Streaming LLM Tokens to 10K Concurrent Users

enum class BackpressurePolicy { DROP_OLDEST, CLOSE_CLIENT }

class ClientSink(
    private val capacity: Int = 64,
    private val policy: BackpressurePolicy = BackpressurePolicy.CLOSE_CLIENT
) {
    private val buffer = Channel<String>(capacity)
    
    suspend fun push(token: String): Boolean = buffer.trySend(token).isSuccess
    
    suspend fun drainTo(writer: suspend (String) -> Unit) {
        for (token in buffer) {
            writer(token)
        }
    }
    
    fun close() = buffer.close()
}

class TokenDispatcher {
    private val activeSinks = ConcurrentHashMap<String, ClientSink>()
    
    fun register(clientId: String): ClientSink {
        val sink = ClientSink()
        activeSinks[clientId] = sink
        return sink
    }
    
    fun deregister(clientId: String) {
        activeSinks.remove(clientId)?.close()
    }
    
    suspend fun broadcast(token: String) {
        activeSinks.values.forEach { sink ->
            val delivered = sink.push(token)
            if (!delivered) {
                handleBackpressure(sink)
            }
        }
    }
    
    private fun handleBackpressure(sink: ClientSink) {
        when (sink.policy) {
            BackpressurePolicy.CLOSE_CLIENT -> sink.close()
            BackpressurePolicy.DROP_OLDEST -> { /* Channel handles overflow internally */ }
        }
    }
}

hout requiring explicit locks, which is essential when thousands of coroutines register and deregister simultaneously.

Step 3: Calculate Real Concurrency Limits

Memory budgeting dictates your actual ceiling. On a standard 4GB container, the JVM reserves approximately 30–40% for metaspace, thread stacks, and GC overhead. This leaves roughly 2.5GB for application heap. The per-connection footprint breaks down as follows:

• Coroutine context & stack: ~1.5 KB
• Bounded channel (64 slots × ~40 bytes per string reference): ~2.5 KB
• HTTP response writer & TCP buffer: ~8 KB
• Connection metadata & routing state: ~1 KB
• Total per connection: ~13 KB

At 10,000 concurrent streams, total overhead is approximately 130 MB. This leaves ample headroom for JVM operations, token string allocation, and burst traffic. The practical limit on a 4GB instance is 10,000–12,000 connections. Attempting to exceed this by increasing buffer sizes or disabling GC tuning will trigger compaction pauses and OOM kills. Horizontal scaling is the only sustainable path beyond this threshold.

Step 4: Orchestrate Graceful Termination

Kubernetes rolling deployments send SIGTERM to pods, which abruptly closes TCP connections if not handled. An SSE client receiving a reset connection has no way to distinguish between a network failure and a deployment. The solution is a structured drain coordinator that signals clients to reconnect before forcing closure.

class DrainCoordinator(private val defaultTimeout: Duration = Duration.ofSeconds(30)) {
    
    suspend fun executeDrain(sinks: Collection<ClientSink>) {
        val job = coroutineScope {
            launch {
                sinks.forEach { it.push("event: reconnect\ndata: {\"reason\":\"deployment\"}\n\n") }
                delay(defaultTimeout)
            }
        }
        
        job.join()
        sinks.forEach { it.close() }
    }
}

The coordinator injects a standard SSE reconnect event into every active buffer. Clients configured with standard retry logic will automatically establish new connections to healthy pods. The coroutineScope ensures that the drain process respects structured concurrency: if the pod is forcefully terminated before the timeout, all child coroutines cancel cooperatively, preventing zombie connections and resource leaks.

Pitfall Guide

1. Unbounded Buffer Accumulation

Explanation: Using Channel(Channel.UNLIMITED) or ArrayList for token storage allows slow clients to accumulate tens of thousands of tokens. At ~40 bytes per token, a single stalled connection can consume 2–5 MB of heap. Multiply this across hundreds of mobile clients, and the container exhausts memory within minutes. Fix: Enforce hard capacity limits (32–128 slots) on all client channels. Treat buffer overflow as a backpressure signal, not an error condition.

2. Head-of-Line Blocking via Shared Queues

Explanation: Routing all tokens through a single shared channel forces every client to wait for the slowest consumer. The LLM generator suspends until the queue drains, increasing time-to-first-token (TTFT) for all users. Fix: Fan-out to isolated per-client channels. Decouple the generator from consumer drain rates using non-blocking trySend or offer operations.

3. Ignoring JVM Heap Overhead

Explanation: Developers often calculate capacity based on total container memory (e.g., 4GB / 13KB ≈ 300K connections). This ignores metaspace, JIT compilation caches, thread stacks, and GC reservation. The actual usable heap is typically 50–60% of total memory. Fix: Budget against usable heap, not total RAM. Reserve 30–40% for JVM internals. Use -XX:MaxDirectMemorySize and -XX:ReservedCodeCacheSize to cap non-heap allocations.

4. Abrupt Pod Termination

Explanation: Kubernetes SIGTERM closes sockets immediately. SSE clients receive TCP RST packets, triggering generic network errors rather than intelligent retries. Users experience broken streams during routine deployments. Fix: Implement a preStop lifecycle hook that triggers the drain coordinator. Inject a retry field in SSE events to standardize client-side reconnection behavior.

5. Leaked Coroutine Contexts

Explanation: Failing to tie SSE coroutines to the request lifecycle creates zombie tasks that continue consuming CPU and memory after client disconnect. Over time, this degrades throughput and increases GC pressure. Fix: Wrap each connection handler in coroutineScope { } tied to the HTTP request. Cancel the scope explicitly on client disconnect or timeout. Use invokeOnCompletion to deregister sinks from the dispatcher.

6. Vertical Scaling Fallacy

Explanation: Increasing container size to 8GB or 16GB to support more connections delays the inevitable. Memory fragmentation, GC pause times, and network socket limits scale non-linearly. A single large pod becomes a blast radius for outages. Fix: Design for horizontal scaling from day one. Use connection-count-based autoscaling metrics. Keep pods small, predictable, and easily replaceable.

7. Missing Client Retry Logic

Explanation: Assuming clients will automatically reconnect without explicit instructions leads to silent failures. Standard HTTP clients do not retry SSE connections on disconnect. Fix: Always include retry: 3000 in the initial SSE stream. Document the reconnect event format. Provide client SDKs that handle exponential backoff and state resumption.

Production Bundle

Action Checklist

• Define bounded channel capacity (32–128) based on acceptable token loss tolerance
• Implement trySend fan-out to prevent upstream blocking
• Calculate per-connection memory footprint and cap concurrency at 80% of usable heap
• Attach coroutineScope to each SSE request lifecycle for automatic cleanup
• Configure Kubernetes preStop hook to trigger drain coordinator before SIGTERM
• Inject retry and reconnect events into SSE streams for client-side resilience
• Set up connection-count-based horizontal pod autoscaling (HPA)
• Load test with simulated slow clients to validate backpressure behavior

Decision Matrix

Scenario	Recommended Approach	Why	Cost Impact
< 5K concurrent streams	Single pod, bounded channels	Simpler ops, predictable memory	Low infrastructure cost
5K–10K concurrent streams	Multi-pod cluster, connection-based HPA	Isolates blast radius, enables zero-downtime deploys	Moderate compute cost, higher network egress
> 10K concurrent streams	Regional edge proxies + central LLM gateway	Reduces latency, offloads fan-out to edge	High architectural complexity, increased CDN/proxy costs
Mobile/Unreliable networks	DROP_OLDEST policy + aggressive retry	Maintains connection, tolerates packet loss	Slightly higher token generation cost due to retries
Enterprise/Compliance	CLOSE_CLIENT policy + audit logging	Strict SLA enforcement, clear failure trails	Lower concurrency ceiling, higher support overhead

Configuration Template

// ktor-application.conf
ktor {
    deployment {
        port = 8080
        watch = []
    }
    application {
        modules = [ com.example.StreamingAppKt.module ]
        maxConnections = 10000
        connectionIdleTimeout = 300000
    }
}

// Application Module
fun Application.module() {
    val dispatcher = TokenDispatcher()
    val drainCoordinator = DrainCoordinator(Duration.ofSeconds(30))
    
    install(CallLogging) {
        level = Level.INFO
    }
    
    routing {
        get("/stream/{clientId}") {
            val clientId = call.parameters["clientId"] ?: return@get call.respondText("Missing ID", status = HttpStatusCode.BadRequest)
            val sink = dispatcher.register(clientId)
            
            call.respondBytesWriter(contentType = ContentType.Text.EventStream) {
                write("retry: 3000\n\n")
                try {
                    sink.drainTo { token ->
                        write("data: $token\n\n")
                        flush()
                    }
                } finally {
                    dispatcher.deregister(clientId)
                }
            }
        }
    }
    
    // PreStop hook integration
    Runtime.getRuntime().addShutdownHook(Thread {
        runBlocking {
            drainCoordinator.executeDrain(dispatcher.activeSinks.values)
        }
    })
}

Quick Start Guide

1. Initialize the dispatcher and drain coordinator in your application startup. Configure channel capacity based on your token loss tolerance (start with 64).
2. Wire the SSE endpoint to register a new ClientSink per request, stream tokens via drainTo, and deregister on completion or disconnect.
3. Attach a shutdown hook that triggers the drain coordinator. Ensure your Kubernetes deployment manifest includes a preStop sleep or HTTP call to allow the drain window to complete.
4. Configure client-side retry logic to respect the retry field and handle event: reconnect payloads. Implement exponential backoff for reconnection attempts.
5. Deploy and monitor connection count, heap usage, and backpressure rejection rates. Adjust autoscaling thresholds based on observed drain times and token throughput.