tutorials/009 - Redshift - Append, Overwrite, Upsert.ipynb (394 lines of code) (raw):
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"[![AWS SDK for pandas](_static/logo.png \"AWS SDK for pandas\")](https://github.com/aws/aws-sdk-pandas)\n",
"\n",
"# 9 - Redshift - Append, Overwrite and Upsert\n",
"\n",
"awswrangler's `copy/to_sql` function has three different `mode` options for Redshift.\n",
"\n",
"1 - `append`\n",
"\n",
"2 - `overwrite`\n",
"\n",
"3 - `upsert`"
]
},
{
"cell_type": "code",
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"source": [
"# Install the optional modules first\n",
"!pip install 'awswrangler[redshift]'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from datetime import date\n",
"\n",
"import pandas as pd\n",
"\n",
"import awswrangler as wr\n",
"\n",
"con = wr.redshift.connect(\"aws-sdk-pandas-redshift\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Enter your bucket name:"
]
},
{
"cell_type": "code",
"execution_count": 3,
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"name": "stdout",
"output_type": "stream",
"text": [
" ···········································\n"
]
}
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"source": [
"import getpass\n",
"\n",
"bucket = getpass.getpass()\n",
"path = f\"s3://{bucket}/stage/\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Enter your IAM ROLE ARN:"
]
},
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"execution_count": 4,
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" ····················································································\n"
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}
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"source": [
"iam_role = getpass.getpass()"
]
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"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating the table (Overwriting if it exists)"
]
},
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"execution_count": 10,
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"text/plain": [
" id value date\n",
"0 2 boo 2020-01-02\n",
"1 1 foo 2020-01-01"
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"source": [
"df = pd.DataFrame({\"id\": [1, 2], \"value\": [\"foo\", \"boo\"], \"date\": [date(2020, 1, 1), date(2020, 1, 2)]})\n",
"\n",
"wr.redshift.copy(\n",
" df=df,\n",
" path=path,\n",
" con=con,\n",
" schema=\"public\",\n",
" table=\"my_table\",\n",
" mode=\"overwrite\",\n",
" iam_role=iam_role,\n",
" primary_keys=[\"id\"],\n",
")\n",
"\n",
"wr.redshift.read_sql_table(table=\"my_table\", schema=\"public\", con=con)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Appending"
]
},
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"cell_type": "code",
"execution_count": 11,
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" <td>2</td>\n",
" <td>boo</td>\n",
" <td>2020-01-02</td>\n",
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" <tr>\n",
" <th>2</th>\n",
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" <td>bar</td>\n",
" <td>2020-01-03</td>\n",
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" id value date\n",
"0 1 foo 2020-01-01\n",
"1 2 boo 2020-01-02\n",
"2 3 bar 2020-01-03"
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"source": [
"df = pd.DataFrame({\"id\": [3], \"value\": [\"bar\"], \"date\": [date(2020, 1, 3)]})\n",
"\n",
"wr.redshift.copy(\n",
" df=df, path=path, con=con, schema=\"public\", table=\"my_table\", mode=\"append\", iam_role=iam_role, primary_keys=[\"id\"]\n",
")\n",
"\n",
"wr.redshift.read_sql_table(table=\"my_table\", schema=\"public\", con=con)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Upserting"
]
},
{
"cell_type": "code",
"execution_count": 12,
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" <tr>\n",
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" id value date\n",
"0 1 foo 2020-01-01\n",
"1 2 xoo 2020-01-02\n",
"2 3 bar 2020-01-03"
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"execution_count": 12,
"metadata": {},
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"source": [
"df = pd.DataFrame({\"id\": [2, 3], \"value\": [\"xoo\", \"bar\"], \"date\": [date(2020, 1, 2), date(2020, 1, 3)]})\n",
"\n",
"wr.redshift.copy(\n",
" df=df, path=path, con=con, schema=\"public\", table=\"my_table\", mode=\"upsert\", iam_role=iam_role, primary_keys=[\"id\"]\n",
")\n",
"\n",
"wr.redshift.read_sql_table(table=\"my_table\", schema=\"public\", con=con)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleaning Up"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"with con.cursor() as cursor:\n",
" cursor.execute(\"DROP TABLE public.my_table\")\n",
"con.close()"
]
}
],
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