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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "id": "06b749e1-90f2-4aa3-9471-77bb564c7f96", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import glob\n", | |
| "import pandas as pd\n", | |
| "import altair as alt\n", | |
| "from altair.expr import datum" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "id": "52b0e009-6038-4a3a-afb2-01f1da94c871", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "frames = []\n", | |
| "for pkl in glob.glob(\"*-main.pkl\"):\n", | |
| " df = pd.read_pickle(pkl)\n", | |
| " df[\"has_index\"] = \"no index\"\n", | |
| " frames.append(df)\n", | |
| "\n", | |
| "for pkl in glob.glob(\"*-metricssss.pkl\"):\n", | |
| " df = pd.read_pickle(pkl)\n", | |
| " df[\"has_index\"] = \"index\"\n", | |
| " frames.append(df)\n", | |
| "\n", | |
| "df = pd.concat(frames)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "id": "0993e86c-b7be-4a81-8fb4-c470ef1ca91c", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>24h</th>\n", | |
| " <th>7d</th>\n", | |
| " <th>30d</th>\n", | |
| " <th>time</th>\n", | |
| " <th>users</th>\n", | |
| " <th>db</th>\n", | |
| " <th>has_index</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>16</td>\n", | |
| " <td>51</td>\n", | |
| " <td>73</td>\n", | |
| " <td>0.012844</td>\n", | |
| " <td>100</td>\n", | |
| " <td>postgres</td>\n", | |
| " <td>no index</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>16</td>\n", | |
| " <td>51</td>\n", | |
| " <td>73</td>\n", | |
| " <td>0.005290</td>\n", | |
| " <td>100</td>\n", | |
| " <td>postgres</td>\n", | |
| " <td>no index</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>16</td>\n", | |
| " <td>51</td>\n", | |
| " <td>73</td>\n", | |
| " <td>0.003802</td>\n", | |
| " <td>100</td>\n", | |
| " <td>postgres</td>\n", | |
| " <td>no index</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>22</td>\n", | |
| " <td>67</td>\n", | |
| " <td>107</td>\n", | |
| " <td>0.009735</td>\n", | |
| " <td>150</td>\n", | |
| " <td>postgres</td>\n", | |
| " <td>no index</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>22</td>\n", | |
| " <td>67</td>\n", | |
| " <td>107</td>\n", | |
| " <td>0.005233</td>\n", | |
| " <td>150</td>\n", | |
| " <td>postgres</td>\n", | |
| " <td>no index</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>49</th>\n", | |
| " <td>11269</td>\n", | |
| " <td>29956</td>\n", | |
| " <td>49099</td>\n", | |
| " <td>0.016288</td>\n", | |
| " <td>65409</td>\n", | |
| " <td>postgres</td>\n", | |
| " <td>index</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50</th>\n", | |
| " <td>11269</td>\n", | |
| " <td>29956</td>\n", | |
| " <td>49099</td>\n", | |
| " <td>0.016571</td>\n", | |
| " <td>65409</td>\n", | |
| " <td>postgres</td>\n", | |
| " <td>index</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>51</th>\n", | |
| " <td>16991</td>\n", | |
| " <td>45051</td>\n", | |
| " <td>73623</td>\n", | |
| " <td>0.055377</td>\n", | |
| " <td>98113</td>\n", | |
| " <td>postgres</td>\n", | |
| " <td>index</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>52</th>\n", | |
| " <td>16991</td>\n", | |
| " <td>45051</td>\n", | |
| " <td>73623</td>\n", | |
| " <td>0.031337</td>\n", | |
| " <td>98113</td>\n", | |
| " <td>postgres</td>\n", | |
| " <td>index</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>53</th>\n", | |
| " <td>16991</td>\n", | |
| " <td>45051</td>\n", | |
| " <td>73623</td>\n", | |
| " <td>0.024112</td>\n", | |
| " <td>98113</td>\n", | |
| " <td>postgres</td>\n", | |
| " <td>index</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>318 rows × 7 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " 24h 7d 30d time users db has_index\n", | |
| "0 16 51 73 0.012844 100 postgres no index\n", | |
| "1 16 51 73 0.005290 100 postgres no index\n", | |
| "2 16 51 73 0.003802 100 postgres no index\n", | |
| "3 22 67 107 0.009735 150 postgres no index\n", | |
| "4 22 67 107 0.005233 150 postgres no index\n", | |
| ".. ... ... ... ... ... ... ...\n", | |
| "49 11269 29956 49099 0.016288 65409 postgres index\n", | |
| "50 11269 29956 49099 0.016571 65409 postgres index\n", | |
| "51 16991 45051 73623 0.055377 98113 postgres index\n", | |
| "52 16991 45051 73623 0.031337 98113 postgres index\n", | |
| "53 16991 45051 73623 0.024112 98113 postgres index\n", | |
| "\n", | |
| "[318 rows x 7 columns]" | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "id": "f4ba80fd-cfb1-4e8b-b405-df564215dd0e", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "\n", | |
| "<div id=\"altair-viz-6dfe741a383044dfa920b8855f929e5a\"></div>\n", | |
| "<script type=\"text/javascript\">\n", | |
| " var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n", | |
| " (function(spec, embedOpt){\n", | |
| " let outputDiv = document.currentScript.previousElementSibling;\n", | |
| " if (outputDiv.id !== \"altair-viz-6dfe741a383044dfa920b8855f929e5a\") {\n", | |
| " outputDiv = document.getElementById(\"altair-viz-6dfe741a383044dfa920b8855f929e5a\");\n", | |
| " }\n", | |
| " const paths = {\n", | |
| " \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n", | |
| " \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n", | |
| " \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n", | |
| " \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n", | |
| " };\n", | |
| "\n", | |
| " function maybeLoadScript(lib, version) {\n", | |
| " var key = `${lib.replace(\"-\", \"\")}_version`;\n", | |
| " return (VEGA_DEBUG[key] == version) ?\n", | |
| " Promise.resolve(paths[lib]) :\n", | |
| " new Promise(function(resolve, reject) {\n", | |
| " var s = document.createElement('script');\n", | |
| " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", | |
| " s.async = true;\n", | |
| " s.onload = () => {\n", | |
| " VEGA_DEBUG[key] = version;\n", | |
| " return resolve(paths[lib]);\n", | |
| " };\n", | |
| " s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n", | |
| " s.src = paths[lib];\n", | |
| " });\n", | |
| " }\n", | |
| "\n", | |
| " function showError(err) {\n", | |
| " outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n", | |
| " throw err;\n", | |
| " }\n", | |
| "\n", | |
| " function displayChart(vegaEmbed) {\n", | |
| " vegaEmbed(outputDiv, spec, embedOpt)\n", | |
| " .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n", | |
| " }\n", | |
| "\n", | |
| " if(typeof define === \"function\" && define.amd) {\n", | |
| " requirejs.config({paths});\n", | |
| " require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n", | |
| " } else {\n", | |
| " maybeLoadScript(\"vega\", \"5\")\n", | |
| " .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n", | |
| " .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n", | |
| " .catch(showError)\n", | |
| " .then(() => displayChart(vegaEmbed));\n", | |
| " }\n", | |
| " })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"hconcat\": [{\"layer\": [{\"mark\": \"point\", \"encoding\": {\"color\": {\"field\": \"has_index\", \"type\": \"nominal\"}, \"x\": {\"field\": \"users\", \"scale\": {\"type\": \"log\"}, \"type\": \"quantitative\"}, \"y\": {\"field\": \"time\", \"scale\": {\"type\": \"log\"}, \"type\": \"quantitative\"}}, \"selection\": {\"selector001\": {\"type\": \"interval\", \"bind\": \"scales\", \"encodings\": [\"x\", \"y\"]}}, \"title\": \"postgres\", \"transform\": [{\"filter\": \"(datum.db === 'postgres')\"}]}, {\"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"has_index\", \"type\": \"nominal\"}, \"x\": {\"field\": \"users\", \"scale\": {\"type\": \"log\"}, \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"mean\", \"field\": \"time\", \"scale\": {\"type\": \"log\"}, \"type\": \"quantitative\"}}, \"title\": \"postgres\", \"transform\": [{\"filter\": \"(datum.db === 'postgres')\"}]}, {\"mark\": 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{\"24h\": 1046, \"7d\": 2645, \"30d\": 4281, \"time\": 0.009989832993596792, \"users\": 5743, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 1046, \"7d\": 2645, \"30d\": 4281, \"time\": 0.004334541969001293, \"users\": 5743, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 1532, \"7d\": 3935, \"30d\": 6400, \"time\": 0.0184612920274958, \"users\": 8614, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 1532, \"7d\": 3935, \"30d\": 6400, \"time\": 0.010720833030063659, \"users\": 8614, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 1532, \"7d\": 3935, \"30d\": 6400, \"time\": 0.006161042023450136, \"users\": 8614, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 2246, \"7d\": 5920, \"30d\": 9632, \"time\": 0.018070375022944063, \"users\": 12921, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 2246, \"7d\": 5920, \"30d\": 9632, \"time\": 0.012882125040050596, \"users\": 12921, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 2246, \"7d\": 5920, \"30d\": 9632, \"time\": 0.004490624996833503, \"users\": 12921, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 3419, \"7d\": 8969, \"30d\": 14526, \"time\": 0.022482374974060804, \"users\": 19381, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 3419, \"7d\": 8969, \"30d\": 14526, \"time\": 0.006214249995537102, \"users\": 19381, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 3419, \"7d\": 8969, \"30d\": 14526, \"time\": 0.006355832971166819, \"users\": 19381, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 5078, \"7d\": 13334, \"30d\": 21777, \"time\": 0.025967374967876822, \"users\": 29071, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 5078, \"7d\": 13334, \"30d\": 21777, \"time\": 0.009381291980389506, \"users\": 29071, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 5078, \"7d\": 13334, \"30d\": 21777, \"time\": 0.007859999954234809, \"users\": 29071, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 7614, \"7d\": 19977, \"30d\": 32746, \"time\": 0.033447083027567714, \"users\": 43606, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 7614, \"7d\": 19977, \"30d\": 32746, \"time\": 0.012083083973266184, \"users\": 43606, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 7614, \"7d\": 19977, \"30d\": 32746, \"time\": 0.011778624961152673, \"users\": 43606, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 11269, \"7d\": 29956, \"30d\": 49099, \"time\": 0.037195916986092925, \"users\": 65409, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 11269, \"7d\": 29956, \"30d\": 49099, \"time\": 0.016287750040646642, \"users\": 65409, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 11269, \"7d\": 29956, \"30d\": 49099, \"time\": 0.016570958017837256, \"users\": 65409, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 16991, \"7d\": 45051, \"30d\": 73623, \"time\": 0.055376833013724536, \"users\": 98113, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 16991, \"7d\": 45051, \"30d\": 73623, \"time\": 0.031336708983872086, \"users\": 98113, \"db\": \"postgres\", \"has_index\": \"index\"}, {\"24h\": 16991, \"7d\": 45051, \"30d\": 73623, \"time\": 0.024112499959301203, \"users\": 98113, \"db\": \"postgres\", \"has_index\": \"index\"}]}}, {\"mode\": \"vega-lite\"});\n", | |
| "</script>" | |
| ], | |
| "text/plain": [ | |
| "alt.HConcatChart(...)" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "charts = alt.hconcat().resolve_scale(y='shared')\n", | |
| "base_chart = alt.Chart(df).encode(\n", | |
| " x=alt.X(\"users\", scale=alt.Scale(type=\"log\")),\n", | |
| " y=alt.Y(\"time\", scale=alt.Scale(type=\"log\")),\n", | |
| " color=\"has_index\",\n", | |
| ")\n", | |
| "for db in df.db.unique():\n", | |
| " chart = base_chart.transform_filter(datum.db == db)\n", | |
| " chart.title = db\n", | |
| " points = chart.mark_point().encode()\n", | |
| " line = chart.mark_line().encode(\n", | |
| " y=alt.Y(\"mean(time)\", scale=alt.Scale(type=\"log\")),\n", | |
| " )\n", | |
| " bar = chart.mark_errorbar(extent=\"ci\")\n", | |
| " charts |= (points + line + bar).interactive()\n", | |
| "charts" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3 (ipykernel)", | |
| "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": "3.10.4" | |
| }, | |
| "widgets": { | |
| "application/vnd.jupyter.widget-state+json": { | |
| "state": {}, | |
| "version_major": 2, | |
| "version_minor": 0 | |
| } | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |
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| """ | |
| Run with: | |
| export PGHOST=127.0.0.1 | |
| export MYSQL_HOST=127.0.0.1 | |
| export PGUSER=user | |
| export PGPASSWORD=pass | |
| for DB in postgres mysql; do | |
| export DB | |
| bash ci/docker-db.sh | |
| bash ci/init-db.sh | |
| done | |
| for branch in main metricssss; do | |
| git switch $branch | |
| for DB in sqlite postgres mysql; do | |
| export DB | |
| if [ $DB != sqlite ]; then | |
| bash ci/docker-db.sh | |
| bash ci/init-db.sh | |
| fi | |
| python profile_index.py -f ${DB}-${branch}.pkl | |
| done | |
| done | |
| """ | |
| import os | |
| import tempfile | |
| import time | |
| from datetime import datetime, timedelta | |
| from functools import partial | |
| from urllib.parse import quote | |
| import click | |
| import numpy as np | |
| import pandas as pd | |
| from jupyterhub import orm | |
| # user distribution: | |
| # 30% chance uniformly in the last 2 days | |
| # 20% chance uniformly in the last 2 weeks | |
| # 50% chance uniformly in the last 2 months | |
| # that should gives return counts: | |
| # 30-day: 75% of users | |
| # 7-day: 46% | |
| # 1-day: 32% | |
| HOURS = 3600 | |
| DAYS = 24 * HOURS | |
| WEEKS = 7 * DAYS | |
| MONTHS = 30 * DAYS | |
| ago_weights = { | |
| 2 * DAYS: 0.3, | |
| 2 * WEEKS: 0.2, | |
| 2 * MONTHS: 0.5, | |
| } | |
| user_counter = 0 | |
| np.random.seed(1) | |
| def populate_db(db, user_count): | |
| global user_counter | |
| have_users = db.query(orm.User).count() | |
| # rely on dict ordering | |
| offset_choices = list(ago_weights.keys()) | |
| weights = list(ago_weights.values()) | |
| new_users = user_count - have_users | |
| offsets = np.random.choice( | |
| offset_choices, size=new_users, p=weights | |
| ) * np.random.random(size=new_users) | |
| now = datetime.utcnow() | |
| for offset in offsets: | |
| user = orm.User(name=f"test-{user_counter}") | |
| user_counter += 1 | |
| seconds = offset | |
| user.last_activity = now - timedelta(seconds=int(offset)) | |
| db.add(user) | |
| db.commit() | |
| def run_query(db): | |
| now = datetime.utcnow() | |
| cutoffs = { | |
| "24h": now - timedelta(hours=24), | |
| "7d": now - timedelta(days=7), | |
| "30d": now - timedelta(days=30), | |
| } | |
| sample = {} | |
| for period, cutoff in cutoffs.items(): | |
| value = db.query(orm.User).filter(orm.User.last_activity >= cutoff).count() | |
| sample[period] = value | |
| return sample | |
| def time_one(f, min_samples=3, max_time=1, max_samples=20): | |
| start = toc = time.perf_counter() | |
| samples = 0 | |
| while samples < min_samples and samples < max_samples and toc - start < max_time: | |
| tic = time.perf_counter() | |
| sample = f() | |
| toc = time.perf_counter() | |
| t = toc - tic | |
| sample["time"] = t | |
| yield sample | |
| samples += 1 | |
| def collect_data(max_n=100_000, max_runtime=60, scale=1.5): | |
| records = [] | |
| start = time.perf_counter() | |
| start_users = total_users = 100 | |
| db_type = os.environ.get("DB", "sqlite") | |
| try: | |
| with tempfile.TemporaryDirectory() as td: | |
| # db and env initialized via jupyterhub ci/docker-db.sh | |
| if db_type == "sqlite": | |
| db_url = f"sqlite:///{td}/test.sqlite" | |
| elif db_type == "postgres": | |
| db_url = f"postgresql://{os.environ['PGUSER']}:{quote(os.environ['PGPASSWORD'])}@{os.environ['PGHOST']}:5432/jupyterhub" | |
| elif db_type == "mysql": | |
| db_url = f"mysql+mysqlconnector://root@{os.environ['MYSQL_HOST']}:3306/jupyterhub" | |
| db = orm.new_session_factory(db_url)() | |
| # delete all users to start | |
| have_users = db.query(orm.User).count() | |
| if have_users: | |
| print(f"Deleting {have_users} users") | |
| sql = orm.User.__table__.delete() | |
| db.execute(sql) | |
| have_users = db.query(orm.User).count() | |
| assert have_users == 0 | |
| f = partial(run_query, db) | |
| while time.perf_counter() - start < max_runtime and total_users < max_n: | |
| populate_db(db, total_users) | |
| time.sleep(1) | |
| for sample in time_one(f): | |
| sample["users"] = total_users | |
| sample["db"] = db_type | |
| print(f"{sample}") | |
| records.append(sample) | |
| total_users = int(scale * total_users) | |
| except KeyboardInterrupt: | |
| return records | |
| return records | |
| @click.command() | |
| @click.option( | |
| "--fname", "-f", default="samples.pkl", help="filename to save dataframe to" | |
| ) | |
| @click.option("--max-n", "-n", default=100_000, help="Max number of users") | |
| @click.option("--max-runtime", "-t", default=60, help="Max time to run") | |
| @click.option( | |
| "--scale", | |
| "-s", | |
| default=1.5, | |
| help="Scale number of users between samples (1.5 = increase by 50% each run)", | |
| ) | |
| def main(fname, max_n, max_runtime, scale): | |
| db = os.environ.get("DB", "sqlite") | |
| print(f"{fname=}, {max_n=}, {max_runtime=}, {scale=}, {db=}") | |
| records = collect_data(max_n=max_n, max_runtime=max_runtime, scale=scale) | |
| df = pd.DataFrame.from_dict(records) | |
| df.to_pickle(fname) | |
| print(f"Wrote {fname}, read with `pandas.from_pickle('{fname}')") | |
| if __name__ == "__main__": | |
| main() |
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