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Transactions ensure database operations are reliable using ACID properties.

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

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.

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

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

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

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

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

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

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

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

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

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

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

Kafka uses retention to keep historical data and compaction to keep the latest state.

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

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

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

Availability is about access; reliability is about correct performance.

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

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

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

Database indexing speeds up data retrieval by allowing quick lookups.

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

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

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

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

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

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

REST API design focuses on creating clean, efficient, and scalable web services.

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

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

The CAP Theorem explains the trade-offs in distributed systems during network failures.

Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.