dtm VS bolt

- using a distributed transaction framework (like https://github.com/dtm-labs/dtm)

But in this article, we introduce a workflow pattern in github.com/dtm-labs/dtm. Under this pattern, a mixture of XA, SAGA and TCC can be applied to different branches in a single distributed transactions, allowing users to customize most of the contents of a distributed transaction, providing great flexibility.

The open source distributed transaction framework https://github.com/dtm-labs/dtm has a two-stage message pattern inside that handles this problem very well. The following is an example of the use of an interbank transfer operation.

The above Sub-transaction Barrier technique, when used in conjunction with the distributed transaction framework https://github.com/dtm-labs/dtm, has been made available in several language SDKs, with the following example code in Go.

Maybe you can take a look at github.com/dtm-labs/dtm which provide built-in TCC pattern

Distributed XA transactions can solve the above business problem. This article presents a solution based on dtm-labs/dtm. DTM is a popular distributed transaction framework which supports XA, Saga, OutBox, and TCC patterns.

Github: https://github.com/dtm-labs/dtm

This article gives an example of implementing a distributed transaction across multiple store engines, Mysql, Redis and Mongo. This example is based on the Distributed Transaction Framework https://github.com/dtm-labs/dtm and will hopefully help to solve your problems in data consistency across microservices.

This crate started out as just a way for me to learn how boltdb works, while learning Rust at the same time. But somehow people started finding and using it and seem to like the simple API, so I figured I might as well share it in case someone else finds it useful too. If you want to know more about my motivations and the history of this crate, you can read the release notes on version 0.8.0!

Some example of embeddable database could be genji, badger and boltdb

Designing Data Intensive applications- specifically chapter 3 and 4 which deal with strategies and algorithms for storing and encoding data to be stored on disk and their pros and cons.

Once you read that, I'll suggest reading the source of a simple embedded key-value database, I wouldn't bother with RDBMs as they are complex beasts and contain way more than you need. BoltDB is a good project to read the source of https://github.com/boltdb/bolt, the whole thing is <10k lines of code and is a full blown production grade system with ACID semantics so packs a lot in those 10k and isn't just merely a toy.

Bolt db and Bolt db's author post to go with it.

https://github.com/boltdb/bolt is a ACID B+ tree key-value store

For a single server, SQLite, or boltdb[0]

I've never had to scale horizontally. I develop in Go and you can get very far along with just vertical scaling (aka beefier hardware).

Therefore I can't give concrete examples of a distributed db-as-a-library.

But all that you need is to extend the functions that fetch data to not just fetch from disk but from "peers" as well. For this to work you need servers (instances) to know about each other, and as you add more they also get added to their peers - sort of like a bittorrent network. I don't think it's difficult to do.

SQLite might not be suited for being distributed (although RQlite[1] claims to have done it).

Making a distributed data storage based on boltdb[0] is probably more feasible.

Whatever the case, there's no reason why a data storage engine can't be a library, even if it's distributed.

[0]: https://github.com/boltdb/bolt

[1]: https://github.com/rqlite/rqlite