In '. Spark.', there are several optimizations that often go unnoticed but can have a significant impact on performance. One such optimization is the use of columnar storage, which organizes data by column instead of row. This provides benefits like better compression, improving query performance for analytical workloads. Another lesser-known optimization is the adaptive query execution, where '. Spark.' dynamically adjusts its execution plan based on the characteristics of the data being processed. This can lead to more efficient resource utilization and faster query execution. Lastly, '. Spark.' supports predicate pushdown, allowing it to push filter operations closer to where the data is stored, reducing the amount of data that needs to be read during processing.

One lesser-known optimization in '. Spark.' is the use of broadcast variables, which allow you to efficiently share large read-only variables across tasks. This can greatly reduce the amount of data that needs to be transferred over the network. Another optimization technique is the use of data locality, where '. Spark.' tries to schedule tasks closer to the data they need, instead of moving the data to the tasks. This can significantly reduce network overhead. Lastly, '. Spark.' can leverage off-heap memory for cache storage, enabling larger in-memory caching and reducing garbage collection overhead.