Any concept.
clear in 60 seconds.

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.

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

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

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

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

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

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

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

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

Transactions ensure database operations are reliable using ACID properties.

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

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

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

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

Database isolation levels determine how transactions interact and affect data consistency.

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

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

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.

Database indexing speeds up data retrieval by allowing quick lookups.

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

Write-Ahead Log ensures data is safely recorded before changes are made.

In distributed systems, timestamps don't always reflect the true order of events.

Read-after-write consistency ensures users see their latest updates immediately.

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

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

Caching helps applications respond faster by serving data from memory instead of hitting the database.

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

Stateless servers are easier to scale because they don't store user session data locally.

Consistent hashing helps distribute data across servers with minimal movement.

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

Consistency models determine what data reads in distributed systems can see.

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

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

Composite indexes speed up database queries that filter or sort by multiple columns.

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

Lamport Timestamps help order events in distributed systems while preserving causality.

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

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

API pagination helps manage large datasets by breaking them into smaller parts.

Cache stampede occurs when many requests hit the database due to a cache miss.

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.

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

Dependency failures can disrupt applications, but resilience patterns can help manage them.

Kafka uses replication and in-sync replicas to ensure data is always available.

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

Kafka Streams uses local state for quick data access and changelogs for recovery.

Cache penetration occurs when requests for non-existent data overload the database.

ZooKeeper watches notify clients of changes, avoiding constant polling and reducing server load.

DNS helps microservices find each other, but it has limitations that may require a service registry.

Partition pruning helps databases skip unnecessary data scans, speeding up queries.

Elasticsearch uses shards for data distribution and replicas for redundancy.

RPC allows one service to call a function in another service over the network.

Crash failures stop a service, while network failures disrupt communication.

Elasticsearch uses master election and quorum to prevent split brain scenarios.