RFC: integrate PiPNN as an alternative graph-index build algorithm

[SeliMeli]

Summary

This RFC proposes adding PiPNN (Pick-in-Partitions Nearest Neighbors, arXiv:2602.21247) as a second graph-construction algorithm for DiskANN's disk index, alongside the existing Vamana builder.

PiPNN produces a graph byte-compatible with Vamana's disk format and search API, at up to 6.3× lower build time on our measured workloads (Enron 10M, BigANN 10M). Vamana remains the default and the only algorithm supported for incremental inserts; PiPNN is the proposed faster path for full rebuilds.

Two-stage integration

• Stage 1 (this RFC): Land PiPNN behind a BuildAlgorithm selector with Vamana as the default. PiPNN is opt-in via a pipnn Cargo feature. Existing build sites see no behavior change. Stage 1 defines explicit milestones (M0–M7) gating Stage 2 readiness.
• Stage 2 (separate proposal, conditional on Stage 1 milestones): Retire Vamana's full-rebuild path. Vamana remains the implementation for incremental inserts via the hybrid update model — PiPNN for bulk/full rebuilds, Vamana for online inserts, full PiPNN rebuild triggered by recall decay.

Highlights

• Disk format and search API are unchanged — the on-disk graph layout, PQ codes, search pipeline, and public Rust types remain backward-compatible. PiPNN configuration is added under a new tagged-enum variant.
• Algorithm description: 4-phase batch builder (Partition / Local k-NN per leaf via GEMM / HashPrune merge / optional final RobustPrune). Detailed in the RFC.
• Honest gap accounting: explicit milestones for checkpoint/resume, scalar-quantization parity, label-filtered indexes, three-tier memory mitigation, production validation, and operational readiness — each required before Stage 2 is credible.
• Memory mitigation: documented three-tier build path (one-shot / disk-edges / merged-shards) selected by the existing build_ram_limit_gb knob, bringing PiPNN's peak RSS to or below Vamana's at a configurable build-time cost.
• Batch-only is a paper-confirmed algorithmic property of PiPNN, not just our implementation. The hybrid update model is the workaround.

Reviewers

Please read the full RFC for trade-offs, benchmark tables, and milestone definitions.

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[Copilot]

Pull request overview

Note

Copilot was unable to run its full agentic suite in this review.

Adds an RFC proposing PiPNN as an opt-in, feature-gated alternative to Vamana for DiskANN disk-index graph construction, keeping disk format and search API unchanged.

Changes:

• Introduces RFC 01049 detailing PiPNN’s algorithm, integration plan, and two-stage rollout
• Specifies a BuildAlgorithm selector design and feature-gating strategy (pipnn)
• Documents benchmark results and Stage-1 milestones gating potential Stage-2 deprecation of Vamana full rebuilds

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[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 90.60%. Comparing base (3d3ed4c) to head (4fe210f).

Additional details and impacted files

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Flag	Coverage Δ
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unittests	90.56% <ø> (+<0.01%)	⬆️

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[SeliMeli]

checkpoint may need to be put in stage2. currently the streaming checkpoing like vamana dose is not fit PIPNN's batch build

[arrayka]

Probably worth noting that today clients update their indices in three main ways:

Incremental — continuously insert and delete vectors in an existing graph.
Full rebuild — rebuild the entire graph from scratch, producing an immutable graph.
Partitioned full rebuild — split points into N clusters, build N separate graphs, then stitch them together with a lightweight merge step to reduce peak memory usage during build.

[SeliMeli]

will update to rfc

[arrayka]

Minor: Please provide full link for convinience

[arrayka]

@arkrishn94, is Local k-NN per leaf a good candidate for being implemented using the flat scan proposal we are working on?
A work item to track this discussion: #1036

[SeliMeli]

the knn build in pipnn is a pair-wise gemm backed (N * N), the scenario is quite different to the flat-scan (1 * N). But we can measure. The flat-scan build knn is more like all-to-all prune option in the paper which is extreamly slow

[arrayka]

builds up to 6.3× faster

This problem statement should clearly call out the trade-offs, constraints, and what we’re optimizing for.

We should also define concrete hypotheses and validate them.
For example: given a fixed set of workers (with N CPU cores, M RAM, and S SSD per node), PiPNN can improve index build throughput (vectors per minute per worker) by driving close to full CPU and RAM utilization. In contrast, Vamana cannot effectively leverage unused RAM to accelerate the build.

In other words, we should lock available CPU, RAM, SSD and compare build duration with the same available resources.
Let's assume that Vamana needs 6GB to build 10M-vector graph, but PipNN needs 10GB.
Which algorithm is faster if we have:

• 3GB RAM available
• 6GB RAM available
• 10GB RAM available
• 20GB RAM available (can PipNN autotune itself to use all available RAM budget to reduce index build time?)

[arrayka]

I don’t think we expect the graph to degrade enough that a full rebuild from scratch is necessary. DiskANN claims that the graph can remain healthy over time with incremental updates.

[SeliMeli]

yes, but other case rebuilt is still an option like: embedding rotations, schema changes, parameter retuning, batch insert. those cases could be benifit from pipnn as well. Will fix the wording

[arrayka]

There are no plans to mutate disk index.

[SeliMeli]

will fix the wording: only inmem vanana could insert to loaded graph, disk index rebuilt