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visualization scripts for A/B tests #4923
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45 changes: 45 additions & 0 deletions
tools/ab/combine.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,45 @@ | ||
| import argparse | ||
| import json | ||
| import os | ||
| from pathlib import Path | ||
|
|
||
| parser = argparse.ArgumentParser( | ||
| description="Combine A/B test fails into groups per test type" | ||
| ) | ||
| parser.add_argument( | ||
| "path", | ||
| help="Path to the directory with failed A/B runs", | ||
| type=Path, | ||
| ) | ||
| args = parser.parse_args() | ||
|
|
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| BLOCK = "test_block_performance" | ||
| NET_THROUGHPUT = "test_network_throughput" | ||
| NET_LATENCY = "test_network_latency" | ||
|
|
||
| block_data = [] | ||
| net_data = [] | ||
| net_lat_data = [] | ||
| for d in os.walk(args.path): | ||
| if "ab.json" in d[-1]: | ||
| path = d[0] + "/ab.json" | ||
| print(path) | ||
| with open(path, "r+") as f: | ||
| lines = f.read() | ||
| j = '{"data":' + lines + "}" | ||
| data = json.loads(j) | ||
| for e in data["data"]: | ||
| match e["performance_test"]: | ||
| case BLOCk: | ||
| block_data.append(e) | ||
| case NET_THROUGHPUT: | ||
| net_data.append(e) | ||
| case NET_LATENCY: | ||
| net_lat_data.append(e) | ||
|
|
||
| with open(f"{NET_LATENCY}.json", "w") as f: | ||
| json.dump({"results": net_lat_data}, f, indent=2, sort_keys=True) | ||
| with open(f"{NET_THROUGHPUT}.json", "w") as f: | ||
| json.dump({"results": net_data}, f, indent=2, sort_keys=True) | ||
| with open(f"{BLOCK}.json", "w") as f: | ||
| json.dump({"fails": block_data}, f, indent=2, sort_keys=True) |
138 changes: 138 additions & 0 deletions
tools/ab/plot.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,138 @@ | ||
| import argparse | ||
| import json | ||
| import os | ||
| from enum import Enum | ||
|
|
||
| import matplotlib.pyplot as plt | ||
| import numpy as np | ||
|
|
||
| plt.style.use("dark_background") | ||
|
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||
|
|
||
| def clamp(min_v, max_v, v): | ||
| return max(min_v, min(max_v, v)) | ||
|
|
||
|
|
||
| def lerp(color_a, color_b, t): | ||
| return ( | ||
| clamp(0.0, 1.0, abs(color_a[0] * (1.0 - t) + color_b[0] * t)), | ||
| clamp(0.0, 1.0, abs(color_a[1] * (1.0 - t) + color_b[1] * t)), | ||
| clamp(0.0, 1.0, abs(color_a[2] * (1.0 - t) + color_b[2] * t)), | ||
| ) | ||
|
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||
|
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| GREY = (0.5, 0.5, 0.5) | ||
| GREEN = (0.1, 0.8, 0.1) | ||
| RED = (0.8, 0.0, 0.1) | ||
|
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| POSITIVE_COLOR = GREEN | ||
| NEGATIVE_COLOR = RED | ||
|
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|
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| class DataType(Enum): | ||
| Block = "block" | ||
| Net = "net" | ||
| NetLatency = "net_latency" | ||
|
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|
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| parser = argparse.ArgumentParser(description="Plot results of A/B test") | ||
| parser.add_argument("path", type=str) | ||
| args = parser.parse_args() | ||
|
|
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| paths = [f"{args.path}/{f}" for f in os.listdir(args.path)] | ||
| for path in paths: | ||
| print(f"processing: {path}") | ||
| with open(path) as f: | ||
| fails = json.load(f)["fails"] | ||
|
|
||
| if not fails: | ||
| print(f"skipping {path}. No data present") | ||
| continue | ||
|
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| instances = set() | ||
| host_kernels = set() | ||
| aggregated = {} | ||
|
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| match fails[0]["performance_test"]: | ||
| case "test_block_performance": | ||
| data_type = DataType.Block | ||
| case "test_network_tcp_throughput": | ||
| data_type = DataType.Net | ||
| case "test_network_latency": | ||
| data_type = DataType.NetLatency | ||
| case _: | ||
| print("unknown data type. skipping") | ||
| continue | ||
|
|
||
| for fail in fails: | ||
| instances.add(fail["instance"]) | ||
| host_kernels.add(fail["host_kernel"]) | ||
|
|
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| if data_type == DataType.Block: | ||
| tag = ( | ||
| fail["instance"], | ||
| fail["host_kernel"], | ||
| fail["guest_kernel"], | ||
| fail["fio_mode"], | ||
| fail["vcpus"], | ||
| fail["io_engine"], | ||
| ) | ||
| elif data_type == DataType.Net: | ||
| tag = ( | ||
| fail["instance"], | ||
| fail["host_kernel"], | ||
| fail["guest_kernel"], | ||
| fail["mode"], | ||
| fail["vcpus"], | ||
| ) | ||
| elif data_type == DataType.NetLatency: | ||
| tag = ( | ||
| fail["instance"], | ||
| fail["host_kernel"], | ||
| fail["guest_kernel"], | ||
| ) | ||
| POSITIVE_COLOR = RED | ||
| NEGATIVE_COLOR = GREEN | ||
|
|
||
| if tag not in aggregated: | ||
| aggregated[tag] = [] | ||
| aggregated[tag].append(fail["diff"]) | ||
|
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| for instance in sorted(instances): | ||
| fig, ax = plt.subplots(len(host_kernels), figsize=(16, 11)) | ||
| if len(host_kernels) == 1: | ||
| ax = [ax] | ||
| fig.tight_layout(pad=8.0) | ||
|
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| for i, host_kernel in enumerate(sorted(host_kernels)): | ||
| data = [] | ||
| for key, value in aggregated.items(): | ||
| if key[0] == instance and key[1] == host_kernel: | ||
| label = "\n".join(key[2:]) | ||
| values = np.array(value) | ||
| mean = np.mean(values) | ||
| std = np.std(values) | ||
| data.append((label, mean, std)) | ||
| data.sort() | ||
| labels = np.array([t[0] for t in data]) | ||
| means = np.array([t[1] for t in data]) | ||
| errors = np.array([t[2] for t in data]) | ||
| colors = [ | ||
| ( | ||
| lerp(GREY, POSITIVE_COLOR, t) | ||
| if 0.0 < t | ||
| else lerp(GREY, NEGATIVE_COLOR, -t) | ||
| ) | ||
| for t in [m / 100.0 for m in means] | ||
| ] | ||
|
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| bar = ax[i].bar(labels, means, yerr=errors, color=colors, ecolor="white") | ||
| bar_labels = [f"{m:.2f} / {s:.2f}" for (m, s) in zip(means, errors)] | ||
| ax[i].bar_label(bar, labels=bar_labels) | ||
| ax[i].set_ylabel("Percentage of change: mean / std") | ||
| ax[i].grid(color="grey", linestyle="-.", linewidth=0.5, alpha=0.5) | ||
| ax[i].set_title( | ||
| f"{data_type}\nInstance: {instance}\nHost kernel: {host_kernel}", | ||
| ) | ||
|
|
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| plt.savefig(f"{args.path}/{data_type}_{instance}.png") |
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