Redis Software Features

Stores data as key-value pairs where key is a unique identifier.

Supports multiple data types like lists, sets, hashes (similar to map), sorted sets etc.

Stores and queries data as JSON-like documents.

Supports multiple data types like lists, sets, hashes (similar to map), sorted sets etc.

Allows users to index at ingest and query endlessly across data.

Notifies specific events like document inserts, updates, replaces, deletes etc and responds in real-time.

Designed to store interconnected data consisting of nodes and edges.

Supports multiple data types like lists, sets, hashes (similar to map), sorted sets etc.

Allows users to index at ingest and query endlessly across data.

Notifies specific events like document inserts, updates, replaces, deletes etc and responds in real-time.

Powerful query language that allows users to access patterns like traversals, JOINs, search, geospatial etc.

Implements auto horizontal data partitioning that allows storing data on more than one node to scale out.

Restores a database to a correct (consistent) state in the event of a failure.

Copy data across multiple servers through master-slave, peer-to-peer replication architecture etc.

Implements auto horizontal data partitioning that allows storing data on more than one node to scale out.

Restores a database to a correct(consistent) state in the event of a failure.

Copy data across multiple servers through master-slave, peer-to-peer replication architecture etc.

Implements auto horizontal data partitioning that allows storing data on more than one node to scale out.

Restores a database to a correct(consistent) state in the event of a failure.

Stores frequently-used data in system memory quickly.

Provides predefined system roles, privileges, and user-defined roles to users.

Provides an audit log to track access and operations performed on databases for regulatory compliance.

Provides encryption capability for all the data at rest using encryption keys.

Provides predefined system roles, privileges, and user-defined roles to users.

Allows integration with external security mechanisms like Kerberos, LDAP authentication etc.

Provides an audit log to track access and operations performed on databases for regulatory compliance.

Provides encryption capability for all the data at rest using encryption keys.

Provides predefined system roles, privileges, and user-defined roles to users.

Allows integration with external security mechanisms like Kerberos, LDAP authentication etc.

Provides an audit log to track access and operations performed on databases for regulatory compliance.

Provides encryption capability for all the data at rest using encryption keys.

Available on multiple operating systems like Linux, Windows, MacOS etc.

Provides support to handle structured, semi-structured, and unstructured data with equal effect.

Available on multiple operating systems like Linux, Windows, MacOS etc.

Allows users to connect business intelligence tools to the database.

Provides support to handle structured, semi-structured, and unstructured data with equal effect.

Allows users to connect business intelligence tools to the database.

Available on multiple operating systems like Linux, Windows, MacOS etc.

Helps interpret SQL queries and determine the fastest method of execution

Helps interpret SQL queries and determine the fastest method of execution

Orchestrates retrieval, reranking, and enrichment steps within RAG workflows

Optimizes chunk selection, context assembly, and grounding specifically for LLM consumption

Enables advanced tuning of lexical, semantic, and reranked retrieval strategies

Provides insights into query behavior, retrieval quality, and relevance performance

Ensures resilience through redundancy, failover, and recovery mechanisms

Manages embedding updates, re-indexing, and model version changes over time

Enables semantic search across text, images, audio, or video using embeddings

Allows teams to bring their own embedding models or LLM providers and change them without re-architecting

Supports near-real-time index updates as source data changes

Enriches content using chunking, metadata inference, entity extraction, or OCR during indexing

Enforces document-, field-, or metadata-level permissions during retrieval

Supports regional data controls, retention rules, and regulatory compliance

Tracks queries, retrieved content, and access decisions for compliance and debugging

Enables fine-grained control over ranking using rules, weights, feedback loops, or learning-to-rank models

Improves retrieval quality through query rewriting, semantic expansion, or intent detection

Supports multi-step retrieval pipelines where initial candidates are reranked using ML or LLM-based approaches

Adjusts retrieval and ranking based on user context, behavior, or application-level signals