ZBSearch vs Orama

ZBSearch is a fork of Orama maintained by the original engineering team. The API is compatible, but the internals have been optimized. This page summarizes benchmark results from the benchmarks/ suite in this repository.

Summary

Core full-text search

Dataset: 1,512 video-game records with title, description, rating, and genres fields. Search benchmarks run against a database populated once before timing; insert benchmarks create a fresh database on each iteration.

ZBSearch is faster across all search scenarios tested here, with the largest gap on long-text queries with complex filters. Indexing throughput is effectively identical between the two engines.

Facets

Vector search

Dataset: 2,000 documents, 128-dimensional vectors.

ZBSearch IVF vs Orama flat

ZBSearch adds an optional IVF index for approximate nearest-neighbor search. Orama does not ship an equivalent in the JS core.

With IVF (nlist=179, nprobe=16), ZBSearch vector search is ~225–508% faster than Orama's flat index on this dataset.

Geosearch (BKD tree)

Dataset: 10,000 geopoints.

The largest geosearch gain is on tight-radius queries, where ZBSearch's BKD implementation avoids scanning the full point set.

Sorted indexes (AVL tree)

Dataset: 10,000 numeric keys.

Persistence size

Serializing the same indexed dataset produces identical on-disk size:

Run the benchmarks yourself

From the repository root, build ZBSearch first, then run any suite in benchmarks/:

cd packages/zbsearch && npm run build
cd ../../benchmarks && npm install

npm run benchmark              # core insert/search
npm run benchmark:facets       # facet workloads
npm run benchmark:vector         # vector search (flat)
npm run benchmark:vector-ivf     # vector flat vs IVF
npm run benchmark:bkd            # geosearch BKD tree
npm run benchmark:avl            # sorted AVL tree
npm run benchmark:bundle-size    # serialized DB size

Results are saved as JSON and HTML charts under benchmarks/benchmark/results/.