marmot
cr-sqlite
marmot | cr-sqlite | |
---|---|---|
33 | 28 | |
1,628 | 2,434 | |
- | 2.6% | |
8.6 | 9.6 | |
3 months ago | 7 days ago | |
Go | Rust | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
marmot
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Distributed SQLite: Paradigm shift or hype?
If you're willing to accept eventual consistency (a big ask, but acceptable in some scenarios) then there are options like marmot [1] that replicate cdc over nats.
[1]: https://github.com/maxpert/marmot
- Marmot: Multi-writer distributed SQLite based on NATS
- Why you should probably be using SQLite
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The Raft Consensus Algorithm
I've written a whole SQLite replication system that works on top of RAFT ( https://github.com/maxpert/marmot ). Best part is RAFT has a well understood and strong library ecosystem as well. I started of with libraries and when I noticed I am reimplementing distributed streams, I just took off the shelf implementation (https://docs.nats.io/nats-concepts/jetstream) and embedded it in system. I love the simplicity and reasoning that comes with RAFT. However I am playing with epaxos these days (https://www.cs.cmu.edu/~dga/papers/epaxos-sosp2013.pdf), because then I can truly decentralize the implementation for truly masterless implementation. Right now I've added sharding mechanism on various streams so that in high load cases masters can be distributed across nodes too.
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SQLedge: Replicate Postgres to SQLite on the Edge
Very interesting! I have question ( out of my experience in https://github.com/maxpert/marmot ) how do get around the boot time, specially when a change log of table is pretty large in Postgres? I've implemented snapshotting mechanism in Marmot as part of quickly getting up to speed. At some level I wonder if we can just feed this PG replication log into NATS cluster and Marmot can just replicate it across the board.
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Show HN: Blueprint for a distributed multi-region IAM with Go and CockroachDB
One of the reasons I started writing Marmot (https://maxpert.github.io/marmot/) was for replicating bunch of tables across regions that were read heavy. I even used it for cache replication (because who cares if it’s a cache miss, but a hit will save me time and money). It’s hard to make such blue prints in early days of product, and by the time you hit a true growth almost everyone builds a custom solution for multi-region IAM.
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Stalwart All-in-One Mail Server (IMAP, JMAP, SMTP)
Amazing I was just looking for a good mail server to configure for my demo. Which reminds me since you folks have mentioned LiteStream, have you tried Marmot (https://github.com/maxpert/marmot); I recently configured Isso with Marmot to scale it out horizontally (https://maxpert.github.io/marmot/demo). I am super curious what kind of write workload on a sub thousand people organization will have and if Marmot can help scale it horizontally without Foundation DB. I always find the the convenience of SQLite amazing.
- Marmot: A distributed SQLite replicator built on top of NATS
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LiteFS Cloud: Distributed SQLite with Managed Backups
Great that you brought it up. I will fill in the perspective of what I am doing for solving this in Marmot (https://github.com/maxpert/marmot). Today Marmot already records changes via installing triggers to record changes of a table, hence all the offline changes (while Marmot is not running) are never lost. Today when Marmot comes up after a long offline (depending upon max_log_size configuration), it realizes that and tries to catch up changes via restoring a snapshot and then applying rest of logs from NATS (JetStream) change logs. I am working on change that will be publishing those change logs to NATS before it restores snapshots, and once it reapplies those changes after restoring snapshot everyone will have your changes + your DB will be up to date. Now in this case one of the things that bothers people is the fact that if two nodes coming up with conflicting rows the last writer wins.
For that I am also exploring on SQLite-Y-CRDT (https://github.com/maxpert/sqlite-y-crdt) which can help me treat each row as document, and then try to merge them. I personally think CRDT gets harder to reason sometimes, and might not be explainable to an entry level developers. Usually when something is hard to reason and explain, I prefer sticking to simplicity. People IMO will be much more comfortable knowing they can't use auto incrementing IDs for particular tables (because two independent nodes can increment counter to same values) vs here is a magical way to merge that will mess up your data.
cr-sqlite
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Show HN: RemoteStorage – sync localStorage across devices and browsers
I'm a happy user of https://github.com/vlcn-io/cr-sqlite/
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Marmot: Multi-writer distributed SQLite based on NATS
If you're interested in this, here are some related projects that all take slightly different approaches:
- LiteSync directly competes with Marmot and supports DDL sync, but is closed source commercial (similar to SQLite EE): https://litesync.io
- dqlite is Canonical's distributed SQLite that depends on c-raft and kernel-level async I/O: https://dqlite.io
- cr-sqlite is a Rust-based loadable extension that adds CRDT changeset generation and reconciliation to SQLite: https://github.com/vlcn-io/cr-sqlite
Slightly related but not really (no multi writer, no C-level SQLite API or other restrictions):
- comdb2 (Bloombergs multi-homed RDMS using SQLite as the frontend)
- rqlite: RDMS with HTTP API and SQLite as the storage engine, used for replication and strong consistency (does not scale writes)
- litestream/LiteFS: disaster recovery replication
- liteserver: active read-only replication (predecessor of LiteSync)
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Offline eventually consistent synchronization using CRDTS
Theory is great, but how can we apply this in practice? Instead of starting from 0, and writing a CRDT, let's try and leverage an existing project to do the heavy lifting. My choice is crSQLITE, an extension for SQLite to support CRDT merging of databases. Under the hood, the extension creates tables to track changes and allow inserting into an event log for merging states of separated peers.
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Local-first software: You own your data, in spite of the cloud (2019)
Also https://github.com/vlcn-io/cr-sqlite/ which is SQLite + CRDTs
Runs/syncs to the browser too which is just lovely.
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I'm All-In on Server-Side SQLite
If you need multiple writers and can handle eventual correctness, you should really be using cr-sqlite[1]. It'll allow you to have any number of workers/clients that can write locally within the same process (so no network overhead) but still guarantee converge to the same state.
[1] https://github.com/vlcn-io/cr-sqlite
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Show HN: ElectricSQL, Postgres to SQLite active-active sync for local-first apps
I am fully on the offline-first bandwagon after starting to use cr-sqlite (https://vlcn.io), which works similar to ElectricSQL.
I thought the bundle size of wasm-sqlite would be prohibitive, but it's surprisingly quick to download and boot. Reducing network reliance solves so many problems and corner-cases in my web app. Having access to local data makes everything very snappy too - the user experience is much better. Even if the user's offline data is wiped by the browser (offline storage limits are a bit of a minefield), it is straightforward to get all synced changes back from the server.
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Launch HN: Tiptap (YC S23) – Toolkit for developing collaborative editors
I didn't know that. Especially the first approach sounds interesting to me, because as far as I know the transactions of Yjs seem to be a problem on heavily changing documents. https://github.com/vlcn-io/cr-sqlite#approach-1-history-free... Thanks!
- Scaling Linear's Sync Engine
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Mycelite: SQLite extension to synchronize changes across SQLite instances
I wonder how this compares to https://vlcn.io?
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Ask HN: Incremental View Maintenance for SQLite?
The short ask: Anyone know of any projects that bring incremental view maintenance to SQLite?
The why:
Applications are usually read heavy. It is a sad state of affairs that, for these kinds of apps, we don't put more work on the write path to allow reads to benefit.
Would the whole No-SQL movement ever even have been a thing if relational databases had great support for materialized views that updated incrementally? I'd like to think not.
And more context:
I'm working to push the state of "functional relational programming" [1], [2] further forward. Materialized views with incremental updates are key to this. Bringing them to SQLite so they can be leveraged one the frontend would solve this whole quagmire of "state management libraries." I've been solving the data-sync problem in SQLite (https://vlcn.io/) and this piece is one of the next logical steps.
If nobody knows of an existing solution, would love to collaborate with someone on creating it.
[1] - https://github.com/papers-we-love/papers-we-love/blob/main/design/out-of-the-tar-pit.pdf
What are some alternatives?
pocketbase - Open Source realtime backend in 1 file
electric - Local-first sync layer for web and mobile apps. Build reactive, realtime, local-first apps directly on Postgres.
litefs - FUSE-based file system for replicating SQLite databases across a cluster of machines
vlcn-orm - Develop with your data model anywhere. Query and load data reactively. Replicate between peers without a central server.
wordpress-playground - Run WordPress in the browser via WebAssembly PHP
edgedb-go - The official Go client library for EdgeDB
mssql-changefeed
imdbench - IMDBench — Realistic ORM benchmarking
rqlite - The lightweight, distributed relational database built on SQLite.
edgedb-cli - The EdgeDB CLI
sqlite3-preload - LD_PRELOAD hack to execute SQLite statements when an SQLite database is opened