pingcap/tikv

Features

• Distributed Key-Value Stores - Provides a consistent and replicated distributed key-value store designed to maintain a reliable source of truth across a cluster.
• Distributed Consensus Stores - Provides a distributed transactional key-value store that maintains consistent state across nodes using Raft consensus.
• Data Replication Strategies - Copies data across multiple nodes in consensus groups to ensure operational continuity and fault tolerance during node failures.
• Cloud-Native Databases - Engineered as a cloud-native database with support for geographic data distribution and seamless horizontal scaling.
• Multi-Raft Sharding - Divides the keyspace into small ranges, each managed by its own independent Raft consensus group for horizontal scalability.
• Consensus Algorithms - Maintains a consistent view of data across all nodes using a Raft-based consensus algorithm to prevent state divergence.
• Distributed Storage Engines - Provides a storage system that shards data across multiple physical nodes with automated balancing and replication.
• Distributed Transaction Processing - Stores key-value data using a distributed layer that guarantees ACID compliance for all transactional operations via consensus protocols.
• Horizontal Scaling - Distributes data across multiple nodes using automated sharding and migration to scale storage and throughput horizontally.
• Log-Structured Merge-Trees - Uses a log-structured merge-tree engine for high-throughput local data persistence on individual nodes.
• Raft Consensus Implementations - Maintains data consistency and availability by synchronizing a replicated log across storage nodes using the Raft protocol.
• Two-Phase Commit Protocols - Ensures ACID compliance across distributed shards using a two-phase commit coordinator to manage prepare and commit workflows.
• Distributed Transaction Management - Handles complex data operations across multiple nodes while ensuring ACID compliance and strict data consistency.
• Dynamic Shard Splitting - Automatically divides oversized data shards into smaller pieces to balance load and manage storage growth.
• Cluster Load Balancing - Automatically redistributes data ranges across the cluster to balance storage and compute load across physical nodes.
• Cross-Zone Data Distribution - Distributes data copies across specific physical zones or geographies to ensure high availability and disaster recovery.
• Coprocessors - Implements a coprocessor for executing filtering and aggregation logic directly on storage nodes to minimize network latency.
• Distributed Computing - Executes filtering and aggregation logic directly on storage nodes to reduce data transfer over the network.
• Regional Replication - Spreads data copies across different physical locations to ensure high availability and protection against regional disasters.
• Storage-Side Computation - Processes data directly on storage nodes using a coprocessor to reduce network traffic and lower system latency.
• Key-Value Stores - Distributed key-value database powered by Rust.

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