Gerard Sans | Axiom 🇬🇧 PRO
Founder of Axiom Masterclass, professional trainings // Forging skills for the new era of AI. GDE in AI, Cloud & Angular. Building London's tech & art nexus @nextai_london. Speaker | MC | Trainer.
by Gerard Sans | @gerardsans
Finding a
bike in London with AWS Amplify
Finding a
bike in London with AWS Amplify
Developer Advocate AWS
Developer Advocate AWS
Benefits
- Transport flexibility
- Reduce traffic emissions
- Reduce traffic congestion
- Health benefits for users
International Speaker
Spoken at 149 events in 37 countries
Bike Sharing
Santander Cycles
Overview
- Since 2010
- +12,000 Bikes
- 778 stations
Boris Bikes
LNDBikes
Fullstack Serverless
🦄
🌩️
No servers to manage
Fault tolerance High availability
Never pay for idle usage
Auto-scales immediately
Serverless
$
AWS AMPLIFY
Categories
interactions
storage
notifications
auth
analytics
function
amplify add <category>
api
hosting
xr
transcribe
rekognition
translate
comprehend
amplify add predictions
polly
London
Unified API
/BikePoint
/BikePoint/id
TfL Unified API
/BikePoint/Search
Data Strategies
slow 3G
fast 3G
[
{
"id": "BikePoints_1",
"commonName": "River Street , Clerkenwell",
"additionalProperties": [{
"key": "NbBikes", "value": "11",
}],
"lat": 51.529163,
"lon": -0.10997
}
// 777 more
]/BikePoint
{
"id": "BikePoints_1",
"commonName": "River Street , Clerkenwell",
"additionalProperties": [{
"key": "NbBikes", "value": "11",
}],
"lat": 51.529163,
"lon": -0.10997
}/BikePoint/BikePoints_1
Loading bike stations
Data Transformations
GeoJSONfeature
BikesPoint
{
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [-0.10997, 51.529163]
},
"properties": {
"id": "BikePoints_1",
"name": "River Street , Clerkenwell"
}
}geoJSON feature
Coordinates = [-0.109971, 51.529163]
51.529163
-0.109971
Coordinates
Data Transformations
GeoJSONfeature
BikesPoint
mapbox Source
mapbox Layer
REST API integration
type BikePoint @model {
id: ID!
name: String!
description: String
location: Location!
bikes: Int
}GraphQL Schema
// request VTL template
{
"version": "2018-05-29",
"method": "GET",
"resourcePath": "/BikePoint/$context.source.id",
}BikePoint.bikes HTTP Resolver
// response VTL template
#set($body = $util.parseJson($ctx.result.body))
#if($ctx.error)
$util.error($ctx.error.message, $ctx.error.type)
#end
#if($ctx.result.statusCode == 200)
$body.additionalProperties[6].value
#else
#return
#endBikePoint.bikes HTTP Resolver
Adding Search
type BikePoint @model @searchable {
id: ID!
name: String!
description: String
location: Location!
bikes: Int
}
type Location {
lat: Float!
lon: Float!
}
type Query {
nearbyBikeStations(location: LocationInput!, m: Int, limit: Int)
}GraphQL Schema
Data Transformations
GeoJSONfeature
BikesPoint
GraphQL API
Elastic Search
# Create index
PUT /bikepoint
# Setup location type as geo_point
PUT /bikepoint/_mapping/doc
{
"properties": {
"location": {
"type": "geo_point"
}
}
}Elastic Search index
mutation addBikePoint {
createBikePoint(input: {
id: "BikePoints_1"
name: "River Street , Clerkenwell"
location: {
lat: 51.529163
lon: -0.10997
}
}) { id }
}Automatic indexing
GET /bikepoint/doc/_search
{
"query": {
"bool" : {
"must" : { "match_all" : {} },
"filter" : {
"geo_distance" : {
"distance" : "500m",
"distance_type": "arc",
"location" : {
"lon": -0.134167, "lat": 51.510239
}Query nearbyBikeStations (1/2)
"sort": [{
"_geo_distance": {
"location": {
"lon": -0.134167, "lat": 51.510239
},
"order": "asc",
"unit": "m",
"distance_type": "arc"
}
}]
}Query nearbyBikeStations (2/2)
Distance Calculations
[lon1, lat1]
[lon2, lat2]
Haversine Formula
Great-circle distance
Distance Calculations
double distance(double lat1, double lon1, double lat2, double lon2) {
return 6378137 * haversine(lat1, lon1, lat2, lon2);
}
double haversine(double lat1, double lon1, double lat2, double lon2) {
double hsinX = Math.sin((lon1 - lon2) * 0.5);
double hsinY = Math.sin((lat1 - lat2) * 0.5);
double h = hsinY * hsinY + (Math.cos(lat1) * Math.cos(lat2) * hsinX * hsinX);
return 2 * Math.atan2(Math.sqrt(h), Math.sqrt(1 - h));
}ElasticSearch arc distance (WGS 84)
More
@undef_obj
@kurtiskemple
@dabit3
Kurt Kemple
Richardo
Nader Dabit
@TheSwaminator
Nikhil Swaminathan
Finding a bike in London with AWS Amplify
By Gerard Sans | Axiom 🇬🇧
Finding a bike in London with AWS Amplify
Are you visiting London and want a bike to move around? Are you with some friends? No worries. In this talk, we are going to build LNDBikes an app to find in real-time how many bikes are available so you and your friends are good to go and enjoy a great ride. We will be using the Transport for London Unified API to query the data and show it in a map using mapbox GL JS. There are more than 750 docking stations across London with 12K bikes. We will build our client using the latest versions of AWS Amplify, GraphQL and Angular. Awesome!
