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okay, let's dive into building a serverless graphql api using jared short's approach, combining aws lambda, api gateway, and a database (i'll use dynamodb for this example, but you can adapt it). i'll provide a comprehensive walkthrough with code examples.

*what is the jared short serverless graphql approach?*

jared short, a prominent figure in the serverless and graphql communities, champions a "serverless first" approach to graphql apis. key principles include:

*focus on lambda functions:* all resolvers (functions that fetch data for graphql fields) are implemented as individual lambda functions. this granular approach leads to scalability, isolation, and optimal resource utilization.
*statelessness:* lambda functions should be stateless. all necessary data should be passed to them during invocation (via event context or database interaction), avoiding reliance on local or cached state.
*api gateway integration:* api gateway acts as a bridge between your graphql client requests and the lambda functions. it routes requests to the appropriate lambda based on the graphql query.
*database as a service:* using a managed database service (like dynamodb) decouples your api from infrastructure management.

*advantages of this approach:*

*scalability:* lambda functions scale automatically based on demand.
*cost efficiency:* pay only for the resources consumed during lambda execution.
*isolation:* each resolver operates in its own isolated environment.
*maintainability:* smaller, focused lambda functions are easier to test and maintain.
*flexibility:* easily add or modify resolvers without redeploying the entire api.
*faster development:* decoupling backend logic from the front-end allows for more rapid development cycles.

*project setup and prerequisites*

1. *aws account:* you'll need an aws account with the necessary permissions to create lambda functions, api gateways, and dynamodb tables.
2. ...

#Serverless #GraphQL #softwaredevelopment
Serverless
GraphQL
Jared Short
API
Cloud Functions
AWS Lambda
Data Fetching
Microservices
Event-Driven Architecture
Scalability
Backend-as-a-Service
Node.js
Performance Optimization
Real-time Data
Development Best Practices