Redisson

Redisson is a Java library and Redis client that functions as a distributed Java object mapper, caching provider, and locking framework. It maps Java collections and concurrency primitives to distributed implementations backed by Redis and Valkey, providing synchronous, asynchronous, and reactive APIs for interacting with these data stores.

The project distinguishes itself by providing a comprehensive suite of distributed coordination tools, including a locking framework for managing semaphores and countdown latches across multiple application nodes. It also serves as a distributed messaging system for implementing pub/sub patterns and reliable queues using event streams.

The framework covers a broad range of capabilities, including distributed state management through shared collections, objects, and transactions. It supports advanced data retrieval via vector similarity search, full-text search, and JSON querying, while offering performance optimizations such as probabilistic data structures, local caching, and command pipelining.

Redisson includes starter dependencies for the Spring Framework and Spring Boot to simplify application configuration and dependency management.

Features

Distributed Collections - Provides thread-safe, shared distributed implementations of standard Java collections like maps, sets, lists, and queues.
Object-to-Redis Mappers - Maps Java collections and concurrency primitives to distributed implementations backed by Redis and Valkey.
Data Serialization Codecs - Implements pluggable data serialization codecs such as JSON, Kryo, and Protobuf for transforming Java objects into byte arrays.
Distributed Caching - Integrates Java applications with Redis or Valkey to improve response times through distributed caching.
Remote Object Mappings - Maps plain Java objects to remote storage with support for persistence, indexing, and cascade handling.
Redis Clients - Provides a Java library for interacting with Redis and Valkey using synchronous, asynchronous, and reactive APIs.
Distributed Locks - Manages concurrent access to shared resources across a network using distributed locks, semaphores, and latches.
Distributed Messaging - Implements a messaging toolkit for pub/sub patterns and reliable queues using Redis and Valkey streams.
Distributed State Management - Synchronizes Java objects and collections across multiple application nodes in a cluster using Redis or Valkey.
Distributed Task Queues - Implements distributed blocking and delayed queues for asynchronous message processing and task distribution.
Event-Driven I/O - Utilizes a Netty-based non-blocking event-driven framework to handle high-throughput network communication.
Pipeline Batching - Groups multiple operations into a single request or pipeline to reduce network round-trips and increase throughput.
Lua Scripting - Executes Lua scripts on the server to ensure multi-step operations are performed as a single atomic unit.
Cluster Topology Management - Provides configuration for various server topologies, including single server, master-slave, sentinel, and cluster modes.
Data Caching - Caches frequently accessed remote data locally to reduce network latency and server load.
Event Streaming Pipelines - Connects to messaging middleware to produce and consume data streams for event-driven architectures.
Full Text Search - Provides full-text search capabilities using specialized indexing for stored data.
JSON Search Indices - Implements search indices for specific paths within JSON documents to enable efficient querying.
Local Caching Layers - Provides a local-first cache layer to store frequently accessed remote data in memory and reduce network round-trips.
Probabilistic Data Structures - Uses bloom filters, hyperloglog, and cuckoo filters for memory-efficient membership and cardinality checks.
Sentinel Management - Provides integration with Redis Sentinel for node health monitoring and automatic failover redirection.
Server-Side Scripting - Enables the execution of server-side Lua scripts to perform complex atomic operations directly on the data store.
Vector Embedding Indexes - Supports high-dimensional vector embedding indexes for similarity searches in AI applications.
Vector Similarity Search - Stores and queries high-dimensional embeddings to enable efficient retrieval for AI applications.
Atomic Counters - Maintains atomic long and double counters across a cluster to track global metrics and sequence numbers.
Distributed Task Schedulers - Runs remote tasks and MapReduce operations across a cluster to distribute computational workloads.
Message Queues - Handles asynchronous messaging and task distribution using reliable, blocking queues, and ring buffers.
Command Pipelining - Groups multiple server commands into a single network request to minimize round-trip latency via pipelining.
Message Delivery Guarantees - Provides a topic-subscription-consumer model with message acknowledgment, grouping, and dead-letter topics.
Message Stream Consumer Groups - Handles append-only logs with support for consumer groups, message acknowledgment, and stream trimming.
Pub-Sub Messaging - Facilitates real-time communication using topics and pattern-based subscriptions with support for sharding.
Topic Sharding - Distributes pub-sub messaging workloads across multiple cluster nodes to increase throughput and scalability.
Distributed Semaphores - Implements distributed semaphores to control access to limited shared resources across a cluster.
Distributed Transaction Coordinators - Coordinates atomic operations across multiple data sources to ensure consistency using a distributed transaction manager.
Rate Limiting - Implements distributed rate-limiting algorithms to cap the number of operations per time window.
Caching and Messaging - Distributed objects and services for Java Redis clients.
Database Tools - Distributed data structures backed by Redis.
Key-Value Stores - A feature-rich Redis client for Java with distributed object support.

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Features