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>
465 lines
15 KiB
Rust
465 lines
15 KiB
Rust
//! Streaming Load Stress Tests
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//!
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//! Tests gRPC streaming performance under extreme load conditions.
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//! Validates concurrent stream handling, backpressure, and message throughput.
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use anyhow::Result;
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use std::sync::atomic::{AtomicU32, 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::broadcast;
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use tokio::time::timeout;
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use ml_training_service::orchestrator::TrainingStatusUpdate;
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/// Helper to create status updates
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fn create_status_update(job_num: u32, sequence: u32) -> TrainingStatusUpdate {
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TrainingStatusUpdate {
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job_id: uuid::Uuid::new_v4(),
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status: ml_training_service::orchestrator::JobStatus::Running,
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progress_percentage: (sequence % 100) as f32,
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current_epoch: sequence,
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total_epochs: 100,
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metrics: std::collections::HashMap::new(),
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message: format!("Job {} - Update {}", job_num, sequence),
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timestamp: chrono::Utc::now(),
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financial_metrics: None,
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resource_usage: ml_training_service::orchestrator::ResourceUsage {
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cpu_usage_percent: 50.0,
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memory_usage_gb: 1.0,
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gpu_usage_percent: Some(75.0),
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gpu_memory_usage_gb: Some(2.0),
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active_workers: 4,
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},
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}
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}
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/// Test 1: 100 Concurrent Watch Streams
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///
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/// Validates that the system can handle 100 concurrent streaming clients
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/// without deadlocks, memory exhaustion, or stream corruption.
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#[tokio::test]
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#[ignore = "Stress test - run explicitly with --ignored"]
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async fn test_100_concurrent_watch_streams() -> Result<()> {
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println!("\n=== Test 1: 100 Concurrent Watch Streams ===");
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let start = Instant::now();
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let (tx, _) = broadcast::channel(1000);
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let active_streams = Arc::new(AtomicU32::new(0));
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let messages_received = Arc::new(AtomicU64::new(0));
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let mut handles = Vec::new();
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// Spawn 100 concurrent stream consumers
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for i in 0..100 {
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let mut rx = tx.subscribe();
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let active = active_streams.clone();
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let received = messages_received.clone();
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let handle = tokio::spawn(async move {
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active.fetch_add(1, Ordering::Relaxed);
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let mut count = 0;
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let stream_timeout = Duration::from_secs(10);
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loop {
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match timeout(stream_timeout, rx.recv()).await {
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Ok(Ok(_update)) => {
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count += 1;
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received.fetch_add(1, Ordering::Relaxed);
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if count >= 100 {
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break;
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}
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}
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Ok(Err(_)) => break, // Channel closed
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Err(_) => break, // Timeout
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}
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}
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active.fetch_sub(1, Ordering::Relaxed);
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count
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});
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handles.push(handle);
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}
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// Give streams time to subscribe
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tokio::time::sleep(Duration::from_millis(100)).await;
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// Send 100 status updates
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for seq in 0..100 {
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let update = create_status_update(0, seq);
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let _ = tx.send(update);
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tokio::time::sleep(Duration::from_millis(10)).await;
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}
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// Wait for all streams to complete
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let mut stream_counts = Vec::new();
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for handle in handles {
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if let Ok(count) = handle.await {
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stream_counts.push(count);
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}
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}
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let elapsed = start.elapsed();
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let total_received = messages_received.load(Ordering::Relaxed);
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let avg_per_stream = if !stream_counts.is_empty() {
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stream_counts.iter().sum::<u64>() / stream_counts.len() as u64
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} else {
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0
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};
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println!("✓ Test 1 Results:");
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println!(" - Duration: {:?}", elapsed);
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println!(" - Active streams: {}/100", stream_counts.len());
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println!(" - Total messages received: {}", total_received);
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println!(" - Avg messages per stream: {}/100", avg_per_stream);
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println!(" - Expected total: {}", 100 * 100);
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assert_eq!(stream_counts.len(), 100, "Expected all 100 streams to complete");
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assert!(avg_per_stream >= 90, "Expected avg ≥90 messages per stream, got {}", avg_per_stream);
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Ok(())
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}
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/// Test 2: 10K Messages/Second Broadcast
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///
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/// Tests high-throughput message broadcasting to validate
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/// the system can sustain 10,000 messages per second.
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#[tokio::test]
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#[ignore = "Stress test - run explicitly with --ignored"]
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async fn test_10k_messages_per_second_broadcast() -> Result<()> {
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println!("\n=== Test 2: 10K Messages/Second Broadcast ===");
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let start = Instant::now();
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let (tx, _) = broadcast::channel(10000);
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let messages_sent = Arc::new(AtomicU64::new(0));
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let messages_received = Arc::new(AtomicU64::new(0));
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// Spawn 10 stream consumers
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let mut consumer_handles = Vec::new();
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for i in 0..10 {
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let mut rx = tx.subscribe();
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let received = messages_received.clone();
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let handle = tokio::spawn(async move {
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let mut count = 0;
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while let Ok(_update) = rx.recv().await {
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count += 1;
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received.fetch_add(1, Ordering::Relaxed);
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if count >= 10000 {
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break;
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}
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}
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count
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});
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consumer_handles.push(handle);
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}
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// Give consumers time to subscribe
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tokio::time::sleep(Duration::from_millis(50)).await;
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// Send messages as fast as possible
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let producer_start = Instant::now();
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let sent = messages_sent.clone();
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let producer_handle = tokio::spawn(async move {
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for seq in 0..10000 {
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let update = create_status_update(0, seq);
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if tx.send(update).is_ok() {
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sent.fetch_add(1, Ordering::Relaxed);
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}
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}
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producer_start.elapsed()
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});
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let send_duration = producer_handle.await?;
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// Wait for consumers to catch up
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tokio::time::sleep(Duration::from_millis(500)).await;
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let sent = messages_sent.load(Ordering::Relaxed);
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let received = messages_received.load(Ordering::Relaxed);
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let throughput = sent as f64 / send_duration.as_secs_f64();
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println!("✓ Test 2 Results:");
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println!(" - Send duration: {:?}", send_duration);
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println!(" - Messages sent: {}", sent);
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println!(" - Messages received: {} (across 10 consumers)", received);
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println!(" - Throughput: {:.0} msg/sec", throughput);
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println!(" - Target: >10,000 msg/sec");
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assert_eq!(sent, 10000, "Expected all 10,000 messages to be sent");
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assert!(throughput >= 10000.0, "Throughput {:.0} msg/sec below 10K target", throughput);
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Ok(())
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}
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/// Test 3: Slow Consumer (Backpressure Validation)
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///
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/// Tests that slow consumers don't block fast producers and that
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/// backpressure mechanisms work correctly.
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#[tokio::test]
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#[ignore = "Stress test - run explicitly with --ignored"]
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async fn test_slow_consumer_backpressure() -> Result<()> {
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println!("\n=== Test 3: Slow Consumer Backpressure ===");
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let start = Instant::now();
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let (tx, _) = broadcast::channel(1000);
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let fast_received = Arc::new(AtomicU64::new(0));
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let slow_received = Arc::new(AtomicU64::new(0));
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// Fast consumer
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let fast_rx = tx.subscribe();
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let fast_count = fast_received.clone();
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let fast_handle = tokio::spawn(async move {
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let mut rx = fast_rx;
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while let Ok(_update) = rx.recv().await {
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fast_count.fetch_add(1, Ordering::Relaxed);
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}
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});
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// Slow consumer (10ms delay per message)
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let slow_rx = tx.subscribe();
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let slow_count = slow_received.clone();
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let slow_handle = tokio::spawn(async move {
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let mut rx = slow_rx;
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while let Ok(_update) = rx.recv().await {
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tokio::time::sleep(Duration::from_millis(10)).await;
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slow_count.fetch_add(1, Ordering::Relaxed);
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}
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});
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// Give consumers time to subscribe
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tokio::time::sleep(Duration::from_millis(50)).await;
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// Send 1000 messages rapidly
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for seq in 0..1000 {
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let update = create_status_update(0, seq);
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let _ = tx.send(update);
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}
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// Wait for processing
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tokio::time::sleep(Duration::from_secs(2)).await;
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// Drop sender to close streams
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drop(tx);
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// Wait for consumers
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timeout(Duration::from_secs(15), async {
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fast_handle.await.ok();
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slow_handle.await.ok();
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}).await?;
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let elapsed = start.elapsed();
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let fast = fast_received.load(Ordering::Relaxed);
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let slow = slow_received.load(Ordering::Relaxed);
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println!("✓ Test 3 Results:");
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println!(" - Duration: {:?}", elapsed);
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println!(" - Fast consumer received: {}/1000", fast);
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println!(" - Slow consumer received: {}/1000", slow);
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println!(" - Backpressure handled: {}", slow < fast);
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assert!(fast >= 900, "Fast consumer should receive most messages, got {}", fast);
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assert!(slow < fast, "Slow consumer should lag behind fast consumer");
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Ok(())
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}
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/// Test 4: Network Latency Simulation (100ms RTT)
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///
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/// Simulates high network latency to validate stream resilience.
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#[tokio::test]
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#[ignore = "Stress test - run explicitly with --ignored"]
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async fn test_network_latency_simulation() -> Result<()> {
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println!("\n=== Test 4: Network Latency Simulation (100ms RTT) ===");
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let start = Instant::now();
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let (tx, _) = broadcast::channel(1000);
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let messages_received = Arc::new(AtomicU64::new(0));
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let mut handles = Vec::new();
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// Spawn 10 consumers with simulated network delay
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for _ in 0..10 {
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let mut rx = tx.subscribe();
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let received = messages_received.clone();
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let handle = tokio::spawn(async move {
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let mut count = 0;
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while let Ok(_update) = rx.recv().await {
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// Simulate 100ms network round-trip
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tokio::time::sleep(Duration::from_millis(100)).await;
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count += 1;
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received.fetch_add(1, Ordering::Relaxed);
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if count >= 50 {
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break;
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}
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}
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count
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});
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handles.push(handle);
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}
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// Give consumers time to subscribe
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tokio::time::sleep(Duration::from_millis(100)).await;
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// Send 50 messages
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for seq in 0..50 {
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let update = create_status_update(0, seq);
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let _ = tx.send(update);
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tokio::time::sleep(Duration::from_millis(20)).await; // Paced sending
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}
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// Wait for all consumers
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for handle in handles {
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handle.await.ok();
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}
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let elapsed = start.elapsed();
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let total_received = messages_received.load(Ordering::Relaxed);
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println!("✓ Test 4 Results:");
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println!(" - Duration: {:?}", elapsed);
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println!(" - Messages received: {} (across 10 consumers)", total_received);
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println!(" - Expected: {}", 50 * 10);
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println!(" - Network latency: 100ms RTT");
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assert!(total_received >= 450, "Expected at least 450 messages, got {}", total_received);
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Ok(())
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}
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/// Test 5: Stream Multiplexing (16 Jobs Per Stream)
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///
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/// Tests multiplexing multiple job updates over single streams.
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#[tokio::test]
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#[ignore = "Stress test - run explicitly with --ignored"]
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async fn test_stream_multiplexing() -> Result<()> {
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println!("\n=== Test 5: Stream Multiplexing (16 Jobs Per Stream) ===");
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let start = Instant::now();
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let (tx, _) = broadcast::channel(2000);
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let messages_received = Arc::new(AtomicU64::new(0));
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let unique_jobs = Arc::new(std::sync::Mutex::new(std::collections::HashSet::new()));
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// Spawn 10 stream consumers
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let mut handles = Vec::new();
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for _ in 0..10 {
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let mut rx = tx.subscribe();
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let received = messages_received.clone();
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let jobs = unique_jobs.clone();
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let handle = tokio::spawn(async move {
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let mut local_jobs = std::collections::HashSet::new();
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while let Ok(update) = rx.recv().await {
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received.fetch_add(1, Ordering::Relaxed);
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local_jobs.insert(update.job_id);
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if local_jobs.len() >= 16 {
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break;
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}
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}
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// Merge into global set
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let mut global = jobs.lock().expect("INVARIANT: Lock should not be poisoned");
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global.extend(local_jobs.iter());
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local_jobs.len()
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});
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handles.push(handle);
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}
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// Give consumers time to subscribe
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tokio::time::sleep(Duration::from_millis(100)).await;
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// Send updates for 16 different jobs
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for job_num in 0..16 {
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for seq in 0..10 {
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let update = create_status_update(job_num, seq);
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let _ = tx.send(update);
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}
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}
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// Wait for consumers
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for handle in handles {
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handle.await.ok();
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}
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let elapsed = start.elapsed();
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let total_received = messages_received.load(Ordering::Relaxed);
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let unique_job_count = unique_jobs.lock().expect("INVARIANT: Lock should not be poisoned").len();
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println!("✓ Test 5 Results:");
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println!(" - Duration: {:?}", elapsed);
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println!(" - Messages received: {}", total_received);
|
|
println!(" - Unique jobs tracked: {}/16", unique_job_count);
|
|
println!(" - Expected messages: {}", 16 * 10 * 10); // 16 jobs * 10 updates * 10 consumers
|
|
|
|
assert_eq!(unique_job_count, 16, "Expected all 16 unique jobs to be tracked");
|
|
assert!(total_received >= 1400, "Expected at least 1400 messages, got {}", total_received);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod benchmarks {
|
|
use super::*;
|
|
|
|
/// Benchmark streaming throughput
|
|
#[tokio::test]
|
|
#[ignore = "Stress test - run with --ignored"]
|
|
async fn bench_streaming_throughput() -> Result<()> {
|
|
println!("\n=== Benchmark: Streaming Throughput ===");
|
|
|
|
let (tx, mut rx) = broadcast::channel(10000);
|
|
|
|
let received_count = Arc::new(AtomicU64::new(0));
|
|
let count = received_count.clone();
|
|
|
|
// Consumer
|
|
let consumer = tokio::spawn(async move {
|
|
while rx.recv().await.is_ok() {
|
|
count.fetch_add(1, Ordering::Relaxed);
|
|
}
|
|
});
|
|
|
|
// Producer
|
|
let start = Instant::now();
|
|
for seq in 0..10000 {
|
|
let update = create_status_update(0, seq);
|
|
if tx.send(update).is_err() {
|
|
break;
|
|
}
|
|
}
|
|
let send_duration = start.elapsed();
|
|
|
|
drop(tx);
|
|
consumer.await.ok();
|
|
|
|
let received = received_count.load(Ordering::Relaxed);
|
|
let throughput = received as f64 / send_duration.as_secs_f64();
|
|
|
|
println!("✓ Throughput Results:");
|
|
println!(" - Messages: {}/10000", received);
|
|
println!(" - Duration: {:?}", send_duration);
|
|
println!(" - Throughput: {:.0} msg/sec", throughput);
|
|
println!(" - Target: >1,000 msg/sec per stream");
|
|
|
|
assert!(throughput >= 1000.0, "Throughput below 1K msg/sec target");
|
|
|
|
Ok(())
|
|
}
|
|
}
|