Phoenix WebSocket connection scaling: Memory overhead vs. reconnection frequency

p>In Phoenix/Elixir applications, the memory overhead for an idle WebSocket connection typically ranges between 2KB and 10KB per process. This footprint includes the BEAM process overhead, the Cowboy socket state, and the Phoenix Channel-specific state. When scaling to millions of connections, this cumulative memory cost is linear and predictable, unlike the non-linear resource spikes during a 'reconnection storm.'

The trade-off is between the constant RAM cost of maintaining idle processes and the high CPU/network cost of TLS handshakes, authentication logic, and process-initialization triggered by mass reconnections.

Resource Cost Comparison

The following table outlines the resource impact profiles:

• Idle Connection: Low CPU, constant memory usage. The BEAM manages these via lightweight processes, requiring minimal cycles for heartbeats.
• Reconnection Event: High CPU, burst memory usage. Each reconnection requires a TLS handshake, WebSocket upgrade, authentication (token validation), and potentially Channel joins/subscriptions.

Optimization Strategy

To balance stale-connection pruning without triggering system instability, follow these configuration steps:

1. Increase Heartbeat Interval: Adjust the heartbeat_interval in your Phoenix Socket or Channel configuration. A longer interval reduces the frequency of 'ping/pong' traffic but keeps dead sockets in memory longer.
2. Implement Exponential Backoff: Ensure the client-side Phoenix JS uses exponential backoff with jitter. This prevents thousands of disconnected clients from hitting the server simultaneously after a network blip.
3. Limit File Descriptors: Ensure the OS-level `ulimit` is set high enough to handle the target connection count, as each WebSocket consumes one file descriptor.

Verification Commands

To verify your specific memory overhead per connection, use the following command in your Elixir console:

# Check total memory used by the BE
erlang:memory_info(:total)

# Monitor the number of processes and their average memory
Process.info()

Note: If memory per process is significantly higher than 10KB, check if you are storing large datasets in the Channel state instead of fetching them on demand or client-side.