Podcast
Questions and Answers
What is the lesson learned for future improvements in query optimization?
What is the lesson learned for future improvements in query optimization?
- Mediocre optimizers are suitable for handling heavy subqueries
- Heavy subqueries should be executed first for better performance
- Using alternative re-optimization algorithms is the best approach
- Fine-grained subqueries are preferred for re-optimization to avoid cardinality estimation errors (correct)
What percentage of the queries belong to the first two categories where QuerySplit outperforms alternative re-optimization algorithms?
What percentage of the queries belong to the first two categories where QuerySplit outperforms alternative re-optimization algorithms?
- 40%
- 70% (correct)
- 50%
- 30%
What is a significant finding about the 'Worse' category of queries?
What is a significant finding about the 'Worse' category of queries?
- The 'Worse' category has a large effect on the overall benchmark performance
- The 'Worse' category is the most frequent type of query
- The 'Worse' category contains a majority of the queries
- The 'Worse' category has minimal effect on the overall benchmark performance (correct)
In the example shown in Figure 21(a), what does the join graph depict?
In the example shown in Figure 21(a), what does the join graph depict?
What mistake does PostgreSQL’s optimizer make in estimating the cardinality of S1?
What mistake does PostgreSQL’s optimizer make in estimating the cardinality of S1?
Which algorithm chose to execute S2 first instead of S1?
Which algorithm chose to execute S2 first instead of S1?
What is the main advantage of QuerySplit compared to robust query processing baselines?
What is the main advantage of QuerySplit compared to robust query processing baselines?
Why do learned cardinality estimation algorithms like NeuroCard and DeepDB achieve limited performance improvement?
Why do learned cardinality estimation algorithms like NeuroCard and DeepDB achieve limited performance improvement?
What is the likely reason re-optimization is more effective and efficient than refining cardinality estimation in improving query performance?
What is the likely reason re-optimization is more effective and efficient than refining cardinality estimation in improving query performance?
What is the reason behind USE having the same performance in both index configurations?
What is the reason behind USE having the same performance in both index configurations?
