"DataScript asks the question: "What if creating a database would be as cheap as creating a Hashmap?"
Mentat is not interested in that. Instead, it's strongly interested in persistence and performance, with very little interest in immutable databases/databases as values or throwaway use."
and:
"Datomic has a beautiful conceptual model. [...] Many of these design decisions are inapplicable to deployed desktop software; indeed, the use of multiple JVM processes makes Datomic's use in a small desktop app, or a mobile device, prohibitive.
Mentat is designed for embedding, initially in an Electron app (Tofino). It is less concerned with exposing consistent database states outside transaction boundaries, because that's less important here, and dropping some of these requirements allows us to leverage SQLite itself."
I'm the author of most of those docs, if you'd like answers to any specific questions from the horse's mouth, so to speak. I'll cycle back around and reply to this comment again in a bit.
You might be interested in reading this much longer piece, which includes a brief code example, and spends more time than the README explaining the motivation for the project.
Yes. Project Mentat fits into a conceptual lineage that includes Freebase's graphd and 2005-onward Semantic Web stores. We have aimed for compatibility with Datomic and DataScript for least surprise, but if you squint there's a little AllegroGraph in the direction.
Datomic's model (both architectural and conceptual) draws from Clojure's concepts of persistence. That model isn't free, so Mentat deviates from it where it makes sense to do so: at present we don't implement querying of history or past states, for example, and when we do it won't be free.
We'll get closer to full Datomic-style datom store capabilities over time, but we'll make different performance tradeoffs.
Thanks for the explanation. For me the key takeaway is:
> at present we don't implement querying of history or past states, for example, and when we do it won't be free.
That's the bit of Datomic that intrigues me and gives the best use-case (I don't have to add data versioning and history in my app-layer) and what I'm looking for in other systems.
Note that we do store the full transaction log, just like Datomic, and Mentat will allow querying of it (and replication, and replay, and…). We haven't implemented history querying yet because we haven't needed it for application code.
The trick with Datomic is that every time you grab a `db` instance, it's a snapshot, and the system's index chunking and storage replication are necessarily built around the ability to continue using those older index chunks, potentially for a very long time.
Most consumers, most of the time, just want to query the store as it stands at that moment, but Datomic peers pay the space and time penalty of keeping and retrieving historical index chunks in order to answer those historical queries.
My current thoughts are:
1. To allow for short-term snapshot querying through something like `db.keep()`, implemented via a SQLite read transaction. That's not free: the database WAL will continue to grow until the read transaction is ended, so it isn't ideal for all workloads, but it'll do.
For some queries it's enough to simply track a last-seen tx value and filter everywhere, but that becomes difficult when cardinality-one and unique-identity properties are considered.
2. The obvious equivalent to Datomic's 'with' is an uncommitted write transaction. Naturally this blocks other writers while it exists, and so alternative implementations (e.g., writing to a complete disk copy of the database, or writing a 'delta' table) might make sense.
At some very hazy point in the future we might try to get SQLite support for this: after all, if we can guarantee that a write transaction won't be committed, we could use a separate WAL file for the `with` and avoid blocking other writers.
3. A longer-term approach to snapshots/DB-as-value is to materialize the datoms at the specified instant in time, either in a temporary table or in a real persisted table. That is: `db.keep_forever()` will give you a new structure to query, and calling code will be responsible for cleaning up that space.
The reason I say "won't be free" is that each of these operations imposes a cost when the feature is used: either SQLite or Mentat will have to do some work to allow an extended period of isolation, to reconstruct some state, or to persist some state.
That's in contrast to Datomic, which imposes some overhead every time index chunks are built or retrieved. It's also an interesting parallel to Clojure vs Rust: Clojure's data structures are persistent by default, giving you snapshots and safety at a cost everyone pays; Rust believes that you shouldn't pay for abstractions you don't use.
When I read the title I was wondering if this was a new project. It's actually a continuation of the Datomish project. I'm glad they changed the name, though.
Can't find any information whatsoever on usage. Not even sure if this meant to be externally accessed or whether this is purely for embedding (in other Rust code?).
It's intended for embedding. When complete Mentat will likely sport a Node module, a network protocol + CLI + explorer, and might well be accessible from Firefox extensions via a JavaScript API.
This looked really familiar to another Mozilla project called Datomish. The project was recently renamed to avoid confusion with Datomic; but apparently that _also_ involved a rewrite into Rust. Details here: https://github.com/mozilla/mentat/issues/133
Unfortunately, this means that the path for using this in non-Node Javascript environments (i.e. browsers) is unknown.
The original implementation was in ClojureScript. This reimplementation is in Rust, and is intended to work anywhere you can run Rust code: inside Node, inside Firefox, and in standalone applications.
We expect a WebExtensions API to wrap this inside Firefoxes at some point, but right now we're focused on the core (re)implementation.
Awesome. Been looking hard for open source datalog-supporting data stores and data processing systems for a couple of years now. This is at least something in this direction, although I might ultimately wish for a full-fledged database or system that could run in a distributed fashion if needed.
RDFox: "A highly scalable in-memory RDF triple store that supports shared memory parallel datalog reasoning. It is a cross-platform software written in C++ that comes with a Java wrapper allowing for an easy integration with any Java-based solution."
Datalog -> Gremlin: It shouldn't be too hard to implement Datalog on top of the Gremlin Graph Virtual Machine so that Datalog compiles down to Gremlin bytecode -- SPARQL and SQL implementations already exist -- and running Datalog on the GVM would allow you to run Datalog on any datastore Apache Tinkerpop supports (all the graph DBs, HBase, Cassandra...):
It's designed to meet our embedding needs: in Firefox for desktops, Firefox for Android, Firefox for iOS, and Project Tofino (Node + Electron).
Widespread adoption a la SQLite is not one of our goals.
Rust meets our goals just fine… and it also produces demonstrably more correct software than C, which is important to us. (Not to mention leveraging the borrow checker and data race avoidance to provide safe near-automatic parallelization, which is a neat trick that's not in C's quiver.)
I would rather build Mentat in Swift or JavaScript than in C.
Datapoint: GNOME's Federico Mena Quintero is working on a file-by-file port of librsvg to Rust. Compiled Rust objects are linked with compiled C objects. Both Rust and C call functions in librsvg's C dependencies, such as Cairo. None of this is weird; it's precisely what Rust was designed for.
"DataScript asks the question: "What if creating a database would be as cheap as creating a Hashmap?"
Mentat is not interested in that. Instead, it's strongly interested in persistence and performance, with very little interest in immutable databases/databases as values or throwaway use."
and:
"Datomic has a beautiful conceptual model. [...] Many of these design decisions are inapplicable to deployed desktop software; indeed, the use of multiple JVM processes makes Datomic's use in a small desktop app, or a mobile device, prohibitive.
Mentat is designed for embedding, initially in an Electron app (Tofino). It is less concerned with exposing consistent database states outside transaction boundaries, because that's less important here, and dropping some of these requirements allows us to leverage SQLite itself."