redisson/redisson

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.

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