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Kubernetes uses Pods, ReplicaSets, and Deployments to manage application availability and updates.

Kubernetes uses a Control Plane to manage Worker Nodes and ensure application reliability.

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

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

Kubernetes Services provide stable endpoints for applications despite changing Pod IPs.

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

Kubernetes Ingress allows one entry point for multiple applications, simplifying routing.

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.

Read replicas allow databases to handle more read requests by distributing them across multiple copies.

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

Cassandra uses a masterless architecture to distribute and replicate data efficiently.

Apache Kafka is a system for managing large volumes of events efficiently.

MongoDB uses sharding to distribute data across multiple servers for better scalability.

Kafka uses partitions to allow multiple consumers to process messages in parallel.

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.

Availability Zones help prevent downtime by isolating failures across multiple locations.

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

REST API design focuses on creating clean, efficient, and scalable web services.

Container networking enables communication between services in different containers.

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

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

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

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

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

Consistent hashing helps distribute data across servers with minimal movement.

Container health checks ensure your app is running and ready to serve users.

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

Kafka uses partitions to allow parallel processing while ensuring message order with keys.

MongoDB uses replica sets for data redundancy and sharding for scalability.

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.

Kafka rebalances consumer groups to manage partition assignments effectively.

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

You can replay Kafka events safely by separating live and replay processes.

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

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