💻 IT & Coding concepts,
clear in 60 seconds.

Distributed caching spreads data across multiple servers for better performance and reliability.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Caching helps applications respond faster by serving data from memory instead of hitting the database.

Cache stampede occurs when many requests hit the database due to a cache miss.

A CDN speeds up web applications by caching content closer to users.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

Cache invalidation is about knowing when to refresh cached data.

Cache eviction policies determine which data to remove when caches are full.

Cache penetration occurs when requests for non-existent data overload the database.

Redis is a fast in-memory data store used for caching and low-latency applications.

Connection pooling reuses database connections but can cause issues if misconfigured.

Distributed locks ensure only one server performs a task, avoiding duplication.

Stateless servers are easier to scale because they don't store user session data locally.

Consistent hashing helps distribute data across servers with minimal movement.

Choosing the right distributed lock depends on your system's needs and existing tools.

DNS helps microservices find each other, but it has limitations that may require a service registry.

API pagination helps manage large datasets by breaking them into smaller parts.

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

Optimizing Elasticsearch involves using filters, caching, and measuring performance.

Fallbacks allow systems to handle failures safely and maintain user experience.

Keep-Alive allows reusing HTTP connections to improve efficiency and reduce overhead.

Sharding splits databases into smaller parts to improve performance and scalability.

Choosing between a shared database and a database per service affects system design significantly.

Distributed backpressure helps manage the flow of work between services to prevent overload.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

Load balancers distribute traffic across multiple servers to enhance performance and reliability.

Database per Service ensures each microservice owns its data, reducing dependencies.

Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.

Service Discovery helps microservices locate each other dynamically without fixed IP addresses.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

Deep pagination in Elasticsearch can slow down searches significantly, but there are efficient methods to handle it.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Load shedding helps systems reject excess requests to maintain performance during high demand.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

Message queues allow services to communicate asynchronously, improving scalability and reliability.

Database replication helps keep data available and allows systems to handle more read requests.