From 68d43b4d303d76836e0f2a4600de5de5e98fefea Mon Sep 17 00:00:00 2001 From: haoyuhuang Date: Fri, 12 Jul 2019 18:52:48 -0700 Subject: [PATCH] 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 --- tools/block_cache_trace_analyzer_plot.py | 403 +++++++++++++++++++++++ 1 file changed, 403 insertions(+) create mode 100644 tools/block_cache_trace_analyzer_plot.py diff --git a/tools/block_cache_trace_analyzer_plot.py b/tools/block_cache_trace_analyzer_plot.py new file mode 100644 index 000000000..22d56b932 --- /dev/null +++ b/tools/block_cache_trace_analyzer_plot.py @@ -0,0 +1,403 @@ +#!/usr/bin/env python3 +import csv +import os +import random +import sys + +import matplotlib.backends.backend_pdf +import matplotlib.pyplot as plt +import numpy as np + + +# Make sure a legend has the same color across all generated graphs. +def get_cmap(n, name="hsv"): + """Returns a function that maps each index in 0, 1, ..., n-1 to a distinct + RGB color; the keyword argument name must be a standard mpl colormap name.""" + return plt.cm.get_cmap(name, n) + + +color_index = 0 +bar_color_maps = {} +colors = [] +n_colors = 60 +linear_colors = get_cmap(n_colors) +for i in range(n_colors): + colors.append(linear_colors(i)) +# Shuffle the colors so that adjacent bars in a graph are obvious to differentiate. +random.shuffle(colors) + + +def num_to_gb(n): + one_gb = 1024 * 1024 * 1024 + if float(n) % one_gb == 0: + return "{}".format(n / one_gb) + # Keep two decimal points. + return "{0:.2f}".format(float(n) / one_gb) + + +def plot_miss_ratio_graphs(csv_result_dir, output_result_dir): + mrc_file_path = csv_result_dir + "/mrc" + if not os.path.exists(mrc_file_path): + return + miss_ratios = {} + print("Processing file {}".format(mrc_file_path)) + with open(mrc_file_path, "r") as csvfile: + rows = csv.reader(csvfile, delimiter=",") + is_header = False + for row in rows: + if not is_header: + is_header = True + continue + cache_name = row[0] + num_shard_bits = int(row[1]) + ghost_capacity = int(row[2]) + capacity = int(row[3]) + miss_ratio = float(row[4]) + config = "{}-{}-{}".format(cache_name, num_shard_bits, ghost_capacity) + if config not in miss_ratios: + miss_ratios[config] = {} + miss_ratios[config]["x"] = [] + miss_ratios[config]["y"] = [] + miss_ratios[config]["x"].append(num_to_gb(capacity)) + miss_ratios[config]["y"].append(miss_ratio) + fig = plt.figure() + for config in miss_ratios: + plt.plot(miss_ratios[config]["x"], miss_ratios[config]["y"], label=config) + plt.xlabel("Cache capacity (GB)") + plt.ylabel("Miss Ratio (%)") + # plt.xscale('log', basex=2) + plt.ylim(ymin=0) + plt.title("RocksDB block cache miss ratios") + plt.legend() + fig.savefig(output_result_dir + "/mrc.pdf", bbox_inches="tight") + + +def sanitize(label): + # matplotlib cannot plot legends that is prefixed with "_" + # so we need to remove them here. + index = 0 + for i in range(len(label)): + if label[i] == "_": + index += 1 + else: + break + data = label[index:] + # The value of uint64_max in c++. + if "18446744073709551615" in data: + return "max" + return data + + +# Read the csv file vertically, i.e., group the data by columns. +def read_data_for_plot_vertical(csvfile): + x = [] + labels = [] + label_stats = {} + csv_rows = csv.reader(csvfile, delimiter=",") + data_rows = [] + for row in csv_rows: + data_rows.append(row) + # header + for i in range(1, len(data_rows[0])): + labels.append(sanitize(data_rows[0][i])) + label_stats[i - 1] = [] + for i in range(1, len(data_rows)): + for j in range(len(data_rows[i])): + if j == 0: + x.append(sanitize(data_rows[i][j])) + continue + label_stats[j - 1].append(float(data_rows[i][j])) + return x, labels, label_stats + + +# Read the csv file horizontally, i.e., group the data by rows. +def read_data_for_plot_horizontal(csvfile): + x = [] + labels = [] + label_stats = {} + csv_rows = csv.reader(csvfile, delimiter=",") + data_rows = [] + for row in csv_rows: + data_rows.append(row) + # header + for i in range(1, len(data_rows)): + labels.append(sanitize(data_rows[i][0])) + label_stats[i - 1] = [] + for i in range(1, len(data_rows[0])): + x.append(sanitize(data_rows[0][i])) + for i in range(1, len(data_rows)): + for j in range(len(data_rows[i])): + if j == 0: + # label + continue + label_stats[i - 1].append(float(data_rows[i][j])) + return x, labels, label_stats + + +def read_data_for_plot(csvfile, vertical): + if vertical: + return read_data_for_plot_vertical(csvfile) + return read_data_for_plot_horizontal(csvfile) + + +def plot_line_charts( + csv_result_dir, + output_result_dir, + filename_suffix, + pdf_name, + xlabel, + ylabel, + title, + vertical, + legend, +): + pdf = matplotlib.backends.backend_pdf.PdfPages(output_result_dir + "/" + pdf_name) + for file in os.listdir(csv_result_dir): + if not file.endswith(filename_suffix): + continue + print("Processing file {}".format(file)) + with open(csv_result_dir + "/" + file, "r") as csvfile: + x, labels, label_stats = read_data_for_plot(csvfile, vertical) + if len(x) == 0 or len(labels) == 0: + continue + # plot figure + fig = plt.figure() + for label_index in label_stats: + plt.plot( + [int(x[i]) for i in range(len(x))], + label_stats[label_index], + label=labels[label_index], + ) + + # Translate time unit into x labels. + if "_60" in file: + plt.xlabel("{} (Minute)".format(xlabel)) + if "_3600" in file: + plt.xlabel("{} (Hour)".format(xlabel)) + plt.ylabel(ylabel) + plt.title("{} {}".format(title, file)) + if legend: + plt.legend() + pdf.savefig(fig) + pdf.close() + + +def plot_stacked_bar_charts( + csv_result_dir, + output_result_dir, + filename_suffix, + pdf_name, + xlabel, + ylabel, + title, + vertical, + x_prefix, +): + global color_index, bar_color_maps, colors + pdf = matplotlib.backends.backend_pdf.PdfPages( + "{}/{}".format(output_result_dir, pdf_name) + ) + for file in os.listdir(csv_result_dir): + if not file.endswith(filename_suffix): + continue + with open(csv_result_dir + "/" + file, "r") as csvfile: + print("Processing file {}/{}".format(csv_result_dir, file)) + x, labels, label_stats = read_data_for_plot(csvfile, vertical) + if len(x) == 0 or len(label_stats) == 0: + continue + # Plot figure + fig = plt.figure() + ind = np.arange(len(x)) # the x locations for the groups + width = 0.5 # the width of the bars: can also be len(x) sequence + bars = [] + bottom_bars = [] + for _i in label_stats[0]: + bottom_bars.append(0) + for i in range(0, len(label_stats)): + # Assign a unique color to this label. + if labels[i] not in bar_color_maps: + bar_color_maps[labels[i]] = colors[color_index] + color_index += 1 + p = plt.bar( + ind, + label_stats[i], + width, + bottom=bottom_bars, + color=bar_color_maps[labels[i]], + ) + bars.append(p[0]) + for j in range(len(label_stats[i])): + bottom_bars[j] += label_stats[i][j] + plt.xlabel(xlabel) + plt.ylabel(ylabel) + plt.xticks( + ind, [x_prefix + x[i] for i in range(len(x))], rotation=20, fontsize=8 + ) + plt.legend(bars, labels) + plt.title("{} filename:{}".format(title, file)) + pdf.savefig(fig) + pdf.close() + + +def plot_access_timeline(csv_result_dir, output_result_dir): + plot_line_charts( + csv_result_dir, + output_result_dir, + filename_suffix="access_timeline", + pdf_name="access_time.pdf", + xlabel="Time", + ylabel="Throughput", + title="Access timeline with group by label", + vertical=False, + legend=True, + ) + + +def plot_reuse_graphs(csv_result_dir, output_result_dir): + plot_stacked_bar_charts( + csv_result_dir, + output_result_dir, + filename_suffix="avg_reuse_interval_naccesses", + pdf_name="avg_reuse_interval_naccesses.pdf", + xlabel="", + ylabel="Percentage of accesses", + title="Average reuse interval", + vertical=True, + x_prefix="< ", + ) + plot_stacked_bar_charts( + csv_result_dir, + output_result_dir, + filename_suffix="avg_reuse_interval", + pdf_name="avg_reuse_interval.pdf", + xlabel="", + ylabel="Percentage of blocks", + title="Average reuse interval", + vertical=True, + x_prefix="< ", + ) + plot_stacked_bar_charts( + csv_result_dir, + output_result_dir, + filename_suffix="access_reuse_interval", + pdf_name="reuse_interval.pdf", + xlabel="Seconds", + ylabel="Percentage of accesses", + title="Reuse interval", + vertical=True, + x_prefix="< ", + ) + plot_stacked_bar_charts( + csv_result_dir, + output_result_dir, + filename_suffix="reuse_lifetime", + pdf_name="reuse_lifetime.pdf", + xlabel="Seconds", + ylabel="Percentage of blocks", + title="Reuse lifetime", + vertical=True, + x_prefix="< ", + ) + plot_line_charts( + csv_result_dir, + output_result_dir, + filename_suffix="reuse_blocks_timeline", + pdf_name="reuse_blocks_timeline.pdf", + xlabel="", + ylabel="Percentage of blocks", + title="Reuse blocks timeline", + vertical=False, + legend=False, + ) + + +def plot_percentage_access_summary(csv_result_dir, output_result_dir): + plot_stacked_bar_charts( + csv_result_dir, + output_result_dir, + filename_suffix="percentage_of_accesses_summary", + pdf_name="percentage_access.pdf", + xlabel="", + ylabel="Percentage of accesses", + title="", + vertical=True, + x_prefix="", + ) + plot_stacked_bar_charts( + csv_result_dir, + output_result_dir, + filename_suffix="percent_ref_keys", + pdf_name="percent_ref_keys.pdf", + xlabel="", + ylabel="Percentage of blocks", + title="", + vertical=True, + x_prefix="", + ) + plot_stacked_bar_charts( + csv_result_dir, + output_result_dir, + filename_suffix="percent_data_size_on_ref_keys", + pdf_name="percent_data_size_on_ref_keys.pdf", + xlabel="", + ylabel="Percentage of blocks", + title="", + vertical=True, + x_prefix="", + ) + plot_stacked_bar_charts( + csv_result_dir, + output_result_dir, + filename_suffix="percent_accesses_on_ref_keys", + pdf_name="percent_accesses_on_ref_keys.pdf", + xlabel="", + ylabel="Percentage of blocks", + title="", + vertical=True, + x_prefix="", + ) + + +def plot_access_count_summary(csv_result_dir, output_result_dir): + plot_stacked_bar_charts( + csv_result_dir, + output_result_dir, + filename_suffix="access_count_summary", + pdf_name="access_count_summary.pdf", + xlabel="Access count", + ylabel="Percentage of blocks", + title="", + vertical=True, + x_prefix="< ", + ) + + +if __name__ == "__main__": + if len(sys.argv) < 3: + print( + "Must provide two arguments: 1) The directory that saves a list of " + "directories which contain block cache trace analyzer result files " + "2) the directory to save plotted graphs." + ) + exit(1) + csv_result_dir = sys.argv[1] + output_result_dir = sys.argv[2] + print( + "Processing directory {} and save graphs to {}.".format( + csv_result_dir, output_result_dir + ) + ) + for csv_relative_dir in os.listdir(csv_result_dir): + csv_abs_dir = csv_result_dir + "/" + csv_relative_dir + result_dir = output_result_dir + "/" + csv_relative_dir + if not os.path.isdir(csv_abs_dir): + print("{} is not a directory".format(csv_abs_dir)) + continue + print("Processing experiment dir: {}".format(csv_relative_dir)) + if not os.path.exists(result_dir): + os.makedirs(result_dir) + plot_miss_ratio_graphs(csv_abs_dir, result_dir) + plot_access_timeline(csv_abs_dir, result_dir) + plot_reuse_graphs(csv_abs_dir, result_dir) + plot_percentage_access_summary(csv_abs_dir, result_dir) + plot_access_count_summary(csv_abs_dir, result_dir) -- GitLab