Snowflake Data Clean Rooms: Secure Python UDTF-Based Templates¶

Set up the environment¶

Execute the following commands to set up the Snowflake environment before using developer APIs to work with a Snowflake Data Clean Room. If you don’t have the SAMOOHA_APP_ROLE role, contact your account administrator.

use role samooha_app_role;
use warehouse app_wh;

Create the clean room¶

Create a name for the clean room. Enter a new clean room name to avoid colliding with existing clean room names. Note that clean room names can only be alphanumeric. Clean room names cannot contain special characters other than spaces and underscores.

set cleanroom_name = 'Custom Secure Python UDTF Demo Clean room';

You can create a new clean room with the clean room name set above. If the clean room name set above already exists as an existing clean room, this process will fail.

This procedure takes approximately 45 seconds to run.

The second argument to provider.cleanroom_init is the distribution of the clean room. This can either be INTERNAL or EXTERNAL. For testing purposes, if you are sharing the clean room to an account in the same organization, you can use INTERNAL to bypass the automated security scan which must take place before an application package is released to collaborators. However, if you are sharing this clean room to an account in a different organization, you must use an EXTERNAL clean room distribution.

call samooha_by_snowflake_local_db.provider.cleanroom_init($cleanroom_name, 'INTERNAL');

In order to view the status of the security scan, use:

call samooha_by_snowflake_local_db.provider.view_cleanroom_scan_status($cleanroom_name);

Once you have created your clean room, you must set its release directive before it can be shared with any collaborator. However, if your distribution was set to EXTERNAL, you must first wait for the security scan to complete before setting the release directive. You can continue running the remainder of the steps while the scan runs and return here before the provider.create_cleanroom_listing step.

In order to set the release directive, call:

call samooha_by_snowflake_local_db.provider.set_default_release_directive($cleanroom_name, 'V1_0', '0');

Cross-region sharing¶

In order to share a clean room with a Snowflake customer whose account is in a different region than your account, you must enable Cross-Cloud Auto-Fulfillment. For information about the additional costs associated with collaborating with consumers in other regions, see Cross-Cloud Auto-Fulfillment costs.

When using developer APIs, enabling cross-region sharing is a two-step process:

1. A Snowflake administrator with the ACCOUNTADMIN role enables Cross-Cloud Auto-Fulfillment for your Snowflake account. For instructions, see Collaborate with accounts in different regions.

2. You execute the provider.enable_laf_for_cleanroom command to enable Cross-Cloud Auto-Fulfillment for the clean room. For example:

   call samooha_by_snowflake_local_db.provider.enable_laf_for_cleanroom($cleanroom_name);
   

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After you have enabled Cross-Cloud Auto-Fulfillment for the clean room, you can add consumers to your listing as usual using the provider.create_cleanroom_listing command. The listing is automatically replicated to remote clouds and regions as needed.

Link the dataset, and set the join policy for the dataset¶

Link Snowflake tables into the clean room. Browse through the list of tables in your Snowflake account and enter the fully qualified table names (Database.Schema.Table) as an array. The procedure automatically makes the table accessible to the clean room by creating a secure view of the table from within the clean room, thereby avoiding any need to make a copy of your table.

call samooha_by_snowflake_local_db.provider.link_datasets($cleanroom_name, ['SAMOOHA_SAMPLE_DATABASE_NAV2.DEMO.CUSTOMERS']);

use role accountadmin;
call samooha_by_snowflake_local_db.provider.register_db('<DATABASE_NAME>');
use role samooha_app_role;

You can see the datasets linked to the clean room using the following procedure:

call samooha_by_snowflake_local_db.provider.view_provider_datasets($cleanroom_name);

In order to figure out which columns to use as the join policy, you can look at your dataset to determine the PII columns. To see the top 10 rows, use this query:

select * from SAMOOHA_SAMPLE_DATABASE_NAV2.DEMO.CUSTOMERS limit 10;

Specify which columns the consumer is allowed to join on when running templates within the clean room. This procedure should be called on identity columns like email. The join policy is “replace only”, so if the function is called again, then the previously set join policy is completely replaced by the new one.

call samooha_by_snowflake_local_db.provider.set_join_policy($cleanroom_name, ['SAMOOHA_SAMPLE_DATABASE_NAV2.DEMO.CUSTOMERS:HEM']);

If you want to view the join policy that has been added to the clean room, call the following procedure.

call samooha_by_snowflake_local_db.provider.view_join_policy($cleanroom_name);

Confidentially load custom Python code as UDFs into the clean room¶

This section loads a simple Python UDTF that takes in the age of a customer and the number of days they have been active, and converts the age to a decade and the number of days to years.

The following API allows you to define your Python functions directly as inline functions into the clean room. Alternatively you can load Python from staged files you’ve uploaded into the clean room stage. See the API reference guide for an example.

call samooha_by_snowflake_local_db.provider.load_python_into_cleanroom(
    $cleanroom_name,
    'mod_days_and_age',                     -- Name of the UDF
    ['age integer', 'days integer'],        -- Arguments of the UDF, specified as (variable name, variable type)
    ['pandas', 'numpy'],                    -- Packages UDF will use
    'table(decade integer, years float)',   -- Return type of UDF
    'ModifyAgeAndDays',                     -- Handler
    $$
import pandas as pd
import numpy as np

class ModifyAgeAndDays:
    def __init__(self):
        self.year = 365

    def process(self, age, days):
        rounded_age = int(np.floor(age / 10)) * 10
        years = np.round(days / self.year)
        yield (rounded_age, years)
    $$
);

-- See the versions available inside the clean room
show versions in application package samooha_cleanroom_Custom_Secure_Python_UDTF_Demo_clean_room;

-- Once the security scan is approved, update the release directive to the latest version
call samooha_by_snowflake_local_db.provider.set_default_release_directive($cleanroom_name, 'V1_0', '1');

Add a custom template using the UDTFs¶

To add a custom analysis template to the clean room you need a placeholder for table names on both the provider and consumer sides, along with join columns from the provider side. In SQL Jinja templates, these placeholders must always be:

• source_table: an array of table names from the provider

• my_table: an array of table names from the consumer

Table names can be made dynamic through using these variables, but they can also be hardcoded into the template if desired using the name of the view linked to the clean room. Column names can either be hardcoded into the template, if desired, or set dynamically through parameters. If they are set through parameters, remember that you need to call the parameters dimensions or measure_column, which need to be arrays, in order for them to be checked against the column policy. You add these as SQL Jinja parameters in the template that will be passed in later by the consumer when querying. The join policies ensure that the consumer cannot join on columns other than the authorized ones.

Alternatively, any argument in a custom SQL Jinja template can be checked for compliance with the join and column policies using the following filters:

• join_policy: checks if a string value or filter clause is compliant with the join policy

• column_policy: checks if a string value or filter clause is compliant with the column policy

• join_and_column_policy: checks if columns used for a join in a filter clause are compliant with the join policy, and that columns used as a filter are compliant with the column policy

For example, in the clause {{ provider_id | sqlsafe | join_policy }}, an input of p.HEM will be parsed to check if p.HEM is in the join policy. Note: Only use the sqlsafe filter with caution as it allows collaborators to put pure SQL into the template.

Note that this function overrides any existing template with the same name. If you want to update any existing template, you can simply call this function again with the updated template.

This template simply scans over a table of customers and then returns the age in decades and the number of days active in terms of years.

call samooha_by_snowflake_local_db.provider.add_custom_sql_template(
    $cleanroom_name,
    'prod_custom_udtf_age_days',
$$
    select decade, years from identifier({{ source_table[0] }}) p, table(cleanroom.mod_days_and_age(identifier({{ dimensions[0] | column_policy }}), identifier({{ dimensions[1] | column_policy }})));
$$
);

OPTIONAL: A more complex custom template using the UDTF¶

This template carries out an additional overlap of a consumer table and a provider table first, before applying the UDTF on the inner join and getting additional data from the overlap.

call samooha_by_snowflake_local_db.provider.add_custom_sql_template(
    $cleanroom_name,
    'prod_custom_udtf_age_days_with_overlap',
$$
    select 
        c.status,
        decade, 
        avg(years) as years,
        sum(c.days_active) as days_active_c
    from 
        identifier({{ source_table[0] }}) p 
        inner join 
        identifier({{ my_table[0] }}) c
        on p.hem = c.hem,
        table(cleanroom.mod_days_and_age(identifier({{ dimensions[0] | column_policy }}), identifier({{ dimensions[1] | column_policy }})))
    group by c.status, decade
    order by c.status, decade
$$
);

View the added templates¶

If you want to view the templates that are currently active in the clean room, call the following procedure.

call samooha_by_snowflake_local_db.provider.view_added_templates($cleanroom_name);

Set the column policy on each table¶

Display the data linked to see the columns present inside the table. To view the top 10 rows, call the following procedure.

select * from SAMOOHA_SAMPLE_DATABASE_NAV2.DEMO.CUSTOMERS limit 10;

Set the columns the consumer can group, aggregate (e.g. SUM/AVG) and generally use in an analysis for every table and template combination. This gives flexibility so the same table can allow different column selections depending on the underlying template. This should only be called after adding the template.

Note that the column policy is replace only, so if the function is called again, then the previously set column policy is completely replaced by the new one.

Column policy should not be used on identity columns like email, HEM, RampID, etc. since you don’t want the consumer to be able to group by these columns. In the production environment, the system will intelligently infer PII columns and block this operation, but this feature is not available in the sandbox environment. It should only be used on columns that you want the consumer to be able to aggregate and group by, like Status, Age Band, Channel, Days Active, etc.

Note that for the “column_policy” and “join_policy” to carry out checks on the consumer analysis requests, all column names MUST be referred to as dimensions or measure_columns in the SQL Jinja template. Make sure you use these tags to refer to columns you want to be checked in custom SQL Jinja templates.

call samooha_by_snowflake_local_db.provider.set_column_policy($cleanroom_name, [
    'prod_custom_udtf_age_days:SAMOOHA_SAMPLE_DATABASE_NAV2.DEMO.CUSTOMERS:AGE_BAND', 
    'prod_custom_udtf_age_days:SAMOOHA_SAMPLE_DATABASE_NAV2.DEMO.CUSTOMERS:DAYS_ACTIVE',
    'prod_custom_udtf_age_days_with_overlap:SAMOOHA_SAMPLE_DATABASE_NAV2.DEMO.CUSTOMERS:AGE_BAND', 
    'prod_custom_udtf_age_days_with_overlap:SAMOOHA_SAMPLE_DATABASE_NAV2.DEMO.CUSTOMERS:DAYS_ACTIVE'
]);

To view the added column policy for the template, call the following procedure.

call samooha_by_snowflake_local_db.provider.view_column_policy($cleanroom_name);

Share with a consumer¶

Finally, add a data consumer to the clean room by adding their Snowflake account locator and account names as shown below. The Snowflake account name must be of the form <ORGANIZATION>.<ACCOUNT_NAME>.

show versions in application package samooha_cleanroom_Custom_Secure_Python_UDTF_Demo_clean_room;

call samooha_by_snowflake_local_db.provider.add_consumers($cleanroom_name, '<CONSMUMER_ACCOUNT_LOCATOR>');

Multiple consumer account locators can be passed into the provider.add_consumers function as a comma separated string, or as separate calls to provider.add_consumers.

If you want to view the consumers who have been added to this clean room, call the following procedure.

call samooha_by_snowflake_local_db.provider.view_consumers($cleanroom_name);

View the clean rooms that have been recently created via the following procedure:

call samooha_by_snowflake_local_db.provider.view_cleanrooms();

View more insights on the clean room recently created via the following procedure.

call samooha_by_snowflake_local_db.provider.describe_cleanroom($cleanroom_name);

Any clean room created can also be deleted. The following command drops the clean room entirely, so any consumers who previously had access to the clean room will no longer be able to use it. If a clean room with the same name is desired in the future, it must be re-initialized using the above flow.

call samooha_by_snowflake_local_db.provider.drop_cleanroom($cleanroom_name);

Set up the environment¶

Execute the following commands to set up the Snowflake environment before using developer APIs to work with a Snowflake Data Clean Room. If you don’t have the SAMOOHA_APP_ROLE role, contact your account administrator.

use role samooha_app_role;
use warehouse app_wh;

Install the clean room¶

Once a clean room share has been installed, the list of clean rooms available can be viewed using the below command.

call samooha_by_snowflake_local_db.consumer.view_cleanrooms();

Assign a name for the clean room that the provider has shared with you.

set cleanroom_name = 'Custom Secure Python UDTF Demo Clean room';

The following command installs the clean room on the consumer account with the associated provider and selected clean room.

This procedure takes approximately 45 seconds to run.

call samooha_by_snowflake_local_db.consumer.install_cleanroom($cleanroom_name, '<PROVIDER_ACCOUNT_LOCATOR>');

Once the clean room has been installed, the provider has to finish setting up the clean room on their side before it is enabled for use. The below function allows you to check the status of the clean room. Once it has been enabled, you should be able to run the Run Analysis command below. It typically takes about 1 minute for the clean room to be enabled.

call samooha_by_snowflake_local_db.consumer.is_enabled($cleanroom_name);

Run the analysis¶

Now that the clean room is installed, you can run the analysis template added to the clean room by the provider using a “run_analysis” command. You can see how each field is determined in the sections below.

The number of datasets passable is constrained by the template the provider has implemented. Some templates require a specific number of tables. The template creator can impose the requirements that they wish to support.

call samooha_by_snowflake_local_db.consumer.run_analysis(
    $cleanroom_name,
    'prod_custom_udtf_age_days',
    [],                                         -- The consumer tables go here
    ['SAMOOHA_SAMPLE_DATABASE_NAV2.DEMO.CUSTOMERS'], -- The provider tables go here
    object_construct(
        'dimensions', ['p.age_band', 'p.days_active']  -- Any parameters the template needs will go here
    )
);

For each of the columns we need to refer to either the dataset filtering “where_clause”, or the dimensions or measure_columns, you can use p. to refer to fields in provider tables, and c. to refer to fields in consumer tables. Use p2, p3, etc. for more than one provider table and c2, c3, etc. for more than one consumer table.

OPTIONAL: Run the analysis¶

The following run_analysis call gets the results for the optional example that first runs the overlap. Note you first have to link your dataset to the cleanroom using the following procedure:

call samooha_by_snowflake_local_db.consumer.link_datasets($cleanroom_name, ['SAMOOHA_SAMPLE_DATABASE_NAV2.DEMO.CUSTOMERS']);

use role accountadmin;
call samooha_by_snowflake_local_db.consumer.register_db('<DATABASE_NAME>');
use role samooha_app_role;

Now, you can run the analysis:

call samooha_by_snowflake_local_db.consumer.run_analysis(
    $cleanroom_name,
    'prod_custom_udtf_age_days_with_overlap',
    ['SAMOOHA_SAMPLE_DATABASE_NAV2.DEMO.CUSTOMERS'],  -- The consumer tables go here
    ['SAMOOHA_SAMPLE_DATABASE_NAV2.DEMO.CUSTOMERS'], -- The provider tables go here
    object_construct(
        'dimensions', ['p.age_band', 'p.days_active']  -- Any parameters the template needs will go here
    )
);

For each of the columns we need to refer to either the dataset filtering “where_clause”, or the dimensions or measure_columns, you can use p. to refer to fields in provider tables, and c. to refer to fields in consumer tables. Use p2, p3, etc. for more than one provider table and c2, c3, etc. for more than one consumer table.

How to determine the inputs to run_analysis¶

To run the analysis, you need to pass in some parameters to the run_analysis function. This section shows you how to determine what parameters to pass in.

Template names

First, you can see the supported analysis templates by calling the following procedure.

call samooha_by_snowflake_local_db.consumer.view_added_templates($cleanroom_name);

Before running an analysis with a template, you need to know what arguments to specify and what types are expected. For custom templates, you can execute the following.

call samooha_by_snowflake_local_db.consumer.view_template_definition($cleanroom_name, 'prod_custom_udtf_age_days');

This can often also contain a large number of different SQL Jinja parameters. The following functionality parses the SQL Jinja template and extracts the arguments that need to be specified in run_analysis into a list.

call samooha_by_snowflake_local_db.consumer.get_arguments_from_template($cleanroom_name, 'prod_custom_udtf_age_days');

Dataset names

If you want to view the dataset names that have been added to the clean room by the provider, call the following procedure. Note that you cannot view the data present in the datasets that have been added to the clean room by the provider due to the security properties of the clean room.

call samooha_by_snowflake_local_db.consumer.view_provider_datasets($cleanroom_name);

You can also see the tables you’ve linked to the clean room by using the following call:

call samooha_by_snowflake_local_db.consumer.view_consumer_datasets($cleanroom_name);

Dimension and measure columns

While running the analysis, you might want to filter, group by and aggregate on certain columns. If you want to view the column policy that has been added to the clean room by the provider, call the following procedure.

call samooha_by_snowflake_local_db.consumer.view_provider_column_policy($cleanroom_name);

Common errors

If you are getting Not approved: unauthorized columns used error as a result of run analysis, you may want to view the join policy and column policy set by the provider again.

call samooha_by_snowflake_local_db.consumer.view_provider_join_policy($cleanroom_name);
call samooha_by_snowflake_local_db.consumer.view_provider_column_policy($cleanroom_name);

It is also possible that you have exhausted your privacy budget, which prevents you from executing more queries. Your remaining privacy budget can be viewed using the below command. It resets daily, or the clean room provider can reset it if they wish.

call samooha_by_snowflake_local_db.consumer.view_remaining_privacy_budget($cleanroom_name);

You can check if Differential Privacy has been enabled for your clean room using the following API:

call samooha_by_snowflake_local_db.consumer.is_dp_enabled($cleanroom_name);