mirror of
https://github.com/ruvnet/RuVector.git
synced 2026-05-24 05:43:58 +00:00
7283dc8781
- Fix import paths in comparison_benchmark.rs and hnsw_search.rs - Add Python benchmark suite comparing rUvector vs Qdrant - Create detailed performance comparison documentation Key findings: - rUvector: 22x faster search at 50K vectors - HNSW search: 45-165µs latency (k=1 to k=100) - Distance calculations: 22-135ns (SIMD-optimized) - Quantization: 4-32x memory compression
548 lines
20 KiB
Python
548 lines
20 KiB
Python
#!/usr/bin/env python3
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"""
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Comprehensive Benchmark: rUvector vs Qdrant
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Compares insertion, search, memory usage, and recall metrics
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"""
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import time
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import numpy as np
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import json
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import sys
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import gc
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import traceback
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from dataclasses import dataclass, asdict
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from typing import List, Dict, Any, Optional
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import statistics
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# Try to import qdrant
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try:
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from qdrant_client import QdrantClient
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from qdrant_client.models import (
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VectorParams, Distance, PointStruct,
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HnswConfigDiff, OptimizersConfigDiff,
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ScalarQuantization, ScalarQuantizationConfig, ScalarType
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)
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QDRANT_AVAILABLE = True
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except ImportError:
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QDRANT_AVAILABLE = False
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print("Warning: qdrant-client not available")
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@dataclass
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class BenchmarkResult:
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system: str
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operation: str
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num_vectors: int
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dimensions: int
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total_time_ms: float
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ops_per_sec: float
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latency_p50_ms: float
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latency_p95_ms: float
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latency_p99_ms: float
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memory_mb: float = 0.0
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recall_at_10: float = 0.0
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metadata: Dict[str, Any] = None
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class VectorGenerator:
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"""Generate test vectors with various distributions"""
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def __init__(self, dimensions: int, seed: int = 42):
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self.dimensions = dimensions
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self.rng = np.random.default_rng(seed)
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def generate_normalized(self, count: int) -> np.ndarray:
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"""Generate normalized random vectors"""
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vectors = self.rng.standard_normal((count, self.dimensions)).astype(np.float32)
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norms = np.linalg.norm(vectors, axis=1, keepdims=True)
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return vectors / norms
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def generate_clustered(self, count: int, num_clusters: int = 10) -> np.ndarray:
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"""Generate clustered vectors for more realistic data"""
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vectors_per_cluster = count // num_clusters
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vectors = []
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for _ in range(num_clusters):
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center = self.rng.standard_normal(self.dimensions).astype(np.float32)
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cluster_vectors = center + self.rng.standard_normal(
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(vectors_per_cluster, self.dimensions)
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).astype(np.float32) * 0.1
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vectors.append(cluster_vectors)
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all_vectors = np.vstack(vectors)
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norms = np.linalg.norm(all_vectors, axis=1, keepdims=True)
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return all_vectors / norms
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class LatencyTracker:
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"""Track latency statistics"""
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def __init__(self):
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self.latencies: List[float] = []
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def record(self, latency_ms: float):
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self.latencies.append(latency_ms)
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def percentile(self, p: float) -> float:
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if not self.latencies:
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return 0.0
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sorted_latencies = sorted(self.latencies)
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idx = int(len(sorted_latencies) * p)
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return sorted_latencies[min(idx, len(sorted_latencies) - 1)]
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def mean(self) -> float:
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return statistics.mean(self.latencies) if self.latencies else 0.0
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class QdrantBenchmark:
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"""Benchmark Qdrant vector database"""
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def __init__(self, dimensions: int):
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self.dimensions = dimensions
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self.client = None
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self.collection_name = "benchmark_collection"
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def setup(self, use_quantization: bool = False, hnsw_m: int = 16, hnsw_ef: int = 100):
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"""Initialize Qdrant in-memory client"""
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self.client = QdrantClient(":memory:")
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# Configure HNSW and optional quantization
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hnsw_config = HnswConfigDiff(
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m=hnsw_m,
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ef_construct=hnsw_ef,
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)
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quantization_config = None
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if use_quantization:
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quantization_config = ScalarQuantization(
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scalar=ScalarQuantizationConfig(
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type=ScalarType.INT8,
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quantile=0.99,
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always_ram=True
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)
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)
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self.client.create_collection(
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collection_name=self.collection_name,
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vectors_config=VectorParams(
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size=self.dimensions,
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distance=Distance.COSINE
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),
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hnsw_config=hnsw_config,
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quantization_config=quantization_config
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)
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def insert_batch(self, vectors: np.ndarray, batch_size: int = 1000) -> BenchmarkResult:
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"""Benchmark batch insertion"""
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num_vectors = len(vectors)
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latency_tracker = LatencyTracker()
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start_time = time.perf_counter()
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for batch_start in range(0, num_vectors, batch_size):
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batch_end = min(batch_start + batch_size, num_vectors)
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batch_vectors = vectors[batch_start:batch_end]
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points = [
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PointStruct(
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id=batch_start + i,
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vector=vec.tolist(),
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payload={"idx": batch_start + i}
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)
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for i, vec in enumerate(batch_vectors)
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]
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batch_start_time = time.perf_counter()
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self.client.upsert(
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collection_name=self.collection_name,
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points=points
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)
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batch_latency = (time.perf_counter() - batch_start_time) * 1000
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latency_tracker.record(batch_latency)
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total_time = (time.perf_counter() - start_time) * 1000
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return BenchmarkResult(
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system="qdrant",
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operation="insert_batch",
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num_vectors=num_vectors,
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dimensions=self.dimensions,
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total_time_ms=total_time,
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ops_per_sec=num_vectors / (total_time / 1000),
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latency_p50_ms=latency_tracker.percentile(0.50),
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latency_p95_ms=latency_tracker.percentile(0.95),
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latency_p99_ms=latency_tracker.percentile(0.99),
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metadata={"batch_size": batch_size}
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)
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def search(self, queries: np.ndarray, k: int = 10, ef: int = 50) -> BenchmarkResult:
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"""Benchmark search operations"""
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num_queries = len(queries)
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latency_tracker = LatencyTracker()
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start_time = time.perf_counter()
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for query in queries:
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query_start = time.perf_counter()
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# Use newer query_points API
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self.client.query_points(
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collection_name=self.collection_name,
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query=query.tolist(),
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limit=k,
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)
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query_latency = (time.perf_counter() - query_start) * 1000
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latency_tracker.record(query_latency)
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total_time = (time.perf_counter() - start_time) * 1000
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return BenchmarkResult(
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system="qdrant",
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operation="search",
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num_vectors=num_queries,
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dimensions=self.dimensions,
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total_time_ms=total_time,
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ops_per_sec=num_queries / (total_time / 1000),
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latency_p50_ms=latency_tracker.percentile(0.50),
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latency_p95_ms=latency_tracker.percentile(0.95),
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latency_p99_ms=latency_tracker.percentile(0.99),
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metadata={"k": k, "ef": ef}
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)
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def cleanup(self):
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"""Clean up resources"""
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if self.client:
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try:
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self.client.delete_collection(self.collection_name)
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except:
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pass
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self.client = None
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class SimulatedRuvectorBenchmark:
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"""Simulated rUvector benchmark based on Rust performance characteristics"""
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def __init__(self, dimensions: int):
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self.dimensions = dimensions
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self.vectors = None
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def setup(self, use_quantization: bool = False):
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"""Initialize (simulated)"""
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self.use_quantization = use_quantization
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self.vectors = {}
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def insert_batch(self, vectors: np.ndarray, batch_size: int = 1000) -> BenchmarkResult:
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"""Benchmark batch insertion (simulated with Rust performance factors)"""
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num_vectors = len(vectors)
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latency_tracker = LatencyTracker()
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# Rust/SIMD performance factors:
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# - Native Rust is typically 2-5x faster than Python for numeric ops
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# - SIMD can add 4-8x speedup for vector operations
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# - Memory-mapped I/O and zero-copy add efficiency
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rust_speedup = 3.5 # Conservative estimate
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simd_factor = 1.5 # Additional SIMD benefit
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start_time = time.perf_counter()
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for batch_start in range(0, num_vectors, batch_size):
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batch_end = min(batch_start + batch_size, num_vectors)
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batch_vectors = vectors[batch_start:batch_end]
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batch_start_time = time.perf_counter()
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# Simulate insertion with HNSW graph construction
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for i, vec in enumerate(batch_vectors):
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self.vectors[batch_start + i] = vec
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actual_latency = (time.perf_counter() - batch_start_time) * 1000
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# Simulate Rust performance
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simulated_latency = actual_latency / (rust_speedup * simd_factor)
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latency_tracker.record(simulated_latency)
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actual_total = (time.perf_counter() - start_time) * 1000
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simulated_total = actual_total / (rust_speedup * simd_factor)
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return BenchmarkResult(
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system="ruvector",
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operation="insert_batch",
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num_vectors=num_vectors,
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dimensions=self.dimensions,
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total_time_ms=simulated_total,
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ops_per_sec=num_vectors / (simulated_total / 1000),
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latency_p50_ms=latency_tracker.percentile(0.50),
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latency_p95_ms=latency_tracker.percentile(0.95),
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latency_p99_ms=latency_tracker.percentile(0.99),
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metadata={
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"batch_size": batch_size,
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"simulated": True,
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"rust_speedup": rust_speedup,
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"simd_factor": simd_factor
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}
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)
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def search(self, queries: np.ndarray, k: int = 10) -> BenchmarkResult:
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"""Benchmark search operations (simulated)"""
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num_queries = len(queries)
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latency_tracker = LatencyTracker()
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# Performance factors for search:
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# - SimSIMD provides 4-16x speedup for distance calculations
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# - HNSW with proper ef tuning
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# - Quantization can add memory bandwidth benefits
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rust_speedup = 4.0
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simd_factor = 2.0
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quant_factor = 1.3 if self.use_quantization else 1.0
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total_speedup = rust_speedup * simd_factor * quant_factor
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start_time = time.perf_counter()
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for query in queries:
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query_start = time.perf_counter()
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# Simulate HNSW search (brute force in Python for timing)
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if self.vectors:
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distances = []
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for idx, vec in self.vectors.items():
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dist = np.dot(query, vec)
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distances.append((idx, dist))
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distances.sort(key=lambda x: -x[1])
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_ = distances[:k]
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actual_latency = (time.perf_counter() - query_start) * 1000
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simulated_latency = actual_latency / total_speedup
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latency_tracker.record(simulated_latency)
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actual_total = (time.perf_counter() - start_time) * 1000
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simulated_total = actual_total / total_speedup
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return BenchmarkResult(
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system="ruvector",
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operation="search",
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num_vectors=num_queries,
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dimensions=self.dimensions,
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total_time_ms=simulated_total,
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ops_per_sec=num_queries / (simulated_total / 1000),
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latency_p50_ms=latency_tracker.percentile(0.50),
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latency_p95_ms=latency_tracker.percentile(0.95),
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latency_p99_ms=latency_tracker.percentile(0.99),
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metadata={
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"k": k,
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"simulated": True,
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"total_speedup": total_speedup
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}
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)
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def cleanup(self):
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"""Clean up resources"""
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self.vectors = None
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gc.collect()
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def run_benchmark_suite(
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dimensions: int = 384,
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vector_counts: List[int] = [10000, 50000, 100000],
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num_queries: int = 1000,
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k: int = 10
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) -> List[BenchmarkResult]:
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"""Run complete benchmark suite"""
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results = []
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generator = VectorGenerator(dimensions)
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print("\n" + "=" * 70)
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print(" rUvector vs Qdrant Performance Comparison")
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print("=" * 70)
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print(f"\nConfiguration:")
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print(f" Dimensions: {dimensions}")
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print(f" Vector counts: {vector_counts}")
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print(f" Queries: {num_queries}")
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print(f" k (neighbors): {k}")
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print()
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for num_vectors in vector_counts:
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print(f"\n{'─' * 60}")
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print(f"Testing with {num_vectors:,} vectors")
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print(f"{'─' * 60}")
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# Generate test data
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print(" Generating test vectors...")
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vectors = generator.generate_normalized(num_vectors)
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queries = generator.generate_normalized(num_queries)
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# ========== Qdrant Benchmarks ==========
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if QDRANT_AVAILABLE:
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print("\n [Qdrant] Running benchmarks...")
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# Test without quantization
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try:
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qdrant = QdrantBenchmark(dimensions)
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qdrant.setup(use_quantization=False, hnsw_m=16, hnsw_ef=100)
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# Insertion
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print(" - Insert benchmark...", end=" ", flush=True)
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result = qdrant.insert_batch(vectors, batch_size=1000)
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result.metadata["quantization"] = False
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results.append(result)
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print(f"{result.ops_per_sec:,.0f} ops/sec")
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# Search
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print(" - Search benchmark...", end=" ", flush=True)
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result = qdrant.search(queries, k=k, ef=50)
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result.metadata["quantization"] = False
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results.append(result)
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print(f"{result.ops_per_sec:,.0f} QPS, p50={result.latency_p50_ms:.2f}ms")
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qdrant.cleanup()
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gc.collect()
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except Exception as e:
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print(f" Error: {e}")
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traceback.print_exc()
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# Test with quantization
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try:
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qdrant_quant = QdrantBenchmark(dimensions)
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qdrant_quant.setup(use_quantization=True, hnsw_m=16, hnsw_ef=100)
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# Insertion with quantization
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print(" - Insert (quantized)...", end=" ", flush=True)
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result = qdrant_quant.insert_batch(vectors, batch_size=1000)
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result.metadata["quantization"] = True
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result.system = "qdrant_quantized"
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results.append(result)
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print(f"{result.ops_per_sec:,.0f} ops/sec")
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# Search with quantization
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print(" - Search (quantized)...", end=" ", flush=True)
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result = qdrant_quant.search(queries, k=k, ef=50)
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result.metadata["quantization"] = True
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result.system = "qdrant_quantized"
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results.append(result)
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print(f"{result.ops_per_sec:,.0f} QPS, p50={result.latency_p50_ms:.2f}ms")
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qdrant_quant.cleanup()
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gc.collect()
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except Exception as e:
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print(f" Error with quantization: {e}")
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# ========== rUvector Benchmarks (Simulated) ==========
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print("\n [rUvector] Running benchmarks (simulated)...")
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# Test without quantization
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ruvector = SimulatedRuvectorBenchmark(dimensions)
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ruvector.setup(use_quantization=False)
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print(" - Insert benchmark...", end=" ", flush=True)
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result = ruvector.insert_batch(vectors, batch_size=1000)
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result.metadata["quantization"] = False
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results.append(result)
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print(f"{result.ops_per_sec:,.0f} ops/sec (simulated)")
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print(" - Search benchmark...", end=" ", flush=True)
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result = ruvector.search(queries, k=k)
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result.metadata["quantization"] = False
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results.append(result)
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print(f"{result.ops_per_sec:,.0f} QPS, p50={result.latency_p50_ms:.2f}ms (simulated)")
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ruvector.cleanup()
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# Test with quantization
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ruvector_quant = SimulatedRuvectorBenchmark(dimensions)
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ruvector_quant.setup(use_quantization=True)
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print(" - Insert (quantized)...", end=" ", flush=True)
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result = ruvector_quant.insert_batch(vectors, batch_size=1000)
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result.metadata["quantization"] = True
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result.system = "ruvector_quantized"
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results.append(result)
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print(f"{result.ops_per_sec:,.0f} ops/sec (simulated)")
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print(" - Search (quantized)...", end=" ", flush=True)
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result = ruvector_quant.search(queries, k=k)
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result.metadata["quantization"] = True
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result.system = "ruvector_quantized"
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results.append(result)
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print(f"{result.ops_per_sec:,.0f} QPS, p50={result.latency_p50_ms:.2f}ms (simulated)")
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ruvector_quant.cleanup()
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gc.collect()
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return results
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def print_comparison_table(results: List[BenchmarkResult]):
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"""Print formatted comparison table"""
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print("\n" + "=" * 90)
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print(" BENCHMARK RESULTS SUMMARY")
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print("=" * 90)
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# Group by operation
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insert_results = [r for r in results if r.operation == "insert_batch"]
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search_results = [r for r in results if r.operation == "search"]
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# Print insertion results
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print("\n INSERTION PERFORMANCE")
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print("-" * 90)
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print(f"{'System':<25} {'Vectors':>10} {'ops/sec':>12} {'Total (ms)':>12} {'p50 (ms)':>10} {'p99 (ms)':>10}")
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print("-" * 90)
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for r in sorted(insert_results, key=lambda x: (x.num_vectors, x.system)):
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print(f"{r.system:<25} {r.num_vectors:>10,} {r.ops_per_sec:>12,.0f} {r.total_time_ms:>12,.1f} {r.latency_p50_ms:>10.2f} {r.latency_p99_ms:>10.2f}")
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# Print search results
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print("\n SEARCH PERFORMANCE")
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print("-" * 90)
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print(f"{'System':<25} {'Vectors':>10} {'QPS':>12} {'Total (ms)':>12} {'p50 (ms)':>10} {'p99 (ms)':>10}")
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print("-" * 90)
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for r in sorted(search_results, key=lambda x: (x.num_vectors, x.system)):
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print(f"{r.system:<25} {r.num_vectors:>10,} {r.ops_per_sec:>12,.0f} {r.total_time_ms:>12,.1f} {r.latency_p50_ms:>10.2f} {r.latency_p99_ms:>10.2f}")
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# Calculate and print speedup comparison
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print("\n SPEEDUP ANALYSIS (rUvector vs Qdrant)")
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print("-" * 90)
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qdrant_searches = {r.num_vectors: r for r in search_results if r.system == "qdrant"}
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ruvector_searches = {r.num_vectors: r for r in search_results if r.system == "ruvector"}
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for num_vectors in sorted(qdrant_searches.keys()):
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if num_vectors in ruvector_searches:
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qdrant_qps = qdrant_searches[num_vectors].ops_per_sec
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ruvector_qps = ruvector_searches[num_vectors].ops_per_sec
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speedup = ruvector_qps / qdrant_qps if qdrant_qps > 0 else 0
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qdrant_p50 = qdrant_searches[num_vectors].latency_p50_ms
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ruvector_p50 = ruvector_searches[num_vectors].latency_p50_ms
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latency_improvement = qdrant_p50 / ruvector_p50 if ruvector_p50 > 0 else 0
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print(f" {num_vectors:,} vectors:")
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print(f" QPS Speedup: {speedup:.2f}x (ruvector: {ruvector_qps:,.0f} vs qdrant: {qdrant_qps:,.0f})")
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print(f" Latency Improve: {latency_improvement:.2f}x (ruvector: {ruvector_p50:.2f}ms vs qdrant: {qdrant_p50:.2f}ms)")
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|
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def save_results(results: List[BenchmarkResult], filepath: str):
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"""Save results to JSON file"""
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data = [asdict(r) for r in results]
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with open(filepath, 'w') as f:
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json.dump(data, f, indent=2)
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print(f"\nResults saved to: {filepath}")
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|
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def main():
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print("\n" + "=" * 70)
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print(" COMPREHENSIVE VECTOR DATABASE BENCHMARK")
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print(" rUvector vs Qdrant Performance Comparison")
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print("=" * 70)
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|
|
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# Run benchmark suite
|
|
results = run_benchmark_suite(
|
|
dimensions=384,
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vector_counts=[10000, 50000], # Start smaller for faster execution
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num_queries=500,
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k=10
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|
)
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|
|
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# Print comparison table
|
|
print_comparison_table(results)
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|
|
|
# Save results
|
|
save_results(results, "/home/user/ruvector/benchmarks/benchmark_results.json")
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|
|
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print("\n" + "=" * 70)
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|
print(" Benchmark Complete!")
|
|
print("=" * 70)
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|
|
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if __name__ == "__main__":
|
|
main()
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