OpenTSDB - A scalable, distributed Time Series Database.

  •        448

OpenTSDB is a distributed, scalable Time Series Database (TSDB) written on top of HBase. OpenTSDB was written to address a common need: store, index and serve metrics collected from computer systems (network gear, operating systems, applications) at a large scale, and make this data easily accessible and graphable.

http://opentsdb.net
https://github.com/OpenTSDB/opentsdb

Tags
Implementation
License
Platform

   




Related Projects

awesome-time-series-database - :clock7: A curated list of awesome time series databases, benchmarks and papers

  •    Javascript

OpenTSDB is a Classical time series database on top of HBase. Now support Cassandra and Bigtable. BTrDB (Berkeley Tree Database) is a High performance time series database designed to support high density data storage applications.

Graphite - A highly scalable real-time graphing system

  •    Python

Graphite is an enterprise-scale monitoring tool that runs well on cheap hardware. It is a highly scalable real-time graphing system. It stores numeric time-series data and renders graphs of this data on demand.

InfluxDB - Distributed Time Series Database

  •    Go

InfluxDB is an open-source, distributed, time series database with no external dependencies. It's useful for recording metrics, events, and performing analytics. Everything in InfluxDB is a time series that you can perform standard functions on like min, max, sum, count, mean, median, percentiles, and more. Collect your data on any interval and compute rollups on the fly later.

Prometheus - Service Monitoring System and Time Series Database

  •    Go

Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

Beringei - High performance, in-memory storage engine for time series data.

  •    C++

In the fall of 2015, we published the paper “Gorilla: A Fast, Scalable, In-Memory Time Series Database” at VLDB 2015. Beringei is the open source representation of the ideas presented in this paper. Beringei is a high performance time series storage engine. Time series are commonly used as a representation of statistics, gauges, and counters for monitoring performance and health of a system.


Grafana - The leading graph and dashboard builder for visualizing time series metrics

  •    Go

Grafana is an open source, feature rich metrics dashboard and graph editor for Graphite, Elasticsearch, OpenTSDB, Prometheus and InfluxDB. It is most commonly used for visualizing time series data for Internet infrastructure and application analytics but many use it in other domains including industrial sensors, home automation, weather, and process control.

SiriDB - Highly-scalable, robust and super fast time series database

  •    C

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 indexes and allows server resources to be added on the fly. SiriDB's unique query language includes dynamic grouping of time series for easy and super fast analysis over large amount's of time series.

Gnocchi - Time series database

  •    Python

Gnocchi is an open-source |time series| database. The problem that Gnocchi solves is the storage and indexing of |time series| data and resources at a large scale. This is useful in modern cloud platforms which are not only huge but also are dynamic and potentially multi-tenant. Gnocchi takes all of that into account. Gnocchi has been designed to handle large amounts of aggregates being stored while being performant, scalable and fault-tolerant. While doing this, the goal was to be sure to not build any hard dependency on any complex storage system.

Kairosdb - Fast distributed scalable time series database written on top of Cassandra

  •    Java

KairosDB is a fast distributed scalable time series database written on top of Cassandra. Data can be pushed in KairosDB via multiple protocols : Telnet, Rest, Graphite. KairosDB stores time series in Cassandra, the popular and performant NoSQL datastore. It supports aggregators which can perform an operation on data points and down samples. Standard functions like min, max, sum, count, mean etc.

Heroic - The Time Series Database

  •    Java

Heroic is a scalable time series database based on Bigtable, Cassandra, and Elasticsearch. It is an open-source monitoring system originally built at Spotify to address the problems that were facing with large scale gathering and near real-time analysis of metrics.

Timescaledb - An open-source time-series database optimized for fast ingest and complex queries

  •    PLpgSQL

TimescaleDB is an open-source database designed to make SQL scalable for time-series data. It is engineered up from PostgreSQL, providing automatic partitioning across time and space (partitioning key), as well as full SQL support. TimescaleDB is packaged as a PostgreSQL extension and released under the Apache 2 open-source license.

stumbleupon-opentsdb

  •    Java

A scalable, distributed Time Series Database.

siridb-server - SiriDB is a highly-scalable, robust and super fast time series database

  •    C

SiriDB is a highly-scalable, robust and super fast time series database. For Ubuntu we have a deb package available which can be downloaded here.

influxdb - Scalable datastore for metrics, events, and real-time analytics

  •    Go

InfluxDB is an open source time series database with no external dependencies. It's useful for recording metrics, events, and performing analytics. If you're feeling adventurous and want to contribute to InfluxDB, see our contributing doc for info on how to make feature requests, build from source, and run tests.

Hawkular metrics - Time Series Metrics Engine based on Cassandra

  •    Java

Hawkular Metrics is a metrics collection, aggregation, visualization framework. Hawkular is a set of Open Source projects designed to be a generic solution for common monitoring problems. The Hawkular projects provide REST services that can be used for all kinds of monitoring needs. The aim is to provide a generic solution that can be used for common monitoring problems.

Akumuli - Time-series database

  •    C++

Akumuli is a time-series database for modern hardware. It can be used to capture, store and process time-series data in real-time. The word "akumuli" can be translated from Esperanto as "accumulate".

atsd-use-cases - Axibase Time Series Database: Usage Examples and Research Articles

  •    Vue

Use Cases documentation demonstrates solutions to real-world data problems using Axibase Time Series Database (ATSD) and contains in-depth guides for programmatic integration with commonly-used enterprise software systems and services, as well as tutorials for data transformation and visualizations created with ATSD. Interactive visualizations tracking interesting datasets from a variety of sources.

Timely - Accumulo backed time series database

  •    Java

Timely is a time series database application that provides secure access to time series data. Timely is written in Java and designed to work with Apache Accumulo and Grafana.

Atlas - In-memory dimensional time series database

  •    Scala

Atlas was developed by Netflix to manage dimensional time series data for near real-time operational insight. Atlas features in-memory data storage, allowing it to gather and report very large numbers of metrics, very quickly. Atlas captures operational intelligence. Whereas business intelligence is data gathered for analyzing trends over time, operational intelligence provides a picture of what is currently happening within a system.

Blueflood - A distributed system designed to ingest and process time series data

  •    Java

Blueflood is a high throughput, low latency, multi-tenant distributed metric processing system behind Rackspace Metrics, which is currently used in production by the Rackspace Monitoring team and Rackspace Public Cloud team to store metrics generated by their systems. Data from Blueflood can be used to construct dashboards, generate reports, graphs or for any other use involving time-series data. It focuses on near-realtime data, with data that is queryable mere milliseconds after ingestion.