Processing 25GB of data in Spark | How many Executors and how much Memory per Executor is required.
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- Опубликовано: 26 сен 2024
- #pyspark #azuredataengineer #databricks #spark
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![Spark [Driver and Executor] Memory Management Deep Dive](http://i.ytimg.com/vi/0ym8OJkA7x0/mqdefault.jpg)
![Spark [Driver and Executor] Memory Management Deep Dive](/img/tr.png)
Your explanation of the Spark cluster and memory configurations was excellent. I really appreciate it!
Good explanation. Spark is all about good resource allocation or use and optimization
Awesome explanation
Simple explanation
Great sir 🙌
Thanq
plz make video on pyspark unit testing
Superb explanation 👌 👏 👍
Wonderful Explanation.
You are simply superb.
Thank you 🙏
perfect video sir
If num of partition is 200 ... And so it the number of core required ... So core size is 128mb ... Right ?
Then how in 3rd block core size turn to 512mb and thus executer is then 4*512 ????
in each core memory should be minimum 4 times of data it is going to process(128mb) roughly it should be minimum 512 mb of memory.
for example you are assigning 25 executors instead of 50 then in each executors there will be 8 cores and parallel task will be run(25*8). Then also it will take 5 mins only to complete the job then how 10min. can you please explain this point once again?
For each executor 2-5 cores should be there, so he is saying he is going to take 4 this number is fixed, if the data size increased or increased
There are 200 cores in total . Each core will use one partition at a time so will use 128MB
Each executor has 4 core so each executor requires 4*128 MB which is 512 mb. Where does extra 4 multiplier came from ?😊
by default, to process a file in one core, we need 4 times the file size memory.
Spark is in memory processing. So it requires min 512mb of memory to perform cache, persist, shuffling and overhead tasks. 1 core handles 1 block of data.
what if we have limited resource? what configuration would you recommend to process 25GB? (16 cores and 32GB)
You would have to choose between an increased partition size or lowered parallelism with an increased number of partitions.
Zuper
Hi,
Does the same study applies if we are working in Data Bricks?
yes, its same logic
Sir,I want to join Job ready program.How to join .Link is not enabled.pls help
Sorry, we are not conducting CSJRP sessions at present. Please check our website www.cleverstudies.in for more details.
Is it that each core would take 4 * partition size memory ?
1 executor.the best configuration is1 executor = 4 cores, 512 mb
There's concept of fat and thin executors
What is use of giving each core 512 mb,if blcok size is 128 MB.
Each block process on a single core,so if each block is 128 mb, why we should give 512mb
To each core?
There will be wastage of memory,Am I right?
Please explain this.
Thanks
The memory is for processing, not for storage.
The min req of executor is 4-5 cores and 512 mb memory. 1 core can handle 1 block data. And as spark is in memory processing so it requires memory space for cache, persist, shuffling etc
In my company the cpu per executor is 5 min and 8 max.
It depends on the use case and resources availability.
@@cleverstudies depends on cluster. We have a state of the art one over $1b data center that can support high cpu’s per executor