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

Database indexing speeds up data retrieval by allowing quick lookups.

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

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

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

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

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

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

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

Transactions ensure database operations are reliable using ACID properties.

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

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

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

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

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

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

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

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

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

FAANG interviewers evaluate problem understanding, architecture, scalability, and communication skills.

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

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

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

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

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

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

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

Elasticsearch mapping defines how fields are indexed and searched, with dynamic and explicit options.

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

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

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

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

In distributed systems, timestamps don't always reflect the true order of events.

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

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

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

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