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.

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