It is crucial to scale your infrastructure in tandem with growing data and processes. To assume that there will consistent performance when your data grows (for example from 100GB to 200GB) is unrealistiic. From experience, it is very essential to perform regular database table reindexing. Additionally, differentiate between real-time and batch processes. Prioritize User experience by scheduling non-essential tasks overnight, reserving daytime resources for critical functions.

To address high-demand performance issues, I’d monitor systems to identify bottlenecks, scale infrastructure (vertically or horizontally), and optimize code and database queries. Leveraging caching, CDNs, and cloud auto-scaling ensures efficient resource use. Stress testing identifies weaknesses, while a clear incident plan mitigates downtime. Continuous improvements maintain resilience and adaptability.

Managing server performance during high-demand periods requires a clear and efficient approach. Start by identifying bottlenecks using monitoring tools to locate issues in the application, database, hardware, or network. Optimize resources by improving code, refining database queries, and implementing caching to reduce load. If demand persists, scale effectively. Upgrade hardware like RAM or SSDs for immediate boosts or use load balancers to distribute traffic evenly. Consider cloud platforms such as AWS, Google Cloud, or Azure for dynamic scaling based on your specific needs. Finally, regular stress testing prepares systems for peak periods. These strategies ensure stable performance under pressure.

Monitoring and Scaling: Use monitoring tools to keep an eye on server performance and auto-scaling services to dynamically adjust resources based on demand.

To tackle performance issues with a server struggling under high-demand applications, start by analyzing the server's resource usage (CPU, memory, and storage) to identify bottlenecks. Optimize the application by improving code efficiency and reducing unnecessary processes. Consider scaling vertically by upgrading hardware or horizontally by distributing the load across additional servers. Implementing load balancing and caching strategies can also improve performance and ensure smoother operation during peak demand.

Uma boa estrutura de observabilidade confere identificação de possíveis ameaças, indicadores para tomada de decisão proativa e até experimentação gradual das possíveis soluções. Como diz o ditado: "previnir é melhor do que remediar." Há e remediar baseado em indicadores/evidências é muito melhor!