Learn how to optimize data fetching in a GraphQL server for queries, mutations and subscriptions, by using data loaders. - graphql softwarearchitecture
This would mean 1 query for N posts and N queries for N authors → ergo, N+1 problem. Ideally, instead of Nquery Then, each resolver could take the author that it needs, by id. Batching queries like this is done differently by different programming languages, and this pattern usually has names like data loader or batch loader.
Other than batching I/O operations, data loaders can and usually do cache the fetched data. This way it can be retrieved from cache if it is needed again, in the lifetime of the data loader instance. Optionally, the cache can be turned off, in which case the data loader will perform only batching. The way to achieve shared data loaders is for the event payload to contain a unique id. Then, each subscriber can use this id to get a specific bag of loaders from a hash map of bags. This bag would have to contain only the common, shared loaders that can be used for every user.
The simplest way to free the memory is to disable the cache option for the data loaders. The queries will be batched, but as soon as the data is distributed to the resolvers, it is cleared from the data loader. The main downside is a potential loss of performance. If the same data is requested again in the lifetime of the data loader instance it would have to be refetched again by the data loader.
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