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In data engineering, we often encounter large files that need to be processed in chunks. Using Snowflake’s Snowpark, you can split a large CSV file into smaller parts and handle each as needed. However, while Snowpark provides powerful in-database processing capabilities, splitting files this way may not be the most efficient method in production environments. Though it’s a valuable exercise for users looking to deepen their understanding of Snowpark. Snowpark enables complex transformations and manipulations that take place entirely within Snowflake’s secure and scalable environment.
In this blog, we’ll:
- Show how to split a large file using Snowpark’s DataFrame transformations.
- Explain the challenges and limitations of this approach in production.
- Recommend alternative methods for production use that maximize efficiency and reduce compute costs.
Step 1: Problem Setup
Let’s assume we have a large CSV file, Customer_Invoice_big.csv, stored in a Snowflake stage. Our objective is to split it into smaller chunks of 100,000 rows each for easier processing and analysis. This task serves as an excellent opportunity to dive into Snowpark, understanding its row-based transformations, window functions, and file operations within Snowflake.
Step 2: Define Schema and Load Data
Define the schema for the CSV file and load it from the stage into a Snowpark DataFrame.
Step 3: Removing Previous Chunked Files
We use LIST to retrieve all files in the stage and REMOVE to delete any existing chunk files.
Step 4: Chunking and Writing Files
Using Snowpark’s DataFrame operations, we can split the data into chunks of 100,000 rows. This logic iterates over each chunk, writing it back to the Snowflake stage.
Output:
Challenges and Limitations
While this approach works, using Snowflake’s resources to split files is generally inefficient for production workloads. Here’s why:
- Warehouse Usage: Splitting large files consumes Snowflake compute resources. A long-running split process could keep your warehouse active for an extended period, increasing costs.
Alternative Solutions for Splitting Large Files
For a more scalable approach, consider using dedicated file-splitting tools outside of Snowflake before uploading to a Snowflake stage:
- Azure Data Factory (ADF): ADF’s Data Flow or Pipelines can split files based on row counts.
- AWS S3 CLI (cp utility): For S3-stored files, the AWS CLI cp command supports parallel uploads and efficient file handling.
- Unix split Command: The Unix split command allows you to split files into smaller parts by line count, ideal for batch processing on servers.
Conclusion
This approach demonstrates how to split files with Snowpark, a useful exercise for learning Snowflake’s capabilities. However, in production, it’s recommended to process large files outside Snowflake, leveraging specialized tools that minimize compute costs and maximize efficiency. This solution offers Snowpark users a deeper understanding of file manipulation but encourages considering the most cost-effective option for production environments.
