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https://github.com/saravanan81java/microservices-with-spring-boot-3

Microservices with Spring Boot 3
https://github.com/saravanan81java/microservices-with-spring-boot-3

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Microservices with Spring Boot 3

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# Microservices with Spring Boot 3 presentation



What We'll Cover Today

- Introduction

- What Are Microservices?

- Key Benefits

- Core Principles of Microservices

- Comparison: Monolithic vs. Microservices

- Key Components in Microservices Architecture

- Challenges and Solutions

- Real-World Use Cases

- Introduction Spring Boot 3

- Q&A



# System Design for Taxi Aggregator Applications (Uber,OLA)



Introduction



Taxi aggregator apps like Uber and OLA have revolutionized transportation by connecting riders and drivers seamlessly. At their core, these platforms rely on sophisticated system designs to handle real-time matching of drivers to riders, dynamic pricing, payment processing, fraud detection, and more. In this article, we will explore the design architecture of such systems.



Key Components in a Taxi Aggregator Application



The architecture of a taxi aggregator app typically comprises several microservices and distributed systems working together to handle a large volume of requests in real-time. Below, we break down each core component:



Architecture Diagram

1. Client Interfaces (Riders and Drivers)



The system starts with the rider and the driver interfaces, which are mobile apps:



Rider App: The rider initiates a ride request by inputting their location, destination, and other details like payment method.

Driver App: The driver’s app continuously sends location data to the server, allowing the platform to monitor supply (available cars).



2. WAF (Web Application Firewall)



All traffic (requests) from the mobile clients is first routed through a Web Application Firewall (WAF). This firewall ensures security by protecting the system from common web-based attacks such as SQL injection, XSS, and DDoS.



3. Load Balancer (LB)



Behind the WAF is a Load Balancer, which distributes incoming requests across multiple servers to prevent any one server from being overwhelmed. Load balancers ensure that the system can scale efficiently, handling spikes in demand during peak hours.



4. Service Layer (HTTP REST, WebSocket Nodes)



The service layer includes both HTTP REST APIs and WebSockets:



HTTP REST: Handles typical request-response communication, such as booking a ride, cancelling it, or fetching ride history.

WebSocket Nodes: Handle real-time communication, crucial for the continuous flow of location data from both riders and drivers.



The use of WebSockets ensures low latency and efficient handling of real-time events, such as the rider’s location update or driver’s proximity to the rider.



5. Kafka and REST API Layer



The real-time data from the WebSockets is passed into Kafka, a distributed streaming platform. Kafka helps manage the massive amounts of real-time data produced by the apps, such as rider requests and driver locations.



Kafka is responsible for stream processing, where events like "rider request received" or "driver accepted ride" are processed in real time.

A REST API layer allows external systems to interact with the data Kafka processes, ensuring scalability across different components.



6. DISCO (Distributed System Coordination)



DISCO is a core component of the system, handling the mapping between rider demand and driver supply. Based on consistent hashing of GPS locations, DISCO selects an appropriate server to manage updates and matching for a given region.



The architecture divides geographical regions into cells, each of which is responsible for managing a subset of driver and rider data.

Cells communicate with each other to balance load and find the optimal driver for a ride request.



For instance, if a rider in Region 5, Cell 57 sends a request, the system finds the closest available driver in neighboring cells (Region 5, Cell 187 or Cell 2089) and sends an update.



7. Hadoop, Hive, HDFS, and Pig for Batch Processing



For data storage and batch processing, the system uses Hadoop with components like:



HDFS (Hadoop Distributed File System): A scalable storage solution for managing large volumes of ride data.

Hive and Pig: Tools for querying and analyzing the stored data to produce reports, such as daily trip summaries or driver earnings.



8. ML Models for Fraud Detection, Routing, Pricing, and ETA Calculation



Machine Learning (ML) models play a critical role in:



Fraud Detection: Identifying suspicious behavior, such as fake bookings or fraudulent drivers.

Routing and ETA Calculation: Calculating the most efficient routes and providing estimated times of arrival (ETAs) based on real-time traffic data.

Dynamic Pricing and Surge Calculation: Adjusting prices dynamically based on supply and demand. For instance, during high-demand times, prices may increase due to a scarcity of available drivers.



These ML models rely on continuous data streaming and real-time processing from the Kafka layer.



9. Analytics with ELK and Jupyter Notebooks



The system collects logs from all services via Kafka and stores them in the ELK Stack (Elasticsearch, Logstash, Kibana). This enables real-time monitoring, visualization, and log analysis.

Jupyter Notebooks: Used for data scientists to perform detailed analytics, build visualizations, and fine-tune machine learning models.



10. Microservices for Backup, Pricing, and Surge Handling



The system relies heavily on microservices to handle different tasks independently:



Pricing Microservice: Determines the ride cost based on distance, demand, and other factors.

Surge Pricing Microservice: Adjusts ride prices in real-time based on demand and supply fluctuations.

Backup Datacenter: Ensures data redundancy and availability even in case of server failures.



These microservices communicate via Kafka, ensuring resilience and fault tolerance in the system. 

Data Models



11. Logs and Monitoring



The system generates logs from all services, which are then passed into Kafka for monitoring and analysis. Using the ELK Stack:



Logstash parses the log data.

Elasticsearch indexes the data.

Kibana provides real-time visualization, enabling system administrators to monitor performance, identify bottlenecks, and troubleshoot issues.



Challenges in System Design



While the system design appears robust, there are several challenges to managing a platform of this scale:



High Availability: The system must be operational 24/7, handling millions of transactions per day.

Scalability: The platform should be able to scale horizontally to handle increasing demand.

Latency: Low-latency communication is crucial for real-time services like ride-matching and location tracking.

Data Integrity and Security: Ensuring secure communication and preventing fraud is critical in a payment-based system.



Conclusion



Taxi aggregator applications like Uber and OLA depend on a complex system architecture, involving components like Kafka, Hadoop, machine learning models, and microservices. Each component plays a crucial role in ensuring seamless real-time experiences for both riders and drivers. By distributing tasks across microservices, ensuring redundancy, and using real-time data processing, these systems manage to deliver fast, scalable, and secure services to millions of users globally. 



# Microservice Architecture - Challenges



While microservices architecture offers numerous advantages, it's not without its drawbacks. The cons of microservices are listed below:



Higher Complexity

Managing a large number of microservices can lead to increased complexity in deployment, monitoring, and orchestration. Coordinating communication between services adds another layer of complexity.



Distributed Systems Challenges

Microservices rely heavily on network communication. This introduces issues such as 



Network latency

Increased network traffic

Potential points of failure



The potential points of failure require the need for robust error handling. Additionally, because microservices are so independent, it can be difficult to track down errors and resolve them.



Operational Overhead

Each microservice needs to be deployed, monitored and maintained separately. This can increase operational overhead compared to a monolithic architecture.



This complexity can be a major challenge for organizations that are not used to working with microservices. 



Data Consistency

Maintaining consistency across multiple services can be challenging. Ensuring data consistency and managing transactions between different services requires careful planning and implementation.



Increased Resource Utilization

Running multiple services means more resources are needed compared to a monolithic architecture, especially considering the additional overhead of managing these services.



Initial Development Complexity

Breaking down an application into microservices requires careful planning and design decisions from the outset. This can lead to increased initial development complexity.



Increased Development Time

Microservices also require more development time than monolithic applications since microservices are more complicated and require more coordination. Additionally, because microservices are deployed independently, it can take longer to get them all up and running. Also, developers need to be familiar with multiple technologies to work on a microservice-based application.



Dependency on DevOps

To be successful with microservices, organizations need to have a strong DevOps team in place. This is due to the fact DevOps is responsible for deploying and managing microservices. Without a good DevOps team, it can be difficult to successfully implement and manage a microservice-based application.



Security Concerns

With numerous services interacting over networks, ensuring proper security measures across all services, including authentication, authorization, and data encryption, becomes crucial and complex.



Testing Challenges

Testing microservices involves testing individual services as well as their interactions. End-to-end testing becomes complex and requires comprehensive strategies.



To effectively test and debug an application, you need to have access to all of the servers and devices that are part of the system. This can be difficult to do in a large, distributed system.



Debugging and Tracing

Identifying issues that span multiple services can be difficult. Debugging and tracing problems across a distributed system require sophisticated tools and practices.



Team Coordination

Different teams often work on different microservices. Coordinating these teams to align on interfaces, protocols, and dependencies can be challenging.