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Snowflake resources programmatically with the Python API for infrastructure automation, DDL, and data operations.","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThe Snowflake Python API allows you to manage Snowflake using Python. Using the API, you're able to create, delete, and modify tables, schemas, warehouses, tasks, and much more, in many cases without needing to write SQL or use the Snowflake Connector for Python. In this Quickstart, you'll learn how to get started with the Snowflake Python API for object and task management with Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with Python\u003C/li\u003E\u003Cli\u003EFamiliarity with Jupyter Notebooks\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to install the Snowflake Python API library\u003C/li\u003E\u003Cli\u003EHow to create a Root object to use the API\u003C/li\u003E\u003Cli\u003EHow to create tables, schemas, and warehouses using the API\u003C/li\u003E\u003Cli\u003EHow to create and manage tasks using the API\u003C/li\u003E\u003Cli\u003EHow to use Snowpark Container Services with the Snowflake Python API\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account (\u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003Etrial\u003C/a\u003E, or otherwise)\u003C/li\u003E\u003Cli\u003EA code editor that supports Jupyter notebooks, or ability to run notebooks in your browser using \u003Ccode\u003Ejupyter notebook\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EMultiple objects within Snowflake\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EInstall the Snowflake Python API\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant\u003C/strong\u003E\nThe Snowflake Python API is currently supported in Python versions 3.8, 3.9., and 3.10.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EBefore installing the Snowflake Python API, we'll start by activating a Python environment.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConda\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EIf you're using conda, you can create and activate a conda environment using the following commands:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Econda create -n <env_name> python==3.10\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Econda activate <env_name>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003Evenv\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EIf you're using venv, you can create and activate a virtual environment using the following commands:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython3 -m venv '.venv'\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esource '.venv/bin/activate'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe Snowflake Python API is available via PyPi. Install it by running the following command:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install snowflake -U\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EConfigure Snowflake\u003C/h2\u003E\n","\u003Cp\u003ELet us create \u003Ccode\u003E$HOME/.snowflake/config.toml\u003C/code\u003E with the following content and update it with your actual credentials. The connection details \u003Ccode\u003Edefault\u003C/code\u003E under table \u003Ccode\u003Econnections\u003C/code\u003E i.e.\u003Ccode\u003Econnections.default\u003C/code\u003E will be used to establish a connection with Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-toml\"\u003E[connections]\n[connections.default]\naccount = "YOUR ACCOUNT NAME"\nuser = "YOUR ACCOUNT USER"\npassword = "YOUR ACCOUNT USER PASSWORD"\n# optional\n# warehouse = "COMPUTE_WH"\n# optional\n# database = "COMPUTE_WH"\n# optional\n# schema = "PUBLIC"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext create a \u003Ccode\u003Econnections_params\u003C/code\u003E dictionary as show below and set the connection to use for the session,\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003E  connection_params = {\n   "connection_name": "default",\n  }\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOverview of the Snowflake Python API\u003C/h2\u003E\n","\u003Cp\u003ELet's quickly take a look at how the Snowflake Python API is organized:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EModule\u003C/strong\u003E\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EDescription\u003C/strong\u003E\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake.core\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDefines an Iterator to represent a certain resource instances fetched from the Snowflake database\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake.core.paging\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E \u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake.core.exceptions\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E \u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake.core.database\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EManages Snowflake databases\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake.core.schema\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EManages Snowflake schemas\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake.core.task\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EManages Snowflake Tasks\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake.core.task.context\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EManage the context in a Snowflake Task\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake.core.task.dagv1\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA set of higher-level APIs than the lower-level Task APIs in snowflake.core.task to more conveniently manage DAGs\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake.core.compute_pool\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EManages Snowpark Container Compute Pools\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake.core.image_repository\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EManages Snowpark Container Image Repositories\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake.core.service\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EManages Snowpark Container Services\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Ccode\u003Esnowflake.core\u003C/code\u003E represents the entry point to the core Snowflake Python APIs that manage Snowflake objects. To use the Snowflake Python API, you'll follow a common pattern:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EEstablish a session using Snowpark or a Python connector connection, representing your connection to Snowflake.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EImport and instantiate the \u003Ccode\u003ERoot\u003C/code\u003E class from \u003Ccode\u003Esnowflake.core\u003C/code\u003E, and pass in the Snowpark session object as an argument. You'll use the resulting \u003Ccode\u003ERoot\u003C/code\u003E object to use the rest of the methods and types in the Snowflake Python API.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EHere's an example of what this pattern typically looks like:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Efrom snowflake.snowpark import Session\nfrom snowflake.core import Root\n\nsession = Session.builder.configs(connection_params).create()\nroot = Root(session)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor more information on various connection options/attributes, see \u003Ca href=\"https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-python-api/snowflake-python-connecting-snowflake\"\u003EConnecting to Snowflake with the Snowflake Python API\u003C/a\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE\u003C/strong\u003E\nThe Snowflake Python API can establish a connection to Snowflake via a Snowpark session or a Python connector connection. In the example above, we opt for a Snowpark session.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ELet's get started!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet up your development environment\u003C/h2\u003E\n","\u003Cp\u003EIn this Quickstart, we'll walk through a Jupyter notebook to incrementally showcase the capabilities of the Snowflake Python API. Let's start by setting up your development environment so that you can run the notebook.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EFirst, download the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-snowflake-python-api/blob/main/getting_started_snowflake_python_api.ipynb\"\u003E\nQuickstart: Getting Started with the Snowflake Python API notebook\u003C/a\u003E from the accompanying repo for this Quickstart.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENext, open the notebook in a code editor that supports Jupyter notebooks (i.e., Visual Studio Code).\nAlternatively, open the notebook in your browser by starting a notebook server with \u003Ccode\u003Ejupyter notebook\u003C/code\u003E and navigating to the notebook in your browser. To do this, you'll need to ensure your environment can run a notebook (be sure to run \u003Ccode\u003Econda install notebook\u003C/code\u003E in your terminal, then start the notebook server).\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ENow run the first cell within the notebook, the one containing the import statements.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Efrom datetime import timedelta\nfrom typing import List\n\nfrom snowflake.snowpark import Session\nfrom snowflake.snowpark.functions import col\nfrom snowflake.core import Root,CreateMode\nfrom snowflake.core.database import Database\nfrom snowflake.core.schema import Schema\nfrom snowflake.core.table import Table, TableColumn, PrimaryKey\nfrom snowflake.core.warehouse import Warehouse\nfrom snowflake.core._common import CreateMode\nfrom snowflake.core.task import StoredProcedureCall, Task\nfrom snowflake.core.task.dagv1 import DAGOperation, DAG, DAGTask\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E\nUpon running this cell, you may be prompted to set your Python kernel. We activated a conda environment earlier, so we'll select conda as our Python kernel (i.e., something like \u003Ccode\u003E~/miniconda3/envs/<your conda env>/bin/python\u003C/code\u003E).\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIn this cell, we import Snowpark and the core Snowflake Python APIs that manage Snowflake objects.\u003C/p\u003E\n","\u003Cp\u003ENext, configure the \u003Ccode\u003Econnection_params\u003C/code\u003E dictionary with your account credentials and run the cell.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Econnection_params = {\n    "connection_name": "default"\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate a Snowpark session and pass in your connection parameters to establish a connection to Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Esession = Session.builder.configs(connection_params).create()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFinally, create a \u003Ccode\u003ERoot\u003C/code\u003E object by passing in your \u003Ccode\u003Esession\u003C/code\u003E object to the \u003Ccode\u003ERoot\u003C/code\u003E constructor. Run the cell.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Eroot = Root(session)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAnd that's it! By running these four cells, we're now ready to use the Snowflake Python API.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate a database, schema, and table\u003C/h2\u003E\n","\u003Cp\u003ELet's use our \u003Ccode\u003Eroot\u003C/code\u003E object to create a database, schema, and table in your Snowflake account.\u003C/p\u003E\n","\u003Cp\u003ECreate a database by running the following cell in the notebook:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Edatabase = root.databases.create(\n  Database(\n    name="PYTHON_API_DB"),\n    mode=CreateMode.or_replace\n  )\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis line of code creates a database in your account called \u003Ccode\u003EPYTHON_API_DB\u003C/code\u003E, and is functionally equivalent to the SQL command \u003Ccode\u003ECREATE OR REPLACE DATABASE PYTHON_API_DB;\u003C/code\u003E. This line of code follows a common pattern for managing objects in Snowflake. Let's examine this line of code in a bit more detail:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ccode\u003Eroot.databases.create()\u003C/code\u003E is used to create a database in Snowflake. It accepts two arguments, a \u003Ccode\u003EDatabase\u003C/code\u003E object and a mode.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWe pass in a \u003Ccode\u003EDatabase\u003C/code\u003E object with \u003Ccode\u003EDatabase(name="PYTHON_API_DB")\u003C/code\u003E, and set the name of the database using the \u003Ccode\u003Ename\u003C/code\u003E argument. Recall that we imported \u003Ccode\u003EDatabase\u003C/code\u003E on line 3 of the notebook.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWe specify the creation mode by passing in the \u003Ccode\u003Emode\u003C/code\u003E argument. In this case, we set it to \u003Ccode\u003ECreateMode.or_replace\u003C/code\u003E, but other valid values are \u003Ccode\u003ECreateMode.if_not_exists\u003C/code\u003E (functionally equivalent to \u003Ccode\u003ECREATE IF NOT EXISTS\u003C/code\u003E in SQL). If a mode is not specified, the default value will be \u003Ccode\u003ECreateMode.error_if_exists\u003C/code\u003E, which means an exception will be raised if the object already exists in Snowflake.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWe'll manage the database programmatically by storing a reference to the database in an object we created called \u003Ccode\u003Edatabase\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENavigate back to the databases section of your Snowflake account. If successful, you should see the \u003Ccode\u003EPYTHON_API_DB\u003C/code\u003E database.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/python_api_db.png\" alt=\"database\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETIP\u003C/strong\u003E:\nIf you use VSCode, then install \u003Ca href=\"https://marketplace.visualstudio.com/items?itemName=snowflake.snowflake-vsc\"\u003ESnowflake plugin\u003C/a\u003E to explore all Snowflake objects from within your editor\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ENext, create a schema and table in that schema by running the following cells:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Eschema = database.schemas.create(\n  Schema(\n    name="PYTHON_API_SCHEMA"),\n    mode=CreateMode.or_replace,\n  )\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Etable = schema.tables.create(\n  Table(\n    name="PYTHON_API_TABLE",\n    columns=[\n      TableColumn(\n        name="TEMPERATURE",\n        datatype="int",\n        nullable=False,\n      ),\n      TableColumn(\n        name="LOCATION",\n        datatype="string",\n      ),\n    ],\n  ),\nmode=CreateMode.or_replace\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe code in both of these cells should look and feel familiar. They follow the pattern you saw in the cell that created the \u003Ccode\u003EPYTHON_API_DB\u003C/code\u003E database. The first cell creates a schema in the database created earlier (note \u003Ccode\u003E.schemas.create()\u003C/code\u003E is called on the \u003Ccode\u003Edatabase\u003C/code\u003E object from earlier). The next cell creates a table in that schema, with two columns and their data types specified - \u003Ccode\u003ETEMPERATURE\u003C/code\u003E as \u003Ccode\u003Eint\u003C/code\u003E and \u003Ccode\u003ELOCATION\u003C/code\u003E as \u003Ccode\u003Estring\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EAfter running these two cells, navigate back to your Snowflake account and confirm that the objects were created.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/db_schema_table.png\" alt=\"schema and table\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERetrieve object data\u003C/h2\u003E\n","\u003Cp\u003ELet's cover a couple of ways to retrieve metadata about an object in Snowflake. Run the following cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Etable_details = table.fetch()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003Efetch()\u003C/code\u003E returns a \u003Ccode\u003ETableModel\u003C/code\u003E object. We can then call \u003Ccode\u003E.to_dict()\u003C/code\u003E on the resulting object to view information about the object. Run the next cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Etable_details.to_dict()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn the notebook, you should see a dictionary printed that contains metadata about the \u003Ccode\u003EPYTHON_API_TABLE\u003C/code\u003E table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003E{\n    "name": "PYTHON_API_TABLE",\n    "kind": "TABLE",\n    "enable_schema_evolution": False,\n    "change_tracking": False,\n    "data_retention_time_in_days": 1,\n    "max_data_extension_time_in_days": 14,\n    "default_ddl_collation": "",\n    "columns": [\n        {"name": "TEMPERATURE", "datatype": "fixed", "nullable": False},\n        {"name": "LOCATION", "datatype": "text", "nullable": True},\n    ],\n    "created_on": datetime.datetime(\n        2024, 5, 9, 8, 59, 15, 832000, tzinfo=datetime.timezone.utc\n    ),\n    "database_name": "PYTHON_API_DB",\n    "schema_name": "PYTHON_API_SCHEMA",\n    "rows": 0,\n    "bytes": 0,\n    "owner": "ACCOUNTADMIN",\n    "automatic_clustering": False,\n    "search_optimization": False,\n    "owner_role_type": "ROLE",\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs you can see, the dictionary above contains information about the \u003Ccode\u003EPYTHON_API_TABLE\u003C/code\u003E table that you created earlier, with detailed information on \u003Ccode\u003Ecolumns\u003C/code\u003E, \u003Ccode\u003Eowner\u003C/code\u003E, \u003Ccode\u003Edatabase\u003C/code\u003E, \u003Ccode\u003Eschema\u003C/code\u003E etc.,\u003C/p\u003E\n","\u003Cp\u003EObject metadata is useful when building business logic in your application. For example, you could imagine building logic that executes depending on certain information about an object. \u003Ccode\u003Efetch()\u003C/code\u003E would be helpful in retrieving object metadata in such scenarios.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EProgrammatically update a table\u003C/h2\u003E\n","\u003Cp\u003ELet's take a look at how you might programmatically add a column to a table. The \u003Ccode\u003EPYTHON_API_TABLE\u003C/code\u003E table currently has two columns, \u003Ccode\u003ETEMPERATURE\u003C/code\u003E and \u003Ccode\u003ELOCATION\u003C/code\u003E, let us add a new column named \u003Ccode\u003EELEVATION\u003C/code\u003E of type \u003Ccode\u003Eint\u003C/code\u003E and set it as the table's \u003Ccode\u003EPrimary Key\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ERun the following cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Etable_details.columns.append(\n    TableColumn(\n      name="elevation",\n      datatype="int",\n      nullable=False,\n      constraints=[PrimaryKey()],\n    )\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote, however, that this line of code does not create the column (you can confirm this by navigating to Snowflake and inspecting the table after running the cell). Instead, this column definition is appended to the array that represents the table's columns in the TableModel. To view this array, take a look at the value of \u003Ccode\u003Ecolumns\u003C/code\u003E in the previous step.\u003C/p\u003E\n","\u003Cp\u003ETo modify the table and add the column, run the next cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Etable.create_or_update(table_details)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn this line, we call \u003Ccode\u003Ecreate_or_update()\u003C/code\u003E on the object representing \u003Ccode\u003EPYTHON_API_TABLE\u003C/code\u003E and pass in the updated value of \u003Ccode\u003Etable_details\u003C/code\u003E. This line adds the \u003Ccode\u003EELEVATION\u003C/code\u003E column to \u003Ccode\u003EPYTHON_API_TABLE\u003C/code\u003E. To quickly confirm, run the following cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Etable.fetch().to_dict()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis should be the output:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003E{\n    "name": "PYTHON_API_TABLE",\n    "kind": "TABLE",\n    "enable_schema_evolution": False,\n    "change_tracking": False,\n    "data_retention_time_in_days": 1,\n    "max_data_extension_time_in_days": 14,\n    "default_ddl_collation": "",\n    "columns": [\n        {"name": "TEMPERATURE", "datatype": "fixed", "nullable": False},\n        {"name": "LOCATION", "datatype": "text", "nullable": True},\n    ],\n    "created_on": datetime.datetime(\n        2024, 5, 9, 8, 59, 15, 832000, tzinfo=datetime.timezone.utc\n    ),\n    "database_name": "PYTHON_API_DB",\n    "schema_name": "PYTHON_API_SCHEMA",\n    "rows": 0,\n    "bytes": 0,\n    "owner": "ACCOUNTADMIN",\n    "automatic_clustering": False,\n    "search_optimization": False,\n    "owner_role_type": "ROLE",\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETake a look at the value of \u003Ccode\u003Ecolumns\u003C/code\u003E, as well as the value of \u003Ccode\u003Econstraints\u003C/code\u003E, which now includes the \u003Ccode\u003EELEVATION\u003C/code\u003E column.\u003C/p\u003E\n","\u003Cp\u003EFinally, visually confirm by navigating to your Snowflake account and inspecting the table.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/add_column.png\" alt=\"add_column\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate, suspend, and delete a warehouse\u003C/h2\u003E\n","\u003Cp\u003EYou can also manage warehouses with the Snowflake Python API. Let's use the API to create, suspend, and delete a warehouse.\u003C/p\u003E\n","\u003Cp\u003ERun the following cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Ewarehouses = root.warehouses\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECalling the \u003Ccode\u003E.warehouses\u003C/code\u003E property on our \u003Ccode\u003Eroot\u003C/code\u003E returns the collection of warehouses associated with our session. We'll manage warehouses in our session using the resulting \u003Ccode\u003Ewarehouses\u003C/code\u003E object.\u003C/p\u003E\n","\u003Cp\u003ERun the next cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Epython_api_wh = Warehouse(\n    name="PYTHON_API_WH",\n    warehouse_size="SMALL",\n    auto_suspend=500,\n)\n\nwarehouse = warehouses.create(python_api_wh,mode=CreateMode.or_replace)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn this cell, we define a new warehouse by instantiating \u003Ccode\u003EWarehouse\u003C/code\u003E and specifying the warehouse's name, size, and auto suspend policy. The auto suspend timeout is in units of seconds. In this case, the warehouse will suspend after 8.33 minutes of inactivity.\u003C/p\u003E\n","\u003Cp\u003EWe then create the warehouse by calling \u003Ccode\u003Ecreate()\u003C/code\u003E on our warehouse collection. We store the reference in the resulting \u003Ccode\u003Ewarehouse_ref\u003C/code\u003E object. Navigate to your Snowflake account and confirm that the warehouse was created.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/create_wh.png\" alt=\"create warehouse\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo retrieve information about the warehouse, run the next cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Ewarehouse_details = warehouse.fetch()\nwarehouse_details.to_dict()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe code in this cell should look familiar, as it follows the same patterns we used to fetch table data in a previous step. The output should be:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003E{'name': 'PYTHON_API_WH',\n 'auto_suspend': 500,\n 'auto_resume': 'true',\n 'resource_monitor': 'null',\n 'comment': '',\n 'max_concurrency_level': 8,\n 'statement_queued_timeout_in_seconds': 0,\n 'statement_timeout_in_seconds': 172800,\n 'tags': {},\n 'type': 'STANDARD',\n 'size': 'Small'\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf we had several warehouses in our session, we could use the API to iterate through them or to search for a specific warehouse. Run the next cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Ewarehouse_list = warehouses.iter(like="PYTHON_API_WH")\nresult = next(warehouse_list)\nresult.to_dict()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn this cell, we call \u003Ccode\u003Eiter()\u003C/code\u003E on the warehouse collection and pass in the \u003Ccode\u003Elike\u003C/code\u003E argument, which is used to return any warehouses that has a name matching the string we pass in. In this case, we passed in the name of the warehouse we defined earlier, but this argument is generally a case-insensitive string that functions as a filter, with support for SQL wildcard characters like \u003Ccode\u003E%\u003C/code\u003E and \u003Ccode\u003E_\u003C/code\u003E. You should see the following output after running the cell, which shows that we successfully returned a matching warehouse:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003E{'name': 'PYTHON_API_WH',\n 'auto_suspend': 500,\n 'auto_resume': 'true',\n 'resource_monitor': 'null',\n 'comment': '',\n 'max_concurrency_level': 8,\n 'statement_queued_timeout_in_seconds': 0,\n 'statement_timeout_in_seconds': 172800,\n 'tags': {},\n 'type': 'STANDARD',\n 'size': 'Small'\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo programmatically modify the warehouse we need to do \u003Ccode\u003Ecreate\u003C/code\u003E with \u003Ccode\u003ECREATE OR REPLACE\u003C/code\u003E mode with modified properties:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Ewarehouse = root.warehouses.create(Warehouse(\n    name="PYTHON_API_WH",\n    warehouse_size="LARGE",\n    auto_suspend=500,\n),mode=CreateMode.or_replace)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EConfirm that the warehouse size was indeed updated to \u003Ccode\u003ELARGE\u003C/code\u003E by running the next cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Ewarehouse.fetch().size\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENavigate to your Snowflake account and confirm the change in warehouse size.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/large_wh.png\" alt=\"large warw\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOPTIONAL\u003C/strong\u003E Delete the warehouse and close your Snowflake session by running the final cell. This is optional so that you can continue to use the warehouse in the subsequent steps. If you run the cell, be sure to create a new warehouse so that you can complete the subsequent steps.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Ewarehouse.delete()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENavigate once again to your Snowflake account and confirm the deletion of the warehouse.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/delete_wh_close_session.png\" alt=\"delete wh close session\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EManaging tasks\u003C/h2\u003E\n","\u003Cp\u003EYou can also manage tasks using the Snowflake Python API. Let's use the API to manage a couple of basic stored procedures using tasks.\u003C/p\u003E\n","\u003Cp\u003EFirst, create a stage within the \u003Cstrong\u003EPYTHON_API_SCHEMA\u003C/strong\u003E called \u003Cstrong\u003ETASKS_STAGE\u003C/strong\u003E. This stage will hold the stored procedures and any dependencies those procedures will need.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Etasks_stage_name = f"{database.name}.{schema.name}.TASKS_STAGE"\ncreate_query = f"CREATE OR REPLACE STAGE {tasks_stage_name}"\nsession.connection.execute_string(create_query)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide: negative\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE\u003C/strong\u003E:\nSince we don't yet have the API for manipulating stages, let us create the \u003Ccode\u003ETASKS_STAGE\u003C/code\u003E stage using SQL.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECreate a couple of basic functions that will be run as stored procedures. \u003Ccode\u003Etrunc()\u003C/code\u003E creates a truncated version of an input table and \u003Ccode\u003Efilter_by_shipmode()\u003C/code\u003E creates a 10 row table created by filtering the \u003Cstrong\u003ESNOWFLAKE_SAMPLE_DATA.TPCH_SF100.LINEITEM\u003C/strong\u003E by ship mode. The functions are intentionally basic in nature and are intended to be used for demonstration purposes.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef trunc(session: Session, from_table: str, to_table: str, count: int) -> str:\n  (\n    session\n    .table(from_table)\n    .limit(count)\n    .write.save_as_table(to_table)\n  )\n  return "Truncated table successfully created!"\n\ndef filter_by_shipmode(session: Session, mode: str) -> str:\n    (\n      session\n      .table("snowflake_sample_data.tpch_sf100.lineitem")\n      .filter(col("L_SHIPMODE") == mode)\n      .limit(10)\n      .write.save_as_table("filter_table")\n    )\n    return "Filter table successfully created!"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDefine two tasks, \u003Ccode\u003Etask1\u003C/code\u003E and \u003Ccode\u003Etask2\u003C/code\u003E, whose definitions are stored procedures that each refer to the functions created. We specify the stage that will hold the contents of the stored procedure, and also specify packages that are dependencies. Task 1 is the root task, so we specify a warehouse and a schedule (run every minute).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Etask1 = Task(\n    "task_python_api_trunc",\n    definition=StoredProcedureCall(\n      trunc,\n      stage_location=f"@{tasks_stage_name}",\n      packages=["snowflake-snowpark-python"],\n    ),\n    warehouse="COMPUTE_WH",\n    schedule=timedelta(minutes=1)\n)\n\ntask2 = Task(\n    "task_python_api_filter",\n    definition=StoredProcedureCall(\n      filter_by_shipmode,\n      stage_location=f"@{tasks_stage_name}",\n      packages=["snowflake-snowpark-python"],\n    ),\n    warehouse="COMPUTE_WH"\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can check more details on Snowflake documentation on \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/stored-procedure/stored-procedures-python\"\u003EWriting stored procedures in Python\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ENow create the two tasks by first retrieving a TaskCollection object (\u003Ccode\u003Etasks\u003C/code\u003E) and adding the two tasks to that object. We will also set \u003Ccode\u003Etask1\u003C/code\u003E as a predecessor to t\u003Ccode\u003Eask2\u003C/code\u003E, which links the tasks, creating a DAG (albeit, a small one).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# create the task into the Snowflake database\ntasks = schema.tasks\n\ntrunc_task = tasks.create(task1, mode=CreateMode.or_replace)\n# should be fully qualified name\ntask2.predecessors = [f"{trunc_task.database.name}.{trunc_task.schema.name}.{trunc_task.name}"]\nfilter_task = tasks.create(task2, mode=CreateMode.or_replace)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENavigate back to your Snowflake account and confirm that the two tasks now exist.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/tasks.png\" alt=\"tasks\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/minidag.png\" alt=\"minidag\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWhen created, tasks are suspended by default. To start them, call \u003Ccode\u003E.resume()\u003C/code\u003E on the task. Run the following cell in the notebook:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etrunc_task.resume()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENavigate to your Snowflake account and observe that the \u003Ccode\u003Etrunc_task\u003C/code\u003E task was started. You can check the status of the task by running the next cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etaskiter = tasks.iter()\nfor t in taskiter:\n    print("Name: ", t.name, "| State: ", t.state)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see an output similar to the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-console\"\u003EName:  TASK_PYTHON_API_FILTER | State:  suspended\nName:  TASK_PYTHON_API_TRUNC | State:  started\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet's wrap things up by suspending the task, and then deleting both tasks. Run the following cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etrunc_task.suspend()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENavigate to your Snowflake account to confirm that the task is indeed suspended.\u003C/p\u003E\n","\u003Cp\u003E(Optional) Finally, delete both tasks by running the following cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etrunc_task.delete()\nfilter_task.delete()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EManaging Directd Acyclic Graphs(DAG)\u003C/h2\u003E\n","\u003Cp\u003EWhen the number of tasks that must be managed becomes very large, individually managing each task can be a challenge. The Snowflake Python API provides functionality to orchestrate tasks with a higher level DAG API. Let's take a look at how it can be used.\u003C/p\u003E\n","\u003Cp\u003EAs part of the next cell we will do,\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate a DAG object by calling the \u003Ccode\u003EDAG\u003C/code\u003E constructor, and specifying a DAG name and schedule.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EDefine DAG-specific tasks using the \u003Ccode\u003EDAGTask\u003C/code\u003E constructor. Note that the constructor accepts the same arguments that were specified in an earlier cell (in the previous step) when using the \u003Ccode\u003EStoredProcedureCall\u003C/code\u003E class.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESpecifie \u003Ccode\u003Edag_task1\u003C/code\u003E as the root task and predecessor to \u003Ccode\u003Edag_task2\u003C/code\u003E, with more convenient syntax.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EDeploys the DAG to the \u003Ccode\u003EPYTHON_API_SCHEMA\u003C/code\u003E schema.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edag_name = "python_api_dag"\ndag = DAG(dag_name, schedule=timedelta(days=1))\nwith dag:\n    dag_task1 = DAGTask(\n        "task_python_api_trunc",\n        StoredProcedureCall(\n            trunc,\n            stage_location=f"@{tasks_stage_name}",\n            packages=["snowflake-snowpark-python"]),\n        warehouse="COMPUTE_WH",\n    )\n    dag_task2 = DAGTask(\n        "task_python_api_filter",\n        StoredProcedureCall(\n            filter_by_shipmode,\n            stage_location=f"@{tasks_stage_name}",\n            packages=["snowflake-snowpark-python"]),\n        warehouse="COMPUTE_WH",\n    )\n    dag_task1 >> dag_task2\ndag_op = DAGOperation(schema)\ndag_op.deploy(dag, mode=CreateMode.or_replace)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENavigate to your Snowflake account and confirm the creation of the DAG.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/dag.png\" alt=\"dag\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/daggraph.png\" alt=\"daggraph\"\u003E\u003C/p\u003E\n","\u003Cp\u003EStart the DAG by starting the root task by running the following cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edag_op.run(dag)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOptionally, navigate to your Snowflake account and confirm that \u003Ccode\u003EPYTHON_API_DAG$TASK_PYTHON_API_TRUNC\u003C/code\u003E task was started. The call to the function will not succeed as we are not calling it with any of its required arguments. The purpose of this cell is simply to demonstrate how to programatically start the DAG.\u003C/p\u003E\n","\u003Cp\u003EFinally, delete the DAG by running the following cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edag_op.delete(dag)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EClean up all the objects that was created with this quickstart and close the session.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edatabase.delete()\nsession.close()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EManaging Snowpark Container Services\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant\u003C/strong\u003E\nAt the time of writing, Snowpark Container Services is in Public Preview in select AWS regions. To use Snowpark Container Services, your Snowflake account must be in one of the select AWS regions. For more information, refer to \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview#available-regions\"\u003ESnowpark Container Services – Available Regions\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ESnowpark Container Services is a fully managed container offering designed to facilitate the deployment, management, and scaling of containerized applications within the Snowflake ecosystem. The service enables users to run containerized workloads directly within Snowflake. Let's take a look at how you can manage Snowpark Container Services with the Snowflake Python API.\u003C/p\u003E\n","\u003Cp\u003EFor this section, we'll switch to a new notebook. The notebook contains \u003Cstrong\u003Esample code\u003C/strong\u003E that runs an NGINX web server using Snowpark Container Services, all running in Snowflake. The notebook is provided for convenience and demonstrative purposes.\u003C/p\u003E\n","\u003Cp\u003EDownload and open the following notebook in your preferred code editor, or with \u003Ccode\u003Ejupyter notebook\u003C/code\u003E: \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-snowflake-python-api/blob/main/snowpark_container_services_python_api.ipynb\"\u003ESnowpark Container Services – Python API\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EIn the first cell, we import the required libraries, create our connection to Snowflake, and instantiate our \u003Ccode\u003ERoot\u003C/code\u003E object. We also create objects to represent references to existing Snowflake objects in a Snowflake account. Our Snowpark Container Services will reside in the \u003Cstrong\u003EPUBLIC\u003C/strong\u003E schema.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom pprint import pprint\nfrom snowflake.core import Root\nfrom snowflake.core.service import ServiceSpecInlineText\nfrom snowflake.snowpark import Session\n\nsession = Session.builder.config("connection_name", "python_api").create()\napi_root = Root(session)\ndatabase = api_root.databases["spcs_python_api_db"]\nschema = database.schemas["public"]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen orchestrating Snowpark Container Services, there are a couple of patterns you'll typically follow:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDefine a compute pool\u003C/strong\u003E – A compute pool represents a set of compute resources (virtual machine nodes). You can think of these compute resources as analogous (but not equivalent) to Snowflake virtual warehouses. The service (in this case, our NGINX service) will run in the compute pool. Compute-intensive services will require high-powered compute pools (i.e., many cores, many GPUs), while less intensive services can run in smaller compute pools (fewer cores). For more information, see \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-compute-pool\"\u003ESnowpark Container Services: Working with compute pools\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDefine the service\u003C/strong\u003E – A service is how you run an application container. The services require, at minimum, a specification and a compute pool. A specification contains the information needed to run the application container, like the path to a container image, endpoints that the services will expose, and more. The specification is written in YML. The compute pool is what the service will run in. For more information, see \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-services\"\u003ESnowpark Container Services: Working with services\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ELet's cover these patterns in the notebook. Take a look at the following cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Enew_compute_pool_def = ComputePool(\n    name="MyComputePool",\n    instance_family="CPU_X64_XS",\n    min_nodes=1,\n    max_nodes=2,\n)\n\nnew_compute_pool = api_root.compute_pools.create(new_compute_pool_def)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn this cell, we define a compute pool using the \u003Ccode\u003EComputePool\u003C/code\u003E constructor, and provide a name, instance family, and the min and max number of nodes. We then create the compute pool and pass in the compute pool definition.\u003C/p\u003E\n","\u003Cp\u003EHere are some details behind the arguments passed into the constructor:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ccode\u003Einstance_family\u003C/code\u003E – An instance family refers to the type of machine you want to provision for the nodes in the compute pool. Different machines have different amounts of compute resources in their compute pools. In this cell, we use \u003Ccode\u003ECPU_X64_XS\u003C/code\u003E, which is the smallest available machine type. See \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-compute-pool#required-parameters\"\u003ECREATE COMPUTE POOL: Required parameters\u003C/a\u003E for more detail.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ccode\u003Emin_nodes\u003C/code\u003E, \u003Ccode\u003Emax_nodes\u003C/code\u003E – The lower and upper bounds for nodes in the compute pool. After creating a compute pool, Snowflake launches the minimum number of nodes. New nodes are created – up to the maximum specified – when the running nodes cannot take any additional workload. This is called \u003Cstrong\u003Eautoscaling\u003C/strong\u003E. For more information, see \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-compute-pool\"\u003ESnowpark Container Services: Working with compute pools\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIt's time to build our service. Take a look at the next cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimage_repository = schema.image_repositories["MyImageRepository"]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn this cell, we retrieve the repository containing our container image. This repository is in the Snowflake account, listed as a stage in the \u003Cstrong\u003EPUBLIC\u003C/strong\u003E schema. We'll need this reference to fetch container image information, which you can see referenced in the next cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom textwrap import dedent\nfrom io import BytesIO\nfrom snowflake.core.service import Service\nsession.use_role("my_role")  # Perhaps this role has permission to create and run a service\nspecification = dedent(f"""\\\n            spec:\n              containers:\n              - name: web-server\n                image: {image_repository.fetch().repository_url}/nginx:latest\n              endpoints:\n              - name: ui\n                port: 80\n                public: true\n             """)\n\nservice_def = Service(\n    name="MyService",\n    compute_pool="MyComputePool",\n    spec=ServiceSpecInlineText(specification),\n    min_instances=1,\n    max_instances=1,\n)\n\nnginx_service = schema.services.create(service_def)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis cell defines the service specification, the service, and creates the service for our NGINX web server. You should note a few things:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ccode\u003Especification\u003C/code\u003E – We define the specification using a Python f-string. The string is formatted as YML. It contains the name of the container, a path to the container image, and endpoints that the service will expose to be able to access the service publicly. Although the specification is defined inline, it could also been a reference to a \u003Cstrong\u003E.yml\u003C/strong\u003E file in a stage.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ccode\u003Eservice_def\u003C/code\u003E – We define a service with the \u003Ccode\u003EService\u003C/code\u003E constructor, passing in a name for the service, the compute pool it should run in, a path to the specification, and the total number of instances for the service. Note that \u003Ccode\u003EServiceSpecInlineText\u003C/code\u003E is used when setting \u003Ccode\u003Espec\u003C/code\u003E in this cell. This is because the specification was defined inline as an f-string. Multiple instances of the service could be run, but in this example we want only one instance of the service to run, which is set via \u003Ccode\u003Emin_instances\u003C/code\u003E and \u003Ccode\u003Emax_instances\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWith the service created, the next cell will output the status of the service.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Epprint(nginx_service.get_service_status(timeout=5))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESample output of the cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-console\"\u003E{'auto_resume': True,\n 'auto_suspend_secs': 3600,\n 'instance_family': 'CPU_X64_XS',\n 'max_nodes': 1,\n 'min_nodes': 1,\n 'name': 'MyService'}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIt'll take a few minutes for the endpoints that are needed to access the service to be provisioned. The next cell isn't specific to Snowpark Container Services or the Snowflake Python API – it simply provides a handy way to inspect whether the endpoints are ready. Note that we fetch the endpoints by calling \u003Ccode\u003E.fetch().public_endpoints\u003C/code\u003E on our service object.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport json, time\nwhile True:\n    public_endpoints = nginx_service.fetch().public_endpoints\n    try:\n        endpoints = json.loads(public_endpoints)\n    except json.JSONDecodeError:\n        print(public_endpoints)\n        time.sleep(15)\n    else:\n        break\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESample output of the cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-console\"\u003EEndpoints provisioning in progress... check back in a few minutes\nEndpoints provisioning in progress... check back in a few minutes\nEndpoints provisioning in progress... check back in a few minutes\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOnce the endpoints are provisioned, the next cell will open the public endpoints in your browser.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eprint(f"Visiting {endpoints['ui']} in your browser. You may need to log in there.")\nimport webbrowser\nwebbrowser.open(f"https://{endpoints['ui']}")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESample output of the cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-console\"\u003EVisiting fekqoap7-orgname-sslpr.snowflakecomputing.app in your browser. You may need to log in there.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf successful, you'll see the NGINX success page in your browser when visiting the endpoint:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/nginx-success.png\" alt=\"minidag\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWith just a few lines of Python, we were able to run an NGINX web server in Snowflake using Snowpark Container Services.\u003C/p\u003E\n","\u003Cp\u003EThe next cells will suspend and delete the compute pool and the service:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Enew_compute_pool_def.suspend()\nnginx_service.suspend()\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Enew_compute_pool_def.delete()\nnginx_service.delete()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! In this Quickstart, you learned the fundamentals for managing Snowflake objects, tasks, DAGs, and Snowpark Container Services using the Snowflake Python API.\u003C/p\u003E\n","\u003Ch3\u003EWhat we've covered\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EInstalling the Snowflake Python API\u003C/li\u003E\u003Cli\u003ECreating databases, schemas, and tables\u003C/li\u003E\u003Cli\u003ERetrieving object information\u003C/li\u003E\u003Cli\u003EProgrammatically updating an object\u003C/li\u003E\u003Cli\u003ECreating, suspending, and deleting warehouses\u003C/li\u003E\u003Cli\u003EHow to manage tasks and DAGs\u003C/li\u003E\u003Cli\u003EHow to manage Snowpark Container Services\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EAdditional resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-python-api/snowflake-python-overview\"\u003ESnowflake Documentation: Snowflake Python API\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-python-api/reference/0.1.0/index.html\"\u003ESnowflake Python API Reference Documentation\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E"],"title":"Getting Started with the Snowflake Python API","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n\n## Overview\n\n\nThe Snowflake Python API allows you to manage Snowflake using Python. Using the API, you're able to create, delete, and modify tables, schemas, warehouses, tasks, and much more, in many cases without needing to write SQL or use the Snowflake Connector for Python. In this Quickstart, you'll learn how to get started with the Snowflake Python API for object and task management with Snowflake.\n\n### Prerequisites\n\n- Familiarity with Python\n- Familiarity with Jupyter Notebooks\n\n### What You’ll Learn\n\n- How to install the Snowflake Python API library\n- How to create a Root object to use the API\n- How to create tables, schemas, and warehouses using the API\n- How to create and manage tasks using the API\n- How to use Snowpark Container Services with the Snowflake Python API\n\n### What You’ll Need\n\n- A Snowflake account ([trial](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides), or otherwise)\n- A code editor that supports Jupyter notebooks, or ability to run notebooks in your browser using `jupyter notebook`\n\n### What You’ll Build\n\n- Multiple objects within Snowflake\n\n\u003C!-- ------------------------ --\u003E\n\n## Install the Snowflake Python API\n\n\n\u003E \n\u003E\n\u003E **Important**\n\u003E The Snowflake Python API is currently supported in Python versions 3.8, 3.9., and 3.10.\n\nBefore installing the Snowflake Python API, we'll start by activating a Python environment.\n\n**Conda**\n\nIf you're using conda, you can create and activate a conda environment using the following commands:\n\n```bash\nconda create -n \u003Cenv_name\u003E python==3.10\n```\n\n```bash\nconda activate \u003Cenv_name\u003E\n```\n\n**venv**\n\nIf you're using venv, you can create and activate a virtual environment using the following commands:\n\n```bash\npython3 -m venv '.venv'\n```\n\n```bash\nsource '.venv/bin/activate'\n```\n\nThe Snowflake Python API is available via PyPi. Install it by running the following command:\n\n```bash\npip install snowflake -U\n```\n\n## Configure Snowflake\n\nLet us create `$HOME/.snowflake/config.toml` with the following content and update it with your actual credentials. The connection details `default` under table `connections` i.e.`connections.default` will be used to establish a connection with Snowflake.\n\n```toml\n[connections]\n[connections.default]\naccount = \"YOUR ACCOUNT NAME\"\nuser = \"YOUR ACCOUNT USER\"\npassword = \"YOUR ACCOUNT USER PASSWORD\"\n# optional\n# warehouse = \"COMPUTE_WH\"\n# optional\n# database = \"COMPUTE_WH\"\n# optional\n# schema = \"PUBLIC\"\n```\n\nNext create a `connections_params` dictionary as show below and set the connection to use for the session,\n\n```py\n  connection_params = {\n   \"connection_name\": \"default\",\n  }\n```\n\n\u003C!-- ------------------------ --\u003E\n\n## Overview of the Snowflake Python API\n\n\nLet's quickly take a look at how the Snowflake Python API is organized:\n\n| **Module**                        | **Description**                                                                                                   |\n| --------------------------------- | ----------------------------------------------------------------------------------------------------------------- |\n| `snowflake.core`                  | Defines an Iterator to represent a certain resource instances fetched from the Snowflake database                 |\n| `snowflake.core.paging`           |                                                                                                                   |\n| `snowflake.core.exceptions`       |                                                                                                                   |\n| `snowflake.core.database`         | Manages Snowflake databases                                                                                       |\n| `snowflake.core.schema`           | Manages Snowflake schemas                                                                                         |\n| `snowflake.core.task`             | Manages Snowflake Tasks                                                                                           |\n| `snowflake.core.task.context`     | Manage the context in a Snowflake Task                                                                            |\n| `snowflake.core.task.dagv1`       | A set of higher-level APIs than the lower-level Task APIs in snowflake.core.task to more conveniently manage DAGs |\n| `snowflake.core.compute_pool`     | Manages Snowpark Container Compute Pools                                                                          |\n| `snowflake.core.image_repository` | Manages Snowpark Container Image Repositories                                                                     |\n| `snowflake.core.service`          | Manages Snowpark Container Services                                                                               |\n\n`snowflake.core` represents the entry point to the core Snowflake Python APIs that manage Snowflake objects. To use the Snowflake Python API, you'll follow a common pattern:\n\n1. Establish a session using Snowpark or a Python connector connection, representing your connection to Snowflake.\n\n2. Import and instantiate the `Root` class from `snowflake.core`, and pass in the Snowpark session object as an argument. You'll use the resulting `Root` object to use the rest of the methods and types in the Snowflake Python API.\n\nHere's an example of what this pattern typically looks like:\n\n```py\nfrom snowflake.snowpark import Session\nfrom snowflake.core import Root\n\nsession = Session.builder.configs(connection_params).create()\nroot = Root(session)\n```\n\nFor more information on various connection options/attributes, see [Connecting to Snowflake with the Snowflake Python API](https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-python-api/snowflake-python-connecting-snowflake).\n\n\u003E \n\u003E\n\u003E **NOTE**\n\u003E The Snowflake Python API can establish a connection to Snowflake via a Snowpark session or a Python connector connection. In the example above, we opt for a Snowpark session.\n\nLet's get started!\n\n\u003C!-- ------------------------ --\u003E\n\n## Set up your development environment\n\n\nIn this Quickstart, we'll walk through a Jupyter notebook to incrementally showcase the capabilities of the Snowflake Python API. Let's start by setting up your development environment so that you can run the notebook.\n\n1. First, download the [\n   Quickstart: Getting Started with the Snowflake Python API notebook](https://github.com/Snowflake-Labs/sfguide-getting-started-snowflake-python-api/blob/main/getting_started_snowflake_python_api.ipynb) from the accompanying repo for this Quickstart.\n\n2. Next, open the notebook in a code editor that supports Jupyter notebooks (i.e., Visual Studio Code).\n   Alternatively, open the notebook in your browser by starting a notebook server with `jupyter notebook` and navigating to the notebook in your browser. To do this, you'll need to ensure your environment can run a notebook (be sure to run `conda install notebook` in your terminal, then start the notebook server).\n\nNow run the first cell within the notebook, the one containing the import statements.\n\n```py\nfrom datetime import timedelta\nfrom typing import List\n\nfrom snowflake.snowpark import Session\nfrom snowflake.snowpark.functions import col\nfrom snowflake.core import Root,CreateMode\nfrom snowflake.core.database import Database\nfrom snowflake.core.schema import Schema\nfrom snowflake.core.table import Table, TableColumn, PrimaryKey\nfrom snowflake.core.warehouse import Warehouse\nfrom snowflake.core._common import CreateMode\nfrom snowflake.core.task import StoredProcedureCall, Task\nfrom snowflake.core.task.dagv1 import DAGOperation, DAG, DAGTask\n```\n\n\u003E \n\u003E\n\u003E **Note**\n\u003E Upon running this cell, you may be prompted to set your Python kernel. We activated a conda environment earlier, so we'll select conda as our Python kernel (i.e., something like `~/miniconda3/envs/\u003Cyour conda env\u003E/bin/python`).\n\nIn this cell, we import Snowpark and the core Snowflake Python APIs that manage Snowflake objects.\n\nNext, configure the `connection_params` dictionary with your account credentials and run the cell.\n\n```py\nconnection_params = {\n    \"connection_name\": \"default\"\n}\n```\n\nCreate a Snowpark session and pass in your connection parameters to establish a connection to Snowflake.\n\n```py\nsession = Session.builder.configs(connection_params).create()\n```\n\nFinally, create a `Root` object by passing in your `session` object to the `Root` constructor. Run the cell.\n\n```py\nroot = Root(session)\n```\n\nAnd that's it! By running these four cells, we're now ready to use the Snowflake Python API.\n\n\u003C!-- ------------------------ --\u003E\n\n## Create a database, schema, and table\n\n\nLet's use our `root` object to create a database, schema, and table in your Snowflake account.\n\nCreate a database by running the following cell in the notebook:\n\n```py\ndatabase = root.databases.create(\n  Database(\n    name=\"PYTHON_API_DB\"),\n    mode=CreateMode.or_replace\n  )\n```\n\nThis line of code creates a database in your account called `PYTHON_API_DB`, and is functionally equivalent to the SQL command `CREATE OR REPLACE DATABASE PYTHON_API_DB;`. This line of code follows a common pattern for managing objects in Snowflake. Let's examine this line of code in a bit more detail:\n\n- `root.databases.create()` is used to create a database in Snowflake. It accepts two arguments, a `Database` object and a mode.\n\n- We pass in a `Database` object with `Database(name=\"PYTHON_API_DB\")`, and set the name of the database using the `name` argument. Recall that we imported `Database` on line 3 of the notebook.\n\n- We specify the creation mode by passing in the `mode` argument. In this case, we set it to `CreateMode.or_replace`, but other valid values are `CreateMode.if_not_exists` (functionally equivalent to `CREATE IF NOT EXISTS` in SQL). If a mode is not specified, the default value will be `CreateMode.error_if_exists`, which means an exception will be raised if the object already exists in Snowflake.\n\n- We'll manage the database programmatically by storing a reference to the database in an object we created called `database`.\n\nNavigate back to the databases section of your Snowflake account. If successful, you should see the `PYTHON_API_DB` database.\n\n![database](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/python_api_db.png)\n\n\u003E \n\u003E\n\u003E **TIP**:\n\u003E If you use VSCode, then install [Snowflake plugin](https://marketplace.visualstudio.com/items?itemName=snowflake.snowflake-vsc) to explore all Snowflake objects from within your editor\n\nNext, create a schema and table in that schema by running the following cells:\n\n```py\nschema = database.schemas.create(\n  Schema(\n    name=\"PYTHON_API_SCHEMA\"),\n    mode=CreateMode.or_replace,\n  )\n```\n\n```py\ntable = schema.tables.create(\n  Table(\n    name=\"PYTHON_API_TABLE\",\n    columns=[\n      TableColumn(\n        name=\"TEMPERATURE\",\n        datatype=\"int\",\n        nullable=False,\n      ),\n      TableColumn(\n        name=\"LOCATION\",\n        datatype=\"string\",\n      ),\n    ],\n  ),\nmode=CreateMode.or_replace\n)\n```\n\nThe code in both of these cells should look and feel familiar. They follow the pattern you saw in the cell that created the `PYTHON_API_DB` database. The first cell creates a schema in the database created earlier (note `.schemas.create()` is called on the `database` object from earlier). The next cell creates a table in that schema, with two columns and their data types specified - `TEMPERATURE` as `int` and `LOCATION` as `string`.\n\nAfter running these two cells, navigate back to your Snowflake account and confirm that the objects were created.\n\n![schema and table](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/db_schema_table.png)\n\n\u003C!-- ------------------------ --\u003E\n\n## Retrieve object data\n\n\nLet's cover a couple of ways to retrieve metadata about an object in Snowflake. Run the following cell:\n\n```py\ntable_details = table.fetch()\n```\n\n`fetch()` returns a `TableModel` object. We can then call `.to_dict()` on the resulting object to view information about the object. Run the next cell:\n\n```py\ntable_details.to_dict()\n```\n\nIn the notebook, you should see a dictionary printed that contains metadata about the `PYTHON_API_TABLE` table.\n\n```py\n{\n    \"name\": \"PYTHON_API_TABLE\",\n    \"kind\": \"TABLE\",\n    \"enable_schema_evolution\": False,\n    \"change_tracking\": False,\n    \"data_retention_time_in_days\": 1,\n    \"max_data_extension_time_in_days\": 14,\n    \"default_ddl_collation\": \"\",\n    \"columns\": [\n        {\"name\": \"TEMPERATURE\", \"datatype\": \"fixed\", \"nullable\": False},\n        {\"name\": \"LOCATION\", \"datatype\": \"text\", \"nullable\": True},\n    ],\n    \"created_on\": datetime.datetime(\n        2024, 5, 9, 8, 59, 15, 832000, tzinfo=datetime.timezone.utc\n    ),\n    \"database_name\": \"PYTHON_API_DB\",\n    \"schema_name\": \"PYTHON_API_SCHEMA\",\n    \"rows\": 0,\n    \"bytes\": 0,\n    \"owner\": \"ACCOUNTADMIN\",\n    \"automatic_clustering\": False,\n    \"search_optimization\": False,\n    \"owner_role_type\": \"ROLE\",\n}\n```\n\nAs you can see, the dictionary above contains information about the `PYTHON_API_TABLE` table that you created earlier, with detailed information on `columns`, `owner`, `database`, `schema` etc.,\n\nObject metadata is useful when building business logic in your application. For example, you could imagine building logic that executes depending on certain information about an object. `fetch()` would be helpful in retrieving object metadata in such scenarios.\n\n\u003C!-- ------------------------ --\u003E\n\n## Programmatically update a table\n\n\nLet's take a look at how you might programmatically add a column to a table. The `PYTHON_API_TABLE` table currently has two columns, `TEMPERATURE` and `LOCATION`, let us add a new column named `ELEVATION` of type `int` and set it as the table's `Primary Key`.\n\nRun the following cell:\n\n```py\ntable_details.columns.append(\n    TableColumn(\n      name=\"elevation\",\n      datatype=\"int\",\n      nullable=False,\n      constraints=[PrimaryKey()],\n    )\n)\n```\n\nNote, however, that this line of code does not create the column (you can confirm this by navigating to Snowflake and inspecting the table after running the cell). Instead, this column definition is appended to the array that represents the table's columns in the TableModel. To view this array, take a look at the value of `columns` in the previous step.\n\nTo modify the table and add the column, run the next cell:\n\n```py\ntable.create_or_update(table_details)\n```\n\nIn this line, we call `create_or_update()` on the object representing `PYTHON_API_TABLE` and pass in the updated value of `table_details`. This line adds the `ELEVATION` column to `PYTHON_API_TABLE`. To quickly confirm, run the following cell:\n\n```py\ntable.fetch().to_dict()\n```\n\nThis should be the output:\n\n```py\n{\n    \"name\": \"PYTHON_API_TABLE\",\n    \"kind\": \"TABLE\",\n    \"enable_schema_evolution\": False,\n    \"change_tracking\": False,\n    \"data_retention_time_in_days\": 1,\n    \"max_data_extension_time_in_days\": 14,\n    \"default_ddl_collation\": \"\",\n    \"columns\": [\n        {\"name\": \"TEMPERATURE\", \"datatype\": \"fixed\", \"nullable\": False},\n        {\"name\": \"LOCATION\", \"datatype\": \"text\", \"nullable\": True},\n    ],\n    \"created_on\": datetime.datetime(\n        2024, 5, 9, 8, 59, 15, 832000, tzinfo=datetime.timezone.utc\n    ),\n    \"database_name\": \"PYTHON_API_DB\",\n    \"schema_name\": \"PYTHON_API_SCHEMA\",\n    \"rows\": 0,\n    \"bytes\": 0,\n    \"owner\": \"ACCOUNTADMIN\",\n    \"automatic_clustering\": False,\n    \"search_optimization\": False,\n    \"owner_role_type\": \"ROLE\",\n}\n```\n\nTake a look at the value of `columns`, as well as the value of `constraints`, which now includes the `ELEVATION` column.\n\nFinally, visually confirm by navigating to your Snowflake account and inspecting the table.\n\n![add_column](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/add_column.png)\n\n\u003C!-- ------------------------ --\u003E\n\n## Create, suspend, and delete a warehouse\n\n\nYou can also manage warehouses with the Snowflake Python API. Let's use the API to create, suspend, and delete a warehouse.\n\nRun the following cell:\n\n```py\nwarehouses = root.warehouses\n```\n\nCalling the `.warehouses` property on our `root` returns the collection of warehouses associated with our session. We'll manage warehouses in our session using the resulting `warehouses` object.\n\nRun the next cell:\n\n```py\npython_api_wh = Warehouse(\n    name=\"PYTHON_API_WH\",\n    warehouse_size=\"SMALL\",\n    auto_suspend=500,\n)\n\nwarehouse = warehouses.create(python_api_wh,mode=CreateMode.or_replace)\n```\n\nIn this cell, we define a new warehouse by instantiating `Warehouse` and specifying the warehouse's name, size, and auto suspend policy. The auto suspend timeout is in units of seconds. In this case, the warehouse will suspend after 8.33 minutes of inactivity.\n\nWe then create the warehouse by calling `create()` on our warehouse collection. We store the reference in the resulting `warehouse_ref` object. Navigate to your Snowflake account and confirm that the warehouse was created.\n\n![create warehouse](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/create_wh.png)\n\nTo retrieve information about the warehouse, run the next cell:\n\n```py\nwarehouse_details = warehouse.fetch()\nwarehouse_details.to_dict()\n```\n\nThe code in this cell should look familiar, as it follows the same patterns we used to fetch table data in a previous step. The output should be:\n\n```py\n{'name': 'PYTHON_API_WH',\n 'auto_suspend': 500,\n 'auto_resume': 'true',\n 'resource_monitor': 'null',\n 'comment': '',\n 'max_concurrency_level': 8,\n 'statement_queued_timeout_in_seconds': 0,\n 'statement_timeout_in_seconds': 172800,\n 'tags': {},\n 'type': 'STANDARD',\n 'size': 'Small'\n}\n```\n\nIf we had several warehouses in our session, we could use the API to iterate through them or to search for a specific warehouse. Run the next cell:\n\n```py\nwarehouse_list = warehouses.iter(like=\"PYTHON_API_WH\")\nresult = next(warehouse_list)\nresult.to_dict()\n```\n\nIn this cell, we call `iter()` on the warehouse collection and pass in the `like` argument, which is used to return any warehouses that has a name matching the string we pass in. In this case, we passed in the name of the warehouse we defined earlier, but this argument is generally a case-insensitive string that functions as a filter, with support for SQL wildcard characters like `%` and `_`. You should see the following output after running the cell, which shows that we successfully returned a matching warehouse:\n\n```py\n{'name': 'PYTHON_API_WH',\n 'auto_suspend': 500,\n 'auto_resume': 'true',\n 'resource_monitor': 'null',\n 'comment': '',\n 'max_concurrency_level': 8,\n 'statement_queued_timeout_in_seconds': 0,\n 'statement_timeout_in_seconds': 172800,\n 'tags': {},\n 'type': 'STANDARD',\n 'size': 'Small'\n}\n```\n\nTo programmatically modify the warehouse we need to do `create` with `CREATE OR REPLACE` mode with modified properties:\n\n```py\nwarehouse = root.warehouses.create(Warehouse(\n    name=\"PYTHON_API_WH\",\n    warehouse_size=\"LARGE\",\n    auto_suspend=500,\n),mode=CreateMode.or_replace)\n```\n\nConfirm that the warehouse size was indeed updated to `LARGE` by running the next cell:\n\n```py\nwarehouse.fetch().size\n```\n\nNavigate to your Snowflake account and confirm the change in warehouse size.\n\n![large warw](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/large_wh.png)\n\n**OPTIONAL** Delete the warehouse and close your Snowflake session by running the final cell. This is optional so that you can continue to use the warehouse in the subsequent steps. If you run the cell, be sure to create a new warehouse so that you can complete the subsequent steps.\n\n```py\nwarehouse.delete()\n```\n\nNavigate once again to your Snowflake account and confirm the deletion of the warehouse.\n\n![delete wh close session](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/delete_wh_close_session.png)\n\n\u003C!-- ------------------------ --\u003E\n\n## Managing tasks\n\n\nYou can also manage tasks using the Snowflake Python API. Let's use the API to manage a couple of basic stored procedures using tasks.\n\nFirst, create a stage within the **PYTHON_API_SCHEMA** called **TASKS_STAGE**. This stage will hold the stored procedures and any dependencies those procedures will need.\n\n```py\ntasks_stage_name = f\"{database.name}.{schema.name}.TASKS_STAGE\"\ncreate_query = f\"CREATE OR REPLACE STAGE {tasks_stage_name}\"\nsession.connection.execute_string(create_query)\n```\n\n\u003E aside: negative\n\u003E\n\u003E **NOTE**:\n\u003E Since we don't yet have the API for manipulating stages, let us create the `TASKS_STAGE` stage using SQL.\n\nCreate a couple of basic functions that will be run as stored procedures. `trunc()` creates a truncated version of an input table and `filter_by_shipmode()` creates a 10 row table created by filtering the **SNOWFLAKE_SAMPLE_DATA.TPCH_SF100.LINEITEM** by ship mode. The functions are intentionally basic in nature and are intended to be used for demonstration purposes.\n\n```python\ndef trunc(session: Session, from_table: str, to_table: str, count: int) -\u003E str:\n  (\n    session\n    .table(from_table)\n    .limit(count)\n    .write.save_as_table(to_table)\n  )\n  return \"Truncated table successfully created!\"\n\ndef filter_by_shipmode(session: Session, mode: str) -\u003E str:\n    (\n      session\n      .table(\"snowflake_sample_data.tpch_sf100.lineitem\")\n      .filter(col(\"L_SHIPMODE\") == mode)\n      .limit(10)\n      .write.save_as_table(\"filter_table\")\n    )\n    return \"Filter table successfully created!\"\n```\n\nDefine two tasks, `task1` and `task2`, whose definitions are stored procedures that each refer to the functions created. We specify the stage that will hold the contents of the stored procedure, and also specify packages that are dependencies. Task 1 is the root task, so we specify a warehouse and a schedule (run every minute).\n\n```py\ntask1 = Task(\n    \"task_python_api_trunc\",\n    definition=StoredProcedureCall(\n      trunc,\n      stage_location=f\"@{tasks_stage_name}\",\n      packages=[\"snowflake-snowpark-python\"],\n    ),\n    warehouse=\"COMPUTE_WH\",\n    schedule=timedelta(minutes=1)\n)\n\ntask2 = Task(\n    \"task_python_api_filter\",\n    definition=StoredProcedureCall(\n      filter_by_shipmode,\n      stage_location=f\"@{tasks_stage_name}\",\n      packages=[\"snowflake-snowpark-python\"],\n    ),\n    warehouse=\"COMPUTE_WH\"\n)\n```\n\nYou can check more details on Snowflake documentation on [Writing stored procedures in Python](https://docs.snowflake.com/en/developer-guide/stored-procedure/stored-procedures-python).\n\nNow create the two tasks by first retrieving a TaskCollection object (`tasks`) and adding the two tasks to that object. We will also set `task1` as a predecessor to t`ask2`, which links the tasks, creating a DAG (albeit, a small one).\n\n```python\n# create the task into the Snowflake database\ntasks = schema.tasks\n\ntrunc_task = tasks.create(task1, mode=CreateMode.or_replace)\n# should be fully qualified name\ntask2.predecessors = [f\"{trunc_task.database.name}.{trunc_task.schema.name}.{trunc_task.name}\"]\nfilter_task = tasks.create(task2, mode=CreateMode.or_replace)\n```\n\nNavigate back to your Snowflake account and confirm that the two tasks now exist.\n\n![tasks](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/tasks.png)\n\n![minidag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/minidag.png)\n\nWhen created, tasks are suspended by default. To start them, call `.resume()` on the task. Run the following cell in the notebook:\n\n```python\ntrunc_task.resume()\n```\n\nNavigate to your Snowflake account and observe that the `trunc_task` task was started. You can check the status of the task by running the next cell:\n\n```python\ntaskiter = tasks.iter()\nfor t in taskiter:\n    print(\"Name: \", t.name, \"| State: \", t.state)\n```\n\nYou should see an output similar to the following:\n\n```console\nName:  TASK_PYTHON_API_FILTER | State:  suspended\nName:  TASK_PYTHON_API_TRUNC | State:  started\n```\n\nLet's wrap things up by suspending the task, and then deleting both tasks. Run the following cell:\n\n```python\ntrunc_task.suspend()\n```\n\nNavigate to your Snowflake account to confirm that the task is indeed suspended.\n\n(Optional) Finally, delete both tasks by running the following cell:\n\n```python\ntrunc_task.delete()\nfilter_task.delete()\n```\n\n\u003C!-- ------------------------ --\u003E\n\n## Managing Directd Acyclic Graphs(DAG)\n\n\nWhen the number of tasks that must be managed becomes very large, individually managing each task can be a challenge. The Snowflake Python API provides functionality to orchestrate tasks with a higher level DAG API. Let's take a look at how it can be used.\n\nAs part of the next cell we will do,\n\n- Create a DAG object by calling the `DAG` constructor, and specifying a DAG name and schedule.\n\n- Define DAG-specific tasks using the `DAGTask` constructor. Note that the constructor accepts the same arguments that were specified in an earlier cell (in the previous step) when using the `StoredProcedureCall` class.\n\n- Specifie `dag_task1` as the root task and predecessor to `dag_task2`, with more convenient syntax.\n\n- Deploys the DAG to the `PYTHON_API_SCHEMA` schema.\n\n```python\ndag_name = \"python_api_dag\"\ndag = DAG(dag_name, schedule=timedelta(days=1))\nwith dag:\n    dag_task1 = DAGTask(\n        \"task_python_api_trunc\",\n        StoredProcedureCall(\n            trunc,\n            stage_location=f\"@{tasks_stage_name}\",\n            packages=[\"snowflake-snowpark-python\"]),\n        warehouse=\"COMPUTE_WH\",\n    )\n    dag_task2 = DAGTask(\n        \"task_python_api_filter\",\n        StoredProcedureCall(\n            filter_by_shipmode,\n            stage_location=f\"@{tasks_stage_name}\",\n            packages=[\"snowflake-snowpark-python\"]),\n        warehouse=\"COMPUTE_WH\",\n    )\n    dag_task1 \u003E\u003E dag_task2\ndag_op = DAGOperation(schema)\ndag_op.deploy(dag, mode=CreateMode.or_replace)\n```\n\nNavigate to your Snowflake account and confirm the creation of the DAG.\n\n![dag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/dag.png)\n\n![daggraph](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/daggraph.png)\n\nStart the DAG by starting the root task by running the following cell:\n\n```python\ndag_op.run(dag)\n```\n\nOptionally, navigate to your Snowflake account and confirm that `PYTHON_API_DAG$TASK_PYTHON_API_TRUNC` task was started. The call to the function will not succeed as we are not calling it with any of its required arguments. The purpose of this cell is simply to demonstrate how to programatically start the DAG.\n\nFinally, delete the DAG by running the following cell:\n\n```python\ndag_op.delete(dag)\n```\n\nClean up all the objects that was created with this quickstart and close the session.\n\n```python\ndatabase.delete()\nsession.close()\n```\n\n\u003C!-- ------------------------ --\u003E\n\n## Managing Snowpark Container Services\n\n\n\u003E \n\u003E\n\u003E **Important**\n\u003E At the time of writing, Snowpark Container Services is in Public Preview in select AWS regions. To use Snowpark Container Services, your Snowflake account must be in one of the select AWS regions. For more information, refer to [Snowpark Container Services – Available Regions](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview#available-regions).\n\nSnowpark Container Services is a fully managed container offering designed to facilitate the deployment, management, and scaling of containerized applications within the Snowflake ecosystem. The service enables users to run containerized workloads directly within Snowflake. Let's take a look at how you can manage Snowpark Container Services with the Snowflake Python API.\n\nFor this section, we'll switch to a new notebook. The notebook contains **sample code** that runs an NGINX web server using Snowpark Container Services, all running in Snowflake. The notebook is provided for convenience and demonstrative purposes.\n\nDownload and open the following notebook in your preferred code editor, or with `jupyter notebook`: [Snowpark Container Services – Python API](https://github.com/Snowflake-Labs/sfguide-getting-started-snowflake-python-api/blob/main/snowpark_container_services_python_api.ipynb).\n\nIn the first cell, we import the required libraries, create our connection to Snowflake, and instantiate our `Root` object. We also create objects to represent references to existing Snowflake objects in a Snowflake account. Our Snowpark Container Services will reside in the **PUBLIC** schema.\n\n```python\nfrom pprint import pprint\nfrom snowflake.core import Root\nfrom snowflake.core.service import ServiceSpecInlineText\nfrom snowflake.snowpark import Session\n\nsession = Session.builder.config(\"connection_name\", \"python_api\").create()\napi_root = Root(session)\ndatabase = api_root.databases[\"spcs_python_api_db\"]\nschema = database.schemas[\"public\"]\n```\n\nWhen orchestrating Snowpark Container Services, there are a couple of patterns you'll typically follow:\n\n- **Define a compute pool** – A compute pool represents a set of compute resources (virtual machine nodes). You can think of these compute resources as analogous (but not equivalent) to Snowflake virtual warehouses. The service (in this case, our NGINX service) will run in the compute pool. Compute-intensive services will require high-powered compute pools (i.e., many cores, many GPUs), while less intensive services can run in smaller compute pools (fewer cores). For more information, see [Snowpark Container Services: Working with compute pools](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-compute-pool).\n\n- **Define the service** – A service is how you run an application container. The services require, at minimum, a specification and a compute pool. A specification contains the information needed to run the application container, like the path to a container image, endpoints that the services will expose, and more. The specification is written in YML. The compute pool is what the service will run in. For more information, see [Snowpark Container Services: Working with services](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-services).\n\nLet's cover these patterns in the notebook. Take a look at the following cell:\n\n```python\nnew_compute_pool_def = ComputePool(\n    name=\"MyComputePool\",\n    instance_family=\"CPU_X64_XS\",\n    min_nodes=1,\n    max_nodes=2,\n)\n\nnew_compute_pool = api_root.compute_pools.create(new_compute_pool_def)\n```\n\nIn this cell, we define a compute pool using the `ComputePool` constructor, and provide a name, instance family, and the min and max number of nodes. We then create the compute pool and pass in the compute pool definition.\n\nHere are some details behind the arguments passed into the constructor:\n\n- `instance_family` – An instance family refers to the type of machine you want to provision for the nodes in the compute pool. Different machines have different amounts of compute resources in their compute pools. In this cell, we use `CPU_X64_XS`, which is the smallest available machine type. See [CREATE COMPUTE POOL: Required parameters](https://docs.snowflake.com/en/sql-reference/sql/create-compute-pool#required-parameters) for more detail.\n\n- `min_nodes`, `max_nodes` – The lower and upper bounds for nodes in the compute pool. After creating a compute pool, Snowflake launches the minimum number of nodes. New nodes are created – up to the maximum specified – when the running nodes cannot take any additional workload. This is called **autoscaling**. For more information, see [Snowpark Container Services: Working with compute pools](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-compute-pool).\n\nIt's time to build our service. Take a look at the next cell:\n\n```python\nimage_repository = schema.image_repositories[\"MyImageRepository\"]\n```\n\nIn this cell, we retrieve the repository containing our container image. This repository is in the Snowflake account, listed as a stage in the **PUBLIC** schema. We'll need this reference to fetch container image information, which you can see referenced in the next cell:\n\n```python\nfrom textwrap import dedent\nfrom io import BytesIO\nfrom snowflake.core.service import Service\nsession.use_role(\"my_role\")  # Perhaps this role has permission to create and run a service\nspecification = dedent(f\"\"\"\\\n            spec:\n              containers:\n              - name: web-server\n                image: {image_repository.fetch().repository_url}/nginx:latest\n              endpoints:\n              - name: ui\n                port: 80\n                public: true\n             \"\"\")\n\nservice_def = Service(\n    name=\"MyService\",\n    compute_pool=\"MyComputePool\",\n    spec=ServiceSpecInlineText(specification),\n    min_instances=1,\n    max_instances=1,\n)\n\nnginx_service = schema.services.create(service_def)\n```\n\nThis cell defines the service specification, the service, and creates the service for our NGINX web server. You should note a few things:\n\n- `specification` – We define the specification using a Python f-string. The string is formatted as YML. It contains the name of the container, a path to the container image, and endpoints that the service will expose to be able to access the service publicly. Although the specification is defined inline, it could also been a reference to a **.yml** file in a stage.\n\n- `service_def` – We define a service with the `Service` constructor, passing in a name for the service, the compute pool it should run in, a path to the specification, and the total number of instances for the service. Note that `ServiceSpecInlineText` is used when setting `spec` in this cell. This is because the specification was defined inline as an f-string. Multiple instances of the service could be run, but in this example we want only one instance of the service to run, which is set via `min_instances` and `max_instances`.\n\nWith the service created, the next cell will output the status of the service.\n\n```python\npprint(nginx_service.get_service_status(timeout=5))\n```\n\nSample output of the cell:\n\n```console\n{'auto_resume': True,\n 'auto_suspend_secs': 3600,\n 'instance_family': 'CPU_X64_XS',\n 'max_nodes': 1,\n 'min_nodes': 1,\n 'name': 'MyService'}\n```\n\nIt'll take a few minutes for the endpoints that are needed to access the service to be provisioned. The next cell isn't specific to Snowpark Container Services or the Snowflake Python API – it simply provides a handy way to inspect whether the endpoints are ready. Note that we fetch the endpoints by calling `.fetch().public_endpoints` on our service object.\n\n```python\nimport json, time\nwhile True:\n    public_endpoints = nginx_service.fetch().public_endpoints\n    try:\n        endpoints = json.loads(public_endpoints)\n    except json.JSONDecodeError:\n        print(public_endpoints)\n        time.sleep(15)\n    else:\n        break\n```\n\nSample output of the cell:\n\n```console\nEndpoints provisioning in progress... check back in a few minutes\nEndpoints provisioning in progress... check back in a few minutes\nEndpoints provisioning in progress... check back in a few minutes\n```\n\nOnce the endpoints are provisioned, the next cell will open the public endpoints in your browser.\n\n```python\nprint(f\"Visiting {endpoints['ui']} in your browser. You may need to log in there.\")\nimport webbrowser\nwebbrowser.open(f\"https://{endpoints['ui']}\")\n```\n\nSample output of the cell:\n\n```console\nVisiting fekqoap7-orgname-sslpr.snowflakecomputing.app in your browser. You may need to log in there.\n```\n\nIf successful, you'll see the NGINX success page in your browser when visiting the endpoint:\n\n![minidag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-snowflake-python-api/nginx-success.png)\n\nWith just a few lines of Python, we were able to run an NGINX web server in Snowflake using Snowpark Container Services.\n\nThe next cells will suspend and delete the compute pool and the service:\n\n```python\nnew_compute_pool_def.suspend()\nnginx_service.suspend()\n```\n\n```python\nnew_compute_pool_def.delete()\nnginx_service.delete()\n```\n\n\u003C!-- ------------------------ --\u003E\n\n## Conclusion\n\n\nCongratulations! In this Quickstart, you learned the fundamentals for managing Snowflake objects, tasks, DAGs, and Snowpark Container Services using the Snowflake Python API.\n\n### What we've covered\n\n- Installing the Snowflake Python API\n- Creating databases, schemas, and tables\n- Retrieving object information\n- Programmatically updating an object\n- Creating, suspending, and deleting warehouses\n- How to manage tasks and DAGs\n- How to manage Snowpark Container Services\n\n### Additional resources\n\n- [Snowflake Documentation: Snowflake Python API](https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-python-api/snowflake-python-overview)\n\n- [Snowflake Python API Reference Documentation](https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-python-api/reference/0.1.0/index.html)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"isDeveloperGuidesPage":false,"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-e07b023bef","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-44e7496932",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-3d5a53590e","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-408291cb0a","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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