Step-by-step tutorial on creating an feature view models.
10 minute read
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.
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:userid_types:- main_id- user_idfeature_views:using_ids:- id:email# primary key for features_by_email viewname:features_by_email- id:salesforce_id# primary key for salesforce_id_stitched_features viewname:salesforce_id_stitched_featuresfeatures:- 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 namename:sample_id_stitching# Project's yaml schema versionschema_version:80# Warehouse connectionconnection:test# Folder containing modelsmodel_folders:- models# Entities in this project and their ids.entities:- name:userid_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.- emailid_types:- name:user_id- name:emailfilters:- type:includeregex:".+@.+"- type:excludevalue:"test@company.com"
Input
The input file file includes the input table references and corresponding SQL for the above-mentioned entities:
inputs:- name:rsIdentifiescontract:# constraints that a model adheres tois_optional:falseis_event_stream:truewith_entity_ids:- userwith_columns:- name:timestamp- name:user_id- name:anonymous_id- name:emailapp_defaults:table:rudder_events_production.web.identifies# one of the WH table RudderStack generates when processing identify or track events.occurred_at_col:timestampids:- select:"user_id"# kind of identity sql to pick this column from above table.type:user_identity:user# as defined in project fileto_default_stitcher:true- select:"anonymous_id"type:anonymous_identity:userto_default_stitcher:true- select:"lower(email)"# can use sql.type:emailentity:userto_default_stitcher:true- name:rsTrackscontract:is_optional:falseis_event_stream:truewith_entity_ids:- userwith_columns:- name:timestamp- name:user_id- name:anonymous_idapp_defaults:table:rudder_events_production.web.tracks# another table in WH maintained by RudderStack processing track events.occurred_at_col:timestampids:- select:"user_id"type:user_identity:userto_default_stitcher:true- select:"anonymous_id"type:anonymous_identity:userto_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_groupentity_key:uservars:- entity_var:name:first_seenselect:min(timestamp::date)from:inputs/rsTrackswhere:properties_country is not null and properties_country != ''- entity_var:name:last_seenselect:max(timestamp::date)from:inputs/rsTracksis_feature:false# Specifies the entity_var is not a feature- entity_var:name:user_lifespanselect:'{{user.last_seen}} - {{user.first_seen}}'description:Life Time Value of a customer- entity_var:name:days_activeselect:count(distinct timestamp::date)from:inputs/rsTracksdescription:No. of days a customer was active- entity_var:name:campaign_sourcedefault_value:"'organic'"- entity_var:name:user_rankdefault_value:-1- entity_var:name:campaign_source_first_touchselect:first_value(context_campaign_source)window:order_by:- timestamp ascpartition_by:- main_idfrom:inputs/rsIdentifieswhere:context_campaign_source is not null and context_campaign_source != ''- input_var:name:num_c_rank_num_b_partitionselect:rank()from:inputs/tbl_cdefault_value:-1window: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_partitionselect: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.
Field
Data type
Description
name
String
Name to identify the var_groups uniquely.
entity_key
String
Specifies the entity to be used.
vars
List
Specifies the entity_var and input_var variables.
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.
Field
Data type
Description
name
String
Name of the entity_var to identify it uniquely.
select
String
Column name/value you want to select from the table. This defines the actual value that will be stored in the variable. You can use simple SQL expressions or select an entity_var as {{entityName.Var(\"entity_var\")}}. It has to be an aggregate operation that ensures the output is a unique value for a given main_id. For example: min(timestamp), count(*), sum(amount) etc. This holds true even when a window function (optional) is used. For example:: first_value(), last_value() etc are valid while rank(), row_number(), etc. are not valid and give unpredictable results.
from
List
Reference to the source table from where data is to be fetched. You can either refer to another model from the same YAML or some other table specified in input YAML.
where
String
Any filters you want to apply on the input table before selecting a value. This must be SQL compatible and should consider the data type of the table.
default_value
String
Default value in case no data matches the filter. When defining default values, make sure to enclose the string values in single quotes followed by double quotes to avoid SQL failure. However, you can use the non-string values without any quotes.
description
String
Textual description of the entity_var.
is_feature
Boolean
Determines whether the entity_var is a feature. The default value is true.
window
Object
Specifies the window function. Window functions in SQL usually have both partition_by and order_by properties. But for entity_var, partition_by is added with main_id as default; so, adding partition_by manually is not supported. If you need partitioning on other columns too, check out input_var where partition_by on arbitrary and multiple columns is supported.
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.
Field
Data type
Description
name
String
Name to store the retrieved data.
select
String
Data to be stored in the name.
from
List
Reference to the source table from where data is to be fetched.
where
String
(Optional) Applies conditions for fetching data.
default_value
String
(Optional) Default value for any entity for which the calculated value would otherwise be NULL.
description
String
(Optional) Textual description.
window
Object
(Optional) Specifies a window over which the value should be calculated.
window
Field
Data type
Description
partition_by
String
(Optional) List of SQL expressions to use in partitioning the data.
order_by
String
(Optional) List of SQL expressions to use in ordering the data.
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.
In the left sidebar, click Database and the corresponding Schema to view the list of all tables. You can hover over a table to see the full table name along with its creation date and time.
Write a SQL query like select * from <table_name> and execute it to see the results:
Open Postico2. If required, create a new connection by entering the relevant details. Click Test Connection followed by Connect.
Click the + icon next to Queries in the left sidebar.
You can click Database and the corresponding schema to view the list of all tables/views.
Double click on the appropriate view name to paste the name on an empty worksheet.
You can prefix SELECT * from the view name pasted previously and suffix LIMIT 10; at the end.
Press Cmd+Enter keys, or click the Run button to execute the query.
Enter your Databricks workspace URL in the web browser and log in with your username and password.
Click the Catalog icon in left sidebar.
Choose the appropriate catalog from the list and click on it to view contents.
You will see list of tables/views. Click the appropriate table/view name to paste the name on worksheet.
You can prefix SELECT * FROM before the pasted view name and suffix LIMIT 10; at the end.
Select the query text. Press Cmd+Enter, or click the Run button to execute the query.
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