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🔍 Introduction
While dashboards provide an overview of system health, alerts
and notifications are the lifelines that help teams react
instantly to problems.
In this chapter, we’ll cover:
By the end, you’ll have a smart monitoring system
that not only visualizes issues but automatically warns you when things
go wrong!
🛠️ Part 1: Setting Up
Alerting with Prometheus
Prometheus includes a native alerting system based on rules
and an external component called Alertmanager.
🔹 How Prometheus Alerting
Works
Component |
Purpose |
Alerting Rules |
Defined in Prometheus
config to evaluate metrics |
Alertmanager |
Manages,
groups, and routes alerts |
Notification
Channels |
Send alerts to email,
Slack, PagerDuty, etc. |
🔥 Defining Alert Rules in
Prometheus
You define alerting rules in a YAML file, typically
referenced in your prometheus.yml:
yaml
groups:
-
name: example-alerts
rules:
- alert: HighCPUUsage
expr: 100 -
(avg(irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100) > 80
for: 5m
labels:
severity: warning
annotations:
summary: "High CPU usage
detected"
description: "CPU usage is above 80%
for more than 5 minutes."
Field |
Purpose |
alert |
Name of the alert |
expr |
PromQL
expression triggering the alert |
for |
How long the condition
must be true |
labels |
Metadata for
filtering/grouping |
annotations |
Human-readable alert
description |
✅ Prometheus evaluates rules
every scrape interval.
🔹 Deploying Alertmanager
Download and run Alertmanager:
bash
docker
run -p 9093:9093 prom/alertmanager
Default
Alertmanager UI:
➡️ http://localhost:9093
🔹 Configuring Prometheus
to Use Alertmanager
In prometheus.yml:
yaml
alerting:
alertmanagers:
- static_configs:
- targets:
- 'localhost:9093'
✅ Now Prometheus sends triggered
alerts to Alertmanager!
📚 Part 2: Configuring
Notifications
Once Alertmanager receives an alert, it decides where
to send it.
🔹 Basic Alertmanager
Config (alertmanager.yml)
yaml
global:
smtp_smarthost: 'smtp.example.com:587'
smtp_from: 'alerts@example.com'
smtp_auth_username: 'alerts@example.com'
smtp_auth_password: 'password'
receivers:
-
name: 'email-notifications'
email_configs:
- to: 'oncall@example.com'
route:
receiver: 'email-notifications'
✅ This sends all alerts to an
email address.
📋 Common Alertmanager
Integrations
Service |
Supported |
Email |
✅ |
Slack |
✅ |
PagerDuty |
✅ |
OpsGenie |
✅ |
Webhook |
✅ Custom receivers |
🔥 Example: Slack Alert
Integration
yaml
receivers:
-
name: 'slack-notifications'
slack_configs:
- api_url: 'https://hooks.slack.com/services/Txxxx/Bxxxx/xxxxxxxx'
channel: '#alerts'
send_resolved: true
✅ Instant alert delivery to your
incident response chat!
📈 Part 3: Advanced
Visualization with Grafana
Grafana’s real power shines with dynamic, interactive,
and advanced dashboards.
🔹 Using Variables and
Templating
Variables make dashboards dynamic — the same
dashboard can adjust based on environment, region, instance, etc.
Example Variable:
sql
label_values(instance)
✅ Dropdown to select servers
dynamically.
Variable Type |
Purpose |
Query |
Dynamic values based
on metrics |
Constant |
Fixed
predefined values |
Custom |
Manual options |
🔥 Dynamic Query Example
Instead of hardcoding:
promql
rate(http_requests_total{instance="server-1"}[5m])
Use a variable:
promql
rate(http_requests_total{instance="$server"}[5m])
✅ Select servers from a dropdown!
🔹 Adding Thresholds and
Color Rules
In panel settings:
🔹 Using Annotations
Annotations mark important events on graphs:
✅ Helpful for correlating
incidents with metric spikes.
📋 Example: Annotating
Deployments
🧩 Part 4: Best Practices
for Effective Alerting and Visualization
Practice |
Reason |
Avoid alert storms |
Group similar alerts |
Use severity labels |
Prioritize
incidents |
Tune alert
thresholds carefully |
Avoid false positives |
Visualize KPIs (not just metrics) |
Focus on
business impact |
Document dashboards
and alerts |
Easier team onboarding |
Test alerts regularly |
Ensure reliability |
🔥 Suggested Alert
Severities
Severity |
Example |
Critical |
Database down, memory
exhausted |
Warning |
CPU usage
above 80%, high error rate |
Info |
Deployment started,
backup completed |
🚀 Conclusion
Monitoring is not just about seeing — it’s about being
notified at the right time with the right context.
In this chapter, you learned:
By mastering alerting and visualization, your monitoring
system evolves from passive data collection to active incident response and
system optimization.
In the next chapter, we’ll cover scaling, securing, and
production hardening your Prometheus + Grafana stack — ensuring it can
handle real-world load!
Knowledge is power. Alerts are action. 🚀
Answer:
Prometheus is used to collect, store, and query time-series metrics from
applications, servers, databases, and services. It scrapes metrics endpoints at
regular intervals, stores the data locally, and allows you to query and trigger
alerts based on conditions like performance degradation or system failures.
Answer:
Grafana is used to visualize and analyze the metrics collected by
Prometheus. It allows users to build interactive, real-time dashboards
and graphs, making it easier to monitor system health, detect anomalies, and
troubleshoot issues effectively.
Answer:
Prometheus scrapes and stores metrics → Grafana queries Prometheus via APIs →
Grafana visualizes the metrics through dashboards and sends alerts if
conditions are met.
Answer:
You can monitor web applications, microservices, databases, APIs, Kubernetes
clusters, Docker containers, infrastructure resources (CPU, memory, disk),
and virtually anything that exposes metrics in Prometheus format (/metrics
endpoint).
Answer:
Prometheus has a built-in Alertmanager component that manages alert
rules, deduplicates similar alerts, groups them, and routes notifications (via
email, Slack, PagerDuty, etc.). Grafana also supports alerting from dashboards
when thresholds are crossed.
Answer:
PromQL (Prometheus Query Language) is a powerful query language used to
retrieve and manipulate time-series data stored in Prometheus. It supports
aggregation, filtering, math operations, and advanced slicing over time
windows.
Answer:
By default, Prometheus is optimized for short-to-medium term storage
(weeks/months). For long-term storage, it can integrate with systems
like Thanos, Cortex, or remote storage solutions to scale and retain
historical data for years.
Answer:
Yes! Prometheus and Grafana are commonly used together to monitor Kubernetes
clusters, capturing node metrics, pod statuses, resource usage, networking,
and service health. Tools like kube-prometheus-stack simplify this
setup.
Answer:
Grafana supports time-series graphs, gauges, bar charts, heatmaps, pie
charts, histograms, and tables. It also allows users to create dynamic
dashboards using variables and templating for richer interaction.
Answer:
Yes, both Prometheus and Grafana are open-source and free to use.
Grafana also offers paid enterprise editions with additional features
like authentication integration (LDAP, SSO), enhanced security, and advanced
reporting for larger organizations.
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