Serverless / On the Edge

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

For Serverless environments, you cannot use Gateway CLI hive-gateway but you can use the createGatewayRuntime function from @graphql-hive/gateway package.

The gateway configuration goes into createGatewayRuntime function instead of gatewayConfig export in gateway.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

Hive Gateway 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 Hive Gateway 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 GraphQL Mesh you need to save the supergraph as a TypeScript file:

gateway.config.ts

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 { createGatewayRuntime, WSTransport } from '@graphql-hive/gateway'
// Let's say you are using WS transport
import supergraph from './supergraph.js'
 
const serveRuntime = createGatewayRuntime({
  supergraph,
  transports: {
    ws: WSTransport
  }
})

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