December 04, 2025•Adrien Grand (Engineer), Morgan Gallant (Engineer), Nikhil Benesch (Engineer)

turbopuffer’s full-text search engine is getting a major upgrade - what we call FTS v2 - resulting in up to 20x better full-text search performance through a combination of a new index structure and an updated query implementation.

FTS v2 is currently in beta, and not all query plans are implemented (yet). We’re about 2 PRs away, soon rolling out to prod for all. If you want in on the beta now, let us know.

What’s new with FTS v2

Large datasets, low top_k values, and queries including frequent terms will get the greatest speedups. Below are some performance comparisons for a diverse set of queries that we computed on an export of English Wikipedia containing a bit more than 5M documents.

turbopuffer FTS latency (k=100, dataset=English Wikipedia export @ ~5M documents)


san francisco  v1 ║░░░ 8ms
v2 ║▓ 3ms
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the who  v1 ║░░░░░░░░░░░░░░░░░ 57ms
v2 ║▓▓ 7ms
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united states  v1 ║░░░░░░ 20ms
constitution  v2 ║▓▓ 5ms
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lord of the rings  v1 ║░░░░░░░░░░░░░░░░░░░░░░░ 75ms
v2 ║▓▓ 6ms
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pop singer songwriter  v1 ║░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 174ms
born 1989 won best  v2 ║▓▓▓▓▓▓ 20ms
country song time     ║
person of the year     ║

turbopuffer FTS latency (ms)

k=100,
English Wikipedia export
~5M documents

174ms
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▓▓ v2                  ░░
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75ms  ░░
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57ms        ░░    ░░
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░░    20ms  ░░    ░░20ms
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8ms   ░░7ms ░░    ░░6ms ░░▓▓
░░3ms ░░▓▓  ░░5ms ░░▓▓  ░░▓▓
░░▓▓  ░░▓▓  ░░▓▓  ░░▓▓  ░░▓▓
══════════════════════════════
q1    q2    q3    q4    q5
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q1 = san francisco

q2 = the who

q3 = united states
constitution

q4 = lord of the rings

q5 = pop singer songwriter
born 1989 won best
country song time
person of the year

Semantic search may get more press, but full-text search is equally important for recall and performance in agent-initiated queries. turbopuffer has a dedicated full-text search team devoted to building the right set of features for modern search. FTS v2 is the next iteration toward our ambition for economical, web-scale search for both humans and agents.

In the purest of turbopuffer traditions, we optimized FTS v1 for simplicity, knowing that we had much room for improvement. With FTS v2, we are very happy to now be achieving search performance that is comparable to best-in-class search libraries like Tantivy and Apache Lucene.

In fact, FTS v2 takes its inspiration directly from Tantivy and Lucene in its architecture. The performance leap is the result of two major changes:

New index structure: We’ve rebuilt the core inverted index structures of full-text search, resulting in a 10x size reduction on-disk. The new format includes important metadata which allows queries to "skip" large chunks of irrelevant postings. 1.

Better search algorithm: Agents write longer queries than humans. We switched to the same MAXSCORE dynamic pruning algorithm as Apache Lucene, which scales better than alternatives for long queries while retaining excellent performance on short queries.

Stay tuned on this — we will soon publish more detailed posts about these two improvements and how they deliver FTS v2’s performance gains.

Beyond performance, FTS v2 introduces several new features to full-text search on turbopuffer:

• A new word_v3 tokenizer for better unicode-aware text segmentation
• Rank by filter to conditionally boost documents matching certain criteria
• Prefix queries for search-as-you-type filtering
• Regex filtering

Other full-text search improvements are already on the way: ranking by attributes, highlighting, better search-as-you-type, fuzziness, and globbing are all on our radar. We’re building features to support state-of-the-art ranking and will prioritize based on both observed query patterns and your feedback.

turbopuffer

We host 1T+ documents, handle writes at 10M+ writes/s, and serve 10k+ queries/s. We are ready for far more. We hope you’ll trust us with your queries.

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