Why Prometheus and Grafana integration concept in Spring Boot? - Purpose & Use Cases

The Big Idea

What if you could see your app's health live and fix problems before they become disasters?

The Scenario

Imagine you run a Spring Boot app and want to know if it's healthy or slow. You try checking logs and guessing what's wrong by opening many files and dashboards manually.

The Problem

This manual way is slow and confusing. You miss important problems because you can't see all data in one place. It's like trying to fix a car without a dashboard showing speed or fuel.

The Solution

Prometheus collects all your app's health and performance data automatically. Grafana shows this data in clear, colorful dashboards. Together, they give you a live view of your app's status, so you can fix issues fast.

Before vs After

✗ Before

Check logs one by one
Open multiple tools
Guess what's wrong

✓ After

Use Prometheus to gather metrics
Use Grafana to visualize metrics
See app health live on dashboard

What It Enables

You get real-time, easy-to-understand insights into your app's health and performance, helping you fix problems before users notice.

Real Life Example

A Spring Boot app suddenly slows down. With Prometheus and Grafana, you spot a spike in CPU use immediately and fix the issue before customers complain.

Key Takeaways

Manual monitoring is slow and error-prone.

Prometheus collects metrics automatically.

Grafana visualizes data for quick understanding.

Practice

1. What is the primary role of Prometheus in a Spring Boot application monitoring setup?

easy

A. To collect metrics by scraping the application's exposed endpoint

B. To visualize metrics using dashboards

C. To expose application metrics automatically

D. To send alerts directly to users

Solution

1. Step 1: Understand Prometheus function

   Prometheus collects metrics by scraping HTTP endpoints exposed by applications.

2. Step 2: Differentiate roles of tools

   Grafana is used for visualization, not collection. Spring Boot exposes metrics but does not collect them.

3. Final Answer:

   To collect metrics by scraping the application's exposed endpoint -> Option A

4. Quick Check:

   Prometheus collects metrics = A [OK]

Hint: Remember: Prometheus scrapes, Grafana shows [OK]

Common Mistakes:

• Confusing Grafana as the collector
• Thinking Spring Boot collects metrics
• Assuming Prometheus sends alerts directly

2. Which dependency must be added to a Spring Boot project to expose metrics for Prometheus scraping?

easy

A. spring-boot-starter-web

B. spring-boot-starter-data-jpa

C. micrometer-registry-prometheus

D. spring-boot-starter-security

Solution

1. Step 1: Identify metrics registry dependency

   Micrometer provides metrics support; the Prometheus registry enables exposing metrics in Prometheus format.

2. Step 2: Exclude unrelated dependencies

   Web starter is for REST APIs, JPA for database, Security for auth; none expose Prometheus metrics.

3. Final Answer:

   micrometer-registry-prometheus -> Option C

4. Quick Check:

   Prometheus metrics need micrometer-registry-prometheus [OK]

Hint: Add micrometer-registry-prometheus for Prometheus metrics [OK]

Common Mistakes:

• Adding only spring-boot-starter-web without metrics registry
• Confusing JPA or Security starters as metrics providers
• Forgetting to include Prometheus registry dependency

3. Given this Spring Boot application.properties snippet:
management.endpoints.web.exposure.include=health,prometheus
management.endpoint.prometheus.enabled=true
What will be the URL path to scrape metrics for Prometheus?

medium

A. /metrics

B. /actuator/metrics

C. /prometheus/metrics

D. /actuator/prometheus

Solution

1. Step 1: Understand actuator endpoint exposure

   Spring Boot exposes actuator endpoints under /actuator by default.

2. Step 2: Identify Prometheus endpoint path

   Prometheus metrics endpoint is /actuator/prometheus when enabled and exposed.

3. Final Answer:

   /actuator/prometheus -> Option D

4. Quick Check:

   Prometheus metrics at /actuator/prometheus [OK]

Hint: Actuator endpoints default under /actuator [OK]

Common Mistakes:

• Using /metrics instead of /actuator/prometheus
• Assuming /prometheus/metrics is default
• Forgetting to enable prometheus endpoint

4. You configured Prometheus to scrape your Spring Boot app, but metrics are not appearing in Grafana. Which is the most likely cause?

medium

A. Prometheus server is down but Grafana is running

B. Prometheus is not scraping the correct endpoint URL

C. Spring Boot app does not have a database connection

D. Grafana does not support Prometheus data sources

Solution

1. Step 1: Check Prometheus scraping configuration

   If Prometheus scrapes wrong URL, no metrics collected, so Grafana shows nothing.

2. Step 2: Exclude other causes

   Grafana supports Prometheus; database unrelated; if Prometheus down, Grafana shows errors.

3. Final Answer:

   Prometheus is not scraping the correct endpoint URL -> Option B

4. Quick Check:

   Wrong scrape URL blocks metrics flow [OK]

Hint: Verify Prometheus scrape URL matches app endpoint [OK]

Common Mistakes:

• Blaming Grafana for lack of support
• Assuming database affects metrics exposure
• Ignoring Prometheus scrape configuration

5. You want to create a Grafana dashboard showing the average response time of your Spring Boot app over the last 5 minutes. Which Prometheus query should you use?

hard

A. rate(http_server_requests_seconds_sum[5m]) / rate(http_server_requests_seconds_count[5m])

B. sum(http_server_requests_seconds_sum) / sum(http_server_requests_seconds_count)

C. avg_over_time(http_server_requests_seconds_sum[5m])

D. count_over_time(http_server_requests_seconds_count[5m])

Solution

1. Step 1: Understand metrics for response time

   http_server_requests_seconds_sum is total time; count is number of requests.

2. Step 2: Calculate average over 5 minutes

   Use rate() to get per-second increase, then divide sum by count for average.

3. Final Answer:

   rate(http_server_requests_seconds_sum[5m]) / rate(http_server_requests_seconds_count[5m]) -> Option A

4. Quick Check:

   Average = sum rate / count rate [OK]

Hint: Divide sum rate by count rate for average response time [OK]

Common Mistakes:

• Using sum without rate causes wrong aggregation
• Using avg_over_time on sum metric alone
• Counting requests instead of averaging time