Improving RelGo's cost estimation techniques to better capture the distribution of attribute values and their impact on query performance can be achieved through several strategies:
Enhanced Cardinality Estimation: Implementing more sophisticated cardinality estimation techniques that consider the distribution of attribute values. This could involve using histograms, which provide a more granular view of data distribution, allowing the optimizer to make more informed decisions about join selectivity and expected result sizes.
Machine Learning-Based Estimation: Leveraging machine learning models to predict cardinalities and costs based on historical query performance data. By training models on past execution statistics, RelGo can learn patterns in data distribution and query performance, leading to more accurate cost estimates.
Dynamic Sampling Techniques: Incorporating dynamic sampling methods that can gather statistics on-the-fly during query execution. This would allow RelGo to adjust its cost estimates based on real-time data distributions, improving the accuracy of its predictions.
Multi-Dimensional Statistics: Extending the cost estimation framework to consider multi-dimensional statistics, which take into account correlations between different attributes. This can help in scenarios where the distribution of one attribute affects the selectivity of another, leading to more accurate estimates.
Feedback Loop Mechanism: Establishing a feedback loop where execution statistics from completed queries are fed back into the optimizer to refine its cost estimation models. This iterative approach can help continuously improve the accuracy of cost estimates over time.
By implementing these enhancements, RelGo can achieve a more nuanced understanding of data distributions, leading to better-informed optimization decisions and improved overall query performance.