olric-data/olric

Features

• Distributed Key-Value Stores - Provides a high-performance distributed key-value store that partitions and replicates data across a cluster of servers.
• Distributed Key-Value Stores - Stores and retrieves data across a cluster of servers using a distributed hash table.
• Key-Value Stores - Provides a high-performance in-memory key-value store for distributed data management.
• Key-Value - Functions as a high-performance distributed store for saving and fetching various data types via unique keys.
• Distributed Data Management - Partitions data across multiple nodes and manages replicas to ensure high availability and system scalability.
• Distributed Data Replication - Ensures data availability and consistency across the cluster using quorum-based replication.
• Data Replication - Synchronizes data across distributed nodes and uses failure detection to maintain reliability.
• Data Replication Strategies - Copies data to backup owners using synchronous or asynchronous replication for fault tolerance.
• Quorum-Based Commit Protocols - Guarantees data accuracy by requiring a minimum number of replica acknowledgments for read and write operations.
• Distributed Data Grids - Provides a shared memory system for managing distributed maps and performing atomic operations across nodes.
• Distributed Routing Tables - Maintains a distributed routing table that maps data partitions to their primary and replica owners for efficient request routing.
• In-Memory Caches - Provides an in-memory data cache with automated time-to-live expiration and memory eviction policies.
• Distributed Memory Caches - Caches transient data in memory across multiple nodes with automated eviction and TTL policies.
• Model-to-Source Routing - Provides routing information mapping data partitions to specific owner nodes for request dispatching.
• Quorum-Based Consistency - Requires a minimum number of successful replica operations before returning a result to the client.
• Cluster Membership Coordination - Tracks active node instances and manages peer discovery to maintain connectivity as nodes join or leave.
• Cluster Membership Protocols - Uses gossip-based protocols for nodes to discover each other and maintain a dynamic cluster topology.
• Automated Node Discovery - Automatically identifies and connects to peer nodes using cloud-native plugins and external discovery services.
• Capacity Scaling - Dynamically adds new nodes to the cluster to increase storage capacity and distribute the workload.
• Distributed Locks - Provides a distributed locking mechanism to coordinate access to shared resources across the cluster.
• Cluster Discovery Services - Locates and joins peer nodes by integrating with cloud providers and cluster discovery services.
• Publish-Subscribe Messaging - Enables producers and consumers to communicate asynchronously via named channels.
• Data Synchronization and Consistency - Sets read and write quorums to balance system performance against data consistency guarantees.
• Pub-Sub Messaging - Implements a pub-sub messaging system to broadcast real-time updates across the cluster.
• Sharded Pub/Sub - Uses glob-style pattern matching to route pub-sub messages to subscribers across the cluster.
• Publish-Subscribe Systems - Distributes messages using specific channel names or pattern-based subscriptions.
• Service Discovery Integrations - Integrates with external naming services and daemons to automatically locate peer nodes within a network.
• Consistent Hashing - Implements consistent hashing to distribute data across nodes and minimize remapping during cluster scaling.
• Split-Brain Prevention - Prevents data divergence by using majority quorums to stop node operations during network partitions.
• Distributed Atomic Operations - Supports atomic increments and replacements of values to ensure consistency across the distributed grid.
• Batch Data Operations - Groups multiple read and write commands into single requests to reduce network overhead.
• Cache Eviction Policies - Manages memory usage by removing outdated entries using time-based or usage-based policies.
• Cursor-Based Iteration - Provides cursor-based iteration to retrieve large distributed datasets across partitions without blocking server resources.
• Conflict Resolution Strategies - Balances throughput and reliability using a combination of quorums and conflict resolution strategies.
• Database Key Scanning - Provides non-blocking iteration to scan keys in a distributed map using match patterns.
• Distributed Query Processing - Executes distributed queries to scan and retrieve keys from maps across multiple cluster nodes.
• Write Conflict Resolution Policies - Resolves data conflicts between diverging versions using a last-write-wins policy based on client timestamps.
• Conflict Resolution - Compares replica timestamps during reads and propagates the latest version to outdated nodes.
• Distributed Map Scanning - Enables retrieving subsets of entries from distributed maps using filtering criteria and limits.
• Cloud Native Orchestration - Automatically discovers peer nodes and manages cluster membership within cloud or containerized environments.
• Standalone or Cluster Deployments - Supports flexible deployment as either a standalone server or a distributed cluster.
• Standalone Service Deployments - Can be run as a standalone server accessed via a standard serialization protocol.
• Split-Brain Prevention Mechanisms - Prevents split-brain scenarios by requiring a majority quorum for node operations during network partitions.
• Pattern-Based Routing - Distributes pub-sub messages across the cluster using glob-style pattern matching for flexible channel subscriptions.
• Embedded Library Deployments - Allows the data grid to be integrated directly into applications for low-latency access.
• Last-Write-Wins Semantics - Reconciles diverging data versions using a last-write-wins policy based on client timestamps.
• LRU Cache Eviction - Automatically removes the least recently used items from memory to prevent system resource exhaustion.
• Cluster Health Monitoring - Retrieves operational statistics and runtime metrics to analyze the overall state of the grid.
• Cluster Monitoring Systems - Tracks network throughput, memory allocation, and connection counts to monitor system health.
• Cluster Topology Monitors - Provides tools to retrieve the cluster routing table and list all members and roles.
• Time-To-Live Expirations - Automatically evicts in-memory entries after a specified time-to-live duration to manage resource limits.

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