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

Monolithic and microservices architectures each have unique advantages and challenges for software development.

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

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

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

Database replication enhances data availability and read performance but has challenges like replication lag.

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

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

Search engines collect, index, retrieve, and rank documents to provide relevant results.

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

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

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

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

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

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

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

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

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

An API Gateway simplifies client access to backend services in microservices.

Choose Elasticsearch for complex text searches, but use databases for structured queries.

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

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

Connection pooling reuses database connections but can cause issues if misconfigured.