19 Questions
What is the primary benefit of partitioning data or functionality across multiple nodes/servers?
to distribute workload and avoid bottlenecks
What is the main advantage of using asynchronous communication in application design?
to defer long-running or non-critical tasks and ensure the main application remains responsive to users
What is the key benefit of breaking down an application into smaller, independent services in a microservices architecture?
it improves resilience and allows teams to work on specific components in parallel
What is the primary purpose of auto-scaling in cloud computing?
to automatically adjust the number of active servers based on the current load
What is the main benefit of deploying an application in multiple data centers or cloud regions?
to reduce latency and improve redundancy
How do microservices architecture and auto-scaling work together to improve system resilience?
by allowing independent scaling of services and adjusting server capacity based on load
In what ways can a system grow?
A system can grow in five dimensions: user base, features, data volume, complexity, and geographic reach.
What is the key difference between vertical scaling and horizontal scaling?
Vertical scaling adds more power to existing machines, while horizontal scaling adds more machines to spread the workload.
What is the primary purpose of load balancing?
To distribute traffic across multiple servers, ensuring no single server becomes overwhelmed.
How can caching improve system performance?
Caching stores frequently accessed data in-memory, reducing the load on the server or database.
What is the role of Content Delivery Networks (CDNs) in scalability?
CDNs distribute static assets closer to users, reducing latency and resulting in faster load times.
Why is scalability important for system growth?
Scalability is essential to handle growing amounts of load by adding resources to the system.
What are some limitations of vertical scaling?
Vertical scaling has limitations in how far you can go in adding power to existing machines.
How can horizontal scaling be beneficial for large systems?
Horizontal scaling is often considered the most effective way to scale for large systems.
Which of the following scalability strategies involves deferring tasks to improve application responsiveness?
Asynchronous communication
What is the primary benefit of deploying an application in multiple data centers or cloud regions?
Reduced latency and improved redundancy
Which of the following scalability strategies involves splitting data or functionality across multiple nodes/servers?
Partitioning
What is the primary purpose of auto-scaling in cloud computing?
To automatically adjust the number of active servers based on the current load
Which of the following scalability strategies allows teams to work on specific components in parallel?
Microservices Architecture
Study Notes
System Growth
- A system can grow in five dimensions:
- Growth in User Base: increased number of requests due to more users
- Growth in Features: introducing new functionality to expand the system's capabilities
- Growth in Data Volume: increased data storage and management due to user activity or logging
- Growth in Complexity: system architecture evolves to accommodate new features, scale, or integrations
- Growth in Geographic Reach: expanding the system to serve users in new regions or countries
Scalability
- Scalability is the property of a system to handle a growing amount of load by adding resources
- There are 10 common ways to make a system scalable:
Scalability Methods
- Vertical Scaling (Scale up): adding more power to existing machines by upgrading server with more RAM, faster CPUs, or additional storage
- Horizontal Scaling (Scale out): adding more machines to the system to spread the workload across multiple servers
- Load Balancing: distributing traffic across multiple servers to ensure no single server becomes overwhelmed
- Caching: storing frequently accessed data in-memory to reduce the load on the server or database
- Content Delivery Networks (CDNs): distributing static assets closer to users to reduce latency and result in faster load times
- Partitioning: splitting data or functionality across multiple nodes/servers to distribute workload and avoid bottlenecks
- Asynchronous communication: deferring long-running or non-critical tasks to background queues or message brokers
- Microservices Architecture: breaking down the application into smaller, independent services that can be scaled independently
- Auto-Scaling: automatically adjusting the number of active servers based on the current load
- Multi-region Deployment: deploying the application in multiple data centers or cloud regions to reduce latency and improve redundancy
System Growth
- A system can grow in five dimensions:
- Growth in User Base: increased number of requests due to more users
- Growth in Features: introducing new functionality to expand the system's capabilities
- Growth in Data Volume: increased data storage and management due to user activity or logging
- Growth in Complexity: system architecture evolves to accommodate new features, scale, or integrations
- Growth in Geographic Reach: expanding the system to serve users in new regions or countries
Scalability
- Scalability is the property of a system to handle a growing amount of load by adding resources
- There are 10 common ways to make a system scalable:
Scalability Methods
- Vertical Scaling (Scale up): adding more power to existing machines by upgrading server with more RAM, faster CPUs, or additional storage
- Horizontal Scaling (Scale out): adding more machines to the system to spread the workload across multiple servers
- Load Balancing: distributing traffic across multiple servers to ensure no single server becomes overwhelmed
- Caching: storing frequently accessed data in-memory to reduce the load on the server or database
- Content Delivery Networks (CDNs): distributing static assets closer to users to reduce latency and result in faster load times
- Partitioning: splitting data or functionality across multiple nodes/servers to distribute workload and avoid bottlenecks
- Asynchronous communication: deferring long-running or non-critical tasks to background queues or message brokers
- Microservices Architecture: breaking down the application into smaller, independent services that can be scaled independently
- Auto-Scaling: automatically adjusting the number of active servers based on the current load
- Multi-region Deployment: deploying the application in multiple data centers or cloud regions to reduce latency and improve redundancy
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