IT systems are not only growing in size and volume but in complexity as well. Thus, it’s only natural that more personnel is required to service these systems.
Moreover, requirements for systems’ fault tolerance are increasing and require more attention from the service personnel along with a quick response to the restoration of the services’ operability.
New and diverse ways of monitoring said parameters are emerging to improve the process of observing and tracking various systems’ operations. It’s challenging to track the performance of systems in terms of time series data since the number of monitored parameters can be too massive for simple human observation. Also, the amount of data that comes in a unit of time is huge.
The Time Series Database aims to efficiently store and monitor IT systems’ data. In this blog post, we will explore where these types of systems are used and how they are arranged using the example of the common Time Series Database Prometheus.
A time series is a series of data points indexed (or listed, or plotted) in chronological order. Most often, the time series is a sequence taken at equidistant points in time that follows one another. Thus, it is a sequence of discrete-time data.
Time series are very often represented by line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, financial mathematics, weather forecasting, intelligent transport and trajectory prediction, earthquake prediction, electroencephalography, automatic control, astronomy, communication technology, and largely in any field of applied science and engineering, which includes time measurements.
There are three entities in time series:
As mentioned on the developer’s website “Prometheus is an open-source systems monitoring and alerting toolkit originally built at SoundCloud. Since its inception in 2012, many companies and organizations have adopted Prometheus, and the project has a very active developer and user community. It is now a standalone open source project and maintained independently of any company. To emphasize this, and to clarify the project’s governance structure, Prometheus joined the Cloud Native Computing Foundation in 2016 as the second hosted project, after Kubernetes.” Prometheus is written mostly in Go and components communicate over HTTP.
For viewing data, Prometheus has its own graphics display system, but it can work in conjunction with systems like Grafana.
Components included in the monitoring system:
Some of the main features of Prometheus are:
Prometheus is not only a time series database; it’s an entire ecosystem of tools that can be attached to expand functionality.
Prometheus monitors a wide variety of systems like servers, databases, individual virtual machines, IoT, machine learning models, and many more. Prometheus is now becoming an indispensable component in high-load microservice systems and SOA.
Prometheus retrieves metrics via HTTP calls to specific endpoints specified in the Prometheus configuration.
Prometheus retrieves data from the address specified in the configuration at regular intervals and provides a mechanism to store and process said data.
Prometheus has a central component called Prometheus Server that stores and monitors certain objects. An object can be anything like a Linux server, an Apache server, a process, a database server, or any other system component that you want to control.
In Prometheus lingo, the main monitoring service is called Prometheus server where the monitoring objects are called targets. A target can be a single server or endpoint validation targets over the following protocols:
The Prometheus server checks the application’s status through the HTTP endpoint.
Each target element that you want to monitor (CPU status, memory, etc.) is called a metric. Prometheus collects metrics from targets over HTTP, stores them locally or remotely, and display them.
The Prometheus server reads targets at given intervals defined by you to collect metrics and store them in a time series database.
Metrics in the Prometheus time series database are stored using the PromQL query language. PromQL enables you to request Prometheus to display the status of a specific target at a given time to obtain metrics.
Prometheus provides client libraries across different languages that you can use to keep your application running. With that being said, Prometheus is not about app monitoring only. You can use exporters to monitor third party systems like a Linux server or a MySQL daemon. The exporter is a piece of software that receives existing metrics from a third party system and exports it to a format that Prometheus can understand.
It’s worth noting that Prometheus uses a standard data model with key-based metrics that may not match the third party system model.
Information Source – https://svitla.com/blog/time-series-database-prometheus
