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Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

Elasticsearch has different node roles that help manage data and scale clusters effectively.

The number of Elasticsearch nodes depends on storage, workload, and performance needs.

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

FAANG engineers use high-level design principles to create scalable systems.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

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

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

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

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

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

Container data can be lost unless stored in persistent volumes.

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

Replicas keep your system available, while snapshots allow for data recovery.

Choosing the right boundaries for microservices is crucial for effective architecture.

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