Top 100 Prometheus Interview Questions and Answers

In this article we are going to cover Prometheus Interview Questions and Answers, Prometheus Scenario Based Interview Questions and Answers, Prometheus PromQL Interview Questions ans Answers.

Prometheus Interview Questions and Answers

What is Prometheus?

Prometheus is an open-source monitoring system that excels at collecting and storing time-series metrics scraped from various targets like servers, applications, and containers. It allows for alerting based on those metrics.

What is PromQL?

PromQL (Prometheus Query Language) is a specific query language used to retrieve and analyze data stored by Prometheus. It allows for filtering, aggregation, and calculations on time-series data.

Why would you use Prometheus & Grafana together?

Prometheus excels at collection and storage, while Grafana is a visualization tool. Together, they provide a complete monitoring solution. Prometheus collects data, and Grafana allows you to see that data in informative dashboards and graphs.

Is Prometheus a time-series database (TSDB)?

Yes, Prometheus functions as a time-series database specifically designed for metrics data.

How do you set up alerting in Prometheus?

Alerting rules are defined within the Prometheus configuration. These rules use PromQL expressions to evaluate metrics and trigger alerts when specific conditions are met. Alertmanager, a separate tool, can then be used to route and manage those alerts.

What are Prometheus exporters?

Exporters are specialized components that translate metrics from various sources (applications, databases, etc.) into a format that Prometheus can understand and scrape.

How can you monitor Kubernetes with Prometheus?

Several exporters are designed specifically for Kubernetes, such as node_exporter (for node-level metrics), kube-state-metrics (for cluster state information), and kubelet (for per-Pod metrics). By scraping these exporters, Prometheus can monitor the health and performance of your Kubernetes cluster.

How would you find the number of Kubernetes Pods per namespace?

kube_pod_info{namespace!=""} | group by (namespace) | count

How can you calculate the HTTP error rate as a percentage of total traffic?

rate(http_requests_total{code=~"5.."}[5m]) / rate(http_requests_total[5m]) * 100

These are just a few examples. Being familiar with PromQL syntax and how to construct queries for specific metrics will be helpful.

What are some challenges you’ve faced working with Prometheus?

This is your chance to showcase your experience. Common challenges might include the complexity of the data model, keeping up with frequent updates, or limited documentation for certain features.

Have you developed any custom Prometheus exporters?

If you have experience creating custom exporters, this can demonstrate your ability to tailor Prometheus to specific monitoring needs.

What is main purpose of Prometheus?

Prometheus is an open-source monitoring and alerting toolkit originally built at SoundCloud. Its primary purpose is to collect metrics from various systems, store them, allow querying and analysis of these metrics, and trigger alerts based on predefined conditions.

How does Prometheus collect metrics?

Prometheus uses a pull-based model, where it scrapes metrics from instrumented targets at regular intervals. Targets expose metrics in a format Prometheus can understand, typically through an HTTP endpoint.

Explain the role of exporters in Prometheus.

Exporters are special types of applications or libraries that expose metrics in a format Prometheus can scrape. They’re often used to instrument existing systems that don’t natively expose Prometheus-compatible metrics.

What is a Prometheus Alertmanager, and how does it work?

Prometheus Alertmanager is responsible for handling alerts generated by Prometheus server. It groups, deduplicates, and routes alerts to the appropriate receivers (like email, PagerDuty, etc.) based on predefined configurations.

What is a Prometheus recording rule, and why is it useful?

Recording rules allow you to precompute frequently needed or computationally expensive expressions and store their results as new time series. This can improve query performance and simplify complex queries.

How does Prometheus handle high availability and scalability?

Prometheus itself doesn’t natively support high availability or horizontal scalability. However, you can set up federation to aggregate data from multiple Prometheus servers, and you can use Prometheus’s remote storage integrations to store data in scalable backends like Thanos or Cortex.

What is PromQL, and how is it used?

PromQL is the query language used to retrieve and manipulate metrics stored in Prometheus. It allows users to perform complex queries, aggregations, and transformations on the collected data.

Explain the concept of labels in Prometheus metrics.

Labels are key-value pairs associated with time series data in Prometheus. They allow for more dimensional data modeling and enable powerful querying and aggregation capabilities.

How does Prometheus handle metric cardinality explosion?

Cardinality explosion occurs when the number of unique time series grows too large, which can strain resources. Prometheus mitigates this by encouraging careful use of labels, setting reasonable retention policies, and using recording rules to precompute commonly used metrics.

Can you describe how you would set up monitoring for a new service using Prometheus?

First, I would instrument the service with a Prometheus client library to expose relevant metrics. Then, I would configure Prometheus to scrape these metrics endpoints. Next, I’d define appropriate alerting rules based on service-level objectives (SLOs) and set up Alertmanager to handle alerts. Finally, I’d write dashboards in Grafana to visualize the collected metrics.

Prometheus Scenario Based Interview Questions and Answers

Scenario 1: Application Performance Degradation

Question: Our e-commerce application’s response times have been increasing for the past hour. How would you use Prometheus to diagnose the issue?

Answer:

1. Identify relevant metrics: Look for metrics related to application performance, such as HTTP request latency, error rate, and thread pool saturation. You can use PromQL to query these metrics for the past hour.
2. Analyze trends: Look for spikes or sustained increases in latency or error rates. This could indicate an application bug, resource overload, or database issues.
3. Correlate metrics: Correlate application metrics with infrastructure metrics like CPU, memory, and network utilization to see if resource constraints are contributing.
4. Drill down: Based on the analysis, further investigate specific components using relevant metrics (e.g., database connection pool size for database issues).

Scenario 2: Alerting Setup and Optimization

Question: We’re receiving too many Prometheus alerts, causing alert fatigue. How would you optimize our alerting rules?

Answer:

1. Review alert rules: Analyze existing alerting rules to identify overly sensitive thresholds or rules triggering for normal fluctuations.
2. Implement alerting suppression: Use techniques like grouping related alerts or defining time-based silencing to reduce alert noise.
3. Refine thresholds: Adjust thresholds based on historical data and acceptable performance levels.
4. Consider alert escalation: Implement escalation policies to route critical alerts to designated personnel while notifying relevant teams for less severe issues.

Scenario 3: Scaling Prometheus for Large Infrastructure

Question: Our infrastructure is growing, and a single Prometheus instance is struggling to keep up. How would you scale the monitoring setup?

Answer:

1. Utilize push gateways: Consider using push gateways to offload scraping tasks from the main Prometheus server, allowing it to focus on aggregation and alerting.
2. Implement horizontal scaling: Explore solutions like Thanos, which allows running multiple Prometheus instances to distribute the scraping load and storage requirements.
3. Leverage Alertmanager: Implement Alertmanager to handle notifications from multiple Prometheus instances and manage alert routing effectively.

Additional Tips:

• Be prepared to discuss PromQL syntax and how to write queries to extract relevant data.
• Showcase your understanding of integrating Prometheus with visualization tools like Grafana for effective monitoring dashboards.
• Highlight your experience with configuring exporters for different applications and infrastructure components.
• If possible, demonstrate your knowledge of advanced topics like recording rules and remote write functionality.

Scenario 4: High CPU Usage Alert

Question: You receive an alert indicating high CPU usage on a critical production server. How would you use Prometheus to diagnose the issue?

Answer:

1. Identify the target: Use the alert details to identify the specific server experiencing high CPU usage.
2. Query relevant metrics: Use PromQL to query metrics related to CPU usage on the server. This might include node_cpu_usage or specific CPU core usage metrics.
3. Analyze trends: Look for spikes or sustained high values in CPU usage over time.
4. Correlate with other metrics: Query for metrics that might explain the high CPU usage, such as process CPU usage (process_cpu_user and process_cpu_system), network traffic (net_bytes_recv and net_bytes_sent), or database connection counts (db_connections).
5. Investigate further: Based on the correlated metrics, you can investigate further. This might involve checking application logs for errors or identifying resource-intensive processes.

Scenario 5: Missing Data from a Service

Question: You notice that data for a specific service is missing from your Prometheus dashboards. How would you troubleshoot the issue?

Answer:

1. Check scraping targets: Verify if the target for the service is still configured correctly in the Prometheus configuration file (prometheus.yml).
2. Exporters: Ensure the exporter for the service is running and healthy on the target machine. You can check logs or use tools like curl to test the exporter endpoint.
3. Firewall rules: If the target is on a different machine, confirm there are no firewall rules blocking Prometheus from scraping data.
4. Prometheus logs: Check the Prometheus server logs for any errors related to scraping the specific target.

Scenario 6: Scaling Prometheus for a Large Infrastructure

Question: Your infrastructure is growing, and the current Prometheus server is struggling to keep up. How would you scale your monitoring solution?

Answer:

There are several ways to scale Prometheus for a large infrastructure:

• Horizontal scaling: Use multiple Prometheus servers running in a distributed setup. Tools like Thanos can be used for centralizing data collection and querying.
• Alertmanager: Utilize Alertmanager to handle alerts from multiple Prometheus servers and deduplicate them before sending notifications.
• Remote write: Configure Prometheus to push scraped data to a central storage solution like Thanos for long-term storage and querying historical data.

Prometheus PromQL Interview Questions and Answers

Question: How do you retrieve the current CPU usage of a specific server named “webserver1”?

Answer:

node_cpu_usage{instance="webserver1"}

Question: Calculate the average HTTP request latency over the last 5 minutes for all your web servers.

Answer:

avg(http_request_latency{job="webserver"}[5m])

Question: Identify the pods with the highest memory usage in the “production” namespace within Kubernetes.

Answer:

container_memory_usage_bytes{namespace="production"} order by desc limit 5

Question: Calculate the rate of increase in database connections over the last 1 hour.

PromQL doesn’t directly support rate of change, but you can achieve it with a derivative function.

Answer:

rate(db_connections{job="database"}[1h])

PromQL Functions and Operators:

Question: How can you filter metrics based on specific label values?

Answer:

PromQL uses curly braces {} for label filtering. You can use equality (=) or regular expressions (=~) for matching values.

Example: Get CPU usage only for cores with “type” label value “logical”.

node_cpu_usage{type="logical"}

Question: Explain the difference between instant vector and range vector in PromQL.

Answer:

• Instant vector: Represents the value of a metric at a specific point in time. Returned by queries without a time range specified.
• Range vector: Represents the values of a metric over a specified time range. Defined by time range qualifiers like [5m] or timestamps.

Understanding these differences helps write accurate queries for historical data retrieval.

Tips for PromQL Interviews:

• Be prepared to explain your thought process while constructing a PromQL query.
• Showcase your knowledge of various PromQL functions like avg, sum, rate, irate, and count.
• Understand how to filter metrics based on label selectors and regular expressions.
• Practice writing PromQL queries for different real-world scenarios like identifying anomalies, calculating averages, or correlating metrics.

• Question: How do you retrieve the current value of the http_requests_total metric for a specific server identified by the label server_id set to “webserver1”?

Answer:

http_requests_total{server_id="webserver1"}

• Question: How can you display the average CPU usage across all servers over the last 1 hour?

Answer:

avg(node_cpu_usage{  } [1h])

• Question: How do you calculate the rate of increase of HTTP requests per second for the past 5 minutes?

Answer:

irate(http_requests_total{  } [5m])

• Question: How can you identify the top 5 servers with the highest average memory usage over the last 15 minutes?

Answer:

topk(5, {instance=""} avg(node_memory_Bytes{  } [15m]))

• Question: Write a PromQL query to show the difference between inbound and outbound network traffic for a specific interface named “eth0” on a server with the label host set to “dbserver”?

Answer:

net_bytes_recv{interface="eth0", host="dbserver"} - net_bytes_sent{interface="eth0", host="dbserver"}

PromQL Functions:

• Question: Explain the difference between avg and irate functions in PromQL.

Answer:

• avg: Calculates the average value of a metric over a specified time range.
• irate: Calculates the rate of change of a metric over a specified time range, essentially showing how much the value is increasing or decreasing per second.
• Question: What does the offset modifier do in a PromQL query?

Answer:

The offset modifier allows you to shift the time window of a query by a specific duration. For example, http_requests_total offset 1h would retrieve data for the http_requests_total metric for the hour preceding the current time.

These are just a few examples, and interviewers might ask follow-up questions to assess your understanding of PromQL functionalities and problem-solving skills.

Here are some additional tips for PromQL interview questions:

• Be familiar with common PromQL functions like avg, irate, min, max, sum, count, increase, and decrease.
• Practice writing PromQL queries for various scenarios like identifying trends, anomalies, or correlations between metrics.
• Understand how to use labels and label selectors to filter and group data in your queries.

Conclusion:

We have covered Prometheus Interview Questions and Answers, Prometheus Scenario Based Interview Questions and Answers, Prometheus PromQL Interview Questions and Answers.

Related Articles:

Terraform Interview Questions and Answers

Reference:

Prometheus official page