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Prometheus Devops Interview Questions Part-3

  Q9: Explain PromQL and provide examples of common queries. PromQL (Prometheus Query Language) is Prometheus's powerful functional language for querying time series data. Think of it as SQL for metrics - it helps you extract meaningful insights from your monitoring data. Core Data Types: Instant Vector : Current value of metrics at a specific time Range Vector : Values over a time period Scalar : Simple numeric values Essential Query Examples: Basic Metric Selection: promql # Get all HTTP requests http_requests_total # Filter by specific conditions http_requests_total { method = "GET" , status = "200" } # Use regex for flexible matching http_requests_total { status =~ "2.." } # All 2xx status codes Rate Calculations: promql # Requests per second over 5 minutes rate ( http_requests_total [ 5m ] ) # Total increase over 1 hour increase ( http_requests_total [ 1h ] ) Aggregations: promql # Sum all requests across instances sum...
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Prometheus Devops Interview Questions Part-2

Q6: Explain Prometheus data model and metric types. Answer: Understanding Prometheus's data model is fundamental to effectively using the monitoring system. The data model defines how metrics are structured, stored, and identified, forming the foundation for all monitoring and alerting capabilities. Prometheus Data Model - The Foundation Core Concept: Prometheus stores all data as time series - sequences of timestamped values belonging to the same metric and the same set of labeled dimensions. Time Series Identity: Each time series is uniquely identified by: Metric Name: What you're measuring (e.g., http_requests_total , cpu_usage_percent ) Labels: Key-value pairs that add dimensions (e.g., method="GET" , status="200" ) Timestamp: When the measurement was taken (Unix timestamp) Value: The actual measurement (64-bit floating-point number) Data Storage Format Standard Format: metric_name{label1="value1", label2="value2"} value ti...
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