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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.

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

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

Kafka uses state stores to remember previous events in stateful stream processing.

Elasticsearch aggregations help analyze data by grouping, calculating, and estimating values.

Availability is about access; reliability is about correct performance.

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

Functional requirements define what a system does, while non-functional requirements define how it performs.

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

HLD provides an overview of system architecture, while LLD details implementation specifics.

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

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

Cache invalidation is about knowing when to refresh cached data.

Elasticsearch aggregations help summarize data using metrics for calculations and buckets for grouping.