Mastering Apache Spark Executor Tuning: A Practical Guide

Optimizing Spark Executors for Maximum Performance

Optimizing Apache Spark performance is crucial for large-scale data processing. One of the key aspects of optimization is tuning executors, which includes configuring the number of executors, executor cores, and executor memory. This guide breaks down the process step-by-step, ensuring your Spark applications run efficiently.

Understanding Spark Executors and Resource Allocation

An executor is a JVM instance that runs Spark tasks. Optimizing the number of executors, cores, and memory is essential for efficient resource utilization.

Case Study: Cluster with 6 Nodes (Each with 16 Cores and 64 GB RAM)

1. Reserve System Resources: Each node requires approximately 1 core and 1GB RAM for OS and Hadoop daemons, leaving 15 cores and 63GB RAM available per node.

2. Choose the Number of Cores per Executor:

   • The ideal number of cores per executor is 5 (for optimal parallelism and task execution).

   • This is a recommended best practice based on research, regardless of the total cores available.

3. Calculate the Number of Executors:

   • Available cores per node: 15

   • Executors per node: 15 / 5 = 3

   • With 6 nodes, the total number of executors: 6 × 3 = 18

   • Subtract 1 executor for ApplicationMaster (YARN): 17 executors

4. Calculate Memory per Executor:

   • Available memory per node: 63GB

   • Memory per executor: 63GB / 3 = 21GB

   • Deduct overhead memory: Max(384MB, 0.07 × 21GB) = 1.47GB

   • Final executor memory: 21GB - 1.47GB ≈ 19GB

Final Configuration:

--num-executors 17--executor-cores 5--executor-memory 19G

Case Study: Cluster with 6 Nodes (Each with 32 Cores and 64 GB RAM)

• Maintain 5 cores per executor for efficiency.

• Executors per node: 32 / 5 ≈ 6

• Total executors: 6 × 6 - 1 = 35

• Memory per executor: 63GB / 6 ≈ 10GB

• Overhead: 0.07 × 10GB ≈ 700MB (round to 1GB)

• Final executor memory: 10GB - 1GB = 9GB

Final Configuration:

--num-executors 35--executor-cores 5--executor-memory 9G

Adjusting Cores and Executors Based on Memory Needs

If a Spark application requires less memory per executor, we can adjust the number of executors accordingly. Example:

• If 10GB per executor is sufficient instead of 19GB, we can allocate 6 executors per node instead of 3.

• This increases the number of executors while reducing the cores per executor (from 5 to 3) to fit within available resources.

• New calculations:

  • Executors per node: 15 cores / 3 cores per executor = 5 executors

  • Total executors: 5 × 6 - 1 = 29

  • Memory per executor: 63GB / 5 ≈ 12GB

  • Overhead: 0.07 × 12GB ≈ 840MB (round to 1GB)

  • Final executor memory: 12GB - 1GB = 11GB

Final Configuration:

--num-executors 29--executor-cores 3--executor-memory 11G

Dynamic Resource Allocation in Spark

Spark supports dynamic allocation of executors, which allows resources to be allocated dynamically based on workload.

Key Configuration Parameters:

• Enable Dynamic Allocation:

spark.dynamicAllocation.enabled = true

Set Executor Limits:

spark.dynamicAllocation.minExecutors = X

spark.dynamicAllocation.maxExecutors = Y

• Specify Initial Executors

spark.dynamicAllocation.initialExecutors = Z

• Trigger Additional Executors:
  spark.dynamicAllocation.schedulerBacklogTimeout = 1s

  • If tasks are waiting in the queue for 1 second, a new executor is requested.

  • The number of requested executors grows exponentially (1, 2, 4, 8, etc.) until maxExecutors is reached.

• Release Idle Executors:

spark.dynamicAllocation.executorIdleTimeout = 60s

• Executors that remain idle for 60 seconds will be removed to free up cluster resources.

Additional Considerations

1. Standalone Mode:

   • By default, Spark allocates one executor per worker node.

   • To increase the number of executors per node, set:

spark.executor.cores = X

2. Memory Fraction:

   • Controls the proportion of JVM heap used for execution vs. storage.

   • If caching is not required, set spark.memory.fraction = 0.1 to allocate maximum memory to computation.

   • Check memory usage with:

sc.getExecutorMemoryStatus.map(a => (a._2._1 - a._2._2)/(1024.0*1024*1024)).sum

Conclusion

Optimizing Spark executors is not a one-size-fits-all process; it depends on the cluster setup, workload, and memory requirements. However, a structured approach to tuning ensures efficient resource utilization and better performance.

Key Takeaways:

• Start with 5 cores per executor for optimal parallelism.

• Divide available memory per node by the number of executors.

• Subtract overhead memory before finalizing executor memory.

• Use dynamic allocation for flexible resource management.

• Monitor and adjust based on workload performance.

By following these best practices, you can ensure that your Spark applications are well-optimized, resource-efficient, and performant.