31–40 of 48 posts
Can Memgraph be used as an embedded database similar to SQLite?
The intended usage isn't that one because Memgraph is built as a server database system. The communication has to go via the binary protocol. But, because Memgraph is implemented in C/C++ and there is the C API for the query modules already in-place, offering an embedded solution is possible. This feature isn't high on the priorities list at the moment. What would be your target platforms? Mobile or maybe something o…
Congrats on the launch! Do you have any plans to release a NodeJs Driver?
Congratulations on your release! Keep up the good work!
How does this compare to Dgraph? Most recent discussion about it: https://news.ycombinator.com/item?id=23031762 Also, does it support GraphQL?
To answer your first question, the main difference between Memgraph and Dgraph seem to be: 1. The graph model: Property graph for Memgraph vs RDF for DGraph (Depending on the use-case you have one model might be better than the other. E.g ontologies are better served with RDF, whereas graphs with a lot of properties and labels are better served by the property graph model) 2. Memgraph is an in-memory first system whe…
How does this compare to Dgraph? Most recent discussion about it: https://news.ycombinator.com/item?id=23031762 Also, does it support GraphQL?
To answer your first question, the main difference between Memgraph and Dgraph seem to be: 1. The graph model: Property graph for Memgraph vs RDF for DGraph (Depending on the use-case you have one model might be better than the other. E.g ontologies are better served with RDF, whereas graphs with a lot of properties and labels are better served by the property graph model) 2. Memgraph is an in-memory first system whe…
I'm community support at Dgraph. Reading that question. I come to clarify some points.
First of all, I don't particularly know Memgraph internally. So, I'll stick to Dgraph and related only.
About Karimtr's reply
1. The graph model: Although Dgraph uses RDF as input data. Dgraph is not a Triple Store per se - and the RDF we have is a customized version, which means that it is not 100% compatible with any RDF model (e.g. Turtle RDF) - But eventually, many RDF syntaxes may be compatible.
The decision to use RDF was made a long time ago, for reasons that I am particularly unaware of. It was long before I joined Dgraph. We also accept JSON as data input. By the way, I also don't understand why Neo4j uses CSV as input data since it is not a Graph standard. RDF itself would be more acceptable than CSV. I assume they use CSV for strategic reasons.
1.1 In practice Dgraph is technically a "Property graph" like. There are no fundamental differences between the Dgraph's graph model with Neo4j other than the language itself and the way the data is stored and injected.
1.2 In Dgraph the data is stored in KV using BadgerDB.
1.3 Ontologies can be represented in any GraphDB. The difference is that Triple Store DBs have a language created to infer data specifically with the concept of ontology. And Triple Stores has standardized data input for this.
2. That's right. However, I think we will soon have the option to keep it in memory. But I don't particularly know how useful this is. Today you can keep some Memory first data, but with the guarantee that they will be saved on disks. This helps in performance when there is no use of NVMe.
3. We have GraphQL+- which is a rich language and inspired by GraphQL. And we also have GraphQL which is an "API" language that is now native in Dgraph. A friendly front-end language. And it works "out of the box" once you mount your schema. Dgraph creates a CRUD model based on your Schema. This reduces production time for your application and less logic on your business side. We are still adding "Black Magic" so that the experience in producing APPs is exceptional. And less code typing.
About performance. I suggest doing a test against ludicrous mode https://discuss.dgraph.io/t/sharing-some-numbers-from-the-lu...
Cheers.
Earlier quoted context omitted.
To answer your first question, the main difference between Memgraph and Dgraph seem to be: 1. The graph model: Property graph for Memgraph vs RDF for DGraph (Depending on the use-case you have one model might be better than the other. E.g ontologies are better served with RDF, whereas graphs with a lot of properties and labels are better served by the property graph model) 2. Memgraph is an in-memory first system whe…
Hi guys, I'm community support at Dgraph. Reading that question. I come to clarify some points. First of all, I don't particularly know Memgraph internally. So, I'll stick to Dgraph and related only. About Karimtr's reply 1. The graph model: Although Dgraph uses RDF as input data. Dgraph is not a Triple Store per se - and the RDF we have is a customized version, which means that it is not 100% compatible with any RDF…
thanks for joining the conversation and clarifying things. I'm far from being an expert on Dgraph so I learned a few things from your answer.
Regarding the data model, that makes sense. I read that Dgraph support properties so I thought you built something along the lines of the RDF+ framework with seems to support properties and labels.
Yes, you're right, ontologies can be represented in any graph but it's clear that RDF is a better option due to the reasons you mentioned and other ones. That's the nice thing about the emergence of different graph databases, you get to pick the right system for the right job :)
When it comes to performance, to be honest, I'm not a big fan of random benchmarks, as it's always tricky to get them right, especially for systems that have big differences. We usually let developers do that for their specific use-cases and we just try to help them set up Memgraph as best as possible. Cheers.
Graph databases have as much to do with data science as they do with any software applications.
Going to guess a VC made them put that there.
Definitely excited to try it out for a project I'm working on with DAG optimizations.
Hey everyone, Memgraph CTO here. This is a massive milestone for us that took almost 3 years to reach and I would like to thank everyone who helped us get here! If you have any questions or feedback, I’ll be around all day to answer :D
Earlier quoted context omitted.
To answer your first question, the main difference between Memgraph and Dgraph seem to be: 1. The graph model: Property graph for Memgraph vs RDF for DGraph (Depending on the use-case you have one model might be better than the other. E.g ontologies are better served with RDF, whereas graphs with a lot of properties and labels are better served by the property graph model) 2. Memgraph is an in-memory first system whe…
Hi guys, I'm community support at Dgraph. Reading that question. I come to clarify some points. First of all, I don't particularly know Memgraph internally. So, I'll stick to Dgraph and related only. About Karimtr's reply 1. The graph model: Although Dgraph uses RDF as input data. Dgraph is not a Triple Store per se - and the RDF we have is a customized version, which means that it is not 100% compatible with any RDF…
Now that the frontend devs have gotten their attention, I hope dgraph plans to give the python community some love as well by improving the client API and asyncio support and even full integration with networkx.