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Prometheus Distributed Monitoring System - A brief review
This article covers an overview of what Prometheus Distributed Monitoring System is and how it works.
Prometheus is an open-source systems monitoring and alerting toolkit with an active ecosystem.
Why is Prometheus used?
Prometheus is an open-source monitoring software that is very popular in the industry. Prometheus is easy to customize, and produces metrics without impacting application performance.
Along with this, Prometheus monitoring can be used to provide clarity into systems and how to run them.
What is Prometheus monitoring used for?
Prometheus is a free software application used for event monitoring and alerting. It records real-time metrics in a time series database (allowing for high dimensionality) built using a HTTP pull model, with flexible queries and real-time alerting.
What is AWS Prometheus?
Amazon Managed Service for Prometheus (AMP) is a Prometheus-compatible monitoring service that makes it easy to monitor containerized applications at scale.
AMP automatically scales as your workloads grow or shrink, and is integrated with AWS security services to enable fast and secure access to data.
What metrics does Prometheus collect?
At this moment, for Prometheus, all metrics are time-series data. The Prometheus client libraries are the ones in charge of aggregating metrics data, like count or sum. Usually, these client libraries—like the Go library from the graphic above—have four types of metrics: counter, gauge, history, and summary.
What is the difference between Grafana and Prometheus?
Grafana and Prometheus, both help us in tackling issues related to complex data in a simplified manner.
Grafana is an open-source visualization software, which helps the users to understand the complex data with the help of data metrics.
Prometheus is an open-source event monitoring and alerting tool.
How does Prometheus monitoring work?
Prometheus scrapes metrics from instrumented jobs, either directly or via an intermediary push gateway for short-lived jobs.
It stores all scraped samples locally and runs rules over this data to either aggregate and record new time series from existing data or generate alerts.
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