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Rum Alternatives
Similar projects and alternatives to rum
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Sevalla
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ripgrep
ripgrep recursively searches directories for a regex pattern while respecting your gitignore
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MeiliSearch
A lightning-fast search engine API bringing AI-powered hybrid search to your sites and applications.
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Typesense
Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch ⚡ 🔍 ✨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences
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quickwit
Cloud-native search engine for observability. An open-source alternative to Datadog, Elasticsearch, Loki, and Tempo.
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InfluxDB
InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
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sonic
🦔 Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.
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tatoeba2
Tatoeba is a platform whose purpose is to create a collaborative and open dataset of sentences and their translations.
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pgx
Discontinued Build Postgres Extensions with Rust! [Moved to: https://github.com/tcdi/pgrx] (by tcdi)
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ora2pg
Ora2Pg is a free tool used to migrate an Oracle database to a PostgreSQL compatible schema. It connects your Oracle database, scan it automatically and extracts its structure or data, it then generates SQL scripts that you can load into PostgreSQL.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better rum alternative or higher similarity.
rum discussion
rum reviews and mentions
Posts with mentions or reviews of rum.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2025-09-01.
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DocumentDB: Comparing Emulation Internals with MongoDB
The steps are similar but more detailed at the PostgreSQL level. The index access utilizes the @<> operator, which checks if a BSON value is within a specified range via a RUM index (DocumentDB uses an extended version of RUM index which provides more metadata than a GIN index). In this case, the index was applied solely for filtering, while a separate Sort step managed the final ordering. This method requires reading all documents before they can be ordered. Although the example is simple, indexed fields may contain arrays, which means a forward scan must return entries in order based on the smallest in the array. This behavior is native to MongoDB but not in PostgreSQL, and it likely explains why entries can't be retrieved in the desired order in the current version of DocumentDB.
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Code Search Is Hard
the rum index has worked well for us on roughly 1TB of pdfs. written by postgrespro, same folks who wrote core text search and json indexing. not sure why rum not in core. we have no problems.
https://github.com/postgrespro/rum -
Is it worth using Postgres' builtin full-text search or should I go straight to Elastic?
If you need ranking, and you have the possibility to install PostgreSQL extensions, then you can consider an extension providing RUM indexes: https://github.com/postgrespro/rum. Otherwise, you'll have to use an "external" FTS engine like ElasticSearch.
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Features I'd Like in PostgreSQL
>Reduce the memory usage of prepared queries
Yes query plan reuse like every other db, this still blows me away PG replans every time unless you explicitly prepare and that's still per connection.
Better full-text scoring is one for me that's missing in that list, TF/IDF or BM25 please see: https://github.com/postgrespro/rum
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Ask HN: Books about full text search
for postgres, i highly recommend the rum index over the core fts. rum is written by postgrespro, who also wrote core fts and json indexing in pg.
https://github.com/postgrespro/rum -
Postgres Full Text Search vs. the Rest
My experience with Postgres FTS (did a comparison with Elastic a couple years back), is that filtering works fine and is speedy enough, but ranking crumbles when the resulting set is large.
If you have a large-ish data set with lots of similar data (4M addresses and location names was the test case), Postgres FTS just doesn't perform.
There is no index that helps scoring results. You would have to install an extension like RUM index (https://github.com/postgrespro/rum) to improve this, which may or may not be an option (often not if you use managed databases).
If you want a best of both worlds, one could investigate this extensions (again, often not an option for managed databases): https://github.com/matthewfranglen/postgres-elasticsearch-fd...
Either way, writing something that indexes your postgres database into elastic/opensearch is a one time investment that usually pays off in the long run.
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Postgres Full-Text Search: A Search Engine in a Database
Mandatory mention of the RUM extension (https://github.com/postgrespro/rum) if this caught your eye. Lots of tutorials and conference presentations out there showcasing the advantages in terms of ranking, timestamps...
You might be just fine adding an unindexed tsvector column, since you've already filtered down the results.
The GIN indexes for FTS don't really work in conjunction with other indices, which is why https://github.com/postgrespro/rum exists. Luckily, it sounds like you can use your existing indices to filter and let postgres scan for matches on the tsvector.
- Postgrespro/rum: RUM access method – inverted index with additional information
- Debugging random slow writes in PostgreSQL
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A note from our sponsor - Sevalla
sevalla.com | 1 Sep 2025
Stats
Basic rum repo stats
12
797
6.6
2 months ago
postgrespro/rum is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of rum is C.
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