Connecting Snowpark to Lambda using LocalStack

In this tutorial, you will explore how you can use Snowpark to connect to a locally running AWS Lambda using LocalStack.

  5 minute read  

Introduction

In this tutorial, you will explore how to connect Snowpark to AWS Lambda locally using LocalStack. Snowpark allows you to query, process, and transform data in a variety of ways using Snowpark Python. In this example, we create a Lambda function that uses Snowpark to:

  • Establish a connection to a local Snowflake database provided by LocalStack.
  • Create a cursor object and execute a simple query to create a table.
  • Insert rows and execute an insert query with executemany and a query to select data from the table.
  • Fetch the results and execute a query to get the current timestamp.

The code in this tutorial is available on GitHub.

Prerequisites

Create the Lambda function

Create a new directory for your lambda function and navigate to it:

$ mkdir -p lambda-snowpark
$ cd lambda-snowpark

Create a new file named handler.py and add the following code:

import snowflake.connector as sf

def lambda_handler(event, context):
    snowflake_params = {
        'user': 'test',
        'password': 'test',
        'account': 'test',
        'warehouse': 'test',
        'database': 'test',
        'schema': 'TEST_SCHEMA',
        'host': 'snowflake.localhost.localstack.cloud'
    }

    # Establish a connection
    connection = sf.connect(**snowflake_params)

    try:
        # Create a cursor object
        cursor = connection.cursor()

        # Execute the query to create a table
        cursor.execute("create or replace table ability(name string, skill string )")

        # Rows to insert
        rows_to_insert = [('John', 'SQL'), ('Alex', 'Java'), ('Pete', 'Snowflake')]

        # Execute the insert query with executemany
        cursor.executemany("insert into ability (name, skill) values (%s, %s)", rows_to_insert)

        # Execute a query to select data from the table
        cursor.execute("select name, skill from ability")

        # Fetch the results
        result = cursor.fetchall()
        print("Total # of rows:", len(result))
        print("Row-1 =>", result[0])
        print("Row-2 =>", result[1])

        # Execute a query to get the current timestamp
        cursor.execute("SELECT CURRENT_TIMESTAMP()")
        current_timestamp = cursor.fetchone()[0]
        print("Current timestamp from Snowflake:", current_timestamp)

    finally:
        # Close the cursor
        cursor.close()

        # Close the connection
        connection.close()

    return {
        'statusCode': 200,
        'body': "Successfully connected to Snowflake and inserted rows!"
    }

In the above code:

  • You import the snowflake.connector module to establish a connection to the Snowflake database.
  • You define the lambda_handler function, which is the entry point for the Lambda function.
  • You define the snowflake_params dictionary with snowflake.localhost.localstack.cloud as the host to connect to the Snowflake emulator.
  • You establish a connection to the Snowflake database using the sf.connect method.
  • You create a cursor object and execute a query to create a table.
  • You insert rows and execute an insert query with executemany and a query to select data from the table.
  • You fetch the results and execute a query to get the current timestamp.
  • You close the cursor and the connection.

Install the dependencies

You can now install the dependencies for your Lambda function. These include:

  • snowflake-connector-python to connect to the Snowflake database.
  • boto3 and botocore to interact with AWS services.

Run the following command:

$ pip3 install \
		--platform manylinux2010_x86_64 \
		--implementation cp \
		--only-binary=:all: --upgrade \
		--target ./libs \
 		snowflake-connector-python==2.7.9 boto3==1.26.153 botocore==1.29.153

Package the Lambda function

Package the Lambda function and its dependencies into a ZIP file. Run the following command:

$ mkdir -p build
$ cp -r libs/* build/
$ (cd build && zip -q -r function-py.zip .)

You have now created a ZIP file named function-py.zip that contains the Lambda function and its dependencies.

Start the LocalStack container

Start your LocalStack container in your preferred terminal/shell.

$ export LOCALSTACK_AUTH_TOKEN=<your_auth_token>
$ DEBUG=1 LAMBDA_RUNTIME_ENVIRONMENT_TIMEOUT=180 localstack start

The DEBUG=1 environment variable is set to enable debug logs. It would allow you to see the SQL queries executed by the Lambda function. The LAMBDA_RUNTIME_ENVIRONMENT_TIMEOUT environment variable is set to increase the Lambda function’s timeout to 180 seconds.

Check the emulator’s availability by running:

$ localstack extensions list


┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ Name                         ┃ Summary                       ┃ Version ┃ Author     ┃ Plugin name ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ localstack-extension-snowfl… │ LocalStack Extension:         │ 0.1.22  │ LocalStack │ snowflake   │
│                              │ Snowflake                     │         │            │             │
└──────────────────────────────┴───────────────────────────────┴─────────┴────────────┴─────────────┘

Deploy the Lambda function

You can now deploy the Lambda function to LocalStack using the awslocal CLI. Run the following command:

$ awslocal lambda create-function \
    --function-name localstack-snowflake-lambda-example \
	--runtime python3.10 \
	--timeout 180 \
	--zip-file fileb://build/function-py.zip \
	--handler handler.lambda_handler \
	--role arn:aws:iam::000000000000:role/lambda-role

After successfully deploying the Lambda function, you will receive a response with the details of the function. You can now invoke the function using the awslocal CLI:

$ awslocal lambda invoke --function-name localstack-snowflake-lambda-example \
	--payload '{"body": "test" }' output.txt

You will receive a response with the details of the invocation. You can view the output in the output.txt file. To see the SQL queries executed by the Lambda function, check the logs by navigating to LocalStack logs (localstack logs).

2024-02-07T17:33:36.763 DEBUG --- [   asgi_gw_3] l.s.l.i.version_manager    : [localstack-snowflake-lambda-example-b0813b21-ad5f-4ec7-8fb4-53147df9695e] Total # of rows: 3
2024-02-07T17:33:36.763 DEBUG --- [   asgi_gw_3] l.s.l.i.version_manager    : [localstack-snowflake-lambda-example-b0813b21-ad5f-4ec7-8fb4-53147df9695e] Row-1 => ('John', 'SQL')
2024-02-07T17:33:36.763 DEBUG --- [   asgi_gw_3] l.s.l.i.version_manager    : [localstack-snowflake-lambda-example-b0813b21-ad5f-4ec7-8fb4-53147df9695e] Row-2 => ('Alex', 'Java')
2024-02-07T17:33:36.771 DEBUG --- [   asgi_gw_3] l.s.l.i.version_manager    : [localstack-snowflake-lambda-example-b0813b21-ad5f-4ec7-8fb4-53147df9695e] Current timestamp from Snowflake: 2024-02-07T17:33:36

Conclusion

LocalStack’s core cloud emulator supports a wide range of AWS services, including Lambda, and provides a local environment for developing and testing your serverless applications. You can now use LocalStack’s Snowpark emulator to connect to locally running AWS services, and develop/test your Snowpark & AWS applications locally.