Block cache analyzer: python script to plot graphs (#5673)
Summary: This PR updated the python script to plot graphs for stats output from block cache analyzer. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5673 Test Plan: Manually run the script to generate graphs. Differential Revision: D16657145 Pulled By: HaoyuHuang fbshipit-source-id: fd510b5fd4307835f9a986fac545734dbe003d28
This commit is contained in:
haoyuhuang
Facebook Github Bot
parent
b1a02ffeab
commit
f4a616ebf9
@ -1,12 +1,17 @@
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#!/usr/bin/env python3
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import csv
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import math
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import os
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import random
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import sys
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.backends.backend_pdf
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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# Make sure a legend has the same color across all generated graphs.
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@ -19,7 +24,7 @@ def get_cmap(n, name="hsv"):
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color_index = 0
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bar_color_maps = {}
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colors = []
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n_colors = 60
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n_colors = 360
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linear_colors = get_cmap(n_colors)
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for i in range(n_colors):
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colors.append(linear_colors(i))
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@ -35,19 +40,20 @@ def num_to_gb(n):
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return "{0:.2f}".format(float(n) / one_gb)
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def plot_miss_ratio_graphs(csv_result_dir, output_result_dir):
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mrc_file_path = csv_result_dir + "/mrc"
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if not os.path.exists(mrc_file_path):
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return
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def plot_miss_stats_graphs(
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csv_result_dir, output_result_dir, file_prefix, file_suffix, ylabel, pdf_file_name
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):
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miss_ratios = {}
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print("Processing file {}".format(mrc_file_path))
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for file in os.listdir(csv_result_dir):
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if not file.startswith(file_prefix):
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continue
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if not file.endswith(file_suffix):
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continue
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print("Processing file {}/{}".format(csv_result_dir, file))
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mrc_file_path = csv_result_dir + "/" + file
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with open(mrc_file_path, "r") as csvfile:
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rows = csv.reader(csvfile, delimiter=",")
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is_header = False
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for row in rows:
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if not is_header:
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is_header = True
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continue
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cache_name = row[0]
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num_shard_bits = int(row[1])
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ghost_capacity = int(row[2])
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@ -58,18 +64,71 @@ def plot_miss_ratio_graphs(csv_result_dir, output_result_dir):
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miss_ratios[config] = {}
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miss_ratios[config]["x"] = []
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miss_ratios[config]["y"] = []
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miss_ratios[config]["x"].append(num_to_gb(capacity))
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miss_ratios[config]["x"].append(capacity)
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miss_ratios[config]["y"].append(miss_ratio)
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fig = plt.figure()
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for config in miss_ratios:
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plt.plot(miss_ratios[config]["x"], miss_ratios[config]["y"], label=config)
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plt.xlabel("Cache capacity (GB)")
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plt.ylabel("Miss Ratio (%)")
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# plt.xscale('log', basex=2)
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plt.plot(
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miss_ratios[config]["x"], miss_ratios[config]["y"], label=config
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)
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plt.xlabel("Cache capacity")
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plt.ylabel(ylabel)
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plt.xscale("log", basex=2)
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plt.ylim(ymin=0)
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plt.title("RocksDB block cache miss ratios")
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plt.title("{}".format(file))
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plt.legend()
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fig.savefig(output_result_dir + "/mrc.pdf", bbox_inches="tight")
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fig.savefig(
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output_result_dir + "/{}.pdf".format(pdf_file_name), bbox_inches="tight"
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)
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def plot_miss_stats_diff_lru_graphs(
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csv_result_dir, output_result_dir, file_prefix, file_suffix, ylabel, pdf_file_name
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):
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miss_ratios = {}
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for file in os.listdir(csv_result_dir):
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if not file.startswith(file_prefix):
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continue
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if not file.endswith(file_suffix):
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continue
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print("Processing file {}/{}".format(csv_result_dir, file))
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mrc_file_path = csv_result_dir + "/" + file
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with open(mrc_file_path, "r") as csvfile:
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rows = csv.reader(csvfile, delimiter=",")
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for row in rows:
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cache_name = row[0]
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num_shard_bits = int(row[1])
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ghost_capacity = int(row[2])
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capacity = int(row[3])
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miss_ratio = float(row[4])
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config = "{}-{}-{}".format(cache_name, num_shard_bits, ghost_capacity)
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if config not in miss_ratios:
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miss_ratios[config] = {}
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miss_ratios[config]["x"] = []
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miss_ratios[config]["y"] = []
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miss_ratios[config]["x"].append(capacity)
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miss_ratios[config]["y"].append(miss_ratio)
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if "lru-0-0" not in miss_ratios:
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return
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fig = plt.figure()
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for config in miss_ratios:
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diffs = [0] * len(miss_ratios["lru-0-0"]["x"])
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for i in range(len(miss_ratios["lru-0-0"]["x"])):
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for j in range(len(miss_ratios[config]["x"])):
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if miss_ratios["lru-0-0"]["x"][i] == miss_ratios[config]["x"][j]:
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diffs[i] = (
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miss_ratios[config]["y"][j] - miss_ratios["lru-0-0"]["y"][i]
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)
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break
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plt.plot(miss_ratios["lru-0-0"]["x"], diffs, label=config)
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plt.xlabel("Cache capacity")
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plt.ylabel(ylabel)
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plt.xscale("log", basex=2)
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plt.title("{}".format(file))
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plt.legend()
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fig.savefig(
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output_result_dir + "/{}.pdf".format(pdf_file_name), bbox_inches="tight"
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)
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def sanitize(label):
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@ -143,6 +202,7 @@ def read_data_for_plot(csvfile, vertical):
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def plot_line_charts(
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csv_result_dir,
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output_result_dir,
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filename_prefix,
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filename_suffix,
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pdf_name,
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xlabel,
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@ -151,11 +211,14 @@ def plot_line_charts(
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vertical,
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legend,
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):
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global color_index, bar_color_maps, colors
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pdf = matplotlib.backends.backend_pdf.PdfPages(output_result_dir + "/" + pdf_name)
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for file in os.listdir(csv_result_dir):
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if not file.endswith(filename_suffix):
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continue
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print("Processing file {}".format(file))
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if not file.startswith(filename_prefix):
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continue
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print("Processing file {}/{}".format(csv_result_dir, file))
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with open(csv_result_dir + "/" + file, "r") as csvfile:
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x, labels, label_stats = read_data_for_plot(csvfile, vertical)
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if len(x) == 0 or len(labels) == 0:
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@ -163,10 +226,15 @@ def plot_line_charts(
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# plot figure
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fig = plt.figure()
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for label_index in label_stats:
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# Assign a unique color to this label.
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if labels[label_index] not in bar_color_maps:
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bar_color_maps[labels[label_index]] = colors[color_index]
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color_index += 1
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plt.plot(
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[int(x[i]) for i in range(len(x))],
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label_stats[label_index],
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[int(x[i]) for i in range(len(x) - 1)],
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label_stats[label_index][:-1],
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label=labels[label_index],
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color=bar_color_maps[labels[label_index]],
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)
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# Translate time unit into x labels.
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@ -239,10 +307,29 @@ def plot_stacked_bar_charts(
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pdf.close()
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def plot_access_timeline(csv_result_dir, output_result_dir):
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def plot_heatmap(csv_result_dir, output_result_dir, filename_suffix, pdf_name, title):
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pdf = matplotlib.backends.backend_pdf.PdfPages(
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"{}/{}".format(output_result_dir, pdf_name)
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)
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for file in os.listdir(csv_result_dir):
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if not file.endswith(filename_suffix):
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continue
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csv_file_name = "{}/{}".format(csv_result_dir, file)
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print("Processing file {}/{}".format(csv_result_dir, file))
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corr_table = pd.read_csv(csv_file_name)
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corr_table = corr_table.pivot("label", "corr", "value")
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fig = plt.figure()
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sns.heatmap(corr_table, annot=True, linewidths=0.5, fmt=".2")
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plt.title("{} filename:{}".format(title, file))
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pdf.savefig(fig)
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pdf.close()
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def plot_timeline(csv_result_dir, output_result_dir):
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plot_line_charts(
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csv_result_dir,
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output_result_dir,
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filename_prefix="",
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filename_suffix="access_timeline",
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pdf_name="access_time.pdf",
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xlabel="Time",
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@ -253,6 +340,109 @@ def plot_access_timeline(csv_result_dir, output_result_dir):
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)
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def convert_to_0_if_nan(n):
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if math.isnan(n):
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return 0.0
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return n
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def plot_correlation(csv_result_dir, output_result_dir):
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# Processing the correlation input first.
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label_str_file = {}
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for file in os.listdir(csv_result_dir):
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if not file.endswith("correlation_input"):
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continue
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csv_file_name = "{}/{}".format(csv_result_dir, file)
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print("Processing file {}/{}".format(csv_result_dir, file))
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corr_table = pd.read_csv(csv_file_name)
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label_str = file.split("_")[0]
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label = file[len(label_str) + 1 :]
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label = label[: len(label) - len("_correlation_input")]
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output_file = "{}/{}_correlation_output".format(csv_result_dir, label_str)
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if output_file not in label_str_file:
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f = open("{}/{}_correlation_output".format(csv_result_dir, label_str), "w+")
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label_str_file[output_file] = f
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f.write("label,corr,value\n")
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f = label_str_file[output_file]
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f.write(
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"{},{},{}\n".format(
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label,
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"LA+A",
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convert_to_0_if_nan(
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corr_table["num_accesses_since_last_access"].corr(
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corr_table["num_accesses_till_next_access"], method="spearman"
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)
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),
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)
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)
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f.write(
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"{},{},{}\n".format(
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label,
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"PA+A",
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convert_to_0_if_nan(
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corr_table["num_past_accesses"].corr(
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corr_table["num_accesses_till_next_access"], method="spearman"
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)
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),
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)
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)
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f.write(
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"{},{},{}\n".format(
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label,
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"LT+A",
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convert_to_0_if_nan(
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corr_table["elapsed_time_since_last_access"].corr(
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corr_table["num_accesses_till_next_access"], method="spearman"
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)
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),
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)
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)
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f.write(
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"{},{},{}\n".format(
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label,
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"LA+T",
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convert_to_0_if_nan(
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corr_table["num_accesses_since_last_access"].corr(
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corr_table["elapsed_time_till_next_access"], method="spearman"
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)
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),
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)
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)
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f.write(
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"{},{},{}\n".format(
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label,
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"LT+T",
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convert_to_0_if_nan(
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corr_table["elapsed_time_since_last_access"].corr(
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corr_table["elapsed_time_till_next_access"], method="spearman"
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)
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),
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)
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)
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f.write(
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"{},{},{}\n".format(
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label,
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"PA+T",
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convert_to_0_if_nan(
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corr_table["num_past_accesses"].corr(
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corr_table["elapsed_time_till_next_access"], method="spearman"
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)
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),
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)
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)
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for label_str in label_str_file:
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label_str_file[label_str].close()
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plot_heatmap(
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csv_result_dir,
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output_result_dir,
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"correlation_output",
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"correlation.pdf",
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"Correlation",
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)
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def plot_reuse_graphs(csv_result_dir, output_result_dir):
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plot_stacked_bar_charts(
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csv_result_dir,
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@ -301,6 +491,7 @@ def plot_reuse_graphs(csv_result_dir, output_result_dir):
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plot_line_charts(
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csv_result_dir,
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output_result_dir,
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filename_prefix="",
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filename_suffix="reuse_blocks_timeline",
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pdf_name="reuse_blocks_timeline.pdf",
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xlabel="",
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@ -370,14 +561,90 @@ def plot_access_count_summary(csv_result_dir, output_result_dir):
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vertical=True,
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x_prefix="< ",
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)
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plot_line_charts(
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csv_result_dir,
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output_result_dir,
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filename_prefix="",
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filename_suffix="skewness",
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pdf_name="skew.pdf",
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xlabel="",
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ylabel="Percentage of accesses",
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title="Skewness",
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vertical=True,
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legend=False,
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)
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def plot_miss_ratio_timeline(csv_result_dir, output_result_dir):
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plot_line_charts(
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csv_result_dir,
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output_result_dir,
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filename_prefix="",
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filename_suffix="3600_miss_ratio_timeline",
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pdf_name="miss_ratio_timeline.pdf",
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xlabel="Time",
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ylabel="Miss Ratio (%)",
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title="Miss ratio timeline",
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vertical=False,
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legend=True,
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)
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plot_line_charts(
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csv_result_dir,
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output_result_dir,
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filename_prefix="",
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filename_suffix="3600_miss_timeline",
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pdf_name="miss_timeline.pdf",
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xlabel="Time",
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ylabel="# of misses ",
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title="Miss timeline",
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vertical=False,
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legend=True,
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)
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plot_line_charts(
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csv_result_dir,
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output_result_dir,
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filename_prefix="",
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filename_suffix="3600_miss_timeline",
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pdf_name="miss_timeline.pdf",
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xlabel="Time",
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ylabel="# of misses ",
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title="Miss timeline",
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vertical=False,
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legend=True,
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)
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plot_line_charts(
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csv_result_dir,
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output_result_dir,
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filename_prefix="",
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filename_suffix="3600_policy_timeline",
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pdf_name="policy_timeline.pdf",
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xlabel="Time",
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ylabel="# of times a policy is selected ",
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title="Policy timeline",
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vertical=False,
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legend=True,
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)
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plot_line_charts(
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csv_result_dir,
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output_result_dir,
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filename_prefix="",
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filename_suffix="3600_policy_ratio_timeline",
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pdf_name="policy_ratio_timeline.pdf",
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xlabel="Time",
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ylabel="Percentage of times a policy is selected ",
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title="Policy timeline",
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vertical=False,
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legend=True,
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)
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if __name__ == "__main__":
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if len(sys.argv) < 3:
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print(
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"Must provide two arguments: 1) The directory that saves a list of "
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"directories which contain block cache trace analyzer result files "
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"2) the directory to save plotted graphs."
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"Must provide two arguments: \n"
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"1) The directory that saves a list of "
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"directories which contain block cache trace analyzer result files. \n"
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"2) the directory to save plotted graphs. \n"
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)
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exit(1)
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csv_result_dir = sys.argv[1]
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@ -396,8 +663,59 @@ if __name__ == "__main__":
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print("Processing experiment dir: {}".format(csv_relative_dir))
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if not os.path.exists(result_dir):
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os.makedirs(result_dir)
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plot_miss_ratio_graphs(csv_abs_dir, result_dir)
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plot_access_timeline(csv_abs_dir, result_dir)
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plot_access_count_summary(csv_abs_dir, result_dir)
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plot_timeline(csv_abs_dir, result_dir)
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plot_miss_ratio_timeline(csv_result_dir, output_result_dir)
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plot_correlation(csv_abs_dir, result_dir)
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plot_reuse_graphs(csv_abs_dir, result_dir)
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plot_percentage_access_summary(csv_abs_dir, result_dir)
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plot_access_count_summary(csv_abs_dir, result_dir)
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plot_miss_stats_graphs(
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csv_abs_dir,
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result_dir,
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file_prefix="",
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file_suffix="mrc",
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ylabel="Miss ratio (%)",
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pdf_file_name="mrc",
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)
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plot_miss_stats_diff_lru_graphs(
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csv_abs_dir,
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result_dir,
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file_prefix="",
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file_suffix="mrc",
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ylabel="Miss ratio (%)",
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pdf_file_name="mrc_diff_lru",
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)
|
||||
# The following stats are only available in pysim.
|
||||
for time_unit in ["1", "60", "3600"]:
|
||||
plot_miss_stats_graphs(
|
||||
csv_abs_dir,
|
||||
result_dir,
|
||||
file_prefix="ml_{}_".format(time_unit),
|
||||
file_suffix="p95mb",
|
||||
ylabel="p95 number of byte miss per {} seconds".format(time_unit),
|
||||
pdf_file_name="p95mb_per{}_seconds".format(time_unit),
|
||||
)
|
||||
plot_miss_stats_graphs(
|
||||
csv_abs_dir,
|
||||
result_dir,
|
||||
file_prefix="ml_{}_".format(time_unit),
|
||||
file_suffix="avgmb",
|
||||
ylabel="Average number of byte miss per {} seconds".format(time_unit),
|
||||
pdf_file_name="avgmb_per{}_seconds".format(time_unit),
|
||||
)
|
||||
plot_miss_stats_diff_lru_graphs(
|
||||
csv_abs_dir,
|
||||
result_dir,
|
||||
file_prefix="ml_{}_".format(time_unit),
|
||||
file_suffix="p95mb",
|
||||
ylabel="p95 number of byte miss per {} seconds".format(time_unit),
|
||||
pdf_file_name="p95mb_per{}_seconds_diff_lru".format(time_unit),
|
||||
)
|
||||
plot_miss_stats_diff_lru_graphs(
|
||||
csv_abs_dir,
|
||||
result_dir,
|
||||
file_prefix="ml_{}_".format(time_unit),
|
||||
file_suffix="avgmb",
|
||||
ylabel="Average number of byte miss per {} seconds".format(time_unit),
|
||||
pdf_file_name="avgmb_per{}_seconds_diff_lru".format(time_unit),
|
||||
)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user