What are the problems with executor memory when using Spark Structured Streaming on Kubernetes?

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There can be multiple problems with executor memory when using Spark Structured Streaming on Kubernetes:

1. Inefficient resource allocation: Kubernetes may not allocate enough memory to the executor, leading to out-of-memory errors during data processing.

2. Lack of dynamic allocation: Kubernetes does not support dynamic allocation of executor resources, which makes it difficult to optimize resource utilization for Spark Structured Streaming jobs.

3. Overcommitment of resources: If multiple Spark jobs are running on the Kubernetes cluster at the same time, there can be overcommitment of resources, leading to memory pressure and slow execution.

4. Containerization overhead: Running Spark executors in containers on Kubernetes adds additional overhead, which can reduce the amount of memory available for Spark processing.

5. Incompatibility issues: Spark may not be fully compatible with the Kubernetes environment, leading to issues with resource management and memory allocation.