Open ignatandrei opened 5 months ago
15) Scalability:
Ability to handle growth without degrading system performance.
It involves horizontal and vertical scaling.
14) Availability:
The system remains operational even after a failure.
It involves redundancy, fault tolerance, load balancing, and failover.
13) Reliability:
The system gives predictable output for a specific period.
It involves reduced downtime, handling errors, and redundancy.
12) Performance:
The system meets response time and throughput needs.
It involves using efficient algorithms, data structures, and caching mechanisms.
11) Consistency:
Every server in a distributed system has the same data view at a time.
It involves choosing between strong and eventual consistency.
10) Partitioning:
Dividing data into smaller parts to distribute traffic across many servers.
It involves partitioning algorithms and partition keys.
9) Data modeling:
Schemas and data structures to efficiently store and represent data.
It involves data normalization and different database types.
8) Caching:
Storing data that is often accessed to reduce latency and improve performance.
It involves cache invalidation and cache eviction techniques.
7) Message queues and Event streaming:
Asynchronous communication to decouple components and handle traffic spikes.
6) Security:
It involves authentication, authorization, encryption, and data privacy.
5) Monitoring and Logging:
Tracking system health and optimizing performance.
It involves metrics collection, log aggregation, and finding issues.
4) Design patterns:
It involves MVC, microservices, serverless, and monolith.
3) Load balancing:
Distributing traffic across many servers for scalability and reliability.
It involves load-balancing algorithms and types.
2) Concurrency:
Using threads, and processes for performance in handling concurrent requests.
1) Testing:
It involves unit testing, integration testing, and performance testing.
15) Scalability:
Ability to handle growth without degrading system performance.
It involves horizontal and vertical scaling.
14) Availability:
The system remains operational even after a failure.
It involves redundancy, fault tolerance, load balancing, and failover.
13) Reliability:
The system gives predictable output for a specific period.
It involves reduced downtime, handling errors, and redundancy.
12) Performance:
The system meets response time and throughput needs.
It involves using efficient algorithms, data structures, and caching mechanisms.
11) Consistency:
Every server in a distributed system has the same data view at a time.
It involves choosing between strong and eventual consistency.
10) Partitioning:
Dividing data into smaller parts to distribute traffic across many servers.
It involves partitioning algorithms and partition keys.
9) Data modeling:
Schemas and data structures to efficiently store and represent data.
It involves data normalization and different database types.
8) Caching:
Storing data that is often accessed to reduce latency and improve performance.
It involves cache invalidation and cache eviction techniques.
7) Message queues and Event streaming:
Asynchronous communication to decouple components and handle traffic spikes.
6) Security:
It involves authentication, authorization, encryption, and data privacy.
5) Monitoring and Logging:
Tracking system health and optimizing performance.
It involves metrics collection, log aggregation, and finding issues.
4) Design patterns:
It involves MVC, microservices, serverless, and monolith.
3) Load balancing:
Distributing traffic across many servers for scalability and reliability.
It involves load-balancing algorithms and types.
2) Concurrency:
Using threads, and processes for performance in handling concurrent requests.
1) Testing:
It involves unit testing, integration testing, and performance testing.