Optimizing Spark executor number, cores, and memory is crucial to improving the performance and efficiency of your Spark applications. Here, I’ll explain the general principles and provide examples accordingly.
Understanding Spark Executors
Spark executors are distributed agents responsible for executing tasks and holding data partitions in memory or disk storage if needed. Each executor runs multiple tasks concurrently through multiple cores. The total resources allocated to a Spark application can significantly impact its performance.
Factors to Consider for Optimization
- Number of Executors
- Number of Cores per Executor
- Executor Memory
Here’s a breakdown of how to determine the optimal configuration for each factor:
Number of Executors
The number of executors you allocate should be based on the total number of nodes and the resources available in your cluster. You can use the formula below for better resource utilization:
Number of Executors = Total Available Cores / Number of Cores per Executor
Number of Cores per Executor
Choosing the right number of cores per executor is essential for balancing parallelism and resource utilization. A common approach is to start with a small number of cores (2-5), and then gradually increase to find the optimal balance. If you have too many cores per executor, you may hit a bottleneck due to excessive garbage collection or resource contention.
Executor Memory
The memory allocated to each executor is crucial for achieving the desired performance. Allocate enough memory to accommodate your data and any necessary overhead, but avoid over-provisioning to prevent wastage of resources. The rule of thumb is:
Executor Memory = (Total Available Memory per Node – Reserved Memory for System and HDFS Daemons) / Number of Executors per Node
Example Configuration in SparkSubmit
Let’s consider an example with the following cluster configuration:
- Total Number of Nodes: 5
- Total Cores per Node: 16
- Total Memory per Node: 64 GB
We can determine the optimal settings as follows:
- Reserved Memory per Node: 8 GB
- Available Memory per Node: 64 GB – 8 GB = 56 GB
- Target: 5 cores per executor
- Number of Executors per Node: 16 / 5 = 3 (approx)
- Executor Memory per Node: 56 GB / 3 ≈ 18 GB
So, the configuration would be:
- Number of Executors: 5 Nodes * 3 Executors/Node = 15
- Number of Cores per Executor: 5
- Executor Memory: 18 GB
SparkSubmit Command Example
Here is the corresponding SparkSubmit command:
spark-submit \
--master yarn \
--deploy-mode cluster \
--num-executors 15 \
--executor-cores 5 \
--executor-memory 18G \
my_spark_application.py
These configurations aim to strike a balance between resource utilization and performance, but you might need to adjust them based on your specific workload and cluster setup.