Introduction
Popular open-source monitoring and alerting toolkit Prometheus is getting more and more attention and use because of how flexible and scalable it is. It was made by SoundCloud and is now part of the Cloud Native Computing Foundation, just like Kubernetes. Because of this, it has become very popular in the cloud native ecosystem. If you work in DevOps, you’ve probably heard of Prometheus, a powerful open-source tool used in many IT environments for real-time monitoring and alerting. It was made to deal with time-series data and is an important part of a modern software architecture. But what is so great about it? Let’s look at the architecture and parts of Prometheus to find out what it can do and what its strengths are. Let’s look at Prometheus architecture as well as main components to learn more about what it can do and how it works.
Prometheus Architecture
Prometheus’ fundamental architecture is simple and centered on the server, which collects and stores time series data. The Prometheus server, a time-series database, a user interface, alerts, and several service discovery techniques are a few examples of the various parts that make up the overall architecture. For gathering metrics data, Prometheus uses a pull-based methodology. This differs from many other monitoring systems, which employ a push model. At certain intervals, Prometheus scrapes or grabs metrics from the applications rather than having the apps push their metrics to the system. By doing away with the necessity for application-side permissions, this paradigm streamlines the system’s management and security features. You can install and execute a single binary to use as Prometheus’ main server. The server executes tasks that scrape metric data from instrumented apps and localize it.
Here’s a high-level overview of the Prometheus architecture and components.
Prometheus Server: This collects and stores metrics from instrumented apps. Additionally, the server is responsible for executing alert rules and sending notifications to the Alertmanager.
Instrumented Application: These are the target applications and services you want to track and monitor. Target applications and services use HTTP endpoint to expose their Metrics.
Alertmanager: It manages alerts sent by the Prometheus server, including silencing, inhibition, aggregation, and the transmission of notifications via email, on-call notification systems, and chat platforms.
Pushgateway: A component developed for scrape-resistant services. It collects and exposes metrics pushed by services to the Prometheus server.
Prometheus Web User Interface and APIs: The integrated web user interface provides a graphical interface for exploring the collected metrics. APIs allow Prometheus to communicate with third-party apps.
External Storage: Via remote write and remote read APIs, Prometheus can transfer its samples to long-term storage systems. External storage is optional and used for long-term data storage and processing.
Key Components
As stated previously, the Prometheus ecosystem comprises of various components, many of which are optional. Because the majority of Prometheus components are written in Go. That’s why they are simple to create and deploy as static binaries. In this section, we will examine each component in depth in an effort to increase your comprehension.
Prometheus Server
The Prometheus Server is the core of the system. This primary component performs the majority of the work. The server is meant to function alone, so there is no need to worry about sophisticated cluster management or distributed data storage. The Prometheus server scrapes measurements, stores time-series data, and executes alarms. It periodically gathers metrics from the specified targets and stores them as time-series data. On the basis of these metrics, alert rules can be built, and when specific conditions are met, an alert is delivered to the Alertmanager.
Targets and Service Discovery
Prometheus monitor applications and services called targets. Prometheus scrape HTTP endpoint of the targets. Service discovery is a Prometheus mechanism for automatically discovering targets in dynamic contexts. Prometheus can identify targets in settings such as Kubernetes, AWS, and more by integrating with several service discovery systems. Prometheus includes numerous service discovery alternatives for discovering scrape targets, such as static configuration and interactions with cloud platform service discovery mechanisms.
Metrics and Time Series Data
The data model is an integral component of Prometheus. Prometheus presents a configurable multidimensional data model. It is based on the concept of time series data, which consists of streams of timestamped values with the same metric and set of labeled dimensions. So, Prometheus collect Metrics which are the quantitative data from your apps and services. Prometheus save these metrics as time-series data. That can be identified by the metric name and key/value pairs. This format enables users to efficiently query their data regardless of its source. Counters (cumulative measurements that grow over time) and Gauges are the two primary forms of metrics (metrics that can arbitrarily go up or down). The data model enables the creation of effective and succinct queries.
Alertmanager
Prometheus contains a highly functional notification system. Alertmanager is responsible for processing alerts. Alertmanager manages alarms sent by the Prometheus server or other clients. It is also responsible for deduplicating, grouping, and routing alerts to the appropriate recipient based on severity and responsible team, such as email, PagerDuty, or OpsGenie. It also has features for suppressing and disabling alarms.
Client Libraries and Exporters
Prometheus includes exporters for connecting existing metrics into Prometheus and client libraries for instrumenting application code. This enables developers to instrument their code and export custom application metrics. In addition, there exist exporters, which are independent services that may be scraped for metrics, for popular third-party applications that cannot be directly instrumented. These convert existing metrics into the format used by Prometheus.
Pushgateway
In situations where the pull model would not be appropriate, such as with short-lived jobs or batch jobs, Prometheus use the Pushgateway instead. The metrics that are generated by the jobs are sent to the Pushgateway, and then Prometheus retrieves the metrics from there.
Prometheus Query Language (PromQL)
The Prometheus Query Language, also known as PromQL, is a query language that is both versatile and powerful, enabling effective analysis of your time-series data. It allows users to pick and aggregate time-series data in real time, as well as build rich visuals for more effective monitoring, and it facilitates the selection and aggregation of time-series data.
External Storage
Prometheus store its collected time-series data in the system’s data storage component. It is possible to save data either locally within the Prometheus server or remotely with systems such as the Thanos or the Cortex. It is good ploy to minimize read and write times on the local storage for effectiveness and dependability.
Conclusion
Prometheus distinguishes out as an effective monitoring tool for complicated IT landscapes. It is reliable and dependable tool, providing multiple dimensions that permit tracking of dependency and respective causality. It also provides exceptional adaptability, dependability, and a functional approach to problem-solving, making it a valuable addition to the modern DevOps arsenal. Prometheus stands out because to its simplicity, dependability, and support for the collecting and querying of multidimensional data.
Its modular architecture and ease of deployment make it the solution of choice for monitoring modern infrastructure. Whether you are monitoring a simple system or a large microservice architecture, Prometheus is capable of handling your metrics efficiently and effectively. Prometheus is a powerful and flexible monitoring solution that supports a wide variety of use cases. It handles monitoring of both machine-centric as well as highly dynamic service-oriented architectures. As the cloud native environment continues to expand, it is evident that Prometheus plays a crucial role.