OpenAtomFoundation/pikiwidb
Pikiwidb
PikiwiDB is a distributed NoSQL database and disk-based key-value store that serves as a Redis-compatible protocol server. It is designed to handle datasets larger than available system memory by utilizing a persistence engine that stores the full dataset on disk.
The system employs a tiered storage model, caching frequently accessed hot data in memory while maintaining the primary volume on disk. It ensures high availability through a replicated data store architecture, using asynchronous binary logs to synchronize data between primary and secondary nodes.
The project supports distributed database scaling via cluster-based data sharding and organizes data into groups to expand the storage layer. Its operational capabilities include system performance monitoring for tracking resource utilization and support for containerized deployment.
Features
- Redis Compatible Stores - A disk-based storage system that implements the Redis protocol for compatibility with existing clients and tools.
- Hybrid Memory-Disk Caches - Balances performance and capacity by caching hot data in RAM while keeping the full dataset on disk.
- Data Replication - Maintains high availability by synchronizing data between primary and secondary nodes using asynchronous binary logs.
- Distributed Sharding Architectures - Uses a distributed sharding architecture to partition data across multiple nodes for horizontal scalability.
- Disk-Backed Stores - Acts as a disk-backed store that caches hot data in memory while persisting the full dataset to disk.
- Unbounded Key-Value Stores - Persists strings, hashes, lists, and sets to disk to handle datasets larger than available system memory.
- Disk-Backed Key-Value Management - Balances frequent RAM access and full disk storage to manage key-value datasets larger than system memory.
- NoSQL Databases - A scalable distributed NoSQL database that organizes data into clusters to manage massive enterprise datasets.
- Memory-Disk Layering - Employs a tiered storage model that layers in-memory caching over a persistent disk-based storage engine.
- Protocol Servers - Implements the standard Redis protocol to ensure full compatibility with existing Redis clients and toolsets.
- Storage Tiering - Balances performance by caching hot data in memory while maintaining the full dataset on disk.
- Distributed Database Clusters - Expands storage capacity across multiple nodes and clusters to handle massive volumes of enterprise data.
- Distributed Storage Clusters - Organizes data into groups using a distributed storage cluster architecture to elastically scale the storage layer.
- Incremental Data Synchronization - Maintains consistency across distributed nodes using a combination of full snapshots and incremental updates.
- Large-Scale Dataset Management - Manages massive volumes of enterprise data across thousands of instances for high-capacity storage.
- Primary-Replica Replication - Implements primary-replica replication using binary logs to maintain continuous data availability.
- Storage Scaling - Allows elastic scaling of storage capacity through both single-machine and clustered deployment modes.
- Database High Availability - Ensures continuous service availability through the coordination of primary and secondary nodes.
- Log Replication Strategies - Synchronizes data from primary to secondary nodes by streaming binary logs for redundancy and high availability.
- Database Performance Monitoring - Tracks memory usage, CPU load, and network traffic to monitor the health and operational speed of the database.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
