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InfluxDB is a time series database (TSDB) developed by the company InfluxData. It is used for storage and retrieval of time series data in fields such as operations monitoring, application metrics, Internet of Things sensor data, and real-time analytics. It also has support for processing data from Graphite.
The analysis highlights History, Events, Measurement and Companies as prominent areas in the source structure around InfluxDB.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
The extracted context around InfluxDB shows recurring relationship patterns in the source. For example, InfluxDB → Another, Combinator-backed, Errplane, In, In February, InfluxData, InfluxData Inc, Mayfield Fund, November, Sapphire Ventures, September, Series, Trinity Ventures Another extracted example is InfluxDB → Companies, InfluxData, InfluxDays, London, New York, San Francisco, The InfluxDays, Those. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
series influxdata data time software written source license database round million points version rust events closed open graphite errplane funding
TTTA extracted 60 structured relationships around InfluxDB. Examples in this analysis include InfluxDB → Developer → InfluxData and InfluxDB → License → MIT, Apache 2.0, Proprietary. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| InfluxDB | Developer | InfluxData | 1.00 | infobox |
| InfluxDB | License | MIT, Apache 2.0, Proprietary | 1.00 | infobox |
| InfluxDB | Operating system | Cross-platform | 1.00 | infobox |
| InfluxDB | Release | 24 September 2013; 12 years ago (2013-09-24) | 1.00 | infobox |
| InfluxDB | Repository | github.com/influxdata/influxdb | 1.00 | infobox |
| InfluxDB | Stable release | 3.9.2 / 30 April 2026; 3 months ago (30 April 2026) | 1.00 | infobox |
| InfluxDB | Type | Time series database | 1.00 | infobox |
| InfluxDB | Website | influxdata.com | 1.00 | infobox |
| InfluxDB | Written in | Rust | 1.00 | infobox |
| operations monitoring | instance of | It is used for storage and retrieval of time series data in fields | 0.80 | text |
| application metrics | instance of | It is used for storage and retrieval of time series data in fields | 0.80 | text |
| Internet of Things sensor data | instance of | It is used for storage and retrieval of time series data in fields | 0.80 | text |
The concept neighborhoods around InfluxDB bring nearby vocabulary together. In this analysis, examples include Influxdata, Source and Software. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For InfluxDB, one of the stronger structural bridges in this analysis connects InfluxDB with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around InfluxDB to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Events, Measurement & Companies, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — InfluxDB · EN edition · Analysis: TopicsToTalkAbout