Metrics collection (Prometheus) in Microservices - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Prometheus client library in Python.

Microservices

from prometheus_client import [1]

Drag options to blanks, or click blank then click option'

ACollectorRegistry

BCounter

Cstart_http_server

DGauge

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a metric type that does not count events.

Importing a function instead of a metric class.

The Counter class is commonly imported to create a metric that counts events.

2fill in blank

medium

Complete the code to start the Prometheus metrics HTTP server on port 8000.

Microservices

start_http_server([1])

Drag options to blanks, or click blank then click option'

A8080

B9090

C8000

D5000

Attempts:

3 left

💡 Hint

Common Mistakes

Using the default Prometheus server port 9090 instead of the exporter port.

Using a port that is not open or commonly used.

Port 8000 is commonly used in examples to expose Prometheus metrics.

3fill in blank

hard

Fix the error in the code to increment the counter metric named 'requests_total'.

Microservices

requests_total = Counter('requests_total', 'Total number of requests')
requests_total[1]()

Drag options to blanks, or click blank then click option'

Ainc

Bincrement

Cadd

Dincrease

Attempts:

3 left

💡 Hint

Common Mistakes

Using method names that do not exist in the Counter class.

Trying to add values with incorrect method names.

The correct method to increase a Prometheus Counter by 1 is inc().

4fill in blank

hard

Fill both blanks to create a Gauge metric and set its value to 5.

Microservices

g = [1]('temperature_celsius', 'Current temperature in Celsius')
g.[2](5)

Drag options to blanks, or click blank then click option'

AGauge

BCounter

Cset

Dinc

Attempts:

3 left

💡 Hint

Common Mistakes

Using Counter instead of Gauge for values that can go up and down.

Using inc() instead of set() to assign a value.

A Gauge metric can be set to any value using the set() method.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that collects metrics only for services with more than 10 requests.

Microservices

metrics = [1]: [2] for [1], [2] in service_requests.items() if [2] > 10

Drag options to blanks, or click blank then click option'

Aservice

Bcount

Cservice_requests

Drequests

Attempts:

3 left

💡 Hint

Common Mistakes

Using the dictionary name as a variable name inside the loop.

Mixing up key and value variable names.

The dictionary comprehension iterates over service_requests.items() with variables service and count. It collects entries where count is greater than 10.

Practice

1. What is the main purpose of Prometheus in a microservices environment?

easy

A. To collect and store metrics from services for monitoring

B. To deploy microservices automatically

C. To manage user authentication

D. To serve web pages to users

Solution

1. Step 1: Understand Prometheus role

   Prometheus is designed to collect numerical data called metrics from running services.

2. Step 2: Identify monitoring purpose

   These metrics help monitor service health and performance in microservices.

3. Final Answer:

   To collect and store metrics from services for monitoring -> Option A

4. Quick Check:

   Prometheus = Metrics collection [OK]

Hint: Prometheus is for metrics, not deployment or auth [OK]

Common Mistakes:

• Confusing Prometheus with deployment tools
• Thinking Prometheus manages users
• Assuming Prometheus serves web content

2. Which YAML configuration snippet correctly defines a Prometheus scrape job for a service at http://localhost:8080/metrics?

easy

A. jobs:
- job: 'myservice'
endpoints: ['localhost:8080']

B. scrape_configs:
- job_name: 'myservice'
static_configs:
- targets: ['http://localhost:8080/metrics']

C. scrape_configs:
- job_name: 'myservice'
static_configs:
- targets: ['localhost:8080']

D. scrape_jobs:
- name: 'myservice'
targets: ['localhost:8080/metrics']

Solution

1. Step 1: Check Prometheus YAML syntax

   Prometheus uses scrape_configs with job_name and static_configs listing targets as host:port without URL path.

2. Step 2: Validate target format

   Targets must be host:port only, no http:// or path like /metrics.

3. Final Answer:

   scrape_configs: - job_name: 'myservice' static_configs: - targets: ['localhost:8080'] -> Option C

4. Quick Check:

   Targets = host:port only [OK]

Hint: Targets list host:port only, no URL scheme or path [OK]

Common Mistakes:

• Including http:// or /metrics in targets
• Using wrong YAML keys like scrape_jobs or jobs
• Misnaming job_name or static_configs

3. Given this Prometheus query: rate(http_requests_total[5m]), what does it calculate?

medium

A. The average rate of HTTP requests per second over the last 5 minutes

B. The current number of active HTTP requests

C. The total number of HTTP requests since service start

D. The maximum number of HTTP requests in the last 5 minutes

Solution

1. Step 1: Understand rate() function

   The rate() function calculates the per-second average increase of a counter over a time window.

2. Step 2: Apply to http_requests_total[5m]

   This means it measures how fast the total HTTP requests counter increased in the last 5 minutes, giving requests per second.

3. Final Answer:

   The average rate of HTTP requests per second over the last 5 minutes -> Option A

4. Quick Check:

   rate() = per-second average increase [OK]

Hint: rate() gives per-second average over time window [OK]

Common Mistakes:

• Thinking rate() returns total count
• Confusing rate() with current active requests
• Assuming rate() returns max value

4. You configured Prometheus to scrape localhost:9090 but no metrics appear. Which fix is correct?

medium

A. Change target to localhost:9090/metrics in YAML

B. Remove job_name from config

C. Restart Prometheus to reload config

D. Add metrics_path: '/metrics' under the scrape job

Solution

1. Step 1: Understand default metrics path

   Prometheus scrapes /metrics path by default, but if the service uses a different path, you must specify it.

2. Step 2: Fix missing metrics path

   Adding metrics_path: '/metrics' explicitly tells Prometheus where to get metrics if not default or to confirm path.

3. Final Answer:

   Add metrics_path: '/metrics' under the scrape job -> Option D

4. Quick Check:

   metrics_path fixes scrape URL [OK]

Hint: Use metrics_path to set correct scrape URL path [OK]

Common Mistakes:

• Adding path in targets instead of metrics_path
• Restarting without config fix
• Removing job_name breaks config

5. You want to monitor error rates in a microservice using Prometheus. The service exposes http_requests_total with labels status and method. Which query shows the error rate (status codes 500-599) over the last 10 minutes as a percentage of all requests?

hard

A. rate(http_requests_total{status=~"5.."}[10m]) / rate(http_requests_total[10m]) * 100

B. sum(rate(http_requests_total{status=~"5.."}[10m])) / sum(rate(http_requests_total[10m])) * 100

C. sum(rate(http_requests_total{status=~"5.."}[10m])) * 100

D. sum(rate(http_requests_total{status!~"5.."}[10m])) / sum(rate(http_requests_total[10m])) * 100

Solution

1. Step 1: Filter error status codes 500-599

   Use regex status=~"5.." to select error codes in the 500 range.

2. Step 2: Calculate error rate as percentage

   Sum the rate of error requests and divide by sum of all requests rate, then multiply by 100 for percentage.

3. Final Answer:

   sum(rate(http_requests_total{status=~"5.."}[10m])) / sum(rate(http_requests_total[10m])) * 100 -> Option B

4. Quick Check:

   Error rate % = error requests / total requests * 100 [OK]

Hint: Sum rates before division for correct percentage [OK]

Common Mistakes:

• Dividing single rates instead of sums
• Using wrong label regex
• Multiplying before division