Monitoring Telegraf-InfluxDB-Grafana

Authored byYogesh Kon May 08, 2017 in Topic Technology
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Telegraf, InfluxDB, and Grafana function as complex (but user-friendly) tools that can enhance the server side functionality. Each agent handles numerous tasks that range from collecting metrics to visualizing time series data for infrastructure. Connecting data sources and visualizing data in a rapid fashion is made simple through the combination of these three monitoring solutions.

Grafana can support a variety of different storage backend for the data source. Utilizing Grafana’s query editor, the user can easily connect data sources and visualize metrics or data without dealing with the hassle of manually configuring each notable factor. The UI of the query editor also grants the user the ability to build complex inquiries without facing the tall task of writing them. This grants the user more work done without sacrificing days in the process. Although many companies utilize Grafana for visualization and analytical purposes, others can also take advantage of its powerful features for domains such as process control or home automation.

Telegraf is a plugin-driver server agent that is written in Go to collect report performance metrics from the preferred system – as well as the services that are also running on the system. Telegraf works in tandem with InfluxDB in that the metrics can be stored within the aforementioned data source. The agent is built to have a minimal memory footprint in order for developers to gain access to a simplified support system for metric collection from commonly used services and third party web APIs, albeit it is not intended to function as a full text search tool replacement. The stored data is conveniently placed in a designated area, allowing the user to locate it and correlate the appropriate source.

A versatile and highly-scalable database, InfluxDB is an open-source time series database that alleviates the problem of Time Series data. Written in Go, the developers of InfluxData optimized InfluxDB to provide the user with a system that can retrieve the appropriate time series data, analytics, application metrics, and others. Developers can utilize InfluxDB as a single node, or as a bunch. The nodes use Raft for data consistency and stores them in the database (similar to that in MySQL). Starting out, the user will create a database and one retention policy (minimum) prior to writing data. A retention policy, in terms of InfluxDB, can be defined as the time period after the data expires; this can be altered by the user to match preferences required.

Furthermore, it also specifies the replication factor for the data point. Data points are measurements that consist of “X” amount of tags and values associated with a point in time that must be associated with a specified database and a retention policy. For example, if a developer is planning on tracking the metrics of disk usage on numerous servers, an agent will periodically report the usage of each disk directly to InfluxDB. With a comprehensive toolset and a wide array of service to obtain metrics and event data, developers gain the flexibility they need to obtain a single, consolidated view of their information.

This article was authored by Yogesh Karachiwala, who is a Senior Director of Engineering at Zymr.



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