Performance Optimization for DHT Lookup Algorithms

[luisawatkins]

Description

This enhancement proposes to optimize the Kademlia DHT implementation by replacing the current O(n²) peer lookup algorithm with an efficient O(n log k) heap-based approach, implementing memory-efficient peer selection, and improving error handling with adaptive delays. The optimization will significantly improve scalability and reduce resource consumption in large peer-to-peer networks.

Motivation

The current DHT implementation has several performance bottlenecks that become critical as network size grows:

Scalability Concerns

• O(n²) complexity in peer lookup operations will cause exponential slowdown as peer count increases
• Memory-intensive operations create unnecessary memory pressure in large networks (1000+ peers)
• Fixed sleep delays in error handling waste CPU cycles and increase latency

Production Impact

• Discovery Time: Slower peer discovery in large networks affects user experience
• Resource Usage: High CPU and memory consumption per node reduces network efficiency
• Network Growth: Current implementation will not scale to enterprise-level peer counts

Competitive Advantage

• Modern DHT implementations use optimized algorithms for better performance
• Improved performance enables larger, more efficient peer-to-peer networks
• Better resource utilization allows for more concurrent operations

Current Implementation

1. Inefficient DHT Lookup Algorithm

File: libp2p/kad_dht/peer_routing.py:180-225

The current implementation uses nested loops and full sorting for each lookup round:

# Current O(n²) implementation
while rounds < MAX_PEER_LOOKUP_ROUNDS:
    rounds += 1
    
    # Find peers we haven't queried yet - O(n) operation
    peers_to_query = [p for p in closest_peers if p not in queried_peers]
    
    # Query peers in parallel
    async with trio.open_nursery() as nursery:
        for peer in peers_batch:
            nursery.start_soon(self._query_single_peer_for_closest, peer, target_key, new_peers)
    
    # Full sorting of all candidates - O(n log n) operation
    all_candidates = closest_peers + new_peers
    closest_peers = sort_peer_ids_by_distance(target_key, all_candidates)[:count]

Problems:

• Creates full sorted lists even when only top-k peers are needed
• Duplicates peer lists in memory during each iteration
• No early termination for convergence detection

2. Memory-Intensive Distance Calculations

File: libp2p/kad_dht/utils.py:145-163

The distance calculation function sorts entire peer lists:

def sort_peer_ids_by_distance(target_key: bytes, peer_ids: list[ID]) -> list[ID]:
    def get_distance(peer_id: ID) -> int:
        peer_hash = multihash.digest(peer_id.to_bytes(), "sha2-256").digest
        return xor_distance(target_key, peer_hash)
    
    # Sorts entire list even when only top-k needed
    return sorted(peer_ids, key=get_distance)

Problems:

• Always sorts complete peer lists regardless of how many peers are actually needed
• Creates temporary lists and performs full sorting operations
• No incremental or streaming approach for large peer sets

3. Fixed Sleep Delays in Error Handling

File: libp2p/stream_muxer/yamux/yamux.py:721

Error handling uses fixed delays regardless of error type:

# Fixed 10ms delay for all error types
await trio.sleep(0.01)

Problems:

• Same delay for temporary network issues vs persistent failures
• No exponential backoff for retry scenarios
• Wastes CPU cycles with unnecessary fixed delays

Impact Assessment

Performance Impact

• Scalability: O(n²) complexity will cause exponential slowdown as peer count grows
• Memory Usage: High memory consumption with large peer sets (1000+ peers)
• Latency: Unnecessary delays in error recovery scenarios

Network Impact

• Discovery Time: Slower peer discovery in large networks
• Resource Usage: Higher CPU and memory consumption per node
• User Experience: Increased connection establishment times

Proposed Solutions

1. Optimize DHT Lookup Algorithm

• Implement heap-based top-k selection instead of full sorting
• Use incremental distance calculations
• Add early termination conditions for convergence

2. Reduce Memory Usage

• Implement streaming peer selection
• Use generators instead of list comprehensions where possible
• Add memory-efficient peer caching

3. Improve Error Handling

• Implement exponential backoff for retries
• Use adaptive sleep delays based on error type
• Add circuit breaker patterns for persistent failures

Expected Performance Improvements

Time Complexity

• Current: O(n²) for peer lookup
• Target: O(n log k) where k is the desired peer count

Memory Usage

• Current: O(n) for each lookup operation
• Target: O(k) where k is the desired peer count

Latency

• Current: Fixed 10ms delays in error handling
• Target: Adaptive delays (1ms-100ms based on error type)

Implementation Priority

High Priority - These optimizations are critical for production scalability:

1. Heap-based peer selection algorithm
2. Memory-efficient distance calculations
3. Adaptive error handling delays

[SuchitraSwain]

I am taking these up @seetadev