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LMDB
sqlightning
C2015–2026
LMDB / sqlightning

sqlightning

SQLite3 ported to use LMDB instead of its original Btree code. See https://github.com/LumoSQL/LumoSQL for maintained fork.

11Distinct people
16Mentions
0Explicit recommenders
336GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

SQLite3 ported to use LMDB instead of its original Btree code. See https://github.com/LumoSQL/LumoSQL for maintained fork.

GitHub

Where it is used

No documented product use was verified in the last source check.

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

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:02:05 AM 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 year16 dated mentions

Observed mentions: 2015-09-01 03:34:46 UTC to 2026-01-07 09:05:22 UTC.

0123452015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20152016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 5 mentions; 1 submissions, 4 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 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): 1 mentions; 1 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
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC101
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC101
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC101
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC404
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC202
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC514
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
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 UTC110
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

1 sources
Eh, you're right on some and wrong on many other points. An embedded key-value store can be a good foundation for an RDBMS - or many other data models. The OpenLDAP Project used BerkeleyDB for more than 15 years. BerkeleyDB architects still point to OpenLDAP as a reference standard for transactional BDB code. http://www.bozemanpass.com/services/bdb.html As the primary author of OpenLDAP's BDB support, and of the LMDB library which now supersedes BDB, I've got quite a deep perspective into this topic. BDB is deadlock-prone, no argument there. But that doesn't mean the embedded engine approach can't work. LMDB is deadlock-proof, and serves perfectly well as the engine for an RDBMS as well as the engine for a hierarchical DB (X.500/LDAP directory), or arbitrary graphs (http://sph.io/content/2faf , https://github.com/pietermartin/thundergraph
Read the rest
, etc...) etc. When you start with a model-less key/value store you can implement any other data model on top. LMDB's robustness is, in a word, flawless. It's crash-proof by design and multiple independent researchers have borne out the integrity of that design. (https://www.usenix.org/conference/osdi14/technical-sessions/... https://www.usenix.org/conference/osdi14/technical-sessions/... ) Your assertion that you can't build a robust multi-client system in a lightweight embedded system is flat wrong. In fact, this approach is the only way to get maximum performance from a software system. LMDB is so much faster than every other data management mechanism in existence, nothing else even comes close. http://symas.com/mdb/#bench Crap tools, crap locking - these are certainly weaknesses in BDB's locking design. LMDB's locking design doesn't have these weaknesses. Querying the size of the DB is an O(1) operation, simply reading a few words out of the DB header. Underdocumentation - I have no idea what you're talking about here. To me, BDB was copiously documented, and I largely owe my understanding of transactional systems to the BDB documentation. Yes, BDB is complicated and has too many moving parts. LMDB doesn't have this flaw either. But I can't take anyone seriously who says BDB was underdocumented. C++ interface - I don't use C++, so no comment here. C >> C++. SQLite on BDB - yes, it's a joke. I've run it, and the performance is pretty awful. But we do far better. https://github.com/LMDB/sqlightning There are many positive and negative lessons in software design to be learned from Berkeley DB. It really started life as a test bed, for learning. In that, I think it succeeded admirably. Its authors were able to experiment with extensible hashing, B+trees, ARIES logging, and a multitude of other important software architecture and data management concepts. LMDB owes much of its design to lessons learned from BDB. LMDB's use of a read-only memory map by default means you can safely allow multi-client access without fearing corruption due to memory bugs. LMDB's exclusive use of mmap instead of complex application-level caching means you avoid all of the horrors of BDB's cache tuning, bloat driving into swap space, and other admin/tuning nightmares. LMDB's crash-proof persistent on-disk format means you don't need to worry about error-prone and inherently unreliable transaction logging mechanisms. All of these features are part of the LMDB design because of our experience working with BDB over the past 15+ years.
In Berkeley DB: Architecture

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