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Spark Memory/worker issues & what is the correct spark configuration?


Spark Memory/worker issues & what is the correct spark configuration?

By : Peter Fusco
Date : November 21 2020, 11:01 PM
I wish this helpful for you As far as I know you should only start one Worker per Node:
http://spark.apache.org/docs/latest/hardware-provisioning.html
code :
export SPARK_EXECUTOR_CORES=4
export SPARK_EXECUTOR_MEMORY=16GB
export SPARK_MASTER_HOST=<Your Master-Ip here>
export SPARK_EXECUTOR_CORES=8
export SPARK_EXECUTOR_MEMORY=16GB
export SPARK_MASTER_HOST=<Your Master-Ip here>
spark.driver.memory              2g


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Spark worker memory

Spark worker memory


By : Hitesh Sutar
Date : March 29 2020, 07:55 AM
it should still fix some issue One other workaround is try setting the following parameters inside the conf/spark-defaults.conf file:
code :
spark.driver.cores              4
spark.driver.memory             2g
spark.executor.memory           4g
How does spark.python.worker.memory relate to spark.executor.memory?

How does spark.python.worker.memory relate to spark.executor.memory?


By : Arumugam
Date : March 29 2020, 07:55 AM
Any of those help Found this thread from the Apache-spark mailing list, and it appears that spark.python.worker.memory is a subset of the memory from spark.executor.memory.
From the thread: "spark.python.worker.memory is used for Python worker in executor"
(Spark skewed join) How to join two large Spark RDDs with highly duplicated keys without memory issues?

(Spark skewed join) How to join two large Spark RDDs with highly duplicated keys without memory issues?


By : jartigas
Date : March 29 2020, 07:55 AM
seems to work fine Actually, this is a standard problem in Spark called "skewed join": one of the sides of the join is skewed, meaning some of its keys are much more frequent that others. Some answers that didn't work out for me can be found here.
The strategy I used is inspired by the GraphFrame.skewedJoin() method defined here and its use in ConnectedComponents.skewedJoin() here. The join will be performed by joining the most frequent keys using a broadcast join and the less frequent keys using a standard join.
Hive on Spark > Yarn mode > spark configuration > what value to give to spark.master

Hive on Spark > Yarn mode > spark configuration > what value to give to spark.master


By : Savitri Angadi
Date : March 29 2020, 07:55 AM
I wish this help you I am trying to HiveQL with my own custom serde (It worked properly with pure Hive). I followed the instruction in: https://cwiki.apache.org/confluence/display/Hive/Hive+on+Spark%3A+Getting+Started , Please, try set spark.master=yarn-client;
What is the differences between spark.{driver,executor}.memory in spark-defaults.conf and SPARK_WORKER_MEMORY in spark-e

What is the differences between spark.{driver,executor}.memory in spark-defaults.conf and SPARK_WORKER_MEMORY in spark-e


By : Andres
Date : March 29 2020, 07:55 AM
Any of those help spark-default.conf,this properties file serves as the default settings file, which is used by the spark-submit script to launch applications in a cluster. The spark-submit script loads the values specified in spark-defaults.conf and passes them on to your application. Note: If you define environment variables in spark-env.sh, those values override any of the property values you set in spark-defaults.conf
depends on your configuration and file selection use "spark.executor.memory" or "SPARK_WORKER_MEMORY" "spark.driver.memory" or "SPARK_DRIVER_MEMORY"
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