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Utils Quickstart

info

The models, functionality, and variables described below are only available from snowplow-utils v0.15.0 and above, as earlier packages do not utilize these variables.

The snowplow-utils package allows you to create your own custom snowplow_base_events_this_run table using macros that generate the required SQL code for you, allowing you to incorporate whatever custom event types, contexts of Snowplow data. Using this package will allow you to leverage the incremental nature of the Snowplow packages, meaning you can more easily build data models using our other packages such as snowplow-web and snowplow-mobile, as well as build your own completely custom packages without having to do the initial heavy lifting yourself. This can, however, be a bit complicated to set up and so for that purpose we've create this quickstart page to guide you through this process.

info

It is only recommended that you use this if you are planning on heavily customizing your Snowplow data modeling setup, whilst still taking advantage of the incremental framework that the existing dbt packages offer. If you are going to be heavily leveraging the existing Snowplow packages (e.g. snowplow-web or snowplow-mobile) then you will not need to leverage this package for the creation of your base tables. Please instead follow the appropriate quickstart guides for the packages you are going to be utilizing instead, such as for web or mobile.

Requirements​

In addition to dbt being installed and connected to your database you need:

In addition to the standard privileges required by dbt, our packages by default write to additional schemas beyond just your profile schema. If your connected user does not have create schema privileges, you will need to ensure that the following schemas exist in your warehouse and the user can create tables in them:

  • <profile_schema>_derived
  • <profile_schema>_scratch
  • <profile_schema>_snowplow_manifest

Alternatively, you can override the output schemas our models write to, see the relevant package configuration page for how to do this.

Please refer to the Official Guide on setting up permissions.

Installation​

Check dbt Hub for the latest installation instructions, or read the dbt docs for more information on installing packages. If you are using multiple packages you may need to up/downgrade a specific package to ensure compatibility.

note

Make sure to run the dbt deps command after updating your packages.yml to ensure you have the specified version of each package installed in your project.

Setup​

info

You can largely skip redundant copy + pasting by cloning the following dbt project repository that we have created in GitHub. You can find it here, and this has all of the boilerplate setup for you already. If you want to customise model names or parameter values, you can still follow the quickstart guide below to help you understand how to do that, and what changing each variable will mean for your models. Feel free to skip steps 1 and 2, however.

1. Override the dispatch order in your project​

To take advantage of the optimized upsert that the Snowplow packages offer you need to ensure that certain macros are called from snowplow_utils first before dbt-core. This can be achieved by adding the following to the top level of your dbt_project.yml file:

dbt_project.yml
dispatch:
  - macro_namespace: dbt
    search_order: ['snowplow_utils', 'dbt']

If you do not do this the package will still work, but the incremental upserts will become more costly over time.

2. Creating the base module in your dbt project​

In your dbt project, create a base folder somewhere within your models directory. Within this base folder, you need to create the 6 .sql files shown below:

my_dbt_project
├── analyses
│   └── .gitkeep
├── dbt_packages
│   ├── dbt_utils
│   └── snowplow_utils
├── logs
├── macros
├── models
│   └── base
│       ├── snowplow_base_quarantined_sessions.sql
│       ├── snowplow_incremental_manifest.sql
│       ├── snowplow_base_new_event_limits.sql
│       ├── snowplow_base_sessions_lifecycle_manifest.sql
│       ├── snowplow_base_sessions_this_run.sql
│       └── snowplow_base_events_this_run.sql
├── seeds
├── snapshots
├── target
├── tests
├── .gitignore
├── dbt_project.yml
├── packages.yml
├── README.md
└── selectors.yml

Once you've created all of these models, you need to call the correct macros in each model to ensure that the correct SQL gets generated for each model. If you'd like to rename any of the models, all you need to do is rename the .sql files listed above.

caution

Please only rename the models with caution and be sure to read the subsequent steps carefully as you may need to modify some of the boilerplate code outlined below to have the macros adapt properly to your naming conventions.

3. Setting up the quarantined sessions macro​

Within the snowplow_base_quarantined_sessions.sql file, you can call the base_create_snowplow_quarantined_sessions macro as follows:

{{
  config(
    materialized='incremental'
  )
}}

{% set quarantined_query = snowplow_utils.base_create_snowplow_quarantined_sessions() %}

{{ quarantined_query }}

This macro doesn't accept any arguments, and simply generates a table which contains a column named session_identifier, containing all session identifiers of sessions that have been quarantined due to exceeding the maximum session length, to avoid long table scans. More information on the sessionization logic and optimization can be found here

4. Setting up the incremental manifest macro​

Next, within the snowplow_incremental_manifest.sql file, you can call the base_create_snowplow_incremental_manifest macro as follows:

{{
  config(
    materialized='incremental'
  )
}}

{% set incremental_manifest_query = snowplow_utils.base_create_snowplow_incremental_manifest() %}

{{ incremental_manifest_query }}

Much like with the quarantined sessions macro, this does not require any arguments and generates the boilerplate for the incremental manifest table that Snowplow leverages.

5. Setting up the new event limits macro​

For the snowplow_base_new_event_limits model, you need to add a few extra macros into the mix, which you can do as follows:

info

Be sure to specify your PACKAGE_NAME when calling the get_enabled_snowplow_models macro.

{{ 
  config(
   post_hook=["{{snowplow_utils.print_run_limits(this)}}"]
   )
}}

{%- set models_in_run = snowplow_utils.get_enabled_snowplow_models(PACKAGE_NAME, graph_object=none, models_to_run="", base_events_table_name='snowplow_base_events_this_run') -%}

{% set min_last_success,
         max_last_success,
         models_matched_from_manifest,
         has_matched_all_models = snowplow_utils.get_incremental_manifest_status(ref('snowplow_incremental_manifest'),
                                                                                 models_in_run) -%}


{% set run_limits_query = snowplow_utils.get_run_limits(min_last_success,
                                                          max_last_success,
                                                          models_matched_from_manifest,
                                                          has_matched_all_models,
                                                          var("snowplow__start_date")) -%}


{{ run_limits_query }}

Here there are a couple of variables that can be used to modify the new_event_limits table setup. Firstly, if you chose to name your snowplow_incremental_manifest model differently, be sure to reference that properly in the get_incremental_manifest_status macro call. If for example, you chose to call the manifest model incremental_manifest by naming your file incremental_manifest.sql instead of the prescribed snowplow_incremental_manifest.sql, then you would reflect that using the following macro call:

info

The below is just an example of a macro call when using custom naming, this isn't something additional to copy and add if you want to set up your snowplow_new_event_limits model!

{% set min_last_success,
         max_last_success,
         models_matched_from_manifest,
         has_matched_all_models = snowplow_utils.get_incremental_manifest_status(ref('incremental_manifest'),
                                                                                 models_in_run) -%}

Secondly, you can choose the starting date of when your Snowplow data was first loaded into your data warehouse/lake. This is reflected in the value of the snowplow__start_date variable, which you may be familiar with if you've used previous Snowplow dbt packages. This variable is typically defined in your dbt_project.yml, and can be defined in that file in the following manner.

dbt_project.yml

vars:
    # to define it globally, use the following notation
    snowplow__start_date: '2020-01-01'

    # to define the variable locally within the project, use the following nesting
    my_dbt_project:
        snowplow__start_date: '2020-01-01'

6. Setting up the sessions lifecycle manifest macro​

For the snowplow_base_sessions_lifecycle_manifest model, you have the following macro call which takes in a lot of parameters to allow for a high level of flexibility in how you can process your Snowplow data:


{% set sessions_lifecycle_manifest_query = snowplow_utils.base_create_snowplow_sessions_lifecycle_manifest(
    var('snowplow__session_identifiers', '[{"schema": "atomic", "field": "domain_sessionid"}]'),
    var('snowplow__session_timestamp', 'collector_tstamp'),
    var('snowplow__user_identifiers', '[{"schema": "atomic", "field": "domain_userid"}]'),
    var('snowplow__quarantined_sessions', 'snowplow_base_quarantined_sessions'),
    var('snowplow__derived_tstamp_partitioned', true),
    var('snowplow__days_late_allowed', 3),
    var('snowplow__max_session_days', 3),
    var('snowplow__app_ids', []),
    var('snowplow__events_schema', 'atomic'),
    var('snowplow__events_table', 'events'),
    var('snowplow__event_limits', 'snowplow_base_new_event_limits'),
    var('snowplow__incremental_manifest', 'snowplow_incremental_manifest'),
    var('snowplow__package_name, 'snowplow')
 ) %}

{{ sessions_lifecycle_manifest_query }}

To get an in-depth explanation of each variable passed here, please refer to the configuration page.

There are several important parameters to consider. The first one is snowplow__session_timestamp, which helps identify the timestamp column used for sessionization. It's recommended to use either collector_tstamp or load_tstamp as sensible values.

Next, we have snowplow__session_identifiers and snowplow__user_identifiers, which expect a map object defining the location of session or user identifiers. In this map, the key represents the name of the context or entity where the identifier can be found. If the identifier is a field in the atomic columns, the key can be set to atomic. The value specifies the name of the field in either the context/entity or the atomic columns.

caution

Currently, we only support session and user identifiers found in atomic fields for Redshift/Postgres. We don't support nested level fields for any warehouses, and for BigQuery you will currently need to do the version management yourself. We will be getting around to supporting this extra functionality soon.

By default, snowplow__session_identifiers is set to [{"schema": "atomic", "field": "domain_sessionid"}], and snowplow__user_identifiers is set to [{"schema": "atomic", "field": "domain_userid"}]. This means that the identifiers for sessions and users are expected to be found in the domain_sessionid and domain_userid fields, respectively.

If you have more than one session or user identifier, you can specify multiple entries in the map. The order in which you list them determines the precedence that the macro will use to look for these field values, and COALESCE them into the common session/user_identifier field. E.g. if you have the following definition for your user_identifier:


[
  {"schema": "my_custom_context", "field": "internal_user_id"},
  {"schema": "atomic", "field": "domain_userid"}
]

The package will first extract internal_user_id from the my_custom_context context, and then use something similar to the following SQL statement: COALESCE(my_custom_context.internal_user_id, events.domain_userid) as user_identifier. This way, if a user is able to identify themselves through logging in which would populate a context called my_custom_context, their internal_user_id is used as a user_identifier. If, however, this is not the case, then the user_identifier field falls back on the value that the domain_userid has.

info

We currently only track one user_identifier value per session in the session_lifecycle_manifest, which means if a user logs in part-way through a session, we would only keep one of those values.

Further, you can specify some additional configurations here such as in which table/schema the events data sits, what the names are of your event_limits and incremental_manifest tables, and some parameters around what the maximum session length is. You should once again be familiar with the majority of these variables if you've used another of our dbt packages before.

7. Setting up the sessions this run macro​

For the snowplow_base_sessions_this_run model, you will need to add a post-hook to the configuration of the model as follows:

{{
    config(
        post_hook=["{{ snowplow_utils.base_quarantine_sessions(var('snowplow__max_session_days', 3), var('snowplow__quarantined_sessions', 'snowplow_base_quarantined_sessions')) }}"]
    )
}}


{% set sessions_query = snowplow_utils.base_create_snowplow_sessions_this_run(
    lifecycle_manifest_table='snowplow_base_sessions_lifecycle_manifest',
    new_event_limits_table='snowplow_base_new_event_limits') %}

{{ sessions_query }}

Here the parameters that are called in both macros are only used to direct the macro to the right model names, so again if you've chosen to modify any of the table names then you should adjust the names in the right macros here. For the base_quarantine_sessions macro you simply pass the maximum session duration in days, which is taken from the snowplow__max_session_days variable, and you specify the name of the snowplow_base_quarantined_sessions table, specified by the snowplow__quarantined_sessions variable.

For the base_create_snowplow_sessions_this_run macro call, you specify the name of the lifecycle_manifest_table and the new_event_limits_table. The boilerplate contains their default names, and so if you have not customised anything you can simply copy this code into your snowplow_base_sessions_this_run model.

8. Setting up the events this run macro​

For the snowplow_base_events_this_run model, you will need to run the following two macros in your model:

{%- set lower_limit, upper_limit = snowplow_utils.return_limits_from_model(ref(var('snowplow__base_sessions', 'snowplow_base_sessions_this_run')),
                                                                          'start_tstamp',
                                                                          'end_tstamp') %}

{% set base_events_query = snowplow_utils.base_create_snowplow_events_this_run(
    var('snowplow__base_sessions', 'snowplow_base_sessions_this_run'),
    var('snowplow__session_identifiers', '[{"schema": "atomic", "field": "domain_sessionid"}]'),
    var('snowplow__session_timestamp', 'collector_tstamp'),
    var('snowplow__derived_tstamp_partitioned', true),
    var('snowplow__days_late_allowed', 3),
    var('snowplow__max_session_days', 3),
    var('snowplow__app_ids', []),
    var('snowplow__events_schema', 'atomic'),
    var('snowplow__events_table', 'events')) %}

{{ base_events_query }}

Here you once again have a number of parameters that the macro can take, and to get an in-depth explanation of each variable passed here, please refer to the configuration page. The variables used here are largely either self-explanatory or overlapping with those in the lifecycle manifest section, except that you can now specify custom names for your snowplow_base_sessions_this_run table through the snowplow__base_sessions variable.

9. Modify your dbt_project.yml​

To properly configure your dbt project to utilise and update the manifest tables correctly, you will need to add the following hooks to your dbt_project.yml

# Completely or partially remove models from the manifest during run start.
on-run-start:
  - "{{ snowplow_utils.snowplow_delete_from_manifest(var('models_to_remove',[]), ref('snowplow_incremental_manifest')) }}"

# Update manifest table with last event consumed per sucessfully executed node/model
on-run-end:
  - "{{ snowplow_utils.snowplow_incremental_post_hook() }}"

The snowplow_delete_from_manifest macro is called to remove models from manifest if specified using the models_to_remove variable, in case of a partial or full refresh. The snowplow_incremental_post_hook is used to update the manifest table with the timestamp of the last event consumed successfully for each Snowplow model.

10. Run your models​

Now that you've configured all of your macros correctly, you can run your dbt project as normal. If you want to try to only run the models you've just created above, you can issue the following command in your terminal:

dbt run --select +snowplow_base_events_this_run

You will have to change the name of the model if you've customized this earlier.

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