pREST VS dgraph

In a previous post, I published an example with PostgREST on YugabyteDB. Here is another one: pREST opens a REST API to PostgreSQL. YugabyteDB is a PostgreSQL-compatible Open-Source Distributed SQL database. It adds horizontal scalability to applications built for PostgreSQL. Let's see how it integrates with pREST.

Pretty sure I started with this: https://github.com/prest/prest/blob/main/cmd/root.go

And from there you can execute your own command and add handlers or other things as you wish.

With pRest, it is possible to create a RESTFul API to access the contents of a Postgres database in a fast and straightforward way. The project, written in Go, can be found on its official website and Github.

So this is something I've been pondering about for a while and I think I've settled to using Directus: https://directus.io/ as my db admin and dbmate for migrations. Alternatively you could use react admin: https://marmelab.com/react-admin/ . The only problem with the latter is that it requires you to build your rest endpoint for CRUD but you can use postgrest: https://postgrest.org/en/stable/ (or it's go alternative prest: https://prestd.com/) for that.

prest / prest

There is PostgREST written in Haskell, but keeping Haskell software in production is not an easy job. With this need pREST was born. Read more.

This feature was implemented in version 1.0.5 as a search filter and works like this:

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)

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.

dgraph - Scalable, Distributed, Low Latency, High Throughput Graph Database.

Now let’s talk about saving questions and answers. The Trie data structure is a great fit for quickly identifying if a question exists in the database and then finding its answer. To store tree nodes and links between them, I used the graph database Dgraph. For this project, I created a free cloud repository on dgraph.io. A TrieNode looks like this:

4. Dgraph