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Tutorial: Part 1

This tutorial covers the basic concepts of SAYN and will get you going quickly. It uses the example project created by sayn init. It assumes SAYN is setup as described in the installation section.

This project generates some random data with a python task and performs some modelling on it with autosql tasks.

Running SAYN

To get started, open a terminal, activate your virtual environment (source sayn_venv/bin/activate) and run the following:

sayn init sayn_tutorial
cd sayn_tutorial
sayn run

This will create a new project with the contents of this tutorial and execute it.

sayn run execution

You can open dev.db and see the tables and views created by sayn run. You can use DB Browser for SQLite in order to view the content of the database. As you can observe, sayn run created a small ETL process which models battles from various tournaments.

That's it, you made your first SAYN run! We will now explain what happens in the background.

Project Overview

The sayn_tutorial folder has the following structure:

├── project.yaml
├── settings.yaml
├── tasks
│   └── base.yaml
├── python
│   ├──
│   └──
├── sql
│   ├── dim_arenas.sql
│   ├── dim_fighters.sql
│   ├── dim_tournaments.sql
│   ├── f_battles.sql
│   ├── f_fighter_results.sql
│   └── f_rankings.sql
├── compile
├── .gitignore
└── requirements.txt

The main files are:

  • project.yaml: defines the SAYN project. It is shared across all collaborators.
  • settings.yaml: defines the individual user's settings. It is unique for each collaborator and should never be pushed to git as it contains credentials.
  • tasks: folder where the task files are stored. Each file is considered a task group.
  • python: folder where scripts for python tasks are stored.
  • sql: folder where SQL files for sql and autosql tasks are stored.
  • logs: folder where SAYN logs are written.
  • compile: folder where compiled SQL queries before execution.

Implementing Your Project

Now let's see how the tutorial project would be created from scratch.

Step 1: Define The Project In project.yaml

The project.yaml file is at the root level of your directory and contains:


  - warehouse

default_db: warehouse

The following is defined:

  • required_credentials: the list of credentials used by the project. In this case we have a single credential called warehouse. The connection details will be defined in settings.yaml.
  • default_db: the database used by sql and autosql tasks. Since we only have 1 credential, this field could be skipped.

Step 2: Define Your Individual Settings With settings.yaml

The settings.yaml file at the root level of your directory and contains:


      warehouse: dev_db
      warehouse: prod_db

default_profile: dev

    type: sqlite
    database: dev.db
    type: sqlite
    database: prod.db

The following is defined:

  • profiles: the definion of profiles for the project. A profile defines the connection between credentials in the project.yaml file and credentials defined below. In this case we define 2 profiles dev and prod.
  • default_profile: the profile used by default at execution time. It can be overriden using sayn run -p prod.
  • credentials: here we define the credentials. In this case we have two for dev and prod, that are used as warehouse on each profile.

Step 3: Define Your Tasks

In SAYN, tasks are defined in yaml files within the tasks folder. Each file is considered a task group. Our project contains only one task group: base.yaml:


    type: python
    class: load_data.LoadData

    type: autosql
    file_name: dim_tournaments.sql
    materialisation: table
      table: dim_tournaments
      - load_data
# ...

The tasks entry contains a map of tasks definitions. In this case we're using two types of tasks:

  • python: lets you define a task written in Python. Python tasks are useful to complete your extraction and load layers if you're using an ELT tool or for data science models defined in Python.
  • autosql: lets you write a SELECT statement while SAYN manages the table or view creation automatically for you. Our example has multiple autosql tasks which create models based on the logs.


Although this tutorial only has one file in the tasks folder, you can separate tasks in multiple files. SAYN automatically includes any file from the tasks folder with a .yaml extension when creating the DAG. Each file is considered a task group.

load_data Task

In our example project the only python task is load_data which creates some synthetic logs and loads them to our database. The code can be found in the class LoadData in python/ Let's have a look at the main elements of a python task:


# ...
from sayn import PythonTask

class LoadData(PythonTask):
    def run(self):
        # Your code here

The above is the beginning of the python task. When the execution of sayn run hits the load_data task the code in the run method will execute.

A task in SAYN can be split into multiple steps, which is useful for debugging when errors occur. In this case, we first generate all data and then we load each dataset one by one. We can define the steps a task will follow with the self.set_run_steps method.


    def run(self):
        # ...
                "Generate Dimensions",
                "Generate Battles",
                "Load fighters",
                "Load arenas",
                "Load tournaments",
                "Load battles",
        # ...

To indicate SAYN what step is executing, we can use the following construct:


    def run(self):
        # ...
        with self.step("Generate Dimensions"):
            # Add ids to the dimensions
            fighters = [
                {"fighter_id": str(uuid4()), "fighter_name": val}
                for id, val in enumerate(self.fighters)
            arenas = [
                {"arena_id": str(uuid4()), "arena_name": val}
                for id, val in enumerate(self.arenas)
            tournaments = [
                {"tournament_id": str(uuid4()), "tournament_name": val}
                for id, val in enumerate(self.tournaments)

Here our "Generate Dimensions" step simply generates the dimension variables with an id.

The final core element is accessing databases. In our project we defined a single credential called warehouse and we made this the default_db. To access this we just need to use self.default_db.



The main method in Database objects is execute which accepts a sql script via parameter and executes it in a transaction. Another method used in this tutorial is load_data which loads a dataset into the database automatically creating a table for it first.

For more information about how to build python tasks, visit the python tasks section.

autosql Tasks

Let's have a look at one of the autosql tasks (dim_tournaments). As you can see in tasks/base.yaml above, we specify a file_name which contains:


SELECT l.tournament_id
     , l.tournament_name
FROM logs_tournaments l

This is a simple SELECT statement that SAYN will use when creating a table called dim_tournaments as defined in the destination field in the base.yaml file.

For more information about setting up autosql tasks, visit the autosql tasks section.

Running Your Project

So far we've used sayn run to execute our project, however SAYN provides more options:

  • sayn run -p [profile_name]: runs the whole project with the specific profile. In our case using the profile prod will create a prod.db SQLite database and process all data there.
  • sayn run -t [task_name]: allows the filtering the tasks to run.

More options are available to run specific components of your SAYN project. All details can be found in the SAYN cli section.

What Next?

You now know the basics of SAYN, congratulations! You can continue learning by going through the Tutorial: Part 2 which shows you several tricks to make your SAYN project more dynamic and efficient.

Otherwise, you can go through the rest of the documentation as SAYN has much more to offer!

Enjoy SAYN :)