Tutorial_9_Spark_Structured_Streaming.ipynb

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 "metadata": {
  "colab": {
   "name": "Tutorial 9 - Spark Structured Streaming.ipynb",
   "provenance": [],
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  "kernelspec": {
   "name": "python3",
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  },
  "language_info": {
   "name": "python"
  }
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 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Spark Structured Streaming Basics"
   ],
   "metadata": {
    "id": "N_chOJqupwtR",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    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   "metadata": {
    "id": "_WDrWugcGoJs",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
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    "![download 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WIfH49fVDwp8knFsLesQ7dP13jfv1UMam8eYVpGsT9+qBikb1EMMOfq0Mpopxi4NLeF/3Gcbe9UDOC2K4ZGY7FEcT5+6Si/LyluV48pBkVPSvpzxcAo0YF+dIr7rRsV5+FvRnjPqYrL5BnFIO4pxeXeoUP7NGnM0+8qhpFbFIOwo2memAqKof/PHzSd5u9Lisv/043nfacxL0cyQweqQV87wvs2OcXAb1AHKuoPN1Xx5MujioHfJsWwcJYO3e84xfDv9kY7Pz7Ke79R0ubGc/sU9yuHr2NtitvZ5xvPG1Tcf4w0P/+l8dx7FU8USYduuyXFStVHFKtQj1bp3KW4rgXFE3FXHvQeUtzGEsXt4EOKx3RBMeg8lgpeHfT8BYpB95OKAWbzZqPNx+H7j9mK4evjiicJug97jmm08yrFdXa9Yih9NFcorjvPKO5rv6q4/Jufy01Ocf+8UnGev6GRK8bHWsH7bsXPho2K28Lhn4VWsbp3jeL29QejTA8AgBfjqBW8TQS8AIBWeTEBb5diJd8uxXDy8NdbxRB4pGCvabniZ+AHdfSAdb7igevDipVcY3GGfjZwPZKljff/kI79cv7TdWBM4teM8tyRrt55j2IY2ayqGs0vKYa1L0j6+hjeu6PxvO2KQe9IXq0YKHxeRw5GS4oVtyNVTDZ9SXHZXjzCc35bB/4OuksjrzMfUAyNn1Cclw9q5HW3GQS9Qy/uZkcvU+yXsQw3EXT0qrj3Km5zN40yLavjvyHvFMV1f7R12SlW7m7X0QMoo1jtGhQroP9AI1fWnq5YGfmwDgyHcCxOVMD7McV5GFYcsmWkatbZioHWEzr65eZTFStVg+IJkZGGqWn6oGLfNytzj2S64rxvVwyuRlJWDKrrivvwI52wOREB7xRJ/1Nx3ncprhMj7dteoTg/P1IMLI+kXXFfkTemd/gJu8PNVqwaDho54D1LB4YkmDLC85rB+rOK+6AjPfd1itW1D+hAUDySn9OBcWKPNeBtmqkYlj6uA8NhjCRRXMajfe4axeUcFCt8j+ZyxdB7WLE6drSq+umKJ5b6FSudj9aPYwl4T1OsXN+skUNoKVZ+f0fxioRjGQ4EAIBjcdSA93j/aAcAYDwYUDxon654+fPhYZVXDBUOH1rgSO5pTOv1ioHEkRj9bPXtaDY33n+0S3i/qxhKXaoD42mO1TzFUOvHGv2GXmO5wdHB8zkSr7isvOLYhA+N8vzfUgx4/lJx7MiRPKB4efF7FKuwDm9PXTEAHsuNwJqhzeUaOUSUYsXkzRp5nfk7xWEg5iueGPikRr67/Jcb7b1AY79E/UieVAzTR1uXmjfVe5ViyHq4VymGHg+OMq3mDaSOx17FdX+0ISlyxcusezRymNrsn6sVT+qMdAn0LsXxeudobGNxnizNefiwYsA60mXzexWrY2dr5PGES4r9/QHFKt7RfE1xfNb36cjjC1tJyxS36/+p0YczqClW8G9ovO7FVqkfC6u4fi5RHOJmpH3bVsXhYBbo6MHo+xX3/82bduZHeV7TDsUAXjr6/rJD0nWKoedHFfv1aIJilf4GxX3l4ctyluKJAa94wmPjKO1TY1oHD7VzPA6/CmM0mWJIPZbP3fWK69iFOnogbBX3m59THKJmtOEP9igO8bNNcYim4znJo8Z7vlcxrP1rjf759rBi/12qo189AQDACUPACwAoumbgNUUv7iB2o2IYPFtHr2w7kTYpBrQzj+P9dypedv0yxbO6L2Y5HCunWLH0/zRyINKmOFzCbsWKv7EEzf+iGIhcpBd3s6/7FKu5TtPRq/ycYsXggxr9zu5GMUwbVAxvR7NZMYg+dYT3fyk9rxianKJY5Xm4JxSX/+tOUntGc6/i8h+pOtApnoB5QKOvO1XFS6q7FUOx8cLpQDXlaMF5pthPbYpB9dEYjRwaHu5pxZNZs3Tk4WjmSPojxX3hHWOc5rOKIX2n4hUIL/ZmkWNhFNfdLYoh5mgOXidmH+H3PYpVuFMVq2dH2wc0jXay5VzFMc+/qRg6jmafDpyQ+v/bu/MoS8r6jOPf3mZjhmVgGFaHTVkcRBBwGUERFcTEDVE0atyIejQxRxSNcTeJC0aFxEhU4jESc6IGoxGMieKC4IoQGMYAIiA4LA7MDrP0dOePp+p003OXun379nTB93NOn56evrfWt+p2PfWr9z2CB1efHkkqfK8k3VNU1W4Zd5RREryvIO2x3Y3V9VQ7X5VdUlxLbo40uslVxRLSp3V5w2hDm9cPk36GbyrmO9mKaUmSJsWAV5JUd91WJs0h4fAccoG2O80H9plq5fwXFN+3jpt/J+tV9kV7PKlCO4j2jxdPhQFSTbWC1gNkQR5v3ZuEwVUDqVtJNdYBdB4a9ZPtWPZ7vK2Yf6uAoJMg5E6qVzr/nmynfdrMv1uzSVtaQNryLsW/J/ouCeXeSKpmG4XAvTZIbmTszth2b1f9WXX/DJMweA6dV8NPhyptZhupQBykcSDZjetJmzyA7bsBOKr4fjEJRau6qJjm8UzvTYNRqleVlt2ALGrw+4NJBenlpF/fKtod/wOkn9lh0jVNFSPkRt82sn/KbTmL7JsRUmHcyb6ZieaS7mbmkXVaQPtugTr5TNxI9uNmEtR22u3RAAnUF5GwuErgX3ZzuIp81lTp5kiSpCkzE6o2JEnqxm7kwqrKo+uQUGkRuZhfSC6ilxTfDyOhYqcXg53YacL8l4yb/1JSkdbp/EfJY+sHA28ilV+fIlXB5SjvvTJC+0eZIcHPPFKBOcT2A7A1mu56UiV7IO0D3n4SVi0iF9Z7Fu/bnzymvxvZ9q1ublcNbCEVwVVfv548yj6/zfw7NZesZ9lFSdmOlpDBorbSuIpsJRlA6oOkSvp8EujdXXxV2Z+dGiRh7h5kP+1HbkTsR6rdRmkcRo9XdXuPMnYTYbpu1nSiavcna4t/V+lnuhNriq+9immPv+FSdgdxfYfTvJ2080cyPTeXSlXbxDaynv003p5LSLv8Ia27zujEHFIlv55Uk7Y7/0DC4HWkDT+CnCs3k2P90OJ3VQPomWQ+Y+eq+WSwtgPIOeBYsl7dPKUx0TAJWodJO59N++5ixptD/h6YS56GGCbL3aqtjZLz/MZinruRmwqSJE0LA15JUp2Vj2DfRyo9mwW8/aSi5ggyeNfjSfg3jwSIq0ng0ckFYCf6ivkfTqqwnljMfz6p7FxdfA0Xr53MI7U3kL49fwc8i/Q3eRUJ8K4i/bfe0c1KtFAlZNmXBD9LST+q7V4/SkKuXUnA0Sz0HiJBweGkf99jSKg7RB6pXV1831Zhnp3oZB+Nf+1ULMNeJHw4EjiBhFO7kra8lrTlTbQO2r5Igog3AK8DXg98g1QH3kBGt2/3SHIVO5HQ79GkivtwciyUQdZqcgxM9WPkZRcIM/Xx9Cp6texrSei4F9sH4I8mx0qV/l3HGyFV7Y8k5+VOuo2YLq26xVhCzsc3076P16oGGXtq4CRyfmoX8JZPGuxCboaU12rzyLGzhhybddBHbuAcQj5zH09C6wWk/a0l56xu+whuZBvpWmGEVMDPprPz2SA5p84nn9mvof1NydHi9Y8o5r8junqSJD2MGfBKkupsIQl476F5wDuPDK51BvBkcpG3nDxSvJz0l3cruQD8D6Z+kKDZxfxPB55KLmhXkEHJVpAq21vIxeNXSRXuZC92byYjg/878HwSdr6TVBX9F6nS/CntB2Lrhblk/zyWXABXXcerSdjUKHRZCJxG9u1S0p/jtaQ/4BWk64qVJMj6eQfzrGKy0+omtBsgge4LgVNISHIT2d/Xk7Z0a/HaTwB/2GY5LyYDbp0JPIMMMPVCUiH4NdJmVjD5Gx8HAS8gbX9hsWw/JQPA/Yq0+9Wk2vi0Sc6jmensh7puhhmr7p54LbAfCcVWTmK6K0n73pfcaKqLfhICziUh9ZYpnO4csp2XMXYDr4orgJ+NW5ZZZN9spNpgejvaAnJcv5Ss+2pyLvlncuyXT5fMBz5KzmdTeUNjlLTxUfIZ3OmTE2Ufz3PJZ8uiDqbxOzIg2+oO5ylJUlcMeCVJdbYvqby6m8YXU7PJBebbSWjxRRJcrWD7aq6yOmcqg6F+4MXAX5LP3IuK+V/bYP6zpnDePyq+DiSh8lPIBfSzgc+T8O/uKZpXVXeRi+3zyUjjUG19+0g16sTqq11JmP0npKLtXDLg2W0NpvFQGXPgDOADpK1cBHydVGdPbEtz6az7iM+SdvEk0l6eBrwLeA7wV8B/03m3DYcV730a6fP3w2SgqUZ9WT5U9k9d7EaOnxVsf1zdS9pNGch3Ot1yGnUySm68DZOK8wHaD4JXxTbSTcA64K1ke1btvqJcpvLG1jZyrM4plvG+KVi+XtkJeBlwDjl3X0jOVY0GwxukNzdjBkkoO0BuPHTa7cY2EqavAi4AvkJnfx9sIvtPkqRpY8ArSaqzJ5AqwctIVdP4CqB+4GRSwboWeD+p0G2mFyHTU4H3kgu9d5EK3WZVSr3ot/KW4uvLJOB9M/Dn5CL1fUzdo8hVlAPeLCIBUDfVWrPIaPJ/RgZFej+p0G3moRAgLgM+RPbZe8io7s2UQUQn23iY9D/6Q1Jd/krSdcO5JJT9cQfT2gM4GzgV+EfgY7SuOnwo7J862ZPso1sY6+e3dB2puD6MDC5VVR+52bae6b951K1REgLeT6pkO+2vtZktJNR8AgnUuwllN5GA+FhyY/P2rpeuN/rJ0wBnk/PGe8gTFa1e3wtDZDsNku3W6WfdZrKN7yfdZZT9qEuSNGP5B7Ukqa6Wkse/7yYVhhMvnoeAl5NA8Xxah7u98moSGJxPKoB2VH+gG8nj+OeQbgteRPpFnE7/R0KKI0kVWjd2IoPJbQD+mtbh7kPF60hfwx8hgX0Vk62Mu4lUCv896ZP1mXQ28N/hpO1fQSp36/BI+Xhl9eQgUz/A2UywDxnw7la2r+C9nrSbpR1OczEJwm5i+/PcCAnYhpiZg95B2ugDpP/udgM6VrWF3NiaR8573dhM9tcCsowz1a6ka5jFwCdpHe720myynQZJUDvc4fu3AL8l2/0Q0rYlSZrRDHglSXV1MqlmupRUF058pHYXMuDPKtKXYTtVwtfRca9rF54tIKOerwGunKL5d+sasr3mksHmJq5D1XWbjF+RbjROJJVV3dibVAveSNbpoW6IVFRuIBXLU/H4eDubgUtIYPdY0o9xFf2kH+l+ctxV6e95pg2ENky6FJlDuiqoohcV+L2wmAx2NUzj7kz+lzye/ixSKV/V88m2uortw7StZHvOo9r2HGX6t+ft5Pz0RBJSToURUhENqWbv5rz6ABn8cD45h063Uap1UbA76RroTtIVUZXp9sKhZFDVe4qvybiNVLgfSz5zJEma0Qx4JUl19DRSIXgbAezGfgAAD/dJREFU6dtvVYPXDJDPuU0krGpnFgnSWl1wDpNHNcvBc1oZKL4eqDj/IXrfdVIfqXgeJZWJE/8O2EiWdw6dVWxWsZEMsLMXcBbdBTjlftpAtb5h51Dvv3mGyPKvpdqj44O0b8tVrCeh1zyqt4e+ca9dX/E985hZIe8wGShpLhkQsJ39SP/IO2odOpnvMhLGfo3GN77uIN1/LAVeVXGai0ifq9tIX84Tz3dbSRcIO1Gt+vQw4Hl03u9zN35Nwu0lwElUC7f7GKv2bbYPbiSfUU8m/cFP1jBjA2SeSG5eVjEVnyvD5NwzQPsK7PJzbwPVqmb7yTas0oar3tiaT/oPfyR52uHmiu+b6Lek//C9yeCTO09yOpIkTYs6X+xIkh5+BoHnkn5BDyEDTTWq3oUMbLOaVGPNbzPdhaSv3mNpHSpsIf3HzqL9qNprSbcRC2n/mPeuwNvIIFfjq4SrOpxUK7UL4WaR4GaEXPROXNe1xddievNo+gXActJ1xkldTOdusi/2pX1QfCDp1mBPpqfytRfuJ215X9o/Pj6fBOgvJNuoUVs6jWqPyu9PAq9bqD4i/DbGBueqUvV2AjmeZ9K+GSED90H6T21lEfBPpPuMHWGE7KMqVfEHkLZxP+ln+a4Gr9lKBsdbT/rrPrDCdN8IHEe69Fje4PdbGBtg7Pg20zqQnCf2YXoD841kEM67gdeT80UrgyREPIvWN11WA39L9tObqXbDoJkbgM+Rc32V8H0x2Zd70N3xtZUEy3PIfmllNbnhugftP3d3IW3ndPKkS7Pq4BFyXjm4wjT7SZj+XNLdyCVk307GetK10m9In+/H4bWzJGkG80NKkjQT9NG62mc2qdr9LBm06UDgoyQIWNPkPVtJ2LCQXJw1C7SeRKp8nkr76sg1wC/IxeuyFtOEhBPXkKD0VS1eeyzwJfII7zCdhxoLgTcA3yYD2uze5HUDZBueSrpLaNS1wfUkRDiZBBFT3VXDKjK423yyL89qM4+XkH6WF0z4//vJ8h9M66q4M4BvkBBuJlWITsbVZB3eTPMg/xBSQfk6ErQ0+jvv8aRvzO+T9tDMYlKVuYAMvFY14IU8En0n8HSaB3qDZODBL9O4An9HuwP4JvAUckOikceS9jWPDIC3IwyTfXUx8Fqa/21/KAkHTyXnm0tbTPNmci45jOyfk5u8bmcSzp9DzimfJ09MTDRKQrLvkQG4Tm8yvSeTQG0LCUWnu5uGK4EfAEeRpw0OavK6BWSdzyXnr/KpjmauIe3jcSRYf0aL1+4M/DE5diZWEW8g+/lucs5/D83Pn8tIYP0EqlfSN7OebJvdSNcdrWwgYf4epC/eZt1dHAv8G1nX22i9r0dJVfjTSXs8icbrPUSC3U+Sz8XPkPNmM9to3+3ECuDT5G+QT5NzYrObbENk376cqevmQ5Kkynr9KKgkSVVsJY8N700+m0aL7wvJI/0HFd/nAj8iF9Y/IEFfM1uAT5AL3D8tpv05EkTMIeHMH5Fg91skFPso8KgW0xwl/QreCLyCXCB+iVQ3LSMXwj9gbGT6jxT//3oSwnyGhKizyKA7LyEXhN8n1XLvK/6/k2D1PuALxXK/jVyAf4WEcreQC+clxbxeSqqZ3k3jfgk3Fct/MvB3wHnFz7PIRfUvyIBmk60GGwX+hwQ855EQ5zTgO8BPSHCxHwmWTi+W4/JinuNDinWk4vpSEpw8iuyHlSQgeSLwSuAx5KL8AtJuetG38HT5AGmrbyBt+dOkLQ2S9X8RCVSuA84kx9Mr2H6df04G/Xs/CYsuLb5fR6rTF5Jj5iwSuJ1HHjHvxHLgb0gbuhD4FLkBsZEcx6cWyzYfOJu0qxs6nEev3UNuJv0BOS8cQwaMuofcMDij+LqGHN+PYse0r1mkbd8KfIy0j6+T/XwTGSTvJHJe2Jvslw/TutuYERLWPkDayhdJkP090uZ2IZXXzybnt0uBt5NuDpq5g7SFi4CPM9Z/elmZfiZpv5eTc+Eypr8Q5X7gHWT9zyTVn18j56blJOA8kQR488m5ZwD4YJtl3Qz8A1nXc8n5+hLgMtItxHrSfo4hlfdHkhtgV5HPsfGuITcMzwf+gtyw+Wbx2ntJkP9i4BRyzL2XnGe7fSLjSrIPX01uKnyJhLlLyefyJcV6bCRt5zjgLaTNXUiO77kkPH8JaZOXk644nge8huY34crw91vFvy8m3VVcQs4da4v5nUJC4PXAW0mY3soqcsweR9rlZrZ/qmWEBNEbyYCe55HK7e+QLk7uIp+vR5Jj7JnkeLya5jefJUnqqQHyx1T5WOjvyR/2kiT12jnkQnGEXDiO/9pI+sH7MQklTiCVMVUv/PtJ1dR/kgu3TeSiawO5mLuGBE3lCNn/QkKRo9tM9wRycTdaLOO64t/fJt0ljLeUXJBuK+a5llyAbiZhyVkkOIBcGN9ELto7CYv6SBXha4tpbiFhxdpi2R4g6/6v5PHedt5Jwojh4v3ri/d/iAf3PXx6sR4fovM+e5eQwH0d2TYbi+XdWPx8Jwkw9qfx/h4kF9S/ZGw/rCm+byLhyckkVIBUJX6XsW090WuL6ZxdYdkHSPCzmu33dyOHkvD6u6SybbIOAb5Kbog8wIP3781k2cu2/A4SRjaqFhwij+t/gRwLW8g+XlN8L7tZOIv2j0Q3szMJeO4spreuWN5yuT9frM8g2b+jpPKukT4SMq4mx0YVpxTT/MDkFh/IdnoBqTAcJcfUerK9fk9C8sXFa59EzmHNqlNnkRtO60gIVEXZZUuz7bIbOd4vJJX7y4AryHG7qZjXJsaeZjiz4nxLQyT8uqJYjrKd3F/8fC85Rps9NTDRXHK+vYux7Vm2vztISFwen88h27PZttqV3DBbSW58tNNPqjtHyX5rZQGplF/Dg7dlubyXMtYOX0aCwjdVWIbB4n2X8eDtubaYxwj5THoZY8dxs3V5DLmRV05nA2Nt8zayLXch2+kyclOyk0HzGjmxmM7wuPltLqY//nOlr1jPS4rfl59F5efItSQonl0s01vJjcpm+/FUsp/fUqzTK8nNhGHGjsnynHgJ7btVKT2OVOgOk8D2mDavP4xUdt/P2N8na4uft5I2/G7ad2MhSVI39iY3E8v89jqKfuLLi8cBUt30ieLnVeTi7rPTupiSpIejWSQ0bBRojpKL3lFyAbWVyT1qP48ErUeTD8V7SeXP9YyFy5AAYoBcsLWrUt2dVCMdQy5Uryq+VjVYxjmkku5oUqG6mgST15KL05FxrxskF6qTGWBogIQyjyYBwJJiWjcW81pZ/NxOH6maPoZcuK8h1Va/mvD+QbJtN1NtILmJZhXvP4Q8yr+YXDCX+2Yt7Qfq2Y1UYx9FwozfkRsCv+HBFd7zyXZuVvU9RPb/JravmmtkHln/8e2nmb5i/tvI9uumu4hBUi12FOmi4l6yrX5STLvcXmV4Mv7/JppNKvuWkX2wkIQUy0nF3X1013dnP6nOPIa0yT4SRP+wWO7xbWZnst0bPeIP2TeDZP9VOTYGyDavOshiM32kYrc8fueSmzA/IgF6uW37STC4kebbew7ZJ+VNjHbKddhM8+2ygJwXy9/PI8fE8cUy/5pUO95ZvG4y55V5JFg7mpxH15KKztsYC7uq6ifHedmGh8h55QrSJsrlK88trabfaZsYJO39Adof44PF9JeQkPsAsr5XkeNtpPgaIvt1c4VpQtrTHLI9jyDHxs7A7eS8d2OxfFWOu9nkmC2nM0CO3Z+Rz6FyGvPIdt9QYZrt7EHawWOK9biqmN99DV67E2Ofu/uQm1xXkxB7fLXsrGJdmu3HwWJeW4qvcrC3fclNkD2LaV7DWBuqet46ggT/q0mV+so2r59FjslDSLvYi9zsubaYf3mDTJKkXtmb3ORdVvy8vPj3uvIFVvBKkh4O+siF7lQ+St1P9YriXsy/1/PqZP26US7vZOc1ndt2Jpjq9e319puudtRLdWtjvVjWdn2WdqIu27KX+71u5+nJzK/Xx023051sm+7noXFekyTVS9MKXvvglSQ9nJQfhFOpkwrHXsy/1/PqpoKzE90u73Ru25lgqte319tvutpRL9WtjfVim0/l+telTfRyv9ftPD2Z+fX6uOl22pN9f13aryTpYcI7jpIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTBrySJEmSJEmSVFMGvJIkSZIkSZJUUwa8kiRJkiRJklRTg03+fxfgROCnwPzpWxxJkiRJkiRJ0jhbgYOAhY1+2SjgHQWGgOcChzV5jSRJkiRJkiSp90ZJEe7+jX7ZKLztK960ADi2d8slSZIkSZIkSerAKMlvl1Jku2UfvCPAL8e9sG96l0uSJEmSJEmS1EaZ214APDD+PwBmA88DPg5cjl0zSJIkSZIkSdJMMgisAM4H7oLtK3UHgJ2BLQ1+J0mSJEmSJEnasYaBTTt6ISRJkiRJkiRJXfp/XLpGCGI1+iYAAAAASUVORK5CYII=)"
   ],
   "metadata": {
    "id": "INiqRzJwG0nm",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "![download 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)"
   ],
   "metadata": {
    "id": "wlCQAeWGG7zx",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Structured Streaming In Theory"
   ],
   "metadata": {
    "id": "0Tg5BaXCFzSK",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Structured Streaming is designed to make it easy to build end-to-end continuous applications using Apache Spark that combine streaming, batch, and interactive queries. For example, Structured Streaming does not use a separate API from DataFrames: you simply write a normal DataFrame (or SQL) computation and launch it on a stream. Structured Streaming will automatically update the result of this computation in an incremental fashion as data arrives."
   ],
   "metadata": {
    "id": "13O6lEYDF4IO",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Transformations and Actions"
   ],
   "metadata": {
    "id": "QSbPiTcZF8xw",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Structured Streaming maintains the same concept of transformations and actions that we have seen throughout this semester. The transformations available in Structured Streaming are, with a fewrestrictions, the exact same transformations that we saw before. The restrictions usually involve some types of queries that the engine cannot incrementalize."
   ],
   "metadata": {
    "id": "oh3gMIVtGAJm",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Input Sources"
   ],
   "metadata": {
    "id": "zOk9yc_QGCuv",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Structured Streaming supports several input sources for reading in a streaming fashion, for example: Apache Kafka, HDFS."
   ],
   "metadata": {
    "id": "lqTiUVODGGPb",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Sinks"
   ],
   "metadata": {
    "id": "FkPMBlHSGIaQ",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Just as sources allow you to get data into Structured Streaming, sinks specify the destination for the result set of that stream, for example: Apache Kafka, Almost any file format."
   ],
   "metadata": {
    "id": "MIKZimx4GL0t",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Triggers"
   ],
   "metadata": {
    "id": "19o4rlIaGPkl",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Whereas output modes define how data is output, triggers define when data is output. In simple words: when Structured Streaming should check for new input data and update its result. By default,Structured Streaming will look for new input records as soon as it has finished processing the last group of input data, giving the lowest latency possible for new results. However, this behavior can lead to writing many small output files when the sink is a set of files. Thus, Spark also supports triggers based on processing time (only look for new data at a fixed interval).\n",
    "\n",
    "By setting processing time triger, data will be processed at intervals according to the trigger. If a trigger is missed because the previous processing has not yet been completed, Spark will skip this processing point, the next processing will be done at the next point and so on."
   ],
   "metadata": {
    "id": "NyzsQf5SGRox",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Event-Time Processing"
   ],
   "metadata": {
    "id": "ny9A_obiGV0A",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Event-time means time fields that are embedded in your data. This means that rather than processing data according to the time it reaches your system, you process it according to the timethat it was generated, even if records arrive out of order at the streaming application due to slow uploads or network delays. Simple in Structured Streaming as the system views the input data as a table. However, under the hood, Structured Streaming can take some special actions when it knows that one of your columns is an event-time field, including optimizing query execution or determining when it is safe to forget state about a time window."
   ],
   "metadata": {
    "id": "LQQm-WLJGYXF",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Structured Streaming in Action"
   ],
   "metadata": {
    "id": "fAVvQ6i6NDBZ",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "%cd \"C:\\Users\\uriah\\OneDrive\\Desktop\\Distributed Information Management/PROJECT/PROJECT PART 2/Colab Locals\""
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uiv8yG-h7YHh",
    "outputId": "de4abda1-356c-4575-c0c7-9943ca81ea7b",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "C:\\Users\\uriah\\OneDrive\\Desktop\\Distributed Information Management\\PROJECT\\PROJECT PART 2\\Colab Locals\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!python -m venv .venv"
   ],
   "metadata": {
    "id": "F0dBKser9ASt",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "!pip uninstall matplotlib"
   ],
   "metadata": {
    "id": "rgkCqgUrEbQh",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "!.venv\\Scripts\\activate\n",
    "!pip install matplotlib --target .venv\\Lib\\site-packages\n",
    "!.venv\\Scripts\\deactivate"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DnFM3bQc9VYn",
    "outputId": "06b4efce-80ee-4f8b-a6d5-817f9752a485",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 27,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Collecting matplotlib\n",
      "  Using cached matplotlib-3.5.3-cp310-cp310-win_amd64.whl (7.2 MB)\n",
      "Collecting fonttools>=4.22.0\n",
      "  Downloading fonttools-4.36.0-py3-none-any.whl (950 kB)\n",
      "     ------------------------------------ 950.4/950.4 kB 395.9 kB/s eta 0:00:00\n",
      "Collecting cycler>=0.10\n",
      "  Using cached cycler-0.11.0-py3-none-any.whl (6.4 kB)\n",
      "Collecting python-dateutil>=2.7\n",
      "  Using cached python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)\n",
      "Collecting pyparsing>=2.2.1\n",
      "  Using cached pyparsing-3.0.9-py3-none-any.whl (98 kB)\n",
      "Collecting pillow>=6.2.0\n",
      "  Using cached Pillow-9.2.0-cp310-cp310-win_amd64.whl (3.3 MB)\n",
      "Collecting numpy>=1.17\n",
      "  Using cached numpy-1.23.2-cp310-cp310-win_amd64.whl (14.6 MB)\n",
      "Collecting kiwisolver>=1.0.1\n",
      "  Using cached kiwisolver-1.4.4-cp310-cp310-win_amd64.whl (55 kB)\n",
      "Collecting packaging>=20.0\n",
      "  Using cached packaging-21.3-py3-none-any.whl (40 kB)\n",
      "Collecting six>=1.5\n",
      "  Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)\n",
      "Installing collected packages: six, pyparsing, pillow, numpy, kiwisolver, fonttools, cycler, python-dateutil, packaging, matplotlib\n",
      "Successfully installed cycler-0.11.0 fonttools-4.36.0 kiwisolver-1.4.4 matplotlib-3.5.3 numpy-1.23.2 packaging-21.3 pillow-9.2.0 pyparsing-3.0.9 python-dateutil-2.8.2 six-1.16.0\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "mlfromscratch 0.0.4 requires cvxopt, which is not installed.\n",
      "gym 0.25.0 requires cloudpickle>=1.2.0, which is not installed.\n",
      "gym 0.25.0 requires gym_notices>=0.0.4, which is not installed.\n",
      "WARNING: Target directory C:\\Users\\uriah\\OneDrive\\Desktop\\Distributed Information Management\\PROJECT\\PROJECT PART 2\\Colab Locals\\.venv\\Lib\\site-packages\\dateutil already exists. Specify --upgrade to force replacement.\n",
      "WARNING: Target directory C:\\Users\\uriah\\OneDrive\\Desktop\\Distributed Information Management\\PROJECT\\PROJECT PART 2\\Colab Locals\\.venv\\Lib\\site-packages\\numpy already exists. Specify --upgrade to force replacement.\n",
      "WARNING: Target directory C:\\Users\\uriah\\OneDrive\\Desktop\\Distributed Information Management\\PROJECT\\PROJECT PART 2\\Colab Locals\\.venv\\Lib\\site-packages\\numpy-1.23.2.dist-info already exists. Specify --upgrade to force replacement.\n",
      "WARNING: Target directory C:\\Users\\uriah\\OneDrive\\Desktop\\Distributed Information Management\\PROJECT\\PROJECT PART 2\\Colab Locals\\.venv\\Lib\\site-packages\\python_dateutil-2.8.2.dist-info already exists. Specify --upgrade to force replacement.\n",
      "WARNING: Target directory C:\\Users\\uriah\\OneDrive\\Desktop\\Distributed Information Management\\PROJECT\\PROJECT PART 2\\Colab Locals\\.venv\\Lib\\site-packages\\six-1.16.0.dist-info already exists. Specify --upgrade to force replacement.\n",
      "WARNING: Target directory C:\\Users\\uriah\\OneDrive\\Desktop\\Distributed Information Management\\PROJECT\\PROJECT PART 2\\Colab Locals\\.venv\\Lib\\site-packages\\six.py already exists. Specify --upgrade to force replacement.\n",
      "WARNING: Target directory C:\\Users\\uriah\\OneDrive\\Desktop\\Distributed Information Management\\PROJECT\\PROJECT PART 2\\Colab Locals\\.venv\\Lib\\site-packages\\__pycache__ already exists. Specify --upgrade to force replacement.\n",
      "WARNING: Target directory C:\\Users\\uriah\\OneDrive\\Desktop\\Distributed Information Management\\PROJECT\\PROJECT PART 2\\Colab Locals\\.venv\\Lib\\site-packages\\bin already exists. Specify --upgrade to force replacement.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import pandas as pd"
   ],
   "metadata": {
    "id": "GFLk1pasFcfb",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 26,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "F_HoQHVHG9eB",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "d0507926-e34d-4160-fefd-d986dbc76ec5",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: findspark in /usr/local/lib/python3.7/dist-packages (2.0.1)\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: pyspark in /usr/local/lib/python3.7/dist-packages (3.3.0)\n",
      "Requirement already satisfied: py4j==0.10.9.5 in /usr/local/lib/python3.7/dist-packages (from pyspark) (0.10.9.5)\n"
     ]
    }
   ],
   "source": [
    "!pip install findspark\n",
    "!pip install pyspark"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import findspark\n",
    "findspark.init()\n",
    "from pyspark.sql import SparkSession\n",
    "import pyspark.sql.functions as F\n",
    "import time\n",
    "import os\n",
    "\n",
    "def init_spark(app_name: str):\n",
    "    spark = SparkSession.builder.appName(app_name).getOrCreate()\n",
    "    sc = spark.sparkContext\n",
    "    return spark, sc\n",
    "\n",
    "spark, sc = init_spark('demo')\n",
    "print(spark.version)\n",
    "print('CPUs:', os.cpu_count())"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 200
    },
    "id": "BCPQk5voJZGo",
    "outputId": "16b00339-00d0-4452-fd13-4a704295669e",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "error",
     "ename": "ModuleNotFoundError",
     "evalue": "ignored",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
      "Input \u001B[1;32mIn [8]\u001B[0m, in \u001B[0;36m<cell line: 1>\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mfindspark\u001B[39;00m\n\u001B[0;32m      2\u001B[0m findspark\u001B[38;5;241m.\u001B[39minit()\n\u001B[0;32m      3\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mpyspark\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01msql\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m SparkSession\n",
      "\u001B[1;31mModuleNotFoundError\u001B[0m: No module named 'findspark'"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Dataset"
   ],
   "metadata": {
    "id": "1adMgmIXU3R_",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Heterogeneity Human Activity Recognition Dataset - the data consists of smartphone and smartwatch sensor readings from a variety of devices."
   ],
   "metadata": {
    "id": "aXBPBElyU7re",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "!unzip /content/activity-data.zip -d /content/"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9dp1z1vtYttP",
    "outputId": "410864b9-33e2-46de-e5b6-f7bb4620dea2",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Archive:  /content/activity-data.zip\n",
      "   creating: /content/activity-data/\n",
      "  inflating: /content/activity-data/part-00000-tid-730451297822678341-1dda7027-2071-4d73-a0e2-7fb6a91e1d1f-0-c000.json  \n",
      "  inflating: /content/activity-data/part-00001-tid-730451297822678341-1dda7027-2071-4d73-a0e2-7fb6a91e1d1f-0-c000.json  \n",
      "  inflating: /content/activity-data/part-00002-tid-730451297822678341-1dda7027-2071-4d73-a0e2-7fb6a91e1d1f-0-c000.json  \n",
      "  inflating: /content/activity-data/part-00003-tid-730451297822678341-1dda7027-2071-4d73-a0e2-7fb6a91e1d1f-0-c000.json  \n",
      "  inflating: /content/activity-data/part-00004-tid-730451297822678341-1dda7027-2071-4d73-a0e2-7fb6a91e1d1f-0-c000.json  \n",
      "  inflating: /content/activity-data/part-00005-tid-730451297822678341-1dda7027-2071-4d73-a0e2-7fb6a91e1d1f-0-c000.json  \n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "static = spark.read.json(\"activity-data/\")\n",
    "static.printSchema()\n",
    "static.show(3)\n",
    "print(f\"Loaded {static.count()} Rows and {len(static.columns)} Columns.\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qeMhl3PwU_yr",
    "outputId": "3f1a1a3f-dfcb-461e-f7f0-61182ca2fdc2",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "root\n",
      " |-- Arrival_Time: long (nullable = true)\n",
      " |-- Creation_Time: long (nullable = true)\n",
      " |-- Device: string (nullable = true)\n",
      " |-- Index: long (nullable = true)\n",
      " |-- Model: string (nullable = true)\n",
      " |-- User: string (nullable = true)\n",
      " |-- gt: string (nullable = true)\n",
      " |-- x: double (nullable = true)\n",
      " |-- y: double (nullable = true)\n",
      " |-- z: double (nullable = true)\n",
      "\n",
      "+-------------+-------------------+--------+-----+------+----+-----+------------+-----------+-------------+\n",
      "| Arrival_Time|      Creation_Time|  Device|Index| Model|User|   gt|           x|          y|            z|\n",
      "+-------------+-------------------+--------+-----+------+----+-----+------------+-----------+-------------+\n",
      "|1424686734964|1424688581018556031|nexus4_2|    1|nexus4|   g|stand|-0.001449585|0.035491943|  0.027999878|\n",
      "|1424686735180|1424688581235108765|nexus4_2|   44|nexus4|   g|stand|-0.006790161|-0.05102539|  0.020523071|\n",
      "|1424686735384|1424688581436677369|nexus4_2|   84|nexus4|   g|stand| 6.866455E-4| 0.01626587|-0.0029754639|\n",
      "+-------------+-------------------+--------+-----+------+----+-----+------------+-----------+-------------+\n",
      "only showing top 3 rows\n",
      "\n",
      "Loaded 468071 Rows and 10 Columns.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Next, let’s create a streaming version of the same Dataset, which will read each input file in the dataset one by one as if it was a stream."
   ],
   "metadata": {
    "id": "Gs-3WqgQFvX1",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hUP_gnykbT8h",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Building Preprocessing Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import pyspark.sql.functions as f\n",
    "from pyspark.ml.feature import VectorAssembler, VectorSizeHint, OneHotEncoder, StringIndexer, IndexToString, SQLTransformer, Interaction, UnivariateFeatureSelector\n",
    "from pyspark.ml import Pipeline\n",
    "from pyspark.ml.classification import RandomForestClassifier, LogisticRegression\n",
    "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n",
    "from pyspark.ml.tuning import CrossValidator, ParamGridBuilder"
   ],
   "metadata": {
    "id": "oSX5efyGSOLZ",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_m5YJLa4ES3o",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "original_features = ['Arrival_Time', 'Creation_Time', 'Device', 'User', 'x', 'y', 'z', 'gt']\n",
    "features = ['Arrival_Time', 'Creation_Time', 'DeviceIndex', 'userIndex', 'x', 'y', 'z']\n",
    "f_statement = str(original_features).strip(\"[]\").replace(\"'\", \"\")\n",
    "\n",
    "feature_interactions_names = [str(f1+' '+f2)\n",
    "                              for i, f1 in enumerate(features) \n",
    "                              for j, f2 in enumerate(features) \n",
    "                              if i < j]\n",
    "\n",
    "features_to_remove = ['Arrival_Time Creation_Time', 'Creation_Time userIndex', 'Arrival_Time x', \n",
    "                      'userIndex x', 'Creation_Time x', 'x z', 'DeviceIndex x', 'DeviceIndex z']\n",
    "for feature in features_to_remove:\n",
    "    feature_interactions_names.remove(feature)\n",
    "\n",
    "feature_interactions = [Interaction(inputCols=s.split(sep=' '), \n",
    "                                    outputCol=s)\n",
    "                        for s in feature_interactions_names]\n",
    "\n",
    "final_features = [*features, *feature_interactions_names]\n",
    "\n",
    "preprocessing_pipeline = Pipeline(stages=[\n",
    "    SQLTransformer(statement=f\"SELECT {f_statement} FROM __THIS__\"),\n",
    "    StringIndexer(inputCol=\"gt\", outputCol=\"label\", handleInvalid=\"keep\"),\n",
    "    StringIndexer(inputCol=\"Device\", outputCol=\"DeviceIndex\", handleInvalid=\"keep\"),\n",
    "    StringIndexer(inputCol=\"User\", outputCol=\"userIndex\", handleInvalid=\"keep\"),\n",
    "    SQLTransformer(\n",
    "        statement=f\"SELECT {f_statement}, (DeviceIndex + 1) AS DeviceIndex, (userIndex + 1) AS userIndex, (label + 1) AS label FROM __THIS__\"),\n",
    "    *feature_interactions,\n",
    "    VectorAssembler(inputCols=final_features,\n",
    "                    outputCol='features'),\n",
    "    VectorSizeHint(inputCol='features', \n",
    "                    size=len(final_features), \n",
    "                    handleInvalid='optimistic'),\n",
    "    SQLTransformer(statement=f\"SELECT {f_statement}, features, label FROM __THIS__\")])"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "len(final_features)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w7-STZelItXN",
    "outputId": "56d98bcd-0741-482e-a60a-0b75ee5e5cc1",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "metadata": {},
     "execution_count": 31
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "learning_pipeline = RandomForestClassifier(numTrees=15, \n",
    "                                           maxDepth=10)"
   ],
   "metadata": {
    "id": "QnDL1-e1Vdjn",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "train, test = static.randomSplit([0.3, 0.7])\n",
    "pipeline = Pipeline(stages=[preprocessing_pipeline, learning_pipeline])\n",
    "model_pipeline = pipeline.fit(train)"
   ],
   "metadata": {
    "id": "XSq5aUITVB5h",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "model_pipeline.save(\"/content/saved_model\")"
   ],
   "metadata": {
    "id": "dgEco60-DIU7",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "!zip -r /content/saved_model.zip /content/saved_model"
   ],
   "metadata": {
    "id": "pASUBLfKD9Gb",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "evaluator = MulticlassClassificationEvaluator()"
   ],
   "metadata": {
    "id": "LOHn9cghZpLD",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Streaming On Data"
   ],
   "metadata": {
    "id": "mkoOcSj9SCfo",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "streaming = spark \\\n",
    "    .readStream \\\n",
    "    .schema(static.schema) \\\n",
    "    .option(\"maxFilesPerTrigger\", 1) \\\n",
    "    .outputMode(\"append\") \\\n",
    "    .json(\"activity-data/\")\n",
    "\n",
    "print(\"Starting Streaming...\")\n",
    "# create a query that reads all the data and saves it to memory sink\n",
    "streamQuery = streaming.writeStream \\\n",
    "                        .format(\"memory\") \\\n",
    "                        .queryName(\"sink\") \\\n",
    "                        .start()\n",
    "\n",
    "# let the stream run for a while first so that the table gets populated\n",
    "snapshot = spark.sql(\"select * from sink\")\n",
    "\n",
    "while len(snapshot.head(1)) == 0:\n",
    "    time.sleep(5)\n",
    "    snapshot = spark.sql(\"select * from sink\")\n",
    "\n",
    "# after there is data to read, give initial, arbitrary prediction and report accuracy\n",
    "print(\"Initial Evaluation:\")\n",
    "eval = evaluator.evaluate(snapshot.withColumn(\"prediction\", f.lit(1)).withColumn(\"label\", f.col(\"gt\")))\n",
    "print(f\"Accuracy: {eval}\")\n",
    "\n",
    "# now give model initial training on the data we predicted\n",
    "print(\"Initial Training...\")\n",
    "snapshot = snapshot.select(*original_features)\n",
    "model = pipeline.fit(snapshot)\n",
    "\n",
    "# every 5 seconds scan for new arrived data, predict and report accuracy and then fit model on the predicted data.\n",
    "# do this until no new data arrives for a sufficient amount of time, then stop streaming.\n",
    "\n",
    "n_loaded = snapshot.count()  # total number of rows loaded so far\n",
    "epoch_id = 0  # iteration/batch/streaming epoch.\n",
    "cold_streams = 0  # cold stream = stream where no new data arrived\n",
    "termination_buffer = 3  # terminate after \"termination_buffer\" cold streams\n",
    "while cold_streams < termination_buffer:\n",
    "\n",
    "    snapshot = spark.sql(\"select * from sink\")\n",
    "    n_current = snapshot.count()\n",
    "\n",
    "    if n_current == n_loaded:\n",
    "        cold_streams += 1\n",
    "        time.sleep(5)\n",
    "        continue\n",
    "    else:\n",
    "        n_loaded = n_current\n",
    "\n",
    "        print(\"==\" * 20)\n",
    "        print(f\"Batch: {epoch_id}\")\n",
    "        print(\"==\" * 20)\n",
    "\n",
    "        print(\"predicting...\")\n",
    "        snapshot = model.transform(snapshot)\n",
    "\n",
    "        print(\"evaluating...\")\n",
    "        print(f\"Accuracy: {evaluator.evaluate(snapshot)}\")\n",
    "\n",
    "        print(\"training model...\")\n",
    "        snapshot = snapshot.select(*original_features)\n",
    "        model = pipeline.fit(snapshot)\n",
    "\n",
    "        epoch_id += 1\n",
    "        time.sleep(5)\n",
    "\n",
    "print(\"ending stream...\")\n",
    "streamQuery.stop()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 779
    },
    "id": "NK0Lv32OZFRi",
    "outputId": "10d40adb-af3a-4caa-91e4-d0862132720b",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "transformedStream is of type: <class 'pyspark.sql.dataframe.DataFrame'>\n",
      "Starting Streaming...\n",
      "transformedStream is of type: <class 'pyspark.sql.dataframe.DataFrame'>\n",
      "========================================\n",
      "Batch: 1\n",
      "========================================\n",
      "evaluating...\n",
      "Accuracy: 0.618935764162393\n",
      "training model...\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "ERROR:root:Exception while sending command.\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/py4j/clientserver.py\", line 516, in send_command\n",
      "    raise Py4JNetworkError(\"Answer from Java side is empty\")\n",
      "py4j.protocol.Py4JNetworkError: Answer from Java side is empty\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/py4j/java_gateway.py\", line 1038, in send_command\n",
      "    response = connection.send_command(command)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/py4j/clientserver.py\", line 540, in send_command\n",
      "    \"Error while sending or receiving\", e, proto.ERROR_ON_RECEIVE)\n",
      "py4j.protocol.Py4JNetworkError: Error while sending or receiving\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "Py4JError",
     "evalue": "ignored",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mPy4JError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-9-1a79453c2b5d>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m()\u001B[0m\n\u001B[1;32m     19\u001B[0m     \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"training model...\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     20\u001B[0m     \u001B[0msnapshot\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0msnapshot\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mselect\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0moriginal_features\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 21\u001B[0;31m     \u001B[0mmodel_pipeline\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpipeline\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msnapshot\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     22\u001B[0m     \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"restarting streaming...\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     23\u001B[0m     \u001B[0mtransformedStream\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmodel_pipeline\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtransform\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mstreaming\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pyspark/ml/base.py\u001B[0m in \u001B[0;36mfit\u001B[0;34m(self, dataset, params)\u001B[0m\n\u001B[1;32m    203\u001B[0m                 \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcopy\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mparams\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    204\u001B[0m             \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 205\u001B[0;31m                 \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    206\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    207\u001B[0m             raise TypeError(\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pyspark/ml/pipeline.py\u001B[0m in \u001B[0;36m_fit\u001B[0;34m(self, dataset)\u001B[0m\n\u001B[1;32m    132\u001B[0m                     \u001B[0mdataset\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mstage\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtransform\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    133\u001B[0m                 \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m  \u001B[0;31m# must be an Estimator\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 134\u001B[0;31m                     \u001B[0mmodel\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mstage\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    135\u001B[0m                     \u001B[0mtransformers\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    136\u001B[0m                     \u001B[0;32mif\u001B[0m \u001B[0mi\u001B[0m \u001B[0;34m<\u001B[0m \u001B[0mindexOfLastEstimator\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pyspark/ml/base.py\u001B[0m in \u001B[0;36mfit\u001B[0;34m(self, dataset, params)\u001B[0m\n\u001B[1;32m    203\u001B[0m                 \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcopy\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mparams\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    204\u001B[0m             \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 205\u001B[0;31m                 \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    206\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    207\u001B[0m             raise TypeError(\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pyspark/ml/wrapper.py\u001B[0m in \u001B[0;36m_fit\u001B[0;34m(self, dataset)\u001B[0m\n\u001B[1;32m    377\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    378\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdataset\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mDataFrame\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m->\u001B[0m \u001B[0mJM\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 379\u001B[0;31m         \u001B[0mjava_model\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_fit_java\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    380\u001B[0m         \u001B[0mmodel\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_create_model\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mjava_model\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    381\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_copyValues\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pyspark/ml/wrapper.py\u001B[0m in \u001B[0;36m_fit_java\u001B[0;34m(self, dataset)\u001B[0m\n\u001B[1;32m    374\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    375\u001B[0m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_transfer_params_to_java\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 376\u001B[0;31m         \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_java_obj\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_jdf\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    377\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    378\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdataset\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mDataFrame\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m->\u001B[0m \u001B[0mJM\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/py4j/java_gateway.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, *args)\u001B[0m\n\u001B[1;32m   1320\u001B[0m         \u001B[0manswer\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mgateway_client\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msend_command\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mcommand\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1321\u001B[0m         return_value = get_return_value(\n\u001B[0;32m-> 1322\u001B[0;31m             answer, self.gateway_client, self.target_id, self.name)\n\u001B[0m\u001B[1;32m   1323\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1324\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0mtemp_arg\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mtemp_args\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pyspark/sql/utils.py\u001B[0m in \u001B[0;36mdeco\u001B[0;34m(*a, **kw)\u001B[0m\n\u001B[1;32m    188\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mdeco\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mAny\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkw\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mAny\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m->\u001B[0m \u001B[0mAny\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    189\u001B[0m         \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 190\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mf\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkw\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    191\u001B[0m         \u001B[0;32mexcept\u001B[0m \u001B[0mPy4JJavaError\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    192\u001B[0m             \u001B[0mconverted\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mconvert_exception\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0me\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mjava_exception\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/py4j/protocol.py\u001B[0m in \u001B[0;36mget_return_value\u001B[0;34m(answer, gateway_client, target_id, name)\u001B[0m\n\u001B[1;32m    334\u001B[0m             raise Py4JError(\n\u001B[1;32m    335\u001B[0m                 \u001B[0;34m\"An error occurred while calling {0}{1}{2}\"\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 336\u001B[0;31m                 format(target_id, \".\", name))\n\u001B[0m\u001B[1;32m    337\u001B[0m     \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    338\u001B[0m         \u001B[0mtype\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0manswer\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mPy4JError\u001B[0m: An error occurred while calling o52.fit"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "query.stop()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 553
    },
    "id": "q0Oj57zal5vz",
    "outputId": "73a5be20-0c8e-4591-e9ab-e96b439bac94",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "ERROR:root:Exception while sending command.\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/py4j/clientserver.py\", line 516, in send_command\n",
      "    raise Py4JNetworkError(\"Answer from Java side is empty\")\n",
      "py4j.protocol.Py4JNetworkError: Answer from Java side is empty\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/py4j/java_gateway.py\", line 1038, in send_command\n",
      "    response = connection.send_command(command)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/py4j/clientserver.py\", line 540, in send_command\n",
      "    \"Error while sending or receiving\", e, proto.ERROR_ON_RECEIVE)\n",
      "py4j.protocol.Py4JNetworkError: Error while sending or receiving\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "Py4JError",
     "evalue": "ignored",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mPy4JError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-15-4e73e23718a4>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m()\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0mquery\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mstop\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pyspark/sql/streaming.py\u001B[0m in \u001B[0;36mstop\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    160\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mstop\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m->\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    161\u001B[0m         \u001B[0;34m\"\"\"Stop this streaming query.\"\"\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 162\u001B[0;31m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_jsq\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mstop\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    163\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    164\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mexplain\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mextended\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mbool\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;32mFalse\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m->\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/py4j/java_gateway.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, *args)\u001B[0m\n\u001B[1;32m   1320\u001B[0m         \u001B[0manswer\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mgateway_client\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msend_command\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mcommand\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1321\u001B[0m         return_value = get_return_value(\n\u001B[0;32m-> 1322\u001B[0;31m             answer, self.gateway_client, self.target_id, self.name)\n\u001B[0m\u001B[1;32m   1323\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1324\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0mtemp_arg\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mtemp_args\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pyspark/sql/utils.py\u001B[0m in \u001B[0;36mdeco\u001B[0;34m(*a, **kw)\u001B[0m\n\u001B[1;32m    188\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mdeco\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mAny\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkw\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mAny\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m->\u001B[0m \u001B[0mAny\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    189\u001B[0m         \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 190\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mf\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkw\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    191\u001B[0m         \u001B[0;32mexcept\u001B[0m \u001B[0mPy4JJavaError\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    192\u001B[0m             \u001B[0mconverted\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mconvert_exception\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0me\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mjava_exception\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/py4j/protocol.py\u001B[0m in \u001B[0;36mget_return_value\u001B[0;34m(answer, gateway_client, target_id, name)\u001B[0m\n\u001B[1;32m    334\u001B[0m             raise Py4JError(\n\u001B[1;32m    335\u001B[0m                 \u001B[0;34m\"An error occurred while calling {0}{1}{2}\"\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 336\u001B[0;31m                 format(target_id, \".\", name))\n\u001B[0m\u001B[1;32m    337\u001B[0m     \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    338\u001B[0m         \u001B[0mtype\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0manswer\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mPy4JError\u001B[0m: An error occurred while calling o1654.stop"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "streaming = spark \\\n",
    "    .readStream \\\n",
    "    .schema(static.schema) \\\n",
    "    .option(\"maxFilesPerTrigger\", 1) \\\n",
    "    .json(\"activity-data/\")\n",
    "\n",
    "no_process = streaming \\\n",
    "    .writeStream \\\n",
    "    .queryName(\"no_process\") \\\n",
    "    .format(\"memory\") \\\n",
    "    .outputMode(\"append\") \\\n",
    "    .start()\n",
    "\n",
    "# i = 0\n",
    "# while i in range(5):\n",
    "#     batch = spark.sql(\"SELECT * FROM no_process\")\n",
    "#     batch.persist()\n",
    "\n",
    "#     if len(batch.head(1)) > 0:\n",
    "#         print(\"==\" * 20)\n",
    "#         print(f\"Batch: {i}\")\n",
    "#         print(\"==\" * 20)\n",
    "#         predictionsDF = model_pipeline.transform(batch)\n",
    "#         print(f\"Accuracy: {evaluator.evaluate(predictionsDF)}\")\n",
    "#         model_pipeline = pipeline.fit(batch)\n",
    "#         print()\n",
    "#         i += 1\n",
    "#     else:\n",
    "#         time.sleep(5)\n",
    "    \n",
    "#     batch.unpersist()\n",
    "\n",
    "no_process.stop()"
   ],
   "metadata": {
    "id": "LrhDnVKW7kzp",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "\n",
    "def foreach_batch_function(batch, epoch_id, model=model_pipeline):\n",
    "    # Transform and write batchDF\n",
    "    pass\n",
    "\n",
    "process = streaming \\\n",
    "    .writeStream \\\n",
    "    .format(\"memory\") \\\n",
    "    .queryName(\"process\") \\\n",
    "    .start()\n",
    "i=0\n",
    "while i<10:\n",
    "    time.sleep(2)\n",
    "    batch = spark.sql(\"SELECT * FROM process\")\n",
    "    batch.persist()\n",
    "    if len(batch.head(1)) > 0:\n",
    "        print(\"==\" * 20)\n",
    "        print(f\"Batch: {i}\")\n",
    "        print(\"==\" * 20)\n",
    "        predictionsDF = model_pipeline.transform(batch)\n",
    "        print(f\"Accuracy: {evaluator.evaluate(predictionsDF)}\")\n",
    "        model_pipeline = pipeline.fit(batch)\n",
    "        print()\n",
    "    else:\n",
    "        time.sleep(5)\n",
    "    \n",
    "    batch.unpersist()\n",
    "    i+=1\n",
    "process.stop()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "mWxk7QEM8pvi",
    "outputId": "182e6b09-4310-429a-fe03-40cab7060e6b",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "========================================\n",
      "Batch: 1\n",
      "========================================\n",
      "Accuracy: 0.6084553022195918\n",
      "\n",
      "========================================\n",
      "Batch: 2\n",
      "========================================\n",
      "Accuracy: 0.6220452554104653\n",
      "\n",
      "========================================\n",
      "Batch: 3\n",
      "========================================\n",
      "Accuracy: 0.6211795329178408\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "Py4JJavaError",
     "evalue": "ignored",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mPy4JJavaError\u001B[0m                             Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-24-cffc6f5d5593>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m()\u001B[0m\n\u001B[1;32m     16\u001B[0m         \u001B[0mpredictionsDF\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmodel_pipeline\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtransform\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mbatch\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     17\u001B[0m         \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34mf\"Accuracy: {evaluator.evaluate(predictionsDF)}\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 18\u001B[0;31m         \u001B[0mmodel_pipeline\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpipeline\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mbatch\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     19\u001B[0m         \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     20\u001B[0m     \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pyspark/ml/base.py\u001B[0m in \u001B[0;36mfit\u001B[0;34m(self, dataset, params)\u001B[0m\n\u001B[1;32m    203\u001B[0m                 \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcopy\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mparams\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    204\u001B[0m             \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 205\u001B[0;31m                 \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    206\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    207\u001B[0m             raise TypeError(\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pyspark/ml/pipeline.py\u001B[0m in \u001B[0;36m_fit\u001B[0;34m(self, dataset)\u001B[0m\n\u001B[1;32m    132\u001B[0m                     \u001B[0mdataset\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mstage\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtransform\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    133\u001B[0m                 \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m  \u001B[0;31m# must be an Estimator\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 134\u001B[0;31m                     \u001B[0mmodel\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mstage\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    135\u001B[0m                     \u001B[0mtransformers\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    136\u001B[0m                     \u001B[0;32mif\u001B[0m \u001B[0mi\u001B[0m \u001B[0;34m<\u001B[0m \u001B[0mindexOfLastEstimator\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pyspark/ml/base.py\u001B[0m in \u001B[0;36mfit\u001B[0;34m(self, dataset, params)\u001B[0m\n\u001B[1;32m    203\u001B[0m                 \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcopy\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mparams\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    204\u001B[0m             \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 205\u001B[0;31m                 \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    206\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    207\u001B[0m             raise TypeError(\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pyspark/ml/wrapper.py\u001B[0m in \u001B[0;36m_fit\u001B[0;34m(self, dataset)\u001B[0m\n\u001B[1;32m    377\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    378\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdataset\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mDataFrame\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m->\u001B[0m \u001B[0mJM\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 379\u001B[0;31m         \u001B[0mjava_model\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_fit_java\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    380\u001B[0m         \u001B[0mmodel\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_create_model\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mjava_model\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    381\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_copyValues\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pyspark/ml/wrapper.py\u001B[0m in \u001B[0;36m_fit_java\u001B[0;34m(self, dataset)\u001B[0m\n\u001B[1;32m    374\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    375\u001B[0m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_transfer_params_to_java\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 376\u001B[0;31m         \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_java_obj\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_jdf\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    377\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    378\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdataset\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mDataFrame\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m->\u001B[0m \u001B[0mJM\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/py4j/java_gateway.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, *args)\u001B[0m\n\u001B[1;32m   1320\u001B[0m         \u001B[0manswer\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mgateway_client\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msend_command\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mcommand\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1321\u001B[0m         return_value = get_return_value(\n\u001B[0;32m-> 1322\u001B[0;31m             answer, self.gateway_client, self.target_id, self.name)\n\u001B[0m\u001B[1;32m   1323\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1324\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0mtemp_arg\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mtemp_args\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pyspark/sql/utils.py\u001B[0m in \u001B[0;36mdeco\u001B[0;34m(*a, **kw)\u001B[0m\n\u001B[1;32m    188\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mdeco\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mAny\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkw\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mAny\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m->\u001B[0m \u001B[0mAny\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    189\u001B[0m         \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 190\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mf\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkw\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    191\u001B[0m         \u001B[0;32mexcept\u001B[0m \u001B[0mPy4JJavaError\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    192\u001B[0m             \u001B[0mconverted\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mconvert_exception\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0me\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mjava_exception\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/py4j/protocol.py\u001B[0m in \u001B[0;36mget_return_value\u001B[0;34m(answer, gateway_client, target_id, name)\u001B[0m\n\u001B[1;32m    326\u001B[0m                 raise Py4JJavaError(\n\u001B[1;32m    327\u001B[0m                     \u001B[0;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 328\u001B[0;31m                     format(target_id, \".\", name), value)\n\u001B[0m\u001B[1;32m    329\u001B[0m             \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    330\u001B[0m                 raise Py4JError(\n",
      "\u001B[0;31mPy4JJavaError\u001B[0m: An error occurred while calling o51.fit.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 560.0 failed 1 times, most recent failure: Lost task 0.0 in stage 560.0 (TID 983) (f5efbdc432d8 executor driver): java.lang.OutOfMemoryError: Java heap space\n\tat org.apache.spark.sql.catalyst.expressions.UnsafeArrayData.toDoubleArray(UnsafeArrayData.java:413)\n\tat org.apache.spark.ml.linalg.VectorUDT.deserialize(VectorUDT.scala:66)\n\tat org.apache.spark.ml.linalg.VectorUDT.deserialize(VectorUDT.scala:28)\n\tat org.apache.spark.sql.catalyst.CatalystTypeConverters$UDTConverter.toScala(CatalystTypeConverters.scala:149)\n\tat org.apache.spark.sql.catalyst.CatalystTypeConverters$UDTConverter.toScalaImpl(CatalystTypeConverters.scala:153)\n\tat org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toScala(CatalystTypeConverters.scala:114)\n\tat org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toScala(CatalystTypeConverters.scala:277)\n\tat org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toScala(CatalystTypeConverters.scala:241)\n\tat org.apache.spark.sql.catalyst.CatalystTypeConverters$.$anonfun$createToScalaConverter$2(CatalystTypeConverters.scala:490)\n\tat org.apache.spark.sql.catalyst.CatalystTypeConverters$$$Lambda$3856/0x00000008415d8840.apply(Unknown Source)\n\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)\n\tat org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)\n\tat org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:760)\n\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)\n\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)\n\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)\n\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)\n\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)\n\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)\n\tat org.apache.spark.storage.memory.MemoryStore.putIterator(MemoryStore.scala:223)\n\tat org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:302)\n\tat org.apache.spark.storage.BlockManager.$anonfun$doPutIterator$1(BlockManager.scala:1508)\n\tat org.apache.spark.storage.BlockManager$$Lambda$2018/0x0000000840e9f840.apply(Unknown Source)\n\tat org.apache.spark.storage.BlockManager.org$apache$spark$storage$BlockManager$$doPut(BlockManager.scala:1435)\n\tat org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1499)\n\tat org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:1322)\n\tat org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:376)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:327)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:365)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:329)\n\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2672)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2608)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2607)\n\tat scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)\n\tat scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2607)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1182)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1182)\n\tat scala.Option.foreach(Option.scala:407)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1182)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2860)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2802)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2791)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:952)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2228)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2249)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2268)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2293)\n\tat org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1021)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:406)\n\tat org.apache.spark.rdd.RDD.collect(RDD.scala:1020)\n\tat org.apache.spark.rdd.PairRDDFunctions.$anonfun$collectAsMap$1(PairRDDFunctions.scala:738)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:406)\n\tat org.apache.spark.rdd.PairRDDFunctions.collectAsMap(PairRDDFunctions.scala:737)\n\tat org.apache.spark.ml.tree.impl.RandomForest$.findBestSplits(RandomForest.scala:663)\n\tat org.apache.spark.ml.tree.impl.RandomForest$.runBagged(RandomForest.scala:208)\n\tat org.apache.spark.ml.tree.impl.RandomForest$.run(RandomForest.scala:302)\n\tat org.apache.spark.ml.classification.RandomForestClassifier.$anonfun$train$1(RandomForestClassifier.scala:161)\n\tat org.apache.spark.ml.util.Instrumentation$.$anonfun$instrumented$1(Instrumentation.scala:191)\n\tat scala.util.Try$.apply(Try.scala:213)\n\tat org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:191)\n\tat org.apache.spark.ml.classification.RandomForestClassifier.train(RandomForestClassifier.scala:138)\n\tat org.apache.spark.ml.classification.RandomForestClassifier.train(RandomForestClassifier.scala:46)\n\tat org.apache.spark.ml.Predictor.fit(Predictor.scala:151)\n\tat java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.base/java.lang.reflect.Method.invoke(Method.java:566)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:282)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)\n\tat py4j.ClientServerConnection.run(ClientServerConnection.java:106)\n\tat java.base/java.lang.Thread.run(Thread.java:829)\nCaused by: java.lang.OutOfMemoryError: Java heap space\n\tat org.apache.spark.sql.catalyst.expressions.UnsafeArrayData.toDoubleArray(UnsafeArrayData.java:413)\n\tat org.apache.spark.ml.linalg.VectorUDT.deserialize(VectorUDT.scala:66)\n\tat org.apache.spark.ml.linalg.VectorUDT.deserialize(VectorUDT.scala:28)\n\tat org.apache.spark.sql.catalyst.CatalystTypeConverters$UDTConverter.toScala(CatalystTypeConverters.scala:149)\n\tat org.apache.spark.sql.catalyst.CatalystTypeConverters$UDTConverter.toScalaImpl(CatalystTypeConverters.scala:153)\n\tat org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toScala(CatalystTypeConverters.scala:114)\n\tat org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toScala(CatalystTypeConverters.scala:277)\n\tat org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toScala(CatalystTypeConverters.scala:241)\n\tat org.apache.spark.sql.catalyst.CatalystTypeConverters$.$anonfun$createToScalaConverter$2(CatalystTypeConverters.scala:490)\n\tat org.apache.spark.sql.catalyst.CatalystTypeConverters$$$Lambda$3856/0x00000008415d8840.apply(Unknown Source)\n\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)\n\tat org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)\n\tat org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:760)\n\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)\n\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)\n\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)\n\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)\n\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)\n\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)\n\tat org.apache.spark.storage.memory.MemoryStore.putIterator(MemoryStore.scala:223)\n\tat org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:302)\n\tat org.apache.spark.storage.BlockManager.$anonfun$doPutIterator$1(BlockManager.scala:1508)\n\tat org.apache.spark.storage.BlockManager$$Lambda$2018/0x0000000840e9f840.apply(Unknown Source)\n\tat org.apache.spark.storage.BlockManager.org$apache$spark$storage$BlockManager$$doPut(BlockManager.scala:1435)\n\tat org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1499)\n\tat org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:1322)\n\tat org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:376)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:327)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:365)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:329)\n\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "process.stop()"
   ],
   "metadata": {
    "id": "pvYsbxQpFnHw",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Rest Of Tutorial"
   ],
   "metadata": {
    "id": "TERax8g-lgUK",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "streamingQuery = streaming\\\n",
    "                .writeStream\\\n",
    "                .queryName(\"no_process\")\\\n",
    "                .format(\"memory\")\\\n",
    "                .outputMode(\"append\")\\\n",
    "                .start()"
   ],
   "metadata": {
    "id": "_ERTL8f8GezA",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "print(\"Query Last Progress:\")\n",
    "print(type(streamingQuery.lastProgress))\n",
    "for k, v in streamingQuery.lastProgress.items():\n",
    "    print(f\"{k}: {v}\")\n",
    "print(\"--\"*10)\n",
    "\n",
    "print(\"Current Query Status:\")\n",
    "for k, v in streamingQuery.status.items():\n",
    "    print(f\"{k}: {v}\")\n",
    "print(\"--\"*10)\n",
    "\n",
    "streamingQuery.stop()"
   ],
   "metadata": {
    "id": "NHFnaEY-J-L8",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "streamingQuery.stop()"
   ],
   "metadata": {
    "id": "RDXOGrsDIzqb",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# this is a df but doesn't hold anything as it's lazy\n",
    "activityCounts = streaming.groupBy(\"gt\").count()\n",
    "\n",
    "# however, calling an action will result with an error as Queries with streaming sources must be executed with writeStream.start()"
   ],
   "metadata": {
    "id": "N90tsIxyVeQ8",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# avoid creating too many shuffle partitions\n",
    "spark.conf.set(\"spark.sql.shuffle.partitions\", 2)"
   ],
   "metadata": {
    "id": "DCudDlMOVh8y",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# Streaming Query\n",
    "# Here the output is written to a memory sink which keeps an in-memory table of the results.\n",
    "# Here we use the complete output mode - this mode rewrites all of the keys along with their counts after every trigger\n",
    "activityQuery = activityCounts\\\n",
    "    .writeStream\\\n",
    "    .queryName(\"activity_counts\")\\\n",
    "    .format(\"memory\")\\\n",
    "    .outputMode(\"complete\")\\\n",
    "    .start()"
   ],
   "metadata": {
    "id": "i-0xbzCSVlzJ",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# our streaming is active\n",
    "# streaming computation has started in the background\n",
    "spark.streams.active"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eP5uyyBeVoMc",
    "outputId": "e2b71be9-4300-4191-a188-fdf20476cce2",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<pyspark.sql.streaming.StreamingQuery at 0x7f4a2f949750>]"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from time import sleep\n",
    "for x in range(5):\n",
    "    spark.sql(\"SELECT * FROM activity_counts\").show()\n",
    "    sleep(2)\n",
    "\n",
    "activityQuery.stop()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "01baVnk5VrRt",
    "outputId": "c3975695-173d-440a-d149-51f7ccbb4bbb",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "+----------+-----+\n",
      "|        gt|count|\n",
      "+----------+-----+\n",
      "|       sit|24620|\n",
      "|      walk|26512|\n",
      "|  stairsup|20905|\n",
      "|      bike|21594|\n",
      "|     stand|22770|\n",
      "|      null|20894|\n",
      "|stairsdown|18729|\n",
      "+----------+-----+\n",
      "\n",
      "+----------+-----+\n",
      "|        gt|count|\n",
      "+----------+-----+\n",
      "|       sit|36929|\n",
      "|      walk|39768|\n",
      "|  stairsup|31357|\n",
      "|      bike|32390|\n",
      "|     stand|34154|\n",
      "|      null|31343|\n",
      "|stairsdown|28094|\n",
      "+----------+-----+\n",
      "\n",
      "+----------+-----+\n",
      "|        gt|count|\n",
      "+----------+-----+\n",
      "|       sit|61546|\n",
      "|      walk|66280|\n",
      "|  stairsup|52259|\n",
      "|      bike|53984|\n",
      "|     stand|56924|\n",
      "|      null|52240|\n",
      "|stairsdown|46826|\n",
      "+----------+-----+\n",
      "\n",
      "+----------+-----+\n",
      "|        gt|count|\n",
      "+----------+-----+\n",
      "|       sit|73855|\n",
      "|      walk|79536|\n",
      "|  stairsup|62710|\n",
      "|      bike|64781|\n",
      "|     stand|68309|\n",
      "|      null|62688|\n",
      "|stairsdown|56192|\n",
      "+----------+-----+\n",
      "\n",
      "+----------+-----+\n",
      "|        gt|count|\n",
      "+----------+-----+\n",
      "|       sit|73855|\n",
      "|      walk|79536|\n",
      "|  stairsup|62710|\n",
      "|      bike|64781|\n",
      "|     stand|68309|\n",
      "|      null|62688|\n",
      "|stairsdown|56192|\n",
      "+----------+-----+\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Transformations on Streams"
   ],
   "metadata": {
    "id": "NClsqye_V8mb",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Selections and Filtering"
   ],
   "metadata": {
    "id": "O7oYlyeaV95G",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "from pyspark.sql.functions import expr\n",
    "\n",
    "simpleTransform = streaming\\\n",
    "    .withColumn(\"stairs\",expr(\"gt like '%stairs%'\"))\\\n",
    "    .where(\"stairs\")\\\n",
    "    .where(\"gt is not null\")\\\n",
    "    .select(\"gt\",\"model\",\"arrival_time\",\"creation_time\")\\\n",
    "    .writeStream\\\n",
    "    .queryName(\"simple_transform\")\\\n",
    "    .format(\"memory\")\\\n",
    "    .outputMode(\"append\")\\\n",
    "    .start()\n",
    "\n",
    "for x in range(3):\n",
    "    spark.sql(\"SELECT * FROM simple_transform\").show()\n",
    "    sleep(2)\n",
    "\n",
    "simpleTransform.stop()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nE9OLa0RV2Dy",
    "outputId": "da000127-ded1-4043-a946-1ca53cd6cf6e",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "+---+-----+------------+-------------+\n",
      "| gt|model|arrival_time|creation_time|\n",
      "+---+-----+------------+-------------+\n",
      "+---+-----+------------+-------------+\n",
      "\n",
      "+--------+------+-------------+-------------------+\n",
      "|      gt| model| arrival_time|      creation_time|\n",
      "+--------+------+-------------+-------------------+\n",
      "|stairsup|nexus4|1424687983725|1424689829780924966|\n",
      "|stairsup|nexus4|1424687984011|1424687982020381820|\n",
      "|stairsup|nexus4|1424687984413|1424687982421559529|\n",
      "|stairsup|nexus4|1424687984818|1424687982827412800|\n",
      "|stairsup|nexus4|1424687985223|1424687983227571661|\n",
      "|stairsup|nexus4|1424687985623|1424687983630373175|\n",
      "|stairsup|nexus4|1424687986026|1424687984033235724|\n",
      "|stairsup|nexus4|1424687986428|1424687984435884650|\n",
      "|stairsup|nexus4|1424687986833|1424687984840816323|\n",
      "|stairsup|nexus4|1424687987240|1424687985246761147|\n",
      "|stairsup|nexus4|1424687987635|1424687985644600852|\n",
      "|stairsup|nexus4|1424687987994|1424689834040863906|\n",
      "|stairsup|nexus4|1424687988200|1424687986208748802|\n",
      "|stairsup|nexus4|1424687988397|1424689834443695938|\n",
      "|stairsup|nexus4|1424687988598|1424689834645111953|\n",
      "|stairsup|nexus4|1424687988804|1424689834846711074|\n",
      "|stairsup|nexus4|1424687989002|1424687987009377464|\n",
      "|stairsup|nexus4|1424687989201|1424687987211001242|\n",
      "|stairsup|nexus4|1424687989409|1424687987417300070|\n",
      "|stairsup|nexus4|1424687989610|1424689835661012116|\n",
      "+--------+------+-------------+-------------------+\n",
      "only showing top 20 rows\n",
      "\n",
      "+--------+------+-------------+-------------------+\n",
      "|      gt| model| arrival_time|      creation_time|\n",
      "+--------+------+-------------+-------------------+\n",
      "|stairsup|nexus4|1424687983725|1424689829780924966|\n",
      "|stairsup|nexus4|1424687984011|1424687982020381820|\n",
      "|stairsup|nexus4|1424687984413|1424687982421559529|\n",
      "|stairsup|nexus4|1424687984818|1424687982827412800|\n",
      "|stairsup|nexus4|1424687985223|1424687983227571661|\n",
      "|stairsup|nexus4|1424687985623|1424687983630373175|\n",
      "|stairsup|nexus4|1424687986026|1424687984033235724|\n",
      "|stairsup|nexus4|1424687986428|1424687984435884650|\n",
      "|stairsup|nexus4|1424687986833|1424687984840816323|\n",
      "|stairsup|nexus4|1424687987240|1424687985246761147|\n",
      "|stairsup|nexus4|1424687987635|1424687985644600852|\n",
      "|stairsup|nexus4|1424687987994|1424689834040863906|\n",
      "|stairsup|nexus4|1424687988200|1424687986208748802|\n",
      "|stairsup|nexus4|1424687988397|1424689834443695938|\n",
      "|stairsup|nexus4|1424687988598|1424689834645111953|\n",
      "|stairsup|nexus4|1424687988804|1424689834846711074|\n",
      "|stairsup|nexus4|1424687989002|1424687987009377464|\n",
      "|stairsup|nexus4|1424687989201|1424687987211001242|\n",
      "|stairsup|nexus4|1424687989409|1424687987417300070|\n",
      "|stairsup|nexus4|1424687989610|1424689835661012116|\n",
      "+--------+------+-------------+-------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Aggregations"
   ],
   "metadata": {
    "id": "rXc1hEoLWE-T",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "deviceModelStats = streaming\\\n",
    "    .groupBy(\"gt\",\"model\")\\\n",
    "    .avg()\\\n",
    "    .drop(\"avg(Arrival_time)\", \"avg(Creation_Time)\", \"avg(Index)\")\\\n",
    "    .writeStream\\\n",
    "    .queryName(\"aggregated\")\\\n",
    "    .format(\"memory\")\\\n",
    "    .outputMode(\"complete\")\\\n",
    "    .start()\n",
    "\n",
    "for x in range(3):\n",
    "    spark.sql(\"SELECT * FROM aggregated\").show()\n",
    "    sleep(2)\n",
    "\n",
    "deviceModelStats.stop()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rr_0fE0MWGZG",
    "outputId": "35dabf96-b655-4abc-db99-cb8f083bb72a",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "+---+-----+------+------+------+\n",
      "| gt|model|avg(x)|avg(y)|avg(z)|\n",
      "+---+-----+------+------+------+\n",
      "+---+-----+------+------+------+\n",
      "\n",
      "+---+-----+------+------+------+\n",
      "| gt|model|avg(x)|avg(y)|avg(z)|\n",
      "+---+-----+------+------+------+\n",
      "+---+-----+------+------+------+\n",
      "\n",
      "+----------+------+--------------------+--------------------+--------------------+\n",
      "|        gt| model|              avg(x)|              avg(y)|              avg(z)|\n",
      "+----------+------+--------------------+--------------------+--------------------+\n",
      "|stairsdown|nexus4|0.023266314895782098|-0.03660373344454885| 0.12851069306839294|\n",
      "|  stairsup|nexus4|-0.02728966545882...|-0.01046383734045...|-0.09644315578757234|\n",
      "|       sit|nexus4|-5.36877579090171...|4.924361915922029E-4|-6.63216907904138...|\n",
      "|     stand|nexus4|-5.16108951313128...|6.595482594642077E-4|8.732156086956401E-5|\n",
      "|      walk|nexus4|-0.00211111378466...|0.003161541477127347|0.006135831273853396|\n",
      "|      bike|nexus4|0.025608492084347567|-0.00865370622082...|-0.07931255272787818|\n",
      "|      null|nexus4|-0.00451458435114...|-5.02948380941881...|0.009844990415162323|\n",
      "+----------+------+--------------------+--------------------+--------------------+\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Joins"
   ],
   "metadata": {
    "id": "urm_yEL5WK6P",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Structured Streaming supports joining a streaming Dataset/DataFrame with a static Dataset/DataFrame as well as another streaming Dataset/DataFrame. The result of the streaming join is generated incrementally, similar to the results of streaming aggregations in the previous section. In this section we will explore what type of joins (i.e. inner, outer, semi, etc.) are supported in the above cases. Note that in all the supported join types, the result of the join with a streaming Dataset/DataFrame will be the exactly the same as if it was with a static Dataset/DataFrame containing the same data in the stream."
   ],
   "metadata": {
    "id": "ruLkgpg236ye",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# joining streaming DataFrames to staticDataFrames\n",
    "\n",
    "historicalAgg = static\\\n",
    "                .groupBy(\"gt\",\"model\")\\\n",
    "                .avg()\n",
    "\n",
    "deviceModelStats = streaming\\\n",
    "    .drop(\"Arrival_Time\",\"Creation_Time\",\"Index\")\\\n",
    "    .groupBy(\"gt\",\"model\")\\\n",
    "    .avg()\\\n",
    "    .join(historicalAgg, [\"gt\",\"model\"])\\\n",
    "    .writeStream.queryName(\"device_counts\")\\\n",
    "    .format(\"memory\")\\\n",
    "    .outputMode(\"complete\")\\\n",
    "    .start()\n",
    "\n",
    "for x in range(3):\n",
    "    spark.sql(\"SELECT * FROM device_counts\").show()\n",
    "    sleep(2)\n",
    "\n",
    "deviceModelStats.stop()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oVP1RFu5WL_H",
    "outputId": "33c336c8-eda6-470e-c938-bac957f108ce",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "+---+-----+------+------+------+-----------------+------------------+----------+------+------+------+\n",
      "| gt|model|avg(x)|avg(y)|avg(z)|avg(Arrival_Time)|avg(Creation_Time)|avg(Index)|avg(x)|avg(y)|avg(z)|\n",
      "+---+-----+------+------+------+-----------------+------------------+----------+------+------+------+\n",
      "+---+-----+------+------+------+-----------------+------------------+----------+------+------+------+\n",
      "\n",
      "+---+-----+------+------+------+-----------------+------------------+----------+------+------+------+\n",
      "| gt|model|avg(x)|avg(y)|avg(z)|avg(Arrival_Time)|avg(Creation_Time)|avg(Index)|avg(x)|avg(y)|avg(z)|\n",
      "+---+-----+------+------+------+-----------------+------------------+----------+------+------+------+\n",
      "+---+-----+------+------+------+-----------------+------------------+----------+------+------+------+\n",
      "\n",
      "+---+-----+------+------+------+-----------------+------------------+----------+------+------+------+\n",
      "| gt|model|avg(x)|avg(y)|avg(z)|avg(Arrival_Time)|avg(Creation_Time)|avg(Index)|avg(x)|avg(y)|avg(z)|\n",
      "+---+-----+------+------+------+-----------------+------------------+----------+------+------+------+\n",
      "+---+-----+------+------+------+-----------------+------------------+----------+------+------+------+\n",
      "\n"
     ]
    }
   ]
  }
 ]
}