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

Transactions ensure database operations are reliable using ACID properties.

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

Database indexing speeds up data retrieval by allowing quick lookups.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

DNS resolution translates a URL into an IP address for web access.

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

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

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

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

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

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

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

An inverted index helps search engines quickly find documents containing specific terms.

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

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

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

HTTP methods tell the server what to do, and status codes tell us the result.

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

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

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

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

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

DNS records like A, CNAME, and MX help route internet traffic and manage services.

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

Availability is about access; reliability is about correct performance.