Document, key-value, column and graph stores scale flexibly.
NoSQL stores trade strict relational structure for scale and flexibility: key-value (Redis), document (MongoDB), column-family (Cassandra), graph (Neo4j). Often eventually consistent and horizontally scalable. Choose by access patterns and consistency needs, not hype.
A session store uses Redis for fast key-value access.
NoSQL databases handle flexible data and very large scale with different trade-offs, so they are chosen for specific workloads.
Document, key-value, column and graph stores scale flexibly.
NoSQL databases handle flexible data and very large scale with different trade-offs, so they are chosen for specific workloads.
When the data is flexible in shape, the access patterns are simple and key-based, or horizontal scaling across many machines is essential.
Many offer weaker consistency by default or allow tuning it, so the guarantees must be checked for each system.
Typical applications include storing user profiles as documents with varying fields, caching session data in a key-value store and modelling a social graph with a graph database.
Study "Indexing" first (topic 78 of 104 in Software & Computing). It covers the groundwork this topic builds on.
Schema-on-read. Sharding. Polyglot persistence.
A session store uses Redis for fast key-value access.
The next topic is "CAP theorem", which builds directly on this one.
It is classed as Advanced in Software & Computing (topic 79 of 104). It is an advanced topic. It assumes you are comfortable with the intermediate topics in Software & Computing.
Want to practise? Open the interactive course for quick checks, quizzes, simulations and progress tracking.