tutorials/025 - Redshift - Loading Parquet files with Spectrum.ipynb

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    "[![AWS SDK for pandas](_static/logo.png \"AWS SDK for pandas\")](https://github.com/aws/aws-sdk-pandas)\n",
    "\n",
    "# 25 - Redshift - Loading Parquet files with Spectrum"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enter your bucket name:"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "# Install the optional modules first\n",
    "!pip install 'awswrangler[redshift]'"
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {},
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    {
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     "text": [
      " ···········································\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "\n",
    "bucket = getpass.getpass()\n",
    "PATH = f\"s3://{bucket}/files/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mocking some Parquet Files on S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "   col0 col1\n",
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     "execution_count": 2,
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   "source": [
    "import pandas as pd\n",
    "\n",
    "import awswrangler as wr\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"col0\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
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    "\n",
    "df"
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "wr.s3.to_parquet(df, PATH, max_rows_by_file=2, dataset=True, mode=\"overwrite\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crawling the metadata and adding into Glue Catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'col0': 'bigint', 'col1': 'string'}, None, None)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "wr.s3.store_parquet_metadata(path=PATH, database=\"aws_sdk_pandas\", table=\"test\", dataset=True, mode=\"overwrite\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the CTAS query to load the data into Redshift storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "con = wr.redshift.connect(connection=\"aws-sdk-pandas-redshift\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"CREATE TABLE public.test AS (SELECT * FROM aws_sdk_pandas_external.test)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with con.cursor() as cursor:\n",
    "    cursor.execute(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running an INSERT INTO query to load MORE data into Redshift storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"col0\": [10, 11],\n",
    "        \"col1\": [\"k\", \"l\"],\n",
    "    }\n",
    ")\n",
    "wr.s3.to_parquet(df, PATH, dataset=True, mode=\"overwrite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"INSERT INTO public.test (SELECT * FROM aws_sdk_pandas_external.test)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "with con.cursor() as cursor:\n",
    "    cursor.execute(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"SELECT * FROM public.test\""
   ]
  },
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   "cell_type": "code",
   "execution_count": 13,
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     "execution_count": 13,
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   "source": [
    "wr.redshift.read_sql_table(con=con, schema=\"public\", table=\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
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    "con.close()"
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