Awesome GitHub RepositoriesTime Series Databases

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

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

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

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

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

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

OpenTSDB este o bază de date distribuită de serii temporale și un motor de metrici conceput pentru stocarea și gestionarea unor volume masive de metrici de sistem cu cardinalitate ridicată. Acesta funcționează ca un depozit de date și o platformă de analiză care permite ingestia de metrici la scară largă și monitorizarea performanței infrastructurii într-un cluster distribuit. Sistemul se distinge printr-o abstractizare a stocării distribuite care suportă mai multe backend-uri, cum ar fi HBase, Cassandra și Google Bigtable. Utilizează un arbore ierarhic de metrici pentru a organiza seriile temporale și folosește indexarea cu identificatori numerici pentru a reduce amprenta de stocare și a accelera căutările pentru metricile etichetate. Proiectul acoperă domenii largi de capabilități, inclusiv analiza datelor de serii temporale cu calcule distribuite de percentile și downsampling, precum și gestionarea cuprinzătoare a metadatelor. Oferă integrare API pentru ingestia și interogarea datelor, caching off-heap pentru optimizarea performanței și instrumente pentru auditarea integrității datelor și analiza anomaliilor. Sistemul este gestionat printr-o interfață linie de comandă pentru administrarea bazei de date și sincronizarea arborelui de metrici.

Beringei is a high performance, in-memory storage engine for time series data.

High performance datastore for time series and tick data

Fast scalable time series database

Time-series database

See gitlab: https://gitlab.com/Project-FiFo/DalmatinerDB/dalmatinerdb

A distributed system designed to ingest and process 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.

Accumulo backed time series database