Building Real-time Serverless Backends with GraphQL

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Heitor Lessa
Specialist Solutions Architect, Amazon Web Services
Building Real-time Serverless Backends
with GraphQL

Why do we need real-time and offline
capabilities in the first place?

Brief intro to Serverless

Build and run
applications
without thinking about
servers
… pay per request not
for idle
“ Scales with usage
Never pay for idle
High availability
built-in
No servers
to provision
or manage
A serverless world…

Lambda is for all application types
Analytics
Operational management
Live Dashboards
Data workflows
Content management
ETL workflows
Interactive Backends
Mobile and web apps
Webhooks and Bots
Autonomous IT
Policy engines
Infrastructure management

Building blocks for serverless applications
AWS Lambda
Amazon DynamoDB
Amazon SNS
Amazon API Gateway Amazon SQS Amazon Kinesis
Amazon S3
Orchestration and State Management
API Management and Real-time Backend Messaging and Queues Analytics
Monitoring and Debugging
Compute Storage Database
AWS X-RayAWS Step Functions Amazon Cognito
User Management and IdP
AWS AppSync Amazon Athena
AWS Lambda@Edge

Has anyone said GraphQL?

GraphQL APIs
Open, declarative data-fetching specification
!= Graph database
Use NoSQL, Relational, HTTP, etc.
Traditional data-fetching GraphQL
/posts
/postInfo
/postJustTitle
/postsByAuthor
/postNameStartsWithX
/commentsOnPost
/posts

GraphQL Spec - Schema
{
"id": "1",
"name": "Get Milk",
“priority": "1"
},
{
"id": “2",
"name": “Go to gym",
“priority": “5"
},…
type Query {
getTodos: [Todo]
}
type Todo {
id: ID!
name: String
description: String
priority: Int
duedate: String
}
query {
getTodos {
id
name
priority
}
}
Model data with
application schema
Client requests what it
needs
Only that data is
returned

GraphQL Spec - Types
Schema
type Event {
id: ID!
name: String
where: String
when: String
description: String
comments: [Comment]
}
type Comment {
commentId: String!
eventId: ID!
content: String!
createdAt: String!
}

GraphQL Spec - Mutation
Schema
Mutation
type Mutation {
createEvent(
name: String!,
when: String!,
where: String!,
description: String!
): Event
deleteEvent(id: ID!): Event
commentOnEvent(
eventId: ID!,
content: String!,
createdAt: String!
): Comment
}

GraphQL Spec - Query
Schema
Mutation
Query
type Query {
getEvent(id: ID!): Event
listEvents(
limit: Int,
nextToken: String
): EventConnection
}

GraphQL Spec - Subscription
Schema
Mutation
Query
Subscription type Subscription {
onComments(eventId: String!): Comment
@aws_subscribe(mutations: ["commentOnEvent"])
}

GraphQL Spec - Subscription
Schema
Mutation
Query
Subscription type Subscription {
onComments(eventId: String!): Comment
@aws_subscribe(mutations: ["commentOnEvent"])
}
type Mutation {
…
commentOnEvent(
eventId: ID!,
content: String!,
createdAt: String!
): Comment
}

GraphQL - Summary
Schema
Mutation
Query
Subscription
Realtime? YES
Batching? YES
Pagination? YES
Relations? YES
Aggregations? YES
Search? YES
Offline? YES

AWS AppSync – Intro

What is AWS AppSync?
AWS AppSync is a managed service for
application data using GraphQL with real-time
capabilities and an offline programming model.
Real-time
Collaboration
Offline Programming
Model with Sync
Flexible Database
Options
Fine-grained
Access Control

How does AWS AppSync work?

AWS AppSync and GraphQL Subscription
Near Realtime updates of data
Event based mode, triggered by Mutations
- Scalable model, designed as a platform for common use-cases
Can be used with ANY data source in AppSync
- Lambda, DynamoDB, Elasticsearch
mutation addPost(
id: 123
title: ”New post!”
author: ”Nadia”)
{
id
title
author
}
data: [{
id: 123,
title: ”New Post!”
author: ”Nadia”
}]
Websockets over MQTT

AWS AppSync – Data Sources, Auth and more
DynamoDB
Table
Lambda
Function Elasticsearch
Service
GraphQL
Schema
Upload
Schema
GraphQL
Query
Mutation
Subscription
Real-time
Offline
AppSync
API
Cognito
User Pool
Legacy
Application

How does AppSync connect to my data sources?

AWS AppSync – Debug Resolver Flow
Amazon CloudWatch logs

What about Offline access?

Offline patterns – Application Considerations
• Local Storage (R/W)
• Order of Operations
• Network State Management
• UI Updates
• Conflict Resolution

AppSync – Debug Resolver Flow
Jane
Version : 2 Updated Document
Jane
Version : 1 New Document
Time
John
John
Jane goes offline
Jane comes back online
John
Jane

AWS AppSync – Conflict Resolution
Conflict resolution in the cloud
1. Server wins
2. Silent reject
3. Custom logic (AWS Lambda)
- Optimistic version check
- Extend with your own checks
Optional
• Client callback for Conflict Resolution is still
available as a fallback
{
"version" : "2017-02-28",
"operation" : "PutItem",
"key" : {
"id" : { "S" : "1" }
},
"condition" : {
"expression" : "attribute_not_exists(id)"
}
}
Example: Check that an ID doesn’t already exist:
"condition" : {
"expression" : "someExpression"
"conditionalCheckFailedHandler" : {
"strategy" : "Custom",
"lambdaArn" : "arn:..."
}
}
Run Lambda if version wrong:

Offline best practices
• Mutations offline – what UIs actually need to be optimistic?
• Use Subscriptions appropriately
• Large payloads/paginated data: Queries
• Frequent updating deltas: Subscriptions
• Be kind to your customer’s battery & CPU!
• Don’t overcomplicate Conflict Resolution
• Data model appropriately, many app actions simply append to a list
• For custom cases, use a AWS Lambda and keep client logic light (race
conditions)

Resources

Additional Resources
aws.amazon.com/appsync/resources
aws-samples/
aws-mobile-appsync-chat-starter-angular
awslabs/
aws-appsync-codegen
dabit3/
vue-graphql-appsync

Demo

Speaker contact
Specialist Solutions Architect
Heitor Lessa
@heitor_lessa
lessa@amazon.com@

Please complete the session survey in the
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Building Real-time Serverless Backends with GraphQL

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