CedarDB, a new Postgres-compatible database developed from research at the Technical University of Munich, showcases impressive capabilities in query decorrelation. The author shares insights from testing CedarDB's handling of complex SQL queries, noting both strengths in its query planner and some early-stage issues. Overall, there is optimism about CedarDB's future as it continues to evolve.

The article discusses techniques for enhancing query performance in PostgreSQL by manipulating its statistics tables. It explains how to use these statistics effectively to optimize query planning and execution, ultimately leading to faster data retrieval. Practical examples and insights into the PostgreSQL system are provided to illustrate these methods.

The article discusses the structural differences between various query operators, specifically focusing on index nested loops joins and hash joins. It emphasizes the importance of understanding these operators' internal structures during query planning to optimize execution, highlighting how this knowledge can lead to more efficient query performance. The piece also touches on the implications of treating operators as black boxes versus recognizing their specific functionalities.

SQL query optimization involves the DBMS determining the most efficient plan to execute a query, with the query optimizer responsible for evaluating different execution plans based on cost. The Plan Explorer tool, implemented for PostgreSQL, visualizes these plans and provides insights into the optimizer's decisions by generating various diagrams. The tool can operate in both standalone and server modes, enabling deeper analysis of query execution and costs.