SE-Radio Episode 328: Bruce Momjian on the Postgres Query Planner

Plan Creation

Robert Blumen and Bruce Momjian explore the intricacies of query plan creation in Postgres. Bruce explains that the process begins with lexing and parsing the SQL text, which is then transformed into an internal structure. This structure undergoes rewriting and optimization before execution 1. He describes query plans as a series of primitive operations, such as sequential scans and joins, arranged in a tree-like format to efficiently execute queries 2.

The database is first going to scan the SQL text for identifiers and keywords. That's called Lexing. Then it's going to parse it.

--- Bruce Momjian

These plans are crucial for handling complex queries involving multiple tables and operations.

   

Optimizer Features

The optimizer in Postgres plays a pivotal role in query performance, employing features like hypothetical indexing and costing. Bruce Momjian notes that developers can use extensions to test hypothetical indexes, allowing them to predict performance improvements without actual implementation 3. Costing, another feature, helps determine the most efficient query plan by comparing relative costs of different execution paths, though these costs don't directly translate to execution time 4.

The costing number is really only meaningful to the extent that it's cheaper than another costing number.

--- Bruce Momjian

This approach ensures that the optimizer selects the most efficient plan available.

   

Optimization Challenges

Optimizing query plans presents significant challenges, as Bruce Momjian highlights the complexity and testing difficulties of the optimizer. He emphasizes the importance of user feedback in refining Postgres, allowing rapid iteration and improvement 5. Despite the challenges, the optimizer often achieves near-perfect plans, balancing between exhaustive and heuristic approaches to manage large datasets 6.

It's incredibly difficult to test. And you're right, if you introduce a bug, it's a major problem.

--- Bruce Momjian

This iterative process is crucial for maintaining high performance and reliability.

   

Temporary Data

Temporary data usage is a key aspect of query planning, often involving the creation of intermediate data sets. Bruce Momjian explains that temporary tables are used when processing data for operations like joins or aggregates, providing a flexible way to handle complex queries 7. These temporary results are akin to variables in programming, storing intermediate states that facilitate efficient query execution 8.

You end up sort of creating the temporary. So as you're running the query, you create sort of a temporary instance of an intermediate state of that data.

--- Bruce Momjian

This method enhances the optimizer's ability to manage large and complex datasets effectively.