tutorials/017 - Partition Projection.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "[![AWS SDK for pandas](_static/logo.png \"AWS SDK for pandas\")](https://github.com/aws/aws-sdk-pandas)\n",
    "\n",
    "# 17 - Partition Projection\n",
    "\n",
    "https://docs.aws.amazon.com/athena/latest/ug/partition-projection.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import getpass\n",
    "from datetime import datetime\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import awswrangler as wr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Enter your bucket name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ···········································\n"
     ]
    }
   ],
   "source": [
    "bucket = getpass.getpass()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Integer projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2021</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value  year  month  day\n",
       "0      1  2019     10   25\n",
       "1      2  2020     11   26\n",
       "2      3  2021     12   27"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"value\": [1, 2, 3], \"year\": [2019, 2020, 2021], \"month\": [10, 11, 12], \"day\": [25, 26, 27]})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "wr.s3.to_parquet(\n",
    "    df=df,\n",
    "    path=f\"s3://{bucket}/table_integer/\",\n",
    "    dataset=True,\n",
    "    partition_cols=[\"year\", \"month\", \"day\"],\n",
    "    database=\"default\",\n",
    "    table=\"table_integer\",\n",
    "    athena_partition_projection_settings={\n",
    "        \"projection_types\": {\"year\": \"integer\", \"month\": \"integer\", \"day\": \"integer\"},\n",
    "        \"projection_ranges\": {\"year\": \"2000,2025\", \"month\": \"1,12\", \"day\": \"1,31\"},\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2021</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value  year  month  day\n",
       "0      3  2021     12   27\n",
       "1      2  2020     11   26\n",
       "2      1  2019     10   25"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wr.athena.read_sql_query(\"SELECT * FROM table_integer\", database=\"default\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Enum projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>São Paulo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Tokio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Seattle</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value       city\n",
       "0      1  São Paulo\n",
       "1      2      Tokio\n",
       "2      3    Seattle"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"value\": [1, 2, 3],\n",
    "        \"city\": [\"São Paulo\", \"Tokio\", \"Seattle\"],\n",
    "    }\n",
    ")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "wr.s3.to_parquet(\n",
    "    df=df,\n",
    "    path=f\"s3://{bucket}/table_enum/\",\n",
    "    dataset=True,\n",
    "    partition_cols=[\"city\"],\n",
    "    database=\"default\",\n",
    "    table=\"table_enum\",\n",
    "    athena_partition_projection_settings={\n",
    "        \"projection_types\": {\n",
    "            \"city\": \"enum\",\n",
    "        },\n",
    "        \"projection_values\": {\"city\": \"São Paulo,Tokio,Seattle\"},\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>São Paulo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Seattle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Tokio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value       city\n",
       "0      1  São Paulo\n",
       "1      3    Seattle\n",
       "2      2      Tokio"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wr.athena.read_sql_query(\"SELECT * FROM table_enum\", database=\"default\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Date projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>dt</th>\n",
       "      <th>ts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>2020-01-01 00:00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>2020-01-01 00:00:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value          dt                  ts\n",
       "0      1  2020-01-01 2020-01-01 00:00:00\n",
       "1      2  2020-01-02 2020-01-01 00:00:01\n",
       "2      3  2020-01-03 2020-01-01 00:00:02"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def ts(x):\n",
    "    return datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\")\n",
    "\n",
    "\n",
    "def dt(x):\n",
    "    return datetime.strptime(x, \"%Y-%m-%d\").date()\n",
    "\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"value\": [1, 2, 3],\n",
    "        \"dt\": [dt(\"2020-01-01\"), dt(\"2020-01-02\"), dt(\"2020-01-03\")],\n",
    "        \"ts\": [ts(\"2020-01-01 00:00:00\"), ts(\"2020-01-01 00:00:01\"), ts(\"2020-01-01 00:00:02\")],\n",
    "    }\n",
    ")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "wr.s3.to_parquet(\n",
    "    df=df,\n",
    "    path=f\"s3://{bucket}/table_date/\",\n",
    "    dataset=True,\n",
    "    partition_cols=[\"dt\", \"ts\"],\n",
    "    database=\"default\",\n",
    "    table=\"table_date\",\n",
    "    athena_partition_projection_settings={\n",
    "        \"projection_types\": {\n",
    "            \"dt\": \"date\",\n",
    "            \"ts\": \"date\",\n",
    "        },\n",
    "        \"projection_ranges\": {\"dt\": \"2020-01-01,2020-01-03\", \"ts\": \"2020-01-01 00:00:00,2020-01-01 00:00:02\"},\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>dt</th>\n",
       "      <th>ts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>2020-01-01 00:00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>2020-01-01 00:00:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value          dt                  ts\n",
       "0      1  2020-01-01 2020-01-01 00:00:00\n",
       "1      2  2020-01-02 2020-01-01 00:00:01\n",
       "2      3  2020-01-03 2020-01-01 00:00:02"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wr.athena.read_sql_query(\"SELECT * FROM table_date\", database=\"default\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Injected projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>uuid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>761e2488-a078-11ea-bb37-0242ac130002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b89ed095-8179-4635-9537-88592c0f6bc3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>87adc586-ce88-4f0a-b1c8-bf8e00d32249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value                                  uuid\n",
       "0      1  761e2488-a078-11ea-bb37-0242ac130002\n",
       "1      2  b89ed095-8179-4635-9537-88592c0f6bc3\n",
       "2      3  87adc586-ce88-4f0a-b1c8-bf8e00d32249"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"value\": [1, 2, 3],\n",
    "        \"uuid\": [\n",
    "            \"761e2488-a078-11ea-bb37-0242ac130002\",\n",
    "            \"b89ed095-8179-4635-9537-88592c0f6bc3\",\n",
    "            \"87adc586-ce88-4f0a-b1c8-bf8e00d32249\",\n",
    "        ],\n",
    "    }\n",
    ")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "wr.s3.to_parquet(\n",
    "    df=df,\n",
    "    path=f\"s3://{bucket}/table_injected/\",\n",
    "    dataset=True,\n",
    "    partition_cols=[\"uuid\"],\n",
    "    database=\"default\",\n",
    "    table=\"table_injected\",\n",
    "    athena_partition_projection_settings={\n",
    "        \"projection_types\": {\n",
    "            \"uuid\": \"injected\",\n",
    "        }\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>value</th>\n",
       "      <th>uuid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>b89ed095-8179-4635-9537-88592c0f6bc3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value                                  uuid\n",
       "0      2  b89ed095-8179-4635-9537-88592c0f6bc3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wr.athena.read_sql_query(\n",
    "    sql=\"SELECT * FROM table_injected WHERE uuid='b89ed095-8179-4635-9537-88592c0f6bc3'\", database=\"default\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Cleaning Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "wr.s3.delete_objects(f\"s3://{bucket}/table_integer/\")\n",
    "wr.s3.delete_objects(f\"s3://{bucket}/table_enum/\")\n",
    "wr.s3.delete_objects(f\"s3://{bucket}/table_date/\")\n",
    "wr.s3.delete_objects(f\"s3://{bucket}/table_injected/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "wr.catalog.delete_table_if_exists(table=\"table_integer\", database=\"default\")\n",
    "wr.catalog.delete_table_if_exists(table=\"table_enum\", database=\"default\")\n",
    "wr.catalog.delete_table_if_exists(table=\"table_date\", database=\"default\")\n",
    "wr.catalog.delete_table_if_exists(table=\"table_injected\", database=\"default\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
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