ArangoDB VS kubernetes

ArangoDB

... or working in an environment or on a problem for which functional patterns apply.

Suppose you are writing a "CRUD" app that writes to a relational database, how do you apply functional programming to that? The whole point of an application like that is that it makes side effects.

In some cases you can break those problems down into functional pieces. Consider Python drivers for a product like

https://www.arangodb.com/

One major problem is that you want drivers that work synchronously and asynchronously, the structure of the average api call is something like

   def query(parameters):

First, you need to choose a specific graph database platform to work with, such as Neo4j, OrientDB, JanusGraph, Arangodb or Amazon Neptune. Once you have selected a platform, you can then start working with graph data using the platform's query language.

Some databases like ArangoDB (https://www.arangodb.com/) allow you to use Javascript instead of SQL.

However, using a type-unsafe, turing-complete language introduces type unsafety and turing-complete problems to the query layer; the usual problems we know and love, such as infinite loops, runtime type errors, exceptions, and the like.

Personally, I'm looking forward to a WASM runtime for databases -- so we can run webassembly on the database. This COULD be carefully designed to be statically checked and, possibly, make it really hard to write runaway loops.

When it comes to graphdb's, my favorite is still ArangoDB, definitely worth checking out if you are looking for alternatives.

https://www.arangodb.com

Personally my favorite db for pet projects is

https://www.arangodb.com/

I think you hear very little about it because ADB users see it as a "secret weapon" to crush their competitors with. I've done large ontology work (MESH and other health ontologies) and IoT work (keep several years of sensor readings for sensors in my house) and workflow systems (select interesting HN articles or jobs I want to apply to) and it has never let me down. I haven't run a real instance serving customers in the cloud though.

For the last few years every eng manager I have worked with has been a fan of

https://www.postgresql.org/

In the early 2000s I thought it overpromised and underdelivered and called it CrashGreSlow but after MySQL got bought by Oracle the pgsql team has worked hard to improve it I think it is great today. It supports all kinds of advanced features such as stored procs, full-text search, JSON equivalent fields, etc.

Hi! I recently came across ArangoDb and used in some POCs, but I really want to know if someone here already used it in a Real World environment or even if chose to not use in a production environment. So... have you ever used ArangoDb? Why? Why not?

For mutual friends, we can build a social graph for every user. Each node in the graph will represent a user and a directional edge will represent followers and followees. After that, we can traverse the followers of a user to find and suggest a mutual friend. This would require a graph database such as Neo4j and ArangoDB.

ArangoDB

This a NOSQL database built for high availability and high scalability, a perfect fit for implementing persistence in microservices. ArangoDB is an open source native multi-model database that supports graph, document and key-value data models allowing users to freely combine all data models in a single query. Dive deeper into this database and its features here.

In the field of software development, efficiency and agility are always sought after. In the era of cloud-native apps, traditional deployment techniques—which are frequently laborious and prone to errors—are starting to become obstacles. This is when Kubernetes and GitOps come in handy.

Are you looking for a hassle-free way to deploy Moodle™ Learning Management Systems (LMS) on Kubernetes? Look no further! Krestomatio presents the LMS Moodle Operator, an open-source Kubernetes Operator designed to simplify the deployment and management of Moodle instances on Kubernetes clusters. Let's dive into what makes this tool a great choice for Moodle administrators and developers alike.

Securing access to your Kubernetes clusters is crucial as inadequate security measures can lead to unauthorized access and potential data breaches. However, navigating the complexities of Kubernetes access security, especially when setting up strong authentication, authorization, and network policies, can be challenging.

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.