Background: Phoenix in Distributed Architectures

Phoenix builds atop the Erlang/OTP concurrency model, leveraging processes and supervision trees for resilience. While this design enables hot code upgrades and seamless failover, misuse—especially in real-time systems—can lead to orphaned processes and oversized ETS tables. These issues are amplified in multi-node deployments when processes are distributed without proper cleanup hooks.

Architectural Implications

Leaked processes in Phoenix can accumulate mailbox messages, retaining user session data or temporary computation results indefinitely. ETS bloat impacts the entire BEAM VM, as large tables are global to the node and not garbage-collected in the same way as process heaps. The result is increased GC pressure, scheduler delays, and cascading performance degradation across all running applications on the same VM.

Diagnostics and Root Cause Analysis

Detecting Process Leaks

Use :observer.start() or recon:proc_count/2 to monitor the number of active processes over time. A steady upward trend without corresponding drops after connection churn often points to supervision misconfiguration.

# Example: Inspect channel process count in IEx
:observer.start()
:recon.proc_count(:current_function, 10)

Identifying ETS Table Growth

List ETS tables and their sizes using :ets.info/1. Large, persistent tables tied to transient operations indicate missing cleanup routines.

# List ETS tables and sizes
:ets.all() |> Enum.map(&{:ets.info(&1, :size)})

Common Pitfalls

  • Failing to terminate channel processes when clients disconnect unexpectedly.
  • Using ETS as a cache without eviction policies.
  • Attaching large state data directly to processes that are never restarted.
  • Improper supervision strategies that restart children without clearing ETS tables.

Step-by-Step Fixes

1. Implement Proper Termination Callbacks

Ensure terminate/2 in channel modules cleans up ETS entries and releases resources.

def terminate(_reason, socket) do
  :ets.delete(:session_cache, socket.assigns.user_id)
  :ok
end

2. Use Bounded ETS Caches

Apply eviction strategies or migrate to external caches like Redis for ephemeral data.

:ets.new(:session_cache, [:set, :public, :named_table, {:read_concurrency, true}])

3. Monitor and Restart Long-Lived Processes

Leverage :recon to find processes with large heaps or long message queues, and restart them gracefully.

4. Partition Real-Time Workloads

Separate channel-handling nodes from API nodes to isolate impact and allow targeted scaling.

Best Practices for Long-Term Stability

  • Integrate process and ETS monitoring into observability platforms like Prometheus + Grafana.
  • Regularly review supervision tree design for orphan process prevention.
  • Adopt process registries (e.g., Registry or Horde) with lifecycle hooks for cleanup.
  • Benchmark with simulated connection churn to test cleanup effectiveness before production rollout.

Conclusion

Phoenix's concurrency model is a strength, but unmanaged processes and ETS tables can silently undermine system performance in enterprise back-end systems. By incorporating disciplined lifecycle management, bounded data storage strategies, and robust supervision designs, teams can preserve Phoenix's real-time performance while ensuring operational stability over years of continuous uptime.

FAQs

1. How can I detect orphaned channel processes without :observer?

Use Telemetry events or instrument Phoenix channels to log process starts and terminations, then compare counts periodically.

2. Is ETS always bad for large-scale Phoenix apps?

No—ETS is highly efficient for in-memory storage, but without eviction or size monitoring, it can cause memory issues in long-running nodes.

3. Can process leaks be caused by faulty third-party libraries?

Yes, especially libraries that spawn processes without linking or proper supervision, leaving them outside normal lifecycle management.

4. Should I restart the BEAM VM to clear ETS tables?

This clears them but is a blunt instrument. Implement cleanup logic to prevent uncontrolled growth without downtime.

5. How do I test ETS cleanup logic under load?

Use tools like Tsung or Locust to simulate connections, then monitor ETS table sizes to ensure they return to baseline after disconnections.