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

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

Choosing between a shared database and a database per service affects system design significantly.

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

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

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.

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

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

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

The choice between SQL and NoSQL databases depends on your application's specific needs.

Failover ensures systems remain operational by managing leader changes during failures.

Load shedding helps systems reject excess requests to maintain performance during high demand.

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.

Network partitions occur when servers are operational but can't communicate, affecting system performance.

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

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

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

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.

B-Trees help databases find data quickly without scanning every row.

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.