Implement comprehensive Runpod deployment with S3 volume mount architecture for FP32 ML model training on Tesla V100 GPUs. ## Infrastructure Components ### Deployment Scripts (scripts/) - runpod_deploy.sh: Master deployment orchestrator (8-step workflow) - runpod_upload.sh: S3 upload for binaries and test data - upload_env_to_runpod.sh: Secure .env credentials upload - runpod_deploy_test.sh: Prerequisites validation ### Docker Configuration - Dockerfile.runpod: Multi-stage CUDA 12.1 runtime (~2GB, no binaries) - entrypoint.sh: Volume verification and training execution - Architecture: Volume mount (NO S3 downloads in pods) ### S3 Configuration - Bucket: se3zdnb5o4 (Iceland region: eur-is-1) - Endpoint: https://s3api-eur-is-1.runpod.io - Structure: binaries/, test_data/, models/, .env ### OpenTofu Infrastructure (terraform/runpod/) - main.tf: Pod and volume resources - variables.tf: Configuration variables - outputs.tf: Pod connection info - Security: NO credentials in state (uses volume .env) ## Deployment Assets Uploaded ### Training Binaries (77MB) - train_tft_parquet (23M) - TFT-225 features - train_mamba2_parquet (22M) - MAMBA-2 state space - train_dqn (22M) - Deep Q-Network - train_ppo (13M) - Proximal Policy Optimization ### Test Data (13.8 MB) - 9 Parquet files: ES.FUT, NQ.FUT, 6E.FUT, ZN.FUT (180-day datasets) ### Credentials - .env file (1.5 KB, private access, chmod 600) ## Documentation ### Deployment Guides - RUNPOD_DEPLOYMENT_READY_SUMMARY.md: Complete deployment status - RUNPOD_VOLUME_DEPLOYMENT_GUIDE.md: Step-by-step guide (42KB) - RUNPOD_DEPLOYMENT_QUICK_START.md: Quick reference - RUNPOD_UPLOAD_GUIDE.md: S3 upload instructions - RUNPOD_VOLUME_CONFIGURATION_COMPLETE.md: S3 setup report - RUNPOD_S3_PARQUET_UPLOAD_REPORT.md: Data upload verification ### Architecture Documentation - RUNPOD_VOLUME_MOUNT_ARCHITECTURE.md: Volume mount design - RUNPOD_S3_ARCHITECTURE_DIAGRAM.txt: S3 API vs filesystem access - DOCKERFILE_RUNPOD_FINAL_SUMMARY.md: Docker image specification ### Decision Documentation - RUNPOD_DEPLOYMENT_CHECKLIST.md: Go/no-go decision matrix (27KB) - RUNPOD_DEPLOYMENT_DECISION_TREE.md: Decision workflow - FP32_RUNPOD_DEPLOYMENT_READY.md: FP32 deployment readiness ## QAT Enhancements ### Core QAT Infrastructure - ml/src/memory_optimization/qat.rs: Enhanced QAT observer (+226 lines) - ml/src/memory_optimization/auto_batch_size.rs: OOM recovery (+84 lines) - ml/src/tft/qat_tft.rs: QAT TFT wrapper (+154 lines) - ml/src/trainers/tft.rs: QAT training integration (+433 lines) - ml/src/qat_metrics_exporter.rs: NEW - QAT metrics export ### QAT Testing - ml/tests/qat_integration_tests.rs: NEW - Integration test suite - ml/tests/qat_gradient_clipping_test.rs: NEW - Gradient clipping tests - ml/tests/qat_device_consistency_test.rs: Device mismatch tests (+205 lines) - ml/tests/qat_accuracy_validation_test.rs: Accuracy validation - ml/tests/qat_tft_integration_test.rs: TFT QAT integration ### QAT Documentation - ml/docs/QAT_GUIDE.md: Comprehensive QAT guide (+616 lines) - ml/docs/QAT_GRADIENT_CHECKPOINTING_WORKAROUND.md: NEW - Workaround guide - QAT_BLOCKERS_ROOT_CAUSE_ANALYSIS.md: P0 blocker analysis (44KB) - QAT_ACCURACY_VALIDATION_REPORT.md: Accuracy comparison - QAT_GRADIENT_CLIPPING_VALIDATION_REPORT.md: Clipping validation ### QAT Monitoring - config/grafana/dashboards/qat-training-metrics.json: NEW - Grafana dashboard ## AWS CLI Configuration ### Credentials Setup - ~/.aws/credentials: Runpod profile configured - Access Key: user_2xxA3XcIFj16yfL3aBon9niiSpr - Secret Key: (from RUNPOD_S3_SECRET) - ~/.aws/config: Iceland region (eur-is-1) ## Production Readiness ### FP32 Models: ✅ READY FOR DEPLOYMENT - DQN: 15-20s training, ~6MB GPU memory - PPO: 7-10s training, ~145MB GPU memory - MAMBA-2: 2-3 min training, ~164MB GPU memory - TFT-225: 3-5 min training, ~500MB GPU memory - Total GPU Budget: 815MB (fits on 4GB+ Tesla V100) ### QAT Models: 🔴 BLOCKED - 24 tests implemented but DO NOT COMPILE (11 errors) - 3 P0 blockers: device mismatch, gradient checkpointing, OOM recovery - Timeline: 1-2 weeks to fix (13h P0 fixes + validation) ### Wave D Features: ✅ OPERATIONAL - 225 features fully integrated - Feature extraction: 5.10μs/bar (196x faster than target) - Wave D backtest: Sharpe 2.00, Win Rate 60%, Drawdown 15% - Database migration 045: Applied cleanly, zero conflicts ## Cost Analysis ### One-Time Setup - Network Volume: $4/month (50GB SSD) - Upload costs: FREE (S3 API included) ### Per Training Run (TFT-225) - GPU: Tesla V100-PCIE-16GB @ $0.29/hr - Training Time: ~4 hours - Cost per run: $1.16 ### Monthly (20 Training Runs) - Storage: $4.00/month - Training: $23.20/month (20 runs × $1.16) - Total: $27.20/month ## Security ### Credentials Management - ✅ NO credentials in Docker image - ✅ NO credentials in Terraform state - ✅ .env gitignored and not committed - ✅ .env file private on S3 (HTTP 401 on public access) - ✅ Docker Hub repository PRIVATE (jgrusewski/foxhunt) ### Access Control - S3 API: Local client uploads only - Volume mount: Pod filesystem access only - Authentication: AWS CLI with Runpod profile required ## Next Steps 1. ✅ COMPLETE: Build Docker image 2. ⏳ PENDING: Push to Docker Hub 3. ⏳ PENDING: Deploy pod via Runpod console 4. ⏳ PENDING: Validate training on Tesla V100 ## Performance Targets - Build time: 5-10 min - Upload time: ~20 sec (90MB total) - Pod startup: ~30 sec - Training time: 3-5 min (TFT-225) - Total deployment: ~40 min from start to first training run ## Test Status - FP32 tests: 597/608 passing (98.2%) - QAT tests: 0/24 passing (compilation errors) - Overall: 2,062/2,086 passing (98.8% excluding QAT) 🤖 Generated with Claude Code (https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
626 lines
20 KiB
Rust
626 lines
20 KiB
Rust
//! Concurrent Clients Stress Tests
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//!
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//! Tests system behavior with many simultaneous clients:
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//! - 1,000 concurrent TLI clients
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//! - 10,000 concurrent WebSocket connections
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//! - Thundering herd scenario (all clients submit at once)
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//! - Performance under degraded conditions
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use anyhow::Result;
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use hdrhistogram::Histogram;
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use std::sync::atomic::{AtomicU64, Ordering};
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use std::sync::Arc;
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use std::time::{Duration, Instant};
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use tokio::sync::Barrier;
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use tokio::task::JoinSet;
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use tracing::{error, info};
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/// Concurrent client test metrics
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#[derive(Debug, Clone)]
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pub struct ConcurrentClientMetrics {
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/// Number of concurrent clients
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pub num_clients: usize,
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/// Total requests sent
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pub total_requests: u64,
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/// Successful requests
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pub successful_requests: u64,
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/// Failed requests
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pub failed_requests: u64,
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/// Latency histogram
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pub latency_histogram: Histogram<u64>,
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/// Fairness score (0-100, 100 = perfectly fair)
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pub fairness_score: f64,
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/// Starvation detected
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pub starvation_detected: bool,
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/// Test duration
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pub duration: Duration,
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/// Test type
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pub test_type: String,
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}
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impl ConcurrentClientMetrics {
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pub fn new(num_clients: usize, test_type: &str) -> Self {
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Self {
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num_clients,
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total_requests: 0,
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successful_requests: 0,
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failed_requests: 0,
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latency_histogram: Histogram::<u64>::new_with_bounds(1, 60_000_000, 3).unwrap(),
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fairness_score: 0.0,
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starvation_detected: false,
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duration: Duration::ZERO,
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test_type: test_type.to_string(),
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}
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}
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pub fn success_rate(&self) -> f64 {
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if self.total_requests == 0 {
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return 0.0;
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}
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(self.successful_requests as f64 / self.total_requests as f64) * 100.0
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}
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pub fn p99_latency_us(&self) -> u64 {
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self.latency_histogram.value_at_quantile(0.99)
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}
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pub fn avg_latency_us(&self) -> f64 {
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self.latency_histogram.mean()
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}
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}
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/// Concurrent client test runner
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pub struct ConcurrentClientTest {
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/// Number of concurrent clients
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num_clients: usize,
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/// Requests per client
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requests_per_client: usize,
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/// Test type
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test_type: TestType,
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/// Metrics
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metrics: Arc<parking_lot::Mutex<ConcurrentClientMetrics>>,
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/// Request counter
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request_counter: Arc<AtomicU64>,
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/// Success counter
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success_counter: Arc<AtomicU64>,
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/// Per-client request counts (for fairness)
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client_counts: Arc<dashmap::DashMap<usize, u64>>,
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}
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#[derive(Debug, Clone, Copy)]
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pub enum TestType {
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/// Regular concurrent clients
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Standard,
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/// Thundering herd (synchronized start)
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ThunderingHerd,
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/// WebSocket-style persistent connections
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WebSocket,
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}
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impl ConcurrentClientTest {
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pub fn new(num_clients: usize, requests_per_client: usize, test_type: TestType) -> Self {
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let test_name = match test_type {
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TestType::Standard => "Standard Concurrent",
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TestType::ThunderingHerd => "Thundering Herd",
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TestType::WebSocket => "WebSocket Persistent",
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};
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Self {
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num_clients,
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requests_per_client,
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test_type,
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metrics: Arc::new(parking_lot::Mutex::new(ConcurrentClientMetrics::new(
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num_clients,
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test_name,
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))),
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request_counter: Arc::new(AtomicU64::new(0)),
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success_counter: Arc::new(AtomicU64::new(0)),
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client_counts: Arc::new(dashmap::DashMap::new()),
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}
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}
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///
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/// # Errors
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/// Returns error if the operation fails
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pub async fn run(&self) -> Result<ConcurrentClientMetrics> {
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info!(
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"Running concurrent client test: {} clients, {:?}",
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self.num_clients, self.test_type
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);
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let start = Instant::now();
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match self.test_type {
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TestType::Standard => self.run_standard().await?,
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TestType::ThunderingHerd => self.run_thundering_herd().await?,
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TestType::WebSocket => self.run_websocket().await?,
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}
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// Calculate fairness
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let fairness = self.calculate_fairness();
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// Finalize metrics
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let mut metrics = self.metrics.lock();
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metrics.duration = start.elapsed();
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metrics.total_requests = self.request_counter.load(Ordering::Relaxed);
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metrics.successful_requests = self.success_counter.load(Ordering::Relaxed);
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metrics.failed_requests = metrics.total_requests - metrics.successful_requests;
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metrics.fairness_score = fairness;
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metrics.starvation_detected = fairness < 50.0;
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info!("Concurrent client test complete: {:?}", metrics);
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Ok(metrics.clone())
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}
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/// Standard concurrent clients
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async fn run_standard(&self) -> Result<()> {
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let mut join_set = JoinSet::new();
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for client_id in 0..self.num_clients {
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let requests = self.requests_per_client;
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let request_counter = Arc::clone(&self.request_counter);
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let success_counter = Arc::clone(&self.success_counter);
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let metrics = Arc::clone(&self.metrics);
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let client_counts = Arc::clone(&self.client_counts);
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join_set.spawn(async move {
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Self::client_workload(
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client_id,
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requests,
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request_counter,
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success_counter,
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metrics,
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client_counts,
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)
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.await
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});
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}
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while let Some(result) = join_set.join_next().await {
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if let Err(e) = result {
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error!("Client failed: {:?}", e);
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}
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}
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Ok(())
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}
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/// Thundering herd: all clients start simultaneously
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async fn run_thundering_herd(&self) -> Result<()> {
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let barrier = Arc::new(Barrier::new(self.num_clients));
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let mut join_set = JoinSet::new();
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for client_id in 0..self.num_clients {
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let barrier = Arc::clone(&barrier);
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let requests = self.requests_per_client;
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let request_counter = Arc::clone(&self.request_counter);
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let success_counter = Arc::clone(&self.success_counter);
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let metrics = Arc::clone(&self.metrics);
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let client_counts = Arc::clone(&self.client_counts);
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join_set.spawn(async move {
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// Wait for all clients to be ready
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barrier.wait().await;
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// All clients start at the same instant
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Self::client_workload(
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client_id,
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requests,
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request_counter,
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success_counter,
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metrics,
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client_counts,
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)
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.await
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});
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}
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while let Some(result) = join_set.join_next().await {
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if let Err(e) = result {
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error!("Thundering herd client failed: {:?}", e);
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}
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}
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Ok(())
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}
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/// WebSocket-style persistent connections
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async fn run_websocket(&self) -> Result<()> {
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let mut join_set = JoinSet::new();
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for client_id in 0..self.num_clients {
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let requests = self.requests_per_client;
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let request_counter = Arc::clone(&self.request_counter);
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let success_counter = Arc::clone(&self.success_counter);
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let metrics = Arc::clone(&self.metrics);
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let client_counts = Arc::clone(&self.client_counts);
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join_set.spawn(async move {
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// Simulate WebSocket connection lifecycle
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Self::websocket_client(
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client_id,
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requests,
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request_counter,
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success_counter,
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metrics,
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client_counts,
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)
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.await
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});
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}
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while let Some(result) = join_set.join_next().await {
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if let Err(e) = result {
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error!("WebSocket client failed: {:?}", e);
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}
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}
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Ok(())
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}
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/// Standard client workload
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async fn client_workload(
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client_id: usize,
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num_requests: usize,
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request_counter: Arc<AtomicU64>,
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success_counter: Arc<AtomicU64>,
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metrics: Arc<parking_lot::Mutex<ConcurrentClientMetrics>>,
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client_counts: Arc<dashmap::DashMap<usize, u64>>,
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) -> Result<()> {
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for _ in 0..num_requests {
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let req_start = Instant::now();
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// Simulate request
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let success = Self::simulate_request(client_id).await;
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let latency = req_start.elapsed();
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// Update counters
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request_counter.fetch_add(1, Ordering::Relaxed);
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if success {
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success_counter.fetch_add(1, Ordering::Relaxed);
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}
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// Record latency
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{
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let mut m = metrics.lock();
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let _ = m.latency_histogram.record(latency.as_micros() as u64);
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}
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// Track per-client requests
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*client_counts.entry(client_id).or_insert(0) += 1;
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// Small delay between requests
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tokio::time::sleep(Duration::from_millis(1)).await;
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}
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Ok(())
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}
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/// WebSocket client simulation
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async fn websocket_client(
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client_id: usize,
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num_messages: usize,
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request_counter: Arc<AtomicU64>,
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success_counter: Arc<AtomicU64>,
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metrics: Arc<parking_lot::Mutex<ConcurrentClientMetrics>>,
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client_counts: Arc<dashmap::DashMap<usize, u64>>,
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) -> Result<()> {
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// Simulate WebSocket handshake
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tokio::time::sleep(Duration::from_millis(10)).await;
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// Send messages over persistent connection
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for _ in 0..num_messages {
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let msg_start = Instant::now();
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let success = Self::simulate_websocket_message(client_id).await;
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let latency = msg_start.elapsed();
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request_counter.fetch_add(1, Ordering::Relaxed);
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if success {
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success_counter.fetch_add(1, Ordering::Relaxed);
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}
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{
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let mut m = metrics.lock();
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let _ = m.latency_histogram.record(latency.as_micros() as u64);
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}
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*client_counts.entry(client_id).or_insert(0) += 1;
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// Simulate message rate (100 msg/sec)
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tokio::time::sleep(Duration::from_millis(10)).await;
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}
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Ok(())
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}
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/// Simulate request
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async fn simulate_request(_client_id: usize) -> bool {
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tokio::time::sleep(Duration::from_micros(50 + rand::random::<u64>() % 450)).await;
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rand::random::<f64>() < 0.999
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}
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/// Simulate WebSocket message
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async fn simulate_websocket_message(_client_id: usize) -> bool {
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tokio::time::sleep(Duration::from_micros(20 + rand::random::<u64>() % 180)).await;
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rand::random::<f64>() < 0.9995
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}
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/// Calculate fairness score (coefficient of variation)
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fn calculate_fairness(&self) -> f64 {
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let counts: Vec<u64> = self
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.client_counts
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.iter()
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.map(|entry| *entry.value())
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.collect();
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if counts.is_empty() {
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return 0.0;
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}
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let mean = counts.iter().sum::<u64>() as f64 / counts.len() as f64;
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let variance = counts
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.iter()
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.map(|&x| {
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let diff = x as f64 - mean;
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diff * diff
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})
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.sum::<f64>()
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/ counts.len() as f64;
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let std_dev = variance.sqrt();
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let cv = if mean > 0.0 { std_dev / mean } else { 0.0 };
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// Convert to fairness score (0-100, lower CV = higher fairness)
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// CV of 0 = 100% fair, CV of 1.0 = 0% fair
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((1.0 - cv.min(1.0)) * 100.0).max(0.0)
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}
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}
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/// Performance under failure test
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pub struct PerformanceUnderFailureTest {
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/// Number of clients
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num_clients: usize,
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/// Failure type
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failure_type: FailureType,
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/// Test duration
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duration: Duration,
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/// Metrics
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metrics: Arc<parking_lot::Mutex<ConcurrentClientMetrics>>,
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}
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#[derive(Debug, Clone, Copy)]
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pub enum FailureType {
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/// Degraded database (50% slower)
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DegradedDatabase,
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/// High network latency (100ms added)
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HighNetworkLatency,
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/// Intermittent Redis failures
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IntermittentRedis,
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}
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impl PerformanceUnderFailureTest {
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pub fn new(num_clients: usize, failure_type: FailureType, duration: Duration) -> Self {
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let test_name = match failure_type {
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FailureType::DegradedDatabase => "Degraded Database",
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FailureType::HighNetworkLatency => "High Network Latency",
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FailureType::IntermittentRedis => "Intermittent Redis",
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};
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Self {
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num_clients,
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failure_type,
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duration,
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metrics: Arc::new(parking_lot::Mutex::new(ConcurrentClientMetrics::new(
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num_clients,
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test_name,
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))),
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}
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}
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///
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/// # Errors
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/// Returns error if the operation fails
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|
pub async fn run(&self) -> Result<ConcurrentClientMetrics> {
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info!(
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"Running performance under failure: {} clients, {:?}",
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|
self.num_clients, self.failure_type
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|
);
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|
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|
let start = Instant::now();
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let mut join_set = JoinSet::new();
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|
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let request_counter = Arc::new(AtomicU64::new(0));
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let success_counter = Arc::new(AtomicU64::new(0));
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|
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for client_id in 0..self.num_clients {
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let duration = self.duration;
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|
let failure_type = self.failure_type;
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let request_counter = Arc::clone(&request_counter);
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let success_counter = Arc::clone(&success_counter);
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|
let metrics = Arc::clone(&self.metrics);
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|
|
|
join_set.spawn(async move {
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|
Self::degraded_client_workload(
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|
client_id,
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|
duration,
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|
failure_type,
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|
request_counter,
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|
success_counter,
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|
metrics,
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|
)
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.await
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|
});
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|
}
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|
|
|
while let Some(result) = join_set.join_next().await {
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|
if let Err(e) = result {
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error!("Degraded client failed: {:?}", e);
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|
}
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|
}
|
|
|
|
let mut metrics = self.metrics.lock();
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|
metrics.duration = start.elapsed();
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|
metrics.total_requests = request_counter.load(Ordering::Relaxed);
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|
metrics.successful_requests = success_counter.load(Ordering::Relaxed);
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|
metrics.failed_requests = metrics.total_requests - metrics.successful_requests;
|
|
|
|
info!("Performance under failure test complete");
|
|
|
|
Ok(metrics.clone())
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|
}
|
|
|
|
async fn degraded_client_workload(
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|
client_id: usize,
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|
duration: Duration,
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|
failure_type: FailureType,
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|
request_counter: Arc<AtomicU64>,
|
|
success_counter: Arc<AtomicU64>,
|
|
metrics: Arc<parking_lot::Mutex<ConcurrentClientMetrics>>,
|
|
) -> Result<()> {
|
|
let start = Instant::now();
|
|
|
|
while start.elapsed() < duration {
|
|
let req_start = Instant::now();
|
|
|
|
// Simulate request under degraded conditions
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|
let success = Self::simulate_degraded_request(client_id, failure_type).await;
|
|
let latency = req_start.elapsed();
|
|
|
|
request_counter.fetch_add(1, Ordering::Relaxed);
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if success {
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|
success_counter.fetch_add(1, Ordering::Relaxed);
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|
}
|
|
|
|
{
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|
let mut m = metrics.lock();
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|
let _ = m.latency_histogram.record(latency.as_micros() as u64);
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|
}
|
|
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|
tokio::time::sleep(Duration::from_millis(10)).await;
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|
}
|
|
|
|
Ok(())
|
|
}
|
|
|
|
async fn simulate_degraded_request(_client_id: usize, failure_type: FailureType) -> bool {
|
|
match failure_type {
|
|
FailureType::DegradedDatabase => {
|
|
// 50% slower queries
|
|
tokio::time::sleep(Duration::from_micros(150 + rand::random::<u64>() % 450)).await;
|
|
rand::random::<f64>() < 0.99
|
|
},
|
|
FailureType::HighNetworkLatency => {
|
|
// Additional 100ms network latency
|
|
tokio::time::sleep(Duration::from_millis(100)).await;
|
|
rand::random::<f64>() < 0.999
|
|
},
|
|
FailureType::IntermittentRedis => {
|
|
// 20% Redis failure rate
|
|
tokio::time::sleep(Duration::from_micros(50 + rand::random::<u64>() % 450)).await;
|
|
rand::random::<f64>() < 0.8
|
|
},
|
|
}
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[tokio::test]
|
|
async fn test_standard_concurrent_clients() {
|
|
let test = ConcurrentClientTest::new(100, 100, TestType::Standard);
|
|
let metrics = test.run().await.expect("Test failed");
|
|
|
|
assert_eq!(metrics.num_clients, 100);
|
|
assert!(metrics.total_requests > 0);
|
|
assert!(metrics.success_rate() > 99.0);
|
|
assert!(
|
|
metrics.fairness_score > 50.0,
|
|
"Should have reasonable fairness"
|
|
);
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_thundering_herd() {
|
|
let test = ConcurrentClientTest::new(500, 50, TestType::ThunderingHerd);
|
|
let metrics = test.run().await.expect("Test failed");
|
|
|
|
assert!(metrics.total_requests > 0);
|
|
assert!(
|
|
metrics.success_rate() > 95.0,
|
|
"Should handle thundering herd gracefully"
|
|
);
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_websocket_connections() {
|
|
let test = ConcurrentClientTest::new(100, 100, TestType::WebSocket);
|
|
let metrics = test.run().await.expect("Test failed");
|
|
|
|
assert!(metrics.total_requests > 0);
|
|
assert!(metrics.success_rate() > 99.0);
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_performance_under_degraded_db() {
|
|
let test = PerformanceUnderFailureTest::new(
|
|
50,
|
|
FailureType::DegradedDatabase,
|
|
Duration::from_secs(5),
|
|
);
|
|
let metrics = test.run().await.expect("Test failed");
|
|
|
|
assert!(metrics.total_requests > 0);
|
|
assert!(
|
|
metrics.success_rate() > 95.0,
|
|
"Should maintain acceptable performance under degradation"
|
|
);
|
|
// Latency should be higher but not catastrophic
|
|
assert!(metrics.avg_latency_us() < 1_000_000.0); // < 1 second
|
|
}
|
|
|
|
#[tokio::test]
|
|
#[ignore = "Resource intensive - run manually"]
|
|
async fn test_1000_concurrent_clients() {
|
|
let _ = tracing_subscriber::fmt::try_init();
|
|
|
|
info!("Running 1,000 concurrent clients test");
|
|
|
|
let test = ConcurrentClientTest::new(1_000, 1_000, TestType::Standard);
|
|
let metrics = test.run().await.expect("Test failed");
|
|
|
|
assert_eq!(metrics.num_clients, 1_000);
|
|
assert!(metrics.success_rate() > 99.0);
|
|
assert!(!metrics.starvation_detected, "No client should be starved");
|
|
assert!(
|
|
metrics.fairness_score > 70.0,
|
|
"Should maintain fairness with 1K clients"
|
|
);
|
|
|
|
info!("1,000 client test results: {:?}", metrics);
|
|
}
|
|
|
|
#[tokio::test]
|
|
#[ignore = "Resource intensive - run manually"]
|
|
async fn test_10000_websocket_connections() {
|
|
let _ = tracing_subscriber::fmt::try_init();
|
|
|
|
info!("Running 10,000 WebSocket connections test");
|
|
|
|
let test = ConcurrentClientTest::new(10_000, 100, TestType::WebSocket);
|
|
let metrics = test.run().await.expect("Test failed");
|
|
|
|
assert_eq!(metrics.num_clients, 10_000);
|
|
assert!(metrics.success_rate() > 99.0);
|
|
assert!(
|
|
metrics.p99_latency_us() < 10_000,
|
|
"P99 latency should be < 10ms"
|
|
);
|
|
|
|
info!("10,000 WebSocket test results: {:?}", metrics);
|
|
}
|
|
}
|