mrniko/redisson

Features

• Distributed Collections - Implements distributed thread-safe maps, sets, lists, and queues for sharing state between application nodes.
• Distributed State Management - Implements shared maps, sets, and queues to maintain consistent application state across a cluster.
• Distributed State Structures - Synchronizes application state using distributed maps, sets, queues, and locks stored remotely.
• Remote Object Mappings - Maps local Java object methods to remote Redis commands through a specialized serialization layer.
• Redis Clients - Acts as a comprehensive Java client library for interacting with Redis and Valkey data stores.
• Atomic Counters - Generates consistent atomic counters and IDs across a network using distributed numeric values.
• Distributed Locks - Provides distributed lock coordination to ensure mutual exclusion across a cluster of application instances.
• Distributed Collections - Provides a comprehensive set of thread-safe distributed collections for sharing state across cluster nodes.
• Cache API Implementations - Provides a JCache compliant caching layer for standardized data storage and retrieval.
• Caching - Combines remote data stores with local caches to reduce network latency for frequent reads.
• Distributed Sharding Architectures - Implements data sharding to distribute large datasets across multiple Redis nodes for horizontal scaling.
• MapReduce Processing Engines - Provides a distributed MapReduce framework to process large datasets in parallel across multiple nodes.
• Probabilistic Data Structures - Implements memory-efficient probabilistic structures like Bloom filters and HyperLogLog for high-volume membership testing.
• Probabilistic Membership Filters - Ships memory-efficient probabilistic structures like Bloom filters for high-volume data membership testing.
• Server-Side Scripting - Uses server-side Lua scripting to execute complex atomic operations directly within the data store.
• Distributed Task Schedulers - Coordinates background processing across a network by executing scheduled jobs and managing distributed atomic counters.
• Task Schedulers - Provides a distributed executor service to run scheduled jobs and remote services across a cluster.
• Standardized Caching APIs - Implements a JCache compliant caching layer for standardized data storage and retrieval.
• Tiered Caching - Implements a multi-tier caching strategy combining local in-memory storage with a remote data store.
• Distributed Task Schedulers - Coordinates distributed tasks and background processing across a network using remote scheduling.
• Pub-Sub Messaging - Coordinates reliable message delivery using topics and sharded topics via a pub/sub architecture.
• Event-Driven Synchronization - Synchronizes local application state by listening to real-time publication channels via a pub/sub mechanism.
• Event-Driven I/O - Utilizes a Netty-based event-driven I/O framework to handle high-concurrency asynchronous network communication.
• Session Managers - Externalizes web session state to a remote store to enable horizontal scaling and failover.
• Database Clients and Drivers - Distributed Java objects and services for Redis.
• Cluster Management and Elasticity - Distributed and scalable data structures for Redis.

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