Graph database Puns

Hi everyone

I'm looking for an embeddable graph database for go. I'm developing a mission critical microservice that will be invoked millions of times per day and need to ensure a minimal response time. Among other APIs, the service needs to run a high-speed BFS to test the existence of a path between two arbitrary nodes.

I have already checked:

• Dagger - currently my favourite but doesn't have HA
• SlingshotDB - AFAIK not embeddable
• EliasDB - HA not mature, APIs seem very embedded to HTTP API

Any pointer and suggestion warmly welcome!

Thanks and happy New year!

https://preview.redd.it/7ql679pkuw481.png?width=1217&format=png&auto=webp&s=bb7860e7bbcc7f424c102808d213193c0fb39ef2

https://preview.redd.it/7r65h2jqtw481.png?width=1229&format=png&auto=webp&s=4ee8f02603e765de8f5d79b3321d7086ac8755b4

https://preview.redd.it/briynbsvxw481.png?width=1192&format=png&auto=webp&s=bdf22f22dd33943de8f4bbf62496b30921aa00c9

More at: https://crinacle.com/2021/12/11/graph-database-update-11-12-21/

I have a Notion database which self-references itself. For example, I have an Items database where an entry called 'Cart' references two others entries in the table 'Wheel' and 'Harness'. This, of course, can nest such that 'Wheel' and 'Harness' are also comprised of other Items in the table.

Is there some kind of tool or website which can use the Notion API to extract this data and make a nodes and edges graph showing which Items are linked to which other ones? (eg as if you were making it in draw.io or using some python network chart tool)

Title

The post was way longer than anticipated so here is the question is short:

Has anyone worked with AWS Neptune in a Serverless project?

About a year ago I started my journey using Serverless. At first I was unsure in my ability to write good backend code using DynamoDB as a database, since my only experience was using SQL. After I shared these doubts with a colleague he offered to setup Aurora for the project. Halfway through setting up Aurora the scope of the project changed and we agreed to continue with DynamoDB (size changed and the learning curve became much less of an issue). Coming from SQL the process slowly transitioned from existential crisis into comfort.

The project has been running for a while now, feedback from this time has made the next steps of the project very clear. We need more focus on user interaction and inter-content interaction. The goalpost has shifted from document storage to more relational data.

Using DynamoDB has been a joy but seeing the shift in requirements I am thinking about what the best database would be. I am leaning towards a Graph database, preferably within AWS, that’d be Neptune.

The resources available for Serverless together with AWS Neptune seem quite sparse. The technology applies to a different market and the service is quite a bit younger, so I don’t see this lack of community written documentation as a strong consideration against opting for Neptune.

Has anybody used AWS Neptune in a Serverless project before? Was there any reason for or against using it?

To explain the shift better: Pre-consideration of a graph database we would show user content on their page, kind of like a simple blog. Right now we would like to support a model of follower/following and serve a page with all the content of users you follow. A (most likely non-hierarchical) tagging system will also be implemented in the near future.

Any help or insights would be greatly appreciated, thank you in advance!

I’ve looked around and haven’t been able to identify a comprehensive, interactive source to catalog, track, and create data outputs related to historical and modern eugenics trends.

If I were to develop something, what sorts of features and functionality would folks be looking for?

I see that even in cases where graph databases would shine (friendships/followers on Instagram/Twitter/Facebook, e-shop products recommendations, etc.), developers prefer to use relational databases.

So what is wrong with graph databases in your opinion?

In my opinion, most answers that we come up with are merely superficial and rather implications of the fundamental difference in the concepts.

So, I gave it some thought:

https://preview.redd.it/mltftzi0wj081.png?width=800&format=png&auto=webp&s=b10a51fd529ce7ae9af2611e2c788f88cb511f75

At its core: How's a graph database different from a relational?

📣 I write articles about concepts in Data Engineering and Data Science. If you liked this article, please follow me on Medium to get updates.❤️

Given the table: Bonuses
    ID         Last    First   Bonus
    1          Doe     John    8000
    2          Smith   Jane    4000
    3          Beck    Sam     1000

row oriented database(e.g. postgresql):
    1,Doe,John,8000;2,Smith,Jane,4000;3,Beck,Sam,1000;

column oriented database(e.g. mariadb):
    1,2,3;Doe,Smith,Beck;John,Jane,Sam;8000,4000,1000;

column family database(e.g. cassandra):
    "Bonuses" : {
        row1 : { "ID":1, "Last":"Doe", "First":"John", "Bonus":8000},
        row2 : { "ID":2, "Last":"Smith", "First":"Jane", "Bonus":4000},
        row3 : { "ID":3, "Last":"Beck", "First":"Sam", "Bonus":1000}
    }

document based database (e.g. mongodb):
    document1  
        { 
            "ID": 1,
            "Last": "Doe",
            "First": "John",
            "Bonus": 8000
        }
    document2
        {
            "ID": 2,
            "Last": "Smith",
            "First": "Jane",
            "Bonus": 4000
        } 
    document3
        {
            "ID": 3,
            "Last": "Beck",
            "First": "Sam",
            "Bonus": 1000
        }

graph database(e.g. neo4j): ???

key-value database(e.g. redis: ???

AGI Laboratory’s long-term goal is to make it easier to build AGI and move towards AGI and Superintelligent systems. Given where we are at from a research standpoint this is in implementing cooperative collective superintelligence systems such as Uplift, as well as e-governance voting, and in infrastructure such as the N-Scale database designed to grow on the fly without human interventions. This means it scales out and stays performant regardless of the amount of data in the system.

From a product standpoint that initially means e-governance voting systems with a focus on filtering out bias for use in politics and organizations as well as licensing the N-Scale Graph Database along with Open Sourcing key AGI related software, such as the mASI and e-governance systems, and supporting the open sourcing of other AGI research software.

Our website is https://agilaboratory.com/ and we also maintain a blog documenting the usage of Uplift, our first collective superintelligence system. You can find that here https://uplift.bio/

A new terminology is coined by Google in 2012 “Knowledge Graph”. This knowledge graph has its own significance in the field of machine learning due to which, performing capabilities of machine learning techniques are getting better day by day with a high accuracy rate. Read more

It can be any good/service. Thanks in advance!

Hello everybody!

Here's that relational vs graph database comparison article I promised I'd put together some time ago.

Would love to hear your thoughts!

Cheers,

Lju

Neo4j just raised a huge $325M Series F. In this article covering the announcement, there is a very striking quote:

>According to Gartner, "By 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision making across the enterprise."

What are your experiences with graph databases? Do you also see this trend?

We're currently using it as a PoC to combine POI with population data. You can find it on our GitHub.

Are Graph Databases (not to be confused with GraphQL: think Neo4j, Neptune and Dgraph) going to replace SQL and NoSQL DBs? Would it be right to say NoSQL is one-dimensional, SQL is two-dimensional while GraphDBs are multi-dimensional (any node can connect to any other node across the DB).

Hey. I have an sqlite database of nodes (id, text) and links (id, a, b, text) where 2 nodes can be associated by an entry in the link table. So for 2 nodes, say {id: 1, text: "joe"} and {id: 2, text: "blue"}, they might have an entry in the link table like {id: 1, a: 1, b: 2, text: "favorite color"}. The link sort of describes the relationship between joe and blue as favorite color. I can query this database to build a collection of nodes and their relationships to and from any node and by the text of their relationship. After doing some research, it looks like a graph database might be something to consider.

I have some basic questions around that sort of db if anyone has tried it before.

1. I assume my structure is the most performant way to handle my current data in a relational database since it's flat. Would I get better performance organizing the same data inside a graph database?
2. I think my data would convert easily to a graph database, but would I lose a ton of flexibility? I'm mostly familiar with mysql, sql server, mongodb, etc - the common flavors of sql & nosql.
3. Is there an implementation of a graph database you would recommend? I like working with golang and am curious about graphql, so I see dgraph might be an option there. Is there a graph database that's good for noobs / simpler use cases? Although maybe I should prefer a more flexible one since I'm moving from common sql / nosql?
4. After doing a bit more research and understanding graphdb a little more, I can also ask if there would be any value / purpose in making the switch. In my case, I am traversing the nodes from a single entry point and series of known relationships. My data is designed to be traversed this way, so maybe I wouldn't gain the benefit of switching? From my example, I would have some known path like root->life->friends->joe->favorite color. Or I would query from the start "favorite color" as a link and get back nodes of a->joe and b->blue.

thank

--edit: I'm realizing there was a better and simpler question I could have asked. In case anyone else comes by - I'd appreciate some perspective on what graph databases are NOT good at.

A new terminology is coined by Google in 2012 “Knowledge Graph”. This knowledge graph has its own significance in the field of machine learning due to which, performing capabilities of machine learning techniques are getting better day by day with a high accuracy rate. Read more