Principle:Duckdb Duckdb Regression Analysis And Reporting
Overview
Regression Analysis And Reporting is the principle of performing multi-dimensional regression analysis that goes beyond simple timing comparisons. While timing-based regression detection catches query performance degradation, a comprehensive regression strategy must also cover other dimensions that affect end-user experience: storage file sizes, extension binary sizes, and client library performance. This principle ensures that releases are validated across all critical metrics, not just raw query execution speed.
Description
The core idea behind Regression Analysis And Reporting is that performance is multi-dimensional. A code change might not slow down any query but could double the size of the database file on disk, or significantly increase the size of a compiled extension, or degrade performance of client libraries (Python, Node.js, etc.). Each of these dimensions requires its own regression analysis.
Key aspects of this principle include:
- Storage size regression -- Monitoring the size of database files (e.g.,
.duckdbfiles) produced by a standard workload. Growth in storage file size can indicate inefficient serialization, unnecessary metadata, or data layout regressions. - Extension binary size regression -- Tracking the compiled size of extension binaries (e.g.,
.duckdb_extensionfiles). Increases in extension size may indicate code bloat, accidental inclusion of debug symbols, or dependency inflation. - Client library performance regression -- Running benchmarks through client APIs (Python, Node.js, Java, etc.) rather than the native C++ benchmark runner. This catches regressions in the binding layer, data marshalling, or API overhead that would not appear in core engine benchmarks.
- Multi-metric reporting -- Each dimension produces its own pass/fail result. A comprehensive regression report aggregates these results to give a holistic view of release quality.
- Baseline management -- Each metric requires a maintained baseline (either stored in version control, in a CI artifact store, or in cloud storage) against which new measurements are compared.
Usage
Apply Regression Analysis And Reporting when:
- As part of CI/CD to validate releases across all critical performance and size metrics.
- When preparing a new release, to ensure that no dimension has regressed compared to the previous release.
- When evaluating large refactoring efforts that could affect storage format, binary size, or client API performance.
- When adding new extensions, to track the baseline size of the new extension and detect future bloat.
Theoretical Basis
This principle draws on several foundational concepts:
- Multi-metric regression testing -- The idea that testing for regressions should cover all user-visible quality dimensions, not just a single metric. This is analogous to how software testing covers both functional correctness and non-functional requirements (performance, size, resource usage).
- Coverage across storage/binary/API dimensions -- Each dimension represents a different contract with the user: storage files should not grow unexpectedly, binaries should remain a reasonable size, and client APIs should maintain their performance characteristics.
- Continuous monitoring -- Regularly measuring and comparing these metrics ensures that regressions are caught early, when they are easiest to diagnose and fix.
- Defense in depth -- No single regression check catches all possible issues. By layering multiple regression checks across different dimensions, the overall system achieves higher confidence that regressions will be detected.