Prometheus: Powerful Open-Source Monitoring & Alerting Toolkit

Welcome to the wild world of Prometheus monitoring! If you’ve ever wondered how to make sense of thousands of metrics, query your data like a pro, or configure Prometheus on Windows machines, you’re in the right place.

What is Prometheus and What Does It Do?

Prometheus is an open-source monitoring and alerting toolkit originally developed at SoundCloud. It helps developers and DevOps engineers to efficiently collect, store, and query metrics. Think of it as a digital watchdog—but instead of barking, it sends alerts when things go haywire.

Prometheus excels at time-series data collection, meaning it continuously scrapes and stores data points indexed by time and labels. Unlike traditional logging solutions, Prometheus is optimized for high-cardinality data, which means you can tag your metrics with as much detail as needed without breaking performance.

How Does Prometheus Work?

Prometheus works on a pull-based model where it scrapes metrics from configured targets at regular intervals. The high-level breakdown on how Prometheus works:

1. Data Collection: Prometheus scrapes metrics from applications, nodes, or external services using exporters.
2. Storage: The data is stored in a time-series database optimized for high-volume, real-time querying.
3. Querying: You can use PromQL (Prometheus Query Language) to extract and analyze metrics.
4. Alerting: Integrated with Alertmanager, it sends notifications when anomalies occur.
5. Visualization: Prometheus integrates with Grafana for dashboards and visualizations.

Prometheus vs. Grafana: What’s the Difference?

Prometheus is a monitoring system that collects data, while Grafana is a visualization tool that uses that data to create dashboards and graphs. Think of Prometheus as your detective gathering clues (metrics), and Grafana as the artist painting a picture with those clues. Together, they can be a powerful combination for data-driven insights and decision-making.

They collaborate when Grafana retrieves data from Prometheus via PromQL when it is integrated by the DevOps teams on top of Prometheus. Both Grafana and Prometheus work with a variety of data sources. Prometheus and Grafana are useful tools for creating dashboards that show system data, experimenting with metrics, and troubleshooting metrics collection-related difficulties.

Passing Multiple Query Parameters in Prometheus Queries

You can pass multiple query parameters in PromQL using curly braces and logical operators. An example is the following command:

node_cpu_seconds_total{mode=”idle”, instance=”localhost:9090″}

Want to query multiple labels at once? Try this command:

node_cpu_seconds_total{mode=~”idle|user”, instance=~”localhost.*”}

🚀Pro tip: Use regex carefully—there’s a Prometheus regex character limit to keep in mind!

Prometheus Metrics for Kubernetes Cronjobs

To send metrics from Kubernetes CronJobs to Prometheus, do these steps:

1. Expose metrics in your CronJob container using an HTTP endpoint.
2. Configure a ServiceMonitor to scrape those metrics.
3. Use PromQL queries to analyze scheduled job executions.

Example K8s Prometheus alert rules:

alert: CronJobFailures
expr: kube_job_status_failed > 0
for: 5m
labels:
severity: critical
annotations:
summary: “CronJob failed”

Prometheus and AWS: AlertManager, IAM, and CLI

When using AWS Prometheus (AMP – Amazon Managed Prometheus), you need proper IAM permissions to interact with it.

Setting Up AWS Prometheus AlertManager Role

When using AWS Prometheus (AMP – Amazon Managed Prometheus), you need proper IAM permissions to interact with it.

Setting Up AWS Prometheus AlertManager Role

{
“Version”: “2012-10-17”,
“Statement”: [
{
“Effect”: “Allow”,
“Action”: [
“aps:ListAlertManagers”,
“aps:PutAlertManagerDefinition”
],
“Resource”: “*”
}
]
}

Using the AWS Prometheus CLI

Use the following commands:

aws aps list-workspaces
aws aps get-metrics –workspace-id=xyz

Prometheus Metrics for Everything!

From pods to databases, Prometheus captures it all. Some essential metric categories:

• Prometheus node exporter: System metrics (CPU, Memory, Disk, Network)
• Prometheus RAID exporter: Disk RAID status and health monitoring
• Prometheus disk RAID monitor: Ensuring disk redundancy
• MongoDB Atlas Metrics Prometheus meta labels: Monitoring MongoDB Atlas with Prometheus

Configuring Prometheus External Service Monitor

Want to scrape metrics from an external API? Use Prometheus external service monitor by using the following commands:

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: external-service-monitor
spec:
endpoints:
– port: metrics
interval: 30s
selector:
matchLabels:
app: external-service

Adding Prometheus Metrics to Open API MUX Golang

Here’s a concise summary of the steps to add Prometheus metrics to an OpenAPI Mux-based Golang application:

• 1) Install Dependencies
  • Use the following commands:
  • go get github.com/prometheus/client_golang/prometheus
  • go get github.com/prometheus/client_golang/prometheus/promhttp

• 2) Register Prometheus Metrics
  • Define custom metrics (requestsTotal, requestDuration).
  • Register them using prometheus.MustRegister().

• 3) Create Middleware to Track Metrics
  • Capture request method and route.
  • Record request count and duration.

• 4) Integrate with Mux Router
  • Attach middleware to the router.
  • Define API routes and the /metrics endpoint.

• 5) Run the Server by using this command:
  • go run main.go
  • Test API at http://localhost:8080/hello.
  • View metrics at http://localhost:8080/metrics.

• 6) Configure Prometheus to Scrape Metrics
  • a) Update prometheus.yml:
    • yam
    • CopyEdit
    • scrape_configs:
    • – job_name: ‘golang_app’ static_configs:
    • targets: [‘localhost:8080’]
  • b) Start Prometheus and query metrics.

Prometheus Cortex: How Long Does It Keep Data in Memory?

When using Prometheus Cortex, data retention depends on configuration:

• In-memory storage: Short-term, optimized for fast reads.
• Long-term storage: AWS S3, Google Cloud Storage, or object storage solutions.

Typical retention in Prometheus globalconfig:
storage.tsdb.retention.time: “15d

Advanced Prometheus Topics

Prometheus Lookback Delta
Lookback delta is used to fill missing data points in queries:
rate(http_requests_total[5m] offset 5m)
To configure it manually, use this command:
query.lookback-delta: “30s”

Prometheus Hot Reload Config
Need to reload configuration without restarting? Use this command:
curl -X POST http://localhost:9090/-/reload

Drop All Prometheus Metrics with a Label
Use relabeling to drop unwanted metrics with the following commands:
metric_relabel_configs:
– source_labels: [“unwanted_label”]
regex: “.*”
action: drop

Prometheus + AI Bots?

While Prometheus AI bots aren’t here (yet), AWS Prometheus AI-driven alerting can help analyze trends and anomalies!

Wrapping Up

Whether you’re monitoring a Kubernetes cluster, integrating with AWS, or exploring Prometheus avalanche data, this guide should give you a solid foundation. 🚀 Now, go forth and monitor everything! And if things go south—Prometheus has your back. (Or at least your logs).

References:

1. Prometheus Official Documentation
2. Grafana + Prometheus Integration
3. AWS Prometheus (Amazon Managed Service for Prometheus – AMP)
4. Prometheus Alerting and Alertmanager
5. Prometheus – Monitoring system & time series database