This code was likely written to support a reporting dashboard or customer analytics feature that displays the total number of recent customer orders. A front-end user, such as a business analyst or sales manager, might use it to track order activity, identify active customers, or monitor sales trends after a certain date. It could be part of a customer order history page, a sales insights panel, or an admin dashboard.
nd performs the following steps:
Retrieve Customers: It executes a query to fetch all customer IDs, first names, and last names from the Customers table and stores them in a slice.
Retrieve Orders for Each Customer: It iterates over the list of customers and queries the Orders table to fetch orderDate values for each customer, sorted in ascending order.
Return the Total Orders Matched
Issues identified
This code has potential issues with unoptimized database query and code, increased database load and lack of observability.
N+1 Query Problem: Executed multiple queries (one per customer), slowing down performance.
Missing Query Execution Time Monitoring: No way to track potentially slow queries.
Unnecessary Data Storage: Stored customers separately before retrieving orders.
Recommended Fixes
The following potentially improves performance primarily by eliminating the N+1 query problem.
Key Change
SELECT c.customerId, c.fname, c.lname, o.orderDate
FROM Customers c
INNER JOIN Orders o ON c.customerId = o.customerId
ORDER BY c.customerId, o.orderDate ASC;
Why?
Performance Boost – Uses a single JOIN query instead of multiple queries.
Cleaner Code – Removes redundant loops and queries.
Lower Memory Usage – Stores combined customer-order data efficiently.
Monitoring Added – Tracks execution time using Prometheus.
This change significantly reduces database calls, making the application faster and more scalable.
Full Code Diff
Recommended Instrumentation: Add Prometheus monitoring in query()
This update adds execution time tracking for database queries, improving performance monitoring and optimization.
Why?
No Performance Tracking – Previously, execution time was not measured.
Potential Scalability Issues – As data grows, queries may slow down.
No Monitoring for Bottlenecks – Difficult to diagnose slow queries.
Advantages
Better Performance Monitoring – Logs execution time for analysis.
Enables Query Optimization – Helps detect and tune slow queries.
Scalability Insights – Ensures queries remain efficient over time.
Supports Alerting – Prometheus can trigger alerts on slow queries.