How can you optimize Spark jobs for better performance in Java?
You can optimize Spark jobs by always using a single partition for your dataset, which ensures that data is processed in one go.
Thatâ€™s incorrect. Using a single partition can actually harm performance, especially for large datasets. Proper partitioning is important for parallelism and performance.
Can you explain how Sparkâ€™s partitioning strategy works and why partitioning is important for performance?