System requirements

Functional Requirements

Core Functionalities:

1. Inventory Tracking:
   • Track inventory at product and SKU levels.
   • Maintain real-time stock quantities across multiple warehouses.
2. Stock Replenishment:
   • Generate automated stock alerts for low inventory levels.
   • Create and track purchase orders for stock replenishment.
3. Order Fulfillment:
   • Allocate inventory for incoming orders.
   • Support multi-location order fulfillment.
4. Reporting and Analytics:
   • Provide real-time insights into stock levels, stock movement, and sales trends.
   • Generate reports on inventory valuation and demand forecasting.
5. Integration with Sales Channels:
   • Sync inventory data with external sales platforms (e.g., Shopify, Amazon).
   • Reconcile stock changes from multiple channels in real time.
6. User Management:
   • Manage user roles and permissions (e.g., admin, warehouse manager).
   • Track user actions for auditing.

Non-Functional Requirements

1. Scalability:
   • Support thousands of products and SKUs, multiple warehouses, and millions of transactions.
2. Reliability:
   • Ensure consistent and accurate inventory updates even during high transaction volumes.
3. Performance:
   • Real-time stock updates with API response times under 200ms.
   • Handle large-scale reporting and analytics efficiently.
4. Availability:
   • Maintain 99.9% uptime to ensure uninterrupted inventory tracking.
5. Data Consistency:
   • Implement strong consistency for critical operations like stock allocation.
6. Security:
   • Protect sensitive data (e.g., product pricing, supplier details) with encryption and secure access controls.
7. Extensibility:
   • Allow seamless integration with third-party platforms and new features.
8. Monitoring and Logging:
   • Enable detailed logging for debugging and tracking stock movements.

Capacity estimation

Estimate the scale of the system you are going to design...

Assumptions:

1. Businesses:
   • Total businesses: 100,000.
   • Daily active businesses: 10% (10,000).
2. Products and SKUs:
   • Average products per business: 1,000.
   • Total products: 100,000×1,000=100 million100,000 \times 1,000 = 100 \, \text{million}100,000×1,000=100million.
3. Transactions:
   • Stock updates per day: 10 million.
   • Peak transactions per second: 10 million24×3600≈115 TPS\frac{10 \, \text{million}}{24 \times 3600} \approx 115 \, \text{TPS}24×360010million​≈115TPS.

Resource Estimation:

1. Storage:
   • Inventory data: 100 million SKUs×1 KB=100 GB100 \, \text{million SKUs} \times 1 \, \text{KB} = 100 \, \text{GB}100million SKUs×1KB=100GB.
   • Transaction logs: 10 million/day×1 KB=10 GB/day10 \, \text{million/day} \times 1 \, \text{KB} = 10 \, \text{GB/day}10million/day×1KB=10GB/day.
2. Bandwidth:
   • Real-time stock updates: 115 TPS×1 KB=115 KB/sec115 \, \text{TPS} \times 1 \, \text{KB} = 115 \, \text{KB/sec}115TPS×1KB=115KB/sec.
   • Sync with sales channels: 1,000 sales channels updating every minute $1,000×1 KB≈1 MB/minute1,000 \times 1 \, \text{KB} \approx 1 \, \text{MB/minute}1,000×1KB≈1MB/minute$.
3. Database:
   • Optimize for high write operations (real-time stock updates) and read-heavy analytics.

API design

Define what APIs are expected from the system...

1. Inventory Management APIs

• POST /api/inventory/update:
  • Updates stock levels for specific SKUs.
  • Inputs: sku_id, quantity, transaction_type (e.g., sale, restock).
• GET /api/inventory/sku/{sku_id}:
  • Fetches stock details for a specific SKU.
  • Outputs: quantity, warehouse_location, last_updated.
• POST /api/inventory/bulk-update:
  • Updates stock levels for multiple SKUs in batch.
  • Inputs: List of SKU updates.

2. Stock Alert APIs

• GET /api/alerts:
  • Retrieves stock alerts for low inventory levels.
  • Outputs: List of SKUs with low stock.
• POST /api/alerts/subscribe:
  • Subscribes a user to stock alerts for specific SKUs.

3. Order Fulfillment APIs

• POST /api/orders/allocate:
  • Allocates inventory for an order.
  • Inputs: order_id, list of sku_id with quantities.
• GET /api/orders/status/{order_id}:
  • Retrieves fulfillment status for an order.

4. Reporting and Analytics APIs

• GET /api/reports/stock-movement:
  • Provides reports on stock movement for a specific period.
• GET /api/reports/demand-forecast:
  • Predicts future stock needs based on sales trends.

5. Integration APIs

• POST /api/integration/sync:
  • Syncs inventory changes with external sales platforms.
  • Inputs: platform_name, sku_id, quantity.

Database design

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...

1. Inventory Database

• Schema Details:
  • Inventory Table:
    • sku_id (Primary Key): Unique identifier for each SKU.
    • product_id (Foreign Key): Associated product ID.
    • quantity: Current stock quantity.
    • warehouse_id: Location of the stock.
    • last_updated: Timestamp of the last update.
• Purpose:
  • Track real-time inventory for each SKU across multiple warehouses.
• Tech Used:
  • Relational Database (e.g., PostgreSQL, MySQL).
• Tradeoff:
  • Pros: Strong consistency for critical inventory operations.
  • Cons: Requires sharding and replicas for scalability with high transaction volumes.

2. Transaction Log Database

• Schema Details:
  • TransactionLogs Table:
    • transaction_id (Primary Key): Unique transaction ID.
    • sku_id: SKU involved in the transaction.
    • quantity: Quantity changed.
    • transaction_type: Type of transaction (e.g., sale, restock).
    • timestamp: Time of the transaction.
• Purpose:
  • Maintain a historical log of all stock changes for auditing and analytics.
• Tech Used:
  • NoSQL Database (e.g., MongoDB, Cassandra).
• Tradeoff:
  • Pros: Optimized for high write throughput and flexible schema.
  • Cons: Querying historical data can be slower compared to relational databases.

3. Alert Database

• Schema Details:
  • Alerts Table:
    • alert_id (Primary Key): Unique identifier for each alert.
    • sku_id: SKU triggering the alert.
    • threshold_quantity: Quantity below which the alert is triggered.
    • user_id: User subscribed to the alert.
• Purpose:
  • Track and trigger alerts for low stock levels.
• Tech Used:
  • Relational Database (e.g., PostgreSQL).
• Tradeoff:
  • Pros: Consistent triggers for stock alerts.
  • Cons: Requires optimization for high-frequency alert processing.

4. Reporting and Analytics Database

• Schema Details:
  • StockReports Table:
    • report_id (Primary Key): Unique identifier for the report.
    • sku_id: SKU for the report.
    • stock_movement: JSON capturing stock inflow/outflow.
    • period: Reporting period.
• Purpose:
  • Store aggregated data for reporting and analytics.
• Tech Used:
  • Columnar Database (e.g., Amazon Redshift, Google BigQuery).
• Tradeoff:
  • Pros: Optimized for read-heavy analytics workloads.
  • Cons: Not suitable for real-time data updates.

High-level design

You should identify enough components that are needed to solve the actual problem from end to end. Also remember to draw a block diagram using the diagramming tool to augment your design. If you are unfamiliar with the tool, you can simply describe your design to the chat bot and ask it to generate a starter diagram for you to modify...

1. Inventory Management Service

Overview:

• Core service that tracks real-time inventory levels.
• Manages stock updates, replenishment, and inventory allocation for orders.

Responsibilities:

• Process stock updates from sales channels and internal operations.
• Provide APIs to query inventory levels and locations.
• Trigger alerts for low stock.

2. Stock Alert Service

Overview:

• Monitors inventory levels and generates alerts when thresholds are breached.
• Ensures businesses stay proactive in replenishing inventory.

Responsibilities:

• Detect low stock levels and trigger notifications.
• Provide real-time alert subscriptions for specific SKUs.

3. Order Fulfillment Service

Overview:

• Allocates inventory for incoming orders.
• Supports multi-warehouse order fulfillment and tracks order statuses.

Responsibilities:

• Reserve stock for orders and update inventory.
• Handle order cancellations by restoring inventory.
• Optimize order allocation across warehouses.

4. Reporting and Analytics Service

Overview:

• Aggregates and analyzes inventory data for insights and trends.
• Generates reports for stock movement, inventory valuation, and demand forecasting.

Responsibilities:

• Provide historical reports on inventory usage.
• Forecast stock needs based on past trends.
• Offer real-time dashboards for inventory health.

5. Integration Service

Overview:

• Syncs inventory changes with external sales platforms (e.g., Shopify, Amazon).
• Handles reconciliation of inventory levels across multiple systems.

Responsibilities:

• Push real-time inventory updates to sales channels.
• Fetch sales and order data from external systems for synchronization.

6. Notification Service

Overview:

• Delivers alerts and notifications to users via email, SMS, or push notifications.

Responsibilities:

• Notify users about low stock levels, replenishment needs, or critical issues.
• Manage user preferences for notification channels.

7. Admin Dashboard

Overview:

• Web-based interface for managing inventory, configuring alerts, and viewing reports.

Responsibilities:

• Allow businesses to configure stock thresholds and notification settings.
• Provide a visual overview of inventory across warehouses.
• Enable manual stock adjustments and audit tracking.

Request flows

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...

1. Stock Update Request

Objective: Update stock levels for an SKU.

Steps:

1. API Gateway:
   • Receives a POST /api/inventory/update request with SKU and quantity.
   • Validates request size and schema, then forwards it to the Inventory Management Service.
2. Inventory Management Service:
   • Validates the SKU and checks if it exists in the database.
   • Updates stock quantity in the Inventory Database.
   • Sends the updated inventory data to the Stock Alert Service for evaluation.
3. Stock Alert Service:
   • Checks if the updated stock falls below threshold levels.
   • Triggers alerts for low stock if thresholds are breached.
4. Integration Service (Optional):
   • Pushes updated stock levels to integrated sales platforms.
5. Response:
   • Confirms successful update or returns an error for invalid SKUs or conflicts.

2. Stock Alert Request

Objective: Retrieve low-stock alerts for a business.

Steps:

1. API Gateway:
   • Receives a GET /api/alerts request from a user.
   • Authenticates the user and forwards the request to the Stock Alert Service.
2. Stock Alert Service:
   • Queries the Alert Database for SKUs with stock levels below threshold.
   • Formats the alert list for the response.
3. Response:
   • Returns a list of low-stock SKUs and their details.

3. Order Fulfillment Request

Objective: Allocate inventory for an incoming order.

Steps:

1. API Gateway:
   • Receives a POST /api/orders/allocate request with order details.
   • Authenticates the request and forwards it to the Order Fulfillment Service.
2. Order Fulfillment Service:
   • Validates the order and SKU availability.
   • Reserves stock in the Inventory Database for each SKU.
   • Updates the order status in the Order Database.
3. Integration Service:
   • Sends order fulfillment details to external sales channels for status updates.
4. Response:
   • Confirms the allocation or returns an error if stock is insufficient.

4. Inventory Report Request

Objective: Generate a report on stock movement.

Steps:

1. API Gateway:
   • Receives a GET /api/reports/stock-movement request with a specified time range.
   • Forwards the request to the Reporting and Analytics Service.
2. Reporting and Analytics Service:
   • Queries the Transaction Log Database for stock movement within the specified time range.
   • Aggregates data and formats the report.
3. Response:
   • Returns the generated report with detailed stock movement statistics.

5. Integration Sync Request

Objective: Sync inventory levels with external platforms.

Steps:

1. API Gateway:
   • Receives a POST /api/integration/sync request with platform details.
   • Forwards the request to the Integration Service.
2. Integration Service:
   • Fetches sales data and reconciles it with the Inventory Database.
   • Updates stock levels based on discrepancies and sends changes back to the platform.
3. Response:
   • Confirms the sync or returns an error for mismatched data.

Detailed component design

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...

1. Inventory Management Service

End-to-End Working:

The Inventory Management Service acts as the backbone of the system, tracking stock levels in real time. When an update request is received (e.g., stock deduction for an order), the service validates the SKU, updates stock in the Inventory Database, and notifies dependent services (e.g., alert or integration services). For batch updates (e.g., from suppliers), it processes multiple SKUs simultaneously, ensuring transactional consistency.

Data Structures/Algorithms:

• Hash Map (In-Memory Cache):
  • Maintains a temporary cache of frequently accessed SKUs for fast read operations.
• Atomic Transactions:
  • Uses ACID-compliant relational database transactions to ensure consistency during concurrent updates.
• Event-Driven Architecture:
  • Publishes stock update events to a message queue (e.g., Kafka) for other services to process asynchronously.

Handling Peak Traffic:

• Horizontal Scaling:
  • Deploy multiple instances of the service behind a load balancer to handle concurrent stock updates.
• Caching:
  • Redis is used to store frequently accessed inventory data, reducing database load.
• Batch Processing:
  • For bulk updates, processes updates in batches to optimize database writes.

Edge Cases:

• Concurrent Updates:
  • Solved with database-level locking or optimistic concurrency control to prevent overwriting changes.
• SKU Not Found:
  • Returns a detailed error message and logs the incident for review.
• Inconsistent Stock Updates:
  • Reconciliation jobs run periodically to compare inventory records with sales channel data.

2. Stock Alert Service

End-to-End Working:

This service continuously monitors inventory levels for predefined thresholds. When an SKU's stock falls below its threshold, it triggers alerts and sends notifications to subscribed users. Businesses can set custom thresholds per SKU.

Data Structures/Algorithms:

• Priority Queue:
  • Prioritizes low-stock SKUs to ensure critical alerts are sent first.
• Threshold Check:
  • Uses simple comparison logic to evaluate whether stock falls below the threshold.

Handling Peak Traffic:

• Distributed Alert Processing:
  • Uses distributed queues (e.g., RabbitMQ) to process alerts across multiple nodes.
• Throttling:
  • Prevents sending duplicate or excessive notifications during high-volume updates.
• Partitioning:
  • SKUs are partitioned by warehouse or category to parallelize alert evaluation.

Edge Cases:

• Delayed Notifications:
  • Implements retry logic to resend failed notifications.
• Incorrect Thresholds:
  • Allows real-time updates to thresholds to prevent unnecessary alerts.

3. Order Fulfillment Service

End-to-End Working:

The service allocates inventory for incoming orders, validates availability, and reserves stock. It supports multi-warehouse fulfillment, prioritizing the nearest warehouse to minimize shipping costs.

Data Structures/Algorithms:

• Warehouse Allocation Algorithm:
  • Calculates the optimal warehouse based on stock availability, location, and shipping SLA.
• Transactional Stock Reservation:
  • Ensures reserved stock is immediately deducted from the inventory to prevent overselling.

Handling Peak Traffic:

• Dynamic Scaling:
  • Autoscaling of fulfillment nodes during high-order volumes ensures smooth processing.
• Asynchronous Processing:
  • Uses message queues to offload non-critical operations (e.g., shipment creation).
• Read Replicas:
  • Queries inventory data from read replicas to minimize load on the primary database.

Edge Cases:

• Insufficient Stock:
  • Suggests alternatives or splits orders across warehouses.
• Order Cancellation:
  • Automatically restores stock for canceled orders.

4. Reporting and Analytics Service

End-to-End Working:

This service aggregates inventory data to provide reports on stock movement, valuation, and demand trends. It processes real-time data for dashboards and batch data for historical analysis.

Data Structures/Algorithms:

• Columnar Storage:
  • Optimized for analytical queries with columnar databases like Redshift or BigQuery.
• Time-Series Indexing:
  • Efficiently queries stock changes over time.

Handling Peak Traffic:

• Data Partitioning:
  • Partitions data by SKU or warehouse for parallel processing.
• Pre-Aggregated Metrics:
  • Stores commonly used metrics (e.g., total stock by category) to reduce computation.

Edge Cases:

• Delayed Data Ingestion:
  • Implements backpressure handling to manage spikes in incoming data.
• Outdated Reports:
  • Refreshes dashboards at defined intervals to balance accuracy and performance.

5. Integration Service

End-to-End Working:

This service syncs inventory data with external sales platforms and reconciles discrepancies. For example, it updates stock on Shopify after an order and fetches sales data to adjust inventory levels.

Data Structures/Algorithms:

• Change Data Capture (CDC):
  • Tracks inventory changes and syncs only the deltas with sales platforms.
• Conflict Resolution:
  • Reconciles mismatches by applying business-defined rules (e.g., prioritize sales channel data).

Handling Peak Traffic:

• Rate Limiting:
  • Limits API calls to prevent exceeding third-party quotas.
• Batch Sync:
  • Combines updates into batches for efficient API usage.

Edge Cases:

• Third-Party Downtime:
  • Queues updates for retry when the external platform is back online.
• Partial Sync Failures:
  • Logs errors and continues processing unaffected records.

Trade offs/Tech choices

Explain any trade offs you have made and why you made certain tech choices...

Relational vs. NoSQL:

• Trade-off: Chose relational databases for inventory due to strong consistency needs, sacrificing schema flexibility.
• Reason: Critical operations like stock updates require ACID guarantees.

Event-Driven Architecture:

• Trade-off: Introduced complexity in managing message queues.
• Reason: Ensures scalability and decouples components for better performance.

Columnar vs. Row Databases:

• Trade-off: Chose columnar databases for analytics over row-based systems, reducing real-time query speed.
• Reason: Optimized for batch reporting and read-heavy workloads.

Failure scenarios/bottlenecks

Try to discuss as many failure scenarios/bottlenecks as possible.

1. Database Overload:
   • Issue: High write traffic can overwhelm the inventory database.
   • Mitigation: Use sharding and write-optimized replicas.
2. Message Queue Overload:
   • Issue: Excessive stock updates may clog the queue.
   • Mitigation: Implement backpressure and prioritize critical events.
3. Integration Failures:
   • Issue: Sync issues with external platforms.
   • Mitigation: Use retry queues and monitor third-party APIs.
4. Stock Mismatch:
   • Issue: Errors in reconciliation lead to overselling.
   • Mitigation: Regular audits and conflict resolution rules.

Future improvements

What are some future improvements you would make? How would you mitigate the failure scenario(s) you described above?

Predictive Analytics:

• Introduce machine learning to forecast demand and prevent stockouts.
• Mitigation: Uses historical trends to guide replenishment decisions.

Dynamic Scaling:

• Implement predictive autoscaling for services based on traffic patterns.
• Mitigation: Ensures consistent performance during spikes.

Advanced Alerting:

• Enable custom alert configurations (e.g., trending low stock).
• Mitigation: Improves proactive inventory management.

Integration Enhancements:

• Add support for more platforms with dynamic API configurations.
• Mitigation: Expands system compatibility and reach.