7 Strategies for Minimizing Latency in Your AWS AppSync API

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AWS AppSync is a serverless GraphQL service that simplifies the creation of unified endpoint GraphQL and real-time APIs. By allowing the integration of diverse data sources, AppSync delivers results to applications in the desired format as defined by your API schema. As your application evolves, there are strategies available to enhance your API’s performance and reduce latency. Below are 7 approaches to optimize your AppSync API.

1. Utilize Lookaheads

   A primary advantage of GraphQL is its flexibility in resolving data at the field level. For instance, consider the following schema:

   type Query {
      getProduct: Product
   }
   
   type Product {
      id: ID!
      name: String!
      productDetails: ProductDetails
   }
   
   type ProductDetails {
       quantityRemaining: Int!
   }
   

   In this example, the id and name of a product may come from a Product database, while the ProductDetails could originate from an Inventory service managed by a different team. This way, applications can selectively request only the data they need, reducing latency. For instance, a simple product list might only need names, while an eCommerce application could require stock quantities.

2. Batching with DynamoDB

   AppSync integrates seamlessly with Amazon DynamoDB, enabling operations across multiple tables. By employing DynamoDB batching, you can minimize latency by accessing several tables simultaneously. This feature allows you to:

   • Query multiple keys in a single request and return results from a table
   • Retrieve records from various tables in one query
   • Write multiple records to one or more tables in bulk
   • Conditionally write or delete records across related tables
3. Lambda Batching

   Consider this AppSync schema:

   type Ticket @model {
     id: ID!
     title: String!
     product: String!
       @index(name: "ByProductAndStatus", sortKeyFields: ["status"], queryField: "listTicketsByProdAndStatus")
     description: String
     requesterId: String!
     requester: User!
     assigneeId: String
     assignee: User
     status: TICKET_STATE!
   }
   
   type User {
     login: ID!
     name: String!
     manager: User
     location: String!
     tel: String!
     department: String
   }
   

   In this case, we use Amplify’s automatic code generation to create a Global Secondary Index (GSI) on our Ticket model for querying tickets by product name and status. However, if each USER for the requester and assignee fields is fetched from a Lambda function querying a MySQL database, we can avoid an N + 1 problem. Configure the Lambda with a batch size to execute once and return an array of results in the order of the Product data resolved. For further details, check out this blog post here.

4. Condition Check Failures

   Regarding AppSync’s interaction with DynamoDB, condition check failures can be managed effectively. When performing a mutation for a specific item, it maps to a PutItem, UpdateItem, or DeleteItem operation in DynamoDB. By specifying a Condition that must be fulfilled for the operation to succeed, you can invoke a Lambda function if these conditions are not met, allowing for better control of the process.

   {
      "version" : "2017-02-28",
      "operation" : "PutItem",
      "key" : {
         "id" : { "S" : "1" }
      },
      "condition" : {
         "expression" : "attribute_not_exists(id)",
         "conditionalCheckFailedHandler" :{ 
           "strategy" : 
               "Custom", 
               "lambdaArn" : "arn:..."
           }
      }
   }
   

   Here, the JSON snippet illustrates a DynamoDB resolver that only succeeds in adding a new record if the item’s id does not already exist. The conditionalCheckFailedHandler key permits a custom strategy, enabling you to specify a Lambda ARN for decision-making on whether to fail or succeed.

5. Implement Local Resolvers

   Front-end applications often do not need to change data but require it to be passed on to another service. Instead of updating a counter or insignificant attribute, which can introduce latency based on network requests, local resolvers facilitate the direct passing of data from the request mapping template to the response mapping template. The advantage here is that subscribed clients are still notified of incoming data. This mechanism enhances AppSync’s Pub/Sub APIs, enabling subscriptions as an independent feature, improving latency without incurring storage costs.

6. Enhance Caching and Compression

   Modern applications benefit from predictable queries and workflows through caching. With caching enabled, incoming requests are first checked against a cache; if the request has been made prior to expiration, the data is served from there. Additionally, enabling compression allows payloads between 1,000 to 10,000,000 bytes to be formatted as gzip or br, provided the client includes the correct Accept-Encoding header. For more information on caching and compression, refer to this authority on the topic.

7. Leverage AWS X-Ray

   With numerous strategies to reduce latency, it can be challenging to prioritize which methods to implement. Thus, the final recommendation is to enable AWS X-Ray tracing for your AppSync API. This feature provides visibility into the request flow as it processes through your API, offering insights similar to the network tab in browsers, revealing the “hops” a request makes before a response is returned.

Conclusion

In this article, we explored various strategies to optimize your AppSync API for reduced latency. Utilizing Lambda functions with batching configured effectively decreases costs as well. While there are many optimization opportunities available, prioritizing latency reduction can significantly enhance customer retention and overall user experience. For more insights, visit the AWS GraphQL page to learn about incorporating AppSync into your applications. Moreover, you might find this resource on Amazon’s employee training approach particularly insightful here.