10 Questions
What is the lesson learned for future improvements in query optimization?
Fine-grained subqueries are preferred for re-optimization to avoid cardinality estimation errors
What percentage of the queries belong to the first two categories where QuerySplit outperforms alternative re-optimization algorithms?
70%
What is a significant finding about the 'Worse' category of queries?
The 'Worse' category has minimal effect on the overall benchmark performance
In the example shown in Figure 21(a), what does the join graph depict?
The execution plan for QuerySplit and IEF
What mistake does PostgreSQL’s optimizer make in estimating the cardinality of S1?
It underestimates the cardinality of S1
Which algorithm chose to execute S2 first instead of S1?
IEF
What is the main advantage of QuerySplit compared to robust query processing baselines?
It is more efficient in handling cardinality estimation errors
Why do learned cardinality estimation algorithms like NeuroCard and DeepDB achieve limited performance improvement?
They don't handle string columns efficiently
What is the likely reason re-optimization is more effective and efficient than refining cardinality estimation in improving query performance?
It leads to query plans that are closer to optimal
What is the reason behind USE having the same performance in both index configurations?
It disables nest-loop join, thus ignoring indexes in its query planning
Learn about the impact of QuerySplit decisions and cardinality estimation errors in database optimization. Understand the importance of fine-grained subqueries for re-optimization to mitigate devastating cardinality estimation errors. Explore the performance effects of different query categories.
Make Your Own Quizzes and Flashcards
Convert your notes into interactive study material.
Get started for free
