tutorials/026 - Amazon Timestream.ipynb

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   "source": [
    "[![AWS SDK for pandas](_static/logo.png \"AWS SDK for pandas\")](https://github.com/aws/aws-sdk-pandas)\n",
    "\n",
    "# 26 - Amazon Timestream"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating resources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import awswrangler as wr\n",
    "\n",
    "database = \"sampleDB\"\n",
    "table_1 = \"sampleTable1\"\n",
    "table_2 = \"sampleTable2\"\n",
    "wr.timestream.create_database(database)\n",
    "wr.timestream.create_table(database, table_1, memory_retention_hours=1, magnetic_retention_days=1)\n",
    "wr.timestream.create_table(database, table_2, memory_retention_hours=1, magnetic_retention_days=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write\n",
    "\n",
    "### Single measure WriteRecord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rejected records: 0\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"time\": [datetime.now()] * 3,\n",
    "        \"dim0\": [\"foo\", \"boo\", \"bar\"],\n",
    "        \"dim1\": [1, 2, 3],\n",
    "        \"measure\": [1.0, 1.1, 1.2],\n",
    "    }\n",
    ")\n",
    "\n",
    "rejected_records = wr.timestream.write(\n",
    "    df=df,\n",
    "    database=database,\n",
    "    table=table_1,\n",
    "    time_col=\"time\",\n",
    "    measure_col=\"measure\",\n",
    "    dimensions_cols=[\"dim0\", \"dim1\"],\n",
    ")\n",
    "\n",
    "print(f\"Number of rejected records: {len(rejected_records)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Multi measure WriteRecord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"time\": [datetime.now()] * 3,\n",
    "        \"measure_1\": [\"10\", \"20\", \"30\"],\n",
    "        \"measure_2\": [\"100\", \"200\", \"300\"],\n",
    "        \"measure_3\": [\"1000\", \"2000\", \"3000\"],\n",
    "        \"tag\": [\"tag123\", \"tag456\", \"tag789\"],\n",
    "    }\n",
    ")\n",
    "rejected_records = wr.timestream.write(\n",
    "    df=df,\n",
    "    database=database,\n",
    "    table=table_2,\n",
    "    time_col=\"time\",\n",
    "    measure_col=[\"measure_1\", \"measure_2\", \"measure_3\"],\n",
    "    dimensions_cols=[\"tag\"],\n",
    ")\n",
    "\n",
    "print(f\"Number of rejected records: {len(rejected_records)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>measure_value::double</th>\n",
       "      <th>dim0</th>\n",
       "      <th>dim1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-08 19:15:32.468</td>\n",
       "      <td>1.0</td>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-08 19:15:32.468</td>\n",
       "      <td>1.2</td>\n",
       "      <td>bar</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-08 19:15:32.468</td>\n",
       "      <td>1.1</td>\n",
       "      <td>boo</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "                     time  measure_value::double dim0 dim1\n",
       "0 2020-12-08 19:15:32.468                    1.0  foo    1\n",
       "1 2020-12-08 19:15:32.468                    1.2  bar    3\n",
       "2 2020-12-08 19:15:32.468                    1.1  boo    2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wr.timestream.query(\n",
    "    f'SELECT time, measure_value::double, dim0, dim1 FROM \"{database}\".\"{table_1}\" ORDER BY time DESC LIMIT 3'\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Unload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = wr.timestream.unload(\n",
    "    sql=f'SELECT time, measure_value, dim0, dim1 FROM \"{database}\".\"{table_1}\"',\n",
    "    path=\"s3://bucket/extracted_parquet_files/\",\n",
    "    partition_cols=[\"dim1\"],\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deleting resources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "wr.timestream.delete_table(database, table_1)\n",
    "wr.timestream.delete_table(database, table_2)\n",
    "wr.timestream.delete_database(database)"
   ]
  }
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