slatedb/slatedb
Slatedb
SlateDB is a cloud-native key-value store and distributed database engine that utilizes a log-structured merge-tree architecture. It serves as a transactional storage layer designed to persist data directly to cloud object storage.
The engine differentiates itself by optimizing read performance for remote storage through the use of bloom filters and multi-level block caching. It employs a single-writer multi-reader model and provides the ability to create zero-copy clones via copy-on-write checkpointing.
The system supports atomic transactions, range queries, and snapshot-based concurrency control to ensure data consistency. It also includes capabilities for capturing data changes to enable real-time synchronization and auditing.
Features
- Log-Structured Merge-Trees - Utilizes a log-structured merge-tree architecture to organize data for high-throughput writes and efficient range queries.
- LSM-Tree Key-Value Stores - Implements a log-structured merge-tree key-value store designed specifically for high-throughput writes to remote cloud storage.
- Concurrent Read-Write Transactions - Provides snapshot-based concurrency control to isolate read operations from write operations.
- Copy-on-Write Transaction Systems - Provides a copy-on-write transactional system to create instant, zero-copy dataset snapshots.
- Data Lake Transaction Managers - Provides transactional management with snapshot isolation for workloads operating on cloud-native data lakes.
- Distributed Databases - Provides a distributed database engine core with atomic transactions and snapshots for building scalable data applications.
- Atomic Transactions - Ensures data consistency by grouping multiple operations into atomic transactions.
- Single-Writer Multi-Reader Stores - Implements a single-writer multi-reader model to ensure consistency while scaling concurrent read operations.
- Block Caches - Speeds up data retrieval by storing frequently accessed blocks in memory and on local disk.
- Object Storage Database Engines - Serves as a database backend that persists data directly to cloud object storage while optimizing read performance.
- Object-Storage Persistence - Persists database records directly to cloud object storage for durable, cost-effective, and unlimited scaling.
- Key-Value - Provides a cloud-native key-value database that persists data directly to object storage.
- Change Data Capture - Streams a real-time record of database modifications for synchronization and auditing.
- Read Performance Optimizations - Implements multi-level block caching and bloom filters to reduce latency when retrieving data from cloud object storage.
- Caching and Performance - Optimizes read performance by utilizing block caches and bloom filters to reduce cloud storage latency.
- Transactional Storage Layers - Provides a transactional persistence layer featuring single-writer concurrency control and zero-copy cloning via checkpoints.
- Automatic Background Compactions - Performs automatic background compactions to merge sorted runs and reclaim disk space.
- Data Lakes - Serves as a high-performance indexing layer for large-scale analytical datasets stored in cloud data lakes.
- Bloom Filters - Implements Bloom filters to optimize data retrieval by skipping unnecessary cloud object storage lookups.
- Zero-Copy Cloning - Creates checkpoints to fork or split data sets without duplicating the physical storage on disk.
- Range Query Structures - Supports efficient range queries to retrieve sequences of keys between specified boundaries.
