5 repository-uri
Storing massive volumes of database records on cloud object storage for durable and unlimited scaling.
Distinct from Large-Scale Data Computation: Focuses on the persistence layer for database scaling rather than general data computation or integration.
Explore 5 awesome GitHub repositories matching data & databases · Object-Storage Persistence. Refine with filters or upvote what's useful.
Thanos is a distributed metrics query engine and monitoring scalability suite designed to provide a unified interface for aggregating data from multiple Prometheus servers and clusters. It functions as a high availability monitoring backend that eliminates single points of failure by deduplicating data from replicated instances. The system enables long-term retention by persisting time-series data to cloud-native object storage, allowing for unlimited historical archiving beyond the limits of local disks. It further optimizes this storage through a downsampling and retention manager that comp
Persists immutable blocks of time-series data to cloud object storage for durable and unlimited scaling.
Lance is a versioned columnar data format and storage engine designed as a multimodal AI lakehouse. It serves as a vector database storage engine and a cloud object store dataset manager, organizing images, video, audio, and embeddings into a unified format optimized for machine learning workflows. The project distinguishes itself by combining a columnar layout for structured data with a specialized blob store for large multimodal tensors. It implements a hybrid search engine that integrates vector similarity search, full-text search, and SQL analytics on a single dataset, supported by a stor
Persists datasets across cloud providers and distributed filesystems using URI schemes to specify storage backends.
Mimir este o bază de date de serii temporale multi-tenant și un magazin de metrici distribuit, conceput pentru telemetrie scalabilă. Servește ca un backend compatibil cu Prometheus, oferind stocare pe termen lung și un motor de interogare scalabil pentru volume masive de date de serii temporale. Sistemul este construit pentru observabilitate multi-tenant, izolând datele de telemetrie și limitele de resurse pentru echipe sau organizații independente în cadrul unui singur cluster. Asigură disponibilitate ridicată și durabilitate prin sharding și replicarea datelor într-un cluster distribuit, utilizând stocarea de obiecte pentru persistență pentru a elimina dependențele de baze de date externe. Proiectul acoperă capabilități vaste, inclusiv agregarea globală a metricilor pentru analiză cross-region și execuția distribuită a interogărilor folosind paralelizarea și caching-ul. De asemenea, integrează instrumente de observabilitate precum alertarea federată, monitorizarea sintetică și fluxuri de lucru de rezolvare a incidentelor bazate pe AI pentru a accelera depanarea. Controalele administrative includ cote de resurse pentru chiriași, override-uri de resurse per-utilizator și shuffle-sharding pentru izolarea sarcinilor de lucru.
Persists massive volumes of time-series data and indexes directly to cloud object storage for scaling.
Helix DB is a distributed graph database and knowledge graph platform that persists nodes and edges on object storage for durable and unlimited scaling. It operates as an ACID-compliant system, ensuring data consistency through serializable snapshot isolation during concurrent operations. The project distinguishes itself by combining a vector search engine and a property graph, utilizing hybrid vector and full-text search to locate entry points for graph traversals. It enables dynamic graph querying through a domain-specific language, allowing complex logic and recursive queries to be execute
Stores massive volumes of graph and vector information in object storage to reduce costs and ensure durability.
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 c
Persists database records directly to cloud object storage for durable, cost-effective, and unlimited scaling.