06/30/2023 | Press release | Distributed by Public on 06/30/2023 07:48
By:Poorna Chand Addala, Senior Data Engineer, Snowflake Center of Excellence, LTIMindtree
We're glad to have you back for the second part of our extensive Snowpark guide. In our previous blog, we delved into the different execution modes of Snowpark code and obtained a more profound comprehension of the inner workings of Snowpark execution. In this article, our attention will shift towards exploring the constraints of language-specific Stored Procedures (SPs) and immersing ourselves in the remarkable solution offered by User-Defined (Table) Functions (UD(T)Fs) to unlock Snowpark's potential.
Snowpark SPs have certain limitations that should be considered:
So, now you might be wondering if there is a way to run your language-specific code in Snowflake, taking advantage of all the nodes of your virtual warehouse. UD(T)Fs are there to help you out.
Fig 10: UD(T)F Execution Flow
Figure 10 illustrates the execution flow of UD(T)Fs:
UD(T)Fs address the SPs problem by allowing distributed execution on multiple nodes. When a UDTF is invoked in a query, Snowflake automatically partitions the input data and distributes it among the nodes in the warehouse. Each node then independently executes the UDTF on its assigned data subset. This parallel execution allows multiple nodes to process different input portions simultaneously, significantly improving performance.
Once the UDTF execution is complete on each node, the intermediate results are combined or merged to produce the final output. Snowflake handles this merging process transparently, providing a unified result set to the query. By leveraging the distributed computing capabilities of the Snowflake data warehouse, UD(T)Fs provide a scalable and performant solution to the SPs problem. They allow for the efficient utilization of multiple nodes and enable parallel processing, thereby overcoming the limitations of executing code on a single node.
In the second part of our Snowpark guide, we examined the limitations of Snowpark Stored Procedures (SPs) and the solution offered by User-Defined (Table) Functions (UD(T)Fs) to overcome these limitations. We looked into the execution flow of UD(T)Fs, which involves Snowflake using multiple interpreter processes to execute Python functions concurrently. Furthermore, we shared best practices for Snowpark usage. By adhering to these practices and harnessing the distributed computing capabilities of Snowpark, users can fully unleash Snowpark's potential for scalable, high-performance, and efficient data processing and analytics.
Senior Data Engineer, Snowflake Center of Excellence, LTIMindtree
Poorna is a valued member of LTIMindtree's Snowflake COE, where he serves as a Senior Data Engineer. He passionately invests his time in augmenting his expertise and refining his mastery in the realm of data engineering. Poorna's insatiable curiosity drives him to dive deep into his areas of interest, helping him stay at the forefront of advancements. He relishes the challenge of tackling complex technical problems and applies his creativity to find innovative solutions.
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