The most effective organizations use metrics to track and comprehend the performance of their infrastructure and applications. One of the most well-known time series databases (TSDB) today is the Prometheus monitoring platform.
In this blog we are going to learn about Prometheus Monitoring:
- What is Prometheus?
- Features of Prometheus
- Prometheus Components
- Prometheus Architecture
- What are metrics?
- Prometheus Metrics Type
- How Does Prometheus Work With Development Environment?
- Kubernetes Monitoring with Prometheus
- Conclusion
What is Prometheus?
Prometheus is a monitoring tool for capturing and processing any time-series that contains only numbers. Along with metrics, individual identifiers, and timestamps, it collects, arranges, and stores them.
Open-source software called Prometheus “scrapes” metrics HTTP endpoints to gather metrics from targets. Platforms for infrastructure (like Kubernetes), applications, and services are all supported “targets” (e.g. database management systems). Prometheus is a flexible metrics collection and alerting tool that works with its companion Alertmanager service.
Features of Prometheus
The key features of Prometheus are:
- A multi-dimensional data model that uses metrics to identify time series data
- Key/value pairs and names
- A flexible query language to take advantage of this dimensionality is PromQL.
- No dependency on distributed storage; individual server nodes are independent
- A pull model over HTTP is used for time series collection.
- An intermediary gateway is supported for pushing time series.
- Targets are found using static configuration or service discovery
- Support for a variety of graphing and dashboarding methods
Prometheus Components
Prometheus has a number of parts that work together to monitor and report on the behavior, performance, and general health of the system. Metrics exposed in plaintext via HTTP endpoints by instrumented applications and services are scraped as the main source of data for data collection.
There are numerous components that make up the Prometheus ecosystem, most of which are optional:
- Time series data is scraped and stored on the primary Prometheus server.
- Instrumenting client libraries for application code
- A push gateway that supports temporary jobs
- Exporters with a specific purpose for products like Graphite, StatsD, HAProxy, etc.
- A notification manager for alerts
- Various support tools
Prometheus Architecture
The Prometheus architecture makes it easier to find and scrape targets that produce the necessary data. A company’s instrumented applications or a third-party application could be the target. The scraped data is stored in Prometheus, where you can use the Prometheus Query Language to examine it (PromQL).
Image Source: prometheus.io
Prometheus scrapes metrics from instrumented jobs, either directly or, for short-lived jobs, via a middleman push gateway. It keeps track of all scraped samples locally and applies rules to them to either create new time series from the existing data or alert users. The gathered data can be displayed using Grafana or another API consumer.
What are metrics?
Metrics are numerical measurements, to use common terminology. Changes are tracked over time in a time series. The metrics that users seek to measure vary depending on the application. It could be request times for a web server, the quantity of active connections or active queries in a database, etc.
Metrics are crucial in helping you understand why your application is performing a certain way. Assume you are using a web application and that it is running slowly. To learn what is happening with your application, you will need some information. For instance, if there are many requests, the application may become sluggish. You can identify the cause and add more servers to handle the load if you have the request count metric.
Prometheus Metrics Type
Prometheus monitors four primary metric types:
1. Counter
One of the most basic metric types is the counter. It is helpful for keeping track of and evaluating values that are only going to rise. You can reset the value to zero and take another measurement once it reaches a specific value.
2. Gauge
Values that rise and fall are measured by gauge metrics. This includes the quantity of concurrent requests or the memory usage at the moment. Usually, the metric is represented by a single numerical value.
3. Summary
Following sampling observations, the summary displays the total number of observations and the sum of observed values. Additionally, it determines variable quantiles over a sliding time window.
4. Histogram
Histograms are used to represent data such as response times, sample sizes, and related observations. It typically provides the total of all observed values and counts in buckets. The metric calculation can be customised to meet your requirements. Note that the location and method used to calculate the statistical quantiles makes a significant difference between summaries and histograms. While histogram quantiles can be computed server side using a promql expression, quantiles for summaries are computed client-side. Choose the statistical metric type that makes sense for your application because both strategies have trade-offs.
How Does Prometheus Work With Development Environment?
Prometheus gathers information based on a number of metrics. For it to be effective, it must support the majority of widely used programming and development languages.
In light of this, Prometheus offers libraries for a diverse range of programming languages. These consist of:
- Go
- Ruby
- Java
- Python
For other widely used languages, libraries are being developed, but they are not yet official. These include Node.js, C#, and Rust. Even short-lived programming tasks like batch processing can be configured to be monitored by Prometheus using PushJobs.
The best method for monitoring applications is to gather information from endpoints.
Endpoints for a variety of metrics are available from the libraries. However, Prometheus needs to be set up that way. Otherwise, if your programme crashes, you run the risk of losing your data.
Kubernetes Monitoring with Prometheus
One of the most frequently used Prometheus use cases is now monitoring for Kubernetes. Like any other system for cloud container applications, Kubernetes can easily become complex. Even though there may be a lot to keep track of, monitoring is crucial if you want your tool or application to be successful.
The health status of cluster components can be examined and reported using the Kubernetes monitoring technique. Thus, the procedure aids in monitoring the use of cluster resources like memory, CPU, and storage.
To learn more about Kubernetes Monitoring with Prometheus click here
Conclusion
Prometheus is an effective tool for gathering and analyzing metrics from devices and programs. Compared to earlier tools, Prometheus’ approach to modern monitoring is much more adaptable.
Related/References
- Visit our YouTube channel “K21Academy”
- Kubernetes Monitoring: Prometheus Kubernetes & Grafana Overview
- Certified Kubernetes Administrator (CKA) Certification Exam
- (CKA) Certification: Step By Step Activity Guides/Hands-On Lab Exercise & Learning Path
- Certified Kubernetes Application Developer (CKAD) Certification Exam
- (CKAD) Certification: Step By Step Activity Guides/Hands-On Lab Exercise & Learning Path
Join FREE Masterclass
To know about what is the Prometheus Certified Associate, why you should learn Prometheus, Job opportunities for Prometheus Certified Associate in the market, and what to study Including Hands-On labs you must perform to clean Prometheus Certified Associate (PCA) certification exam by registering for our FREE Masterclass Waitlist.
The post Prometheus Monitoring: An Introduction For Beginners appeared first on Cloud Training Program.