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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.

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

SLIs measure service performance, SLOs set reliability targets, and SLAs are customer agreements.

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.

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

Elasticsearch mapping defines how fields are indexed and searched, with dynamic and explicit options.

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

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

Elasticsearch offers two pagination methods: from & size for shallow pages and search_after for deep pages.

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

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 ILM automates index management to reduce storage costs and meet data needs.

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

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