Snowflake Data Clean Rooms: Secure Python UDTF-Based Templates

This topic describes the provider and consumer flows needed to programmatically set up a clean room, share it with a consumer, and run analyses on it using secure Python UDTFs loaded into the clean room. In this flow, a provider loads secure Python code for a UDTF into the clean room using an API that keeps the underlying Python code completely confidential from the consumer. The provider allows the consumer to access their UDTF by exposing a simple template for them to call it by. This makes sure that the consumer is using it only in the way intended by the provider.

The key aspects of this flow above other examples are:

  1. Provider:

    a. Securely load a confidential Python UDTF into a new clean room.

    b. Create a simple custom SQL Jinja template calling the Python UDTF.

    c. Share it with a consumer.

  2. Consumer:

    a. Examine the template provided within the clean room (note: the underlying Python code is completely confidential, only the SQL Jinja template will be visible).

    b. Run an analysis within the clean room using the UDTF.

This topic covers some simple examples using UDTFs. More complex examples involving using Python UDFs or Python for Machine Learning are also available.

Prerequisites

You need two separate Snowflake accounts to complete this flow. Use the first account to execute the provider’s commands, then switch to the second account to execute the consumer’s commands.

Provider

Note

The following commands should be run in a Snowflake worksheet in the provider account.

Set up the environment

The following commands are required for running the developer edition of a Snowflake Data Clean Room. If you don’t have the following role, contact your account administrator.

use role samooha_app_role;
use warehouse app_wh;
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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';
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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');
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In order to view the status of the security scan, use:

call samooha_by_snowflake_local_db.provider.view_cleanroom_scan_status($cleanroom_name);
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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');
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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.

In developer edition, 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.

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)
    $$
);
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Note

Loading Python into the clean room creates a new patch for the clean room. If your clean room distribution is set to EXTERNAL, you need to wait for the security scan to complete, then update the default release directive using:

-- 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');
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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

All provider/consumer tables must be referenced using these arguments since the name of the secure view actually linked to the clean room will be different to the table name. Critically, provider table aliases MUST be p (or p1), p2, p3, p4, etc. and consumer table aliases must be c (or c1), c2, c3, etc. This is required in order to enforce security policies in the clean room.

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

Note

In order to call the following procedures, make sure you have first set the release directive using provider.set_default_release_directive. You can see the latest available version and patches using:

show versions in application package samooha_cleanroom_Custom_Secure_Python_UDTF_Demo_clean_room;
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call samooha_by_snowflake_local_db.provider.add_consumers($cleanroom_name, '<CONSMUMER_ACCOUNT_LOCATOR>');
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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);
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View the clean rooms that have been recently created via the following procedure:

call samooha_by_snowflake_local_db.provider.view_cleanrooms();
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View more insights on the clean room recently created via the following procedure.

call samooha_by_snowflake_local_db.provider.describe_cleanroom($cleanroom_name);
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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);
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Note

The provider flow is now finished. Switch to the consumer account to continue with consumer flow.

Consumer

Note

The following commands should be run in a Snowflake worksheet in the consumer account

Set up the environment

The following commands are required for running the developer edition of a Snowflake Data Clean Room.

use role samooha_app_role;
use warehouse app_wh;
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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();
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Assign a name for the clean room that the provider has shared with you.

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

Note

Before running the analysis, you can alter the warehouse size, or use a new, bigger, warehouse size if your tables are large.

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
    )
);
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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']);
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Note

If this step doesn’t work even though your table exists, it is likely the SAMOOHA_APP_ROLE role has not yet been given access to it. If so, switch to the ACCOUNTADMIN role, call the below procedure on the database, and then switch back for the rest of the flow:

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