EUeudoxus·Oct 9, 2025
Would love to hear how this compares to another popular go based full text search engine (with a not too dissimilar name) https://github.com/blevesearch/bleve?
In Show HN: I wrote a full text search engine in Goblevesearch
bleve
Go2014–2025
blevesearch / bleve
bleve
A modern text/numeric/geo-spatial/vector indexing library for go
19Distinct people
27Mentions
0Explicit recommenders
11.3KGitHub stars
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What it is
A modern text/numeric/geo-spatial/vector indexing library for go
GitHub
Where it is used
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Also in these discussions
- quickwit-oss/tantivy · linked in 8 of the same discussions
- meilisearch/meilisearch · linked in 4 of the same discussions
- toshi-search/toshi · linked in 4 of the same discussions
Mentioned in the same Hacker News threads. That is not proof they are substitutes.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.
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Mentions over time
HN mentions by year27 dated mentions
Observed mentions: 2014-09-06 18:35:24 UTC to 2025-10-09 19:41:29 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2014 | 2014-01-01 00:00:00 UTC | 2015-01-01 00:00:00 UTC | 2 | 1 | 1 |
| 2015 | 2015-01-01 00:00:00 UTC | 2016-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 4 | 0 | 4 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 7 | 0 | 7 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 5 | 0 | 5 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 1 | 1 | 0 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
27 sourcesBleve – a modern text indexing library in go
4 points · Open discussionThank you for this comparison. I would also like to know how Bleve Search (https://github.com/blevesearch/bleve) turns out.
I have for many years now a small search engine project in my free-time pipeline, but I'm before crawling even and I intend to sit for searching part after some of that.
In Sonic: Fast, lightweight and schema-less search backendI've never worked on a project that encompasses as many computer science algorithms as a search engine. There are a lot of topics you can lookup in "Information Storage and Retrieval":
- Tries (patricia, radix, etc...)
- Trees (b-trees, b+trees, merkle trees, log-structured merge-tree, etc..)
- Consensus (raft, paxos, etc..)
- Block storage (disk block size optimizations, mmap files, delta storage, etc..)
- Probabilistic filters (hyperloloog, bloom filters, etc...)
- Binary Search (sstables, sorted inverted indexes, roaring bitmaps)
- Ranking (pagerank, tf/idf, bm25, etc...)
- NLP (stemming, POS tagging, subject identification, sentiment analysis etc...)
- HTML (document parsing/lexing)
- Images (exif extraction, removal, resizing / proxying, etc...)
- Queues (SQS, NATS, Apollo, etc...)
- Clustering (k-means, density, hierarchical, gaussi
Read the rest
an distributions, etc...)
- Rate limiting (leaky bucket, windowed, etc...)
- Compression
- Applied linear algebra
- Text processing (unicode-normalization, slugify, sanitation, lossless and lossy hashing like metaphone and document fingerprinting)
- etc...
I'm sure there is plenty more I've missed. There are lots of generic structures involved like hashes, linked-lists, skip-lists, heaps and priority queues and this is just to get 2000's level basic tech.
If you are comfortable with Go or Rust you should look at the latest projects in this space:
- https://github.com/quickwit-oss/tantivy
- https://github.com/valeriansaliou/sonic
- https://github.com/mosuka/phalanx
- https://github.com/meilisearch/MeiliSearch
- https://github.com/blevesearch/bleve
- https://github.com/thomasjungblut/go-sstables
A lot of people new to this space mistakenly think you can just throw elastic search or postgres fulltext search in front of terabytes of records and have something decent. The problem is that search with good rankings often requires custom storage so calculations can be sharded among multiple nodes and you can do layered ranking without passing huge blobs of results between systems.
Source: I'm currently working on the third version of my search engine and I've been studying this for 10 years.
There isn't a one-size-fits all approach, but I've never worked on a project that encompasses as many computer science algorithms as a search engine.
- Tries (patricia, radix, etc...)
- Trees (b-trees, b+trees, merkle trees, log-structured merge-tree, etc..)
- Consensus (raft, paxos, etc..)
- Block storage (disk block size optimizations, mmap files, delta storage, etc..)
- Probabilistic filters (hyperloloog, bloom filters, etc...)
- Binary Search (sstables, sorted inverted indexes)
- Ranking (pagerank, tf/idf, bm25, etc...)
- NLP (stemming, POS tagging, subject identification, etc...)
- HTML (document parsing/lexing)
- Images (exif extraction, removal, resizing / proxying, etc...)
- Queues (SQS, NATS, Apollo, etc...)
- Clustering (k-means, density, hierarchical, gaussian distributions, etc...)
- Rate limiting (leaky bucket, windowed, etc.
Read the rest
..)
- text processing (unicode-normalization, slugify, sanitation, lossless and lossy hashing like metaphone and document fingerprinting)
- etc...
I'm sure there is plenty more I've missed. There are lots of generic structures involved like hashes, linked-lists, skip-lists, heaps and priority queues and this is just to get 2000's level basic tech.
- https://github.com/quickwit-oss/tantivy
- https://github.com/valeriansaliou/sonic
- https://github.com/mosuka/phalanx
- https://github.com/meilisearch/MeiliSearch
- https://github.com/blevesearch/bleve
- https://github.com/thomasjungblut/go-sstables
A lot of people new to this space mistakenly think you can just throw elastic search or postgres fulltext search in front of terabytes of records and have something decent. That might work for something small like a curated collection of a few hundred sites.
Search in SQL databases is a tough beast to get it right. And given that we support MySQL and Postgres both, it gets even harder to support quirks of both of them.
In enterprise editions, the only addition is Elasticsearch. But in our open-source version, we do have support for https://github.com/blevesearch/bleve. Although, it's in beta, we have a lot of customers using it.
I am wondering if you have tried using it and didn't like it?
In Mattermost – open-source platform for secure collaborationThe litestream project was created by https://github.com/benbjohnson who wrote https://github.com/boltdb/bolt (a key value store) which has been instrumental (from my point of view) in the Go community as one of the original choices for an embedded database as it was not only fast, but had transactions with stable snapshots.
It was used by https://github.com/blevesearch/bleve, https://github.com/etcd-io/etcd, and number of other projects.
These days, https://github.com/dgraph-io/badger is often favored because of it's improved write throughput.
In A database for 2022FWIW...
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(Setting aside the fact that Logstash is JRuby/Java app easily eating said gigabytes of heap)
Do JSON logs take gigabytes?
If they do for you then yes, gigabytes of disk and memory are pretty much guaranteed. Also things tend to pile up with time (even on a few weeks horizon).
In all honesty, I believe a finely crafted native code solution for this problem could achieve x3 less ram usage and x2-3 indexing/search performance. Going beyond that is also possible but will take remarkable engineering.
Update: to expand on the last point, c++ solutions are typically closed sourced.
Rust and Go both have interesting open-source full text engines:
https://github.com/tantivy-search/tantivy
https://github.com/blevesearch/bleve
In near future I totally see someone producing a great open-source distributed search project that is at least on par with
Read the rest
today’s ES core feature set.
So my interface to all of my text files is Redis.
I use the simple Redis data structures to store
the data and with a common interface I am able
to populate all sorts of different tools for indexing
and search.
Then from there I use the following tools...
elasticsearch
https://github.com/blevesearch/bleve
Recently I have been experimenting with...
https://github.com/meilisearch/MeiliDB
By maintaining Redis as my interface engine, I can
experiment with all sorts of different tools in
different programming languages...
Over time I have grown to love Redis as my data store...
Of course I have a simple way of getting the common text
files that I write my daily journal in into Redis.
And then from there its just a matter of the common interface into the different indexing / search technologies.
In I find journaling indispensableElasticsearch is very popular because it works well for generic searching and can be customized for lots of unique scenarios. There's competition on the infrastructure side using something other than Java/JVM though:
For Rust, there's Toshi: https://github.com/toshi-search/Toshi which is built on top of Tantivy: https://github.com/tantivy-search/tantivy
For C++, there's Xapiland: https://github.com/Kronuz/Xapiand
For Go, there's Blast: https://github.com/mosuka/blast built on Bleve: https://github.com/blevesearch/bleve
In Ask HN: Are Lucene/Solr/ES Still Used for Search?We recently added an ASCII folding filter, which may help: https://github.com/blevesearch/bleve/pull/1070
In Bleve: Full-text search and indexing for GoWe were made aware of this in September:
https://github.com/blevesearch/bleve/issues/783#issuecomment...
In Bleve: Full-text search and indexing for GoAlways nice to see more alternatives to Elasticsearch. That project could be so much better with some proper planning and focus.
There's also Toshi: https://github.com/toshi-search/Toshi which is built on top of Tantivy: https://github.com/tantivy-search/tantivy
And for C++, there's Xapiland: https://github.com/Kronuz/Xapiand
And for Go, there's Blast: https://github.com/mosuka/blast built on Bleve: https://github.com/blevesearch/bleve
In Sonic: Fast, lightweight and schemaless search back end in RustShowing the 15 most recent of 27 sources. Enable JavaScript to search and browse every indexed mention.