A python script to plot graphs for cvs files generated by block_cache_trace_analyzer
Summary: Pull Request resolved: https://github.com/facebook/rocksdb/pull/5563 Test Plan: Manually run the script on files generated by block_cache_trace_analyzer. Differential Revision: D16214400 Pulled By: HaoyuHuang fbshipit-source-id: 94485eed995e9b2b63e197c5dfeb80129fa7897f
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haoyuhuang
Facebook Github Bot
parent
61876614dc
commit
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403
tools/block_cache_trace_analyzer_plot.py
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403
tools/block_cache_trace_analyzer_plot.py
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#!/usr/bin/env python3
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import csv
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import os
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import random
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import sys
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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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# Make sure a legend has the same color across all generated graphs.
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def get_cmap(n, name="hsv"):
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"""Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
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RGB color; the keyword argument name must be a standard mpl colormap name."""
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return plt.cm.get_cmap(name, n)
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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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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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# Shuffle the colors so that adjacent bars in a graph are obvious to differentiate.
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random.shuffle(colors)
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def num_to_gb(n):
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one_gb = 1024 * 1024 * 1024
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if float(n) % one_gb == 0:
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return "{}".format(n / one_gb)
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# Keep two decimal points.
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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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miss_ratios = {}
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print("Processing file {}".format(mrc_file_path))
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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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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(num_to_gb(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.ylim(ymin=0)
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plt.title("RocksDB block cache miss ratios")
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plt.legend()
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fig.savefig(output_result_dir + "/mrc.pdf", bbox_inches="tight")
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def sanitize(label):
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# matplotlib cannot plot legends that is prefixed with "_"
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# so we need to remove them here.
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index = 0
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for i in range(len(label)):
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if label[i] == "_":
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index += 1
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else:
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break
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data = label[index:]
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# The value of uint64_max in c++.
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if "18446744073709551615" in data:
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return "max"
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return data
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# Read the csv file vertically, i.e., group the data by columns.
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def read_data_for_plot_vertical(csvfile):
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x = []
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labels = []
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label_stats = {}
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csv_rows = csv.reader(csvfile, delimiter=",")
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data_rows = []
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for row in csv_rows:
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data_rows.append(row)
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# header
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for i in range(1, len(data_rows[0])):
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labels.append(sanitize(data_rows[0][i]))
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label_stats[i - 1] = []
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for i in range(1, len(data_rows)):
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for j in range(len(data_rows[i])):
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if j == 0:
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x.append(sanitize(data_rows[i][j]))
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continue
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label_stats[j - 1].append(float(data_rows[i][j]))
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return x, labels, label_stats
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# Read the csv file horizontally, i.e., group the data by rows.
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def read_data_for_plot_horizontal(csvfile):
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x = []
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labels = []
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label_stats = {}
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csv_rows = csv.reader(csvfile, delimiter=",")
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data_rows = []
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for row in csv_rows:
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data_rows.append(row)
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# header
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for i in range(1, len(data_rows)):
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labels.append(sanitize(data_rows[i][0]))
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label_stats[i - 1] = []
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for i in range(1, len(data_rows[0])):
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x.append(sanitize(data_rows[0][i]))
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for i in range(1, len(data_rows)):
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for j in range(len(data_rows[i])):
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if j == 0:
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# label
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continue
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label_stats[i - 1].append(float(data_rows[i][j]))
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return x, labels, label_stats
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def read_data_for_plot(csvfile, vertical):
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if vertical:
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return read_data_for_plot_vertical(csvfile)
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return read_data_for_plot_horizontal(csvfile)
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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_suffix,
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pdf_name,
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xlabel,
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ylabel,
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title,
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vertical,
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legend,
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):
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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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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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continue
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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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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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label=labels[label_index],
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)
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# Translate time unit into x labels.
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if "_60" in file:
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plt.xlabel("{} (Minute)".format(xlabel))
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if "_3600" in file:
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plt.xlabel("{} (Hour)".format(xlabel))
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plt.ylabel(ylabel)
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plt.title("{} {}".format(title, file))
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if legend:
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plt.legend()
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pdf.savefig(fig)
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pdf.close()
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def plot_stacked_bar_charts(
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csv_result_dir,
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output_result_dir,
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filename_suffix,
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pdf_name,
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xlabel,
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ylabel,
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title,
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vertical,
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x_prefix,
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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(
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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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with open(csv_result_dir + "/" + file, "r") as csvfile:
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print("Processing file {}/{}".format(csv_result_dir, file))
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x, labels, label_stats = read_data_for_plot(csvfile, vertical)
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if len(x) == 0 or len(label_stats) == 0:
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continue
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# Plot figure
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fig = plt.figure()
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ind = np.arange(len(x)) # the x locations for the groups
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width = 0.5 # the width of the bars: can also be len(x) sequence
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bars = []
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bottom_bars = []
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for _i in label_stats[0]:
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bottom_bars.append(0)
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for i in range(0, len(label_stats)):
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# Assign a unique color to this label.
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if labels[i] not in bar_color_maps:
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bar_color_maps[labels[i]] = colors[color_index]
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color_index += 1
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p = plt.bar(
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ind,
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label_stats[i],
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width,
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bottom=bottom_bars,
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color=bar_color_maps[labels[i]],
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)
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bars.append(p[0])
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for j in range(len(label_stats[i])):
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bottom_bars[j] += label_stats[i][j]
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plt.xlabel(xlabel)
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plt.ylabel(ylabel)
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plt.xticks(
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ind, [x_prefix + x[i] for i in range(len(x))], rotation=20, fontsize=8
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)
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plt.legend(bars, labels)
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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_access_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_suffix="access_timeline",
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pdf_name="access_time.pdf",
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xlabel="Time",
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ylabel="Throughput",
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title="Access timeline with group by label",
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vertical=False,
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legend=True,
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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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output_result_dir,
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filename_suffix="avg_reuse_interval_naccesses",
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pdf_name="avg_reuse_interval_naccesses.pdf",
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xlabel="",
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ylabel="Percentage of accesses",
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title="Average reuse interval",
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vertical=True,
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x_prefix="< ",
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)
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plot_stacked_bar_charts(
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csv_result_dir,
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output_result_dir,
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filename_suffix="avg_reuse_interval",
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pdf_name="avg_reuse_interval.pdf",
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xlabel="",
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ylabel="Percentage of blocks",
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title="Average reuse interval",
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vertical=True,
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x_prefix="< ",
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)
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plot_stacked_bar_charts(
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csv_result_dir,
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output_result_dir,
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filename_suffix="access_reuse_interval",
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pdf_name="reuse_interval.pdf",
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xlabel="Seconds",
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ylabel="Percentage of accesses",
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title="Reuse interval",
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vertical=True,
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x_prefix="< ",
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)
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plot_stacked_bar_charts(
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csv_result_dir,
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output_result_dir,
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filename_suffix="reuse_lifetime",
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pdf_name="reuse_lifetime.pdf",
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xlabel="Seconds",
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ylabel="Percentage of blocks",
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title="Reuse lifetime",
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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_suffix="reuse_blocks_timeline",
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pdf_name="reuse_blocks_timeline.pdf",
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xlabel="",
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ylabel="Percentage of blocks",
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title="Reuse blocks timeline",
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vertical=False,
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legend=False,
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)
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def plot_percentage_access_summary(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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output_result_dir,
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filename_suffix="percentage_of_accesses_summary",
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pdf_name="percentage_access.pdf",
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xlabel="",
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ylabel="Percentage of accesses",
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title="",
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vertical=True,
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x_prefix="",
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)
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plot_stacked_bar_charts(
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csv_result_dir,
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output_result_dir,
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filename_suffix="percent_ref_keys",
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pdf_name="percent_ref_keys.pdf",
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xlabel="",
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ylabel="Percentage of blocks",
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title="",
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vertical=True,
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x_prefix="",
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)
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plot_stacked_bar_charts(
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csv_result_dir,
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output_result_dir,
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filename_suffix="percent_data_size_on_ref_keys",
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pdf_name="percent_data_size_on_ref_keys.pdf",
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xlabel="",
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ylabel="Percentage of blocks",
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title="",
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vertical=True,
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x_prefix="",
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)
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plot_stacked_bar_charts(
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csv_result_dir,
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output_result_dir,
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filename_suffix="percent_accesses_on_ref_keys",
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pdf_name="percent_accesses_on_ref_keys.pdf",
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xlabel="",
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ylabel="Percentage of blocks",
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title="",
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vertical=True,
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x_prefix="",
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)
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def plot_access_count_summary(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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output_result_dir,
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filename_suffix="access_count_summary",
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pdf_name="access_count_summary.pdf",
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xlabel="Access count",
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ylabel="Percentage of blocks",
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title="",
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vertical=True,
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x_prefix="< ",
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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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)
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exit(1)
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csv_result_dir = sys.argv[1]
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output_result_dir = sys.argv[2]
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print(
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"Processing directory {} and save graphs to {}.".format(
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csv_result_dir, output_result_dir
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)
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)
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for csv_relative_dir in os.listdir(csv_result_dir):
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csv_abs_dir = csv_result_dir + "/" + csv_relative_dir
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result_dir = output_result_dir + "/" + csv_relative_dir
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if not os.path.isdir(csv_abs_dir):
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print("{} is not a directory".format(csv_abs_dir))
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continue
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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_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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Block a user