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feat: Dockerize collector / CI-CD for PDF release (#1)
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Reviewed-on: #1
Co-authored-by: Mirko Milovanovic <mir_ko@me.com>
Co-committed-by: Mirko Milovanovic <mir_ko@me.com>
This commit was merged in pull request #1.
This commit is contained in:
Mirko Milovanovic
2025-02-09 23:17:27 +00:00
committed by kobim
parent 0a25f67dd3
commit a74f6093ec
35 changed files with 54 additions and 1082 deletions
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63
peertube/datavis/CRUD/mongodb-convert-to.ts.js Normal file
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/* global use, db */
// MongoDB Playground
// To disable this template go to Settings | MongoDB | Use Default Template For Playground.
// Make sure you are connected to enable completions and to be able to run a playground.
// Use Ctrl+Space inside a snippet or a string literal to trigger completions.
// The result of the last command run in a playground is shown on the results panel.
// By default the first 20 documents will be returned with a cursor.
// Use 'console.log()' to print to the debug output.
// For more documentation on playgrounds please refer to
// https://www.mongodb.com/docs/mongodb-vscode/playgrounds/
use("statistics");
db.peertube.aggregate([
{
$set: {
player: {
$function: {
lang: "js",
args: ["$player"],
body: function (str) {
try {
return JSON.parse(str);
} catch (e) {
if (typeof str === "object") {
return str;
} else {
return null;
}
}
},
},
},
peers: {
$function: {
lang: "js",
args: ["$peers"],
body: function (str) {
try {
return JSON.parse(str);
} catch (e) {
if (typeof str === "object") {
return str;
} else {
return null;
}
}
},
},
},
},
},
{
$out: {
db: "statistics",
coll: "peertube_ts",
timeseries: {
timeField: "timestamp",
metaField: "tags",
granularity: "seconds",
},
},
},
]);

141
peertube/datavis/CRUD/sessions-metrics.js Normal file
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/* global use, db */
// MongoDB Playground
// To disable this template go to Settings | MongoDB | Use Default Template For Playground.
// Make sure you are connected to enable completions and to be able to run a playground.
// Use Ctrl+Space inside a snippet or a string literal to trigger completions.
// The result of the last command run in a playground is shown on the results panel.
// By default the first 20 documents will be returned with a cursor.
// Use 'console.log()' to print to the debug output.
// For more documentation on playgrounds please refer to
// https://www.mongodb.com/docs/mongodb-vscode/playgrounds/
use("statistics");
let formattedDate = (date) => ({
unix: { $toLong: date },
iso: { $toString: date },
});
db.getCollection("peertube_ts").aggregate([
{
$sort: { timestamp: 1 }
},
{
$group: {
_id: "$tags.session",
host: { $first: "$tags.host" },
firstTimestamp: { $first: "$timestamp" },
lastTimestamp: { $last: "$timestamp" },
firstTimestampWithPeers: {
$min: {
$cond: {
if: { $gt: [{ $size: { $ifNull: ["$peers", []] } }, 0] },
then: "$timestamp",
else: null,
}
}
},
maxNumberOfPeers: {
$max: {
$size: { $ifNull: ["$peers", []] },
}
},
minNumberOfPeers: {
$min: {
$size: { $ifNull: ["$peers", []] },
}
}
}
},
// Lookup other sessions that may have peers
{
$lookup: {
from: "peertube_ts",
let: { currentSession: "$_id", ftp: "$firstTimestampWithPeers", fst: "$firstTimestamp" },
pipeline: [
{
$match: {
$expr: {
$and: [
{ $ne: ["$tags.session", "$$currentSession"] },
{ $lt: ["$timestamp", "$$ftp"] },
{ $gte: ["$timestamp", "$$fst"] },
{ $gt: [{ $size: { $ifNull: ["$peers", []] } }, 0] }
]
}
}
}
],
as: "concurrentSessions"
}
},
// Mark whether peers existed in other sessions before starting
{
$addFields: {
concurrentSessions: {
$gt: [{ $size: "$concurrentSessions" }, 0]
}
}
},
{
$group: {
_id: "$host",
sessions: {
$push: {
id: "$_id",
startTime: formattedDate("$firstTimestamp"),
endTime: formattedDate("$lastTimestamp"),
duration: {
$divide: [
{
$subtract: ["$lastTimestamp", "$firstTimestamp"],
},
1000,
]
},
firstPeerConnection: {
$cond: {
if: { $eq: ["$firstTimestampWithPeers", null] },
then: null,
else: {
time: {
date: formattedDate("$firstTimestampWithPeers"),
elapsedFromStart: {
$divide: [
{
$subtract: ["$firstTimestampWithPeers", "$firstTimestamp"],
},
1000,
]
}
},
concurrentSessions: "$concurrentSessions",
}
}
},
maxPeers: { $max: "$maxNumberOfPeers" },
minPeers: { $min: "$minNumberOfPeers" }
}
}
}
},
{
$set: {
sessions: {
$sortArray: {
input: "$sessions",
sortBy: { id: 1 }
}
}
}
},
{
$project: {
_id: 0,
host: "$_id",
sessions: "$sessions"
}
},
{
$sort: { host: 1 }
}
]);

287
peertube/datavis/plot.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import plotly.express as px\n",
"import plotly.graph_objects as pgo\n",
"import scipy as sp\n",
"from pymongo import MongoClient\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"client = MongoClient(\"mongodb://stats_user:%40z%5EVFhN7q%25vzit@192.168.86.120:27017/?authSource=statistics\")\n",
"db = client.statistics\n",
"\n",
"formatted_date = lambda date: {\n",
" \"unix\": {\"$toLong\": date},\n",
" \"iso\": {\"$toString\": date},\n",
"}\n",
"\n",
"pipeline = [\n",
" {\"$sort\": {\"timestamp\": 1}},\n",
" {\n",
" \"$group\": {\n",
" \"_id\": \"$tags.session\",\n",
" \"host\": {\"$first\": \"$tags.host\"},\n",
" \"firstTimestamp\": {\"$first\": \"$timestamp\"},\n",
" \"lastTimestamp\": {\"$last\": \"$timestamp\"},\n",
" \"firstTimestampWithPeers\": {\n",
" \"$min\": {\n",
" \"$cond\": {\n",
" \"if\": {\"$gt\": [{\"$size\": {\"$ifNull\": [\"$peers\", []]}}, 0]},\n",
" \"then\": \"$timestamp\",\n",
" \"else\": None,\n",
" }\n",
" }\n",
" },\n",
" \"maxNumberOfPeers\": {\n",
" \"$max\": {\"$size\": {\"$ifNull\": [\"$peers\", []]}},\n",
" },\n",
" \"minNumberOfPeers\": {\n",
" \"$min\": {\"$size\": {\"$ifNull\": [\"$peers\", []]}},\n",
" },\n",
" }\n",
" },\n",
" {\n",
" \"$lookup\": {\n",
" \"from\": \"peertube_ts\",\n",
" \"let\": {\"currentSession\": \"$_id\", \"ftp\": \"$firstTimestampWithPeers\", \"fst\": \"$firstTimestamp\"},\n",
" \"pipeline\": [\n",
" {\n",
" \"$match\": {\n",
" \"$expr\": {\n",
" \"$and\": [\n",
" {\"$ne\": [\"$tags.session\", \"$$currentSession\"]},\n",
" {\"$lt\": [\"$timestamp\", \"$$ftp\"]},\n",
" {\"$gte\": [\"$timestamp\", \"$$fst\"]},\n",
" {\"$gt\": [{\"$size\": {\"$ifNull\": [\"$peers\", []]}}, 0]},\n",
" ]\n",
" }\n",
" }\n",
" }\n",
" ],\n",
" \"as\": \"concurrentSessions\",\n",
" }\n",
" },\n",
" {\n",
" \"$addFields\": {\n",
" \"concurrentSessions\": {\"$gt\": [{\"$size\": \"$concurrentSessions\"}, 0]}\n",
" }\n",
" },\n",
" {\n",
" \"$group\": {\n",
" \"_id\": \"$host\",\n",
" \"sessions\": {\n",
" \"$push\": {\n",
" \"id\": \"$_id\",\n",
" \"startTime\": formatted_date(\"$firstTimestamp\"),\n",
" \"endTime\": formatted_date(\"$lastTimestamp\"),\n",
" \"duration\": {\n",
" \"$divide\": [\n",
" {\"$subtract\": [\"$lastTimestamp\", \"$firstTimestamp\"]},\n",
" 1000,\n",
" ]\n",
" },\n",
" \"firstPeerConnection\": {\n",
" \"$cond\": {\n",
" \"if\": {\"$eq\": [\"$firstTimestampWithPeers\", None]},\n",
" \"then\": None,\n",
" \"else\": {\n",
" \"time\": {\n",
" \"date\": formatted_date(\"$firstTimestampWithPeers\"),\n",
" \"elapsedFromStart\": {\n",
" \"$divide\": [\n",
" {\"$subtract\": [\"$firstTimestampWithPeers\", \"$firstTimestamp\"]},\n",
" 1000,\n",
" ]\n",
" }\n",
" },\n",
" \"concurrentSessions\": \"$concurrentSessions\",\n",
" }\n",
" }\n",
" },\n",
" \"maxPeers\": {\"$max\": \"$maxNumberOfPeers\"},\n",
" \"minPeers\": {\"$min\": \"$minNumberOfPeers\"},\n",
" }\n",
" }\n",
" }\n",
" },\n",
" {\n",
" \"$set\": {\n",
" \"sessions\": {\n",
" \"$sortArray\": {\n",
" \"input\": \"$sessions\",\n",
" \"sortBy\": {\"id\": 1},\n",
" }\n",
" }\n",
" }\n",
" },\n",
" {\n",
" \"$project\": {\n",
" \"_id\": 0,\n",
" \"host\": \"$_id\",\n",
" \"sessions\": \"$sessions\",\n",
" }\n",
" },\n",
" {\"$sort\": {\"host\": 1}},\n",
"]\n",
"\n",
"result = db.peertube_ts.aggregate(pipeline)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Extract data from the result cursor\n",
"data = []\n",
"for host in result:\n",
" for session in host['sessions']:\n",
" if session['firstPeerConnection'] and session['firstPeerConnection']['time']:\n",
" elapsed = session['firstPeerConnection']['time']['elapsedFromStart']\n",
" concurrent_sessions = session['firstPeerConnection']['concurrentSessions']\n",
" data.append((elapsed, concurrent_sessions))\n",
"\n",
"# Convert to a DataFrame for easier plotting\n",
"df = pd.DataFrame(data, columns=['Elapsed', 'ConcurrentSessions'])\n",
"\n",
"# Convert boolean column to integers\n",
"df['ConcurrentSessions'] = df['ConcurrentSessions'].astype(int)\n",
"\n",
"# Print some statistics\n",
"print(\"Mean time until first peer connection: {:.2f}s\".format(df['Elapsed'].mean()))\n",
"print(\"Median time until first peer connection: {:.2f}s\".format(df['Elapsed'].median()))\n",
"print(\"Number of sessions with concurrent sessions: {}\".format(df['ConcurrentSessions'].sum()))\n",
"print(\"Number of sessions without concurrent sessions: {}\".format(df['ConcurrentSessions'].count() - df['ConcurrentSessions'].sum()))\n",
"\n",
"# Revert concurrent sessions column to boolean for plotting\n",
"df['ConcurrentSessions'] = df['ConcurrentSessions'].astype(bool)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot the histogram of the elapsed time until first peer connection\n",
"# Color the bars based on the number of concurrent sessions and add a legend\n",
"fig = px.histogram(df, x='Elapsed', color='ConcurrentSessions', barmode='overlay', nbins=100)\n",
"fig.update_layout(\n",
" title='Elapsed time until first peer connection',\n",
" xaxis_title='Elapsed time (s)',\n",
" yaxis_title='Count',\n",
" legend_title='Had concurrent sessions',\n",
")\n",
"fig.show()\n",
"\n",
"# Plot the line chart of the elapsed time until first peer connection\n",
"fig = px.line(df, x=df.index, y='Elapsed', markers=True)\n",
"fig.update_layout(\n",
" title='Elapsed time until first peer connection',\n",
" xaxis_title='Session index',\n",
" yaxis_title='Elapsed time (s)',\n",
")\n",
"fig.show()\n",
"\n",
"# Plot the cumulative distribution of the elapsed time until first peer connection\n",
"# Color the lines based on the number of concurrent sessions and add a legend\n",
"fig = px.ecdf(df, x='Elapsed', color='ConcurrentSessions')\n",
"fig.update_layout(\n",
" title='Cumulative distribution of elapsed time until first peer connection',\n",
" xaxis_title='Elapsed time (s)',\n",
" yaxis_title='Cumulative probability',\n",
" legend_title='Had concurrent sessions',\n",
")\n",
"fig.show()\n",
"\n",
"# Plot the histogram of the number of concurrent sessions\n",
"fig = px.histogram(df, x='ConcurrentSessions', histnorm='percent')\n",
"fig.update_layout(\n",
" title='Number of concurrent sessions',\n",
" xaxis_title='Had concurrent sessions',\n",
" yaxis_title='Percentage',\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot the histogram of the elapsed time until first peer connection using seaborn\n",
"plt.figure(figsize=(10, 6))\n",
"sns.histplot(df, x='Elapsed', hue='ConcurrentSessions', multiple='stack', bins=100)\n",
"plt.title('Elapsed time until first peer connection')\n",
"plt.xlabel('Elapsed time (s)')\n",
"plt.ylabel('Count')\n",
"plt.legend(title='Had concurrent sessions', labels=['True', 'False'])\n",
"plt.show()\n",
"\n",
"# Plot the line chart of the elapsed time until first peer connection using seaborn\n",
"plt.figure(figsize=(10, 6))\n",
"sns.lineplot(data=df, x=df.index, y='Elapsed', marker='o')\n",
"plt.title('Elapsed time until first peer connection')\n",
"plt.xlabel('Session index')\n",
"plt.ylabel('Elapsed time (s)')\n",
"plt.show()\n",
"\n",
"# Plot the cumulative distribution of the elapsed time until first peer connection using seaborn\n",
"plt.figure(figsize=(10, 6))\n",
"sns.ecdfplot(df, x='Elapsed', hue='ConcurrentSessions')\n",
"plt.title('Cumulative distribution of elapsed time until first peer connection')\n",
"plt.xlabel('Elapsed time (s)')\n",
"plt.ylabel('Cumulative probability')\n",
"plt.legend(title='Had concurrent sessions', labels=['True', 'False'])\n",
"plt.show()\n",
"\n",
"# Plot the histogram of the number of concurrent sessions using seaborn\n",
"plt.figure(figsize=(10, 6))\n",
"sns.histplot(df, x='ConcurrentSessions', stat='percent', discrete=True)\n",
"plt.title('Number of concurrent sessions')\n",
"plt.xlabel('Had concurrent sessions')\n",
"plt.ylabel('Percentage')\n",
"plt.legend(title='Had concurrent sessions', labels=['True', 'False'])\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.13.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

9
peertube/datavis/requirements.txt Normal file
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pymongo
pandas
matplotlib
seaborn
numpy
scipy
plotly
nbformat
ipykernel