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

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.

Database indexing speeds up data retrieval by allowing quick lookups.

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

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

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.

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

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

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

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

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 Schema Evolution allows safe changes to message structures without breaking existing consumers.

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

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

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

Transactions ensure database operations are reliable using ACID properties.

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

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

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

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