performance_benchmarks.py

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"""
import matplotlib.pyplot as plt
import numpy as np
import datetime
from benchmark_server import BenchmarkData
from matplotlib.patches import Polygon
import os
from datetime import timedelta
from itertools import product
from functools import reduce

if __name__ == "__main__":
    plt.style.use("Solarize_Light2")
    files = [f for f in os.listdir("../benchmark-output") if f.startswith("performance-benchmark")]
    data = [BenchmarkData.parse_file(f"../benchmark-output/{f}") for f in files]
    data.sort(key=lambda x: x.binary_size)
    data_fetch_targets = [m.fetch_target_files for m in data]
    data_update_repo = [m.update_repo for m in data]
    data_fetch_signature = [m.fetch_signature for m in data]
    data_fetch_binary = [m.fetch_binary for m in data]
    print(data_fetch_binary)
    def helper(acc: dict, a):
        if a.binary_size not in acc:
            acc[a.binary_size] = []
        acc[a.binary_size].append(a)
        return acc
    data_by_size = reduce(helper, data, {})
    data_fetch_binary_by_size = {k:[d.fetch_binary for d in vals] for (k,vals) in data_by_size.items()}
    data_fetch_binary_by_size = {k: [timedelta(seconds=d["secs"] + d["nanos"]*1e-9).total_seconds() for d in vals] for (k,vals) in data_fetch_binary_by_size.items()}
    print(data_fetch_binary_by_size)
    data_verify_binary = [m.verify_binary for m in data]
    data_fetch_targets = [timedelta(seconds=d["secs"] + d["nanos"]*1e-9).total_seconds() for d in data_fetch_targets]
    data_update_repo = [timedelta(seconds=d["secs"] + d["nanos"]*1e-9).total_seconds() for d in data_update_repo]
    data_fetch_signature = [timedelta(seconds=d["secs"] + d["nanos"]*1e-9).total_seconds() for d in data_fetch_signature]
    data_fetch_binary = [timedelta(seconds=d["secs"] + d["nanos"]*1e-9).total_seconds() for d in data_fetch_binary]
    data_verify_binary_full = [timedelta(seconds=d["secs"] + d["nanos"]*1e-9).total_seconds() for d in data_verify_binary]
    data_verify_binary = {k:[d.verify_binary for d in vals] for (k,vals) in data_by_size.items()}
    data_verify_binary = {k: [timedelta(seconds=d["secs"] + d["nanos"]*1e-9).total_seconds() for d in vals] for (k,vals) in data_verify_binary.items()}
    
    #       of each data type for the addition
    data_full = {k:[
        (timedelta(seconds=d.fetch_target_files["secs"] + d.fetch_target_files["nanos"] * 1e-9)
        + timedelta(seconds=d.update_repo["secs"] + d.update_repo["nanos"] * 1e-9)
        + timedelta(seconds=d.fetch_signature["secs"] + d.fetch_signature["nanos"] * 1e-9)
        + timedelta(seconds=d.fetch_binary["secs"] + d.fetch_binary["nanos"] * 1e-9)
        + timedelta(seconds=d.verify_binary["secs"] + d.verify_binary["nanos"] * 1e-9)
        ).total_seconds()
        for d in vals
    ] for (k,vals) in data_by_size.items()}
    data_no_tuf = {k:[
        (timedelta(seconds=d.fetch_signature["secs"] + d.fetch_signature["nanos"] * 1e-9)
        + timedelta(seconds=d.fetch_binary["secs"] + d.fetch_binary["nanos"] * 1e-9)
        + timedelta(seconds=d.verify_binary["secs"] + d.verify_binary["nanos"] * 1e-9)
        ).total_seconds()
        for d in vals
    ] for (k,vals) in data_by_size.items()}
    # data_full_synthetic = [sum(t for t in d) for d in product(data_fetch_targets,data_update_repo,data_fetch_signature,data_fetch_binary,data_verify_binary)]
    # data_no_tuf_synthetic = [sum(t for t in d) for d in product(data_fetch_signature, data_fetch_binary, data_verify_binary)]
    # basic plot
    fig, axs = plt.subplots(3, 2)
    
    
    axs[0, 0].boxplot(data_fetch_targets)
    axs[0, 0].set_title("Fetch TUF Targets")
    axs[0, 0].set_ylabel("time (s)")
    axs[1, 0].set_ylabel("time (s)")
    axs[0, 1].boxplot(data_update_repo)
    axs[0, 1].set_title("TUF Repo Update")
    axs[1, 0].boxplot(data_fetch_signature)
    axs[1, 0].set_title("Signature Fetching")
    axs[1, 1].set_title("Verification Duration by size")
    axs[2, 0].set_title("Binary fetch duration by size")
    axs[1, 1].boxplot(
        [data_verify_binary_full] + list(data_verify_binary.values()),
        tick_labels=["all samples"] + [f"{k/1024:.1f} KiB" for k in data_verify_binary.keys()] 
        )


    axs[2, 0].plot(
        [0, 1] + [f"{k/1024:.1f} KiB" for k in data_fetch_binary_by_size.keys()],
        [None, None] + [np.average(v) for v in data_fetch_binary_by_size.values()],
        linestyle="--"
        )    
    axs[2, 0].boxplot(
        [data_fetch_binary] + list(data_fetch_binary_by_size.values()),
        tick_labels=["all samples"] + [f"{k/1024:.1f} KiB" for k in data_fetch_binary_by_size.keys()]
        )
    axs[2, 1].plot(
        [0] + [f"{k/1024:.1f} KiB" for k in data_full.keys()],
        [None] + [np.average(v) for v in data_full.values()],
        label="TUF + BT",
        linestyle="dotted",
        )    
    axs[2, 1].plot(
        [0] + [f"{k/1024:.1f} KiB" for k in data_no_tuf.keys()],
        [None] + [np.average(v) for v in data_no_tuf.values()],
        label="BT only",
        linestyle="dashed",
        )
    axs[2, 1].boxplot(
        data_full.values(),
        tick_labels=[f"{k/1024:.1f} KiB" for k in data_full.keys()],
        )
    axs[2, 0].set_ylabel("time (s)")

    axs[2, 1].boxplot(
        data_no_tuf.values(),
        tick_labels=[f"{k/1024:.1f} KiB" for k in data_no_tuf.keys()]
        )

    axs[2, 1].legend()
    axs[2, 1].set_title("Cumulative Duration by size")

    fig.subplots_adjust(left=0.08, right=0.98, bottom=0.05, top=0.9,
    hspace=0.4, wspace=0.3)
    plt.show()