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pisa-engine
pisa
C++2019–2026
pisa-engine / pisa

pisa

PISA: Performant Indexes and Search for Academia

3Distinct people
15Mentions
0Explicit recommenders
1.1KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

PISA: Performant Indexes and Search for Academia

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year15 dated mentions

Observed mentions: 2019-05-02 15:12:42 UTC to 2026-04-20 17:14:37 UTC.

0369122019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 11 mentions; 11 submissions, 0 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC11110
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC110
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC101
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC101
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

15 sources
More on latency and search: We benchmark against 10 other search APIs on fresh news queries. The median was around 1.2s; we came in around 166ms and scored highest on answer accuracy (89% vs 84% for the next cluster). Latency matters because agents loop. A 1.2s first call eats the budget for follow-ups — you get one shot at framing the query. At sub-250ms the agent can actually search, read, reformulate, and search again. Measuring this stuff carefully is something I've been at for a while. My ECIR 2019 paper (linked below) was an exhaustive study of 11 index compression methods across 5 query processing algorithms on standard collections — the codebase became PISA, which a lot of IR folks still use for research. Almost ten years later, the workload has changed completely (agents, not humans), but the benchmarking discipline is the same.
Read the rest
In Show HN: Seltz – The fastest, high quality, search API for AI agents
> We haven't discussed other solutions like Partitioned Elias-Fano indexes or Tree-Encoded Bitmaps. We simply did not investigate them. There are no efficient off-the-shelf implementations of these solutions, so we had to implement one. I guess they mean there are no implementations in Rust? The Partitioned Elias-Fano (PEF) paper links to the C++ implementation ([1], which I wrote) used for the experiments, it was efficient 9 years ago :) The library was later forked [2] and used in a few other papers, I believe it is somewhat maintained. The EF core algorithm implemented in folly [3] may be a bit faster, and implementing partitioning on top of that is relatively easy. It would definitely compress much better than roaring bitmaps. In terms of performance, it depends on the access patterns. If very sparse (large jumps), PEF would likely be
Read the rest
faster, if dense (visit a large fraction of the bitmap), it'd be slower. It is possible to squeeze a bit more compression out of PEF by introducing a chunk type for Elias-Fano of the chunk complement (for very dense chunks), but you lose the operation of skipping to a given position, which is however not needed in inverted indexes (you only need to skip past a given id, and that can be supported efficiently). That is not mentioned in the paper because at the time I thought the skip-to-position operation was a non-negotiable. [1] https://github.com/ot/ds2i/ [2] https://github.com/pisa-engine/pisa [3] https://github.com/facebook/folly/blob/main/folly/experiment...
In A compressed indexable bitset
FWIW... 1. Toshi https://github.com/toshi-search/Toshi (Rust, 3.1k stars) 2. Tantivy https://github.com/tantivy-search/tantivy (Rust, 4.5k stars) 3. PISA https://github.com/pisa-engine/pisa (C++, 486 stars) 4. Bleve https://github.com/blevesearch/bleve (Go, 7.4k stars) 5. Sonic https://github.com/valeriansaliou/sonic (Rust, 10.9k stars) 6. Partial comparison https://mosuka.github.io/search-benchmark-game/ (tantivy Vs lucene Vs pisa Vs bleve) 7. Bayard https://github.com/bayard-search/bayard (Rust, on top of Tantivy, 1.4k stars) 8. Blast https://github.com/mosuka/blast (Go, on top of Bleve, 930 stars) Algolia alternatives with some compatibility 1. MeiliSearch https://github.com/meilisearch/MeiliSearch (Rust, 12.4k stars) 2. typesense https://github.com/typesense/typesense (C++, 5.1k stars)
In ClickHouse as an alternative to Elasticsearch for log storage and analysis

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