  Although the many efforts to apply deep reinforcement learning to query
optimization in recent years, there remains room for improvement as query
optimizers are complex entities that require hand-designed tuning of workloads
and datasets. Recent research present learned query optimizations results
mostly in bulks of single workloads which focus on picking up the unique traits
of the specific workload. This proves to be problematic in scenarios where the
different characteristics of multiple workloads and datasets are to be mixed
and learned together. Henceforth, in this paper, we propose BitE, a novel
ensemble learning model using database statistics and metadata to tune a
learned query optimizer for enhancing performance. On the way, we introduce
multiple revisions to solve several challenges: we extend the search space for
the optimal Abstract SQL Plan(represented as a JSON object called ASP) by
expanding hintsets, we steer the model away from the default plans that may be
biased by configuring the experience with all unique plans of queries, and we
deviate from the traditional loss functions and choose an alternative method to
cope with underestimation and overestimation of reward. Our model achieves
19.6% more improved queries and 15.8% less regressed queries compared to the
existing traditional methods whilst using a comparable level of resources.
