How to use dbt to manage Materialize

dbt has become the standard for data transformation (“the T in ELT”). It combines the accessibility of SQL with software engineering best practices, allowing you to not only build reliable data pipelines, but also document, test and version-control them.

In this guide, we’ll cover how to use dbt and Materialize to transform streaming data in real time — from model building to continuous testing.

Setup

Minimum requirements: dbt v0.18.1+

Setting up a dbt project with Materialize is similar to setting it up with any other database that requires a non-native adapter. To get up and running, you need to:

1. Install the dbt-materialize plugin (optionally using a virtual environment):

   python3 -m venv dbt-venv         # create the virtual environment
   source dbt-venv/bin/activate     # activate the virtual environment
   pip install dbt-materialize      # install the adapter
   

   The installation will include dbt-core and the dbt-postgres dependency. To check that the plugin was successfully installed, run:

   dbt --version
   

   materialize should be listed under “Plugins”. If this is not the case, double-check that the virtual environment is activated!

2. To get started, make sure you have Materialize installed and running.

Create and configure a dbt project

A dbt project is a directory that contains all dbt needs to run and keep track of your transformations. At a minimum, it must have a project file (dbt_project.yml) and at least one model (.sql).

To create a new project, run:

dbt init <project_name>

This command will bootstrap a starter project with default configurations and create a profiles.yml file, if it doesn’t exist.

Connect to Materialize

dbt manages all your connection configurations (or, profiles) in a file called profiles.yml. By default, this file is located under ~/.dbt/.

1. Locate the profiles.yml file in your machine:

   dbt debug --config-dir
   

   Note: If you started from an existing project but it’s your first time setting up dbt, it’s possible that this file doesn’t exist yet. You can manually create it in the suggested location.

2. Open profiles.yml and adapt it to connect to your Materialize instance using the reference profile configuration.

   As an example, the following profile would allow you to connect to a Materialize instance running locally (dev):

   default:
     outputs:
   
       dev:
         type: materialize
         threads: 1
         host: localhost
         port: 6875
         user: materialize
         pass: password
         dbname: materialize
         schema: public
   
     target: dev
   

   The target parameter allows you to configure the target environment that dbt will use to run your models.

3. To test the connection to Materialize, run:

   dbt debug
   

   If the output reads All checks passed!, you’re good to go! The dbt documentation has some helpful pointers in case you run into errors.

Build and run dbt models

In dbt, a model is a SELECT statement that encapsulates a data transformation you want to run on top of your database. For dbt to know how to persist (or not) a transformation, the model needs to be associated with a materialization strategy.

dbt models

When you use dbt with Materialize, your models stay up-to-date without manual or configured refreshes. This allows you to efficiently transform streaming data using the same thought process you’d use for batch transformations on top of any other database.

1. Create a model for each SQL statement you’re planning to deploy. Each individual model should be stored as a .sql file under the directory defined by source-paths in dbt_project.yml.

   As an example, we’ll use the SQL statements in our getting started guide and re-write them as dbt models.

   Creating a source

   You can instruct dbt to create a source in Materialize using the custom source materialization:

   {{ config(materialized='source') }}
   
   {% set source_name %}
       {{ mz_generate_name('market_orders_raw') }}
   {% endset %}
   
   CREATE SOURCE {{ source_name }}
   FROM PUBNUB
   SUBSCRIBE KEY 'sub-c-4377ab04-f100-11e3-bffd-02ee2ddab7fe'
   CHANNEL 'pubnub-market-orders'
   

   The mz_generate_name macro allows you to generate a fully-qualified name from a base object name. Here, source_name would be compiled to materialize.public.market_orders_raw.

   Creating a view

   dbt models are materialized as views by default, so to create a view in Materialize you can simply provide the SQL statement in the model (and skip the materialized configuration parameter):

   SELECT
       ((text::jsonb)->>'bid_price')::float AS bid_price,
       (text::jsonb)->>'order_quantity' AS order_quantity,
       (text::jsonb)->>'symbol' AS symbol,
       (text::jsonb)->>'trade_type' AS trade_type,
       to_timestamp(((text::jsonb)->'timestamp')::bigint) AS ts
   FROM {{ ref('market_orders_raw') }}
   

   One thing to note here is that the model depends on the source defined in the previous step. To express this dependency and track the lineage of your project, you can use the dbt ref() function (#8744).

   Creating a materialized view

   This is where Materialize goes beyond dbt’s incremental models (and traditional databases), with materialized views that continuously update as the underlying data changes:

   {{ config(materialized='materializedview') }}
   
   SELECT symbol,
          AVG(bid_price) AS avg
   FROM {{ ref('market_orders') }}
   GROUP BY symbol
   

   When should you use what? We recommend using materializedview models exclusively for your core business logic to ensure that you’re not consuming more memory than needed in Materialize. Intermediate or staging views should use the view materialization type instead.

2. Run the dbt models:

   dbt run
   

   This command generates executable SQL code from any model files under the specified directory and runs it in the target environment. You can find the compiled statements under /target/run and target/compiled in the dbt project folder.

3. Using a new terminal window, connect to Materialize to double-check that all objects have been created:

   psql -U materialize -h localhost -p 6875 materialize
   

   materialize=> SHOW SOURCES;
   
          name
   -------------------
    market_orders_raw
   
    materialize=> SHOW VIEWS;
   
          name
   -------------------
    avg_bid
    market_orders
   

That’s it! From here on, Materialize makes sure that your models are incrementally updated as new data streams in, and that you get fresh and correct results with millisecond latency whenever you query your views.

Materializations

dbt models are materialized as views by default, but can be configured to use a different materialization type through the materialized configuration parameter. This parameter can be set directly in the model file using:

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

Because Materialize is optimized for real-time transformations of streaming data and the core of dbt is built around batch, the dbt-materialize adapter implements a few custom materialization types:

Test and document a dbt project

Continuous testing

Using dbt in a streaming context means that you’re able to run data quality and integrity tests non-stop, and monitor failures as soon as they happen. This is useful for unit testing during the development of your dbt models, and later in production to trigger real-time alerts downstream.

1. To configure your project for continuous testing, add a tests property to dbt_project.yml with the store_failures configuration:

   tests:
     mz_get_started:
       marts:
         +store_failures: true
         +schema: 'etl_failure'
   

   This will instruct dbt to create a materialized view for each configured test that can keep track of failures over time. By default, test views are created in a schema suffixed with dbt_test__audit. To specify a custom suffix, use the schema config.

   Note: As an alternative, you can specify the --store-failures flag when running dbt test.

2. Add tests to your models using the tests property in the model configuration .yml files:

   models:
     - name: avg_bid
       description: 'Computes the average bid price'
       columns:
         - name: symbol
           description: 'The stock ticker'
           tests:
             - not_null
             - unique
   

   The type of test and the columns being tested are used as a base for naming the test materialized views. For example, the configuration above would create views named not_null_avg_bid_symbol and unique_avg_bid_symbol.

3. Run the tests:

   dbt test
   

   When configured to store_failures, this command will create a materialized view for each test using the respective SELECT statements, instead of doing a one-off check for failures as part of its execution.

   This guarantees that your tests keep running in the background as views that are automatically updated as soon as an assertion fails.

4. Using a new terminal window, connect to Materialize to double-check that the schema storing the tests has been created, as well as the test materialized views:

   psql -U materialize -h localhost -p 6875 materialize
   

   materialize=> SHOW SCHEMAS;
   
          name
   -------------------
    public
    public_etl_failure
   
    materialize=> SHOW VIEWS FROM public_etl_failure;;
   
          name
   -------------------
    not_null_avg_bid_symbol
    unique_avg_bid_symbol
   

With continuous testing in place, you can then build alerts off of the test materialized views using any common PostgreSQL-compatible client library and TAIL (see the Python cheatsheet for a reference implementation).

Documentation

dbt can automatically generate documentation for your project as a shareable website. This brings data governance to your streaming pipelines, speeding up life-saving processes like data discovery (where to find what data) and lineage (the path data takes from source(s) to sink(s), as well as the transformations that happen along the way).

1. Optionally, create a .yml file with helpful properties about your project resources (like model and column descriptions, or tests) and add it to directory where your models live:

   version: 2
   
   sources:
     - name: public
       description: "Public schema"
       tables:
         - name: market_orders_raw
           description: "Market order data source (PubNub)"
   models:
     - name: market_orders
       description: "Converts market order data to proper data types"
   
     - name: avg_bid
       description: "Computes the average bid price"
       columns:
         - name: symbol
           description: "The stock ticker"
           tests:
             - not_null
         - name: avg
           description: "The average bid price"
   

2. To generate documentation for your project, run:

   dbt docs generate
   

   dbt will grab any additional project information and Materialize catalog metadata, then compile it into .json files (manifest.json and catalog.json, respectively) that can be used to feed the documentation website. You can find the compiled files under /target, in the dbt project folder.

3. Launch the documentation website. By default, this command starts a web server on port 8000:

   dbt docs serve #--port <port>
   

4. In a browser, navigate to localhost:8000. There, you can find an overview of your dbt project, browse existing models and metadata, and in general keep track of what’s going on.

   If you click View Lineage Graph in the lower right corner, you can even inspect the lineage of your streaming pipelines!