โ07-12-2024 08:48 PM
The following error occured when running .count() on a big sparkDF.
Py4JJavaError: An error occurred while calling o465.count. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 6 in stage 3.0 failed 4 times, most recent failure: Lost task 6.3 in stage 3.0 (TID 32) (10.139.64.6 executor 0): java.lang.NullPointerException
Data has close to 1.2mil records and have some nested json objects, so it's pretty big. I am using cluster with 64GB memory and 16GB cores and 16gb worker with 4gb cores.
Is there a way to solve this count? Is increasing cluster size the only solution?
โ07-13-2024 01:10 PM - edited โ07-13-2024 01:11 PM
DBR : ?
Number of workers: ?
number of driver node : assuming 1 driver
โ07-13-2024 02:50 PM
DBR: 13.3
Min 2 and max 8 workers
Driver node is 1
โ07-14-2024 12:53 AM
can you provide complete stacktrace ?
โ07-14-2024 05:53 AM
@sinclair - Can you please use num_workers=2 or 5 which will be constant instead of using auto scaling ?
Meantime i am trying to reproduce the same issue in my end !
โ07-14-2024 09:14 AM
Hi @sinclair,
Maybe try to increase number of partitions. There are no enough details, but assuming this error occur on worker due to insufficient memory, increasing partition number can help
โ07-19-2024 05:56 AM
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