Search Results for "structured storage"

Unlike traditional LSM-tree storage engines, SlateDB writes data to object storage to provide bottomless storage capacity, high durability, and easy replication. SlateDB is an embedded storage engine built as a log-structured merge-tree. Unlike traditional LSM-tree storage engines, SlateDB writes data to object storage (S3, GCS, ABS, MinIO, Tigris, and so on). Leveraging object storage allows SlateDB to provide bottomless storage capacity, high durability, and easy replication. ...

Databend is an open source cloud-native data warehouse designed for large-scale analytics and modern data workloads. Built in Rust, the system focuses on high performance, scalability, and efficient data processing for analytical queries. It is designed with a separation of compute and storage, allowing compute nodes to scale independently while storing data in object storage systems. This architecture enables cost-efficient storage and elastic scaling for workloads that involve large...

...It also includes tools for running single-node or multi-node networks, enabling developers to simulate distributed consensus systems locally. The architecture is designed for extensibility, allowing custom consensus algorithms, storage models, and transaction logic to be integrated.

Flow is an ultra-lightweight, high-performance teleprompter for Windows built with Rust and Tauri. Designed for creators, presenters, and educators, Flow delivers a smooth, distraction-free reading surface with minimal CPU and RAM usage. Key Features: - 5 Playback Modes: Offline Voice Tracking (via local Vosk models), Word Highlight, Line-by-Line, Arrow Guide, and Continuous Smooth Scrolling. - Script Library & Manager: Save, search, organize, and switch between multiple scripts locally...

...A crash-safe monotonic ID generator capable of generating 75-125 million unique ID's per second. Zstd compression (use the compression build feature, disabled by default). Cpu-scalable lock-free implementation. Flash-optimized log-structured storage. Uses modern b-tree techniques such as prefix encoding and suffix truncation for reducing the storage costs of long keys with shared prefixes. If keys are the same length and sequential then the system can avoid storing 99%+ of the key data in most cases, essentially acting like a learned index. sled performs prefix encoding on long keys with similar prefixes that are grouped together in a range, as well as suffix truncation to further reduce the indexing costs of long keys.