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Pandas, a popular Python library, is a fantastic tool for small to moderately sized data, but it struggles with large-scale datasets. Snowpark, combined with Modin, offers a powerful alternative by enabling scalable, distributed operations directly in Snowflake’s cloud infrastructure. This blog will explore the key differences between Pandas DataFrames and Snowpark DataFrames (enhanced by Modin), demonstrate their respective strengths. This will also presents how Snowpark solves challenges that Pandas faces with large datasets.
Scenario: Comparing Pandas with Snowpark for Big Data Processing
Scenario: Comparing Pandas with Snowpark for Big Data Processing
Imagine you’re working with a customer data warehouse that holds billions of records. If you try to load all that data into a Pandas DataFrame on your local machine, you’ll likely run into memory issues. Pandas was not designed for such large-scale operations. Pandas operates by loading data entirely into memory, which becomes a significant bottleneck with large datasets. If your dataset exceeds your system’s memory capacity, you may experience long processing times or memory crashes.
Additionally, Pandas eagerly evaluates every operation—meaning that when you apply a transformation, it’s immediately executed, and the result is stored in memory. This leads to high memory usage, especially when you need to chain multiple transformations.
Enter Snowpark DataFrames
Snowpark DataFrames, on the other hand, operate within Snowflake’s cloud infrastructure. Instead of downloading and loading the data into memory on your local machine, Snowpark keeps the data in the cloud and allows you to run distributed operations directly on Snowflake’s powerful platform.
Moreover, Snowpark uses lazy evaluation, which means that operations does not execute until you request them. You can build up a chain of transformations without triggering computation until you’re ready. When the time comes to run the process, all the transformations are executed efficiently on Snowflake’s cloud infrastructure.
Modin:
The Role of Modin:
Snowflake’s integration of Modin enhances Snowpark’s capabilities by enabling scalable pandas operations across distributed environments. Modin is an open-source library designed to provide the same interface as pandas, but with the ability to distribute operations across multiple cores and machines.
By building pandas on Snowflake using Modin, developers can continue to use familiar pandas syntax and functions while leveraging the speed and scalability of Snowflake. Snowpark DataFrames seamlessly integrate with pandas, so when using pandas on Snowflake, Modin translates the pandas operations into SQL queries that Snowflake can process natively. This means that, unlike Pandas which would struggle to handle large datasets on a single machine, pandas on Snowflake allows you to work with the same commands but in a distributed manner.
Below is the snowpark code where we try to compare the Native pandas vs Snowflake Pandas uses Modin library. We have considered the dataset of 150M data.
Please note : import snowflake.snowpark.modin.plugin
When I tried to execute the above code using SMALL,MEDIUM or LARGE Warehouse I got the below error.
The main reason for error native_pd_df = snowpark_df.to_pandas() as it tried to load the entire 150M dataset into memory.
Now I tried to run the same code using snowpark optimized warehouse (Medium) and below are the stats.
Took 23.125818234999997 seconds to read a table with 150000000 rows
into Snowpark pandas!
Native pandas took 238.75985168800003 seconds to read the data!
Filtering for Brand on snowpark dataframe took 0.4241870580001432 seconds
Filtering for Brand on Native pandas dataframe took 74.24011083599999 seconds
Aggregation by brand on snowpark dataframe took 0.16337677999990774 seconds
Aggregation by brand on native pandas dataframe took 9.816949344000022 seconds
Sorting on snowpark dataframe took 0.0021869040001547546 seconds
Sorting on native pandas dataframe took 94.72202191499991 seconds
In addition to it , I removed the native pandas code and execute the snowpark using modin on XS small warehouse and surprisingly got completed in 70 sec
Took 68.1728376929999 seconds to read a table with 150000000 rows into Snowpark pandas!
Filtering for Brand on snowpark dataframe took 0.44459223900003053 seconds
Aggregation by brand on snowpark dataframe took 0.1747894229999929 seconds
Conclusion:
Scaling Pandas with Snowpark and Modin
The integration of Modin into Snowpark DataFrames brings the best of both worlds: the simplicity and familiarity of pandas combined with the power and scalability of Snowflake’s cloud infrastructure. When working with massive datasets that outgrow Pandas’ capabilities, Snowpark enables you to continue using your familiar pandas workflows, but at a much larger scale and without the memory limitations of a single machine.
