16 个仓库
Databases optimized for time-series data processing.
Explore 16 awesome GitHub repositories matching part of an awesome list · Time Series Databases. Refine with filters or upvote what's useful.
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
IoT-oriented time-series database with high-performance ingestion.
TimescaleDB is an open-source PostgreSQL extension that adds native time-series capabilities to the database. At its core, it transforms standard PostgreSQL tables into hypertables—automatically partitioned by time intervals—so data is stored in fixed-size chunks without manual sharding. The extension includes a library of over 200 built-in SQL functions purpose-built for time-series workloads, such as time bucketing, gap filling, percentile estimation, and time-weighted averages. What distinguishes TimescaleDB from generic PostgreSQL is its set of integrated time-series features that work th
Time series storage built on top of PostgreSQL.
QuestDB is a high-performance, distributed time-series database designed for the ingestion, storage, and analysis of massive datasets. It functions as a real-time analytics platform that utilizes a columnar storage engine to optimize disk input and output, enabling efficient analytical scans and complex windowing operations on streaming data. The platform distinguishes itself through specialized capabilities for handling asynchronous time-series streams, including advanced join algorithms that align disparate data sets based on precise timestamp lookups. It supports high-volume ingestion thro
Database for processing time-series data.
VictoriaMetrics is a high-performance, scalable time series database and observability platform designed for long-term storage and analysis of metric, log, and trace data. It functions as a unified backend for monitoring ecosystems, offering full compatibility with industry-standard protocols and query languages. The system is built to handle massive data volumes through a distributed architecture that supports horizontal scaling and efficient data lifecycle management. The platform distinguishes itself through a storage engine that utilizes consistent hashing for data sharding and log-struct
Fast and resource-efficient time-series database compatible with Prometheus.
Thanos is a CNCF cloud native monitoring tool that provides a highly available and scalable extension to the Prometheus ecosystem. It functions as a global query engine, a long-term storage system, and a metric downsampler. The project enables a unified interface to aggregate and query metrics across multiple distributed clusters from a single view. It maintains historical data beyond local retention limits by persisting time-series metrics in object storage and eliminates data gaps by merging metrics from redundant server pairs. The system includes capabilities for reducing the resolution o
Components for highly available metric systems with unlimited storage.
Druid is a distributed columnar store and online analytical processing database designed for real-time analytics. It functions as a SQL analytics platform and a streaming data ingestion engine, allowing for the analysis of large datasets with low latency to support interactive dashboards and high-concurrency operational workloads. The system integrates a streaming data ingestion engine that loads information via batch or streaming processes to enable immediate analysis of arriving data. It provides high-performance analytical processing to execute slice-and-dice queries on massive data volume
Distributed data store for powering interactive analytical applications.
OpenTSDB 是一个分布式时间序列数据库和指标引擎,专为存储和管理海量高基数系统指标而设计。它作为一个数据存储和分析平台,支持跨分布式集群的大规模指标摄取和基础设施性能监控。 该系统以其支持 HBase、Cassandra 和 Google Bigtable 等多个后端的分布式存储抽象而著称。它利用分层指标树来组织时间序列,并采用数字标识符索引来减少存储占用并加速标记指标的查找。 该项目涵盖了广泛的能力领域,包括具有分布式百分位数计算和降采样功能的时间序列数据分析,以及全面的元数据管理。它提供用于数据摄取和查询的 API 集成、用于性能优化的堆外缓存,以及用于数据完整性审计和异常分析的工具。 该系统通过用于数据库管理和指标树同步的命令行界面进行管理。
Scalable time series database built on HBase.
Beringei is a high performance, in-memory storage engine for time series data.
In-memory time-series database for high-speed metrics.
High performance datastore for time series and tick data
High-performance storage for tick and time series data.
Fast scalable time series database
Scalable time-series storage built on top of Cassandra.
Time-series database
Numeric time-series database for real-time data accumulation.
See gitlab: https://gitlab.com/Project-FiFo/DalmatinerDB/dalmatinerdb
Fast distributed database for metrics storage.
A distributed system designed to ingest and process time series data
Distributed system for ingesting and processing time-series data.
SiriDB is a highly-scalable, robust and super fast time series database. Build from the ground up SiriDB uses a unique mechanism to operate without a global index and allows server resources to be added on the fly. SiriDB's unique query language includes dynamic grouping of time series for easy analysis over large amounts of time series.
Robust and scalable time-series database with cluster support.
Accumulo backed time series database
Secure time-series database application based on Accumulo.