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calculate the number of parallel tasks that can be executed in a Databricks PySpark cluster

manish1987c
New Contributor II

I want to confirm if this understanding is correct ???

To calculate the number of parallel tasks that can be executed in a Databricks PySpark cluster with the given configuration, we need to consider the number of executors that can run on each node and the number of tasks each executor can handle.

Here’s the breakdown:

Number of Nodes: 10
CPU Cores per Node: 16
RAM per Node: 64 GB
Executor Size: 5 CPU cores and 20 GB RAM per executor
Background Process: 1 CPU core and 4 GB RAM per node
Each node has 1 CPU core and 4 GB RAM reserved for background processes, leaving us with 15 CPU cores and 60 GB RAM available for executors per node.

Given that each executor requires 5 CPU cores and 20 GB RAM, you can run 3 executors per node (since 15 cores/5 cores per executor = 3 executors and 60 GB RAM/20 GB RAM per executor = 3 executors).

Since you have 10 nodes, you can run a total of 30 executors across the cluster (10 nodes * 3 executors per node).

Now, by default, each executor runs one task per core. Since each executor has 5 CPU cores, each executor can run 5 parallel tasks.

Therefore, the total number of parallel tasks that can be executed across the cluster is 150 (30 executors * 5 tasks per executor).

So, with the provided cluster configuration, your Databricks PySpark cluster can execute 150 parallel tasks.

 

1 REPLY 1

Kaniz
Community Manager
Community Manager

Hi @manish1987c, Your understanding is almost correct

  1. Node Configuration:

    • You have 10 nodes in your Databricks PySpark cluster.
    • Each node has 16 CPU cores and 64 GB RAM.
  2. Executor Size:

    • Each executor requires 5 CPU cores and 20 GB RAM.
    • Additionally, there’s a background process on each node that reserves 1 CPU core and 4 GB RAM.
  3. Available Resources per Node:

    • Subtract the resources reserved for the background process:
      • CPU cores available for executors: 15 cores (16 cores - 1 core for background process)
      • RAM available for executors: 60 GB (64 GB - 4 GB for background process)
  4. Number of Executors per Node:

    • Divide the available resources by the executor requirements:
      • Executors per node based on CPU cores: 3 executors (15 cores / 5 cores per executor)
      • Executors per node based on RAM: 3 executors (60 GB RAM / 20 GB RAM per executor)
  5. Total Executors Across the Cluster:

    • Since you have 10 nodes, the total number of executors is 30 (10 nodes * 3 executors per node).
  6. Parallel Tasks:

    • By default, each executor runs one task per core.
    • Since each executor has 5 CPU cores, each executor can run 5 parallel tasks.
  7. Total Parallel Tasks:

    • Multiply the number of executors by the tasks per executor:
      • Total parallel tasks across the cluster: 150 (30 executors * 5 tasks per executor).

So, with the provided cluster configuration, your Databricks PySpark cluster can indeed execute 150 parallel tasks. Great job on your understanding! 😊👍