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Elasticsearch uses shards for data distribution and replicas for redundancy.

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

Elasticsearch distributes search queries across shards to find and retrieve results quickly.

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

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

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

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

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

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

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

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

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

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

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

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

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

Redis is a fast in-memory data store used for caching and low-latency applications.