What Is Prometheus?
Prometheus is a cloud-native platform, such as Kubernetes, that is meant to offer monitoring and alerting capability. This functionality is provided by an open-source technology called Prometheus. It has the ability to gather and store measurements as time-series data, stamping information with the current time as it is recorded. Moreover, it is able to gather and record labels, which are pairs of keys and values that are optional.
The Following are Important Aspects of Prometheus:
- The multidimensional data model makes use of time-series information, which may be recognized by metric names and key-value pair combinations.
- A versatile querying language that is able to take use of a multi-dimensional data model is known as PromQL.
- There is no dependency on distributed storage, and each individual server node maintains its independence.
- Pull model — Prometheus is able to gather time-series data by actively “pulling” data via HTTP. This methodology is known as the pull model.
- Pushing time-series data is something that may be done with the assistance of an intermediate gateway.
- Discovering monitoring targets is possible either via static configuration or through service discovery.
- The visualization capabilities of Prometheus include a wide variety of graphs and dashboards.
SoundCloud was the company that first developed Prometheus in the year 2012. Since it was first released, Prometheus has rapidly grown into a widely used monitoring tool that is maintained by an open-source community of volunteers. Prometheus became a part of the Cloud Native Computing Foundation (CNCF) in 2016, and it is currently a project that has graduated from CNCF.
How Exactly Does the Prometheus Monitoring System Function?
An exposed HTTP endpoint is necessary for Prometheus in order to collect metrics. As an endpoint becomes accessible, Prometheus is able to immediately begin scraping numerical data, capturing it as a time series, and storing it in a local database that is specifically designed to accommodate time-series data. Integration with remote storage repositories is also possible with the Prometheus platform.
Users have the ability to use queries in order to build temporary time series from the source. The titles and labels assigned to these metrics serve to define these series. The queries themselves are constructed in a special language called PromQL, which gives users the ability to select and aggregate time-series data in real-time. You may also use PromQL to assist you in the establishment of alert conditions, which can then lead to alerts being sent to other systems such as email, PagerDuty, or Slack.
Tabular or graphical representations of the data that have been gathered may be shown by Prometheus through its user interface which is web-based. Integration with third-party visualization solutions such as Grafana is also possible via the use of application programming interfaces (APIs).
What Kind of Data Can Be Obtained With Prometheus?
You may monitor a wide range of infrastructure and application metrics with the help of the Prometheus monitoring tool, which is a flexible monitoring solution. The following are some examples of typical applications:
Metrics About Service
Prometheus is often put to use to gather numerical measurements from services that are online around the clock and have HTTP endpoints via which metric data may be accessed. This may be accomplished by hand or using a variety of different client libraries. The data that Prometheus exposes are presented in a straightforward format that consists of a new line for each statistic and line feed characters to separate them. Prometheus is able to query and scrape metrics from the file based on the supplied path, port, and hostname since the file has been published on an HTTP server.
Moreover, distributed services, which are those that are executed on different servers, may be managed with the help of Prometheus. Each instance has its own identity and broadcasts its own metrics, which Prometheus may use to differentiate between them.
Measurements for the Host
You are able to monitor the operating system to determine whether a server is functioning continually at 100% CPU or if the hard drive on a server has reached its capacity. You may install a specialized exporter on the host computer in order to gather information about the operating system and then publish it at a place that is accessible over HTTP.
Uptime/Status of the Website
Prometheus does not normally monitor the uptime or status of websites; however, you may add this functionality by using a black box exporter. When querying an endpoint, you must first give the destination URL before doing an uptime check in order to get information such as the response time of the website. In the prometheus.yml configuration file, you identify the hosts that will be queried, and then you use relabel configs to make sure that Prometheus utilizes the blackbox exporter.
You may use the Push Gateway to show metrics to Prometheus over an HTTP endpoint in order to determine whether or not a cronjob is operating at the intervals that you have defined. You are able to compare the timestamp of the most recent successful task (i.e. a backup job) in Prometheus with the present time by sending the timestamp of the job to the Gateway. The monitor will time out and sound an alarm if the amount of time that has elapsed is greater than the threshold that was set.
Why Should Kubernetes Monitoring Be Done Using Prometheus?
Due to the fact that it was developed specifically for use in cloud-native environments, Prometheus is a popular option for Kubernetes monitoring. The use of Prometheus to monitor Kubernetes workloads offers a number of important advantages, including the following:
The use of key-value pairs in a multidimensional data model generates a parallel to the way in which Kubernetes organizes infrastructure information via the application of labels. Due to the similarities between the two, it is guaranteed that precise time-series data may be gathered and analyzed by Prometheus.
Prometheus offers straightforward and simple exposing of metrics by providing an accessible format and a set of protocols. It guarantees that the measurements are comprehensible to humans and can be distributed via the industry-standard HTTP protocol.
Service discovery is accomplished by the regular scraping of targets by the Prometheus hosting server. Since measurements are fetched rather than pushed, services and applications are exempt from the need to continuously generate data. The Prometheus servers have access to a variety of methods that may be used to automatically locate scrape targets. It is possible, for instance, to set up the servers such that they filter and match container information.
Components that are both modular and readily accessible – Composable services are accountable for a wide variety of tasks, including but not limited to metric collecting, graphical representation, and alerting. Every one of these providers offers sharding as well as redundancy support.
Find out more by reading our in-depth guide on using Prometheus with Kubernetes.
Types of the Prometheus Measure
There are four primary kinds of metrics that are provided by the Prometheus client libraries. On the other hand, the Prometheus server does not yet record these metrics as distinct categories of data at this time. Instead, it condenses all of the information into a time series that is not written.
This is a measure that adds up over time. When it is restarted, its value may either continue to climb as it has been doing all along or it can be reset to zero. It symbolizes a single counter that has been steadily going up.
There are a number of applications that are suitable for counter-metrics. You could, for instance, use it to represent the number of requests that have been fulfilled, errors that have occurred, or tasks that have been finished. You should never reveal data using counters, especially ones that might go down, such as the number of processes that are currently executing.
This metric only has one numerical value to represent it, and that value can change in any arbitrary way. Measuring things like the amount of memory that is currently being used or temperatures typically requires the use of a gauge.
A histogram samples observations, such as request durations or response sizes. It then counts the observations in a configurable bucket. A histogram can also provide a total sum of all the observed values.
A summary may sample observations, such as request durations and answer sizes. In addition to this, it is able to produce a total count of all the observations as well as a total sum of all the values that were observed. It has the ability to compute quantiles in a sliding time frame that may be customized.
Recommended Methods and Procedures for Prometheus Monitoring
The following is a list of various critical best practices that should be followed while establishing Prometheus monitoring.
Choose the Most Competent Exporter.
Exporters are used by Prometheus to get metrics from systems that are difficult to scrape, such as HAProxy and Linux-based operating systems. Exporters are client libraries that are installed on the target system and are responsible for sending metrics to Prometheus once they have been exported.
While all of the Prometheus exporters offer comparable capabilities, you should choose the exporter that is best appropriate for the tasks you need to do. This may have a significant impact on how well your Kubernetes monitoring approach is implemented. You are able to do research on the many exporters that are accessible and assess how each one deals with the parameters that are pertinent to your workloads. It is also important that you evaluate the quality of the exporter using criteria such as user reviews, current updates, and security warnings.
Study the documentation of the exporter you’ve selected and educate yourself on the proper approach to label your measurements so that they offer context. Figure out how to provide labeling that is consistent across all of the various monitoring targets. You have the ability to personalize and define your own data; however, you should be aware that creating labels eats up resources. At a more macro level, having an excessive number of labels might drive up the total resource expenditures. Because of this, you should do your best to utilize as many as ten labels.
Activate Notifications That Can Be Taken Action On
You may improve the effectiveness of your performance monitoring by developing a clear plan for your alerting approach. You should begin by determining which events or metrics are essential to monitoring, and after that, you should create a realistic threshold that is able to identify problems before they may have an effect on your end customers. To get optimal results, you should choose a threshold that does not result in alert fatigue. In addition to this, you need to check that the notifications are correctly set so that they are sent to the relevant team at the appropriate time.
Calico’s Capabilities for Monitoring and Observability of Containers
Both the Calico Cloud and the Calico Enterprise products assist identify and fix problems with the performance, connectivity, and security policy of microservices that are operating on Kubernetes clusters as quickly as possible throughout the whole stack. They provide crucial capabilities for monitoring and observability of containers and Kubernetes that are not accessible with Prometheus. These major characteristics are as follows:
A point-to-point, topographical depiction of traffic flow and policy that demonstrates how workloads inside the cluster are talking with one another and across which namespaces is referred to as a dynamic service graph. In addition, it has the additional ability to filter resources, store views, and address service-related problems.
The DNS dashboard offers an interactive user interface together with unique DNS analytics, which helps to speed the debugging and problem resolution processes associated with DNS-related issues in Kubernetes systems.
Dynamic Packet Capture is a feature that helps speed up the process of troubleshooting performance hotspots and connection problems by capturing packets from a particular pod or set of pods with defined packet sizes and lengths.
Provides a centralized, all-encompassing view of service-to-service traffic in the Kubernetes cluster to detect anomalous behavior such as attempts to access applications or restricted URLs, as well as scans for particular URLs. Application-level observability (Also known as ALO) is a feature that was introduced in version 1.0 of the Kubernetes open-source container orchestration platform.
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