Key broker metrics in Kafka - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the total number of messages produced to the broker.

Kafka

total_messages = broker_metrics.[1]

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

Amessages_in_per_sec

Bbytes_out_total

Cbytes_in_total

Dmessages_in_total

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a per second rate metric instead of total count.

The messages_in_total metric shows the total number of messages produced to the broker.

2fill in blank

medium

Complete the code to get the current rate of bytes sent out by the broker per second.

Kafka

bytes_out_rate = broker_metrics.[1]

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Abytes_out_total

Bmessages_out_per_sec

Cbytes_out_per_sec

Dbytes_in_per_sec

Attempts:

3 left

💡 Hint

Common Mistakes

Using total bytes instead of per second rate.

The bytes_out_per_sec metric gives the current rate of bytes sent out by the broker each second.

3fill in blank

hard

Fix the error in accessing the broker's request handler average idle percent.

Kafka

idle_percent = broker_metrics.[1]

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Arequest_handler_avg_idle_percent

Brequest_handler_idle_avg_percent

Crequest_handler_avg_idle

Drequest_handler_idle_percent_avg

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing the order of words in the metric name.

The correct metric name is request_handler_avg_idle_percent which shows the average idle percent of request handlers.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each topic to its total bytes in.

Kafka

topic_bytes_in = {topic: broker_metrics[topic][1] for topic in topics if broker_metrics[topic][1] [2] 0}

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A['bytes_in_total']

B>

C>= 0

D['messages_in_total']

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong metric keys or wrong comparison operators.

The first blank accesses the bytes_in_total metric for each topic. The second blank filters topics with bytes in greater than zero.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of topics with messages in per second greater than 1000, mapping topic to that rate.

Kafka

high_rate_topics = { [1] : broker_metrics[[2]][3] for [2] in topics if broker_metrics[[2]][3] > 1000 }

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Atopic

B'messages_in_per_sec'

C[ 'messages_in_per_sec' ]

Dmessages_in_per_sec

Attempts:

3 left

💡 Hint

Common Mistakes

Using variable names instead of string keys for metrics.

The dictionary maps each topic to its messages_in_per_sec metric value. The metric is accessed with square brackets and the string key.