I’m pulling my hair out over here. I’ve got this Spark 3 job that’s throwing a fit. The error message looks exactly like the one in that old SPARK-15000 issue. But here’s the kicker - the JIRA ticket doesn’t have a solution!
I tried the usual suspects. Yanked out the cache() calls. Ditched the show() statements. No dice. The error’s still there, mocking me.
Anyone else run into this nightmare? I’m all ears for solutions. Maybe there’s some secret Spark 3 voodoo I’m missing? Help a fellow dev out!
PS: If it matters, I’m running this on a pretty beefy cluster. Thought throwing more hardware at it might help, but nope.
I’ve encountered a similar issue with Spark 3 recently. After extensive troubleshooting, I found that upgrading to the latest minor version of Spark 3 resolved the problem.
It appears that some bugs related to SPARK-15000 persisted in earlier 3.x releases, and addressing them through an upgrade made a notable difference. Reviewing your cluster configuration for any mismatches between Spark and Hadoop versions is also a worthwhile investigation.
If upgrading isn’t feasible, consider adjusting your job’s partitioning strategy or tweaking the executor memory settings. These approaches helped mitigate the issue in my case without requiring major code changes.
I’ve dealt with this Spark 3 issue before, and it’s a real pain. One thing that helped me was digging into the Spark UI during job execution. It revealed some skewed partitions that were causing memory issues.
To address it, I implemented custom partitioning on the problematic RDDs using a more evenly distributed key. This balanced the workload across executors and resolved the error.
Another trick was to use broadcast joins for smaller datasets instead of regular joins. This reduced the shuffle and helped avoid the dreaded SPARK-15000-like error.
If you’re still stuck, try enabling dynamic allocation with spark.dynamicAllocation.enabled=true. It helped my cluster better manage resources during heavy operations.
Remember, sometimes these errors are symptoms of suboptimal query plans. Analyzing and optimizing your queries might be the key to solving this stubborn issue.
hey pete, i ran into this nightmare too. what worked for me was tweaking the spark.sql.shuffle.partitions parameter. try bumping it up or down, depending on ur data size. also, check ur spark.memory.fraction setting. messing with these helped me dodge that SPARK-15000 error. good luck!