Research notebook

Giving Small Runs Better Bloom Filters: saved-count analysis

Edit a Python cell and run it here. Your code runs in this browser tab; the notebook runtime and packages are served by this site.

Python starts when you choose Run.

Inspect the saved Bloom-filter counts

This page reads the five recorded seeds for each allocation and recomputes the published means. The complete 250,000-query-per-policy run remains available in the full experiment notebook and reproducibility package. The saved five-seed probe comparison is shown below. It is available before running the analysis cell.

False-positive table probes per 1,000 absent lookups for uniform and size-aware allocation, averaged across five seeds.

Python · cell 1
Uniform mean: 0.032848 probes/query
Allocated mean: 0.017340 probes/query
Reduction from the saved counts: 47.21%

Compare the five saved seeds

Each bar is false-positive probes per 1,000 absent queries. Edit the data-loading cell or plotting options to inspect the recorded variation.

Python · cell 2

Python runs locally in this page. Saved results and figures are visible before execution.

Download full experiment notebook · Download saved-count analysis