Dataloader

How Dataloaders are Used and Their Benefits

Based on PR #2716, dataloaders were introduced to optimize GraphQL performance by batching and caching requests. This document provides an in-depth explanation of their implementation, usage, and advantages.

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What is a DataLoader?

A DataLoader is a utility for batching and caching requests in a GraphQL environment. It allows efficient resolution of GraphQL queries by queuing up multiple requests during execution and batching them into a single query to the database or API.

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Implementation of Dataloaders

1. Defining the DataLoader Type:

   • A specific ITeamsDataLoader type is created to handle team entities.
   • This type is derived from a generic IDataLoader interface for flexibility and reuse.

   export type ITeamsDataLoader = IDataLoader<ITeam>;

2. Integrating Dataloaders in Service Interfaces:

   • To ensure seamless access throughout the application, the teamsDataLoader is added to the IServices interface.
   • This allows dependency injection and promotes consistency.

   export interface IServices {
       teamService: ITeamService;
       accountUserDataLoader: IAccountUserDataLoader;
       teamsDataLoader: ITeamsDataLoader;
   }

3. Creating the DataLoader Instance:

   • The DataLoader instance is initialized to handle batching and caching logic.
   • Below is an example of how a DataLoader might be initialized and how batched results are mapped back to GraphQL child resolvers:

   import DataLoader from 'dataloader';
   import { getTeamsByIds } from './teamRepository';
   
   export const createTeamsDataLoader = (): ITeamsDataLoader => {
       return new DataLoader<string, ITeam>(async (teamIds) => {
           // Fetch all teams in a single batch based on provided IDs
           const teams = await getTeamsByIds(teamIds);
   
           // Map the fetched teams back to their corresponding IDs
           return teamIds.map((id) => teams.find((team) => team.id === id) || new Error(`Team not found: ${id}`));
       });
   };

   Explanation of Mapping:

   • Input: A list of teamIds queued by the DataLoader (e.g., ['team1', 'team2', 'team3']).
   • Process: Fetch the data for all teamIds in a single query (e.g., getTeamsByIds(teamIds)).
   • Mapping Back:
     • Use the input teamIds to order the results so they correspond to the original request order.
     • If a team is missing, return an error or handle it gracefully.

   This ensures that the batched result aligns with the request order, even if the database query returns the results in a different sequence.

4. Using the DataLoader in GraphQL Resolvers:

   • Resolvers can now delegate data fetching to the DataLoader, ensuring efficient data retrieval and proper mapping back to the GraphQL schema.

   const resolvers = {
       Query: {
           team: async (_: any, { id }: { id: string }, { services }: { services: IServices }) => {
               return services.teamsDataLoader.load(id); // Delegates to the DataLoader
           },
       },
       User: {
           teams: async (parent: IUser, _: any, { services }: { services: IServices }) => {
               // Batch fetch all team IDs associated with the user
               return services.teamsDataLoader.loadMany(parent.teamIds);
           },
       },
   };

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Benefits of Using Dataloaders

1. Batching Requests:

   • Groups multiple identical or related queries into a single request.
   • Reduces the overhead caused by multiple queries, improving efficiency.

   Example: Without DataLoader:

   Query: getTeam(1)
   Query: getTeam(2)
   Query: getTeam(3)

   With DataLoader:

   Single Query: getTeams([1, 2, 3])

2. Caching:

   • Automatically caches the results of requests made during the same execution cycle.
   • Prevents fetching the same data more than once, reducing redundant calls.

   Example:

   First Request: getTeam(1) -> Fetch from database
   Second Request: getTeam(1) -> Return from cache

3. Improved Performance:

   • Fewer queries mean reduced latency and lower server/database load.
   • Scales better as the number of concurrent requests increases.

4. Solves the N+1 Problem:

   • In GraphQL, resolvers often make repetitive requests (N+1 Problem). DataLoader batches these queries, solving this inefficiency.

   Example:

   Query: Get all teams for users
   Team Resolvers: Fetch team for each user -> N queries
   With DataLoader: Fetch all teams in one query

5. Mapping Ensures Correctness:

   • By maintaining a consistent mapping of inputs to outputs, DataLoader guarantees that each GraphQL child resolver receives the data it requested, even if some IDs are missing.

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Key Steps to Map Batched Results Back to GraphQL Children

1. Use the Input IDs for Consistent Ordering:

   • The input IDs passed to the DataLoader (teamIds) must dictate the order of the output array.

2. Handle Missing Data Gracefully:

   • If the requested ID is not found in the fetched data, return an error or null to the corresponding resolver.
   • Example:

     teamIds.map((id) => teams.find((team) => team.id === id) || new Error(`Team not found: ${id}`));

3. Leverage loadMany for Collections:

   • When fetching collections of data (e.g., all teams for a user), use loadMany to handle multiple IDs at once.
   • Example:

     const teamData = await services.teamsDataLoader.loadMany(parent.teamIds);

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Example Usage in GraphQL Context

Here’s how DataLoader optimizes GraphQL child resolvers:

query {
    user(id: "123") {
        id
        name
        teams {
            id
            name
        }
    }
}

• The User resolver fetches teamIds for the user.
• The teams field uses loadMany to batch all teamIds into a single request.
• DataLoader maps the results back to each team field for the user in the correct order.

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Changes Required to Add DataLoader to Existing Code

Steps to Integrate DataLoader

1. Create the DataLoader File:

   • Create a file named teams-dataloader.ts for defining the DataLoader logic.
   • This file will contain the initialization and batching logic for the TeamsDataLoader.

2. Modify types.ts to Add DataLoader Type:

   • Update the TYPES constant to include a new type for TeamsDataLoader.

3. Update Service Interfaces:

   • Add the TeamsDataLoader to the IServices interface, ensuring it can be accessed across the application.

4. Implement DataLoader in Team Service:

   • Use the TeamsDataLoader in the ITeamService implementation for efficient data fetching.

5. Inject DataLoader in Dependency Container:

   • Register the TeamsDataLoader in your dependency injection container, ensuring it is initialized and injected where required.

Files Modified for DataLoader Implementation

Here is a list of files modified for integrating DataLoader, along with a new file for the TeamsDataLoader.

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New File: teams-dataloader.ts

Here you can use the BulkDataLoader to simplify the boilerplate code.

import { injectable, inject } from 'inversify';
import { BulkDataLoader } from '@common-stack/store-mongo';
import { ITeamService, TYPES } from '@adminide-stack/account-api-core';
import { IAccountTeam as ITeam } from '@adminide-stack/core';

@injectable()
export class TeamsDataLoader extends BulkDataLoader<ITeam & { id: string }> {
    constructor(
        @inject(TYPES.ITeamService)
        teamService: ITeamService,
    ) {
        super(teamService as never); // Pass the teamService to the BulkDataLoader utility
    }
}

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Modified File: types.ts

 export const TYPES = {
     IOrganizationRepository: Symbol('IOrganizationRepository'),
     IOrganizationMicroservice: Symbol('IOrganizationMicroservice'),
     AccountUserDataLoader: Symbol('AccountUserDataLoader'),
+    TeamsDataLoader: Symbol('TeamsDataLoader'),
 };

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Modified File: services.ts

 import { IPreferencesService } from '@adminide-stack/core';
 import { IOrganizationService } from './organization-service';
 import { IAccountService } from './account-service';
 import { ITeamsDataLoader, ITeamService } from './team-service';
 import { IAccountUserDataLoader } from './account-user-data-loader';
 import { ICountryService } from '@container-stack/territory';
 
 export interface IServices {
     teamService: ITeamService;
     preferenceService?: IPreferencesService;
     countryService?: ICountryService;
     accountUserDataLoader: IAccountUserDataLoader;
+    teamsDataLoader: ITeamsDataLoader;
 }

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Modified File: team-service.ts

 import { IBaseService, IDataLoader } from '@common-stack/store-mongo';

 export type ITeamsDataLoader = IDataLoader<ITeam>;

 export interface ITeamService extends IBaseService<ITeam> {
     getTeam(id: string): Promise<ITeam>;

     getTeamByName(orgName: string, teamName: string): Promise<ITeam>;
 }

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Modified File: Dependency Injection Container

Ensure the TeamsDataLoader is registered in your DI container:

container.bind<IDataLoader<ITeam>>(TYPES.TeamsDataLoader).toDynamicValue(() => createTeamsDataLoader());

For more details on how to use generic dataloader you can read Generic Dataloader from common-stac

Guidelines for Developers

1. Create a DataLoader for Each Entity:

   • Define a specific DataLoader type for each data entity (e.g., ITeamsDataLoader, IUsersDataLoader).

2. Ensure Proper Mapping in Batch Functions:

   • Always map the batched results back to the original input keys.

3. Use Dependency Injection:

   • Pass DataLoader instances via service interfaces (IServices) to ensure easy access across the application.

4. Gracefully Handle Errors:

   • Ensure missing or invalid IDs are handled correctly without breaking the execution.

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For more details on implementation, review the files changed in PR #2716.

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