postings

Inverted-index postings lists with segment-style updates. Supports u32 term frequencies (classical IR) and f32 weights (SPLADE / learned-sparse retrieval).

Data Model & Invariants

Doc IDs: u32. Must be dense/contiguous for optimal compression.
Ordering: Postings lists are always sorted by Doc ID.
Updates: Segment-based. Deletions are tombstones; updates are delete+add.
Storage: In-memory by default. Persistence via durability (optional).

Usage

[dependencies]
postings = “0.1”

Example (index + candidates):

use postings::{PostingsIndex, PlannerConfig};

let mut idx = PostingsIndex::new();
idx.add_document(0, &["the".to_string(), "quick".to_string(), "fox".to_string()])
    .unwrap();
idx.add_document(1, &["quick".to_string(), "brown".to_string(), "dog".to_string()])
    .unwrap();

// Conjunctive (AND) candidates.
assert_eq!(
    idx.candidates_all_terms(&["quick".to_string(), "dog".to_string()]),
    vec![1]
);

let cfg = PlannerConfig::default();
let plan = idx.plan_candidates(&["quick".to_string()], cfg);
assert!(matches!(plan, postings::CandidatePlan::Candidates(_)));

Features

postings/serde: enable serde for the in-memory structures.
postings/persistence: enable save/load helpers via durability + postcard.
postings/sbits: enable succinct monotone sequences (Elias–Fano) under postings::codec::ef.
postings/positional: enable positional postings (postings::positional::PosingsIndex).
postings/cnk-compression: enable optional compressed-candidate helpers under postings::positional::cnk_candidates.

Optional: positional postings

Enable positional postings behind a feature flag:

[dependencies]
postings = { version = "0.1", features = ["positional"] }

Then use postings::positional::PosingsIndex for phrase/proximity evaluation.

Development