You're facing high traffic on your application. How do you maintain performance while scaling up?

The very first thing to do in terms of handling sudden increase in traffic is to scale the resources vertically in order to cater to the increased load in a traditional environment, then if it's in a distributed environment we need to scale horizontally, which in most cases happens automatically through cloud and Kubernetes predominantly. This should be a multifacetedapproach wherein one needs to review all aspects of the application to scout for possible performance bottlenecks. Start offloading the data which doesn't change frequently but is accessed repeatedly,into a Read Optimized Datastore, use Distributed Caching to enhance the performance multifold, something like Redis, Hazelcast come in handy here.Use a write-optimized nosql db.

Missing here following: – know and use features provided by your programming language and environment – always have in the back of your mind KISS and DRY

- Continuously profile and benchmark your application to identify bottlenecks and optimize slow code paths. - Ensure that the algorithms and data structures used in your application are optimized for performance.

Code optimisation is the process of streamlining your code to increase its efficiency, which lowers the amount of time and resources needed to run it. The performance of your application can be greatly enhanced by this procedure. Here's how to make your code more efficient: 1. Efficient Algorithms and Data Structures 2. Reduce Redundant Calculations 3. Lazy Loading 4. Minimize Resource Consumption 5. Profiling and Benchmarking The practice of caching involves keeping copies of frequently requested data in a temporary storage location (cache) in order to expedite the processing of subsequent requests for that data. Here's how caching can be used and optimised: 1. Identify Cacheable Data 2. Types of Caching 3. Cache Invalidation Strategies

All of this should be reviewed before deployment. If traffic is the problem, if the code works, this is the least cost effective way of handling a traffic issue.

Along with Vertical scaling and Horizontal scaling which are obvious choice to handle this kind of situation, we should also looking at fine tuning queries used in applications , try to implement DB partitions, indexes so that DB will be of optimum performance and having cache database and avoid a trip to application database for most common operations.

The process of modifying your IT infrastructure's capacity to satisfy the demands of your application is known as "scaling resources." This guarantees that, without sacrificing availability or speed, your application can manage fluctuating traffic volumes, from extended periods of low usage to abrupt spikes. Vertical scaling and horizontal scaling are the two main forms of scaling. 1. Vertical Scaling Vertical scaling involves increasing the capacity of an existing server or instance by adding more resources, such as CPU, RAM, or storage. Advantages of Vertical Scaling: > Simplicity > No Code Changes Required > Reduced Complexity Disadvantages of Vertical Scaling > Limits to Growth > Single Point of Failure > Cost Inefficiency Cont 1/2

We tried addressing scaling using both HPA and KEDA for Azure Kubernetes Service. Using HPA we were able to scale the applications based on simple metrics like CPU or Memory usage which is a reactive scaling strategy. Integrating KEDA to AKS gives advanced capabilities, for example scaling based on the number of messages in a Queue. This allows left shifting the auto scaling of applications, without impact in performance.

1. Horizontal Scaling (Scaling Out): - Distribute the load by adding more servers or instances to handle the traffic. Use load balancers to distribute incoming requests evenly across multiple servers. - Implement auto-scaling to automatically add or remove instances based on traffic load. Services like AWS Auto Scaling or Kubernetes Horizontal Pod Autoscaler can help with this. 2. Vertical Scaling (Scaling Up): - If the application is monolithic or it's not easy to distribute, you might scale vertically by increasing the CPU, memory, or storage capacity of existing servers. This is less flexible than horizontal scaling but can be useful for short-term traffic spikes.

Horizontal Scaling: Use tools like Kubernetes or Docker Swarm for container orchestration and scaling across multiple servers. Vertical Scaling: Tools like AWS EC2 or Google Cloud Compute Engine allow you to increase server resources (CPU, RAM). Auto-Scaling: Utilize cloud services such as AWS Auto Scaling, Azure Virtual Machine Scale Sets, or Google Cloud Autoscaler for dynamic resource management. Load Balancing: Implement with AWS Elastic Load Balancing, Azure Load Balancer, or NGINX to distribute traffic effectively.

Optimize Database Performance: - Split the database into smaller, more manageable pieces (shards) to distribute the load and improve performance. - Use read replicas to handle read-heavy workloads, reducing the load on the primary database. - Implement caching (using Redis, Memcached) to reduce the number of database queries by storing frequently accessed data in memory. - Use connection pooling to reuse database connections, which reduces the overhead of establishing new connections for each request. - Ensure that your database is properly indexed to speed up query performance. - Optimize your database queries to reduce execution time and resource consumption.

Anything that reads/writes to disk is (probably) going to be your slowest component. If this is the case, explore your usage pattern and see if things like write-through caches can help or if eventual consistency strategies are suitable for your use case

Indexing: Use database indexing (e.g., MySQL, PostgreSQL) to speed up query performance. Query Optimization: Analyze and optimize slow queries with tools like SQL Profiler or EXPLAIN plans. Sharding: Distribute data across multiple databases with techniques like horizontal sharding to balance load (e.g., MongoDB, Cassandra). Read Replicas: Implement read replicas (e.g., AWS RDS Read Replicas) to handle read-heavy workloads and reduce primary database strain.

Key Benefits of Load Balancing: Even Distribution of Traffic: Load balancers ensure that traffic is evenly distributed across all available servers, preventing any one server from being overwhelmed and thus avoiding performance degradation. Scalability: As traffic increases, load balancers allow you to add more servers to the pool, ensuring that your application can handle the load without compromising performance. Redundancy and High Availability: Load balancers provide redundancy by rerouting traffic to healthy servers if one fails. This ensures continuous uptime and minimizes the risk of outages.

- Use load balancers to distribute traffic across multiple servers or instances, preventing any single server from becoming overwhelmed. - For geographically distributed users, use global load balancing to direct users to the nearest data center, reducing latency and balancing traffic.

Weight Distribution When scaling horizontally, load balancing is essential. To ensure that no single server is overloaded, it entails dividing up incoming network traffic among several servers. Essential Elements of Load Distribution Traffic distribution: Uses techniques like round-robin, least connections, or IP hash to divide up client requests among servers. Medical Examinations: keeps an eye on the servers' condition and only routes traffic to those that are operating correctly. Failover: To maintain high availability in the event of a server failure, the load balancer reroutes traffic to other servers that are available.

Creating geographic distribution of servers across time zones, so peak loads by geographic time provides peak time distribution to less busy areas, improves utilization and less resource waste. Don’t put all your eggs in one basket. Best Defence against power outages, geographic disaster which includes cloud bases.

Load Balancing: Efficient load balancing ensures optimal performance and resource use. Implement these strategies: Use Tools: Employ tools like AWS Elastic Load Balancer, Azure Load Balancer, or NGINX to distribute traffic evenly across servers. Configure Algorithms: Use algorithms like Round Robin, Least Connections, or IP Hash to direct traffic effectively. Health Checks: Set up health checks to monitor server status and redirect traffic from unhealthy servers. Auto-Scaling Integration: Combine load balancers with auto-scaling to dynamically adjust capacity based on traffic.

-Selecting the Right Monitoring Tool We used AppDynamics for tracking key metrics such as response times, server load, and error rates. -Setting Up Metric Collection: Configured monitoring tool to collect data. -Defining Thresholds & Alerts: Defined thresholds for each key metric based on historical data & traffic patterns. -Setup Real-Time Alerts: Setting up alerts to immediately notify the team of potential issues, ensuring they were identified & addressed before impacting end-users. -Dashboards: Created dashboards that provided a visual representation of the key metrics. -Refining: Made adjustments to the thresholds & alerts to reduce false positives and ensure accuracy. -Maintenance and Updates: Regularly reviewed monitoring setup.

Monitoring Systems: Effective monitoring is crucial for planning growth and scaling your application. Here's how: Performance Metrics: Use tools like New Relic, Prometheus, or Datadog to track key performance indicators (KPIs) such as CPU usage, memory consumption, and response times. Capacity Planning: Analyze historical data to forecast future resource needs and identify trends for scaling decisions. Alerts and Notifications: Set up alerts for anomalies and thresholds to proactively address potential issues before they impact users. Bottleneck Identification: Use monitoring to detect performance bottlenecks and plan infrastructure upgrades or optimizations.