Serverless / On the Edge

Mesh Serve can be deployed on the edge. This means that you can deploy your Mesh Serve to a serverless environment like AWS Lambda, Cloudflare Workers, or Azure Functions.

For Serverless environments, you cannot use Serve CLI mesh-serve but you can use the createServeRuntime function from @graphql-mesh/serve-runtime package.

The serve configuration goes into createServeRuntime function instead of serveConfig export in mesh.config.ts file.

Distributed Caching

But you need to be aware of the limitations of these environments. For example, in-memory caching is not possible in these environments. So you have to setup a distributed cache like Redis or Memcached.

See here to configure cache storage.

Bundling problem

Mesh Serve cannot import the required dependencies manually, and load the supergraph from the file system. So if you are not using a schema registry such as GraphQL Hive or Apollo GraphOS, we need to save the supergraph as a code file (supergraph.js or supergraph.ts) and import it.

Loading the supergraph from a file

For example, in Mesh Compose you need to save the supergraph as a TypeScript file:

import { defineConfig } from '@graphql-mesh/compose-cli'
 
export const composeConfig = defineConfig({
  output: 'supergraph.ts',
  subgraph: [
    //...
  ]
})

In supergraph.ts file, you need to export the supergraph:

export default /* GraphQL */ `
  #...
`

Then you need to import the supergraph in your serverless function:

import { createServeRuntime } from '@graphql-mesh/serve-runtime'
// Let's say you are using REST transport
import rest from '@graphql-mesh/transport-rest'
import supergraph from './supergraph.js'
 
const serveRuntime = createServeRuntime({
  supergraph,
  transports: {
    rest
  }
})