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How to increase executor memory in Databricks jobs

amitkmaurya
New Contributor III

May be I am new to Databricks that's why I have confusion.

Suppose I have worker memory of 64gb in Databricks job max 12 nodes...

and my job is failing due to Executor Lost due to 137 (OOM if found on internet).

So, to fix this I need to increase executor memory.

But how to increase it, choosing more workers or choosing 128gb worker? which one will work.?

Help me on this..

2 ACCEPTED SOLUTIONS

Accepted Solutions

raphaelblg
Honored Contributor
Honored Contributor

Hello @amitkmaurya ,

Increasing compute resources may not always be the best strategy. To gain more insights into each executor's memory usage, check the cluster metrics tab and Spark UI for your cluster. If one executor has a much higher memory usage than the others, it could indicate a data skew issue.

Executor OOM issues can be caused by several factors, including poorly distributed partitions (skew), excessive GC and poorly optimized jobs overall. For a detailed root cause analysis and potential mitigation, please contact Databricks Support.

Best regards,

Raphael Balogo
Sr. Technical Solutions Engineer
Databricks

View solution in original post

amitkmaurya
New Contributor III

Hi @raphaelblg ,

I have solved this issue. Yes, in my case data skewness was the issue that was causing this executor OOM, so adding repartition just before writing resolved this skewness. I didn't change any workers or driver memory.

Thanks for your help..!!

View solution in original post

2 REPLIES 2

raphaelblg
Honored Contributor
Honored Contributor

Hello @amitkmaurya ,

Increasing compute resources may not always be the best strategy. To gain more insights into each executor's memory usage, check the cluster metrics tab and Spark UI for your cluster. If one executor has a much higher memory usage than the others, it could indicate a data skew issue.

Executor OOM issues can be caused by several factors, including poorly distributed partitions (skew), excessive GC and poorly optimized jobs overall. For a detailed root cause analysis and potential mitigation, please contact Databricks Support.

Best regards,

Raphael Balogo
Sr. Technical Solutions Engineer
Databricks

amitkmaurya
New Contributor III

Hi @raphaelblg ,

I have solved this issue. Yes, in my case data skewness was the issue that was causing this executor OOM, so adding repartition just before writing resolved this skewness. I didn't change any workers or driver memory.

Thanks for your help..!!

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