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

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Elasticsearch offers three main autocomplete strategies, each with unique benefits and trade-offs.

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

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

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

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

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

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

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.

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

Microservices can communicate using five key patterns based on their needs.

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

Jitter helps spread out client retries to avoid traffic spikes.

L4 and L7 load balancers differ in how they route network traffic based on layers of the OSI model.

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

Elasticsearch index templates automatically apply settings to new indices based on patterns.

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