tutorials/014 - Schema Evolution.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",
    "# 14 - Schema Evolution\n",
    "\n",
    "awswrangler supports new **columns** on Parquet and CSV datasets through:\n",
    "\n",
    "- [wr.s3.to_parquet()](https://aws-sdk-pandas.readthedocs.io/en/3.9.2b1/stubs/awswrangler.s3.to_parquet.html#awswrangler.s3.to_parquet)\n",
    "- [wr.s3.store_parquet_metadata()](https://aws-sdk-pandas.readthedocs.io/en/3.9.2b1/stubs/awswrangler.s3.store_parquet_metadata.html#awswrangler.s3.store_parquet_metadata) i.e. \"Crawler\"\n",
    "- [wr.s3.to_csv()](https://aws-sdk-pandas.readthedocs.io/en/3.9.2b1/stubs/awswrangler.s3.to_csv.html#awswrangler.s3.to_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import awswrangler as wr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enter your bucket name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ···········································\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "\n",
    "bucket = getpass.getpass()\n",
    "path = f\"s3://{bucket}/dataset/\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the Dataset\n",
    "### Parquet Create"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "\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>id</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>boo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id value\n",
       "0   1   foo\n",
       "1   2   boo"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"id\": [1, 2],\n",
    "        \"value\": [\"foo\", \"boo\"],\n",
    "    }\n",
    ")\n",
    "\n",
    "wr.s3.to_parquet(df=df, path=path, dataset=True, mode=\"overwrite\", database=\"aws_sdk_pandas\", table=\"my_table\")\n",
    "\n",
    "wr.s3.read_parquet(path, dataset=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### CSV Create"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"id\": [1, 2],\n",
    "        \"value\": [\"foo\", \"boo\"],\n",
    "    }\n",
    ")\n",
    "\n",
    "wr.s3.to_csv(df=df, path=path, dataset=True, mode=\"overwrite\", database=\"aws_sdk_pandas\", table=\"my_table\")\n",
    "\n",
    "wr.s3.read_csv(path, dataset=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Schema Version 0 on Glue Catalog (AWS Console)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Glue Console](_static/glue_catalog_version_0.png \"Glue Console\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appending with NEW COLUMNS\n",
    "### Parquet Append"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>value</th>\n",
       "      <th>date</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>bar</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>boo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id value        date   flag\n",
       "0   3   bar  2020-01-03   True\n",
       "1   4  None  2020-01-04  False\n",
       "2   1   foo         NaN    NaN\n",
       "3   2   boo         NaN    NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\"id\": [3, 4], \"value\": [\"bar\", None], \"date\": [date(2020, 1, 3), date(2020, 1, 4)], \"flag\": [True, False]}\n",
    ")\n",
    "\n",
    "wr.s3.to_parquet(\n",
    "    df=df,\n",
    "    path=path,\n",
    "    dataset=True,\n",
    "    mode=\"append\",\n",
    "    database=\"aws_sdk_pandas\",\n",
    "    table=\"my_table\",\n",
    "    catalog_versioning=True,  # Optional\n",
    ")\n",
    "\n",
    "wr.s3.read_parquet(path, dataset=True, validate_schema=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### CSV Append\n",
    "\n",
    "Note: for CSV datasets due to [column ordering](https://docs.aws.amazon.com/athena/latest/ug/types-of-updates.html#updates-add-columns-beginning-middle-of-table), by default, schema evolution is disabled. Enable it by passing `schema_evolution=True` flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\"id\": [3, 4], \"value\": [\"bar\", None], \"date\": [date(2020, 1, 3), date(2020, 1, 4)], \"flag\": [True, False]}\n",
    ")\n",
    "\n",
    "wr.s3.to_csv(\n",
    "    df=df,\n",
    "    path=path,\n",
    "    dataset=True,\n",
    "    mode=\"append\",\n",
    "    database=\"aws_sdk_pandas\",\n",
    "    table=\"my_table\",\n",
    "    schema_evolution=True,\n",
    "    catalog_versioning=True,  # Optional\n",
    ")\n",
    "\n",
    "wr.s3.read_csv(path, dataset=True, validate_schema=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Schema Version 1 on Glue Catalog (AWS Console)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Glue Console](_static/glue_catalog_version_1.png \"Glue Console\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading from Athena"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>id</th>\n",
       "      <th>value</th>\n",
       "      <th>date</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>bar</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>boo</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id value        date   flag\n",
       "0   3   bar  2020-01-03   True\n",
       "1   4  None  2020-01-04  False\n",
       "2   1   foo        None   <NA>\n",
       "3   2   boo        None   <NA>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wr.athena.read_sql_table(table=\"my_table\", database=\"aws_sdk_pandas\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wr.s3.delete_objects(path)\n",
    "wr.catalog.delete_table_if_exists(table=\"my_table\", database=\"aws_sdk_pandas\")"
   ]
  }
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