SoundCloud first developed Prometheus back in 2012. Since its creation, Prometheus Monitoring Tool has grown to be a well-liked monitoring tool that is supported by a diverse group of contributors. Prometheus joined the Cloud Native Computing Foundation (CNCF) in 2016 and has since graduated from the organization.
An open-source monitoring and alerting toolkit with scalability in mind is called Prometheus. It is widely utilized in the world of containerized and cloud-native applications. Prometheus monitoring tool is often used in Kubernetes environments to monitor various aspects of applications and infrastructure, collecting metrics from targets like HTTP endpoints, databases, and other systems. It scrapes metrics from these targets using a pull-based model and stores the information in a time-series database.
Prometheus’ main characteristics
- Multidimensional data model: Allows users to analyze data in a variety of ways to learn more about the functionality and health of the system.
- PromQL is a strong and user-friendly query language for aggregating and querying metrics.
- Effective time-series storage: To make it simple to query and analyze historical data, all collected metrics are stored in a time-series database.
- Pull model for metric collection: Scrapes targets on a regular basis to gather metrics data, enabling it to scale horizontally to watch over big and complex systems.
- Pushing time-series data to Prometheus is supported, making it simple to monitor customized applications and services.
- Automatic target discovery for monitoring: A built-in mechanism for service discovery that automatically finds and keeps track of new services as they are added to a system.
- Built-in visualization tools: Offers a number of built-in visualization tools, including integration with Grafana and a straightforward graphing user interface.
- Strong query capabilities: Enables users to construct intricate queries that filter, aggregate, and transform data, facilitating in-depth system analysis.
- Ease of operation designed to be simple to use, with an easy installation procedure and configuration.
- Accurate alerting system: This system’s built-in alerting feature allows you to set up rules that will send you alerts when certain metric values or patterns occur. This way, you can proactively find and fix system problems.
- Client libraries for easy instrumentation: These libraries are available for a number of well-known programming languages, making it simple to instrument unique applications and services.
- Integrations with numerous platforms and tools: allows for easy integration with a wide range of other tools and platforms, making it simple to monitor distributed, complex systems in a variety of settings.
Prometheus Monitoring: How Does It Operate?
Prometheus monitoring needs an exposed HTTP endpoint in order to collect metrics. Prometheus can begin scraping numerical data as soon as an endpoint is available, record it as a time series, and store it in a local database designed to store time-series data. Remote storage repositories can also be integrated with Prometheus monitoring.
To generate temporary times series from the source, users can use queries. The names and labels of the metrics used to define these series. PromQL, a special language that enables users to pick and aggregate time-series data in real time, is used to write queries. You can create alert conditions with PromQL that will send notifications to outside systems like email, PagerDuty, or Slack.
Prometheus’ web-based user interface allows for the display of collected data in tabular or graph form. Additionally, you can integrate third-party visualization programs like Grafana using APIs.
What Can Prometheus Be Used To Monitor?
You can use Prometheus, a flexible monitoring tool, to keep an eye on a range of infrastructure and application metrics. Here are a few typical use scenarios.
Metrics for Services
Prometheus monitoring is frequently used to gather numerical metrics from services that operate continuously and permit HTTP endpoint access to metric data. Manual labor or a variety of client libraries can accomplish this. Prometheus exposes data in a straightforward format, with a new line for each metric and line feed characters to denote separation. Based on the specified path, port, and hostname, Prometheus can query and scrape metrics from the file that is published on an HTTP server.
Additionally, distributed services that run across multiple hosts can be implemented using Prometheus. Each instance has a name that Prometheus can distinguish and publishes its own metrics.
Website Uptime/Up Status
Prometheus typically doesn’t keep track of the status of websites, but you can do so by using a blackbox exporter. In order to obtain information such as the website’s response time, you must perform an uptime check and specify the target URL to query an endpoint. In order to make sure Prometheus uses the blackbox exporter, you define the hosts to be queried in the prometheus.yml configuration file.
Prometheus can keep an eye on IoT systems and gadgets. It can gather metrics data on things like battery life, network latency, and device temperature and notify administrators of problems.
It can keep track of security-related statistics like login attempts, network activity, and system logs and notify administrators of security breaches or other problems when necessary.
Business Metrics Monitoring
Prometheus has the ability to keep track of financial metrics like revenue, sales, and customer retention. It can help you make data-driven decisions and offer insights into the state of your company.
You can check the operating system to see if a server is running at 100% CPU all the time or if its hard drive is full. Installing a specialized exporter on the host will allow you to gather the operating system details and publish them somewhere that can be accessed over HTTP.
You can display metrics to Prometheus through an HTTP endpoint using the Push Gateway to determine whether a cronjob is running at the predetermined intervals. You can compare the current time in Prometheus with the timestamp of the most recent successful job (a backup job) that was pushed to the Gateway. The monitor times out and sends out an alert if the time goes over the predetermined threshold.
Why Should You Monitor Kubernetes Using Prometheus?
Due to the fact that it was created for a cloud-native environment, Prometheus is a popular option for Kubernetes monitoring. The following are a few major advantages of using Prometheus to track Kubernetes workloads:
• Multidimensional data model – The way Kubernetes organizes infrastructure metadata using labels and key-value pairs is similar. This similarity guarantees that Prometheus can gather and analyze time-series data with accuracy.
• Accessible format and protocols – Prometheus allows for quick and easy metric exposure. It makes sure that metrics can be published over a regular HTTP connection and are readable by humans.
• Service discovery – The Prometheus server scans targets on a regular basis. Metrics are pulled rather than pushed, so services and applications are not required to continuously emit data. Several methods can be used by Prometheus servers to automatically find targets for scraping. For instance, you can set up the servers to match and filter container metadata.
• Modular and highly available components – Composable services are in charge of performing metric collection, graphical visualization, alerting, and more. They are composed of modular and highly available components. Redundancy and sharding are supported by each of these services.
Metric Types in Prometheus
Prometheus’ client libraries provide four basic categories of metrics. These metrics are not currently saved by the Prometheus server as different data types. It flattens all data into an untyped time series instead.
This metric is cumulative. It stands for a single monotonically increasing counter, and on restart, it can either increase in value or reset to zero.
There are numerous use cases where counter metrics are appropriate. It can be used, for instance, to show the quantity of errors, requests, or tasks that have been fulfilled. Counters should never be used to display values that are subject to change, such as the number of active processes.
This metric represents a single numerical value that may be arbitrarily increased or decreased. Values like current memory usage or temperatures are frequently measured using a gauge.
A histogram compiles data from observations, such as response times or request durations. The observations are then added up in a customizable bucket. A histogram can also show the sum of all the values that were observed.
A summary can include representative observations like the lengths of requests and the dimensions of responses. A total count of the observations as well as the sum of all observed values can also be provided. Over a sliding time window, it can compute quantiles that are configurable.
Prometheus is a highly adaptable and potent solution because it provides extensive capabilities for monitoring your systems and applications. Prometheus can effectively gather, analyse, and alert you about metrics data whether you run a cloud-native setup or a more traditional IT infrastructure.
Keep in mind that following the suggestions made above is essential if you want to get the most out of Prometheus Monitoring. You can make sure that your systems and applications run as efficiently as possible by doing this.
The Prometheus community has achieved many significant milestones over the years, and we are excited to see how this tool keeps getting better and better.