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Message queues allow services to communicate asynchronously, improving scalability and reliability.

Distributed locks ensure only one server performs a task, avoiding duplication.

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

Choosing the right distributed lock depends on your system's needs and existing tools.

Distributed backpressure helps manage the flow of work between services to prevent overload.

Fallbacks allow systems to handle failures safely and maintain user experience.

The Saga Pattern manages distributed transactions using local transactions and compensating actions.

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

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

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

Microservices and modular monoliths differ mainly in deployment and communication methods.

Availability is about access; reliability is about correct performance.

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.

Database per Service ensures each microservice owns its data, reducing dependencies.

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

The Token Bucket algorithm helps manage how many requests a server can handle.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

The Circuit Breaker pattern controls service calls to prevent failures from spreading.

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