Comprehensive guide to benchmarking vector databases covering performance testing methodologies, standard benchmarks like ANN-Benchmarks, and best practices for evaluating throughput, latency, and accuracy.
Loading more......
ANN-Benchmarks:
VectorDBBench (Zilliz):
MyScale VDB Benchmark:
Query Latency:
Throughput:
Index Build Time:
Recall:
Memory: Peak and average CPU: Utilization Disk I/O: Read/write patterns Network: Bandwidth requirements
Standard Datasets:
Choose Based On:
Baseline:
Filtered Search:
Concurrent Load:
Mixed Workload:
Index Parameters:
Query Parameters:
Warm-up:
Measurement Period:
Statistical Analysis:
import time
import numpy as np
def benchmark_queries(db, queries, k=10, warmup=100):
# Warm-up
for q in queries[:warmup]:
db.search(q, k)
# Measure
latencies = []
for q in queries[warmup:]:
start = time.perf_counter()
results = db.search(q, k)
latencies.append(time.perf_counter() - start)
# Analyze
return {
'p50': np.percentile(latencies, 50),
'p95': np.percentile(latencies, 95),
'p99': np.percentile(latencies, 99),
'mean': np.mean(latencies),
'qps': len(latencies) / sum(latencies)
}
def calculate_recall(approx_results, exact_results, k):
"""Calculate recall@k"""
correct = len(set(approx_results[:k]) & set(exact_results[:k]))
return correct / k
Cloud Considerations:
Self-Hosted:
Include:
Visualize:
Be Skeptical:
Red Flags:
Calculate:
Cost per 1M queries =
(instance_cost/hour * query_time_hours) / 1M queries
Compare:
Setup:
Tools: