Metadata-Version: 2.1
Name: snowconn
Version: 3.8.3
Summary: Python utilities for connection to the Snowflake data warehouse
Home-page: https://github.com/Daltix/snowconn
Author: Daltix NV
Author-email: snowconn@daltix.com
License: UNKNOWN
Description: # SnowConn
        
        This repository is a wrapper around the [snowflake SQLAlchemy](https://docs.snowflake.net/manuals/user-guide/sqlalchemy.html)
        library. It manages the creation of connections and provides a few convenience functions that should be good enough
        to cover most use cases yet be flexible enough to allow additional wrappers to be written around to serve more specific
        use cases for different teams.
        
        ---
        
        ## Installation
        
        To install latest version released to pypi with pip:
        
        ```bash
        pip install snowconn
        ```
        
        To install the latest version directly from the repo:
        
        ```bash
        pip install 'git+ssh://git@github.com/Daltix/SnowConn.git@master#egg=snowconn'
        ```
        
        If you want to use pandas functionality (read/write from/to pandas dataframes) you can install
        as follows:
        
        ```bash
        pip install snowconn[pandas]
        ```
        
        If you want to enable SSO authentication you can install as follows:
        
        ```bash
        pip install snowconn[storage]
        ```
        
        If you want to install all functionality (AWS secrets manager connection, SSO, pandas) you can install as follows:
        
        ```bash
        pip install snowconn[all]
        ```
        
        ---
        
        ## Connection
        
        Everything is implemented in a single `SnowConn` class. To import it is always the same:
        
        ```py
        from snowconn import SnowConn
        ```
        
        ### (1) Connection using your own personal creds
        
        Install [snowsql](https://docs.snowflake.net/manuals/user-guide/snowsql-install-config.html)
        and configure `~/.snowsql/config` as per the instructions
        
        You can test that it is correctly installed by then executing `snowsql`
        from the command line.
        
        *WARNING* Be sure to configure your account name like the following:
        
        ```
        accountname = ACCOUNT_ID.REGION
        ```
        
        *(example `accountname = eq90000.eu-west-1`)*
        
        If you don't include the region part (`eu-west-1` in the example above), it will hang for about a minute and then give you a permission denied.
        
        Now that you are able to execute `snowsql` to successfully connect, you are ready to use the `SnowConn.connect` function:
        
        ```py
        with SnowConn.connect() as conn:
            # your conn. code here
        ```
        That's it you are connected! You can connect to a specific schema / database with the following:
        
        ```py
        with SnowConn.connect('daltix_database', 'public') as conn:
            # your conn. code here
        ```
        
        ** NOTE: Connect using SSO **
        If you are using SSO (Okta or others), you need to update your .snowsqlk/config with the following modifications:
        - Include an "authenticator" line, [see here](https://docs.snowflake.com/en/user-guide/admin-security-fed-auth-use.html#using-sso-with-client-applications-that-connect-to-snowflake) for possible values
        and their meaning).
        - replace username for your username (instead of your snowflake username)
        
        
        ### (2) Connection using AWS Secrets Manager
        
        You need to have boto3 installed which you can do so with the following:
        
        ```
        pip install boto3
        ```
        
        Now you must satisfy the folloing requirements:
        
        1. Have a secret stored in an accessable aws account
        1. The secret must have the following keys:
            - `USERNAME`
            - `PASSWORD`
            - `ACCOUNT`
            - `ROLE`
        
        For this example, we will assume the `price_plotter` is the secret manager that we will be using.
        
        Now that you know the name of the secret, you MUST be sure that the context in which it is running has access to read
        that secret. Once this is done, you can now execute the following code:
        
        ```py
        with SnowConn.connect(methods=['secretsmanager'], credsman_name='price_plotter') as conn:
            # your conn. code here
        ```
        
        Alternatively you can use the specific `connect_secretsmanager` method:
        
        ```py
        with SnowConn.connect_secretsmanager('price_plotter') as conn:
            # your conn. code here
        ```
        
        And you are connected! You can also pass the database and schema along
        
        ```py
        with SnowConn.connect_secretsmanager('price_plotter', 'daltix', 'public') as conn:
            # your conn. code here
        ```
        
        An example of a policy that gives access to the `price_plotter` looks like this:
        
        ```
        {
            "Version": "2012-10-17",
            "Statement": [
                {
                    "Sid": "VisualEditor0",
                    "Effect": "Allow",
                    "Action": [
                        "secretsmanager:GetResourcePolicy",
                        "secretsmanager:GetSecretValue",
                        "secretsmanager:DescribeSecret",
                        "secretsmanager:ListSecretVersionIds"
                    ],
                    "Resource": "arn:aws:secretsmanager:eu-west-1:<your-account-number>:secret:price_plotter-AdcNpp"
                }
            ]
        }
        ```
        
        And an example of this in a serverless.yml looks like this:
        
        ```
        iamRoleStatements:
            - Effect: Allow
              Action:
                - secretsmanager:DescribeSecret
                - secretsmanager:List*
              Resource:
                - "*"
            - Effect: Allow
              Action:
                - secretsmanager:*
              Resource:
                - { Fn::Sub: "arn:aws:secretsmanager:${AWS::Region}:${AWS::AccountId}:secret:price_plotter-??????" }
        ```
        
        ---
        
        ## API
        
        Now that you're connected, there are a few low-level functions that you can use to programatically interact with
        the snowflake tables that you have access to.
        
        The rest of these examples assume that you have used one of the above methods to connect and have access to the
        `daltix.public.price` table.
        
        ### Creating a connection
        
        Creating a connection is very easy (see examples above for connection options):
        
        ```py
        with SnowConn.connect() as conn:
            # your conn. code here
        ```
        
        You can also create connections manually without using a context (this is not recommended, see *Known Issues* section below), make sure to close the connection after you are done:
        
        ```py
        conn = SnowConn.connect()
        # your conn. code here
        conn.close() # close the connection when done
        ```
        
        ### execute_simple
        
        The exc_simple function is used for when you have a single statement to execute and the result set can fit into memory. It
        takes a single argument which a string of the SQL statement that you with to execute. Take the following for example:
        
        ```py
        >>> conn.execute_simple('select * from price limit 1;')
        [{'DALTIX_ID': '0d3c30353035a6ab5747237a1f2600bbf5ddd27401372c5effe0f2790a88ad56', 'SHOP': 'ahed', 'COUNTRY': 'de', 'PRODUCT_ID': '616846.0', 'LOCATION': 'base', 'PRICE': 37.99, 'PROMO_PRICE': None, 'PRICE_STD': None, 'PROMO_PRICE_STD': None, 'UNIT': None, 'UNIT_STD': None, 'IS_MAIN': True, 'VENDOR': None, 'VENDOR_STD': None, 'DOWNLOADED_ON': datetime.datetime(2018, 11, 18, 0, 0, 1), 'DOWNLOADED_ON_LOCAL': datetime.datetime(2018, 11, 18, 1, 0, 1), 'DOWNLOADED_ON_DATE': datetime.date(2018, 11, 18), 'IS_LATEST_PRICE': False}]
        ```
        
        ### execute_string
        
        If you have multiple sql statements in a single string that you want to execute or the resultset is larger than
        will fit into memory, this is the function that you want to use. It returns a list of cursors that are a result
        of each of the statements that are contained in the string. See [here](https://docs.snowflake.net/manuals/user-guide/python-connector-api.html#execute_string) for the full documentation.
        
        ```py
        >>> conn.execute_string('create temporary table price_small as (select * from price limit 1); select * from price_small;')
        [<snowflake.connector.cursor.SnowflakeCursor object at 0x10f537898>, <snowflake.connector.cursor.SnowflakeCursor object at 0x10f52c588>]
        ```
        
        ### execute_file
        
        If you have the contents of an sql file that you want to execute, you can use this function. For example:
        
        ```bash
        echo "select * from price limit 1;" > query.sql
        ```
        
        ```py
        >>> conn.execute_file('query.sql')
        >>> [<snowflake.connector.cursor.SnowflakeCursor object at 0x1188d6390>]
        ```
        This also returns a list of cursors the same as `execute_string` does. In fact, this function is nothing more than a very
        simple wrapper around `execute_string`.
        
        ### read_df
        
        Use this function to read the results of a query into a dataframe. Note that pandas is NOT a dependency of this repo so
        if you want to use it you must satisfy this dependency yourself.
        
        It takes one sql string as an argument and returns a dataframe.
        
        ```bash
        >>> conn.read_df('select daltix_id, downloaded_on, price from price limit 5;')
                                                daltix_id       downloaded_on  price
        0  0d3c30353035a6ab5747237a1f2600bbf5ddd27401372c 2018-11-18 00:00:01  37.99
        1  f5be8a5da3bde2da6a63fcad4e5c30823027324092234c 2018-11-18 00:00:02   9.99
        2  f5be8a5da3bde2da6a63fcad4e5c30823027324092234c 2018-11-18 00:00:02   0.40
        3  807e2a7706b8c515264fa55bed3891d5685ac5ee0148f0 2018-11-18 00:00:04   3.70
        4  1e56339f99dc866cd4b87679aa686556a5ad2398d00c95 2018-11-18 00:00:06   3.76
        >>>
        ```
        
        ### write_df
        
        Use this to write a dataframe to Snowflake. This is a very thin wrapper around the pandas [DataFrame.to_sql()](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_sql.html) function.
        
        Unfortunately, it doesn't play nice with dictionaries and arrays so the use cases are quite limited. Hopefully
        we will improve upon this in the future.
        
        ### get_current_role
        
        Returns the current role.
        
        ### close
        
        Use this to cleanly close all connections that have ever been associated with this instance of SnowConn. If you don't
        use this your process will hang for a while without saying anything before it actually exits.
        
        ## Accessing the connection objects directly
        
        These functions are mostly wrappers around 2 connection libraries:
        
        - [The snowflake python connector](https://docs.snowflake.net/manuals/user-guide/python-connector-api.html)
        - [The snowflake SQLAlchemy library](https://docs.snowflake.net/manuals/user-guide/sqlalchemy.html)
        
        Should you need to use either of these yourself, you can ask for the connections yourself with the following
        functions:
        
        ### get_raw_connection
        
        This will return the instance of a snowflake connector which is documented [here](https://docs.snowflake.net/manuals/user-guide/python-connector-api.html#connect). It is a good choice if you have very simple needs and for some reason none
        of the functions in the rest of this repo are serving your needs.
        
        ### get_alchemy_engine
        
        This is the result of [create_engine()](https://docs.snowflake.net/manuals/user-guide/sqlalchemy.html#connection-parameters)
        which was called during `connect()` . It does not represent an active connection to the database
        but rather acts as a factory for connections.
        
        This is useful for using the most commonly abstracted things in other libraries such as dashboards, pandas, etc. 
        However, like SQLAlchemy in general, despite being very widely supported and feature-complete, it is not the simplest 
        API so it should probably not be your first choice unless you know exactly that you need it.
        
        ### get_connection
        
        This returns the result of the creating the sqlalchemy engine and then calling `connect()` on it. Unlike the result
        of `get_alchemy_engine` this represents an active connection to Snowflake and this has a session associated with it.
        
        You can see the object documentation [here](https://docs.snowflake.net/manuals/user-guide/sqlalchemy.html#parameters-and-behavior)
        
        ## Known issues
        
        There is a bug with `snowflake-connector` which causes some connections to Snowflake to not close properly in certain circumstances. This can cause timeout errors.
        
        You can handle this in two ways: the first is to wrap usage of the connection in a `try/finally` block to ensure the connection is explicitly closed, like this:
        ```
        from snowconn import SnowConn
        conn = SnowConn.connect(...)
        try:
            result = execute_string(query) # or result = read_df(query), etc
        finally:
            conn.close()
        ```
        
        The second way is to use SnowConn with the `with` syntax, as follows:
        ```
        with SnowConn.connect() as conn:
            conn.read_df(...)
        ```
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: pandas
Provides-Extra: storage
Provides-Extra: all
