JMX metrics in Kafka - Mini Project: Build & Apply

Collecting Kafka JMX Metrics

📖 Scenario: You are managing a Kafka server and want to monitor its performance. Kafka exposes many metrics through JMX (Java Management Extensions). You will write a simple setup to collect some key JMX metrics from Kafka.

🎯 Goal: Build a small program that connects to Kafka's JMX interface, selects specific metrics, and prints their values.

📋 What You'll Learn

Create a dictionary with Kafka JMX metric names and their object names

Add a variable to specify the JMX port number

Write a loop to simulate fetching metric values from JMX

Print the collected metrics in a readable format

💡 Why This Matters

🌍 Real World

Kafka administrators monitor JMX metrics to keep track of message rates, data throughput, and broker health.

💼 Career

Understanding how to collect and process Kafka JMX metrics is useful for roles in DevOps, Site Reliability Engineering, and backend system monitoring.

1

Setup Kafka JMX metrics dictionary

Create a dictionary called kafka_metrics with these exact entries: 'MessagesInPerSec': 'kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec', 'BytesInPerSec': 'kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec', and 'BytesOutPerSec': 'kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec'.

Kafka

# Create the kafka_metrics dictionary with exact entries
# Your code here

Need a hint?

Use curly braces to create a dictionary. Each key is a metric name string, and each value is the exact JMX object name string.

2

Set the JMX port number

Create a variable called jmx_port and set it to 9999.

Kafka

kafka_metrics = {
    'MessagesInPerSec': 'kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec',
    'BytesInPerSec': 'kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec',
    'BytesOutPerSec': 'kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec'
}
# Set the JMX port number
# Your code here

Need a hint?

Just assign the number 9999 to the variable named jmx_port.

3

Simulate fetching metric values

Write a for loop using variables metric and object_name to iterate over kafka_metrics.items(). Inside the loop, create a dictionary called collected_metrics before the loop and add each metric with a simulated value of 100.

Kafka

kafka_metrics = {
    'MessagesInPerSec': 'kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec',
    'BytesInPerSec': 'kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec',
    'BytesOutPerSec': 'kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec'
}
jmx_port = 9999

# Create collected_metrics dictionary
collected_metrics = {}

# Loop over kafka_metrics.items() and add metric with value 100
# Your code here

Need a hint?

Initialize collected_metrics as an empty dictionary. Use a for loop with metric and object_name to add each metric with value 100.

4

Print the collected metrics

Write a print statement to display the collected_metrics dictionary.

Kafka

kafka_metrics = {
    'MessagesInPerSec': 'kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec',
    'BytesInPerSec': 'kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec',
    'BytesOutPerSec': 'kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec'
}
jmx_port = 9999

collected_metrics = {}
for metric, object_name in kafka_metrics.items():
    collected_metrics[metric] = 100

# Print the collected_metrics dictionary
# Your code here

Need a hint?

Use print(collected_metrics) to show the dictionary.