Prometheus Interview Questions and Answers for 10 years experience

1. What is Prometheus and how does it work?

   • Answer: Prometheus is an open-source monitoring and alerting system. It works by periodically scraping metrics from targets (applications, servers, etc.) exposed via an HTTP endpoint. These metrics are time-series data, meaning they include a timestamp along with the value. Prometheus stores this data and provides functionalities for querying, visualizing, and alerting based on pre-defined rules.

2. Explain the concept of pull vs. push model in monitoring. Which model does Prometheus use and why?

   • Answer: Prometheus uses a pull model. In a pull model, Prometheus actively scrapes metrics from configured targets at specified intervals. A push model, conversely, would involve targets actively sending metrics to a central server. Prometheus uses a pull model for several reasons: it simplifies the target's implementation, it ensures consistent scraping intervals even if the target is temporarily unavailable, and it allows for more robust error handling and retry mechanisms.

3. What are PromQL selectors and how do you use them to filter time-series data?

   • Answer: PromQL selectors are used to filter and select time series data based on label values. They use the syntax `{label_name="label_value"}`. For example, `http_requests_total{method="GET"}` would select only time series related to GET requests. You can combine multiple selectors using commas to create more specific filters.

4. Describe different PromQL operators and their usage.

   • Answer: PromQL offers various operators including arithmetic (+, -, *, /, %), comparison (=, !=, >, <, >=, <=), logical (and, or, unless), aggregation (sum, avg, min, max, count, stddev, stdvar), and binary operators. These operators are used to perform calculations, comparisons, and aggregations on time series data. For example, `sum(http_requests_total)` sums all `http_requests_total` metrics.

5. Explain the concept of labels and their importance in Prometheus.

   • Answer: Labels are key-value pairs that provide metadata about a specific time series. They are crucial for filtering and aggregating data. Labels allow you to group metrics from different sources based on common characteristics (e.g., application version, environment, hostname). This allows for flexible querying and analysis.

6. What are recording rules and how do they work in Prometheus?

   • Answer: Recording rules allow you to define new metrics based on existing ones using PromQL expressions. They are evaluated periodically and store the results as new time series. This allows you to create derived metrics (e.g., calculating error rates from request counts and error counts) without modifying the applications themselves.

7. How do alerting rules function in Prometheus? Describe the components.

   • Answer: Alerting rules define conditions under which alerts should be triggered. They consist of a PromQL expression and a set of labels defining the alert's severity, summary, and description. When the PromQL expression evaluates to true for a given duration (specified by the `for` clause), an alert is fired and sent to configured alertmanagers.

8. Explain the role of the Alertmanager in Prometheus.

   • Answer: The Alertmanager is responsible for receiving alerts from Prometheus, grouping them, deduplicating them, and routing them to appropriate notification channels (e.g., email, PagerDuty, Slack). It provides features like silencing, inhibition, and routing based on various labels and conditions.

9. What are the different ways to configure Prometheus to scrape targets?

   • Answer: Prometheus can scrape targets defined in its `prometheus.yml` configuration file, using static configurations or service discovery mechanisms. Service discovery integrates with various systems (e.g., Consul, etcd, Kubernetes) to dynamically discover and update the list of targets without manual intervention.

10. How does Prometheus handle high cardinality? What are the strategies to mitigate it?

    • Answer: High cardinality refers to a large number of unique label combinations in your time series data, which can impact performance and storage. Strategies to mitigate it include: reducing the number of labels, using label aggregation functions in PromQL, using histogram or summary metrics instead of counters for distributions, and employing techniques like relabeling to consolidate similar time series.

11. Question 99: Describe a challenging Prometheus deployment you've managed and how you overcame the issues.

    • Answer: [Detailed description of a challenging deployment scenario, including the problems faced, the steps taken to diagnose and resolve them, and the lessons learned. This should be a specific and detailed example, demonstrating problem-solving skills.]

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