Apache Spark Memory Management | Unified Memory Management

In this comprehensive video, we dive into the crucial topic of memory management in Apache Spark. Memory plays a vital role in the performance and resource utilization of Spark applications, and understanding the memory management mechanisms is key to optimizing your Spark jobs.

Join us as we explore the inner workings of Spark's memory management and uncover strategies to enhance the efficiency of your data processing workflows. This video covers a range of memory-related concepts, including:

Spark Memory Architecture:

Gain insights into the different memory regions in Spark, such as the Execution Memory, Storage Memory, and User Memory.
Understand how these memory regions are allocated and utilized during Spark job execution.
Learn about the significance of the Memory Manager and its role in managing memory resources.
Memory Allocation and Configuration:

Learn best practices for setting optimal memory configurations based on your application's requirements and available cluster resources.
Memory Usage and Monitoring:

Explore techniques for monitoring memory usage in Spark applications, including tools like Spark Web UI and monitoring APIs.
Understand how to interpret memory metrics and diagnose memory-related issues.
Learn strategies for optimizing memory usage, such as data serialization and caching.
Garbage Collection and Memory Tuning:

Delve into Spark's garbage collection (GC) mechanisms and their impact on memory management.
Discover techniques for tuning garbage collection settings to achieve better memory utilization and minimize GC overhead.
By the end of this video, you'll have a solid understanding of Apache Spark's memory management mechanisms and practical insights into optimizing memory usage for improved performance and resource efficiency.

Whether you're a data engineer, data scientist, or Spark enthusiast, this video will equip you with valuable knowledge to fine-tune memory management in your Spark applications.

Don't miss out on this opportunity to enhance your expertise in Apache Spark memory management. Hit play and unlock the secrets to optimizing performance and resource utilization in your Spark jobs!

[yustas]

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.

[bramar]

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

[anupambiswas]

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

[shubhamshingi]

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

[nibeshranjanprusty]

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.

[sanskarsuman]

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???

[rajendraprasad]

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

[ahyanroboking]

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 ?

[saurabhgulati]

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

[saurav]

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

[gobieee]

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

[SuperDinuu]

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

[sujaykbful]

Can you explain erasure coding vs replication

[Manisood]

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

[gsekhar]

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

[fuatylmaz]

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

[shyamsundar]

What is the reserved used for or responsible for ?

[i_ambhosale]

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

[shubhamgupta]

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

[projjalchakraborty]

How to resolve OOM using above discussed concept???