"A Q-learning RL agent autonomously learns optimal Spark configurations by observing dataset characteristics, experimenting with different settings, and learning from performance feedback. Combining an RL agent with Adaptive Query Execution (AQE) outperforms either approach alone, with RL choosing optimal initial configurations and AQE adapting them at runtime. Bucketing continuous dataset features (rows, size, cardinality, skew) into discrete categories allows tabular Q-learning to generalize across similar workloads, solving the fundamental challenge of learning from limited examples rather than requiring identical datasets for every decision."
"Starting with aggressive exploration (ε=0.3) and gradually reducing it (ε=0.05) allows the agent to discover optimal configurations early while increasingly exploiting learned knowledge for stable production performance. The partition optimizer agent provides a reusable design that can be extended to other configuration domains, such as memory, cores, and cache, where each agent can independently learn policies for its specific area."
A Q-learning RL agent learns optimal Spark configurations by observing dataset characteristics, experimenting with different settings, and using performance feedback to update policies. Continuous dataset features such as rows, size, cardinality, and skew are bucketed into discrete categories so tabular Q-learning can generalize across similar workloads and learn from limited examples. Combining the RL agent with Adaptive Query Execution (AQE) yields better results: RL selects strong initial configurations while AQE adapts at runtime. An exploration schedule beginning at ε=0.3 and decaying to ε=0.05 enables early discovery and later stable exploitation. The partition optimizer design is reusable and extensible to domains like memory, cores, and cache.
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