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Feature View

Once you have done identity stitching to unify the identity of your users across all the cross-platforms, you can evaluate and maintain the required features/traits for each identified user using a feature view model.

Prerequisites

• A basic Profile Builder project by following the Profile Builder CLI steps.
• Structure of a Profile Builder project and the parameters used in different files.
• Identity Stitching model as Feature View reuses its output to extract the required features/traits.

Feature View model

You can define and extract the required features/traits for an entity from your data warehouse using the feature view model. Once done, you can send them to the downstream destinations. A destination could either be the Activation API or any Reverse ETL destination that RudderStack supports. Each such destination requires data in the form of a table with an ID column and one or more feature columns.

You can use the Feature View model to access the entity features based on any ID type and create a view having all or a specified set of entity features across the project. It also lets you unify the traits/features (defined using entity_vars) and ML models to generate a comprehensive customer 360 table.

RudderStack assigns a default name to the model, if not provided, and adds all the available features on the entity into a view by default. The feature_view model creates the following views after each run:

• View of the current run, hash, and sequence number.
• Final view that always looks back at the most recent run.

The feature_view model is almost an exact replica of the entity var table in a view format. The only difference is that:

• The entity var table always has all the entity_vars defined for a particular entity.
• In feature view, if you explicitly define an entity_var solely as a helper var, (thereby not needed for activation) you can ensure that the var is not used for any activation tables. Feature view will only have entity_vars that do not have the is_feature key set to false.

It provides a quick actionable view with the main_id as the primary key. It also acts as the base (or data source) for the custom feature view model which is created using other entity aliases as primary keys.

The pb_project.yaml file for a default feature view model:

...
entities:
  - name: user
    id_types:
      - main_id
      - user_id
    feature_views:
      using_ids:
        - id: email # primary key for features_by_email view
          name: features_by_email
        - id: salesforce_id # primary key for salesforce_id_stitched_features view
          name: salesforce_id_stitched_features
      features:
        - from: models/feature_table_1 # include everything from `feature_table_1` (default behaviour if `include` is not specified)
          include: ["*"]
        - from: models/feature_table_2 # exclude 'middle_name' feature from `feature_table_2`
          exclude: ["middle_name"] 

As seen in the above YAML, the feature_views key is defined within the entity block under a single entity. This configuration will output two views (features_by_email and salesforce_id_stitched_features) from the entity/all cohort - a default cohort which gets defined automatically when you run the project.

Sample project

This sample project uses the output of an identity stitching model as an input to create a feature view. The following sections describe how to define your PB project files:

Project detail

The pb_project.yaml file defines the project details such as name, schema version, connection name and the entities which represent different identifiers.

You can define all the identifiers from different input sources you want to stitch together as a user_main_id:

You need to add main_id to the list only if you have defined main_id_type: main_id in the ID stitcher spec.

# Project name
name: sample_id_stitching
# Project's yaml schema version
schema_version: 80
# Warehouse connection
connection: test
# Folder containing models
model_folders:
  - models
# Entities in this project and their ids.
entities:
  - name: user
    id_types:
      - main_id # You need to add `main_id` to the list only if you have defined `main_id_type: main_id` in the id stitcher spec.
      - user_id # one of the identifier from your data source.
      - email
id_types:
  - name: user_id
  - name: email
    filters:
      - type: include
        regex: ".+@.+"
      - type: exclude
        value: "test@company.com"

Input

The input file file includes the input table references and corresponding SQL for the above-mentioned entities:

inputs:
- name: rsIdentifies
  contract: # constraints that a model adheres to
    is_optional: false
    is_event_stream: true
    with_entity_ids:
      - user
    with_columns:
      - name: timestamp
      - name: user_id
      - name: anonymous_id
      - name: email
  app_defaults:
    table: rudder_events_production.web.identifies # one of the WH table RudderStack generates when processing identify or track events.
    occurred_at_col: timestamp
    ids:
      - select: "user_id" # kind of identity sql to pick this column from above table.
        type: user_id
        entity: user # as defined in project file
        to_default_stitcher: true
      - select: "anonymous_id"
        type: anonymous_id
        entity: user
        to_default_stitcher: true
      - select: "lower(email)" # can use sql.
        type: email
        entity: user
        to_default_stitcher: true
- name: rsTracks
  contract:
    is_optional: false
    is_event_stream: true
    with_entity_ids:
      - user
    with_columns:
      - name: timestamp
      - name: user_id
      - name: anonymous_id
  app_defaults:
    table: rudder_events_production.web.tracks # another table in WH maintained by RudderStack processing track events.
    occurred_at_col: timestamp
    ids:
      - select: "user_id"
        type: user_id
        entity: user
        to_default_stitcher: true
      - select: "anonymous_id"
        type: anonymous_id
        entity: user
        to_default_stitcher: true

Model

The feature view model lets you define and extract the features/traits from your warehouse tables. Each feature is defined using an entity_var.

A sample profiles.yaml file specifying a feature view model:

var_groups:
  - name: first_group
    entity_key: user
    vars:
      - entity_var:
          name: first_seen
          select: min(timestamp::date)
          from: inputs/rsTracks
          where: properties_country is not null and properties_country != ''
      - entity_var:
          name: last_seen
          select: max(timestamp::date)
          from: inputs/rsTracks
          is_feature: false # Specifies the entity_var is not a feature
      - entity_var:
          name: user_lifespan
          select: '{{user.last_seen}} - {{user.first_seen}}'
          description: Life Time Value of a customer
      - entity_var:
          name: days_active
          select: count(distinct timestamp::date)
          from: inputs/rsTracks
          description: No. of days a customer was active
      - entity_var:
          name: campaign_source
          default_value: "'organic'"
      - entity_var:
          name: user_rank
          default_value: -1
      - entity_var:
          name: campaign_source_first_touch
          select: first_value(context_campaign_source)
          window:
              order_by:
                  - timestamp asc
              partition_by:
                  - main_id
          from: inputs/rsIdentifies
          where: context_campaign_source is not null and context_campaign_source != ''
      - input_var:
          name: num_c_rank_num_b_partition
          select: rank()
          from: inputs/tbl_c
          default_value: -1
          window:
            partition_by:
              - '{{tbl_c}}.num_b'
            order_by:
              - '{{tbl_c}}.num_c asc'
          where: '{{tbl_c}}.num_b >= 10'
      - entity_var:
          name: min_num_c_rank_num_b_partition
          select: min(num_c_rank_num_b_partition)
          from: inputs/tbl_c

var_groups

The var_groups field groups all the vars under it and provides the provision to define any configuration keys that need to be shared across vars.

entity_var

The entity_var field provides inputs for the feature view model. This variable stores the data temporarily, however, you can choose to store its data permanently by specifying the name in it as a feature in the features key.

input_var

The syntax of input_var is similar to entity_var, with the only difference that instead of each value being associated to a row of the feature view, it’s associated with a row of the specified input. While you can think of an entity_var as adding a helper column to the feature view, you can consider an input_var as adding a helper column to the input.

If more than one input_var are required to derive an entity_var, then all the input_var must be defined on the same table.

window

In window option, main_id is not added by default, it can be any arbitrary list of columns from the input table. So if a feature should be partitioned by main_id, you must add it in the partition_by key.

Output

After running the project, you can view the generated material tables.

A sample output containing the results in Snowflake:

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