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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.

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.

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

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.

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

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

Eager rebalancing revokes all assignments, while cooperative allows incremental changes.

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

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

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

Kafka commit strategies determine how offsets are managed during message processing.

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.

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 aggregations help analyze data by grouping, calculating, and estimating values.

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.