trino_iceberg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from typing import Optional\n",
    "from trino.dbapi import connect\n",
    "\n",
    "def query_from_trino(sql: str) -> Optional[pd.DataFrame]:\n",
    "    with connect(\n",
    "        host=\"127.0.0.1\",\n",
    "        port=8080,\n",
    "        user=\"mark\",\n",
    "        catalog=\"iceberg\",\n",
    "        schema=\"iot_sensor\",\n",
    "    ) as conn:\n",
    "        cur = conn.cursor()\n",
    "        cur.execute(sql)\n",
    "        if cur.description is None:\n",
    "            return\n",
    "        fields = [des.name for des in cur.description]\n",
    "        rows = cur.fetchall()\n",
    "        records = [dict(zip(fields, row)) for row in rows]\n",
    "    return pd.DataFrame(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43800</td>\n",
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      "text/plain": [
       "   total\n",
       "0  43800"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"select count(1) as total from iceberg.iot_sensor.equipment_data\"\n",
    "query_from_trino(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>temperature (°c)</th>\n",
       "      <th>vibration (mm/s)</th>\n",
       "      <th>pressure (pa)</th>\n",
       "      <th>rpm</th>\n",
       "      <th>maintenance required</th>\n",
       "      <th>temp_change</th>\n",
       "      <th>vib_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01 00:00:00</td>\n",
       "      <td>0.548793</td>\n",
       "      <td>0.509840</td>\n",
       "      <td>0.619918</td>\n",
       "      <td>0.741161</td>\n",
       "      <td>1</td>\n",
       "      <td>0.501129</td>\n",
       "      <td>0.499619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 03:00:00</td>\n",
       "      <td>0.544862</td>\n",
       "      <td>0.500041</td>\n",
       "      <td>0.519955</td>\n",
       "      <td>0.931955</td>\n",
       "      <td>0</td>\n",
       "      <td>0.471992</td>\n",
       "      <td>0.602794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-01 04:00:00</td>\n",
       "      <td>0.423622</td>\n",
       "      <td>0.031649</td>\n",
       "      <td>0.323663</td>\n",
       "      <td>0.799867</td>\n",
       "      <td>0</td>\n",
       "      <td>0.440103</td>\n",
       "      <td>0.264552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01 01:00:00</td>\n",
       "      <td>0.715185</td>\n",
       "      <td>0.587573</td>\n",
       "      <td>0.802121</td>\n",
       "      <td>0.292862</td>\n",
       "      <td>1</td>\n",
       "      <td>0.584882</td>\n",
       "      <td>0.538630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-01 02:00:00</td>\n",
       "      <td>0.602748</td>\n",
       "      <td>0.294453</td>\n",
       "      <td>0.965546</td>\n",
       "      <td>0.624416</td>\n",
       "      <td>1</td>\n",
       "      <td>0.444534</td>\n",
       "      <td>0.352514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp  temperature (°c)  vibration (mm/s)  pressure (pa)  \\\n",
       "0 2019-01-01 00:00:00          0.548793          0.509840       0.619918   \n",
       "1 2019-01-01 03:00:00          0.544862          0.500041       0.519955   \n",
       "2 2019-01-01 04:00:00          0.423622          0.031649       0.323663   \n",
       "3 2019-01-01 01:00:00          0.715185          0.587573       0.802121   \n",
       "4 2019-01-01 02:00:00          0.602748          0.294453       0.965546   \n",
       "\n",
       "        rpm  maintenance required  temp_change  vib_change  \n",
       "0  0.741161                     1     0.501129    0.499619  \n",
       "1  0.931955                     0     0.471992    0.602794  \n",
       "2  0.799867                     0     0.440103    0.264552  \n",
       "3  0.292862                     1     0.584882    0.538630  \n",
       "4  0.624416                     1     0.444534    0.352514  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"\"\"\n",
    "    SELECT\n",
    "        timestamp,\n",
    "        \"temperature (°c)\",\n",
    "        \"vibration (mm/s)\",\n",
    "        \"pressure (pa)\",\n",
    "        \"rpm\",\n",
    "        \"maintenance required\",\n",
    "        \"temp_change\",\n",
    "        \"vib_change\"\n",
    "    FROM iceberg.iot_sensor.equipment_data limit 5\n",
    "\"\"\"\n",
    "query_from_trino(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>maintenance_count</th>\n",
       "      <th>total</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>min_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-12-30</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>0.917329</td>\n",
       "      <td>0.023276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-12-29</td>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>0.925015</td>\n",
       "      <td>0.078644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-12-28</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>0.971311</td>\n",
       "      <td>0.011073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>0.987483</td>\n",
       "      <td>0.013627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-12-26</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>0.968619</td>\n",
       "      <td>0.022891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1820</th>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>0.962207</td>\n",
       "      <td>0.004623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>0.976781</td>\n",
       "      <td>0.039119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1822</th>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>0.988395</td>\n",
       "      <td>0.096035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1823</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>0.944686</td>\n",
       "      <td>0.018719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1824</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>0.978638</td>\n",
       "      <td>0.020148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1825 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             dt  maintenance_count  total  max_temp  min_temp\n",
       "0    2023-12-30                 13     24  0.917329  0.023276\n",
       "1    2023-12-29                 11     24  0.925015  0.078644\n",
       "2    2023-12-28                  8     24  0.971311  0.011073\n",
       "3    2023-12-27                 13     24  0.987483  0.013627\n",
       "4    2023-12-26                 14     24  0.968619  0.022891\n",
       "...         ...                ...    ...       ...       ...\n",
       "1820 2019-01-05                  9     24  0.962207  0.004623\n",
       "1821 2019-01-04                 11     24  0.976781  0.039119\n",
       "1822 2019-01-03                  9     24  0.988395  0.096035\n",
       "1823 2019-01-02                 11     24  0.944686  0.018719\n",
       "1824 2019-01-01                 12     24  0.978638  0.020148\n",
       "\n",
       "[1825 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"\"\"\n",
    "    SELECT\n",
    "        DATE_TRUNC('day', timestamp) AS dt, \n",
    "        COUNT_IF(\"maintenance required\" = 1) as maintenance_count,\n",
    "        count(1) as total,\n",
    "        MAX(\"temperature (°c)\") AS max_temp,\n",
    "        MIN(\"temperature (°c)\") AS min_temp\n",
    "    FROM iceberg.iot_sensor.equipment_data\n",
    "    GROUP BY DATE_TRUNC('day', timestamp)\n",
    "    ORDER BY dt DESC\n",
    "\"\"\"\n",
    "query_from_trino(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>made_current_at</th>\n",
       "      <th>snapshot_id</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>is_current_ancestor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-12-06 12:47:33.507000+08:00</td>\n",
       "      <td>2625041281614463484</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-12-06 14:40:15.627000+08:00</td>\n",
       "      <td>5065079296734979223</td>\n",
       "      <td>2.625041e+18</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-12-06 14:49:57.272000+08:00</td>\n",
       "      <td>343143447224900953</td>\n",
       "      <td>2.625041e+18</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-12-06 15:04:51.169000+08:00</td>\n",
       "      <td>828904674460009984</td>\n",
       "      <td>2.625041e+18</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   made_current_at          snapshot_id     parent_id  \\\n",
       "0 2024-12-06 12:47:33.507000+08:00  2625041281614463484           NaN   \n",
       "1 2024-12-06 14:40:15.627000+08:00  5065079296734979223  2.625041e+18   \n",
       "2 2024-12-06 14:49:57.272000+08:00   343143447224900953  2.625041e+18   \n",
       "3 2024-12-06 15:04:51.169000+08:00   828904674460009984  2.625041e+18   \n",
       "\n",
       "   is_current_ancestor  \n",
       "0                 True  \n",
       "1                False  \n",
       "2                False  \n",
       "3                 True  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show iceberg table snapshots\n",
    "\n",
    "sql = \"\"\"\n",
    "    SELECT * FROM iceberg.iot_sensor.\"equipment_data$history\"\n",
    "\"\"\"\n",
    "query_from_trino(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   rows\n",
       "0     4"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert new records\n",
    "sql = \"\"\"\n",
    "INSERT INTO iceberg.iot_sensor.equipment_data VALUES\n",
    "(TIMESTAMP '2024-12-06 00:00:00', 0.148793, 0.509840, 0.619918, 0.741161, 1, 0.501129, 0.499619),\n",
    "(TIMESTAMP '2024-12-06 01:00:00', 0.348793, 0.509840, 0.519918, 0.741161, 1, 0.501129, 0.499619),\n",
    "(TIMESTAMP '2024-12-06 02:00:00', 0.248793, 0.509840, 0.419918, 0.741161, 0, 0.501129, 0.499619),\n",
    "(TIMESTAMP '2024-12-06 03:00:00', 0.448793, 0.509840, 0.319918, 0.741161, 0, 0.501129, 0.499619)\n",
    "\"\"\"\n",
    "query_from_trino(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total\n",
       "0  43804"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"select count(1) as total from iceberg.iot_sensor.equipment_data\"\n",
    "query_from_trino(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>committed_at</th>\n",
       "      <th>snapshot_id</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>operation</th>\n",
       "      <th>manifest_list</th>\n",
       "      <th>summary</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-12-06 12:47:33.507000+08:00</td>\n",
       "      <td>2625041281614463484</td>\n",
       "      <td>NaN</td>\n",
       "      <td>append</td>\n",
       "      <td>s3://datalakehouse/iot_sensor/equipment_data_4...</td>\n",
       "      <td>{'added-files-size': '2514477', 'added-data-fi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-12-06 14:40:15.627000+08:00</td>\n",
       "      <td>5065079296734979223</td>\n",
       "      <td>2.625041e+18</td>\n",
       "      <td>append</td>\n",
       "      <td>s3://datalakehouse/iot_sensor/equipment_data_4...</td>\n",
       "      <td>{'trino_query_id': '20241206_064014_00041_zvxf...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-12-06 14:49:57.272000+08:00</td>\n",
       "      <td>343143447224900953</td>\n",
       "      <td>2.625041e+18</td>\n",
       "      <td>append</td>\n",
       "      <td>s3://datalakehouse/iot_sensor/equipment_data_4...</td>\n",
       "      <td>{'trino_query_id': '20241206_064957_00054_zvxf...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-12-06 15:04:51.169000+08:00</td>\n",
       "      <td>828904674460009984</td>\n",
       "      <td>2.625041e+18</td>\n",
       "      <td>append</td>\n",
       "      <td>s3://datalakehouse/iot_sensor/equipment_data_4...</td>\n",
       "      <td>{'trino_query_id': '20241206_070450_00007_ffu7...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      committed_at          snapshot_id     parent_id  \\\n",
       "0 2024-12-06 12:47:33.507000+08:00  2625041281614463484           NaN   \n",
       "1 2024-12-06 14:40:15.627000+08:00  5065079296734979223  2.625041e+18   \n",
       "2 2024-12-06 14:49:57.272000+08:00   343143447224900953  2.625041e+18   \n",
       "3 2024-12-06 15:04:51.169000+08:00   828904674460009984  2.625041e+18   \n",
       "\n",
       "  operation                                      manifest_list  \\\n",
       "0    append  s3://datalakehouse/iot_sensor/equipment_data_4...   \n",
       "1    append  s3://datalakehouse/iot_sensor/equipment_data_4...   \n",
       "2    append  s3://datalakehouse/iot_sensor/equipment_data_4...   \n",
       "3    append  s3://datalakehouse/iot_sensor/equipment_data_4...   \n",
       "\n",
       "                                             summary  \n",
       "0  {'added-files-size': '2514477', 'added-data-fi...  \n",
       "1  {'trino_query_id': '20241206_064014_00041_zvxf...  \n",
       "2  {'trino_query_id': '20241206_064957_00054_zvxf...  \n",
       "3  {'trino_query_id': '20241206_070450_00007_ffu7...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"\"\"\n",
    "    SELECT * FROM iceberg.iot_sensor.\"equipment_data$snapshots\"\n",
    "\"\"\"\n",
    "query_from_trino(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform Time travel\n",
    "sql = \"\"\"\n",
    "CALL iceberg.system.rollback_to_snapshot('iot_sensor', 'equipment_data', 2625041281614463484)\n",
    "\"\"\"\n",
    "query_from_trino(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total\n",
       "0  43800"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"select count(1) as total from iceberg.iot_sensor.equipment_data\"\n",
    "query_from_trino(sql)"
   ]
  }
 ],
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