EmpFriendly

EmpFriendly [Redis Hackathon on DEV 2022] - Support your employees and strive to be better

This project will show the overview of microservices architecture that design base on Cloud-Native approach (actually, some parts still missing because need to compete with the times), In this regards, I design to show how we can implement it from scratch including RESTful/GraphQL API, Aggregation Pattern, Asynchronous messaging with Pub/Sub and Streams.

Postman Example

Kubernetes deployment

Location Streaming screenshots

Streaming Example in Redisinsights

Overview video

Here's a short video that explains the project and how it uses Redis:

Demo/Walkthrough video

Here's a video that shows how api work and explain implementation in some technical:

How it works

How Architecture look like?

More Informations are inside the architecture directory

Scenario 1 - Admin level (who can manage on this role) manipulate data

Scenario 2 - Pay IC Points

Scenario 3 - Give rating to restaurant/driver

Scenario 4 - Driver accepted Deliver/Journey => Start collect location of drivers

How the data is stored:

Use Redis OM Spring and Redis OM Python as base libraries to work on Below is JSON format of each document related

Employee

{
  "id": "d37b2d0b-c06d-429c-b56d-7465c3959993",
  "firstName": "Thanaphoom",
  "lastName": "Babparn",
  "age": 25,
  "email": "thanaphoom.babparn@empfriendly.dev",
  "addressLoc": "100.7433723,14.0364895",
  "address": {
    "houseNumber": "109/1070",
    "city": "Thanyaburi",
    "state": "Pathum Thani",
    "postalCode": "12110",
    "country": "Thailand"
  },
  "tags": [
    "SOFTWARE_ENGINEER",
    "DEVOPS",
    "CLOUD_ENGINEER",
    "BACKEND_DEVELOPER"
  ],
  "type": "ORDINARY",
  "createdDate": 1660784666190
}

JourneyOrder

{
  "id": "jrny01GAZY3KGMG8RRACYD81EH4EPK",
  "status": "WAITING",
  "requesterId": "d37b2d0b-c06d-429c-b56d-7465c3959993",
  "driverId": "8985954f-7925-42c9-a1e5-e6b4bad5fd6c",
  "createdDate": 1661076623113
}

DeliverOrder

{
  "id": "jrny01GAZY3KGMG8RRACYD81EH4EPK",
  "status": "WAITING",
  "requesterId": "d37b2d0b-c06d-429c-b56d-7465c3959993",
  "driverId": "8985954f-7925-42c9-a1e5-e6b4bad5fd6c",
  "createdDate": 1661076623113
}

OrderStatusTracking

{
  "id": "01GAZY48D16FQ1ZKZJKQRKN4E7",
  "orderType": "DELIVER",
  "status": "WAITING",
  "orderId": "dlv01GAZY4898A4M205Q0QWBVR9YS",
  "employeeId": "d37b2d0b-c06d-429c-b56d-7465c3959993",
  "createdDate": 1661076644324
}
{
  "id": "01GAZYVBDWTTBBJBHE2G1A003A",
  "orderType": "JOURNEY",
  "orderId": "jrny01GAZYJHHRX0SXT08BDJTYD0KY",
  "employeeId": "8985954f-7925-42c9-a1e5-e6b4bad5fd6c",
  "status": "ACCEPTED",
  "createdDate": "2022-08-21T17:23:21.087"
}

Location

{
  "id": "01GAZZWNPFBY97HMNK5KM7G8HH",
  "referenceId": "dlv01GAZYJKRS640VGQVTBCYEQXD3",
  "driverId": "8985954f-7925-42c9-a1e5-e6b4bad5fd6c",
  "timestamp": 1661078492839,
  "geoPoint": "12.645436856205208,69.10387306037327"
}

Tracking

{
  "id": "dlv01GAZYJKRS640VGQVTBCYEQXD3",
  "requesterId": "d37b2d0b-c06d-429c-b56d-7465c3959993",
  "driverId": "8985954f-7925-42c9-a1e5-e6b4bad5fd6c",
  "type": "DELIVER"
}

Point

{
  "referenceId":"d37b2d0b-c06d-429c-b56d-7465c3959993",
  "current": 500100,
  "type": "INDIVIDUAL"
}

Payment

{
  "id": "01GAZDMREPWATKPJTB2N4M1CZ7",
  "from": "d37b2d0b-c06d-429c-b56d-7465c3959993",
  "to": "c820da4c-d8de-4229-848b-33e5f183a22c",
  "point": 120.0,
  "method": "QR_CODE_SCAN",
  "type": "INDIVIDUAL_DEBIT",
  "createdDate": 1661059359334
}

PointChangeHistory

{
  "id": "01GAZDMSPKNQ9G3TQ298SM3D22",
  "referenceId": "d37b2d0b-c06d-429c-b56d-7465c3959993",
  "point": -120.0,
  "balancePoint": 500220.0,
  "createdDate": 1661059360527
}

==== SAME TRANSACTION ====

{
  "id": "01GAZDMSQ1QW6QVX8CRH8FJ4B0",
  "referenceId": "c820da4c-d8de-4229-848b-33e5f183a22c",
  "point": 120.0,
  "balancePoint": 220.0,
  "createdDate": 1661059360580
}

RatingHistory

{
  "pk": "01GB2WZ8QKQTMRG8422SH59H60",
  "user_id": "d37b2d0b-c06d-429c-b56d-7465c3959993",
  "target_id": "a3cbc4f1-3636-4db9-bb42-36c49ba655b9",
  "type": "RESTAURANT",
  "rate": 4,
  "timestamp": 1661176088890
}

Restaurant

{
  "pk": "01GAZ0HSPWKWA60J6CMQ11892G",
  "restaurant_id": "a3cbc4f1-3636-4db9-bb42-36c49ba655b9",
  "name": "SHOP-1"
}

Restaurant QR Code

{
  "pk": "01GAZG9PHX6Q119B8EDXFQRJK5",
  "restaurant_id": "d31710fc-ba84-4ef9-ae2e-3c8e38e1c84c",
  "status": "active"
}

How the data is accessed:

Use Redis OM Spring and Redis OM Python as base libraries to work on

Python Example - QR service

Use JsonModel to perform operation

import datetime

from redis_om import (Field, JsonModel)

class QRCode(JsonModel):
    payment_id: str = Field(index=True)
    status: str = Field(index=True)
    created_date: datetime.datetime

class RestaurantQRCode(JsonModel):
    restaurant_id: str = Field(index=True)
    status: str = Field(index=True)

Java/Kotlin Example - EmployeeRepository

package dev.tpcoder.empfriendly.employee.repository;

import com.redis.om.spring.repository.RedisDocumentRepository;
import dev.tpcoder.empfriendly.employee.model.Employee;
import java.util.Set;
import org.springframework.data.geo.Distance;
import org.springframework.data.geo.Point;
import org.springframework.stereotype.Repository;

@Repository
public interface EmployeeRepository extends RedisDocumentRepository<Employee, String> {

  Iterable<Employee> findByAddressLocNear(Point point, Distance distance);

  Iterable<Employee> findByFirstNameAndLastName(String firstName, String lastName);

  Iterable<Employee> findByAddress_City(String city);

  Iterable<Employee> findByTags(Set<String> skills);

  Iterable<Employee> findByTagsContainingAll(Set<String> skills);

  Iterable<Employee> findByType(String employeeType);

  Iterable<Employee> search(String text);
}

How to run it locally?

Prerequisites

Including development and deployments

Java 17 Option1 - sdkman | Option2 - Microsoft-OpenJDK
Python 3.10.4
Docker
Kubernetes Cluster Option1 - Kind | Option2 - minikube
kubectl
Redis Cloud
Postman

Local installation

For deployment purpose only

Install Kubernetes Cluster of choices
Create Database in Redis Cloud
Follow "How to run it locally?" instructions

Transform Redis Cloud URL to base64

Note: the redis url format will be redis://{USERNAME}:{PASSWORD}@{REDIS_HOST_URL}:{REDIS_PORT}

echo 'redis://{USERNAME}:{PASSWORD}@{REDIS_HOST_URL}:{REDIS_PORT}' | base64

Replace your REDIS URL in redis-sc.yaml

After you got Base64 txt data, replace it to redis-sc.yaml

REDIS_OM_URL: <YOUR_REDIS_URL in base64>
to
REDIS_OM_URL: BASE64_RESULT

Add Stream Group in RedisInsight

XGROUP CREATE location-stream-event location-stream-event $ MKSTREAM

Start Microservices with Kubernetes Cluster

kubectl create -f ./k8s
cd k8s
kubectl create -f ./microservice

or

kubectl apply -f ./k8s
cd k8s
kubectl apply -f ./microservice

Port Forward Service (Required 3 Terminals)

kubectl -n emp-friendly port-forward service/emp-friendly-general 9000:50200
kubectl -n emp-friendly port-forward service/emp-friendly-driver 9001:50201
kubectl -n emp-friendly port-forward service/emp-friendly-management 9002:50202

Calling the API with Postman

Import Postman Collection: RedisHackathonDev2022.postman_collection.json to your postman (or use cURL)

Including Aggregator Collection - General, Driver, Management related

Deployment

To make deploys work, you need to create free account on Redis Cloud

Appendix 1 - List of hardcode initialize id

USER_ID - ORDINARY

d37b2d0b-c06d-429c-b56d-7465c3959993

ddf5757a-ca21-41f4-b668-836d7755d70d

1636a414-1f16-45b9-8e36-28507c108be9

USER_ID - DRIVER

8985954f-7925-42c9-a1e5-e6b4bad5fd6c

1b4a4de9-0eca-4c73-97c6-e1b9df06678e

93e16962-57d0-4a27-b8f4-8db20f29b25a

RESTAURANT_ID

a3cbc4f1-3636-4db9-bb42-36c49ba655b9

d72d77fb-e96d-4d9a-964d-f2bf605c7e0b

c820da4c-d8de-4229-848b-33e5f183a22c

f084e35a-f745-4c86-b0d5-aae81bd632a9

d31710fc-ba84-4ef9-ae2e-3c8e38e1c84c

marttp/emp-friendly