Continuous Data Syncs

In this guide, we will sync issues from Github as tickets. It entails:

1. Adding a folder in your repo where your Integration code will live
2. Defining a ticket data model & configuring the sync
3. Writing minimal code to fetch issues and transform them into tickets

​

1. Create your integrations folder

Regardless of your programming language, in your repo, run:

npx nango init && cd nango-integrations # Your new integrations folder

​

2. Configure your Integration

Open the nango.yaml config file and define a new github-tickets sync and ticket model:

integrations:
	github-tickets:
		provider: github
		runs: 'every hour'
		returns:
			- ticket
models:
	ticket:
		id: uuid
		author: string
		author_id: uuid
		...

Then run:

npx nango g # Generates the model & scaffolding

​

3. Write your Integration code

Open the github-tickets.ts which contains the following template:

import { NangoSync, NangoHelper } from '@nangohq/node';
import { ticket } from '@nangohq/models';

class GitHubTicketSync extends NangoSync {

async function fetchData(nango: NangoHelper): { [ticket.name]: [ticket.type] } {
	// Integration code goes here.
}

export GitHubSync;

Fill in the fetchData method with your integration code:

// API requests: handles auth, retries, rate-limiting, pagination and more.
const ghIssues = nango.get('/v1/issues', paginated=true);

// Mapping Github issues to tickets.
const tickets = ghIssues.map(issue => {
    ticket.field.id: issue.unique_id,
    ticket.field.author: issue.username,
    ticket.field.author_id: issue.username_id,
    ...
});

// Return strongly-typed objects.
return { [ticket.name]: tickets }

Finally, deploy your code to Nango:

npx nango deploy

That’s it! When a user authorizes Github with Nango Auth, their issues will sync continuously! Nango provides:

• An SDK & API to access objects (strongly-typed)
• A webhook to get real-time updates (strongly-typed)
• A cache with fresh & highly-available data
• Built-in tooling for easy API requests (pagination, retries, rate-limit handling, transient errors etc.)
• Monitoring & Management console
• Automatic scaling & reliability