karimtr

Hey everyone! Happy New Year to all :D

I'm launching the London Observability Engineering, and I’m scheduling speakers and topics right now, so if you have something to share about Observability, I'd love to speak to you!

Here are a few topics I'm thinking of covering:

Next-gen instrumentation with eBPF & OpenTelemetry, GenAI in Observability, implementing observability for legacy systems, API observability, and data observability.

What else should we talk about?

Hey everyone!

I just published this short intro to K8sGPT.

Btw, this isn't just another "ChatGPT wrapper."

Let me know if I missed anything :)

I spent the last couple of days catching up with the OpenTofu Vs. Terraform saga, and put together this short 5min overview.

I also included a few resources (Articles, podcasts, etc.) you can check out if you're curious to learn more.

Nothing groundbreaking here, but I hope it helps those who haven't been keeping up with the whole thing.

Hey everyone,

I put together this short newsletter exploring Crossplane.

My goal is to introduce you to Crossplane in 5 minutes or less so you can decide if this is something interesting for you to explore further.

I hope it's helpful!

This looks very similar to this: https://techcrunch.com/2019/09/13/element-ai-raises-151m-on-...

The reality is that RPA, AI, ML, etc ... are still years away when it comes to the industrial sectors (e.g. manufacturing, energy, oil & gas, etc).

Sure there are many PoCs, smaller projects, but in my experience, working on data centric projects with a few fortune 500 industrial companies, none of these technologies have been successfully implemented in production.

Perhaps for some back office & business-level applications, but certainly not for any production related use-cases (e.g. predictive maintenance).

Are there any specific topics you would like to learn? (e.g. PLC programming, Machine Vision, IoT, HMI development, ...)

Also is your goal to learn enough to get a job?

Congrats on the beta launch!

This is great! Thanks for putting together and sharing it :)

Cheers!

Anyone stopped using Fitbit as a result of this?

Hey HN,

Karim from Memgraph here. We just launched a project we've been toying around with for a while now, called Memgraph Playground.

The initial idea was to let anyone to learn Cypher (a popular graph query language) and get an idea of what it's like to work with graphs without any friction:

- In-browser experience. No downloads required.

- No sign up required

- Preloaded datasets with many more to come

- Step-by-step tutorials with example queries & explanations

The project is still in the early days, and we're planning to add more functionality, but for now you can:

- Run read Cypher queries only. Wright queries aren't supported

- Run some popular graph algorithms (Breadth First Search, Weighted Shortest Path). We'll be adding many more.

- Visualize your results as a graph or in a table. We'll be adding more styling options.

I hope you find this tool useful and fun. If you have any suggestions or questions please drop them in the comments below.

Cheers!

The Oxford-AstraZeneca vaccine has unfortunately been heavily politicized in the UK. The reporting around it has always sounded a bit nationalistic.

For the past few months, the government has been using every opportunity to justify Brexit. The vaccine approval process being one of those.

Came across your IH a few months ago.

Those revenues are incredible for a local cookie company with a single kitchen.

Well done!

There is huge potential around better data usage & analytics in agriculture & crop science. You're domain expertise would place you in a unique position compared to other data scientists with more of a generic background.

I've personally spoken to a few large enterprises in the agricultural sector who are just beginning to build out their data science department. It seems like the industry as a whole is just getting started in data science.

Also, there are many promising startups that are emerging in space. E.g. Vertical farming

Hey HN,

Karim here from Memgraph.

One of the biggest changes in our latest Memgraph 1.2 release, was the addition of Bolt v4 and v4.1 support.

We thought that it would be interesting to discuss the Bolt Protocol for those of you who are interested in graph databases, and explore what goes into being wire-compatible with another database system.

Please let us know if you have any questions. We're here to help.

Thanks!

*supports

No, it's not an RDF store. Memgraph is a support the Label Property Graph model.

It's not open source, but you can use the free community version: https://memgraph.com/download

Dgraph is actually very different on many aspects like data model, query language, even overall focus & target use-cases.

In my opinion it will be tricky to have a fair comparison. You can have a look at the benchmarks they published against Neo4j. It seemed like there was a lot of disagreements on both side.

What other graph databases would you like us to have a look at?

That's a good point. Benchmarking different systems is always tricky to get right, but we'll get something out soon.

The focus of these benchmarks was to explain to our existing community how we improved things and share with the broader software engineering community has worked for us in the hope it could help others working on graph projects.

Hey HN!

We've just released this blog post exploring some of the changes we made over the past few years that helped us reduce memory usage by as much as 50% and improve throughput towards near-linear scalability.

We'd love to get your thoughts.

Cheers!

This is a super cool and useful tool! I just shared it with a few people I know in the museum world (mostly curators) and they're super excited about it :D I'll revert back if they share any feedback/ideas.

Can users somehow input feedback to improve search results?

Hey Michel,

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.

Sorry, I missed the second part of you question. You should be able to use the Neo4j GraphQL with Memgraph although we didn't fully test it yet(https://github.com/neo4j-graphql/neo4j-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 where is Dgraph is a disk-based system. Again depending on your use-case and the performance you are looking for, an in-memory system might be better suited.

3. Query language & Ecosystem: we support Cypher and the Bolt Protocol (Same as Neo4j) so we work with a lot of the existing graph tools.

In terms of performance, we don't have official benchmarks yet but we have a few clients that test Memgraph and Dgraph and reported a 3-5x in read performance and about an 8x in write performance for their specific workloads.

I hope this answers your question.

Good question :D We haven't spent much time trying out RedisGraph, nor we ran any kind of benchmarks at his point, but from what I can see, I would say that the main differences would:

1. Memgraph is a native GraphDB which means that it's specifically engineered to support graph and graph only whereas RedisGraph is a module that runs on top of key-value store (nothing wrong with that, but it might have some limitation when it comes to performance on complex graph algorithms and traversals)

2. Cypher coverage – We cover about 80% of the cypher query language, where I think RedisGraph covers only a small portion (But I hear that they're improving on that)

Apart from that, I guess you would really need to test both when for specific use-cases to judge performance and scalability. We'll try to provide some benchmarks in the next few weeks.

I hope this helps :D Have you used RedisGraph? Any thoughts?

Karim from Memgraph here. That's a good question we get really often. As you can imagine we had our hands full for the past few years building a DB from scratch, and so we didn't really have the resources to put together an open-source project the way we imagine it. It's something we'll explore this year to try to come up with the best way to do it.

Karim from Memgraph here. Thanks for the questions. Are you referring to geospatial capabilities? If I understand correctly, you would like to add geospatial coordinates to specific elements in the graph (e.g. power stations, power lines etc) and be able to search and query for specific areas (e.g. countries, cities, etc)?

If that's the case, although we don't have direct support for Geospatial data format, you can easily encode it into your graph by setting coordinate as a property on your nodes and edges.

Does this answer your question?

Awesome! Thanks for sharing :D

Easier to build something quicky but definitely not the best option for DBs. It's always about tradeoffs I guess.

Interesting. I'll have a look. Thanks for sharing :D