Airflow metrics with Prometheus - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of integrating Prometheus with Airflow?

Prometheus collects and stores metrics from Airflow to monitor its performance and health in real time.

beginner

Which Airflow component exposes metrics for Prometheus to scrape?

The Airflow webserver exposes metrics on an HTTP endpoint that Prometheus scrapes.

beginner

Name one common Airflow metric that Prometheus can collect.

One common metric is the number of running DAG runs, which helps track active workflows.

intermediate

How do you enable Prometheus metrics in Airflow's configuration?

Set `metrics.prometheus.enabled = True` in the airflow.cfg file or environment variables.

intermediate

What is the role of the Prometheus scrape config in monitoring Airflow?

It tells Prometheus where and how often to collect metrics from the Airflow metrics endpoint.

Which Airflow component typically exposes metrics for Prometheus?

A Airflow webserver

B Airflow scheduler

C Airflow worker

D Airflow database

The Airflow webserver exposes the metrics endpoint that Prometheus scrapes.

What configuration setting enables Prometheus metrics in Airflow?

A prometheus.metrics.active = yes

B metrics.prometheus.enabled = True

C enable.prometheus.metrics = on

D metrics.enabled.prometheus = true

The correct setting is metrics.prometheus.enabled = True in airflow.cfg or environment variables.

What does Prometheus use to find Airflow metrics?

A Scrape configuration with Airflow metrics endpoint URL

B Direct database queries

C Airflow CLI commands

D Airflow logs

Prometheus scrapes metrics from the HTTP endpoint specified in its scrape config.

Which metric would help monitor how many workflows are currently running in Airflow?

A Number of Airflow users

B CPU usage of Airflow server

C Disk space on Airflow host

D Number of running DAG runs

The number of running DAG runs metric shows active workflows.

Why is monitoring Airflow with Prometheus useful?

A To run Airflow tasks faster

B To replace Airflow's scheduler

C To track Airflow health and performance in real time

D To store Airflow logs permanently

Prometheus helps monitor Airflow's health and performance by collecting metrics.

Explain how Prometheus collects metrics from Airflow and why this is helpful.

Think about how Prometheus finds and uses Airflow data.

Describe the steps to enable and configure Prometheus metrics in Airflow.

Focus on configuration changes and Prometheus setup.

Practice

1. What is the main purpose of integrating Prometheus with Airflow metrics?

easy

A. To schedule Airflow DAGs automatically

B. To store Airflow logs permanently

C. To monitor Airflow task and system health in real-time

D. To send emails from Airflow tasks

Solution

1. Step 1: Understand Airflow metrics role

   Airflow metrics provide data about task execution and system performance.

2. Step 2: Identify Prometheus integration purpose

   Prometheus collects and visualizes these metrics for monitoring and alerting.

3. Final Answer:

   To monitor Airflow task and system health in real-time -> Option C

4. Quick Check:

   Monitoring = B [OK]

Hint: Prometheus is for monitoring, not scheduling or logging [OK]

Common Mistakes:

• Confusing monitoring with scheduling
• Thinking Prometheus stores logs
• Assuming Prometheus sends emails

2. Which configuration option must be enabled in Airflow to expose metrics for Prometheus scraping?

easy

A. metrics_enabled = False

B. statsd_on = True

C. prometheus_exporter = False

D. logging_level = DEBUG

Solution

1. Step 1: Identify Airflow metric exposure setting

   Airflow uses StatsD to collect metrics, so enabling StatsD is required.

2. Step 2: Match correct config option

   The correct option to enable StatsD is statsd_on = True.

3. Final Answer:

   statsd_on = True -> Option B

4. Quick Check:

   Enable StatsD = A [OK]

Hint: Enable StatsD to expose metrics for Prometheus [OK]

Common Mistakes:

• Disabling metrics with false flags
• Confusing logging level with metrics
• Assuming Prometheus exporter is off by default

3. Given this Airflow config snippet:

[metrics]
statsd_on = True
statsd_host = "localhost"
statsd_port = 8125

What will Prometheus scrape from Airflow?

medium

A. Airflow task execution metrics via StatsD exporter

B. Airflow DAG definitions

C. Airflow database contents

D. Airflow log files

Solution

1. Step 1: Analyze config snippet

   The snippet enables StatsD metrics collection on localhost port 8125.

2. Step 2: Understand Prometheus scraping

   Prometheus scrapes metrics exposed by StatsD exporter, which includes task execution data.

3. Final Answer:

   Airflow task execution metrics via StatsD exporter -> Option A

4. Quick Check:

   StatsD metrics = D [OK]

Hint: StatsD config means Prometheus scrapes metrics, not logs or DB [OK]

Common Mistakes:

• Thinking Prometheus scrapes DAG code
• Assuming Prometheus reads database directly
• Confusing logs with metrics

4. You enabled StatsD in Airflow but Prometheus shows no metrics. What is a likely cause?

medium

A. Prometheus scrape config missing or incorrect

B. Airflow DAGs are paused

C. Airflow scheduler is disabled

D. Airflow logs are too large

Solution

1. Step 1: Check Prometheus scraping setup

   If Prometheus shows no metrics, it may not be scraping the StatsD exporter endpoint correctly.

2. Step 2: Rule out unrelated causes

   Paused DAGs or scheduler off do not stop metrics exposure; large logs do not affect metrics scraping.

3. Final Answer:

   Prometheus scrape config missing or incorrect -> Option A

4. Quick Check:

   Scrape config issue = C [OK]

Hint: Check Prometheus scrape config if no metrics appear [OK]

Common Mistakes:

• Blaming paused DAGs for missing metrics
• Assuming scheduler off stops metrics
• Thinking logs size affects metrics

5. You want to create an alert in Prometheus for Airflow tasks that fail more than 5 times in 10 minutes. Which Prometheus query is correct?

hard

A. count(airflow_task_failures_total) == 5

B. sum(airflow_task_success_total) > 5

C. rate(airflow_task_failures_total[5m]) < 5

D. increase(airflow_task_failures_total[10m]) > 5

Solution

1. Step 1: Understand alert condition

   The alert triggers if failures increase by more than 5 in 10 minutes.

2. Step 2: Match Prometheus query

   increase(metric[10m]) > 5 checks if failures increased over 5 in last 10 minutes.

3. Step 3: Eliminate wrong options

   sum(airflow_task_success_total) > 5 checks successes, not failures; C uses wrong time and comparison; D counts current samples, not increase.

4. Final Answer:

   increase(airflow_task_failures_total[10m]) > 5 -> Option D

5. Quick Check:

   Use increase() over 10m > 5 for failure alert [OK]

Hint: Use increase() over time window to detect failure spikes [OK]

Common Mistakes:

• Using success metric instead of failure
• Using rate() incorrectly for count threshold
• Counting samples instead of increase