Cache memory causing duplicates restricting merge to happen in Fabric notebook

Question

Hello,

I'm encountering an issue with a merge operation in a notebook, where I'm accessing tables from a lakehouse. The merge command fails with a duplicate error. However, when I query the table using SQL Server Management Studio (SSMS) connected to the lakehouse, it shows zero duplicates. I suspected a caching problem and attempted to resolve it by disabling the cache using the following code:
spark.conf.set("spark.synapse.vegas.useCache", "false")

df.cache()

df.unpersist()

I also manually switched environments within the notebook and found no duplicates. The perplexing aspect is that the issue persists when the notebook is triggered via a pipeline, even though there are no duplicates when tested manually. What could be the potential reasons behind this discrepancy, and how can it be addressed?

Answer 1

Double-check the data in your lakehouse to ensure that there are no actual duplicates. Sometimes, issues might arise from discrepancies in the data source. How does caching work in Apache Spark ? :

Caching in Apache Spark is an optimization technique that stores data in memory to improve query performance. In your code, you've attempted to disable caching using spark.conf.set("spark.synapse.vegas.useCache", "false"). However, the behavior of caching might still be influenced by other settings and the Spark DataFrame lineage.

Make sure there are no previous cache operations on the df DataFrame before performing the merge. Caching can be persistent, and if df was cached before, it might still be in memory despite your attempts to disable caching. Try also to clear the cache before performing the merge using df.unpersist() to ensure that the DataFrame is not cached when the merge is executed.

df.unpersist()
merged_df = df.merge(...)