Snowpark

Get started with Snowpark in LocalStack for Snowflake

3 minute read

Introduction

Snowpark library is a developer library for querying and processing data at scale in Snowflake. Snowflake currently provides Snowpark libraries for three languages: Java, Python, and Scala. The Snowflake emulator facilitates testing Snowpark queries without connecting to the actual Snowflake instance. This guide provides instructions on using the Snowflake emulator in conjunction with Snowpark.

Snowpark for Python

The Snowflake emulator supports the development and testing of Snowpark Python code in a local development environment. You can install the Snowpark Python library using the following command:

$ pip install snowflake-snowpark-python

In this getting started guide, we’ll use the Snowpark Python library to establish a connection to the Snowflake emulator and employ a DataFrame to query a table named sample_product_data.

Create a Snowpark Session

The Snowflake emulator operates on snowflake.localhost.localstack.cloud. To create a Snowpark session in Python, use the following code:

from snowflake.snowpark import *
from snowflake.snowpark.functions import *

connection_parameters = {
    "user": "test",
    "password": "test",
    "account": "test",
    "warehouse": "test",
    "host": "snowflake.localhost.localstack.cloud",
}

session = Session.builder.configs(connection_parameters).create()

Create a table

You can create a table named sample_product_data and fill the table with some data by executing SQL statements. Add the following Python code to create the table:

session.sql('CREATE OR REPLACE TABLE sample_product_data (id INT, parent_id INT, category_id INT, name VARCHAR, serial_number VARCHAR, key INT, "3rd" INT)').collect()
[Row(status='Table SAMPLE_PRODUCT_DATA successfully created.')]
session.sql("""
    INSERT INTO sample_product_data VALUES
    (1, 0, 5, 'Product 1', 'prod-1', 1, 10),
    (2, 1, 5, 'Product 1A', 'prod-1-A', 1, 20),
    (3, 1, 5, 'Product 1B', 'prod-1-B', 1, 30),
    (4, 0, 10, 'Product 2', 'prod-2', 2, 40),
    (5, 4, 10, 'Product 2A', 'prod-2-A', 2, 50),
    (6, 4, 10, 'Product 2B', 'prod-2-B', 2, 60),
    (7, 0, 20, 'Product 3', 'prod-3', 3, 70),
    (8, 7, 20, 'Product 3A', 'prod-3-A', 3, 80),
    (9, 7, 20, 'Product 3B', 'prod-3-B', 3, 90),
    (10, 0, 50, 'Product 4', 'prod-4', 4, 100),
    (11, 10, 50, 'Product 4A', 'prod-4-A', 4, 100),
    (12, 10, 50, 'Product 4B', 'prod-4-B', 4, 100)
""").collect()

You can verify that the table was created and the data was inserted by executing the following Python code:

session.sql("SELECT count(*) FROM sample_product_data").collect()

The following output should be displayed:

[Row(COUNT=12)]

Construct a DataFrame

You can now construct a DataFrame from the data in the sample_product_data table using the following Python code:

df_table = session.table("sample_product_data")

You can see the first 10 rows of the DataFrame by executing the following Python code:

df_table.show(10)

The following output should be displayed:

-------------------------------------------------------------------------------------
|"ID"  |"PARENT_ID"  |"CATEGORY_ID"  |"NAME"      |"SERIAL_NUMBER"  |"KEY"  |"3RD"  |
-------------------------------------------------------------------------------------
|1     |0            |5              |Product 1   |prod-1           |1      |10     |
|2     |1            |5              |Product 1A  |prod-1-A         |1      |20     |
|3     |1            |5              |Product 1B  |prod-1-B         |1      |30     |
|4     |0            |10             |Product 2   |prod-2           |2      |40     |
|5     |4            |10             |Product 2A  |prod-2-A         |2      |50     |
|6     |4            |10             |Product 2B  |prod-2-B         |2      |60     |
|7     |0            |20             |Product 3   |prod-3           |3      |70     |
|8     |7            |20             |Product 3A  |prod-3-A         |3      |80     |
|9     |7            |20             |Product 3B  |prod-3-B         |3      |90     |
|10    |0            |50             |Product 4   |prod-4           |4      |100    |
-------------------------------------------------------------------------------------

Transform the DataFrame

You can perform local transformations on the DataFrame. For example, you can filter rows with the value of ‘id’ equal to 1:

df = session.table("sample_product_data").filter(col("id") == 1)
df.show()

The following output should be displayed:

------------------------------------------------------------------------------------
|"ID"  |"PARENT_ID"  |"CATEGORY_ID"  |"NAME"     |"SERIAL_NUMBER"  |"KEY"  |"3RD"  |
------------------------------------------------------------------------------------
|1     |0            |5              |Product 1  |prod-1           |1      |10     |
------------------------------------------------------------------------------------

Furthermore, you can also select specific columns:

df = session.table("sample_product_data").select(col("id"), col("name"), col("serial_number"))
df.show()

The following output should be displayed:

---------------------------------------
|"ID"  |"NAME"      |"SERIAL_NUMBER"  |
---------------------------------------
|1     |Product 1   |prod-1           |
|2     |Product 1A  |prod-1-A         |
|3     |Product 1B  |prod-1-B         |
|4     |Product 2   |prod-2           |
|5     |Product 2A  |prod-2-A         |
|6     |Product 2B  |prod-2-B         |
|7     |Product 3   |prod-3           |
|8     |Product 3A  |prod-3-A         |
|9     |Product 3B  |prod-3-B         |
|10    |Product 4   |prod-4           |
---------------------------------------