Apache Spark Memory Management | Unified Memory Management 

spark.memory.fraction expresses the size of M as a fraction of the (JVM heap space - 300MiB) (default 0.6). The rest of the space (40%) is reserved for user data structures, internal metadata in Spark, and safeguarding against OOM errors in the case of sparse and unusually large records.

I really appreciate your time and efforts in making quality videos. Please explain us how these different memory allocations cause problems or exceptions. How to solve these exceptions or problems. A screen shot of the possible issues and code/configuration changes to solve the issue will be really helpful and we would be really greatful if you could provide these details as well. Once again I appreciate your work and efforts

Very well explained. But a follow up video on the practical implementation will be appreciated. Anyways great effort!!

very good explanation and up to the point, thanks for this

Great Video !!

Good information. thanks

Hi Sir,the doubts fog clearing from mind after watching your spark videos,kindly make one session on real-time project from requirement to deployment it will very helpful ,Thank you.

Well explained

Hi, Your videos are giving a good real time knowledge on spark and i thank you for that.. Could you please make a video on how to submit spark code(Pyspark) using shell script. also how to submit a spark job using shell script if both can be done differently. Thanks in advance

you said that reservedmemory is a part of executor memory but in diagram you are showing 1gb of executor memory plus 300 m of reserved memory???

Can u pls show how to monitor this memory usage and distribution via spark URL in your next upload?

amazing

how do we find out if any executor is overallocated memory with --executor-memory but actually the job needs very less memory than provided executor memory parameter . Does this cause spark executer to reserve this memory and not being useful for other executors ?

nice

good explanation, btw, Is there any way to increase the executor memory dynamically?

Can you explain erasure coding vs replication

I respect your effort but I expected more detailed video not such a basic video

Where does resource manager such as yarn overhead lies in executor memory?

Can u pls do video on data skewness and on schema registry

If execution memory can evict blocks of data from storage memory, what happens to those evicted blocks if they are to be consumed again Will they be computed again and stored again

If data size is 16Gb and memory is 20GB what will use cache or persist?

What is the reserved used for or responsible for ?

Hello sir, I have some questions if you could answer in free time when i read spark.read.csv(and provide inferSchema=True) Does it take all rows to guess the datatype of a column what is sampleRatio option in spark.read.csv ? is it related to infershema can i tell spark to use all rows while infering the schema for a column

How to verify this if storage memory is not evicting execution memory

How to resolve OOM using above discussed concept???

१ video on How to validate data..?

Hi Harjeet, I am experienced professional who need some help in understanding current market conditions for future planning purposes (a kind of short call for mentoring). Please let me know if you can provide 10-15 min time.

Sir Is Hadoop and Spark are dying 😞

vak mugi