How-to Guide for Postgres using SQLExecuteQueryOperator¶
Introduction¶
Apache Airflow has a robust trove of operators that can be used to implement the various tasks that make up your workflow. Airflow is essentially a graph (Directed Acyclic Graph) made up of tasks (nodes) and dependencies (edges).
A task defined or implemented by a operator is a unit of work in your data pipeline.
The purpose of this guide is to define tasks involving interactions with a PostgreSQL database with
the SQLExecuteQueryOperator.
Note
Previously, PostgresOperator was used to perform this kind of operation. After deprecation this has been removed. Please use SQLExecuteQueryOperator instead.
Common Database Operations with SQLExecuteQueryOperator¶
To use the SQLExecuteQueryOperator to carry out PostgreSQL request, two parameters are required: sql and conn_id.
These two parameters are eventually fed to the DbApiHook object that interacts directly with the Postgres database.
Creating a Postgres database table¶
The code snippets below are based on Airflow-2.0
# create_pet_table, populate_pet_table, get_all_pets, and get_birth_date are examples of tasks created by
# instantiating the Postgres Operator
ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
DAG_ID = "postgres_operator_dag"
with DAG(
dag_id=DAG_ID,
start_date=datetime.datetime(2020, 2, 2),
schedule="@once",
catchup=False,
) as dag:
create_pet_table = SQLExecuteQueryOperator(
task_id="create_pet_table",
sql="""
CREATE TABLE IF NOT EXISTS pet (
pet_id SERIAL PRIMARY KEY,
name VARCHAR NOT NULL,
pet_type VARCHAR NOT NULL,
birth_date DATE NOT NULL,
OWNER VARCHAR NOT NULL);
""",
)
Dumping SQL statements into your operator isn’t quite appealing and will create maintainability pains somewhere
down to the road. To prevent this, Airflow offers an elegant solution. This is how it works: you simply create
a directory inside the DAG folder called sql and then put all the SQL files containing your SQL queries inside it.
Your dags/sql/pet_schema.sql should like this:
-- create pet table
CREATE TABLE IF NOT EXISTS pet (
pet_id SERIAL PRIMARY KEY,
name VARCHAR NOT NULL,
pet_type VARCHAR NOT NULL,
birth_date DATE NOT NULL,
OWNER VARCHAR NOT NULL);
Now let’s refactor create_pet_table in our DAG:
create_pet_table = SQLExecuteQueryOperator(
task_id="create_pet_table",
conn_id="postgres_default",
sql="sql/pet_schema.sql",
)
Inserting data into a Postgres database table¶
Let’s say we already have the SQL insert statement below in our dags/sql/pet_schema.sql file:
-- populate pet table
INSERT INTO pet VALUES ( 'Max', 'Dog', '2018-07-05', 'Jane');
INSERT INTO pet VALUES ( 'Susie', 'Cat', '2019-05-01', 'Phil');
INSERT INTO pet VALUES ( 'Lester', 'Hamster', '2020-06-23', 'Lily');
INSERT INTO pet VALUES ( 'Quincy', 'Parrot', '2013-08-11', 'Anne');
We can then create a SQLExecuteQueryOperator task that populate the pet table.
populate_pet_table = SQLExecuteQueryOperator(
task_id="populate_pet_table",
conn_id="postgres_default",
sql="sql/pet_schema.sql",
)
Fetching records from your Postgres database table¶
Fetching records from your Postgres database table can be as simple as:
get_all_pets = SQLExecuteQueryOperator(
task_id="get_all_pets",
conn_id="postgres_default",
sql="SELECT * FROM pet;",
)
Passing Parameters into SQLExecuteQueryOperator for Postgres¶
SQLExecuteQueryOperator provides parameters attribute which makes it possible to dynamically inject values into your
SQL requests during runtime. The BaseOperator class has the params attribute which is available to the SQLExecuteQueryOperator
by virtue of inheritance. While both parameters and params make it possible to dynamically pass in parameters in many
interesting ways, their usage is slightly different as demonstrated in the examples below.
To find the birth dates of all pets between two dates, when we use the SQL statements directly in our code, we will use the
parameters attribute:
get_birth_date = SQLExecuteQueryOperator(
task_id="get_birth_date",
conn_id="postgres_default",
sql="SELECT * FROM pet WHERE birth_date BETWEEN SYMMETRIC %(begin_date)s AND %(end_date)s",
parameters={"begin_date": "2020-01-01", "end_date": "2020-12-31"},
)
Now lets refactor our get_birth_date task. Now, instead of dumping SQL statements directly into our code, let’s tidy things up
by creating a sql file. And this time we will use the params attribute which we get for free from the parent BaseOperator
class.
-- dags/sql/birth_date.sql
SELECT * FROM pet WHERE birth_date BETWEEN SYMMETRIC {{ params.begin_date }} AND {{ params.end_date }};
get_birth_date = SQLExecuteQueryOperator(
task_id="get_birth_date",
conn_id="postgres_default",
sql="sql/birth_date.sql",
params={"begin_date": "2020-01-01", "end_date": "2020-12-31"},
)
Enable logging of database messages sent to the client¶
SQLExecuteQueryOperator provides hook_params attribute that allows you to pass add parameters to DbApiHook.
You can use enable_log_db_messages to log database messages or errors emitted by the RAISE statement.
call_proc = SQLExecuteQueryOperator(
task_id="call_proc",
conn_id="postgres_default",
sql="call proc();",
hook_params={"enable_log_db_messages": True},
)
Passing Server Configuration Parameters into PostgresOperator¶
SQLExecuteQueryOperator provides hook_params attribute that allows you to pass add parameters to DbApiHook.
You can pass options argument this way so that you specify command-line options
sent to the server at connection start.
get_birth_date = SQLExecuteQueryOperator(
task_id="get_birth_date",
sql="SELECT * FROM pet WHERE birth_date BETWEEN SYMMETRIC %(begin_date)s AND %(end_date)s",
parameters={"begin_date": "2020-01-01", "end_date": "2020-12-31"},
hook_params={"options": "-c statement_timeout=3000ms"},
)
The complete Postgres Operator DAG¶
When we put everything together, our DAG should look like this:
# create_pet_table, populate_pet_table, get_all_pets, and get_birth_date are examples of tasks created by
# instantiating the Postgres Operator
ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
DAG_ID = "postgres_operator_dag"
with DAG(
dag_id=DAG_ID,
start_date=datetime.datetime(2020, 2, 2),
schedule="@once",
catchup=False,
) as dag:
create_pet_table = SQLExecuteQueryOperator(
task_id="create_pet_table",
sql="""
CREATE TABLE IF NOT EXISTS pet (
pet_id SERIAL PRIMARY KEY,
name VARCHAR NOT NULL,
pet_type VARCHAR NOT NULL,
birth_date DATE NOT NULL,
OWNER VARCHAR NOT NULL);
""",
)
populate_pet_table = SQLExecuteQueryOperator(
task_id="populate_pet_table",
sql="""
INSERT INTO pet (name, pet_type, birth_date, OWNER)
VALUES ( 'Max', 'Dog', '2018-07-05', 'Jane');
INSERT INTO pet (name, pet_type, birth_date, OWNER)
VALUES ( 'Susie', 'Cat', '2019-05-01', 'Phil');
INSERT INTO pet (name, pet_type, birth_date, OWNER)
VALUES ( 'Lester', 'Hamster', '2020-06-23', 'Lily');
INSERT INTO pet (name, pet_type, birth_date, OWNER)
VALUES ( 'Quincy', 'Parrot', '2013-08-11', 'Anne');
""",
)
get_all_pets = SQLExecuteQueryOperator(task_id="get_all_pets", sql="SELECT * FROM pet;")
get_birth_date = SQLExecuteQueryOperator(
task_id="get_birth_date",
sql="SELECT * FROM pet WHERE birth_date BETWEEN SYMMETRIC %(begin_date)s AND %(end_date)s",
parameters={"begin_date": "2020-01-01", "end_date": "2020-12-31"},
hook_params={"options": "-c statement_timeout=3000ms"},
)
create_pet_table >> populate_pet_table >> get_all_pets >> get_birth_date
Conclusion¶
In this how-to guide we explored the Apache Airflow SQLExecuteQueryOperator to connect to PostgreSQL Database. Let’s quickly highlight the key takeaways.
It is best practice to create subdirectory called sql in your dags directory where you can store your sql files.
This will make your code more elegant and more maintainable.
And finally, we looked at the different ways you can dynamically pass parameters into our PostgresOperator
tasks using parameters or params attribute and how you can control the session parameters by passing
options in the hook_params attribute.