient over time.
Phoenix for Elixir Application Monitoring with Telemetry and Tracing
Monitoring starts with gaining visibility into the core operations of your application. Phoenix provides out-of-the-box support for Telemetry, enabling developers to track key metrics such as response times, database query performance, and request rates.
Phoenix emits Telemetry events for things like HTTP requests, database interactions, and process life cycles. These metrics allow you to pinpoint performance bottlenecks and detect abnormal behavior.
Tools like AppSignal seamlessly integrate with Phoenix and Ecto, providing dashboards for tracking application performance, error rates, and system health.
Error Tracking and Alerts
Even with the best architecture, errors are inevitable in production. A good error-tracking system ensures that you are alerted to issues as soon as they occur, allowing for quick resolution and minimal impact on users.
AppSignal captures errors, including crashes, unhandled exceptions, database timeouts, and more! Here's an example of how an Ecto error looks in AppSignal:
You can also configure alerts based on error frequency or severity to be notified about critical issues in real time.
This can be really useful. For example, a surge of 500 Internal Server Error responses can indicate an overloaded node or an unexpected database issue.
Tracking Metrics Across Nodes
Distributed Phoenix applications require centralized monitoring to aggregate data from all nodes. Without this, diagnosing issues across multiple instances becomes difficult. Some key node metrics to track include:
- Node Uptime: Track how long each node has been online and watch for frequent restarts.
- Memory and Process Utilization: Monitor the number of BEAM processes and memory consumption on each node.
- PubSub Metrics: Track the number of connected WebSocket clients and broadcast messages across the cluster.
Wrapping Up
In this post, we explored key strategies for deploying and scaling distributed Phoenix applications. We’ve covered the essential practices needed for building resilient, scalable systems. With monitoring tools and proactive maintenance, you can ensure your app is ready to meet future demands.
Together, parts one and two provide a comprehensive guide to developing, deploying, and maintaining distributed Phoenix applications.
Mastering these techniques will empower you to deliver robust systems capable of handling real-world challenges.
Happy coding!
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