|
| 1 | +#!/usr/bin/env python3 |
| 2 | +""" |
| 3 | +P95 Search Time Latency Demo Program |
| 4 | +
|
| 5 | +This program simulates search operations and computes the 95th percentile (p95) latency. |
| 6 | +It includes timing measurements, data generation, and statistical analysis. |
| 7 | +""" |
| 8 | + |
| 9 | +import time |
| 10 | +import random |
| 11 | +import numpy as np |
| 12 | +import statistics |
| 13 | +from typing import List, Dict, Any |
| 14 | +from dataclasses import dataclass |
| 15 | +from concurrent.futures import ThreadPoolExecutor |
| 16 | +import json |
| 17 | + |
| 18 | + |
| 19 | +class LatencyAnalyzer: |
| 20 | + """Analyzes search latencies and computes statistics.""" |
| 21 | + |
| 22 | + def __init__(self, results: list[float]): |
| 23 | + self.results: List[float] = results |
| 24 | + |
| 25 | + def get_latencies(self) -> List[float]: |
| 26 | + """Extract latency values from all results.""" |
| 27 | + return self.results |
| 28 | + |
| 29 | + def compute_statistics(self) -> Dict[str, float]: |
| 30 | + """Compute comprehensive latency statistics.""" |
| 31 | + if not self.results: |
| 32 | + return {} |
| 33 | + |
| 34 | + latencies = self.get_latencies() |
| 35 | + |
| 36 | + return { |
| 37 | + "count": len(latencies), |
| 38 | + "min_ms": min(latencies), |
| 39 | + "max_ms": max(latencies), |
| 40 | + "mean_ms": statistics.mean(latencies), |
| 41 | + "median_ms": statistics.median(latencies), |
| 42 | + "p50_ms": np.percentile(latencies, 50), |
| 43 | + "p90_ms": np.percentile(latencies, 90), |
| 44 | + "p95_ms": np.percentile(latencies, 95), # The main metric |
| 45 | + "p99_ms": np.percentile(latencies, 99), |
| 46 | + "std_dev_ms": statistics.stdev(latencies) if len(latencies) > 1 else 0, |
| 47 | + } |
| 48 | + |
| 49 | + def print_statistics(self): |
| 50 | + """Print formatted statistics to console.""" |
| 51 | + stats = self.compute_statistics() |
| 52 | + if not stats: |
| 53 | + print("No results to analyze.") |
| 54 | + return |
| 55 | + |
| 56 | + print("\n" + "=" * 50) |
| 57 | + print("SEARCH LATENCY ANALYSIS") |
| 58 | + print("=" * 50) |
| 59 | + print(f"Total searches: {stats['count']}") |
| 60 | + print(f"Min latency: {stats['min_ms']:.2f} ms") |
| 61 | + print(f"Max latency: {stats['max_ms']:.2f} ms") |
| 62 | + print(f"Mean latency: {stats['mean_ms']:.2f} ms") |
| 63 | + print(f"Median latency: {stats['median_ms']:.2f} ms") |
| 64 | + print("-" * 30) |
| 65 | + print("PERCENTILES:") |
| 66 | + print(f"P50 (median): {stats['p50_ms']:.2f} ms") |
| 67 | + print(f"P90: {stats['p90_ms']:.2f} ms") |
| 68 | + print(f"P95: {stats['p95_ms']:.2f} ms ⭐") # Highlighted |
| 69 | + print(f"P99: {stats['p99_ms']:.2f} ms") |
| 70 | + print("-" * 30) |
| 71 | + print(f"Standard deviation: {stats['std_dev_ms']:.2f} ms") |
| 72 | + print("=" * 50) |
| 73 | + |
| 74 | + |
| 75 | +with open("./fixture/memobase/results_0710_3000.json", "r") as f: |
| 76 | + data = json.load(f) |
| 77 | +latencies = [] |
| 78 | + |
| 79 | + |
| 80 | +for k in data.keys(): |
| 81 | + for d in data[k]: |
| 82 | + latencies.append(d["speaker_1_memory_time"] * 1000) |
| 83 | + latencies.append(d["speaker_2_memory_time"] * 1000) |
| 84 | + |
| 85 | + |
| 86 | +analyzer = LatencyAnalyzer(latencies) |
| 87 | +analyzer.print_statistics() |
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