# Performance Optimization Guide
[AIR-3][AIS-3][BPC-3][RES-3]
**AI Labeling**: This documentation is AI-generated with technical review and validation.
**Date**: June 7, 2025
## Overview
Comprehensive performance optimization guide for Anya Core, covering system optimization, Bitcoin protocol performance, Web5 efficiency, ML system acceleration, and monitoring strategies.
## Table of Contents
- [Performance Philosophy](#performance-philosophy)
- [System Performance](#system-performance)
- [Bitcoin Performance](#bitcoin-performance)
- [Web5 Performance](#web5-performance)
- [ML Performance](#ml-performance)
- [Memory Optimization](#memory-optimization)
- [Network Optimization](#network-optimization)
- [Storage Optimization](#storage-optimization)
- [Monitoring and Profiling](#monitoring-and-profiling)
## Performance Philosophy
Our performance optimization approach:
1. **Measure First**: Profile before optimizing
2. **Focus on Bottlenecks**: Optimize the critical path
3. **Incremental Improvement**: Small, measurable gains
4. **Real-World Scenarios**: Optimize for actual usage patterns
5. **Maintainable Performance**: Don't sacrifice code quality
## System Performance
### CPU Optimization
```rust
// Use efficient data structures
use ahash::AHashMap; // Faster than std::HashMap
use smallvec::SmallVec; // Stack allocation for small vectors
// Example: Optimized transaction processing
pub struct OptimizedTransactionProcessor {
// Use AHashMap for better performance
utxo_cache: AHashMap<Txid, Utxo>,
// SmallVec for typical small collections
pending_txs: SmallVec<[Transaction; 16]>,
}
impl OptimizedTransactionProcessor {
// Batch processing for better cache locality
pub fn process_batch(&mut self, transactions: &[Transaction]) -> Result<Vec<ProcessResult>> {
let mut results = Vec::with_capacity(transactions.len());
// Sort transactions by fee rate for optimal processing order
let mut sorted_txs: SmallVec<[_; 16]> = transactions
.iter()
.enumerate()
.collect();
sorted_txs.sort_by_key(|(_, tx)| tx.fee_rate());
for (idx, tx) in sorted_txs {
let result = self.process_single_transaction(tx)?;
results.push(result);
}
Ok(results)
}
// Inline hot paths
#[inline(always)]
fn process_single_transaction(&mut self, tx: &Transaction) -> Result<ProcessResult> {
// Fast path for common cases
if tx.is_coinbase() {
return Ok(ProcessResult::Coinbase);
}
// Optimized validation logic
self.validate_transaction_fast(tx)
}
}
```
### Concurrency Optimization
```rust
use tokio::sync::{RwLock, Semaphore};
use std::sync::Arc;
use rayon::prelude::*;
// Concurrent transaction validation
pub struct ConcurrentValidator {
semaphore: Arc<Semaphore>,
utxo_set: Arc<RwLock<UtxoSet>>,
}
impl ConcurrentValidator {
pub async fn validate_transactions_parallel(
&self,
transactions: Vec<Transaction>
) -> Result<Vec<ValidationResult>> {
// Process transactions in parallel with controlled concurrency
let results = futures::future::try_join_all(
transactions.into_iter().map(|tx| {
let validator = self.clone();
async move {
let _permit = validator.semaphore.acquire().await?;
validator.validate_single(tx).await
}
})
).await?;
Ok(results)
}
// CPU-intensive validation using Rayon for parallelism
fn validate_signatures_parallel(&self, tx: &Transaction) -> Result<bool> {
tx.inputs
.par_iter()
.try_for_each(|input| -> Result<()> {
if !self.verify_signature(input)? {
return Err(anyhow!("Invalid signature"));
}
Ok(())
})?;
Ok(true)
}
}
```
## Bitcoin Performance
### Transaction Processing Optimization
```rust
// Optimized UTXO management
use dashmap::DashMap; // Concurrent HashMap
use parking_lot::RwLock; // Faster RwLock
pub struct OptimizedUtxoSet {
// Concurrent access for read-heavy workloads
utxos: DashMap<OutPoint, Utxo>,
// Cache for frequently accessed UTXOs
hot_cache: RwLock<lru::LruCache<OutPoint, Utxo>>,
}
impl OptimizedUtxoSet {
pub fn get_utxo(&self, outpoint: &OutPoint) -> Option<Utxo> {
// Check hot cache first
if let Some(utxo) = self.hot_cache.read().get(outpoint) {
return Some(utxo.clone());
}
// Check main storage
if let Some(utxo) = self.utxos.get(outpoint) {
// Add to hot cache
self.hot_cache.write().put(*outpoint, utxo.clone());
Some(utxo.clone())
} else {
None
}
}
// Batch operations for better performance
pub fn add_utxos_batch(&self, utxos: Vec<(OutPoint, Utxo)>) {
for (outpoint, utxo) in utxos {
self.utxos.insert(outpoint, utxo);
}
}
}
```
### Script Execution Optimization
```rust
// Optimized script execution
pub struct OptimizedScriptEngine {
op_cache: AHashMap<ScriptHash, OpCode>,
execution_stats: Arc<RwLock<ExecutionStats>>,
}
impl OptimizedScriptEngine {
// Pre-compile frequently used scripts
pub fn precompile_script(&mut self, script: &Script) -> Result<CompiledScript> {
let hash = script.hash();
if let Some(compiled) = self.compiled_cache.get(&hash) {
return Ok(compiled.clone());
}
let compiled = self.compile_script(script)?;
self.compiled_cache.insert(hash, compiled.clone());
Ok(compiled)
}
// Optimized execution with early termination
pub fn execute_optimized(&self, script: &CompiledScript) -> Result<bool> {
let mut stack = SmallVec::<[StackItem; 32]>::new();
for op in script.operations() {
match op {
// Fast path for common operations
OpCode::Op_Dup => {
if let Some(top) = stack.last() {
stack.push(top.clone());
} else {
return Ok(false);
}
}
OpCode::Op_Hash160 => {
if let Some(item) = stack.pop() {
let hash = ripemd160_sha256(&item.data);
stack.push(StackItem::from(hash));
} else {
return Ok(false);
}
}
// Early termination for OP_RETURN
OpCode::Op_Return => return Ok(false),
_ => {
// Handle other operations
self.execute_operation(op, &mut stack)?;
}
}
}
Ok(stack.len() == 1 && stack[0].is_true())
}
}
```
## Web5 Performance
### DID Resolution Optimization
```rust
use moka::sync::Cache; // High-performance cache
use std::time::Duration;
pub struct OptimizedDidResolver {
// Multi-level caching
l1_cache: Cache<DidId, DidDocument>,
l2_cache: Arc<RwLock<sled::Db>>,
network_client: Arc<HttpClient>,
}
impl OptimizedDidResolver {
pub fn new() -> Result<Self> {
let l1_cache = Cache::builder()
.max_capacity(10_000)
.time_to_live(Duration::from_secs(300)) // 5 minutes
.time_to_idle(Duration::from_secs(60)) // 1 minute
.build();
let l2_cache = Arc::new(RwLock::new(
sled::open("did_cache.db")?
));
Ok(Self {
l1_cache,
l2_cache,
network_client: Arc::new(HttpClient::new()),
})
}
// Optimized resolution with caching
pub async fn resolve_did(&self, did: &DidId) -> Result<DidDocument> {
// L1 cache check
if let Some(doc) = self.l1_cache.get(did) {
return Ok(doc);
}
// L2 cache check
if let Ok(data) = self.l2_cache.read().get(did.as_bytes())? {
if let Ok(doc) = serde_json::from_slice::<DidDocument>(&data) {
self.l1_cache.insert(did.clone(), doc.clone());
return Ok(doc);
}
}
// Network resolution with timeout
let doc = tokio::time::timeout(
Duration::from_secs(10),
self.resolve_from_network(did)
).await??;
// Update caches
self.l1_cache.insert(did.clone(), doc.clone());
let data = serde_json::to_vec(&doc)?;
self.l2_cache.write().insert(did.as_bytes(), data)?;
Ok(doc)
}
// Batch resolution for multiple DIDs
pub async fn resolve_dids_batch(&self, dids: &[DidId]) -> Result<Vec<DidDocument>> {
let futures = dids.iter().map(|did| self.resolve_did(did));
let results = futures::future::try_join_all(futures).await?;
Ok(results)
}
}
```
### DWN Data Transfer Optimization
```rust
use bytes::Bytes;
use flate2::read::GzDecoder;
use flate2::write::GzEncoder;
use flate2::Compression;
pub struct OptimizedDwnClient {
connection_pool: deadpool::managed::Pool<HttpConnection>,
compression_level: Compression,
}
impl OptimizedDwnClient {
// Streaming upload with compression
pub async fn upload_data_stream<R: AsyncRead + Send>(
&self,
reader: R,
content_type: &str,
) -> Result<RecordId> {
let mut encoder = GzEncoder::new(Vec::new(), self.compression_level);
let mut buffer = [0u8; 8192];
let mut reader = reader;
// Stream and compress data
loop {
let n = reader.read(&mut buffer).await?;
if n == 0 break;
encoder.write_all(&buffer[..n])?;
}
let compressed_data = encoder.finish()?;
// Upload compressed data
let conn = self.connection_pool.get().await?;
let response = conn.upload_bytes(&compressed_data, content_type).await?;
Ok(response.record_id)
}
// Parallel chunk upload for large files
pub async fn upload_large_file(&self, file_path: &Path) -> Result<RecordId> {
const CHUNK_SIZE: usize = 1024 * 1024; // 1MB chunks
let file = File::open(file_path).await?;
let metadata = file.metadata().await?;
let file_size = metadata.len() as usize;
let chunks = (file_size + CHUNK_SIZE - 1) / CHUNK_SIZE;
let mut upload_futures = Vec::with_capacity(chunks);
for i in 0..chunks {
let start = i * CHUNK_SIZE;
let end = std::cmp::min(start + CHUNK_SIZE, file_size);
let client = self.clone();
let path = file_path.to_owned();
upload_futures.push(async move {
client.upload_chunk(&path, start, end).await
});
}
let chunk_ids = futures::future::try_join_all(upload_futures).await?;
// Combine chunks
let record_id = self.combine_chunks(chunk_ids).await?;
Ok(record_id)
}
}
```
## ML Performance
### Model Inference Optimization
```rust
use candle_core::{Device, Tensor};
use candle_nn::VarBuilder;
use half::f16; // Use half precision for memory efficiency
pub struct OptimizedInferenceEngine {
device: Device,
models: AHashMap<String, OptimizedModel>,
batch_queue: Arc<RwLock<VecDeque<InferenceRequest>>>,
}
impl OptimizedInferenceEngine {
// Batch inference for better GPU utilization
pub async fn run_batch_inference(&self) -> Result<()> {
loop {
let batch = self.collect_batch().await;
if batch.is_empty() {
tokio::time::sleep(Duration::from_millis(10)).await;
continue;
}
// Group by model type for efficient batching
let mut model_batches: AHashMap<String, Vec<InferenceRequest>> = AHashMap::new();
for request in batch {
model_batches.entry(request.model_name.clone())
.or_default()
.push(request);
}
// Process each model batch in parallel
let futures = model_batches.into_iter().map(|(model_name, requests)| {
let engine = self.clone();
async move {
engine.process_model_batch(&model_name, requests).await
}
});
futures::future::try_join_all(futures).await?;
}
}
// Optimized tensor operations
fn optimize_tensor_ops(&self, input: &Tensor) -> Result<Tensor> {
// Use in-place operations when possible
let mut result = input.clone();
// Fused operations for better performance
result = result
.to_dtype(candle_core::DType::F16)? // Use half precision
.to_device(&self.device)?;
// Optimize memory layout
if !result.is_contiguous() {
result = result.contiguous()?;
}
Ok(result)
}
}
// Optimized model structure
pub struct OptimizedModel {
weights: AHashMap<String, Tensor>,
compute_graph: ComputeGraph,
memory_pool: MemoryPool,
}
impl OptimizedModel {
// Pre-allocate tensors for known input sizes
pub fn preallocate_tensors(&mut self, batch_sizes: &[usize]) -> Result<()> {
for &batch_size in batch_sizes {
let input_shape = &[batch_size, self.input_dim];
let tensor = Tensor::zeros(input_shape, candle_core::DType::F16, &self.device)?;
self.memory_pool.add_preallocated(batch_size, tensor);
}
Ok(())
}
// Zero-copy inference when possible
pub fn inference_zero_copy(&self, input: &Tensor) -> Result<Tensor> {
// Check if we can reuse pre-allocated tensors
if let Some(output_tensor) = self.memory_pool.get_reusable(input.shape()) {
// Perform in-place computation
self.compute_graph.execute_inplace(input, output_tensor)
} else {
// Fall back to regular inference
self.compute_graph.execute(input)
}
}
}
```
### Agent System Performance
```rust
use std::sync::atomic::{AtomicU64, Ordering};
use crossbeam::channel;
pub struct HighPerformanceAgentCoordinator {
agents: Vec<Arc<dyn Agent>>,
task_queue: channel::Receiver<Task>,
result_sender: channel::Sender<AgentResult>,
metrics: PerformanceMetrics,
}
impl HighPerformanceAgentCoordinator {
// Lock-free task distribution
pub async fn distribute_tasks(&self) -> Result<()> {
let worker_count = self.agents.len();
let (task_tx, task_rx) = channel::unbounded();
let (result_tx, result_rx) = channel::unbounded();
// Spawn worker threads for each agent
let workers: Vec<_> = self.agents.iter().enumerate().map(|(id, agent)| {
let agent = agent.clone();
let task_rx = task_rx.clone();
let result_tx = result_tx.clone();
let metrics = self.metrics.clone();
tokio::spawn(async move {
while let Ok(task) = task_rx.recv() {
let start = std::time::Instant::now();
match agent.execute_task(task).await {
Ok(result) => {
metrics.record_success(id, start.elapsed());
let _ = result_tx.send(AgentResult::Success(result));
}
Err(e) => {
metrics.record_error(id, start.elapsed());
let _ = result_tx.send(AgentResult::Error(e));
}
}
}
})
}).collect();
// Task distribution loop
while let Ok(task) = self.task_queue.recv() {
if let Err(_) = task_tx.send(task) {
break; // All workers have shut down
}
}
// Wait for workers to complete
futures::future::try_join_all(workers).await?;
Ok(())
}
}
// Lock-free performance metrics
#[derive(Clone)]
pub struct PerformanceMetrics {
task_count: Arc<AtomicU64>,
success_count: Arc<AtomicU64>,
error_count: Arc<AtomicU64>,
total_duration: Arc<AtomicU64>,
}
impl PerformanceMetrics {
pub fn record_success(&self, agent_id: usize, duration: Duration) {
self.task_count.fetch_add(1, Ordering::Relaxed);
self.success_count.fetch_add(1, Ordering::Relaxed);
self.total_duration.fetch_add(duration.as_nanos() as u64, Ordering::Relaxed);
}
pub fn record_error(&self, agent_id: usize, duration: Duration) {
self.task_count.fetch_add(1, Ordering::Relaxed);
self.error_count.fetch_add(1, Ordering::Relaxed);
self.total_duration.fetch_add(duration.as_nanos() as u64, Ordering::Relaxed);
}
pub fn get_stats(&self) -> PerformanceStats {
let task_count = self.task_count.load(Ordering::Relaxed);
let success_count = self.success_count.load(Ordering::Relaxed);
let error_count = self.error_count.load(Ordering::Relaxed);
let total_duration = self.total_duration.load(Ordering::Relaxed);
PerformanceStats {
task_count,
success_rate: if task_count > 0 { success_count as f64 / task_count as f64 } else { 0.0 },
average_duration: if task_count > 0 {
Duration::from_nanos(total_duration / task_count)
} else {
Duration::ZERO
},
}
}
}
```
## Memory Optimization
### Memory Pool Implementation
```rust
use std::alloc::{GlobalAlloc, Layout, System};
use std::ptr::NonNull;
use parking_lot::Mutex;
// Custom memory allocator for frequently allocated types
pub struct PoolAllocator<T> {
free_list: Mutex<Vec<NonNull<T>>>,
chunk_size: usize,
}
impl<T> PoolAllocator<T> {
pub fn new(initial_capacity: usize) -> Self {
let mut allocator = Self {
free_list: Mutex::new(Vec::with_capacity(initial_capacity)),
chunk_size: 1000,
};
allocator.grow_pool();
allocator
}
pub fn allocate(&self) -> Option<NonNull<T>> {
let mut free_list = self.free_list.lock();
if free_list.is_empty() {
drop(free_list);
self.grow_pool();
free_list = self.free_list.lock();
}
free_list.pop()
}
pub fn deallocate(&self, ptr: NonNull<T>) {
let mut free_list = self.free_list.lock();
free_list.push(ptr);
}
fn grow_pool(&self) {
let layout = Layout::array::<T>(self.chunk_size).unwrap();
unsafe {
let ptr = System.alloc(layout) as *mut T;
if !ptr.is_null() {
let mut free_list = self.free_list.lock();
for i in 0..self.chunk_size {
let item_ptr = ptr.add(i);
free_list.push(NonNull::new_unchecked(item_ptr));
}
}
}
}
}
// Memory-efficient data structures
use compact_str::CompactString; // More memory-efficient strings
use tinyvec::TinyVec; // Stack-allocated small vectors
pub struct MemoryOptimizedTransaction {
// Use CompactString for better memory efficiency
txid: CompactString,
// TinyVec for small input/output lists
inputs: TinyVec<[TxInput; 4]>,
outputs: TinyVec<[TxOutput; 2]>,
// Pack small fields together
version: u32,
lock_time: u32,
}
```
### Zero-Copy Deserialization
```rust
use zerocopy::{AsBytes, FromBytes, LayoutVerified};
// Zero-copy transaction parsing
#[repr(C)]
#[derive(FromBytes, AsBytes)]
pub struct RawTransaction {
version: [u8; 4],
input_count: u8,
// Variable-length fields follow
}
impl RawTransaction {
// Parse transaction without copying data
pub fn parse_zero_copy(data: &[u8]) -> Result<ParsedTransaction<'_>> {
let (header, rest) = LayoutVerified::<_, RawTransaction>::new_from_prefix(data)
.ok_or_else(|| anyhow!("Invalid transaction header"))?;
let version = u32::from_le_bytes(header.version);
let mut cursor = rest;
// Parse inputs without copying
let mut inputs = Vec::new();
for _ in 0..header.input_count {
let (input, remaining) = TxInput::parse_zero_copy(cursor)?;
inputs.push(input);
cursor = remaining;
}
// Parse outputs similarly
let outputs = self.parse_outputs_zero_copy(cursor)?;
Ok(ParsedTransaction {
version,
inputs,
outputs,
raw_data: data,
})
}
}
```
## Network Optimization
### Connection Pooling
```rust
use deadpool::managed::{Manager, Pool, RecycleResult};
use std::net::SocketAddr;
// High-performance connection pool
pub struct BitcoinConnectionManager {
addr: SocketAddr,
max_connections: usize,
}
#[async_trait]
impl Manager for BitcoinConnectionManager {
type Type = BitcoinConnection;
type Error = anyhow::Error;
async fn create(&self) -> Result<BitcoinConnection, Self::Error> {
let stream = TcpStream::connect(self.addr).await?;
// Set TCP options for better performance
stream.set_nodelay(true)?;
stream.set_keepalive(Some(Duration::from_secs(30)))?;
Ok(BitcoinConnection::new(stream))
}
async fn recycle(&self, conn: &mut BitcoinConnection) -> RecycleResult<Self::Error> {
if conn.is_healthy().await {
RecycleResult::Ok(())
} else {
RecycleResult::Fail(anyhow!("Connection unhealthy"))
}
}
}
// Network message batching
pub struct BatchedNetworkClient {
pool: Pool<BitcoinConnectionManager>,
batch_buffer: Arc<Mutex<Vec<Message>>>,
batch_timer: Arc<Mutex<Option<tokio::time::Instant>>>,
}
impl BatchedNetworkClient {
// Send messages in batches for better network efficiency
pub async fn send_message(&self, message: Message) -> Result<()> {
let mut buffer = self.batch_buffer.lock().await;
buffer.push(message);
// Set timer for first message in batch
let mut timer = self.batch_timer.lock().await;
if timer.is_none() {
*timer = Some(tokio::time::Instant::now() + Duration::from_millis(10));
// Schedule batch flush
let client = self.clone();
tokio::spawn(async move {
tokio::time::sleep(Duration::from_millis(10)).await;
client.flush_batch().await.ok();
});
}
// Flush immediately if batch is full
if buffer.len() >= 100 {
drop(timer);
drop(buffer);
self.flush_batch().await?;
}
Ok(())
}
async fn flush_batch(&self) -> Result<()> {
let mut buffer = self.batch_buffer.lock().await;
let mut timer = self.batch_timer.lock().await;
if buffer.is_empty() {
return Ok(());
}
let messages = std::mem::take(&mut *buffer);
*timer = None;
drop(buffer);
drop(timer);
// Send batch over single connection
let conn = self.pool.get().await?;
conn.send_batch(&messages).await?;
Ok(())
}
}
```
## Storage Optimization
### Database Performance
```rust
use sqlx::{PgPool, Row};
use std::collections::VecDeque;
// Optimized database operations
pub struct OptimizedStorage {
pool: PgPool,
write_buffer: Arc<Mutex<VecDeque<WriteOperation>>>,
read_cache: Arc<RwLock<lru::LruCache<String, CachedData>>>,
}
impl OptimizedStorage {
// Batch write operations
pub async fn batch_write(&self, operations: Vec<WriteOperation>) -> Result<()> {
let mut tx = self.pool.begin().await?;
// Group operations by type for better performance
let mut inserts = Vec::new();
let mut updates = Vec::new();
let mut deletes = Vec::new();
for op in operations {
match op {
WriteOperation::Insert(data) => inserts.push(data),
WriteOperation::Update(data) => updates.push(data),
WriteOperation::Delete(id) => deletes.push(id),
}
}
// Execute batched operations
if !inserts.is_empty() {
self.batch_insert(&mut tx, inserts).await?;
}
if !updates.is_empty() {
self.batch_update(&mut tx, updates).await?;
}
if !deletes.is_empty() {
self.batch_delete(&mut tx, deletes).await?;
}
tx.commit().await?;
Ok(())
}
// Optimized bulk insert
async fn batch_insert(&self, tx: &mut sqlx::Transaction<'_, sqlx::Postgres>, data: Vec<InsertData>) -> Result<()> {
// Use COPY for large bulk inserts
if data.len() > 1000 {
let mut writer = tx.copy_in_raw("COPY transactions (txid, data, block_height) FROM STDIN WITH (FORMAT binary)").await?;
for item in data {
// Write binary data directly
writer.write_all(&item.to_binary()).await?;
}
writer.finish().await?;
} else {
// Use batch INSERT for smaller datasets
let query = "INSERT INTO transactions (txid, data, block_height) VALUES ";
let mut params = Vec::new();
let mut placeholders = Vec::new();
for (i, item) in data.iter().enumerate() {
let base = i * 3;
placeholders.push(format!("(${}, ${}, ${})", base + 1, base + 2, base + 3));
params.push(&item.txid as &(dyn ToSql + Sync));
params.push(&item.data as &(dyn ToSql + Sync));
params.push(&item.block_height as &(dyn ToSql + Sync));
}
let full_query = format!("{}{}", query, placeholders.join(", "));
sqlx::query(&full_query)
.bind_all(params)
.execute(&mut **tx)
.await?;
}
Ok(())
}
}
```
## Monitoring and Profiling
### Performance Metrics
```rust
use prometheus::{Counter, Histogram, Gauge, register_counter, register_histogram, register_gauge};
use std::time::Instant;
// Performance monitoring
pub struct PerformanceMonitor {
transaction_counter: Counter,
transaction_duration: Histogram,
active_connections: Gauge,
memory_usage: Gauge,
}
impl PerformanceMonitor {
pub fn new() -> Result<Self> {
Ok(Self {
transaction_counter: register_counter!(
"transactions_total",
"Total number of processed transactions"
)?,
transaction_duration: register_histogram!(
"transaction_duration_seconds",
"Transaction processing duration"
)?,
active_connections: register_gauge!(
"active_connections",
"Number of active network connections"
)?,
memory_usage: register_gauge!(
"memory_usage_bytes",
"Current memory usage in bytes"
)?,
})
}
// Automatic performance tracking
pub fn track_transaction<F, T>(&self, f: F) -> T
where
F: FnOnce() -> T,
{
let start = Instant::now();
let result = f();
let duration = start.elapsed();
self.transaction_counter.inc();
self.transaction_duration.observe(duration.as_secs_f64());
result
}
// Memory usage tracking
pub fn update_memory_usage(&self) {
if let Ok(usage) = self.get_memory_usage() {
self.memory_usage.set(usage as f64);
}
}
fn get_memory_usage(&self) -> Result<usize> {
// Platform-specific memory usage collection
#[cfg(target_os = "linux")]
{
let status = std::fs::read_to_string("/proc/self/status")?;
for line in status.lines() {
if line.starts_with("VmRSS:") {
let parts: Vec<&str> = line.split_whitespace().collect();
if parts.len() >= 2 {
let kb: usize = parts[1].parse()?;
return Ok(kb * 1024);
}
}
}
}
#[cfg(target_os = "macos")]
{
// macOS-specific implementation
use libc::{getpid, proc_pidinfo, PROC_PIDTASKINFO};
// Implementation details...
}
Ok(0)
}
}
// Profiling integration
pub struct CpuProfiler {
profiler: Arc<Mutex<Option<cpuprofiler::PROFILER>>>,
}
impl CpuProfiler {
pub fn start_profiling(&self, output_path: &str) -> Result<()> {
let mut profiler = self.profiler.lock().unwrap();
cpuprofiler::PROFILER.lock().unwrap().start(output_path)?;
Ok(())
}
pub fn stop_profiling(&self) -> Result<()> {
cpuprofiler::PROFILER.lock().unwrap().stop()?;
Ok(())
}
}
```
## Best Practices
### Performance Testing
```rust
// Automated performance regression testing
#[cfg(test)]
mod performance_tests {
use super::*;
use criterion::{black_box, Criterion};
#[test]
fn performance_regression_test() {
let mut c = Criterion::default();
// Baseline performance requirements
c.bench_function("transaction_validation", |b| {
let tx = create_test_transaction();
b.iter(|| {
validate_transaction(black_box(&tx))
})
});
// Assert performance requirements
let measurement = c.measurement_time(Duration::from_secs(10));
// Transaction validation should complete in <1ms
assert!(measurement.mean_execution_time() < Duration::from_millis(1));
}
}
```
### Optimization Guidelines
1. **Profile First**: Always measure before optimizing
2. **Focus on Hot Paths**: Optimize the most frequently executed code
3. **Memory Locality**: Keep related data close together
4. **Reduce Allocations**: Pool objects and reuse memory
5. **Batch Operations**: Group similar operations together
6. **Use Appropriate Data Structures**: Choose the right tool for the job
7. **Optimize for the Common Case**: Make common operations fast
## Resources
- [Rust Performance Book](https://nnethercote.github.io/perf-book/)
- [Criterion.rs](https://github.com/bheisler/criterion.rs) - Benchmarking
- [Flamegraph](https://github.com/flamegraph-rs/flamegraph) - Profiling
- [Perf](https://perf.wiki.kernel.org/) - Linux performance tools
---
This performance guide provides comprehensive optimization strategies for all Anya Core systems and is maintained by the development team.