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List functional requirements for the system (Ask the chat bot for hints if stuck.)...
The inventory management in build for applications like amazon or shopify to manage their users
1) Admin role management add/update/delete users
2) Add new items to the inventory
3) update items in the inventory
4) Set minium Stock alerts when levels falls below the minium threshold
5) Process Incoming orders and update the inventory
6) Allow users to use analytical service to generate dashboards and analyze analytics
List non-functional requirements for the system...
1) System should be highly scalable it should scale as the number of users incrases or more products get added
2) System should be fault tolorent
Estimate the scale of the system you are going to design...
Based on the discussion with the interviewer
No of qps for admin related queries -> 5 -10
No of qps for staff members who are responssible for upadating the inventory 10-20
No of qps of Customers who will be buying products - >100,000 ->1000,0000
Define what APIs are expected from the system...
Wei will be using below rest calls for user and inventory update
Post /v1/User -> This api is used to create new user
{
userId: "",
firstName:"",
lastName:""
}
Delete /v1/User -> This api is used to delete a user
{
userId:""
}
For staff memeber
Post /v1/update - To add or delete the products from the inventory
{
[
{
product :"Product Name",
quantity:1,
addOrRemove:"add"
},
{
product :"Product Name2",
quantity:3,
addOrRemove:"remove"
}
]
}
For Customers - This api is used to add or remove the product from the inventory
Post /v1/update
{
[
{
product :"Product Name",
quantity:1,
addOrRemove:"remove"
},
{
product :"Product Name2",
quantity:3,
addOrRemove:"remove"
}
]
}
Defining the system data model early on will clarify how data will flow among different components of the system. Also you could draw an ER diagram using the diagramming tool to enhance your design...
As the system contains tasks like adding new products to the inventory or deleting few products from the inventory so we will use relational database which provides acid capabilities and also different isolation isolation read uncommited, read commited, repetable read and serializable
In the database schema we will have a products table which will store the productId, product Quantity,amount,minQuantityForAlarm
and we will have another table users which will store the user details and also details of admin and staff members.
we can create index on product name in products table and userName in user Table which will help us in efficient loopkup
Each user makes a request to the API gateway, which incorporates rate limiters and robust authentication mechanisms to prevent unauthorized access and mitigate DDoS attacks. For adding, updating, or deleting users, the request is directed to the user service, which interacts with the users database to manage user details. When adding or deleting inventory, the user or staff member calls the inventory service, which then accesses the inventory database to store the data. To address race conditions and prevent simultaneous updates, the system employs both pessimistic and optimistic locking methods; pessimistic locking involves locking rows before updates, ensuring data integrity but with slower performance, while optimistic locking checks the product version and only applies changes if the version remains unchanged since the transaction began, allowing for higher concurrency.mplement deadlock detection algorithms that periodically check for circular dependencies among transactions. Once detected, abort one of the transactions to break the deadlock.
Implementation: Most modern DBMSs have built-in deadlock detection and resolution mechanisms. Ensure that transactions are designed to minimize the risk of deadlocks by acquiring locks in a consistent order Additionally, to handle out-of-stock scenarios during high demand, the system implements inventory reservation mechanisms and enforces checks to prevent stock levels from reaching zero or negative, ensuring that orders are processed only when sufficient stock is available.
For storing object images, the system utilizes an object storage service like Amazon S3, enabling efficient and scalable retrieval of images for the front end. To scale the database effectively, sharding is based on the product name, distributing products into specific shards according to the range of their initial alphabetic character, which enhances performance and manageability. Kafka is employed to decouple the inventory and user services, allowing them to scale independently based on demand, and is also leveraged for event sourcing to manage state changes within the system efficiently. This architecture supports analytics services that generate dashboards and insights from client orders; if an order quantity falls below a predefined threshold, an alert is triggered to notify the user. Notifications are sent using third-party email services or a custom-built email service utilizing SMTP, POP, or IMAP protocols, ensuring users are promptly informed when product quantities reach critical levels. As we will get alerts at critical levels and hence user will not be allowed to make further orders when threshold is reached.
To further optimize performance, frequently accessed inventory data is cached using Redis or Memcached, significantly reducing database load and improving response times. Comprehensive rollback capabilities are integrated to revert changes in case of errors or failures during inventory updates, maintaining data integrity through transactional outboxes or saga patterns for distributed transactions. Disaster recovery strategies are established, including regular automated backups, geo-redundant data replication, and failover clusters to handle database failures or data corruption, ensuring business continuity. Monitoring and observability are maintained using tools like Prometheus and Grafana for real-time insights into system health and performance metrics, alongside distributed tracing tools such as Jaeger or Zipkin to pinpoint bottlenecks and enhance operational excellence.
Security is further reinforced through data encryption both at rest and in transit, role-based access control (RBAC), and comprehensive auditing of sensitive operations. The system design emphasizes optimizing transaction scopes to be as short and efficient as possible, minimizing lock durations and reducing contention. Advanced load balancing techniques distribute traffic efficiently across services, and cache invalidation policies ensure data consistency. Additionally, the architecture supports graceful degradation strategies to maintain functionality during partial system failures, ensuring a seamless user experience. By addressing potential gaps such as concurrent updates, out-of-stock scenarios, rollback capabilities, and disaster recovery, while incorporating advanced performance engineering, event-driven architecture, and comprehensive monitoring, the system achieves high scalability, reliability, and efficiency, making it well-suited for enterprise-scale applications like Amazon or Shopify.
Explain how the request flows from end to end in your high level design. Also you could draw a sequence diagram using the diagramming tool to enhance your explanation...
Each user makes a request to the API gateway, which incorporates rate limiters and robust authentication mechanisms to prevent unauthorized access and mitigate DDoS attacks. For adding, updating, or deleting users, the request is directed to the user service, which interacts with the users database to manage user details. When adding or deleting inventory, the user or staff member calls the inventory service, which then accesses the inventory database to store the data. To address race conditions and prevent simultaneous updates, the system employs both pessimistic and optimistic locking methods; pessimistic locking involves locking rows before updates, ensuring data integrity but with slower performance, while optimistic locking checks the product version and only applies changes if the version remains unchanged since the transaction began, allowing for higher concurrency. Additionally, to handle out-of-stock scenarios during high demand, the system implements inventory reservation mechanisms and enforces checks to prevent stock levels from reaching zero or negative, ensuring that orders are processed only when sufficient stock is available.
For storing object images, the system utilizes an object storage service like Amazon S3, enabling efficient and scalable retrieval of images for the front end. To scale the database effectively, sharding is based on the product name, distributing products into specific shards according to the range of their initial alphabetic character, which enhances performance and manageability. Kafka is employed to decouple the inventory and user services, allowing them to scale independently based on demand, and is also leveraged for event sourcing to manage state changes within the system efficiently. This architecture supports analytics services that generate dashboards and insights from client orders; if an order quantity falls below a predefined threshold, an alert is triggered to notify the user. Notifications are sent using third-party email services or a custom-built email service utilizing SMTP, POP, or IMAP protocols, ensuring users are promptly informed when product quantities reach critical levels.
To further optimize performance, frequently accessed inventory data is cached using Redis or Memcached, significantly reducing database load and improving response times. Comprehensive rollback capabilities are integrated to revert changes in case of errors or failures during inventory updates, maintaining data integrity through transactional outboxes or saga patterns for distributed transactions. Disaster recovery strategies are established, including regular automated backups, geo-redundant data replication, and failover clusters to handle database failures or data corruption, ensuring business continuity. Monitoring and observability are maintained using tools like Prometheus and Grafana for real-time insights into system health and performance metrics, alongside distributed tracing tools such as Jaeger or Zipkin to pinpoint bottlenecks and enhance operational excellence.
Security is further reinforced through data encryption both at rest and in transit, role-based access control (RBAC), and comprehensive auditing of sensitive operations. The system design emphasizes optimizing transaction scopes to be as short and efficient as possible, minimizing lock durations and reducing contention. Advanced load balancing techniques distribute traffic efficiently across services, and cache invalidation policies ensure data consistency. Additionally, the architecture supports graceful degradation strategies to maintain functionality during partial system failures, ensuring a seamless user experience. By addressing potential gaps such as concurrent updates, out-of-stock scenarios, rollback capabilities, and disaster recovery, while incorporating advanced performance engineering, event-driven architecture, and comprehensive monitoring, the system achieves high scalability, reliability, and efficiency, making it well-suited for enterprise-scale applications like Amazon or Shopify.
Dig deeper into 2-3 components and explain in detail how they work. For example, how well does each component scale? Any relevant algorithm or data structure you like to use for a component? Also you could draw a diagram using the diagramming tool to enhance your design...
Each user makes a request to the API gateway, which incorporates rate limiters and robust authentication mechanisms to prevent unauthorized access and mitigate DDoS attacks. For adding, updating, or deleting users, the request is directed to the user service, which interacts with the users database to manage user details. When adding or deleting inventory, the user or staff member calls the inventory service, which then accesses the inventory database to store the data. To address race conditions and prevent simultaneous updates, the system employs both pessimistic and optimistic locking methods; pessimistic locking involves locking rows before updates, ensuring data integrity but with slower performance, while optimistic locking checks the product version and only applies changes if the version remains unchanged since the transaction began, allowing for higher concurrency. Additionally, to handle out-of-stock scenarios during high demand, the system implements inventory reservation mechanisms and enforces checks to prevent stock levels from reaching zero or negative, ensuring that orders are processed only when sufficient stock is available.
For storing object images, the system utilizes an object storage service like Amazon S3, enabling efficient and scalable retrieval of images for the front end. To scale the database effectively, sharding is based on the product name, distributing products into specific shards according to the range of their initial alphabetic character, which enhances performance and manageability. Kafka is employed to decouple the inventory and user services, allowing them to scale independently based on demand, and is also leveraged for event sourcing to manage state changes within the system efficiently. This architecture supports analytics services that generate dashboards and insights from client orders; if an order quantity falls below a predefined threshold, an alert is triggered to notify the user. Notifications are sent using third-party email services or a custom-built email service utilizing SMTP, POP, or IMAP protocols, ensuring users are promptly informed when product quantities reach critical levels.
To further optimize performance, frequently accessed inventory data is cached using Redis or Memcached, significantly reducing database load and improving response times. Comprehensive rollback capabilities are integrated to revert changes in case of errors or failures during inventory updates, maintaining data integrity through transactional outboxes or saga patterns for distributed transactions. Disaster recovery strategies are established, including regular automated backups, geo-redundant data replication, and failover clusters to handle database failures or data corruption, ensuring business continuity. Monitoring and observability are maintained using tools like Prometheus and Grafana for real-time insights into system health and performance metrics, alongside distributed tracing tools such as Jaeger or Zipkin to pinpoint bottlenecks and enhance operational excellence.
Security is further reinforced through data encryption both at rest and in transit, role-based access control (RBAC), and comprehensive auditing of sensitive operations. The system design emphasizes optimizing transaction scopes to be as short and efficient as possible, minimizing lock durations and reducing contention. Advanced load balancing techniques distribute traffic efficiently across services, and cache invalidation policies ensure data consistency. Additionally, the architecture supports graceful degradation strategies to maintain functionality during partial system failures, ensuring a seamless user experience. By addressing potential gaps such as concurrent updates, out-of-stock scenarios, rollback capabilities, and disaster recovery, while incorporating advanced performance engineering, event-driven architecture, and comprehensive monitoring, the system achieves high scalability, reliability, and efficiency, making it well-suited for enterprise-scale applications like Amazon or Shopify.
Explain any trade offs you have made and why you made certain tech choices...
1) Using sql database can help us to handle transactions when many updates are made simultaneously
Try to discuss as many failure scenarios/bottlenecks as possible.
What are some future improvements you would make? How would you mitigate the failure scenario(s) you described above?
1) Use maching learning techniquest to futher improve the recomendation