Overview

In this quick start guide, you'll get up and running with SQLMesh's scaffold generator. This example project will run locally on your computer using DuckDB as an embedded SQL engine.

Before beginning, ensure that you meet all the prerequisites for using SQLMesh.

Project structure

This project demonstrates key SQLMesh features by walking through the SQLMesh workflow on a simple data pipeline. This section describes the project structure and the SQLMesh concepts you will encounter as you work through it.

The project contains three models with a CSV file as the only data source:

┌─────────────┐
│seed_data.csv│
└────────────┬┘
             │
            ┌▼─────────────┐
            │seed_model.sql│
            └─────────────┬┘
                          │
                         ┌▼────────────────────┐
                         │incremental_model.sql│
                         └────────────────────┬┘
                                              │
                                             ┌▼─────────────┐
                                             │full_model.sql│
                                             └──────────────┘

Although the project is simple, it touches on all the primary concepts needed to use SQLMesh productively.

Plans

SQLMesh's key actions are creating and applying plans to environments.

A SQLMesh environment is an isolated namespace containing models and the data they generated. The most important environment is prod ("production"), which consists of the databases behind the applications your business uses to operate each day. Environments other than prod provide a place where you can test and preview changes to model code before they go live and affect business operations.

A SQLMesh plan contains a comparison of one environment to another and the set of changes needed to bring them into alignment. For example, if a new SQL model was added, tested, and run in the dev environment, it would need to be added and run in the prod environment to bring them into alignment. SQLMesh identifies all such changes and classifies them as either breaking or non-breaking.

Breaking changes are those that invalidate data already existing in an environment. For example, if a WHERE clause was added to a model in the dev environment, existing data created by that model in the prod environment are now invalid because they may contain rows that would be filtered out by the new WHERE clause. Other changes, like adding a new column to a model in dev, are non-breaking because all the existing data in prod are still valid to use - only new data must be added to align the environments.

After SQLMesh creates a plan, it summarizes the breaking and non-breaking changes so you can understand what will happen if you apply the plan. It will prompt you to "backfill" data to apply the plan - in this context, backfill is a generic term for updating or adding to a table's data (including an initial load or full refresh).

Model kinds

A plan's actions are determined by the kinds of models the project uses. This example project uses three model kinds:

1. SEED models read data from CSV files stored in the SQLMesh project directory.
2. FULL models fully refresh (rewrite) the data associated with the model every time the model is run.
3. INCREMENTAL_BY_TIME_RANGE models use a date/time data column to track which time intervals are affected by a plan and process only the affected intervals when a model is run.

Project directories and files

SQLMesh uses a scaffold generator to initiate a new project. The generator will create multiple sub-directories and files for organizing your SQLMesh project code.

See the CLI, Notebook, or UI quickstart guides for details on how to initiate a SQLMesh project with the scaffold generator.

The scaffold generator will create the following configuration file and directories:

• config.yaml
  • The file for project configuration. Refer to configuration.
• ./models
  • SQL and Python models. Refer to models.
• ./seeds
  • Seed files. Refer to seeds.
• ./audits
  • Shared audit files. Refer to auditing.
• ./tests
  • Unit test files. Refer to testing.
• ./macros
  • Macro files. Refer to macros.

It will also create the files needed for this quickstart example:

• ./models
  • full_model.sql
  • incremental_model.sql
  • seed_model.sql
• ./seeds
  • seed_data.csv
• ./audits
  • assert_positive_order_ids.sql
• ./tests
  • test_full_model.yaml

Project configuration

SQLMesh project-level configuration parameters are specified in the config.yaml file in the project directory.

This example project uses the embedded DuckDB SQL engine, so its configuration specifies duckdb as the local gateway's connection and the local gateway as the default.

The command to run the scaffold generator requires a default SQL dialect for your models, which it places in the config model_defaults dialect key. In this example, we specified the duckdb SQL dialect as the default:

gateways:
    local:
        connection:
            type: duckdb
            database: ./db.db

default_gateway: local

model_defaults:
    dialect: duckdb

Learn more about SQLMesh project configuration here.

Project data

The data used in this example project is contained in the seed_data.csv file in the /seeds project directory. The data reflects sales of 3 items over 7 days in January 2020.

The file contains three columns, id, item_id, and ds, which correspond to each row's unique ID, the sold item's ID number, and the date the item was sold, respectively.

This is the complete dataset:

id	item_id	ds
1	2	2020-01-01
2	1	2020-01-01
3	3	2020-01-03
4	1	2020-01-04
5	1	2020-01-05
6	1	2020-01-06
7	1	2020-01-07

Project models

We now briefly review each model in the project.

The first model is a SEED model that imports seed_data.csv. This model consists of only a MODEL statement because SEED models do not query a database.

In addition to specifying the model name and CSV path relative to the model file, it includes the column names and data types of the columns in the CSV. It also sets the grain of the model to the columns that collectively form the model's unique identifier, id and ds.

MODEL (
    name sqlmesh_example.seed_model,
    kind SEED (
        path '../seeds/seed_data.csv'
    ),
    columns (
        id INTEGER,
        item_id INTEGER,
        ds VARCHAR
    ),
    grain [id, ds]
);

The second model is an INCREMENTAL_BY_TIME_RANGE model that includes both a MODEL statement and a SQL query selecting from the first seed model.

The MODEL statement's kind property includes the required specification of the data column containing each record's timestamp. It also includes the optional start property specifying the earliest date/time for which the model should process data and the cron property specifying that the model should run daily. It sets the model's grain to columns id and ds.

The SQL query includes a WHERE clause that SQLMesh uses to filter the data to a specific date/time interval when loading data incrementally:

MODEL (
    name sqlmesh_example.incremental_model,
    kind INCREMENTAL_BY_TIME_RANGE (
        time_column ds
    ),
    start '2020-01-01',
    cron '@daily',
    grain [id, ds]
);

SELECT
    id,
    item_id,
    ds,
FROM
    sqlmesh_example.seed_model
WHERE
    ds between @start_ds and @end_ds

The final model in the project is a FULL model. In addition to properties used in the other models, its MODEL statement includes the audits property. The project includes a custom assert_positive_order_ids audit in the project audits directory; it verifies that all item_id values are positive numbers. It will be run every time the model is executed.

MODEL (
  name sqlmesh_example.full_model,
  kind FULL,
  cron '@daily',
  grain item_id,
  audits [assert_positive_order_ids],
);

SELECT
  item_id,
  count(distinct id) AS num_orders,
FROM
    sqlmesh_example.incremental_model
GROUP BY item_id

Project guides

Choose a SQLMesh API to work through the example project:

• Command-line interface
• Jupyter notebook
• Web user interface