Navigating Row_Number() Non-Idempotency

In the realm of data engineering, challenges often present themselves in the most unexpected ways. Recently, I embarked on a journey with Databricks that led me to an intricate puzzle — one that took days of exploration and experimentation to crack: the enigmatic non-idempotency of the Row_Number() window function.

My goal seemed deceptively straightforward: process data in manageable 100-record chunks and dispatch these packets as messages to a designated message queue. This operation was driven by the memory limitations of the message queue system, necessitating careful segmentation of data. Armed with my trusty knowledge of the Row_Number() function, I began crafting a solution on my dataframe.

However, as I initiated a loop to process each batch of 100 records, an unexpected pattern emerged. Duplicate entries were making their way into the message queue, confounding my meticulous approach. Intriguingly, the root cause of this issue lay in the division of sequence numbers during runtime, driven by the partition count allocated to the Spark job.

To illustrate, consider a scenario where I had a dataset of 1000 records. The loop, designed to iterate over 100-record batches, looped back to the first 500 records after reaching the 1000th record. In effect, this dispatched the same set of 500 messages twice to the message queue.

This perplexing behavior led me to an intriguing discovery: the distributed nature of Spark was influencing the behavior of my code. The realization prompted a deeper exploration into the intricacies of distributed systems and their implications for data engineering tasks.

Armed with this newfound knowledge, I embarked on finding a solution to this non-idempotency challenge. My answer lay in persisting the dataframe in a temporary location, thereby anchoring the sequence values to the core of the dataframe itself. Upon retrieval and re-creation as a dataframe, the loop operation ran flawlessly. This strategy ensured that the sequence integrity remained undisturbed, unaffected by runtime divisions.

The journey through this challenge was a profound reminder of the complexity that distributed systems introduce into our data engineering endeavors. It highlighted the importance of approaching problems with a blend of analytical thinking, creativity, and an unwavering commitment to finding solutions.

As we navigate the ever-evolving landscape of data engineering, challenges such as this illuminate the dynamic nature of our field. Each puzzle unraveled adds to our arsenal of problem-solving techniques, fostering growth and expertise. So, next time you encounter an enigma, remember that it’s an opportunity to delve deeper into the world of data engineering, one revelation at a time.

By the way Databricks has an identity values generator. Please check out this link for further details https://www.databricks.com/blog/2022/08/08/identity-columns-to-generate-surrogate-keys-are-now-available-in-a-lakehouse-near-you.html

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