go-memdb VS dgraph

For another in-memory database example, you could also check out https://github.com/hashicorp/go-memdb, but it's based on a radix tree implementation.

I just think its a glorified map. Yeah, for example go-memdb list some of its advantages in their https://github.com/hashicorp/go-memdb/blob/master/README.md README. But it is not clear whether you gain any performance gain over a map except for avoiding the locking as explained.

>So we invested what probably amounts to two or three weeks of engineering time into designing in-memory indexes that are transactionally consistent

I'll be interested to see that part if it does get open sourced. I've used https://github.com/hashicorp/go-memdb which sounds similar.

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.