DevOps Monitoring

The DevOps monitoring solution using InfluxDB provides developers with better visibility into their apps, networks, containers, routers, switches, private and public clouds, and environments.

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Why a purpose-built time series database?

Time series data are measurements or events that are tracked, monitored, downsampled, and aggregated over time. This could be server metrics, application performance monitoring, network data, sensor data, events, clicks, trades in a market, and many other types of analytics data.

Why InfluxDB for DevOps monitoring?

Emerging trends such as microservices, containerization, elastic storage, software defined networking, and hybrid clouds all keep pushing the boundaries of what constitutes DevOps monitoring. It can encompass private and public cloud infrastructure (PaaS, SaaS, website), applications and database instances, and the entire infrastructure and network servers, routers, and switches. Monitoring helps to identify and resolve problems before they affect critical business processes; and to plan for upgrades before outdated systems begin to cause failures.

The InfluxDB platform can handle a high volume of real-time writes, is purpose-built from the ground up for irregular (events) and regular (metrics) monitoring, and offers retention policies to maintain performance and availability.

The platform delivers specific functions to do aggregation and summation using time-based functions directly in Flux lang, and allows for large-range scans of many records very quickly.

The functional architecture of the InfluxData DevOps monitoring platform

Reference-Architecture-DevOps-Monitoring-InfluxData-08.10.2022v1

“InfluxDB provided a horizontally-scalable, time-series-optimized database with a lightweight agent that could be deployed anywhere we needed to push data to a central location.”

Chris Ruscio, Solutions Architect, Allscripts

Performance and innovation with open standards

We built InfluxDB 3.0 in Rust using the FDAP stack

Flight

Transport columnar data at high speeds based on Arrow format

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DataFusion

Fast query execution engine written in Rust

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Arrow

Optimized for running large analytical workloads

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Parquet

Open column-oriented file format designed for efficient data storage and retrieval

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FDAP