How does Hive optimize query execution when utilizing Apache Spark as the execution engine?
- Cost-Based Optimization
- Dynamic Partitioning
- Partition Pruning
- Vectorization
Hive optimizes query execution for Apache Spark by leveraging techniques like Partition Pruning, Cost-Based Optimization, and Vectorization, reducing the workload and enhancing performance during data processing. Dynamic Partitioning further enhances storage and retrieval efficiency by dynamically managing partitions.
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