Are you trying to know more about Prometheus Distributed Monitoring System?
This guide is for you.
Prometheus is an open-source monitoring system developed by SoundCloud which stores all its data in a time-series database.
Also, it allows a multi-dimensional data-model and a powerful query language ensuring to generate more accurate reports.
Here at Ibmi Media, as part of our Server Management Services, we regularly help our Customers to handle servers with Prometheus distributed monitoring system.
In this context, we shall look into the features of Prometheus as well as how it works.
Main features of Prometheus Distributed Monitoring System:
Prometheus works very well in a distributed, cloud-native environment.
Below are some main features of the Prometheus distributed monitoring system:
1. Firstly, it has a multi-dimensional data model with time series data identified by metric name and key/value pairs.
2. PromQL, a flexible query language to leverage this dimensionality.
3. And it does not rely on distributed storage; single server nodes are autonomous.
4. Further, the time series collection occurs via a pull model over HTTP.
5. Also, it supports pushing time series via an intermediary gateway.
6. And discovers targets via service discovery or static configuration.
7. Supports multiple modes of graphing and dashboarding.
Components of Prometheus Distributed Monitoring System
The Prometheus ecosystem consists of multiple components, many of which are optional:
1. The main Prometheus server which scrapes and stores time-series data
2. Client libraries for instrumenting application code
3. A push gateway for supporting short-lived jobs
4. Special-purpose exporters for services like HAProxy, StatsD, Graphite, etc.
5. An alert manager to handle alerts
6. And Various support tools
Most Prometheus components are written in Go, making them easy to build and deploy as static binaries.
The architecture of Prometheus Distributed Monitoring System
Prometheus scrapes metrics from instrumented applications, either directly or via an intermediary push gateway.
Moreover, it accepts and stores pushed metrics and exposes a scrapable API for Prometheus.
Generally, it comprises a file-based data store that scales well and is efficient.
In addition, Prometheus has a simple query language that allows us to evaluate and aggregate the time series data.
Finally, Prometheus exposes a REST API for external consumers, such as dashboards.
Where can we use Prometheus?
Prometheus works well for recording any purely numeric time series. However, it fits both machine-centric monitoring as well as monitoring of highly dynamic service-oriented architectures.
Moreover, each Prometheus server is standalone as it does not depend upon network storage or other remote services.
Monitoring with Prometheus is implemented in the DevOps industry, Healthcare, Financial Services, and so on.
When does Prometheus not fit in?
Though we can view the statistics are available about our system, even under failure conditions. If we need 100% accuracy, such as for per-request billing, Prometheus is not apt as the collected data will not be detailed and complete enough.
How does Prometheus Distributed Monitoring System work?
Prometheus is a pull-based monitoring system.
First, we need to extract metrics from our systems.
Following are the different ways in which this can be done:
1. By 'instrumenting' our application: Exposing Prometheus compatible metrics on a given URL. Above all, Prometheus defines it as a target and scraps it on a given period.
2. Using the prebuilt exporters: Prometheus has an entire collection of exporters for existing technologies.
3. Pushgateway: sometimes we have applications or jobs that do not expose metrics directly.
Prometheus monitoring rich ecosystem
We know that Prometheus is a time-series database.
However, when dealing with time-series databases, we often need to visualize them, analyze them, and have some custom alerting on them.
Following are the tools that compose Prometheus ecosystem to enrich its functionalities :
1. Alertmanager: Prometheus pushes alerts to the Alertmanager via custom rules defined in configuration files. And from there, we can export them to multiple endpoints such as Pagerduty or Slack.
2. Data visualization: similarly to Grafana, we can visualize our time series directly in Prometheus Web UI. After that, we can easily filter and have a concrete overview of different targets.
3. Service discovery: Prometheus can discover our targets dynamically and automatically scrap new targets on demand. Moreover, this is particularly handy with containers that can change their addresses dynamically depending on demand.
Factors to consider in Prometheus monitoring system
Following are some factors which we must consider while using Prometheus:
1. Full ingestion compatibility that really supports all Prometheus features
The vendor/tool/SaaS solution should be able to consume data from any entity that can produce Prometheus metrics.
2. PromQL compatibility
The PromQL helps to extract information stored by Prometheus. In addition, PromQL enables us to ask for metrics on specific services or specific users. Also, it enables us to aggregate or segment data.
To be truly compatible with Prometheus, the solution has to be hot-swappable. In other words, it should be able to work with the existing dashboards, alerts, and scripts.
4. Access Controls
Access controls are another security issue we should consider when evaluating tools.
Kubernetes simplifies the deployment, scaling, and management of containerized applications and microservices. Further, this helps to keep services up and running.
However, to identify and resolve underlying problems, such as slow performance, failed deployments, and connection errors, we need to gather and visualize in-depth the following:
c. performance data from across
Not having access to both real-time information and contextual data makes it difficult to correlate the metrics in our environment so we can solve problems more quickly.
6. Compatibility with existing alerts
Generally, Prometheus faces a scalability problem, as we need to ensure it supports all levels of alerting.
However, the key to achieve this is by using Alert Manager functionality, which in turn requires 100% ingestion and PromQL compatibility.
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