This guide provides comprehensive strategies for achieving the performance targets outlined in the architecture plan: sub-50ms search response times, <10ms autocomplete, 60 FPS scrolling, and <16ms markdown rendering. These optimizations are critical for user experience and system scalability.
- Search: <50ms (cached), <200ms (uncached)
- Autocomplete: <10ms response time
- Chat: First token <100ms, streaming at 50+ tokens/sec
- Virtual Scrolling: 60 FPS with 100+ messages
- Markdown Rendering: <16ms per message
- Memory Usage: <100MB for typical workload
- Startup Time: <2 seconds
- Cache Hit Rate: >90%
- Error Rate: <0.1%
- CPU Usage: <50% under normal load
- Memory Leaks: Zero tolerance
use lru::LruCache;
use dashmap::DashMap;
use std::sync::Arc;
use parking_lot::RwLock;
/// Multi-level caching system
pub struct HierarchicalCache<K, V> {
// L1: In-memory LRU cache (fastest)
l1_cache: Arc<RwLock<LruCache<K, V>>>,
// L2: Compressed cache for larger data
l2_cache: Arc<DashMap<K, CompressedValue<V>>>,
// L3: Persistent cache (optional)
l3_cache: Option<Box<dyn PersistentCache<K, V>>>,
// Cache metrics
metrics: CacheMetrics,
}
#[derive(Clone)]
struct CompressedValue<V> {
compressed: Vec<u8>,
_phantom: std::marker::PhantomData<V>,
}
impl<K, V> HierarchicalCache<K, V>
where
K: Clone + Eq + std::hash::Hash + Send + Sync + 'static,
V: Clone + Send + Sync + serde::Serialize + for<'de> serde::Deserialize<'de>,
{
pub fn new(l1_size: usize, l2_size: usize) -> Self {
Self {
l1_cache: Arc::new(RwLock::new(LruCache::new(
std::num::NonZeroUsize::new(l1_size).unwrap()
))),
l2_cache: Arc::new(DashMap::with_capacity(l2_size)),
l3_cache: None,
metrics: CacheMetrics::new(),
}
}
pub async fn get(&self, key: &K) -> Option<V> {
// Try L1 first
if let Some(value) = self.l1_cache.read().get(key) {
self.metrics.record_hit(CacheLevel::L1);
return Some(value.clone());
}
// Try L2
if let Some(compressed) = self.l2_cache.get(key) {
self.metrics.record_hit(CacheLevel::L2);
// Decompress and promote to L1
let value = self.decompress(&compressed.compressed)?;
self.l1_cache.write().put(key.clone(), value.clone());
return Some(value);
}
// Try L3 if available
if let Some(l3) = &self.l3_cache {
if let Some(value) = l3.get(key).await {
self.metrics.record_hit(CacheLevel::L3);
// Promote through levels
self.l2_cache.insert(key.clone(), self.compress(&value));
self.l1_cache.write().put(key.clone(), value.clone());
return Some(value);
}
}
self.metrics.record_miss();
None
}
pub async fn put(&self, key: K, value: V) {
// Store in L1
self.l1_cache.write().put(key.clone(), value.clone());
// Compress and store in L2
self.l2_cache.insert(key, self.compress(&value));
// Store in L3 if available
if let Some(l3) = &self.l3_cache {
let _ = l3.put(&key, &value).await;
}
}
fn compress(&self, value: &V) -> CompressedValue<V> {
// Use lz4 for fast compression
let serialized = bincode::serialize(value).unwrap();
let compressed = lz4::block::compress(&serialized)
.unwrap_or(serialized); // Fallback to uncompressed
CompressedValue {
compressed,
_phantom: std::marker::PhantomData,
}
}
fn decompress(&self, compressed: &[u8]) -> Option<V> {
// Try to decompress
if let Ok(decompressed) = lz4::block::decompress(compressed, None) {
bincode::deserialize(&decompressed).ok()
} else {
// Fallback: try direct deserialization
bincode::deserialize(compressed).ok()
}
}
}
enum CacheLevel {
L1,
L2,
L3,
}
struct CacheMetrics {
l1_hits: std::sync::atomic::AtomicU64,
l2_hits: std::sync::atomic::AtomicU64,
l3_hits: std::sync::atomic::AtomicU64,
misses: std::sync::atomic::AtomicU64,
}
impl CacheMetrics {
fn record_hit(&self, level: CacheLevel) {
match level {
CacheLevel::L1 => self.l1_hits.fetch_add(1, std::sync::atomic::Ordering::Relaxed),
CacheLevel::L2 => self.l2_hits.fetch_add(1, std::sync::atomic::Ordering::Relaxed),
CacheLevel::L3 => self.l3_hits.fetch_add(1, std::sync::atomic::Ordering::Relaxed),
};
}
fn record_miss(&self) {
self.misses.fetch_add(1, std::sync::atomic::Ordering::Relaxed);
}
fn hit_rate(&self) -> f64 {
let total_hits = self.l1_hits.load(std::sync::atomic::Ordering::Relaxed)
+ self.l2_hits.load(std::sync::atomic::Ordering::Relaxed)
+ self.l3_hits.load(std::sync::atomic::Ordering::Relaxed);
let total_requests = total_hits + self.misses.load(std::sync::atomic::Ordering::Relaxed);
if total_requests == 0 {
0.0
} else {
total_hits as f64 / total_requests as f64
}
}
}/// Proactive cache warming for predictable access patterns
pub struct CacheWarmer {
cache: Arc<HierarchicalCache<String, CachedSearchResult>>,
access_patterns: Arc<RwLock<AccessPatternTracker>>,
warmer_task: Option<tokio::task::JoinHandle<()>>,
}
impl CacheWarmer {
pub fn new(cache: Arc<HierarchicalCache<String, CachedSearchResult>>) -> Self {
Self {
cache,
access_patterns: Arc::new(RwLock::new(AccessPatternTracker::new())),
warmer_task: None,
}
}
pub fn start(&mut self) {
let cache = self.cache.clone();
let patterns = self.access_patterns.clone();
self.warmer_task = Some(tokio::spawn(async move {
let mut interval = tokio::time::interval(Duration::from_secs(60));
loop {
interval.tick().await;
// Get predicted queries
let predictions = patterns.read().predict_next_queries(10);
// Warm cache with predicted queries
for query in predictions {
if !cache.contains_key(&query).await {
if let Some(result) = Self::prefetch_result(&query).await {
cache.put(query, result).await;
}
}
}
}
}));
}
pub async fn record_access(&self, query: &str) {
self.access_patterns.write().record_access(query);
}
async fn prefetch_result(query: &str) -> Option<CachedSearchResult> {
// Implement predictive fetching based on user patterns
// This could use ML or simple frequency analysis
None
}
}
struct AccessPatternTracker {
// Query frequency count
frequencies: HashMap<String, usize>,
// Sequential access patterns
sequences: VecDeque<String>,
// Time-based patterns
temporal_patterns: HashMap<String, Vec<Instant>>,
}/// High-performance autocomplete with binary search
pub struct AutocompleteEngine {
// Sorted list of all terms
sorted_terms: Vec<String>,
// Prefix index for faster lookups
prefix_index: HashMap<String, Vec<usize>>,
// Fuzzy search cache
fuzzy_cache: LruCache<String, Vec<AutocompleteSuggestion>>,
}
impl AutocompleteEngine {
pub fn new(terms: Vec<String>) -> Self {
let mut sorted_terms = terms;
sorted_terms.sort_unstable();
let mut prefix_index = HashMap::new();
// Build prefix index (for first 3 characters)
for (idx, term) in sorted_terms.iter().enumerate() {
for len in 1..=std::cmp::min(3, term.len()) {
let prefix = term[..len].to_lowercase();
prefix_index
.entry(prefix)
.or_insert_with(Vec::new)
.push(idx);
}
}
Self {
sorted_terms,
prefix_index,
fuzzy_cache: LruCache::new(std::num::NonZeroUsize::new(1000).unwrap()),
}
}
/// Get suggestions for a partial query (<10ms target)
pub fn get_suggestions(&mut self, query: &str, limit: usize) -> Vec<AutocompleteSuggestion> {
let start = Instant::now();
let suggestions = if query.len() >= 2 {
// Use prefix index for exact matches
self.get_prefix_suggestions(query, limit)
} else {
// Use fuzzy search for very short queries
self.get_fuzzy_suggestions(query, limit)
};
// Log performance
let duration = start.elapsed();
if duration > Duration::from_millis(10) {
log::warn!("Autocomplete took {:?} for query: {}", duration, query);
}
suggestions
}
fn get_prefix_suggestions(&self, prefix: &str, limit: usize) -> Vec<AutocompleteSuggestion> {
let prefix_lower = prefix.to_lowercase();
if let Some(indices) = self.prefix_index.get(&prefix_lower) {
// Get all terms with matching prefix
let mut suggestions = Vec::with_capacity(indices.len());
for &idx in indices {
if let Some(term) = self.sorted_terms.get(idx) {
suggestions.push(AutocompleteSuggestion {
text: term.clone(),
score: 1.0, // Perfect match for prefix
highlight_range: 0..prefix.len(),
});
}
}
// Sort and limit
suggestions.sort_by(|a, b| {
b.score.partial_cmp(&a.score)
.unwrap_or(std::cmp::Ordering::Equal)
.then_with(|| a.text.cmp(&b.text))
});
suggestions.truncate(limit);
suggestions
} else {
// Fallback to binary search
self.binary_search_suggestions(prefix, limit)
}
}
fn binary_search_suggestions(&self, prefix: &str, limit: usize) -> Vec<AutocompleteSuggestion> {
let prefix_lower = prefix.to_lowercase();
// Find insertion point using binary search
let start_idx = match self.sorted_terms.binary_search_by(|term| {
term.to_lowercase().as_str().cmp(&prefix_lower)
}) {
Ok(idx) => idx,
Err(idx) => idx,
};
// Collect matches from insertion point
let mut suggestions = Vec::new();
let end_idx = std::cmp::min(start_idx + limit * 2, self.sorted_terms.len());
for term in &self.sorted_terms[start_idx..end_idx] {
if term.to_lowercase().starts_with(&prefix_lower) {
suggestions.push(AutocompleteSuggestion {
text: term.clone(),
score: 0.9,
highlight_range: 0..prefix.len(),
});
} else if !suggestions.is_empty() {
// We've passed all potential matches
break;
}
}
suggestions.truncate(limit);
suggestions
}
fn get_fuzzy_suggestions(&mut self, query: &str, limit: usize) -> Vec<AutocompleteSuggestion> {
// Check cache first
if let Some(cached) = self.fuzzy_cache.get(query) {
return cached.clone();
}
// Use Jaro-Winkler distance for fuzzy matching
let mut matches: Vec<_> = self.sorted_terms
.iter()
.map(|term| {
let distance = jaro_winkler(query, term);
AutocompleteSuggestion {
text: term.clone(),
score: distance,
highlight_range: self.find_highlight_range(query, term),
}
})
.filter(|s| s.score > 0.7) // Only good matches
.collect();
// Sort by score
matches.sort_by(|a, b| b.score.partial_cmp(&a.score).unwrap());
matches.truncate(limit);
// Cache result
self.fuzzy_cache.put(query.to_string(), matches.clone());
matches
}
}
/// Fast Jaro-Winkler distance implementation
fn jaro_winkler(s1: &str, s2: &str) -> f64 {
if s1 == s2 {
return 1.0;
}
let len1 = s1.len();
let len2 = s2.len();
if len1 == 0 || len2 == 0 {
return 0.0;
}
let match_distance = std::cmp::max(len1, len2) / 2 - 1;
let mut s1_matches = vec![false; len1];
let mut s2_matches = vec![false; len2];
let mut matches = 0;
let mut transpositions = 0;
// Find matches
for i in 0..len1 {
let start = std::cmp::max(0, i as i32 - match_distance as i32) as usize;
let end = std::cmp::min(i + match_distance + 1, len2);
for j in start..end {
if !s2_matches[j] && s1.as_bytes()[i] == s2.as_bytes()[j] {
s1_matches[i] = true;
s2_matches[j] = true;
matches += 1;
break;
}
}
}
if matches == 0 {
return 0.0;
}
// Count transpositions
let mut k = 0;
for i in 0..len1 {
if s1_matches[i] {
while !s2_matches[k] {
k += 1;
}
if s1.as_bytes()[i] != s2.as_bytes()[k] {
transpositions += 1;
}
k += 1;
}
}
let jaro = (
matches as f64 / len1 as f64
+ matches as f64 / len2 as f64
+ (matches - transpositions / 2) as f64 / matches as f64
) / 3.0;
// Winkler prefix bonus
let prefix_len = std::cmp::min(
4,
s1.chars()
.zip(s2.chars())
.take_while(|&(a, b)| a == b)
.count()
);
jaro + prefix_len as f64 * 0.1 * (1.0 - jaro)
}use tokio::sync::{mpsc, oneshot, CancellationToken};
use futures::stream::{self, StreamExt};
/// Concurrent search coordinator with cancellation support
pub struct ConcurrentSearchCoordinator {
search_services: Vec<Arc<dyn SearchService>>,
result_aggregator: ResultAggregator,
performance_tracker: Arc<PerformanceTracker>,
}
impl ConcurrentSearchCoordinator {
pub async fn search(
&self,
query: SearchQuery,
options: SearchOptions,
) -> Result<SearchResults, SearchError> {
let start = Instant::now();
let cancellation_token = CancellationToken::new();
let timeout = Duration::from_millis(options.timeout_ms.unwrap_or(5000));
// Create channels for results
let (result_tx, mut result_rx) = mpsc::channel(100);
let (completion_tx, completion_rx) = oneshot::channel();
// Spawn search tasks for all services
let mut handles = Vec::new();
for service in &self.search_services {
let service = service.clone();
let query = query.clone();
let result_tx = result_tx.clone();
let token = cancellation_token.clone();
let handle = tokio::spawn(async move {
let service_start = Instant::now();
// Race between search and cancellation
tokio::select! {
result = service.search(query.clone()) => {
let duration = service_start.elapsed();
match result {
Ok(results) => {
let _ = result_tx.send(Ok((results, duration))).await;
}
Err(e) => {
let _ = result_tx.send(Err(e)).await;
}
}
}
_ = token.cancelled() => {
log::debug!("Search cancelled for service: {}", service.service_id());
}
}
});
handles.push(handle);
}
// Drop our sender to close when all tasks are done
drop(result_tx);
// Spawn aggregation task
let aggregator = self.result_aggregator.clone();
let performance_tracker = self.performance_tracker.clone();
let query_id = query.id.clone();
let aggregation_handle = tokio::spawn(async move {
let mut all_results = Vec::new();
let mut service_times = Vec::new();
while let Some(result) = result_rx.recv().await {
match result {
Ok((results, duration)) => {
all_results.push(results);
service_times.push(duration);
}
Err(e) => {
log::warn!("Search service error: {}", e);
}
}
}
// Aggregate results
let final_results = aggregator.aggregate(all_results).await;
// Record performance metrics
let total_time = start.elapsed();
performance_tracker.record_search_operation(
query_id,
total_time,
service_times,
final_results.len()
).await;
final_results
});
// Wait for either completion, timeout, or cancellation
tokio::select! {
results = aggregation_handle => {
let _ = completion_tx.send(()).await;
Ok(results?)
}
_ = tokio::time::sleep(timeout) => {
cancellation_token.cancel();
Err(SearchError::Timeout(timeout))
}
}
}
pub async fn search_stream(
&self,
query: SearchQuery,
) -> impl Stream<Item = Result<PartialSearchResult, SearchError>> {
let (tx, rx) = mpsc::channel(10);
for service in &self.search_services {
let service = service.clone();
let query = query.clone();
let tx = tx.clone();
tokio::spawn(async move {
let mut stream = service.search_stream(query).await?;
while let Some(result) = stream.next().await {
if tx.send(Ok(result)).await.is_err() {
break; // Receiver dropped
}
}
Ok::<(), SearchError>(())
});
}
drop(tx); // Drop sender after spawning all tasks
Box::pin(stream::unfold(rx, |mut rx| async {
match rx.recv().await {
Some(result) => Some((result, rx)),
None => None,
}
}))
}
}
/// Smart result aggregation with deduplication and ranking
pub struct ResultAggregator {
deduplicator: ResultDeduplicator,
ranker: ResultRanker,
}
impl ResultAggregator {
pub async fn aggregate(
&self,
result_sets: Vec<SearchResults>,
) -> SearchResults {
let start = Instant::now();
// Flatten all results
let all_results: Vec<SearchResult> = result_sets
.into_iter()
.flat_map(|r| r.results)
.collect();
// Deduplicate based on content similarity
let unique_results = self.deduplicator.deduplicate(all_results).await;
// Re-rank based on relevance and diversity
let ranked_results = self.ranker.rank_results(unique_results).await;
let aggregation_time = start.elapsed();
log::debug!(
"Aggregated {} results in {:?}",
ranked_results.len(),
aggregation_time
);
SearchResults {
query_id: uuid::Uuid::new_v4().to_string(),
results: ranked_results,
total_found: ranked_results.len(),
aggregation_time: Some(aggregation_time),
}
}
}/// Optimized virtual scrolling with height caching
pub struct VirtualScrollList<T> {
items: Vec<T>,
viewport_height: f32,
estimated_item_height: f32,
// Height cache for rendered items
height_cache: LruCache<usize, f32>,
// Scroll state
scroll_offset: f32,
total_height: f32,
// Rendering optimization
render_buffer: usize, // Extra items to render above/below viewport
visible_range: Range<usize>,
}
impl<T> VirtualScrollList<T> {
pub fn new(viewport_height: f32) -> Self {
Self {
items: Vec::new(),
viewport_height,
estimated_item_height: 40.0,
height_cache: LruCache::new(std::num::NonZeroUsize::new(1000).unwrap()),
scroll_offset: 0.0,
total_height: 0.0,
render_buffer: 5,
visible_range: 0..0,
}
}
pub fn set_items(&mut self, items: Vec<T>) {
self.items = items;
self.recalculate_total_height();
}
pub fn scroll_to(&mut self, offset: f32) {
self.scroll_offset = offset.clamp(0.0, self.total_height - self.viewport_height);
self.update_visible_range();
}
/// Get items that should be rendered
pub fn get_visible_items(&self) -> Vec<(usize, &T, f32)> {
let mut visible_items = Vec::new();
let mut current_y = 0.0;
// Find start position
for (idx, _) in self.items.iter().enumerate() {
let height = self.get_item_height(idx);
if current_y + height >= self.scroll_offset {
// This item is at or below the top of viewport
if current_y > self.scroll_offset + self.viewport_height {
// This item is below the bottom of viewport
break;
}
visible_items.push((idx, &self.items[idx], current_y));
}
current_y += height;
}
visible_items
}
fn get_item_height(&self, index: usize) -> f32 {
self.height_cache
.get(&index)
.copied()
.unwrap_or(self.estimated_item_height)
}
fn update_item_height(&mut self, index: usize, height: f32) {
self.height_cache.put(index, height);
self.recalculate_total_height();
}
fn recalculate_total_height(&mut self) {
self.total_height = self
.items
.iter()
.enumerate()
.map(|(idx, _)| self.get_item_height(idx))
.sum();
}
fn update_visible_range(&mut self) {
let start_y = self.scroll_offset;
let end_y = start_y + self.viewport_height;
// Find items intersecting viewport
let mut current_y = 0.0;
let mut start_idx = 0;
let mut end_idx = 0;
for (idx, _) in self.items.iter().enumerate() {
let height = self.get_item_height(idx);
if current_y + height < start_y {
start_idx = idx + 1;
} else if current_y <= end_y {
end_idx = idx + 1;
}
current_y += height;
}
// Add render buffer
self.visible_range = {
let buffered_start = start_idx.saturating_sub(self.render_buffer);
let buffered_end = std::cmp::min(
end_idx + self.render_buffer,
self.items.len()
);
buffered_start..buffered_end
};
}
/// Smooth scroll animation
pub fn smooth_scroll_to(
&mut self,
target_offset: f32,
duration: Duration,
) -> impl Stream<Item = f32> {
let start_offset = self.scroll_offset;
let distance = target_offset - start_offset;
let start_time = Instant::now();
Box::pin(stream::unfold(
(start_offset, distance, start_time),
move |(current_offset, remaining_distance, start_time)| async move {
let elapsed = start_time.elapsed();
let progress = (elapsed.as_secs_f64() / duration.as_secs_f64()).min(1.0);
// Ease-in-out animation
let eased = if progress < 0.5 {
2.0 * progress * progress
} else {
1.0 - 2.0 * (1.0 - progress) * (1.0 - progress)
};
let new_offset = start_offset + distance * eased as f32;
if progress >= 1.0 {
None
} else {
Some((new_offset, (new_offset, remaining_distance, start_time)))
}
}
))
}
}
/// GPUI integration for virtual scrolling
impl<T> Render for VirtualScrollList<T>
where
T: Render + Clone,
{
fn render(&mut self, cx: &mut Context<Self>) -> impl IntoElement {
let visible_items = self.get_visible_items();
div()
.size_full()
.overflow_hidden()
.relative()
.child(
div()
.absolute()
.top(px(0.0))
.left(px(0.0))
.w(px(10.0)) // Scrollbar width
.h(px(self.total_height))
.bg(rgb(0xf0f0f0))
)
.child(
div()
.relative()
.top(px(self.scroll_offset))
.w_full()
.children(
visible_items.into_iter().map(|(idx, item, y)| {
div()
.absolute()
.top(px(y))
.left(px(0.0))
.w_full()
.child(item.clone())
})
)
)
}
}/// High-performance markdown renderer with incremental updates
pub struct IncrementalMarkdownRenderer {
// Parsed markdown AST
ast_nodes: Vec<MarkdownNode>,
// Render cache for static content
render_cache: LruCache<String, RenderedMarkdown>,
// Streaming support
streaming_content: String,
last_rendered_length: usize,
// Performance optimization
dirty_ranges: Vec<RenderRange>,
render_budget: Duration,
}
impl IncrementalMarkdownRenderer {
pub fn new() -> Self {
Self {
ast_nodes: Vec::new(),
render_cache: LruCache::new(std::num::NonZeroUsize::new(100).unwrap()),
streaming_content: String::new(),
last_rendered_length: 0,
dirty_ranges: Vec::new(),
render_budget: Duration::from_millis(16), // 60 FPS
}
}
/// Stream markdown content and render incrementally
pub fn stream_content(&mut self, content: &str) -> RenderedMarkdown {
let start = Instant::now();
// Append new content
self.streaming_content.push_str(content);
// Parse only the new content
let new_nodes = self.parse_incremental(content);
// Mark dirty ranges
self.mark_dirty_ranges(new_nodes.len());
// Render within budget
let rendered = self.render_within_budget(start);
rendered
}
fn parse_incremental(&mut self, new_content: &str) -> Vec<MarkdownNode> {
let start_pos = self.last_rendered_length;
let parser = pulldown_cmark::Parser::new(&new_content[start_pos..]);
let mut new_nodes = Vec::new();
for event in parser {
match event {
Event::Start(tag) => {
new_nodes.push(MarkdownNode::start(tag, start_pos + new_content.len()));
}
Event::End(tag_end) => {
new_nodes.push(MarkdownNode::end(tag_end, start_pos + new_content.len()));
}
Event::Text(text) => {
new_nodes.push(MarkdownNode::text(text.to_string(), start_pos + new_content.len()));
}
_ => {}
}
}
self.ast_nodes.extend(new_nodes.clone());
self.last_rendered_length = self.streaming_content.len();
new_nodes
}
fn render_within_budget(&mut self, start: Instant) -> RenderedMarkdown {
let mut rendered = Vec::new();
let mut current_pos = 0;
// Render dirty ranges first
for range in &self.dirty_ranges {
if start.elapsed() > self.render_budget {
break; // Budget exceeded
}
let partial = self.render_range(range.start..range.end);
rendered.extend(partial);
current_pos = range.end;
}
// Clear rendered ranges
self.dirty_ranges.clear();
RenderedMarkdown {
elements: rendered,
total_height: self.calculate_height(&rendered),
render_time: start.elapsed(),
}
}
fn render_range(&self, range: Range<usize>) -> Vec<RenderedElement> {
let mut elements = Vec::new();
let mut in_code_block = false;
let mut code_block_language = None;
for node in &self.ast_nodes[range] {
match node {
MarkdownNode::Start(Tag::CodeBlock(lang)) => {
in_code_block = true;
code_block_language = lang.clone();
}
MarkdownNode::End(TagEnd::CodeBlock) => {
in_code_block = false;
code_block_language = None;
}
MarkdownNode::Text(text) => {
if in_code_block {
elements.push(RenderedElement::CodeBlock {
language: code_block_language.clone(),
content: text.clone(),
});
} else {
// Render inline markdown
let inline_elements = self.render_inline_text(text);
elements.extend(inline_elements);
}
}
MarkdownNode::Start(Tag::Heading(level)) => {
elements.push(RenderedElement::Heading {
level: *level,
text: String::new(), // Will be filled by following text
});
}
_ => {}
}
}
elements
}
fn render_inline_text(&self, text: &str) -> Vec<RenderedElement> {
// Fast inline rendering without regex
let mut elements = Vec::new();
let mut current_text = String::new();
let mut chars = text.chars().peekable();
while let Some(ch) = chars.next() {
match ch {
'*' => {
if let Some(next_ch) = chars.peek() {
if *next_ch == '*' {
// Bold text
if !current_text.is_empty() {
elements.push(RenderedElement::Text(current_text));
current_text.clear();
}
chars.next(); // Consume second *
// Collect bold text until **
let mut bold_text = String::new();
while let Some(ch) = chars.next() {
if ch == '*' {
if let Some(next_ch) = chars.peek() {
if *next_ch == '*' {
chars.next(); // Consume second *
elements.push(RenderedElement::Bold(bold_text));
break;
}
}
}
bold_text.push(ch);
}
} else {
current_text.push(ch);
}
} else {
current_text.push(ch);
}
}
'`' => {
if !current_text.is_empty() {
elements.push(RenderedElement::Text(current_text));
current_text.clear();
}
// Collect inline code
let mut code_text = String::new();
while let Some(ch) = chars.next() {
if ch == '`' {
break;
}
code_text.push(ch);
}
elements.push(RenderedElement::InlineCode(code_text));
}
_ => {
current_text.push(ch);
}
}
}
if !current_text.is_empty() {
elements.push(RenderedElement::Text(current_text));
}
elements
}
}
#[derive(Debug, Clone)]
enum MarkdownNode {
Start(pulldown_cmark::Tag, usize),
End(pulldown_cmark::TagEnd, usize),
Text(String, usize),
}
#[derive(Debug, Clone)]
enum RenderedElement {
Text(String),
Bold(String),
Italic(String),
InlineCode(String),
CodeBlock {
language: Option<String>,
content: String,
},
Heading {
level: u32,
text: String,
},
}
struct RenderedMarkdown {
elements: Vec<RenderedElement>,
total_height: f32,
render_time: Duration,
}/// Generic object pool for expensive allocations
pub struct ObjectPool<T> {
objects: Arc<Mutex<Vec<T>>>,
factory: Box<dyn Fn() -> T + Send + Sync>,
max_size: usize,
created: Arc<AtomicUsize>,
reused: Arc<AtomicUsize>,
}
impl<T> ObjectPool<T>
where
T: Send + Sync,
{
pub fn new<F>(factory: F, initial_size: usize, max_size: usize) -> Self
where
F: Fn() -> T + Send + Sync + 'static,
{
let mut objects = Vec::with_capacity(initial_size);
for _ in 0..initial_size {
objects.push(factory());
}
Self {
objects: Arc::new(Mutex::new(objects)),
factory: Box::new(factory),
max_size,
created: Arc::new(AtomicUsize::new(initial_size)),
reused: Arc::new(AtomicUsize::new(0)),
}
}
pub async fn get(&self) -> PooledObject<T> {
let mut objects = self.objects.lock().await;
let object = if let Some(obj) = objects.pop() {
self.reused.fetch_add(1, Ordering::Relaxed);
obj
} else {
self.created.fetch_add(1, Ordering::Relaxed);
(self.factory)()
};
PooledObject {
object: Some(object),
pool: self.objects.clone(),
}
}
pub fn stats(&self) -> PoolStats {
PoolStats {
created: self.created.load(Ordering::Relaxed),
reused: self.reused.load(Ordering::Relaxed),
available: self.objects.lock().now_or_poison().len(),
}
}
}
/// RAII wrapper for pooled objects
pub struct PooledObject<T> {
object: Option<T>,
pool: Arc<Mutex<Vec<T>>>,
}
impl<T> Drop for PooledObject<T> {
fn drop(&mut self) {
if let Some(obj) = self.object.take() {
// Return to pool if not at capacity
let mut pool = self.pool.now_or_poison();
if pool.len() < pool.capacity() {
pool.push(obj);
}
}
}
}
impl<T> std::ops::Deref for PooledObject<T> {
type Target = T;
fn deref(&self) -> &Self::Target {
self.object.as_ref().unwrap()
}
}
impl<T> std::ops::DerefMut for PooledObject<T> {
fn deref_mut(&mut self) -> &mut Self::Target {
self.object.as_mut().unwrap()
}
}
#[derive(Debug)]
struct PoolStats {
created: usize,
reused: usize,
available: usize,
}
/// Pre-configured pools for common types
pub struct CommonPools {
string_pool: ObjectPool<String>,
vec_pool: ObjectPool<Vec<u8>>,
hash_map_pool: ObjectPool<std::collections::HashMap<String, String>>,
}
impl CommonPools {
pub fn new() -> Self {
Self {
string_pool: ObjectPool::new(
|| String::with_capacity(256),
100,
1000
),
vec_pool: ObjectPool::new(
|| Vec::with_capacity(1024),
50,
500
),
hash_map_pool: ObjectPool::new(
|| std::collections::HashMap::with_capacity(16),
25,
250
),
}
}
pub async fn get_string(&self) -> PooledObject<String> {
self.string_pool.get().await
}
pub async fn get_vec(&self) -> PooledObject<Vec<u8>> {
self.vec_pool.get().await
}
pub async fn get_hash_map(&self) -> PooledObject<std::collections::HashMap<String, String>> {
self.hash_map_pool.get().await
}
}/// Zero-copy string operations for performance
pub struct ZeroCopyStringOperations;
impl ZeroCopyStringOperations {
/// Extract prefix without allocation
pub fn extract_prefix(s: &str, n: usize) -> Option<&str> {
if s.is_char_boundary(n) {
Some(&s[..n])
} else {
None
}
}
/// Split on first occurrence without allocation
pub fn split_once(s: &str, delimiter: &str) -> Option<(&str, &str)> {
if let Some(pos) = s.find(delimiter) {
Some((&s[..pos], &s[pos + delimiter.len()..]))
} else {
None
}
}
/// Check if string starts with prefix (optimized)
pub fn starts_with_fast(s: &str, prefix: &str) -> bool {
if prefix.len() > s.len() {
return false;
}
let s_bytes = s.as_bytes();
let prefix_bytes = prefix.as_bytes();
// Use SIMD-accelerated comparison if available
#[cfg(target_arch = "x86_64")]
{
use std::arch::x86_64::*;
// Check 16 bytes at a time
let chunks = prefix_bytes.len() / 16;
for i in 0..chunks {
let s_chunk = unsafe {
_mm_loadu_si128(s_bytes.as_ptr().add(i * 16) as *const __m128i)
};
let p_chunk = unsafe {
_mm_loadu_si128(prefix_bytes.as_ptr().add(i * 16) as *const __m128i)
};
let cmp = unsafe { _mm_cmpeq_epi8(s_chunk, p_chunk) };
let mask = unsafe { _mm_movemask_epi8(cmp) };
if mask != 0xFFFF {
return false;
}
}
// Check remaining bytes
for i in (chunks * 16)..prefix_bytes.len() {
if s_bytes[i] != prefix_bytes[i] {
return false;
}
}
true
}
#[cfg(not(target_arch = "x86_64"))]
{
// Fallback to standard comparison
s_bytes.starts_with(prefix_bytes)
}
}
}
/// Zero-copy JSON parsing
pub struct ZeroCopyJsonParser {
buffer: Vec<u8>,
}
impl ZeroCopyJsonParser {
pub fn new() -> Self {
Self {
buffer: Vec::with_capacity(8192),
}
}
/// Parse JSON without string allocations
pub fn parse_str(&mut self, s: &str) -> Result<ZeroCopyValue, serde_json::Error> {
// Copy to buffer if needed
if s.as_bytes().len() > self.buffer.capacity() {
self.buffer.reserve(s.as_bytes().len() - self.buffer.capacity());
}
self.buffer.clear();
self.buffer.extend_from_slice(s.as_bytes());
// Parse using simd-json for zero-copy parsing
#[cfg(feature = "simd-json")]
{
let mut parsed = simd_json::to_owned_value(&self.buffer)
.map_err(|e| serde_json::Error::custom(e.to_string()))?;
Ok(ZeroCopyValue::from(parsed))
}
#[cfg(not(feature = "simd-json"))]
{
// Fallback to standard JSON parsing
let parsed: serde_json::Value = serde_json::from_str(s)?;
Ok(ZeroCopyValue::from(parsed))
}
}
}
#[derive(Debug, Clone)]
pub enum ZeroCopyValue<'a> {
Null,
Bool(bool),
Number(f64),
String(&'a str),
Array(Vec<ZeroCopyValue<'a>>),
Object(Vec<(&'a str, ZeroCopyValue<'a>)>),
}
impl<'a> ZeroCopyValue<'a> {
pub fn as_str(&self) -> Option<&'a str> {
match self {
ZeroCopyValue::String(s) => Some(s),
_ => None,
}
}
pub fn as_f64(&self) -> Option<f64> {
match self {
ZeroCopyValue::Number(n) => Some(*n),
_ => None,
}
}
}/// Real-time performance monitoring dashboard
pub struct PerformanceMonitor {
metrics: Arc<RwLock<ComponentMetrics>>,
alerts: Arc<Mutex<Vec<PerformanceAlert>>>,
history: Arc<RwLock<VecDeque<Snapshot>>>,
max_history: usize,
}
impl PerformanceMonitor {
pub fn new() -> Self {
Self {
metrics: Arc::new(RwLock::new(ComponentMetrics::default())),
alerts: Arc::new(Mutex::new(Vec::new())),
history: Arc::new(RwLock::new(VecDeque::with_capacity(1000))),
max_history: 1000,
}
}
/// Start real-time monitoring
pub fn start_monitoring(&self) -> tokio::task::JoinHandle<()> {
let metrics = self.metrics.clone();
let history = self.history.clone();
let alerts = self.alerts.clone();
tokio::spawn(async move {
let mut interval = tokio::time::interval(Duration::from_millis(100));
loop {
interval.tick().await;
// Take snapshot
let snapshot = {
let metrics = metrics.read().await;
Snapshot {
timestamp: Instant::now(),
memory_usage: get_memory_usage(),
cpu_usage: get_cpu_usage(),
response_times: metrics.response_times.clone(),
error_rate: metrics.error_rate,
throughput: metrics.throughput,
}
};
// Store in history
{
let mut history = history.write().await;
if history.len() >= 1000 {
history.pop_front();
}
history.push_back(snapshot.clone());
}
// Check for alerts
Self::check_alerts(&snapshot, &alerts).await;
}
})
}
async fn check_alerts(snapshot: &Snapshot, alerts: &Arc<Mutex<Vec<PerformanceAlert>>>) {
let mut new_alerts = Vec::new();
// Memory usage alert
if snapshot.memory_usage > 512 * 1024 * 1024 {
new_alerts.push(PerformanceAlert {
alert_type: AlertType::HighMemoryUsage,
severity: AlertSeverity::Warning,
message: format!("Memory usage: {}MB", snapshot.memory_usage / 1024 / 1024),
timestamp: Utc::now(),
});
}
// Response time alert
if let Some(p95) = snapshot.response_times.get(&Percentile::P95) {
if *p95 > Duration::from_millis(100) {
new_alerts.push(PerformanceAlert {
alert_type: AlertType::SlowResponse,
severity: AlertSeverity::Error,
message: format!("P95 response time: {:?}", p95),
timestamp: Utc::now(),
});
}
}
// Add alerts if any
if !new_alerts.is_empty() {
let mut alerts = alerts.lock().await;
alerts.extend(new_alerts);
}
}
}
#[derive(Debug, Clone)]
struct Snapshot {
timestamp: Instant,
memory_usage: usize,
cpu_usage: f64,
response_times: HashMap<Percentile, Duration>,
error_rate: f64,
throughput: f64,
}
#[derive(Debug, Clone)]
enum Percentile {
P50,
P95,
P99,
}
fn get_memory_usage() -> usize {
// Use platform-specific APIs to get actual memory usage
#[cfg(unix)]
{
use std::fs;
let status = fs::read_to_string("/proc/self/status").unwrap();
for line in status.lines() {
if line.starts_with("VmRSS:") {
let parts: Vec<&str> = line.split_whitespace().collect();
return parts[1].parse::<usize>().unwrap() * 1024;
}
}
}
0 // Fallback
}
fn get_cpu_usage() -> f64 {
// Use platform-specific APIs to get CPU usage
// This is a placeholder implementation
0.0
}This comprehensive performance optimization guide provides concrete implementations for achieving the ambitious performance targets. The key strategies include intelligent caching, concurrent operations, virtual scrolling, incremental rendering, and memory optimization techniques that together will ensure the Terraphim AI system delivers a responsive, smooth user experience.