7 Rule Engines of Cortex: A Powerful Python Monitoring System

Hi, I’m Huan Ge! Today, I want to take you on a journey to explore a powerful Python monitoring system—Cortex, especially its cool 7 rule engines. Cortex can help us monitor various metrics in real-time and issue alerts promptly when anomalies occur, just like a diligent sentinel guarding the stability of our system. Are you ready? Let’s get started!

Cortex is an open-source, horizontally scalable, multi-tenant Prometheus-as-a-Service. In simple terms, it can be understood as an enhanced version of Prometheus, capable of handling massive monitoring data and supporting various alerting rule engines. Imagine if your system is large-scale with huge data volumes, the ordinary Prometheus might struggle, and that’s when Cortex comes into play.

The rule engine of Cortex is like the decision-making center of the brain, judging whether monitoring data is abnormal based on pre-set rules. Cortex provides 7 different rule engines, each with its unique advantages and applicable scenarios. It’s like having a multifunctional toolbox where you can choose the right tool based on different needs.

1. recording rule: The recording rule, as the name suggests, can pre-calculate some commonly used expressions and save the results, avoiding repeated calculations and improving query efficiency. It’s like preparing in advance, saving time during the exam.

2. alerting rule: The alerting rule is one of the most commonly used rule engines. It can determine whether metrics are abnormal based on set thresholds and send notifications when alerts are triggered. It’s like an alarm that constantly reminds us to pay attention to potential risks.

3. aggregation rule: The aggregation rule can aggregate multiple metrics for calculations, such as calculating averages, maximums, minimums, etc. It’s like calculating the class average, giving a quick overview of the overall situation.

4. absent rule: The absent rule is used to detect whether metrics have stopped reporting data. It’s like taking attendance to find out which students are absent.

5. on-the-fly alerting rule: The on-the-fly alerting rule is similar to the alerting rule, but it does not require pre-defined rules and can dynamically create alerts during queries. It’s like cramming at the last minute; although not a long-term solution, it can be useful in emergencies.

6. metric query alert rule: The metric query alert rule triggers alerts based on PromQL query results. It’s like filtering students based on specific conditions, such as identifying students who failed mathematics.

7. cortex rule: The Cortex rule is a unique rule engine of Cortex, capable of implementing more complex alerting logic. It’s like a custom solution tailored to your needs.

# Example: Using alerting rule to monitor CPU usage
groups:
- name: high_cpu_usage
  rules:
  - alert: HighCPUUsage
    expr: cpu_usage_idle{instance="my_server"} < 0.1
    for: 5m
    labels:
      severity: critical
    annotations:
      summary: "High CPU usage on {{ $labels.instance }}"
      description: "CPU usage on {{ $labels.instance }} is below 10% for more than 5 minutes."

Code Explanation: This code defines an alert rule group named <span>high_cpu_usage</span>, which contains an alert rule named <span>HighCPUUsage</span>. When the CPU idle rate of the <span>my_server</span> instance is below 10% for more than 5 minutes, an alert will be triggered, marking the severity as <span>critical</span>.

• The rule engines of Cortex are very flexible and can be combined based on actual needs.
• When setting alert rules, pay attention to threshold settings to avoid false positives or missed alerts.
• You can refer to the official Cortex documentation for more details about the rule engines.

Friends, today’s Python learning journey ends here! Remember to write code, and feel free to ask questions in the comments. Wishing everyone happy learning and continuous progress in Python!

Disclaimer: This article is purely handwritten; original work is not easy, and plagiarism is prohibited!