You're juggling urgent database performance demands. How do you prioritize long-term optimization goals?

Assessing Impact: Focus on optimizations with the highest long-term benefits and immediate impact. Balancing Short and Long Term: Allocate time for quick fixes while steadily progressing on larger optimization projects. Automating Monitoring: Implement automated tools to continuously monitor performance and alert you to issues. Documenting Issues: Keep detailed records of recurring problems to address root causes systematically. Regular Reviews: Schedule periodic performance reviews to adjust and refine your optimization strategy.

Assessing the urgency when dealing with urgent database performance requirements is an important skill: - Classify requirements into high, medium, and low priority based on their impact on the system and users. - Use criteria such as impact to the business, determine the number of users affected - Determine the impact of the issue on business operations. Affects revenue and user experience. - Use technical tools to determine the root cause of the problem

Continuously monitoring database performance is important to help detect and resolve problems promptly: - Use database performance monitoring tools such as Prometheus, Grafana, New Relic, DataDog, CloudWatch(AWS) - Build a warning system for important indicators such as (tablespace capacity, log, archivelog, etc.) to detect problems early - Build visual dashboards to monitor performance. (use Kibana, Grafana.. to create charts) - Ensure real-time dashboard for continuous monitoring by the database administration team. - Develop periodic reports on database performance to evaluate current status and identify performance trends. This helps predict and prevent incidents.

Balance resources to ensure more stable system operation - Evaluate the current resources (CPU, memory, disk space) of the system. - Efficient resource allocation, using cloud solutions to flexibly allocate resources according to actual needs, such as auto-scaling and load balancing. - Prioritize resources for critical services and applications, while reducing resources for less important tasks in times of emergency. - Use load balancing techniques to distribute work evenly across database servers. Tools like (HAProxy, NGINX, or integrated cloud tools).

In the real-world application, a database (DB) grows bigger day by day. Even a small change may have a big impact on the DB. So when addressing DB performance demands, we should consider both current and long-term optimization. Query optimization plays a major role in the DB performance. Because it directly impacts application performance. 1. Creating indexes 2. "Normalize" the database 3. select only the needed columns 4. Using "LIMIT" while updating and deleting the queries. 5. Analyze query performance using the "EXPLAIN statement" In addition, remove the duplicates in the table. These steps help to fix the performance issues and definitely increase the speed of query response. So UX will be improved.

"You can't build a long-term future on short-term thinking" As the proverb says, Database (DB) is the most valuable asset of the organization. The DB size will definitely grow in the future. So planning for future growth is a major concern in the DB. While working on the optimization, need to consider: 1. Scalability => DB partitioning: a) horizontal partitioning - (Row-based partitioning) b) vertical partitioning - (Column-based partitioning) => Sharding: a large database is divided into smaller data and distributed across multiple servers. a) Geo-location b) user ID, or time-based 2. Load Balancing 3. Master-Slave concept - (Read from the slave DB. write into the master DB)