I just installed spark on my local computer and try to run some simple code to test if everything is good, but it keeps showing me: Python worker exited unexpectedly (crashed). this is the code I’m running:

When performing the distinct operation in Spark,

I have a block of pyspark code, about 40 lines, with only lazy transformations. I’ve painstakingly removed every action method since I need to delay execution outside of this block of code. However, the block of code when benchmarked still takes 15 minutes! Is there a reason why that may be the case? Might execution trigger if a DAG got too large? Is there a way I can confirm if actions are being taken?

Currently, I am exploring Driver Process in Apache Spark. I Have few questions around apache spark:

Currently i am exploring Driver Process in Apache Spark. Have few questions around apache spark. Questions are

What are the challenges to apply groupby transfermation before doing shuffling and combining all partions grouped data and then again finally applying groupby transfermation? So that we can avoid data shuffling and reduce the time and resources

I have a simple pyspark setup with a local master and no hive installed.

I try to identify which fields of my final data frame origins from which fields in the input data frames

The following merge clause supports updates, and additional rows. However, if a row is deleted it will still show the row that was deleted. Can someone help modify the Merge clause such that rows that have been deleted won’t appear.

I am running Spark jobs on Kubernetes. Usernames are fixed for all users, so I was attempting to set the SPARK_USER environmental variable so that I could distinguish my jobs from others’ on the Spark History Server. However, I found that setting this env var breaks writing to S3.