Files
foxhunt/tests/load_test_trading_service.rs
jgrusewski 83629f9ca8 feat(deployment): Complete Runpod GPU deployment infrastructure
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>
2025-10-24 01:11:43 +02:00

690 lines
25 KiB
Rust

//! Comprehensive Load Test for Trading Service
//!
//! Tests the trading service against production requirements:
//! - 10K orders/sec throughput target
//! - P50, P95, P99 latency measurements
//! - 100+ concurrent connections
//! - Order matching 1-6μs P99 baseline
//! - Database performance under load
//! - Resource monitoring (CPU, memory, connections)
//!
//! Run with: cargo test --package tests --test load_test_trading_service --release -- --nocapture
use std::sync::atomic::{AtomicU64, Ordering};
use std::sync::Arc;
use std::time::{Duration, Instant, SystemTime};
use tokio::time::timeout;
use tonic::transport::Channel;
use tonic::{Request, Status};
use uuid::Uuid;
// gRPC generated code
pub mod trading {
tonic::include_proto!("trading");
}
use trading::trading_service_client::TradingServiceClient;
use trading::{OrderSide, OrderType, SubmitOrderRequest, TimeInForce};
/// Performance metrics aggregator
#[derive(Debug, Clone)]
struct PerformanceMetrics {
latencies_ns: Vec<u64>,
successful_orders: AtomicU64,
failed_orders: AtomicU64,
total_orders: AtomicU64,
test_duration: Duration,
}
impl PerformanceMetrics {
fn new() -> Self {
Self {
latencies_ns: Vec::new(),
successful_orders: AtomicU64::new(0),
failed_orders: AtomicU64::new(0),
total_orders: AtomicU64::new(0),
test_duration: Duration::ZERO,
}
}
fn record_success(&self, latency_ns: u64) {
self.successful_orders.fetch_add(1, Ordering::Relaxed);
self.total_orders.fetch_add(1, Ordering::Relaxed);
}
fn record_failure(&self) {
self.failed_orders.fetch_add(1, Ordering::Relaxed);
self.total_orders.fetch_add(1, Ordering::Relaxed);
}
fn calculate_percentiles(mut latencies: Vec<u64>) -> (u64, u64, u64, u64, u64) {
if latencies.is_empty() {
return (0, 0, 0, 0, 0);
}
latencies.sort_unstable();
let len = latencies.len();
let min = latencies[0];
let p50 = latencies[len / 2];
let p95 = latencies[(len as f64 * 0.95) as usize];
let p99 = latencies[(len as f64 * 0.99) as usize];
let max = latencies[len - 1];
(min, p50, p95, p99, max)
}
fn print_summary(&self, latencies: &[u64]) {
let successful = self.successful_orders.load(Ordering::Relaxed);
let failed = self.failed_orders.load(Ordering::Relaxed);
let total = self.total_orders.load(Ordering::Relaxed);
let success_rate = if total > 0 {
(successful as f64 / total as f64) * 100.0
} else {
0.0
};
let throughput = if self.test_duration.as_secs_f64() > 0.0 {
successful as f64 / self.test_duration.as_secs_f64()
} else {
0.0
};
let (min, p50, p95, p99, max) = Self::calculate_percentiles(latencies.to_vec());
println!("\n╔═══════════════════════════════════════════════════════════╗");
println!("║ TRADING SERVICE LOAD TEST RESULTS ║");
println!("╠═══════════════════════════════════════════════════════════╣");
println!(
"║ Test Duration: {:.2}s",
self.test_duration.as_secs_f64()
);
println!("║ Total Orders: {}", total);
println!(
"║ Successful Orders: {} ({:.2}%)",
successful, success_rate
);
println!("║ Failed Orders: {}", failed);
println!("║ Throughput: {:.0} orders/sec", throughput);
println!("╠═══════════════════════════════════════════════════════════╣");
println!("║ LATENCY METRICS ║");
println!("╠═══════════════════════════════════════════════════════════╣");
println!(
"║ Min Latency: {:.2}ms ({:.2}μs)",
min as f64 / 1_000_000.0,
min as f64 / 1_000.0
);
println!(
"║ P50 Latency: {:.2}ms ({:.2}μs)",
p50 as f64 / 1_000_000.0,
p50 as f64 / 1_000.0
);
println!(
"║ P95 Latency: {:.2}ms ({:.2}μs)",
p95 as f64 / 1_000_000.0,
p95 as f64 / 1_000.0
);
println!(
"║ P99 Latency: {:.2}ms ({:.2}μs)",
p99 as f64 / 1_000_000.0,
p99 as f64 / 1_000.0
);
println!(
"║ Max Latency: {:.2}ms ({:.2}μs)",
max as f64 / 1_000_000.0,
max as f64 / 1_000.0
);
println!("╚═══════════════════════════════════════════════════════════╝");
// Performance assessment
println!("\n📊 PERFORMANCE ASSESSMENT:");
if throughput >= 10_000.0 {
println!(
"✅ Throughput target ACHIEVED: {:.0} orders/sec (target: 10K orders/sec)",
throughput
);
} else {
println!(
"⚠️ Throughput BELOW target: {:.0} orders/sec (target: 10K orders/sec)",
throughput
);
}
if p99 < 100_000_000 {
// 100ms in nanoseconds
println!(
"✅ P99 latency GOOD: {:.2}ms (< 100ms)",
p99 as f64 / 1_000_000.0
);
} else {
println!(
"⚠️ P99 latency HIGH: {:.2}ms (> 100ms)",
p99 as f64 / 1_000_000.0
);
}
if success_rate >= 99.0 {
println!("✅ Success rate EXCELLENT: {:.2}%", success_rate);
} else if success_rate >= 95.0 {
println!("⚠️ Success rate ACCEPTABLE: {:.2}%", success_rate);
} else {
println!("❌ Success rate POOR: {:.2}%", success_rate);
}
}
}
/// Create a test order request
fn create_order_request(index: u64) -> SubmitOrderRequest {
let symbols = vec!["BTC/USD", "ETH/USD", "SOL/USD", "AVAX/USD", "MATIC/USD"];
let symbol = symbols[(index % symbols.len() as u64) as usize].to_string();
SubmitOrderRequest {
order_id: Uuid::new_v4().to_string(),
symbol,
side: if index % 2 == 0 {
OrderSide::Buy.into()
} else {
OrderSide::Sell.into()
},
order_type: OrderType::Limit.into(),
quantity: (1.0 + (index % 10) as f64 * 0.1).to_string(),
price: Some((50000.0 + (index % 1000) as f64).to_string()),
time_in_force: TimeInForce::GoodTillCancel.into(),
}
}
/// Connect to Trading Service
async fn connect_trading_service(
) -> Result<TradingServiceClient<Channel>, Box<dyn std::error::Error>> {
let endpoint = "http://localhost:50052";
println!("🔌 Connecting to Trading Service at {}", endpoint);
let channel = Channel::from_static("http://localhost:50052")
.connect_timeout(Duration::from_secs(10))
.timeout(Duration::from_secs(30))
.connect()
.await?;
let client = TradingServiceClient::new(channel);
println!("✅ Connected successfully");
Ok(client)
}
/// Test 1: Baseline latency with single client
#[tokio::test]
async fn test_1_baseline_latency() -> Result<(), Box<dyn std::error::Error>> {
println!("\n╔═══════════════════════════════════════════════════════════╗");
println!("║ TEST 1: BASELINE LATENCY (Single Client) ║");
println!("╚═══════════════════════════════════════════════════════════╝");
let mut client = connect_trading_service().await?;
let num_requests = 1000;
let mut latencies = Vec::with_capacity(num_requests);
println!("📊 Sending {} orders sequentially...", num_requests);
let start_time = Instant::now();
for i in 0..num_requests {
let request = create_order_request(i as u64);
let req_start = Instant::now();
let result = client.submit_order(Request::new(request)).await;
let latency_ns = req_start.elapsed().as_nanos() as u64;
latencies.push(latency_ns);
if result.is_err() && i < 5 {
eprintln!("❌ Order {} failed: {:?}", i, result.err());
}
}
let test_duration = start_time.elapsed();
let (min, p50, p95, p99, max) = PerformanceMetrics::calculate_percentiles(latencies.clone());
println!("\n📈 BASELINE RESULTS:");
println!(" Duration: {:.2}s", test_duration.as_secs_f64());
println!(
" Throughput: {:.0} orders/sec",
num_requests as f64 / test_duration.as_secs_f64()
);
println!(" Min Latency: {:.2}ms", min as f64 / 1_000_000.0);
println!(" P50 Latency: {:.2}ms", p50 as f64 / 1_000_000.0);
println!(" P95 Latency: {:.2}ms", p95 as f64 / 1_000_000.0);
println!(" P99 Latency: {:.2}ms", p99 as f64 / 1_000_000.0);
println!(" Max Latency: {:.2}ms", max as f64 / 1_000_000.0);
Ok(())
}
/// Test 2: Concurrent connections (100 clients)
#[tokio::test]
async fn test_2_concurrent_connections() -> Result<(), Box<dyn std::error::Error>> {
println!("\n╔═══════════════════════════════════════════════════════════╗");
println!("║ TEST 2: CONCURRENT CONNECTIONS (100 Clients) ║");
println!("╚═══════════════════════════════════════════════════════════╝");
let num_clients = 100;
let orders_per_client = 100;
let metrics = Arc::new(PerformanceMetrics::new());
let latencies = Arc::new(tokio::sync::Mutex::new(Vec::new()));
println!(
"🚀 Spawning {} concurrent clients ({} orders each)...",
num_clients, orders_per_client
);
let start_time = Instant::now();
let mut tasks = Vec::new();
for client_id in 0..num_clients {
let metrics_clone = Arc::clone(&metrics);
let latencies_clone = Arc::clone(&latencies);
let task = tokio::spawn(async move {
let mut client = match connect_trading_service().await {
Ok(c) => c,
Err(e) => {
eprintln!("❌ Client {} connection failed: {}", client_id, e);
return;
},
};
for order_idx in 0..orders_per_client {
let request =
create_order_request((client_id * orders_per_client + order_idx) as u64);
let req_start = Instant::now();
match timeout(
Duration::from_secs(5),
client.submit_order(Request::new(request)),
)
.await
{
Ok(Ok(_response)) => {
let latency_ns = req_start.elapsed().as_nanos() as u64;
metrics_clone.record_success(latency_ns);
latencies_clone.lock().await.push(latency_ns);
},
Ok(Err(status)) => {
metrics_clone.record_failure();
if order_idx < 2 {
eprintln!(
"❌ Client {} order {} failed: {}",
client_id, order_idx, status
);
}
},
Err(_) => {
metrics_clone.record_failure();
if order_idx < 2 {
eprintln!("⏱️ Client {} order {} timed out", client_id, order_idx);
}
},
}
}
});
tasks.push(task);
}
// Wait for all clients to complete
for task in tasks {
let _ = task.await;
}
let test_duration = start_time.elapsed();
let latencies_vec = latencies.lock().await.clone();
let metrics_final = PerformanceMetrics {
latencies_ns: latencies_vec.clone(),
successful_orders: AtomicU64::new(metrics.successful_orders.load(Ordering::Relaxed)),
failed_orders: AtomicU64::new(metrics.failed_orders.load(Ordering::Relaxed)),
total_orders: AtomicU64::new(metrics.total_orders.load(Ordering::Relaxed)),
test_duration,
};
metrics_final.print_summary(&latencies_vec);
Ok(())
}
/// Test 3: Sustained load (5 minutes)
#[tokio::test]
#[ignore = "Run explicitly with --ignored"]
async fn test_3_sustained_load() -> Result<(), Box<dyn std::error::Error>> {
println!("\n╔═══════════════════════════════════════════════════════════╗");
println!("║ TEST 3: SUSTAINED LOAD (5 Minutes) ║");
println!("╚═══════════════════════════════════════════════════════════╝");
let test_duration_secs = 300; // 5 minutes
let num_clients = 50;
let target_rate_per_sec = 200; // 10K total / 50 clients = 200 per client
let metrics = Arc::new(PerformanceMetrics::new());
let latencies = Arc::new(tokio::sync::Mutex::new(Vec::new()));
let shutdown = Arc::new(AtomicU64::new(0));
println!(
"🚀 Starting {} clients for {} seconds...",
num_clients, test_duration_secs
);
println!(
"🎯 Target: {:.0} orders/sec total",
num_clients as f64 * target_rate_per_sec as f64
);
let start_time = Instant::now();
let mut tasks = Vec::new();
for client_id in 0..num_clients {
let metrics_clone = Arc::clone(&metrics);
let latencies_clone = Arc::clone(&latencies);
let shutdown_clone = Arc::clone(&shutdown);
let task = tokio::spawn(async move {
let mut client = match connect_trading_service().await {
Ok(c) => c,
Err(e) => {
eprintln!("❌ Client {} connection failed: {}", client_id, e);
return;
},
};
let mut order_count = 0u64;
let delay_micros = 1_000_000 / target_rate_per_sec; // microseconds between orders
while shutdown_clone.load(Ordering::Relaxed) == 0 {
let request = create_order_request(order_count);
let req_start = Instant::now();
match timeout(
Duration::from_secs(5),
client.submit_order(Request::new(request)),
)
.await
{
Ok(Ok(_response)) => {
let latency_ns = req_start.elapsed().as_nanos() as u64;
metrics_clone.record_success(latency_ns);
latencies_clone.lock().await.push(latency_ns);
},
Ok(Err(_)) => {
metrics_clone.record_failure();
},
Err(_) => {
metrics_clone.record_failure();
},
}
order_count += 1;
// Rate limiting
tokio::time::sleep(Duration::from_micros(delay_micros)).await;
}
});
tasks.push(task);
}
// Run for specified duration
tokio::time::sleep(Duration::from_secs(test_duration_secs)).await;
// Signal shutdown
shutdown.store(1, Ordering::Relaxed);
// Wait for all clients to complete
for task in tasks {
let _ = task.await;
}
let test_duration = start_time.elapsed();
let latencies_vec = latencies.lock().await.clone();
let metrics_final = PerformanceMetrics {
latencies_ns: latencies_vec.clone(),
successful_orders: AtomicU64::new(metrics.successful_orders.load(Ordering::Relaxed)),
failed_orders: AtomicU64::new(metrics.failed_orders.load(Ordering::Relaxed)),
total_orders: AtomicU64::new(metrics.total_orders.load(Ordering::Relaxed)),
test_duration,
};
metrics_final.print_summary(&latencies_vec);
Ok(())
}
/// Test 4: Database under load
#[tokio::test]
async fn test_4_database_performance() -> Result<(), Box<dyn std::error::Error>> {
println!("\n╔═══════════════════════════════════════════════════════════╗");
println!("║ TEST 4: DATABASE PERFORMANCE ║");
println!("╚═══════════════════════════════════════════════════════════╝");
// This test measures order submission which triggers database writes
let num_orders = 5000;
let mut client = connect_trading_service().await?;
println!(
"📊 Submitting {} orders to measure database performance...",
num_orders
);
let start_time = Instant::now();
let mut success_count = 0;
let mut failure_count = 0;
for i in 0..num_orders {
let request = create_order_request(i);
match client.submit_order(Request::new(request)).await {
Ok(_) => success_count += 1,
Err(e) => {
failure_count += 1;
if failure_count <= 5 {
eprintln!("❌ Order {} failed: {}", i, e);
}
},
}
}
let duration = start_time.elapsed();
let throughput = success_count as f64 / duration.as_secs_f64();
println!("\n📈 DATABASE PERFORMANCE:");
println!(" Duration: {:.2}s", duration.as_secs_f64());
println!(" Successful: {}", success_count);
println!(" Failed: {}", failure_count);
println!(" DB Writes/sec: {:.0}", throughput);
if throughput >= 2000.0 {
println!("✅ Database performance GOOD: {:.0} writes/sec", throughput);
} else {
println!(
"⚠️ Database performance: {:.0} writes/sec (expected >2000)",
throughput
);
}
Ok(())
}
/// Test 5: Resource monitoring
#[tokio::test]
async fn test_5_resource_monitoring() -> Result<(), Box<dyn std::error::Error>> {
println!("\n╔═══════════════════════════════════════════════════════════╗");
println!("║ TEST 5: RESOURCE MONITORING ║");
println!("╚═══════════════════════════════════════════════════════════╝");
// Check service health
let health_url = "http://localhost:8081/health";
println!("🏥 Checking service health at {}...", health_url);
match reqwest::get(health_url).await {
Ok(response) => {
println!("✅ Health check response: {}", response.status());
if let Ok(body) = response.text().await {
println!(" Body: {}", body);
}
},
Err(e) => {
println!("⚠️ Health check failed: {}", e);
},
}
// Check Prometheus metrics
let metrics_url = "http://localhost:9092/metrics";
println!("\n📊 Checking Prometheus metrics at {}...", metrics_url);
match reqwest::get(metrics_url).await {
Ok(response) => {
if let Ok(body) = response.text().await {
// Parse relevant metrics
let lines: Vec<&str> = body
.lines()
.filter(|line| !line.starts_with('#') && !line.is_empty())
.collect();
println!("✅ Found {} metric entries", lines.len());
// Show some key metrics
for line in lines.iter().take(10) {
if line.contains("orders") || line.contains("latency") || line.contains("cpu") {
println!(" {}", line);
}
}
}
},
Err(e) => {
println!("⚠️ Metrics check failed: {}", e);
},
}
Ok(())
}
/// Test 6: Production readiness assessment
#[tokio::test]
async fn test_6_production_readiness() -> Result<(), Box<dyn std::error::Error>> {
println!("\n╔═══════════════════════════════════════════════════════════╗");
println!("║ TEST 6: PRODUCTION READINESS ASSESSMENT ║");
println!("╚═══════════════════════════════════════════════════════════╝");
let num_clients = 50;
let orders_per_client = 200;
let metrics = Arc::new(PerformanceMetrics::new());
let latencies = Arc::new(tokio::sync::Mutex::new(Vec::new()));
println!(
"🎯 Production simulation: {} clients, {} orders each",
num_clients, orders_per_client
);
let start_time = Instant::now();
let mut tasks = Vec::new();
for client_id in 0..num_clients {
let metrics_clone = Arc::clone(&metrics);
let latencies_clone = Arc::clone(&latencies);
let task = tokio::spawn(async move {
let mut client = match connect_trading_service().await {
Ok(c) => c,
Err(_) => return,
};
for order_idx in 0..orders_per_client {
let request =
create_order_request((client_id * orders_per_client + order_idx) as u64);
let req_start = Instant::now();
match client.submit_order(Request::new(request)).await {
Ok(_) => {
let latency_ns = req_start.elapsed().as_nanos() as u64;
metrics_clone.record_success(latency_ns);
latencies_clone.lock().await.push(latency_ns);
},
Err(_) => {
metrics_clone.record_failure();
},
}
}
});
tasks.push(task);
}
for task in tasks {
let _ = task.await;
}
let test_duration = start_time.elapsed();
let latencies_vec = latencies.lock().await.clone();
let metrics_final = PerformanceMetrics {
latencies_ns: latencies_vec.clone(),
successful_orders: AtomicU64::new(metrics.successful_orders.load(Ordering::Relaxed)),
failed_orders: AtomicU64::new(metrics.failed_orders.load(Ordering::Relaxed)),
total_orders: AtomicU64::new(metrics.total_orders.load(Ordering::Relaxed)),
test_duration,
};
metrics_final.print_summary(&latencies_vec);
// Production readiness criteria
let successful = metrics_final.successful_orders.load(Ordering::Relaxed);
let total = metrics_final.total_orders.load(Ordering::Relaxed);
let success_rate = (successful as f64 / total as f64) * 100.0;
let throughput = successful as f64 / test_duration.as_secs_f64();
let (_, _, _, p99, _) = PerformanceMetrics::calculate_percentiles(latencies_vec);
println!("\n🎯 PRODUCTION READINESS:");
let mut passed = 0;
let mut total_checks = 0;
// Check 1: Success rate
total_checks += 1;
if success_rate >= 99.0 {
println!("✅ Success rate: {:.2}% (>= 99%)", success_rate);
passed += 1;
} else {
println!("❌ Success rate: {:.2}% (< 99%)", success_rate);
}
// Check 2: Throughput
total_checks += 1;
if throughput >= 5000.0 {
println!("✅ Throughput: {:.0} orders/sec (>= 5000)", throughput);
passed += 1;
} else {
println!("⚠️ Throughput: {:.0} orders/sec (< 5000)", throughput);
}
// Check 3: P99 latency
total_checks += 1;
let p99_ms = p99 as f64 / 1_000_000.0;
if p99_ms < 100.0 {
println!("✅ P99 latency: {:.2}ms (< 100ms)", p99_ms);
passed += 1;
} else {
println!("⚠️ P99 latency: {:.2}ms (>= 100ms)", p99_ms);
}
println!("\n📊 OVERALL: {}/{} checks passed", passed, total_checks);
if passed == total_checks {
println!("🎉 PRODUCTION READY!");
} else {
println!("⚠️ Not ready for production deployment");
}
Ok(())
}