JMX metrics in Kafka - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the JMX port for Kafka broker.

Kafka

KAFKA_JMX_OPTS="-Dcom.sun.management.jmxremote.port=[1]"

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

A9999

B9092

C2181

D8080

Attempts:

3 left

💡 Hint

Common Mistakes

Using Kafka broker port 9092 as JMX port.

Using Zookeeper port 2181 as JMX port.

The default JMX port for Kafka metrics is often set to 9999 to avoid conflicts.

2fill in blank

medium

Complete the code to enable JMX remote access without authentication.

Kafka

KAFKA_JMX_OPTS="$KAFKA_JMX_OPTS -Dcom.sun.management.jmxremote.authenticate=[1]"

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

Ano

Byes

Ctrue

Dfalse

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'no' or 'yes' instead of boolean false or true.

Setting authentication to true when trying to disable it.

Setting com.sun.management.jmxremote.authenticate to false disables authentication for JMX remote access.

3fill in blank

hard

Fix the error in the JMX options to enable SSL for JMX remote connections.

Kafka

KAFKA_JMX_OPTS="$KAFKA_JMX_OPTS -Dcom.sun.management.jmxremote.ssl=[1]"

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

Atrue

Benable

Con

Dyes

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'enable' or 'yes' instead of true.

Using uppercase True which may cause issues.

The property com.sun.management.jmxremote.ssl expects a boolean value like true or false to enable or disable SSL.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that collects JMX metrics for topics with names longer than 5 characters.

Kafka

metrics = {topic: metrics_data[topic][1] for topic in topics if len(topic) [2] 5}

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

A["message_count"]

B>

C<

D['bytes_in']

Attempts:

3 left

💡 Hint

Common Mistakes

Using parentheses instead of square brackets for dictionary keys.

Using less than '<' instead of greater than '>' in condition.

The comprehension collects the 'message_count' metric for topics whose name length is greater than 5.

5fill in blank

hard

Fill all three blanks to filter and transform JMX metrics into a new dictionary with uppercase topic names and only metrics with values above 100.

Kafka

filtered_metrics = [1]: metrics_data[topic] for topic in topics if metrics_data[topic][[2]] [3] 100

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

Atopic.upper()

B'message_count'

C>

D'bytes_in'

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase topic names instead of uppercase.

Filtering on 'bytes_in' instead of 'message_count'.

Using '<' instead of '>' in the condition.

This comprehension creates a dictionary with uppercase topic names as keys and filters metrics where 'message_count' is greater than 100.

Practice

1. What is the primary purpose of JMX metrics in Kafka?

easy

A. To configure Kafka brokers dynamically

B. To send messages between Kafka topics

C. To monitor Kafka's internal performance and health

D. To store data permanently in Kafka

Solution

1. Step 1: Understand JMX metrics role

   JMX metrics provide data about Kafka's internal state and performance.

2. Step 2: Identify the correct purpose

   Monitoring performance and health is the main use of JMX metrics in Kafka.

3. Final Answer:

   To monitor Kafka's internal performance and health -> Option C

4. Quick Check:

   JMX metrics = Monitoring Kafka [OK]

Hint: JMX metrics are for monitoring, not messaging or storage [OK]

Common Mistakes:

• Confusing metrics with message sending
• Thinking JMX configures Kafka brokers
• Assuming JMX stores Kafka data

2. Which of the following is the correct way to identify a Kafka JMX metric?

easy

A. Using a topic name and partition number

B. Using a unique ObjectName and attribute

C. Using a consumer group ID

D. Using a broker ID and port number

Solution

1. Step 1: Recall JMX metric identification

   Each JMX metric is accessed by a unique ObjectName and attribute in Kafka.

2. Step 2: Compare options

   Only Using a unique ObjectName and attribute correctly describes the identification method for JMX metrics.

3. Final Answer:

   Using a unique ObjectName and attribute -> Option B

4. Quick Check:

   JMX metric ID = ObjectName + attribute [OK]

Hint: JMX metrics use ObjectName and attribute, not topic or group IDs [OK]

Common Mistakes:

• Mixing JMX metric IDs with Kafka topic or consumer IDs
• Confusing broker info with metric identifiers
• Assuming partition number identifies metrics

3. Given the following Java snippet to get a Kafka JMX metric attribute, what will be the output if the metric value is 150?

ObjectName name = new ObjectName("kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec");
MBeanServer mbs = ManagementFactory.getPlatformMBeanServer();
Integer value = (Integer) mbs.getAttribute(name, "Count");
System.out.println(value);

medium

A. MBeanServerException

B. null

C. Count

D. 150

Solution

1. Step 1: Understand the code's purpose

   The code queries the JMX MBeanServer for the 'Count' attribute of the specified metric.

2. Step 2: Interpret the output

   If the metric value is 150, the printed output will be the integer 150.

3. Final Answer:

   150 -> Option D

4. Quick Check:

   Metric value printed = 150 [OK]

Hint: JMX attribute value prints directly if metric exists [OK]

Common Mistakes:

• Expecting attribute name instead of value
• Assuming null if metric exists
• Confusing exceptions with normal output

4. You try to read a Kafka JMX metric but get a javax.management.InstanceNotFoundException. What is the most likely cause?

medium

A. The ObjectName used does not match any registered MBean

B. The Kafka broker is down

C. The attribute name is misspelled

D. The MBeanServer is not initialized

Solution

1. Step 1: Understand the exception meaning

   InstanceNotFoundException means the requested MBean ObjectName is not found in the MBeanServer.

2. Step 2: Identify the cause

   If the ObjectName does not match any registered MBean, this exception occurs.

3. Final Answer:

   The ObjectName used does not match any registered MBean -> Option A

4. Quick Check:

   InstanceNotFoundException = Wrong ObjectName [OK]

Hint: Check ObjectName spelling if InstanceNotFoundException occurs [OK]

Common Mistakes:

• Blaming attribute name for InstanceNotFoundException
• Assuming broker down causes this exception
• Thinking MBeanServer is uninitialized

5. You want to monitor Kafka's message throughput using JMX metrics and alert if MessagesInPerSec.Count exceeds 1000. Which approach correctly combines JMX metric reading and alert logic in Java?

hard

A. Query the MBeanServer for the metric attribute, then compare the value to 1000 and trigger alert if greater

B. Subscribe to Kafka topic and count messages manually, ignoring JMX metrics

C. Use Kafka consumer lag metrics instead of MessagesInPerSec

D. Restart Kafka broker when MessagesInPerSec.Count exceeds 1000

Solution

1. Step 1: Understand monitoring with JMX

   To monitor throughput, read the MessagesInPerSec.Count attribute from Kafka's JMX MBeanServer.

2. Step 2: Implement alert logic

   Compare the retrieved metric value to 1000 and trigger an alert if it exceeds this threshold.

3. Final Answer:

   Query the MBeanServer for the metric attribute, then compare the value to 1000 and trigger alert if greater -> Option A

4. Quick Check:

   Read metric + compare + alert = Correct approach [OK]

Hint: Read metric value then compare to threshold for alert [OK]

Common Mistakes:

• Ignoring JMX metrics and counting messages manually
• Using unrelated metrics like consumer lag
• Restarting broker instead of alerting