Prometheus is a monitoring platform that scrapes metrics from monitored targets’ HTTP endpoints. Prometheus is particularly appealing to cloud-native enterprises because of its native interaction with Kubernetes and other cloud-native technologies. As data quantities expand, Prometheus infrastructure monitoring can become time-consuming.

What is the Prometheus Endpoint?

Prometheus endpoints are HTTP interfaces offered by various applications and services that give metrics in a Prometheus-compatible manner. These endpoints use the OpenMetrics exposition format, ensuring interoperability with Prometheus monitoring. Prometheus tracks and analyzes metrics from various endpoints, allowing users to acquire deep insights into the performance and health of their infrastructure, allowing them to detect and rectify issues early on.

How Does Prometheus Endpoint Work?

Prometheus Endpoint is a Go-based application, and it can be run directly on a server, within a Docker container, or as part of a Kubernetes cluster.

A job to scrape Kubernetes would include the Kubernetes API server endpoint. The Kubernetes API then returns the endpoints for scraping the current nodes or pods.

Applications can provide metrics endpoints to Prometheus through client libraries in various languages.

You may also use separate exporters to collect metrics from individual applications and make them available to Prometheus.

Prometheus Endpoint does not have the functionality to store metrics in many locations.

However, because metrics are not consumed when requested, it is possible to operate many Prometheus instances for the same services to ensure redundancy.

Step-By-Step Guide To Implement of Prometheus Endpoint

Step 1: Install Prometheus

For your operating system, download and extract the Prometheus tarball.

Step 2: Run Prometheus

In the next step, you have to proceed to the location where Prometheus has been extracted, then launch the program by utilizing the default configuration file.

./prometheus --config.file=prometheus.yml

Step 3: Update prometheus.yml Configuration

Edit the prometheus.yml configuration file so that Prometheus’s scrape task is included. Normally, this is included by default, but if it’s not, add the settings listed below:

global:
  scrape_interval: 15s # Default scraping interval

scrape_configs:
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']

OUTPUT

Step 4: Instrument Your Application

To expose metrics, incorporate instrumentation into the application code. Here’s a simple Python example:

from prometheus_client import start_http_server, Summary
import random
import time

# Create a metric to track time spent and requests made.
REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request')

# Decorate function with metric.
@REQUEST_TIME.time()
def process_request(t):
    """A dummy function that takes some time."""
    time.sleep(t)

if __name__ == '__main__':
    # Start up the server to expose the metrics.
    start_http_server(8000)
    # Generate some requests.
    while True:
        process_request(random.random())

OUTPUT

Best Practices of Prometheus Endpoint

• Control over traffic volumes: Push metric systems can be overwhelmed by enormous amounts of data, while Prometheus only receives metrics when it requests them, even if the service is extremely busy.
• Easy reconfiguration: Since Prometheusof has the duty of getting metrics if a change in the configuration is required, it just needs to be done in Prometheus.
• Outage detection: As Prometheus understands what it should be monitoring, outages are recognized immediately when a request fails.

Common Troubleshooting issues of Prometheus Enpoints setup

• Configuration errors: Misconfigured Prometheus can cause several problems. Use Protocol to validate your configuration files at all times.
• No event-driven metrics: Each value is a point-in-time fetched on request, which doesn’t work for short-lived processes and batches, because metrics are only available for a brief amount of time.
• Storage: The standard storage approach makes use of your server’s resources. This might not be devoted to Prometheus and may be pricey depending on the platform you’re using.
• Redundancy: Prometheus saves to a single location, unlike Graphite, which can save data to storage clusters.

Examples of Prometheus

• Alerting on Critical Events: Establish Prometheus alerting rules for important metrics thresholds (such as high CPU use and low disk space).
• Monitoring a Web Server: The nginx-prometheus-exporter is mostly used to set up a web server to expose metrics. These metrics are set up for Prometheus to scrape.
• Kubernetes Cluster Monitoring: Using the kube-prometheus-stack, Prometheus is set up in a Kubernetes cluster. Metrics are stored from nodes, deployed apps, and the Kubernetes API server.

Conclusion

In this article, we have learned about Prometheus Endpoint. Prometheus endpoints are critical for ensuring that modern applications and systems run smoothly and reliably. Prometheus does not have the functionality to store metrics in many locations.

Prometheus Endpoint – FAQs

What is the default port for the Prometheus endpoint?

Prometheus endpoint by default listens on port 9090.

What are endpoint controls?

Endpoint solutions provide stronger security against dangers such as data loss and leakage by utilizing data encryption and data access controls.

How long can Prometheus store data?

Data that does not meet the retention requirements is automatically deleted. By default, the retention is set to 15 days.

How many time series can Prometheus handle?

Prometheus can handle millions of time series. However, you must change the storage settings for this. Essentially, you want to allow a fixed amount of chunks from each time series to be stored in RAM. This is the storage’s default value.