Set default "spark.driver.maxResultSize" from the notebook

[mhansinger]

Hello,

I would like to set the default "spark.driver.maxResultSize" from the notebook on my cluster. I know I can do that in the cluster settings, but is there a way to set it by code?

I also know how to do it when I start a spark session, but in my case I directly load from the feature store and want to transform my pyspark data frame to pandas.

from databricks import feature_store
import pandas as pd
import pyspark.sql.functions as f
from os.path import join
 
fs = feature_store.FeatureStoreClient()
 
prediction_data = fs.read_table(name=NAME)
 
prediction_data_pd = prediction_data.toPandas()

[Kaniz]

Hi @Maximilian Hansinger​ ,

Please try this:-

from pyspark import SparkContext
from pyspark import SparkConf
 
conf = SparkConf()
          .setMaster('yarn') \
          .setAppName('xyz') \
          .set('spark.driver.extraClassPath', '/usr/local/bin/postgresql-42.2.5.jar') \
          .set('spark.executor.instances', 4) \
          .set('spark.executor.cores', 4) \
          .set('spark.executor.memory', '10g') \
          .set('spark.driver.memory', '15g') \
          .set('spark.memory.offHeap.enabled', True) \
          .set('spark.memory.offHeap.size', '20g') \
          .set('spark.dirver.maxResultSize', '4096') 
 
spark_context = SparkContext(conf=conf)

[mhansinger]

Hi @Kaniz Fatma​  thanks for your reply.

Not sure if that helps. When I check after execution of your code with

spark.conf.get("spark.driver.maxResultSize")

I still get the default "spark.dirver.maxResultSize", instead of 4096.

[Kaniz]

Hi @Maximilian Hansinger​ , Alternatively try this:-

from pyspark.sql import SparkSession
 
spark = (SparkSession.builder
    .master('yarn') # depends on the cluster manager of your choice
    .appName('xyz')
    .config('spark.driver.extraClassPath', '/usr/local/bin/postgresql-42.2.5.jar')
    .config('spark.executor.instances', 4)
    .config('spark.executor.cores', 4)
    .config('spark.executor.memory', '10g')
    .config('spark.driver.memory', '15g')
    .config('spark.memory.offHeap.enabled', True)
    .config('spark.memory.offHeap.size', '20g')
    .config('spark.dirver.maxResultSize', '4096') 
)
sc = spark.sparkContext

[Atanu · Accepted solution]

@Maximilian Hansinger​  may be you can follow this -

https://kb.databricks.com/jobs/job-fails-maxresultsize-exception.html

[Anonymous]

@Maximilian Hansinger​ - Would you let us know how it goes, please?

[Anonymous]

Hi @Maximilian Hansinger​ 

Just wanted to check in if you were able to resolve your issue. If yes, would you be happy to mark the answer as best? If not, please tell us so we can help you.

Thanks!