Column order matters in the PostgreSQL sort cost model, impacting execution time significantly. Choosing the optimal order of sorting columns can vastly affect computational efforts.
Proposed modifications to the cost model aim to consider the number of columns in the sorting tuple and the number of duplicates, providing additional tools for query plan optimization.
Adjusting the PostgreSQL optimizer's cost model involves balancing costs for various operations, taking into account factors like extended statistics, column order, and the complexity of sorting.
Incremental sort was added in PostgreSQL 2020 to enhance sorting strategies and improve efficiency in handling large datasets and analytical queries.
Estimation instability in PostgreSQL's sort operations can lead to unexpected query plans and performance differences, emphasizing the importance of careful estimation.
The vulnerability in PostgreSQL's optimizer code showcases how the choice of expression evaluation can impact query performance, highlighting a need for optimization improvements.
In MSSQL to PostgreSQL migrations, challenges like query slowdowns may arise, with some queries taking significantly longer to execute in PostgreSQL compared to MSSQL.
Join algorithm selection and parallelism are two key advantages contributing to MSSQL's impressive query execution speed.
Multi-clause selectivity estimation in MSSQL allows for more precise cardinality estimation in complex join queries, giving it an edge over PostgreSQL in certain scenarios.
Developers are looking for a new query protocol to address performance issues caused by the multi-query approach.
There is a need for more uniformity in ORM languages and for ORMs to provide functionality comparable to full-fledged SQL queries.
The idea of directly translating object-oriented language statements into a parse tree without the need for an intermediary layer is gaining traction for improved performance.
The Asymmetric Join (AJ) technique in PostgreSQL allows for more efficient parallel append operations by individually connecting each partition with a non-partitioned relation and merging results.
One advantage of the Asymmetric Join technique is the independent choice of join strategy for each partition, leading to improved table scan filtering and reduced hash table sizes.
Considerations for implementing the Asymmetric Join include growing search space for plans, restrictions on the inner and outer relations, and the necessity of checking partitioning schemes for different plain and partitioned relation combinations.