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Project Structure

Know the specifications of a site configuration file, PB project structure, configuration files, and their parameters.

8 minute read

Once you complete the Profile Builder CLI steps, you will be able to see the Profiles project on your machine.

Site configuration file

RudderStack creates a site configuration file (~/.pb/siteconfig.yaml) while creating a warehouse connection. It contains the following details including secrets (if any):

Warehouse connection details and its credentials.
Git repository connection credentials (if any).

If you have multiple Profiles projects and they use different warehouse connections, you can store the details for multiple connections in the same site configuration file.

A sample site configuration file containing multiple warehouse connection details is shown below:

connections:
  prod-db-profile:
      target: dev
      outputs:
          dev:
              account: inb828.us-west-3
              dbname: MAT_STORE
              password: password
              role: PROFILES_ROLE
              schema: AB_SCHEMA
              type: snowflake
              user: rik
              warehouse: PROD_WAREHOUSE
  test-db-profile:
      target: test
      outputs:
          db:
              access_token: dabasihasdho
              catalog: rs_dev
              host: adb-98.18.azuredatabricks.net
              http_endpoint: /sql/1.0/warehouses/919uasdn92h
              port: 443
              schema: rs_profiles
              type: databricks
              user: johndoe@abc.onmicrosoft.com
          dev:
              account: uk12.us-west-1
              dbname: RUDDERSTACK_DB
              password: password
              role: RS_ROLE
              schema: RS_PROFILES
              type: snowflake
              user: johndoe
              warehouse: RS_WAREHOUSE
          redshift_v1:
              dbname: warehouse_rs
              host: warehouse.abc.us-east-3.redshift.amazonaws.com
              password: password
              port: 5419
              schema: rs_profiles
              type: redshift
              user: redshift_user
          redshift_v2:
              workgroup_name: warehouse_workgroup
              region: us-east-1
              driver: v2
              sslmode: require
              dbname: warehouse_rs
              schema: rs_profiles
              type: redshift
              access_key_id: ******************
              secret_access_key: ******************************
           big:
              credentials:
                auth_provider_x509_cert_url: https://www.googleapis.com/oauth2/v1/certs
                auth_uri: https://accounts.google.com/o/oauth2/auth
                client_email: johndoe@big-query-integration-poc.iam.gserviceaccount.com
                client_id: "123345678909872"
                client_x509_cert_url: https://www.googleapis.com/robot/v1/metadata/x509/johndoe%40big-query-integration-poc.iam.gserviceaccount.com
                private_key: |
                    -----BEGIN PRIVATE KEY-----                    
                   ## private key
                    -----END PRIVATE KEY-----
                private_key_id: 5271368bhjbd72y278222e233w23e231e
              project_id: big-query-integration-poc
                token_uri: https://oauth2.googleapis.com/token
                type: service_account
                project_id: rs_profiles
              schema: rs_profiles
              type: bigquery
              user: johndoe@big-query-integration-poc.iam.gserviceaccount.com
gitcreds:
 - reporegex: "git@github.com:REPO_OWNER/*" # in case of ssh url
   key: |
       -----BEGIN OPENSSH PRIVATE KEY-----
       **********************************************************************
       **********************************************************************
       **********************************************************************
       **********************************************************************
       ****************************************************************
       -----END OPENSSH PRIVATE KEY-----       
 - reporegex: "https://github.com/rudderlabs/*" # https url
   basic_auth:
     username: oauth2
     password: ... # your personal access token with read permission
py_models:
    enabled: true # in case you are using Python models in your project, else set it to false
    python_path: /opt/anaconda3/bin/python # the path where Python is installed (run `which python` to get the full path). If `py_models` is not enabled, set it to `""`. For Windows, you may pass the path value as: python.exe
    credentials_presets: null
    allowed_git_urls_regex: ""
cache_dir: /Users/YOURNAME/.pb/WhtGitCache/ # For Windows, the directory path will have forward slash (\)
filepath: /Users/YOURNAME/.pb/siteconfig.yaml # For Windows, the file path will have forward slash (\)

Profiles project structure

The following image shows the folder structure of the project:

`pb_project.yaml`

The pb_project.yaml file contains the project details like the name, schema version, warehouse connection, entityEntity refers to a digital representation of a class of real world distinct objects for which you can create a profile. names along with ID types, etc.

A sample pb_project.yaml file with entity type as user:

name: sample_attribution
schema_version: 54
connection: test
include_untimed: true
model_folders:
  - models
entities:
  - name: user
    id_types:
      - main_id
      - user_id
      - anonymous_id
      - email
packages:
  - name: corelib
    url: "https://github.com/rudderlabs/rudderstack-profiles-corelib/tag/schema_{{best_schema_version}}"

# Profiles can also use certain model types defined in Python.
# Examples include ML models. Those dependencies are specified here.
python_requirements:
  - profiles-pycorelib==0.1.0

The following table explains the fields used in the above file:

Field	Data type	Description
`name`	String	Name of the project.
`schema_version`	Integer	Project’s YAML version. Each new schema version comes with improvements and added functionalities.
`connection`	String	Connection name from `siteconfig.yaml` used for connecting to the warehouse.
`include_untimed`	Boolean	Determines if inputs having no timestamps should be allowed. If true, data without timestamps is included when running the models.
`model_folders`	String	Names of folders where model files are stored.
`entities`	List	Lists all the entities used in the project for which you can define models. Each entry for an entity here is a JSON object specifying entity’s name and attributes.
`packages`	List	List of packages with their name and URL. Optionally, you can also extend ID types filters for including or excluding certain values from this list.

`entities`

Field	Data type	Description
`name`	String	Name of the entity used in the project.
`id_types`	List	List of all identifier types associated with the current entity.

The identifiers listed in id_types may have a many-to-one relationship with an entity but each ID must belong to a single entity.
For example, a user entity might have id_types as the salesforce_id, anonymous_id, email, and session_id (a user may have many session IDs over time). However, it should not include something like ip_address, as a single IP can be used by different users at different times and it is not considered as a user identifier.

`packages`

You can import library packages in a project signifying where the project inherits its properties from.

Field	Data type	Description
`name`	String	Specify a name.
`url`	String	HTTPS URL of the lib package, with a tag for the best schema version.

`inputs.yaml`

The inputs.yaml file lists all the input sources (tables/views) which should be used to obtain values for models and eventually create output tables.

It also specifies the table/view along with column name and SQL expression for retrieving values. The input specification may also include metadata, and the constraints on those columns.

A sample inputs.yaml file:

inputs:
  - name: salesforceTasks
    contract:
      is_optional: false
      is_event_stream: true
      with_entity_ids:
        - user
      with_columns:
        - name: activitydate
        - name: whoid
    app_defaults:
      table: salesforce.task
      # For BigQuery, it is recommended to use view (view: _views_<view_name>) instead of table for event streaming data sets.
      occurred_at_col: activitydate
      ids:
        # column name or sql expression
        - select: "whoid" 
          type: salesforce_id
          entity: user
          to_default_stitcher: true
  - name: salesforceContact
    contract:
      is_optional: false
      is_event_stream: true
      with_entity_ids:
        - user
      with_columns:
        - name: createddate
        - name: id
        - name: email
    app_defaults:
      table: salesforce.contact
      # For BigQuery, it is recommended to use view (view: _views_<view_name>) instead of table for event streaming data sets.
      occurred_at_col: createddate
      ids:
        - select: "id"
          type: salesforce_id
          entity: user
          to_default_stitcher: true
        - select: "case when lower(email) like any ('%gmail%', '%yahoo%') then lower(email)  else split_part(lower(email),'@',2) end"
          type: email
          entity: user
          to_default_stitcher: true
  - name: websitePageVisits
    contract:
      is_optional: false
      is_event_stream: true
      with_entity_ids:
        - user
      with_columns:
        - name: timestamp
        - name: anonymous_id
        - name: context_traits_email
        - name: user_id
    app_defaults:
      table: autotrack.pages
      # For BigQuery, it is recommended to use view (view: _views_<view_name>) instead of table for event streaming data sets.
      occurred_at_col: timestamp
      ids:
        - select: "anonymous_id"
          type: rudder_anon_id
          entity: user
          to_default_stitcher: true
        # below sql expression check the email type, if it is gmail and yahoo return email otherwise spilt email return domain of email.  
        - select: "case when lower(coalesce(context_traits_email, user_id)) like any ('%gmail%', '%yahoo%') then lower(coalesce(context_traits_email, user_id))  \
              else split_part(lower(coalesce(context_traits_email, user_id)),'@',2) end"
          type: email
          entity: user
          to_default_stitcher: true

The following table explains the fields used in the above file:

Field	Data type	Description
`name`	String	Name of the input model.
`contract`	Dictionary	A model contract provides essential information about the model like the necessary columns and entity IDs that it should contain. This is crucial for other models that depend on it, as it helps find errors early and closer to the point of their origin.
`app_defaults`	Dictionary	Values that input defaults to when you run the project directly. For library projects, you can remap the inputs and override the app defaults while importing the library projects.

`contract`

Field	Data type	Description
`is_optional`	Boolean	Whether the model’s existence in the warehouse is mandatory.
`is_event_stream`	Boolean	Whether the table/view is a series/stream of events. A model that has a `timestamp` column is an event stream model.
`with_entity_ids`	List	List of all entities with which the model is related. A model M1 is considered related to model M2 if there is an ID of model M2 in M1’s output columns.
`with_columns`	List	List of all ID columns that this contract is applicable for.

`app_defaults`

Field	Data type	Description
`table`/`view`	String	Name of the warehouse table/view containing the data. You can prefix the table/view with an external schema or database in the same warehouse, if applicable. Note that you can specify either a table or view but not both.
`occurred_at_col`	String	Name of the column in table/view containing the timestamp.
`ids`	List	Specifies the list of all IDs present in the source table along with their column names (or column SQL expressions). Note: Some input columns may contain IDs of associated entities. By their presence, such ID columns associate the row with the entity of the ID. The ID Stitcher may use these declarations to automatically discover ID-to-ID edges.

`ids`

Field	Data type	Description
`select`	String	Specifies the column name to be used as the identifier. You can also specify a SQL expression if some transformation is required. Note: You can also refer table from another Database/Schema in the same data warehouse. For example, `table: <database_name>.<schema_name>.<table_name>`.
`type`	String	Type of identifier. All the ID types of a project are declared in `pb_project.yaml`. You can specify additional filters on the column expression. Note: Each ID type is linked only with a single entity.
`entity`	String	Entity name defined in the `pb_project.yaml` file to which the ID belongs.
`to_default_stitcher`	Boolean	Set this optional field to `false` for the ID to be excluded from the default ID stitcher.

`profiles.yaml`

The profiles.yaml file lists entity_vars / input_vars used to create the output tables under var_groups:.

Field	Data type	Description
`name`	String	A unique name for the var_group.
`entity_key`	String	The entity to which the var_group belongs to.
`vars`	Object	This section is used to specify variables, with the help of `entity_var` and `input_var`. Aggregation on stitched ID type is done by default and is implicit.

Optionally, you can create models using the above vars. The following fields are common for all the model types:

Field	Data type	Description
`name`	String	Name of the model. Note that a table with the same name is created in the data warehouse. For example, if you define the name as `user_table`, the output table will be named something like `Material_user_table_<rest-of-generated-hash>_<timestamp-number>`.
`model_type`	String	Defines the type of model. Possible values are: `id_stitcher`, `feature_table_model`, and `sql_template`.
`model_spec`	Object	Creates a detailed configuration specification for the target model. Different schema is applicable for different model types as explained in each section below.

RudderStack supports the following model types:

`README.md`

The README.md file provides a quick overview on how to use PB along with SQL queries for data analysis.

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