Re: Support Required: Issue with PySpark Code Execution Order

You have got two separate things going on: You did materialize but your unionByName is failing because the two DataFrames don’t actually have the same column set/schema at the moment of the union, even if printSchema() you looked at earlier seemed to match. Why unionByName is complaining (common

Re: Support Required: Issue with PySpark Code Execution Order

Thanks for support. I tried using action(count()) after persist to materialize the DataFrame results. However, I’m still facing a column count mismatch issue when performing unionByName. I have verified the column count and names in both DataFrames using printSchema, and it shows that both DataFra

Re: Support Required: Issue with PySpark Code Execution Order

Hi Karthick, The problem seems to be that you were performing transformation/recipe on three data frames without materialisation, then writing back to that target table. Each MERGE re-evaluated its “recipe” at a different time, so they saw different snapshots → flaky/empty results. Fix (short +

Re: Support Required: Issue with PySpark Code Execution Order

Hi *Ángel*, Thank you for checking on this. I’ll review the points you mentioned and get back to you with an update. Hi *Mich*, Looping you in here — could you please assist in reviewing this issue and share your inputs or suggestions? Your expertise would be really helpful in resolving it. Than

Re: Support Required: Issue with PySpark Code Execution Order

Have you tried disabling AQE? El dom, 10 ago 2025, 20:48, Karthick N escribió: > Hi Team, > > I’m facing an issue with the execution order in the PySpark code snippet > below. I’m not certain whether it’s caused by lazy evaluation, Spark plan > optimization, or something else. > > *Issue:* > Fo

Re: Support Required: Issue with PySpark Code Execution Order

Short: spark uses lazy eval. This is the long answer The whole of this is taken from Google Gemini https://aistudio.google.com/ Of course. This is a classic and often subtle issue in Spark

Support Required: Issue with PySpark Code Execution Order

Hi Team, I’m facing an issue with the execution order in the PySpark code snippet below. I’m not certain whether it’s caused by lazy evaluation, Spark plan optimization, or something else. *Issue:* For the same data and scenario, during some runs, one of the final views is not returning any data.