When a database struggles with high traffic, here’s how I would bring back responsiveness initially by checking and Evaluate Indexing & Optimize Queries. A well-organized indexing strategy is essential for quick data retrieval. Also, review query code—especially loops that repeatedly query the database—and find alternatives to minimize load. Replacing nested loops with batch processing or more efficient joins can make a big difference.

If the database is slowing down from high traffic, a good first step is to check which queries are taking the longest to complete. You might find that adding indexes or refining those queries can make a noticeable difference in speed. Another option is to use caching, which temporarily stores frequently requested data so the database doesn’t have to process the same requests over and over. If these steps don’t fully resolve the issue, you might consider scaling up by adding more database servers to balance the load or even upgrading your hardware. Staying on top of performance helps you spot issues early, ensuring that everything keeps running smoothly as traffic grows.

For your workload always consider 1. Tuning your query, execution plan 2. Doing proper indexing and periodically maintain it. 3. Checking any bottleneck at cpu, memory or disk i/o 4. Planning about historical data removed from important tables. 5. Reviewing application and checking data caching

To handle a database under heavy traffic, I’d start by optimizing indexing, focusing on frequently accessed columns to improve retrieval speed. Next, I'd review and refine queries, especially looking at loops or nested queries that create multiple database calls, replacing them with batch processing or optimized joins to reduce load. Implementing caching for frequently requested data is essential, as it minimizes database hits and enhances response times. Lastly, scaling infrastructure—either vertically by adding resources like CPU and memory or horizontally using sharding and replication—distributes load across servers, ensuring system responsiveness during traffic spikes.

To improve database responsiveness during heavy traffic, optimize queries and indexing strategies for efficient data access. Implement connection pooling, load balancing, and read replicas to distribute traffic, along with caching mechanisms to reduce the load. Regular maintenance tasks like updating statistics and rebuilding indexes are essential, as is monitoring performance metrics for quick issue responses. Use asynchronous processing for non-critical tasks, denormalization for faster reads, and batch processing for multiple transactions. Additionally, review your application logic and schema regularly and conduct load testing to identify bottlenecks. These strategies will enhance responsiveness effectively.