dive VS dgraph

Dive is an open-source tool that allows you to explore the various layers of a Docker image. It shows you the content of each layer and helps you identify voluminous or unnecessary parts.

Dive is a great tool for debugging this. I like image reduction work just because it gives me a chance to play with Dive: https://github.com/wagoodman/dive

One easy low hanging fruit I see a LOT for ballooning image sizes is people including the kitchen sink SDK/CLI for their cloud provider (like AWS or GCP), when they really only need 1/100 of that. The full versions of both of these tools are several hundred mb each

This post is going to be shorter. I'd like to highlight a tool that I really enjoy working with called "Dive" It is an essential tool when working to build and optimize docker containers.

Whether you work with Docker regularly or even create your own Docker containers, Dive is a great tool for streamlining image sizes, potentially helping you save storage costs and speed up deployments.

Dgraph: A distributed and scalable graph database known for high performance. It's a good fit for large-scale graph processing, offering a GraphQL-like query language and gRPC API support.

Dgraph (GraphQL, DQL)

Have you tried dgraph.io?

DGraph – A distributed GraphQL database with a graph backend.

How does it compare to, say grakn (renamed https://vaticle.com/, I think?), or draph (https://dgraph.io/), or Ontotext's GraphDB (https://www.ontotext.com/products/graphdb/), or Datomic?

Consul Connect service mesh has a higher memory footprint, so on a small cluster with e5-medium nodes (2 vCPUs, 4 GB memory), you will only be able to support a maximum of 6 side-car proxies. In order to get an application like Dgraph working, which will have 6 nodes (3 Dgraph Alpha pods and 3 Dgraph Zero pods) for high availability along with at least one client, a larger footprint with more robust Kubernetes worker nodes were required.