webdav-python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "from pathlib import Path\n",
    "from getpass import getpass\n",
    "from webdav3.client import Client\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class LargeFile():\n",
    "    def __init__(self, file_size, file_fp):\n",
    "        self.file_fp = file_fp\n",
    "        self.file_size = file_size\n",
    "        os.system(f\"fallocate -l {file_size} {str(file_fp)}\")\n",
    "\n",
    "    @property\n",
    "    def data(self):\n",
    "        return self.file_fp\n",
    "\n",
    "class ManyFileDir():\n",
    "    def __init__(self, file_size, n_files, dir_fp):\n",
    "        self.file_size = file_size\n",
    "        self.n_files = n_files\n",
    "        self.dir_fp = Path(dir_fp)\n",
    "        self.dir_fp.mkdir(exist_ok=True, parents=True)\n",
    "        for i_file in range(n_files):\n",
    "            file_fp = Path(dir_fp, f\"{file_size}_{i_file}.bin\")\n",
    "            os.system(f\"fallocate -l {file_size} {str(file_fp)}\")\n",
    "\n",
    "    @property\n",
    "    def data(self):\n",
    "        return self.dir_fp\n",
    "\n",
    "class BenchMarkData():\n",
    "    def __init__(self, local_data_dir):\n",
    "        self.local_data_dir = Path(local_data_dir)\n",
    "        self.local_data_dir.mkdir(exist_ok=True, parents=True)\n",
    "        self.benches = {\n",
    "            \"500M\": LargeFile(\"500M\", Path(local_data_dir, \"500M.bin\")),\n",
    "            \"2G\": LargeFile(\"2K\", Path(local_data_dir, \"2G.bin\")),\n",
    "            \"2000x200K\": ManyFileDir(\"200K\", 20, Path(local_data_dir, \"2000x200K\")),\n",
    "        }\n",
    "        self.tmp_dir = self.local_data_dir / \"temp\"\n",
    "\n",
    "    def run_filecount(self, bencher, file_size=\"200K\"):\n",
    "        # for bench_name, file_obj in self.benches.items():\n",
    "        result_dict = {}\n",
    "        for count in [1, 4, 8, 10, 20, 30]:\n",
    "            file_obj = ManyFileDir(file_size, count, Path(self.local_data_dir, f\"{count}x{file_size}\"))\n",
    "            start = time.perf_counter()\n",
    "            bencher.upload(file_obj)\n",
    "            end = time.perf_counter()\n",
    "            print(count, end-start)\n",
    "            result_dict[count] = end-start\n",
    "        return result_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = Path.home() / Path(\"cache\", \"uu-storage\")\n",
    "bench = BenchMarkData(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WebDavBencher():\n",
    "    def __init__(self, server, username, password, datadir=\"test\"):\n",
    "        self.client = Client({\n",
    "            \"webdav_hostname\": server,\n",
    "            \"webdav_login\": username,  # User name here\n",
    "            \"webdav_password\": passwd,\n",
    "            \"webdav_timeout\": 120,  # Time out longer for bigger files.\n",
    "            \"disable_check\": True\n",
    "        })\n",
    "        self.datadir = datadir\n",
    "        self.client.mkdir(datadir)\n",
    "\n",
    "    def upload(self, file_obj):\n",
    "        remote_dir = f\"{self.datadir}/{file_obj.data.name}\"\n",
    "        self.client.mkdir(remote_dir)\n",
    "        self.client.upload(remote_dir, file_obj.data)\n",
    "\n",
    "    def download(self, file_or_dir_name, tmp_dir):\n",
    "        Path(tmp_dir).mkdir(exist_ok=True, parents=True)\n",
    "        self.client.download(f\"{self.datadir}/{file_or_dir_name}\", tmp_dir)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "passwd = getpass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "bencher = WebDavBencher(\"https://surfdrive.surf.nl/files/remote.php/nonshib-webdav/\",\n",
    "                        \"3154254@soliscom.uu.nl\",\n",
    "                        passwd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2.3512058799969964\n",
      "4 3.4364371140036383\n",
      "8 5.260656395999831\n",
      "10 6.163306111004204\n",
      "20 10.152705783999409\n",
      "30 14.325760086001537\n"
     ]
    }
   ],
   "source": [
    "data = bench.run_filecount(bencher)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data.keys(), data.values())\n",
    "plt.plot(np.arange(30), 2+0.41*np.arange(30))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}