development where software is composed of small independent services that communicate over well-defined APIs. These services are owned by small, self-contained teams.
on a single responsibility or business capability. Example: A "Payment Service" handles all payment-related functions but doesn’t manage user data. Why it Matters: Simplifies service logic, improves maintainability, and reduces dependencies. AWS Service Alignment: AWS Lambda functions can be designed around SRP, keeping each function small and focused.
APIs rather than direct connections. Example: A “User Service” communicates with a “Billing Service” through RESTful APIs rather than sharing a database. Why it Matters: Reduces dependencies, allows independent updates and deployments. AWS Service Alignment: API Gateway is often used to create decoupled microservices that communicate over HTTP.
ensuring that services don't share a single database. Example: The "Order Service" manages order data independently from the "Product Service." Why it Matters: Prevents tight coupling at the database level, allows independent scaling and updates. AWS Service Alignment: Use Amazon DynamoDB or RDS for individual service databases.
session data is stored locally between requests. Example: Each API call to the "Auth Service" is independent, without reliance on previous interactions. Why it Matters: Stateless services scale more easily because instances don’t need to share state. AWS Service Alignment: AWS Lambda functions are inherently stateless and ideal for this architecture.
on its own resource needs and traffic patterns. Example: The "Search Service" scales up to handle increased search requests without affecting other services. Why it Matters: Optimizes resource usage and reduces costs by scaling only the necessary services. AWS Service Alignment: Amazon ECS, EKS, or Lambda Auto Scaling can scale individual services.
are a design pattern where actions within a system are triggered by events, rather than by direct service-to-service communication. AWS provides various services that facilitate the implementation of event-driven architectures, helping to decouple systems, increase scalability, and improve flexibility.
or changes in state, such as a file being uploaded to an S3 bucket, a user signing up, or a message arriving in a queue. Event Producers: Services or components that generate events, such as Amazon S3, Amazon DynamoDB, or custom applications. Event Consumers: Services that listen for and respond to events. These can be AWS Lambda functions, Amazon SNS, SQS, etc. Event Brokers: These manage the routing and delivery of events from producers to consumers, like Amazon EventBridge or SNS.
traffic or user activity increases and your current resources can't handle the load, leading to slow performance or downtime. 2) Cost Optimization: To avoid over-provisioning or underutilization, scaling helps in aligning resources with current demand to optimize costs. 3) Performance Degradation: If you notice longer response times, high CPU or memory usage, or 4) frequent timeouts, it could be time to scale. 4) Event-based Workloads: During scheduled events (e.g., marketing campaigns, product launches) that are expected to drive traffic spikes. 5) Global Expansion: If you're serving users across different regions and need to scale infrastructure to provide low-latency services globally.
number of EC2 instances based on traffic patterns and predefined conditions. When to use: If your application runs on EC2 and traffic is highly variable. How to set up: Define scaling policies (target tracking, step scaling, or scheduled scaling). 1. Set scaling triggers based on metrics like CPU utilization or memory usage. 2.
instances and automatically scales up to handle increases in traffic. When to use: If your application needs to distribute traffic across multiple EC2 instances for high availability. How to set up: Add EC2 instances to a target group, and ELB will automatically distribute incoming traffic.
services scale containerized workloads across clusters of EC2 or Fargate instances. When to use: When running microservices or containerized applications. How to set up: Enable Service Auto Scaling for ECS tasks or Kubernetes Pods. Define scaling policies based on CPU, memory, or custom CloudWatch metrics.
increasing the number of function instances in response to events, with no manual intervention. When to use: For stateless, event-driven workloads (e.g., API requests, stream processing). How to set up: Simply deploy the function, and AWS handles scaling based on the number of incoming events.
CloudWatch to monitor key performance metrics (CPU, memory, request rates) and set thresholds for triggering scaling actions. Use Elastic Load Balancing: Combine ELB with Auto Scaling to ensure high availability. Design for Fault Tolerance: Spread your infrastructure across multiple Availability Zones or regions to ensure resilience. Automate as Much as Possible: Rely on AWS services like Auto Scaling and Lambda, which handle scaling automatically, reducing operational overhead.
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