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DBMS > InfluxDB

InfluxDB System Properties

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Editorial information provided by DB-Engines
NameInfluxDB
DescriptionDBMS for storing time series, events and metrics
Primary database modelTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score8.70
Rank#40  Overall
#2  Time Series DBMS
Websitewww.influxdata.com/­time-series-platform/­influxdb
Technical documentationdocs.influxdata.com/­influxdb
Initial release2013
Current releasev1.3, July 2017
License infoCommercial or Open SourceOpen Source infoMIT-License
Cloud-based infoOnly available as a cloud serviceno
Implementation languageGo
Server operating systemsLinux
OS X infothrough Homebrew
Data schemeschema-free
Typing infopredefined data types such as float or dateNumeric data and Strings
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.no
Secondary indexesno
SQL infoSupport of SQLSQL-like query language
APIs and other access methodsHTTP API
JSON over UDP
Supported programming languages.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala
Server-side scripts infoStored proceduresno
Triggersno
Partitioning methods infoMethods for storing different data on different nodesSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Foreign keys infoReferential integrityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano
Concurrency infoSupport for concurrent manipulation of datayes
Durability infoSupport for making data persistentyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoDepending on used storage engine
User concepts infoAccess controlsimple rights management via user accounts
More information provided by the system vendor
InfluxDB
Specific characteristics

InfluxData includes the open source time-series database InfluxDB. It supports high write loads, large data set storage, and conserves space thru downsampling, automatically expiring and deleting unwanted data as well as backup and restore. InfluxDB also makes it easy to analyze data by providing an easy-to-use SQL-like query language.
You can graph and visualize your data with the open source project Chronograf, and perform ad hoc exploration of your data. It includes support for templates and a library of intelligent, pre-configured dashboards for common data sets. In addition, InfluxDB supports other tools such as Grafana.

Competitive advantages

Time to Value

An end-to-end platform, InfluxData is ready to go on the cloud or via download. Deploy and begin building your application right on top in minutes, not days or weeks. Built to maximize developer happiness, InfluxData is elegant and simple to use, free of external dependencies, yet open and flexible enough for complex deployments. Get real value faster.

Real Action in Real-Time

InfluxData gives you visibility with real-time access out of the gate so you can quickly find value in your data — identify patterns, predict the future, control systems, and turn insight into action. Whether your data comes from humans, machines, or sensors, get instant insights and stay ahead of the curve.

Fast, Scalable, Available

InfluxData’s powerful engine is fast, supporting millions of writes per second, meeting the demands of even the largest monitoring and IoT deployments. And with native clustering, InfluxData offers high availability — eliminating single points of failure — and simple scale-out.

Typical application scenarios

IoT & Sensor Monitoring

Instrumentation of every available surface in the material world, from machines to humans is a reality. Everything inside and outside the compute is emitting a relentless stream of metrics and events. The sheer volume of data is astronomical, and growing exponentially, creating unique opportunities for businesses that can monitor and react to this data in real-time. Traditional monitoring solutions just don’t have the speed, footprint, and uptime needed for this market.

DevOps Monitoring

Emerging trends like microservices, containerization, elastic storage, Software Defined Networking, Hybrid clouds, etc, all keep pushing the boundaries of what constitutes DevOps Monitoring. The number of endpoints and the variety of metrics that need monitoring forces a reconsideration of the one-size fits all mentality. Modern DevOps monitoring needs to be flexible enough to handle unique application and infrastructure metrics on a common framework.

Real-Time Analytics

The volume of metrics and events is beyond what any human is able to realistically interpret and take action. Machine learning with real-time analytics is crucial in finding the “signal from the noise”. Whether the organization needs real-time analytics to buy and sell equities, perform predictive maintenance on a machine before it fails, adjust prices based on customer behavior – processing, analyzing and acting on the time-series data in real-time is the problem to solve.

Key customers

Complete list of refererence customers are at https://www.influxdata.com/customers/testimonials/ but include eBay, Cisco, Nordstrom, Nest, SolarCity, and hundreds of others

Market metrics

Fastest growing database to garnish 10,000 github stars

Over 54,000 servers deployed

Hundreds of customers

Licensing and pricing models

Open source core with closed source clustering available either on-premise or on the cloud as a managed service. All offerings are available via subscription. 

3rd party products and services

We invite representatives of 3rd party vendors to contact us for presenting information about their offerings here.

More resources
InfluxDB
DB-Engines blog posts

Why Build a Time Series Data Platform?
20 July 2017, Paul Dix (guest author)

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

show all

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