mirror of
https://github.com/libp2p/gossipsub-hardening.git
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577 lines
18 KiB
Plaintext
577 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Pubsub Test Analysis Notebook\n",
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"\n",
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"## Usage\n",
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"\n",
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"This notebook analyzes the output of a single pubsub test execution.\n",
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"\n",
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"You must run all the cells (`Cell > Run All` menu) to load the data and generate the charts.\n",
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"\n",
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"This may take quite some time and require considerable RAM on the first\n",
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"run, but the results will be cached to `ANALYSIS_DIR/pandas` for future runs.\n",
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"\n",
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"<p/>\n",
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"\n",
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"<details>\n",
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" <summary>Expand to show example data</summary>\n",
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"\n",
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"### `scores`\n",
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"\n",
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"The `scores` `DataFrame` contains peer score events, indexed by timestamp.\n",
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"\n",
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"**Example**:\n",
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"\n",
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"| timestamp | observer | peer | score |\n",
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"|-------------------------------|------------------------------------------------------|------------------------------------------------------|-----------|\n",
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"| 2020-03-31 16:46:22.675526100 | 12D3KooWM1Q8EazdTBaYidtmAnaEqjtAzCGkYypzjgUmDU3bNpAS | 12D3KooWGHEfmKenMpsGDu1xEuVw7itsEMMT6oBVbYx642627Etw | 0.0027 |\n",
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"| 2020-03-31 16:46:22.675526100 | 12D3KooWM1Q8EazdTBaYidtmAnaEqjtAzCGkYypzjgUmDU3bNpAS | 12D3KooWN5fa46NhuVP9as8ANmZ54gAM5cPuhTuu1bMAY5wvgoPG | 16.5617 |\n",
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"\n",
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"\n",
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"- `observer` is the peer assigning the score\n",
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"- `peer` is the peer receiving the score\n",
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"\n",
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"### `metrics`\n",
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"\n",
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"The `metrics` `DataFrame` contains aggregated tracer metrics.\n",
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"\n",
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"**Example**:\n",
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"\n",
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"| | published | rejected | delivered | duplicates | droppedrpc | peersadded | peersremoved | topicsjoined | topicsleft | peer | sent_rpcs | sent_messages | sent_grafts | sent_prunes | sent_iwants | sent_ihaves | recv_rpcs | recv_messages | recv_grafts | recv_prunes | recv_iwants | recv_ihaves |\n",
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"|----|-------------|------------|-------------|--------------|--------------|--------------|----------------|----------------|--------------|------------------------------------------------------|-------------|-----------------|---------------|---------------|---------------|---------------|-------------|-----------------|---------------|---------------|---------------|---------------|\n",
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"| 0 | 0 | 0 | 721 | 0 | 0 | 2 | 1 | 1 | 0 | 12D3KooWM1Q8EazdTBaYidtmAnaEqjtAzCGkYypzjgUmDU3bNpAS | 722 | 721 | 0 | 0 | 0 | 0 | 727 | 721 | 2 | 0 | 0 | 0 |\n",
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"| 1 | 0 | 0 | 721 | 0 | 0 | 2 | 1 | 1 | 0 | 12D3KooWN5fa46NhuVP9as8ANmZ54gAM5cPuhTuu1bMAY5wvgoPG | 724 | 721 | 1 | 0 | 0 | 0 | 726 | 721 | 1 | 0 | 0 | 0 |\n",
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"\n",
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" \n",
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"</details>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"parameters"
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]
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},
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"outputs": [],
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"source": [
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"# Parameters in this cell can be overriden using papermill\n",
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"\n",
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"# path to directory contaning output from the extract_test_outputs method in analyze.py\n",
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"ANALYSIS_DIR=\".\"\n",
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"\n",
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"# dir to save figure images\n",
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"FIGURE_OUT=\"./figures\"\n",
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"\n",
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"# path to zip file containing all figures\n",
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"FIGURE_ZIP_OUT=\"./figures.zip\"\n",
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"\n",
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"# font sizes\n",
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"SMALL_SIZE = 8\n",
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"MEDIUM_SIZE = 10\n",
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"BIGGER_SIZE = 18\n",
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"\n",
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"# If no cached 'meshes.gz' file exists when loading data, setting\n",
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"# DERIVE_MESHES to True causes the mesh state to be derived from\n",
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"# trace events. This will add several minutes to the load time,\n",
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"# but the results will be cached to disk.\n",
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"DERIVE_MESHES=False\n",
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"\n",
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"# When DERIVE_MESHES is true, you can change the sample frequency\n",
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"# by setting MESH_SAMPLE_FREQ. This controls the size of the time window\n",
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"# for each mesh \"snapshot\".\n",
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"MESH_SAMPLE_FREQ='5s'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import toml\n",
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"import ipywidgets as widgets\n",
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"from pprint import pprint\n",
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"import pathlib\n",
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"import seaborn as sns\n",
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"from durations import Duration\n",
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"\n",
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"import notebook_helper\n",
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"from notebook_helper import no_scores_message, load_pandas, archive_figures, p25, p50, p75, p95, p99\n",
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"\n",
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"# load a helper to save figures to FIGURE_OUT\n",
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"save_fig = notebook_helper.save_fig_fn(FIGURE_OUT)\n",
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"\n",
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"# render charts in a larger, zoomable style\n",
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"%matplotlib notebook\n",
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"\n",
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"# turn off autosaving for the notebook\n",
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"%autosave 0\n",
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"\n",
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"# prettify the colors\n",
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"sns.set(color_codes=True)\n",
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"\n",
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"# helper to set font sizes for charts\n",
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"def set_chart_fontsize(size):\n",
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" plt.rc('font', size=size) # controls default text sizes\n",
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" plt.rc('axes', titlesize=size) # fontsize of the axes title\n",
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" plt.rc('axes', labelsize=size) # fontsize of the x and y labels\n",
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" plt.rc('xtick', labelsize=size) # fontsize of the tick labels\n",
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" plt.rc('ytick', labelsize=size) # fontsize of the tick labels\n",
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" plt.rc('legend', fontsize=size) # legend fontsize\n",
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" plt.rc('figure', titlesize=size) # fontsize of the figure title\n",
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"\n",
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" \n",
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"# set chart fonts to BIGGER_SIZE by default\n",
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"set_chart_fontsize(BIGGER_SIZE)\n",
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"\n",
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"# load data\n",
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"print('loading test data from ' + ANALYSIS_DIR)\n",
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"\n",
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"tables = load_pandas(ANALYSIS_DIR, derive_meshes_if_missing=DERIVE_MESHES, mesh_sample_freq=MESH_SAMPLE_FREQ)\n",
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"scores = tables['scores']\n",
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"metrics = tables['metrics']\n",
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"cdf = tables['cdf']\n",
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"pdf = tables['pdf']\n",
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"peers = tables['peers']\n",
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"meshes = tables.get('meshes', notebook_helper.empty_meshes_table())\n",
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"\n",
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"# resample score index into 5s windows for plotting later\n",
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"if not scores.empty:\n",
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" print('resampling peer scores')\n",
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" resample_interval = '5s'\n",
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" sampled = scores.resample(resample_interval)\n",
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" honest_sampled = scores.where(scores['honest']).resample(resample_interval)\n",
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" attacker_sampled = scores.where(scores['honest'] == False).resample(resample_interval)\n",
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"\n",
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"honest_peers = peers.where(peers['honest']).dropna(how='all')\n",
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"attack_peers = peers.where(~peers['honest']).dropna(how='all').dropna('columns')\n",
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"\n",
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"t_warm = honest_peers['t_warm'].max()\n",
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"t_run = honest_peers['t_run'].max()\n",
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"t_cool = honest_peers['t_cool'].max()\n",
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"t_complete = honest_peers['t_complete'].max()\n",
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"\n",
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"# TODO: support multiple waves of attackers with different connect times\n",
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"attack_connect_start = attack_peers['t_connect'].min()\n",
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"attack_connect_end = attack_peers['t_connect'].max()\n",
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"\n",
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"params_panel, test_params = notebook_helper.test_params_panel(ANALYSIS_DIR)\n",
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"\n",
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"try:\n",
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" s = test_params['PEER_SCORE_PARAMS']['Topics']['blocks']['MeshMessageDeliveriesActivation']\n",
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" d = Duration(s)\n",
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" d = pd.Timedelta(d.to_seconds(), unit='seconds')\n",
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" mesh_activation_start = attack_connect_start + d\n",
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" mesh_activation_end = attack_connect_end + d\n",
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"except BaseException as err:\n",
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" print('error determining mesh activation window: ', err) \n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Test Parameters\n",
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"\n",
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"The cell below shows the parameters that were used to create the composition file for this test run.\n",
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"\n",
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"You can access the parameter values from other cells via the `test_params` dict, e.g.:\n",
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"\n",
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"```python\n",
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"from durations import Duration\n",
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"warmup = Duration(test_params['T_WARM'])\n",
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"print('warmup seconds: {}'.format(warmup.to_seconds()))\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"params_panel"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Aggregations"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Latency Distribution"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### CDF"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig = notebook_helper.plot_latency_cdf(cdf)\n",
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"save_fig(fig, 'latency-cdf')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### PDF"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig = notebook_helper.plot_latency_pdf(pdf)\n",
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"save_fig(fig, 'latency-pdf')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### PDF (above p99)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig = notebook_helper.plot_latency_pdf_above_quantile(pdf, quantile=0.99)\n",
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"save_fig(fig, 'latency-pdf-over-p99')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Tracestat summary\n",
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"Only Publish and Deliver counts are accurate, the rest are filtered."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(notebook_helper.tracestat_summary(ANALYSIS_DIR))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Aggregated tracer metrics (per-peer)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"metrics[['published', 'delivered', 'rejected', 'duplicates', 'droppedrpc']].agg([np.min, np.max, np.median, np.mean]).rename(columns={'amax': 'max', 'amin': 'min', 'amedian': 'median', 'amean': 'mean'})\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### All peer scores, aggregated across the test runtime"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"if not scores.empty:\n",
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" scores['score'].agg({'min': np.min, 'max': np.max, 'median': np.median, 'mean': np.mean})\n",
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"else:\n",
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" no_scores_message()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Aggregated score values for peers with negative scores\n",
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"\n",
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"- `observer` is the peer assigning the score. \n",
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"- `peer` is the peer receiving the score."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"if not scores.empty:\n",
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" neg = scores.where(scores['score'] < 0).groupby(['peer', 'observer'])\n",
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" n = neg.agg({'score': [np.min, np.max, np.median, np.mean]})\n",
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" n\n",
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"else:\n",
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" no_scores_message()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Show honest peers with negative scores, joined with tracer metrics"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"if not scores.empty:\n",
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" # select columns from metrics table\n",
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" m = metrics[['peer', 'published', 'delivered', 'rejected']]\n",
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"\n",
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" pd.DataFrame(n).merge(m, on='peer').groupby('peer').head()\n",
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"else:\n",
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" no_scores_message()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### global min/max score over time"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"time_annotations = [\n",
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" {'label': 'warmup complete', 'time': t_run},\n",
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" {'label': 'attackers connect', 'time': attack_connect_start, 'end_time': attack_connect_end},\n",
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" {'label': 'delivery penalty activation', 'time': mesh_activation_start, 'end_time': mesh_activation_end},\n",
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" {'label': 'cooldown begin', 'time': t_cool},\n",
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"]\n",
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"\n",
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"def annotate_score_plot(plot, label, legend_anchor=None):\n",
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" notebook_helper.annotate_score_plot(plot, label, legend_anchor=legend_anchor, time_annotations=time_annotations)\n",
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"\n",
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"if not scores.empty:\n",
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" fig, ax = plt.subplots(4, figsize=(11, 12))\n",
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" plt.subplots_adjust(hspace=0.5)\n",
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" fig.suptitle(\"Min / Max Peer Scores\")\n",
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" plot = sampled['score'].agg([np.min, np.max]).plot(ax=ax[0])\n",
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" annotate_score_plot(plot, 'All Peers min/max')\n",
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" \n",
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" plot = honest_sampled['score'].agg([np.min, np.max]).plot(ax=ax[1])\n",
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" annotate_score_plot(plot, 'Honest Peers min/max')\n",
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" \n",
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" plot = attacker_sampled['score'].agg([np.min, np.max]).plot(ax=ax[2])\n",
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" \n",
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" # draw last plot with legend anchored below\n",
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" legends = annotate_score_plot(plot, 'Attacker Peers min/max', legend_anchor=(0, -0.2))\n",
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" \n",
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" # hide bottom plot so legend is visible\n",
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" ax[3].set_visible(False)\n",
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" \n",
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" # write png\n",
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" save_fig(fig, 'score-min-max')\n",
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"else:\n",
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" no_scores_message()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### global mean / median score over time"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"if not scores.empty: \n",
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" # create 4 subplots - three for charts and one that we'll hide to make blank space\n",
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" # for the legend\n",
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" fig, ax = plt.subplots(4, figsize=(11, 12))\n",
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" plt.subplots_adjust(hspace=0.5)\n",
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" fig.suptitle('Mean / Median Peer Scores')\n",
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" plot = sampled['score'].agg([np.mean, np.median]).plot(ax=ax[0])\n",
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" annotate_score_plot(plot, 'All Peers mean/median')\n",
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" \n",
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" plot = honest_sampled['score'].agg([np.mean, np.median]).plot(ax=ax[1])\n",
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" annotate_score_plot(plot, 'Honest Peers mean/median')\n",
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" \n",
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" plot = attacker_sampled['score'].agg([np.mean, np.median]).plot(ax=ax[2])\n",
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" annotate_score_plot(plot, 'Attacker Peers mean/median', legend_anchor=(0, -0.2))\n",
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" \n",
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" # hide bottom plot so legend is visible\n",
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" ax[3].set_visible(False)\n",
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" \n",
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" save_fig(fig, 'score-mean-median')\n",
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"else:\n",
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|
" no_scores_message()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### mean score distribution (all peers)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"if not scores.empty:\n",
|
|
" plot = scores[['peer', 'score']].groupby('peer').mean().plot.hist(bins=20)\n",
|
|
" fig = plot.get_figure()\n",
|
|
" fig.suptitle('Global score distribution (all peers)')\n",
|
|
" save_fig(fig, 'score-global-distribution')\n",
|
|
"else:\n",
|
|
" no_scores_message()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### score distributions (honest vs attacker)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"aggregations = [np.min, np.max, p25, p75, p95]\n",
|
|
"kwargs = {'kind': 'hist', 'subplots': True, 'sharex': True, 'sharey': True, 'figsize': (8, 10)}\n",
|
|
"\n",
|
|
"if not scores.empty:\n",
|
|
" honest_by_peer = scores.where(scores['honest']).groupby('peer')\n",
|
|
" attacker_by_peer = scores.where(scores['honest'] == False).groupby('peer')\n",
|
|
"\n",
|
|
" plots = honest_by_peer['score'].agg(aggregations).plot(title='Score distributions (honest peers)', **kwargs)\n",
|
|
" for p in plots:\n",
|
|
" p.set_ylabel('freq')\n",
|
|
" fig = plots[0].get_figure()\n",
|
|
" save_fig(fig, 'score-distributions-honest')\n",
|
|
"\n",
|
|
"\n",
|
|
" plots = attacker_by_peer['score'].agg(aggregations).plot(title='Score distributions (attacker peers)', **kwargs)\n",
|
|
" for p in plots:\n",
|
|
" p.set_ylabel('freq')\n",
|
|
" fig = plots[0].get_figure()\n",
|
|
" save_fig(fig, 'score-distributions-attacker')\n",
|
|
"else:\n",
|
|
" no_scores_message()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"outputs": [],
|
|
"source": [
|
|
"if not meshes.empty:\n",
|
|
" fig = plt.figure()\n",
|
|
" m = meshes.drop(columns=['mesh', 'peer'])\n",
|
|
" sns.relplot(kind=\"line\", ci=\"sd\", data=m)\n",
|
|
" save_fig(fig, 'mesh-counts')\n",
|
|
"else:\n",
|
|
" notebook_helper.no_meshes_message()"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"outputs": [],
|
|
"source": [
|
|
"# zip all figure images into a bundle\n",
|
|
"archive_figures(FIGURE_OUT, FIGURE_ZIP_OUT)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
} |