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

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

Elasticsearch ILM automates index management to reduce storage costs and meet data needs.

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

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

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.

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

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

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

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

Transactions ensure database operations are reliable using ACID properties.

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

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

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

Elasticsearch Data Streams simplify time-series data management with one name for many indices.

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

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

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

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

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.

Cache invalidation is about knowing when to refresh cached data.

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

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

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

A CDN speeds up web applications by caching content closer to users.

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

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

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

Availability is about access; reliability is about correct performance.

Elasticsearch ILM automates data management through defined lifecycle phases.

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

Request-Response asks for a result, while Events notify that something has happened.

REST, RPC, and gRPC are different methods for communication in software systems.