noms
sirdb
noms | sirdb | |
---|---|---|
11 | 4 | |
7,502 | 562 | |
- | - | |
1.9 | 0.0 | |
over 2 years ago | 10 months ago | |
Go | JavaScript | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
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noms
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How Dolt Stores Table Data
This is from 2022. It is based on Noms [1], which is no longer maintained (they forked it).
I think the Noms doc linked from this article [2] is clearer than the article itself. That said I sill cannot turn my head around to grasp how this entire thing work tbh. I hope they wrote a peer reviewed paper to serve the audience better.
[1] https://github.com/attic-labs/
[2] https://github.com/attic-labs/noms/blob/master/doc/intro.md#...
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I was wrong. CRDTs are the future
I am. But i know very little about CRDTs lol, so we'll see how that goes. I'm interested in converting some immutable, local-first data warehouse tooling i enjoy to a CRDT version. Prior it was more.. Git-like. Basically just Git with data structures inspired-massively from Noms[1].
The thing i've found most interesting is it appears[2] that CRDT backends need to expose CRDT flavored types to users. Which is to say how i'm writing this combines the notion of a type, say `[i32]` with how you want the merges to work. CRDT works great but based on my amateur-hour researching on the subject i don't feel you can write a single CRDT merge strategy for a single data type ala `[i32]` and have it be always correct. Applications need to indicate enough context on what makes sense for a given data type.
So yea, i agree with you. I'm interested in making a database-like thing, backed by CRDTs, but i also have seen very few general purpose implementations with CRDTs. It feels like i'm breaking "new ground", while having no idea what i'm doing and having no intention of being an actual researcher here. I'm just making apps i enjoy heh.
[1]: https://github.com/attic-labs/noms
- Building a decentralized database
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Picking low-hanging memory usage bugs of an open source database
Most of the changes are in the noms package which used to live in a separate repo (https://github.com/attic-labs/noms), but Dolt has since adopted them.
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Downsides of Offline First
Not much more to say other than Noms was my favorite project (https://github.com/attic-labs/noms) for a while until acquisition and the engineers are now the ones behind Replicache (https://replicache.dev/).
I think this is going to be the next "Realm" that works everywhere.
- calling Format() on a time struct in a golang program changes the default Location's timezone information in the rest of the program
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Steps to build Database System from sratch?
The storage layer based on Noms: https://github.com/attic-labs/noms
- Noms: The versioned, forkable, syncable database
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Dolt is Git for Data: a SQL database that you can fork, clone, branch, merge
Noms might be what youβre looking for (https://github.com/attic-labs/noms). Dolt is actually a fork of Noms.
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CondensationDB: Build secure and collaborative apps [open-source]
People that are interested in a similar feature set should check out https://github.com/attic-labs/noms and the SQL fork of Noms, https://github.com/dolthub/dolt
sirdb
- Show HN: Sirdb β simple Git diffable toy database on the filesystem
- Show HN: SirDB β Git-diffable database on your filesystem in JSON
- Git forkable, syncable, diffable JSON database
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Dolt is Git for Data: a SQL database that you can fork, clone, branch, merge
I find a balance between this using git on JSON files. And I build the JSON files into a database (1 file per record, 1 directory per table, subdirectories for indexes). The whole thing is pretty beautiful, and it's functioning well for a user-account, access management database I'm running in production. I like that I can go back and do:
`git diff -p` to see the users who have signed up recently, for example.
You can get the code, over at: https://github.com/i5ik/sirdb
The advantages of this approach are using existing unix tooling for text files, solid versioning, easy inspect-ability, and leveraging the filesystem B-Tree indexing as a fast index structure (rather than having to write my b-trees). Another advantage is hardware-linked scaling. For example, if I use regular hard disks, it's slower. But if I use SSDs it's faster. And i should also be possible to mount the DB as a RAM disk and make it super fast.
The disadvantages are that the database side still only supports a couple of operations (like exact, multikey searches, lookup by ID, and so on) rather than a rich query language. I'm OK with that for now, and I'm also thinking of using skiplists in future to get nice ordering property for the keys in an index so I can easily iterate and page over those.
What are some alternatives?
rqlite - The lightweight, distributed relational database built on SQLite.
SheetJS js-xlsx - π SheetJS Spreadsheet Data Toolkit -- New home https://git.sheetjs.com/SheetJS/sheetjs
dat - Go Postgres Data Access Toolkit
nessie - Nessie: Transactional Catalog for Data Lakes with Git-like semantics
dolt - Dolt β Git for Data
sql-migrate - SQL schema migration tool for Go.
skeema - Declarative pure-SQL schema management for MySQL and MariaDB
cockroach - CockroachDB - the open source, cloud-native distributed SQL database.
levigo - levigo is a Go wrapper for LevelDB
ObjectBox Go Database - Embedded Go Database, the fast alternative to SQLite, gorm, etc.
FlockDB - A distributed, fault-tolerant graph database
GCache - An in-memory cache library for golang. It supports multiple eviction policies: LRU, LFU, ARC