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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.

Database indexing speeds up data retrieval by allowing quick lookups.

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.

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

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.

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

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.

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

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.

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

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

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

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

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

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

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

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

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

Cache eviction policies determine which data to remove when caches are full.

Consistent hashing helps distribute data across servers with minimal movement.

Replicas keep your system available, while snapshots allow for data recovery.

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

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

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.

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

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

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

Physical time tells when events happen, while logical time tells their order.

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

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

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

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.

Primary shards partition data, while replica shards duplicate it for redundancy.

Elasticsearch ILM automates data management through defined lifecycle phases.

Logs, metrics, and traces help engineers debug issues in software systems.

FAANG engineers use high-level design principles to create scalable systems.

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.

Structured logging helps in quickly identifying issues by organizing log data consistently.