Challenges Executing JavaScript-Based Stored Procedures in Snowflake via Airflow DAGs

Addressing Execution Failures in Snowflake Stored Procedures with Airflow DAGs

When working with Airflow DAGs to automate processes on Snowflake, executing JavaScript-based stored procedures can present unique challenges. One common issue developers encounter is transaction failure, especially when using scoped transactions in Snowflake. This is a critical obstacle, as the failure leads to the rollback of the transaction, disrupting workflows.

The error becomes more prevalent when using Airflow 2.5.1 in conjunction with the Python Snowflake connector 2.9.0. This combination appears to trigger issues with handling transactions within stored procedures, which rely on JavaScript. The error message commonly seen in these cases is: "Scoped transaction started in stored procedure is incomplete and it was rolled back."

Understanding how the stored procedure handles exceptions is vital for troubleshooting. In most cases, the procedure starts with a "BEGIN TRANSACTION," commits it, and if any issues arise, it rolls back the transaction. This standard flow seems to break when combined with the Snowflake and Airflow versions in use, making resolution tricky for developers.

In this article, we will explore the specific problem and examine potential solutions that can help resolve this execution issue. By addressing the underlying causes and adjusting our configuration, we aim to create a more reliable and robust automation process.

Understanding the Execution of Snowflake Stored Procedures via Airflow DAGs

The provided scripts serve as a bridge between Airflow DAGs and Snowflake, enabling the automation of running JavaScript-based stored procedures in Snowflake. In the first script, we utilize the SnowflakeOperator to call the stored procedure from within an Airflow task. This operator is crucial because it abstracts the complexities of connecting to Snowflake and executing SQL statements. By providing parameters such as the Snowflake connection ID, schema, and SQL command, we ensure the stored procedure is invoked correctly with the necessary context.

The stored procedure in question handles critical database transactions using scoped transaction blocks. These transactions are crucial for ensuring that multiple SQL commands execute as one unit, preserving data integrity. Specifically, the script attempts to start a transaction with a BEGIN TRANSACTION, then commits if successful, or performs a rollback in case of errors. The error handling mechanism is vital, as it allows the script to undo any incomplete changes if something goes wrong, ensuring that no partial data is written.

The second approach, which uses Python's snowflake.connector, offers more flexibility by allowing direct interaction with Snowflake from within a Python function. This method bypasses the SnowflakeOperator and lets you have more control over the connection and transaction handling. The script explicitly opens a connection, initiates the transaction, and calls the stored procedure. If the procedure fails, it raises an exception, triggering a rollback to ensure no unwanted data is saved.

This combination of methods demonstrates two ways to solve the problem of executing JavaScript-based stored procedures in Snowflake via Airflow. While the first approach is simpler and tightly integrated with Airflow's task orchestration, the second approach provides a more customizable and fine-grained control of error handling. Both approaches emphasize the importance of scoped transactions and the need for proper rollback mechanisms in case of failure. By modularizing these scripts, developers can easily reuse them across various Airflow DAGs while maintaining performance and ensuring data consistency.

# Import necessary libraries
from airflow import DAG
from airflow.providers.snowflake.operators.snowflake import SnowflakeOperator
from datetime import datetime
# Define default arguments for the DAG
default_args = {
    'owner': 'airflow',
    'start_date': datetime(2024, 10, 1),
    'retries': 1
}
# Create the DAG for scheduling
dag = DAG('snowflake_stored_procedure_dag', default_args=default_args, schedule_interval='@daily')
# Define the SQL command for invoking the stored procedure
create_config_table = """
CALL {target_schema}.STORED_PROCEDURE(
    '{target_schema}', '{storageIntegration}', '{s3_uri}')
;"""
# Define the Snowflake operator task
call_CONFIG_DATA_LOAD = SnowflakeOperator(
    task_id='call_CONFIG_DATA_LOAD',
    snowflake_conn_id='snowflake_conn',
    database='DB_NAME',
    schema='SCHEMA_NAME',
    role='ROLE_NAME',
    warehouse='WAREHOUSE_NAME',
    sql=create_config_table,
    dag=dag
)
# Test the operator
call_CONFIG_DATA_LOAD

# Import necessary libraries
import snowflake.connector
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime
# Define connection and transaction function
def execute_snowflake_procedure(kwargs):
    conn = snowflake.connector.connect(
        user='USERNAME',
        password='PASSWORD',
        account='ACCOUNT_NAME')
    try:
        conn.cursor().execute("BEGIN TRANSACTION")
        conn.cursor().execute("CALL SCHEMA_NAME.STORED_PROCEDURE()")
        conn.cursor().execute("COMMIT")
    except Exception as e:
        conn.cursor().execute("ROLLBACK")
        raise Exception(f"Transaction failed: {e}")
# Set up DAG
default_args = {
    'owner': 'airflow',
    'start_date': datetime(2024, 10, 1)
}
dag = DAG('snowflake_procedure_with_error_handling', default_args=default_args)
run_snowflake_procedure = PythonOperator(
    task_id='run_snowflake_procedure',
    python_callable=execute_snowflake_procedure,
    provide_context=True,
    dag=dag
)

Exploring Alternatives to Handling Snowflake Transactions in Airflow

One important aspect that hasn't been discussed yet is the possibility of using Snowflake's Task feature instead of relying entirely on Airflow to manage stored procedures. Snowflake Tasks are built-in scheduling and execution components that can automate specific processes directly within Snowflake. While Airflow offers a broader orchestration scope, using Snowflake Tasks in combination with Airflow allows for more localized, efficient execution of database-related tasks. This setup can offload certain jobs to Snowflake, reducing the load on Airflow DAGs.

Another critical area to explore is the integration of multi-step transactions in Snowflake. JavaScript-based stored procedures in Snowflake often require careful management of complex multi-step operations that involve several database changes. By incorporating these steps into the stored procedure directly, you minimize the chances of incomplete transactions or rollbacks. This requires careful management of transaction isolation levels to ensure that no external process interferes with the execution of these multi-step operations, guaranteeing data consistency and preventing race conditions.

Lastly, leveraging Airflow's advanced features such as XCom to pass data between tasks can enhance how you manage dynamic SQL calls. For example, instead of hardcoding values into your stored procedure calls, you can pass parameters dynamically using XCom. This not only increases the flexibility of your Airflow DAGs but also allows for more scalable and maintainable solutions when orchestrating workflows that involve Snowflake stored procedures. By making the entire process more dynamic, you reduce redundancy and improve efficiency.