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.
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.
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:
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.
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!
To send metrics from Kubernetes CronJobs to Prometheus, do these steps:
Example K8s Prometheus alert rules:
alert: CronJobFailures
expr: kube_job_status_failed > 0
for: 5m
labels:
severity: critical
annotations:
summary: “CronJob failed”
When using AWS Prometheus (AMP – Amazon Managed Prometheus), you need proper IAM permissions to interact with it.
When using AWS Prometheus (AMP – Amazon Managed Prometheus), you need proper IAM permissions to interact with it.
{
“Version”: “2012-10-17”,
“Statement”: [
{
“Effect”: “Allow”,
“Action”: [
“aps:ListAlertManagers”,
“aps:PutAlertManagerDefinition”
],
“Resource”: “*”
}
]
}
Use the following commands:
aws aps list-workspaces
aws aps get-metrics –workspace-id=xyz
From pods to databases, Prometheus captures it all. Some essential metric categories:
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
Here’s a concise summary of the steps to add Prometheus metrics to an OpenAPI Mux-based Golang application:
When using Prometheus Cortex, data retention depends on configuration:
Typical retention in Prometheus globalconfig:
storage.tsdb.retention.time: “15d
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
While Prometheus AI bots aren’t here (yet), AWS Prometheus AI-driven alerting can help analyze trends and anomalies!
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).
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