Key broker metrics in Kafka - Step-by-Step Execution

Process Flow - Key broker metrics

Start Kafka Broker

↓

Collect Metrics

↓

Broker Metrics: Request Rate, Latency, Throughput

↓

Monitor Disk Usage, Network I/O, CPU

↓

Trigger Alerts if Thresholds Exceeded

↓

Adjust Broker or Topic Configurations

↓

Repeat Monitoring Cycle

This flow shows how a Kafka broker collects and monitors key metrics continuously to ensure smooth operation.

Execution Sample

Kafka

metrics = broker.get_metrics()
for metric in metrics:
    print(f"{metric.name}: {metric.value}")

This code fetches key metrics from a Kafka broker and prints each metric's name and value.

Process Table

Step	Action	Metric Name	Metric Value	Output
1	Retrieve metric from metrics list	request_rate	1500 req/s	None
2	Retrieve metric from metrics list	request_latency	5 ms	None
3	Retrieve metric from metrics list	bytes_in_per_sec	10 MB/s	None
4	Retrieve metric from metrics list	bytes_out_per_sec	9 MB/s	None
5	Retrieve metric from metrics list	disk_usage	70%	None
6	Print metric	request_rate	1500 req/s	request_rate: 1500 req/s
7	Print metric	request_latency	5 ms	request_latency: 5 ms
8	Print metric	bytes_in_per_sec	10 MB/s	bytes_in_per_sec: 10 MB/s
9	Print metric	bytes_out_per_sec	9 MB/s	bytes_out_per_sec: 9 MB/s
10	Print metric	disk_usage	70%	disk_usage: 70%
11	End of metrics list			Execution stops after all metrics printed

💡 All key broker metrics have been printed, so the loop ends.

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	Final
metrics	empty	[request_rate,...]	[request_rate,...]	[request_rate,...]	[request_rate,...]	[request_rate,...]	[request_rate,...]
metric.name	none	request_rate	request_latency	bytes_in_per_sec	bytes_out_per_sec	disk_usage	none
metric.value	none	1500 req/s	5 ms	10 MB/s	9 MB/s	70%	none

Key Moments - 2 Insights

Why do we see multiple metrics printed one after another instead of just one?

Because the code loops through all metrics returned by get_metrics(), printing each metric's name and value as shown in execution_table rows 6 to 10.

What happens if the disk_usage metric value goes above 90%?

The monitoring system would trigger an alert to warn about high disk usage, prompting action to avoid broker issues. This is implied in the concept_flow step 'Trigger Alerts if Thresholds Exceeded'.

Visual Quiz - 3 Questions

Look at the execution_table, what is the metric value printed at step 8?

A 10 MB/s

B 9 MB/s

C 5 ms

D 1500 req/s

💡 Hint

Check the 'Metric Value' column at step 8 in the execution_table.

At which step does the code print the disk_usage metric?

A Step 6

B Step 5

C Step 10

D Step 11

💡 Hint

Look for the 'Print metric' action with 'disk_usage' in the 'Metric Name' column.

If the broker had an additional metric 'cpu_usage', how would the execution_table change?

A Replace 'disk_usage' metric with 'cpu_usage' metric.

B Add a new row for 'cpu_usage' metric after step 5 and before printing steps.

C No change, metrics are fixed.

D Remove all printing steps.

💡 Hint

New metrics appear as new rows in the metrics list before printing, as shown in steps 1-5.

Concept Snapshot

Key broker metrics in Kafka include request rate, latency, throughput, disk usage, and CPU.
These metrics are collected continuously by the broker.
Monitoring them helps detect performance issues early.
Metrics are accessed via broker APIs and printed or logged.
Alerts can be triggered if metrics exceed safe thresholds.

Full Transcript

This visual execution trace shows how a Kafka broker collects and prints key metrics like request rate, latency, bytes in/out per second, and disk usage. The code calls get_metrics() to get a list of metrics, then loops through each metric to print its name and value. The execution table details each step, showing metric names and values fetched and printed. Variables track the metrics list and current metric's name and value. Key moments clarify why multiple metrics print and what happens if disk usage is high. The quiz tests understanding of metric values at specific steps and how adding a new metric affects the flow. The snapshot summarizes the importance and usage of key broker metrics for monitoring Kafka brokers.