graphql-code-generator VS dgraph

The @graphql-codegen library is a bridge between your GraphQL schema and the actual code you write. It automates the generation of boilerplate code, ensuring that your data fetching layer is always up to date with your content structure.

In typescript and typescript-resolvers plugin v4.0.1, we have reverted the default ID Scalar input type to string. Read the pull request for more details.

See https://github.com/dotansimha/graphql-code-generator with @graphql-codegen/typescript-graphql-request plugin

Graphql Codegen is a code generation library for GraphQL that enables developers to generate custom code. It provides us developers with the ability to generate type definitions, query builders, documentation, and more by analyzing our GraphQL schemas. This makes it easier and faster to build GraphQL applications and reduces the time spent coding.

It is already incoming, with an example in the repository. 😇 https://github.com/dotansimha/graphql-code-generator/pull/8863

The actual package is @ graphql-codegen/cli

GraphQL Codegen generates frontend types and React hooks from our GraphQL request definitions.

There are two main approaches to keeping the types of the GraphQL schema and entities in business logic in sync. You can generate the schema based on your TypeScript code (e.g. TypeGraphQL), or you can generate types based on your schema (e.g. GraphQL Code Generator). We opted for the latter since it slotted right into our existing GraphQL server implementation using Apollo Server.

For querying in React make sure you use GraphQL code generator https://www.the-guild.dev/graphql/codegen

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.