Spark : Calculation of executor memory in Spark – A complete info.

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The executor memory is the amount of memory allocated to each executor in a Spark cluster. It determines the amount of data that can be processed in memory and can significantly affect the performance of your Spark applications. Therefore, it’s important to carefully calculate the amount of executor memory that your Spark applications need.

To calculate the executor memory, you need to consider the following factors:

  1. Available cluster resources: The amount of memory available in your cluster should be considered when calculating the executor memory. You don’t want to allocate more memory than what’s available, as it can lead to performance issues or even failures.
  2. Application requirements: The amount of executor memory required by your Spark application depends on the size of your data and the complexity of your processing logic. For example, if you’re processing a large dataset or performing complex computations, you may need more executor memory.
  3. Overhead: Spark needs some memory overhead to manage tasks and shuffle data. You should allocate enough memory for overhead to ensure that your application doesn’t run out of memory.

Here’s the formula to calculate the executor memory:

executor_memory = (total_memory * 0.8 - memory_overhead) / num_executors
  1. total_memory is the total memory available in your cluster. You can get this information from your cluster manager, such as YARN or Mesos.
  2. memory_overhead is the amount of memory allocated for Spark overhead. The default value is 10% of the executor memory, but you can adjust it using the spark.yarn.executor.memoryOverhead or spark.executor.memoryOverhead configuration properties.
  3. num_executors is the number of executors that you want to run in your Spark application. You can adjust it using the spark.executor.instances configuration property.

For example, let’s say you have a cluster with 100 GB of memory and you want to run 4 executors with 4 GB of memory each. To calculate the executor memory, you can use the following formula:

executor_memory = (100 GB * 0.8 – 4 GB * 0.1) / 4 = 18.5 GB

This means that you should allocate 18.5 GB of memory to each executor to ensure optimal performance.

Calculating the executor memory in Spark is an important task to ensure that your applications run efficiently and avoid out-of-memory errors. By taking into account the available cluster resources, application requirements, and overhead, you can determine the optimal amount of executor memory for your Spark applications.

Another example
If we want to provide each executor 5GB of memory, we must declare that 5GB Plus max(384mb, 10% of 5GB) (off heap memory) = 5.5GB.
Java heap memory 5 GB : (5 * 1024 MB = 5120 MB )
Reserved memory 300 MB
Usable memory 5120 MB – 300 MB

4820 MB

Spark memory Usable memory * spark.memory.fraction

4820 MB * 0.6

2892 MB

Spark storage memory Spark memory * spark.memory.storageFraction
  2892 MB * 0.5

1446 MB

Spark execution memory Spark memory * (1.0 – spark.memory.storageFraction)

2892 MB * ( 1 – 0.5)

2892 MB * 0.5

1446 MB

User memory Usable memory * (1.0 — spark.memory.fraction)

4820 MB * (1.0 – 0.6)

4820 MB * 0.4

1928 MB

  4820 MB * (1.0 – 0.6)

Spark important urls to refer

  1. Spark Examples
  2. PySpark Blogs
  3. Bigdata Blogs
  4. Spark Interview Questions
  5. Official Page
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