8 Repos
Storage engines that organize data into immutable, compressed chunks for efficient access.
Distinguishing note: Focuses on the storage format and structure rather than the query engine.
Explore 8 awesome GitHub repositories matching data & databases · Log-Structured Storage. Refine with filters or upvote what's useful.
InfluxDB is a specialized time series database platform engineered for the high-speed ingestion, compression, and retrieval of timestamped data at scale. It functions as a distributed metrics platform, providing the infrastructure necessary to organize and analyze massive volumes of time-stamped information to identify trends, patterns, and anomalies within complex data streams. The platform distinguishes itself through a functional dataflow engine that utilizes a specialized programming language for complex analytical transformations and automated tasks. This architecture is supported by a p
Organizes incoming data into sorted immutable files to optimize write throughput and range-based queries.
Loki is a horizontally scalable, highly available log aggregation engine designed to store and query massive volumes of unstructured log data. It functions as a distributed observability platform that correlates logs, metrics, and traces to provide comprehensive visibility into the health and performance of complex infrastructure. The system distinguishes itself through a distributed query execution model that processes large datasets in parallel across cluster nodes. It utilizes label-based stream indexing and a distributed index to map log data to specific chunks, enabling rapid retrieval w
Compresses and stores data in immutable chunks within object storage.
TDengine is a distributed time-series database designed for the high-speed ingestion, compression, and retrieval of timestamped metrics and sensor data. It functions as a SQL-compatible analytics engine, allowing users to perform complex operations on massive volumes of time-ordered information using standard relational syntax. The platform is built to serve as a backend foundation for industrial IoT environments, managing real-time data streams and device metadata through a cluster-based architecture. The system distinguishes itself through a distributed sharding architecture that uses consi
Appends incoming data to memory-resident buffers and sequential log files to minimize disk seek latency during writes.
This project serves as a comprehensive technical reference for the architecture and design of data-intensive applications. It provides a structured analysis of the fundamental principles required to build reliable, scalable, and maintainable software systems, covering the core trade-offs inherent in modern data infrastructure. The repository explores the mechanics of distributed data management, including strategies for replication, partitioning, and achieving consensus across multiple nodes. It details the design of storage engines, indexing techniques, and transaction management models, whi
Organizes data into immutable, compressed chunks for efficient access in storage engines.
Neon is a serverless PostgreSQL database platform designed with a decoupled storage and compute architecture. It functions as a multi-tenant system that isolates data and compute resources for independent users on shared cloud infrastructure, utilizing a specialized PostgreSQL storage engine. The platform features a database branching system that allows for the creation of isolated, instant copies of a database for testing and development. It further distinguishes itself with an HTTP-based SQL gateway, enabling the execution of queries via HTTP requests and JSON responses without the need for
Utilizes a log-structured storage engine to persist write-ahead logs to remote object storage.
This project is a curated directory of software, frameworks, and educational resources designed for building, scaling, and maintaining distributed data processing and storage architectures. It serves as a comprehensive index for the distributed computing ecosystem, helping users identify the appropriate tools for managing large-scale information systems. The repository functions as a central hub for data engineering, offering categorized access to technologies that support batch and stream processing, machine learning, and interactive querying. By organizing these resources, it assists in the
Uses append-only file structures to optimize write throughput and retrieval in distributed storage systems.
This project is a curated collection of academic papers, books, and technical resources designed for studying the architecture and implementation of database management systems. It serves as a comprehensive educational guide for engineers and researchers looking to understand the fundamental principles behind modern data storage and retrieval. The repository distinguishes itself by providing structured learning paths across critical database domains, including the design of persistent storage engines, the mechanics of query optimization, and the complexities of distributed transaction managem
Provides documentation on log-structured storage engines that use sequential appends to optimize write performance and recovery.
Fluvio ist eine verteilte Event-Streaming-Plattform und eine Cloud-native Streaming-Engine, die für das Sammeln, Persistieren und Replizieren von Echtzeit-Datenströmen über einen verteilten Cluster hinweg entwickelt wurde. Sie fungiert als Echtzeit-Datenpipeline für den Aufbau zustandsbehafteter Workflows, die Daten zwischen externen Quellen und Senken aufnehmen, anreichern und exportieren. Die Plattform zeichnet sich durch die Verwendung von WebAssembly zur Ausführung kompilierter Module für In-Line-Datentransformationen und -filterung aus. Dies ermöglicht die Ausführung benutzerdefinierter Geschäftslogik, um Informationen während der Übertragung umzuformen, ohne den Cluster neu starten zu müssen. Das System deckt ein breites Spektrum an Funktionen ab, einschließlich connector-basierter Datenaufnahme aus externen Protokollen, log-strukturierter unveränderlicher Speicherung mit Zero-Copy-IO und horizontaler Clusterskalierung. Es unterstützt die Erstellung komplexer ereignisgesteuerter Pipelines, die zustandsbehaftete Verarbeitung, fensterbasierte Aggregationen und partitionierte Datenverteilung nutzen. Die Engine kann als leichtgewichtiges Binärprogramm auf diversen Systemarchitekturen bereitgestellt werden, einschließlich ARM64-IoT-Geräten für die Datenverarbeitung am Edge.
Uses a log-structured storage engine to save immutable, append-only message segments for high-performance writes.