Database & Data Concepts Glossary
ACID, BASE, CAP theorem, CRUD, ORM, N+1, sharding, indexing — every database term decoded.
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ACID — Explained with Examples
ACID is a set of database properties — Atomicity, Consistency, Isolation, Durability — that guarantee reliable processing of database transactions.
✓ LiveBASE — Explained with Examples
BASE (Basically Available, Soft State, Eventually Consistent) is a distributed data model prioritizing availability over the strong consistency of ACID.
✓ LiveCAP Theorem — Explained with Examples
CAP Theorem states that a distributed data system can guarantee at most two of three properties: Consistency, Availability, and Partition Tolerance.
✓ LiveCRUD — Explained with Examples
CRUD is an acronym for Create, Read, Update, Delete — the four basic operations for persistent storage in databases and APIs.
✓ LiveORM — Explained with Examples
ORM (Object-Relational Mapping) maps database tables to programming language objects, letting developers work with data using familiar OOP syntax instead of SQL.
✓ LiveODM — Explained with Examples
ODM (Object-Document Mapping) maps JSON-like documents in NoSQL databases to programming language objects, similar to how ORMs map relational tables.
✓ LiveN+1 Query Problem — Explained with Examples
The N+1 query problem occurs when an application executes N additional database queries to load related data for each of N parent records, causing performance degradation.
✓ LiveSharding — Explained with Examples
Sharding is a database architecture pattern that horizontally partitions data across multiple database instances to improve scalability and performance.
✓ LiveDatabase Index — Explained with Examples
A database index is a data structure (like a B-tree or hash table) that speeds up data retrieval operations at the cost of additional storage and write overhead.
✓ LiveReplication — Explained with Examples
Database replication copies data from one database server to others, ensuring redundancy, high availability, and improved read performance across distributed systems.
✓ LivePartitioning — Explained with Examples
Database partitioning divides a large table into smaller, manageable segments while keeping them under the same database server for improved performance.
✓ LiveDatabase Migration — Explained with Examples
A database migration is a version-controlled change to a database schema, allowing teams to evolve the database incrementally alongside application code.
✓ LiveConnection Pooling — Explained with Examples
Connection pooling reuses a cache of database connections to avoid the overhead of establishing a new connection for every request.
✓ LiveDatabase Transaction — Explained with Examples
A database transaction is a unit of work that groups multiple operations into a single all-or-nothing execution with ACID guarantees.
✓ LiveIsolation Levels — Explained with Examples
Database isolation levels control how transaction concurrency is managed, balancing consistency against performance across four defined levels.
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