The QDBase Solution

Dramatic Performance Improvement

The presorting and order-based approach drastically improved processing times. A 3-step funnel analysis spanning 14 days, executed on a 16-core, 128GB virtual machine, produced results in just 10 seconds. This represents a significant improvement compared to traditional SQL-based methods, which often required minutes or even hours to complete similar analyses on datasets of comparable size. The efficiency gains were largely attributed to the elimination of redundant joins and subqueries, as well as the ability to process ordered data sequentially in memory. By reducing the need for external buffering and minimizing computational overhead, the approach achieved exceptional scalability and responsiveness.

Maintainable and Extendable

The implementation of SPL allowed for concise and modular code. Unlike SQL, which required extensive modifications to accommodate additional funnel steps, the SPL code was easily adaptable. Developers could parameterize event types and funnel windows, enabling the same core logic to be reused for varied analytical scenarios without extensive rework.

Accurate and Reliable

By leveraging ordered data storage and an intuitive processing flow, the results were highly reliable and consistent. This approach minimized the risk of errors inherent in complex SQL joins and ensured accurate conversion rate calculations across all funnel steps.

QDBase Feature Highlights

• Dynamic Parameters: Event types, time periods, and funnel window durations were parameterized for flexibility.
• Code Efficiency: Each funnel step was processed by iterating through ordered data, reducing memory overhead and improving readability.
• Custom Grouping: Groups were processed individually, avoiding the need for large-scale joins and external storage buffering.

Conclusion

Using QDBase for ordered storage and funnel analysis improved performance significantly, reducing execution time and simplifying development complexity. The approach met and exceeded customer expectations, setting a new benchmark for efficient funnel analysis in large-scale e-commerce environments.

Read the full in-depth study, with code examples on the SCUDATA Blog