【Advanced】Crawler Task Monitoring and Alerting Mechanism: Using Prometheus and Grafana to Monitor Crawler Operation Status

# Advanced Chapter: Crawler Task Monitoring and Alert Mechanism: Utilizing Prometheus and Grafana to Monitor Crawler Operation Status

## 2.1 Prometheus Metric System and Data Model

Prometheus employs a time-series database model to store monitoring data in the form of time series. Each time series consists of the following elements:

- **Metric name**: A unique string that identifies the metric, e.g., `http_requests_total`.
- **Labels**: Key-value pairs used to categorize and filter time series, e.g., `method=GET`.
- **Timestamp**: The time when the data point was recorded in the time series.
- **Value**: The numerical value of the metric at a specific point in time, such as the total number of requests.

The Prometheus metric system is based on the following principles:

- **Single Responsibility Principle (SRP)**: Each metric measures a single specific aspect.
- **Minimum Granularity Principle**: Metrics should be as fine-grained as possible for easy aggregation and analysis.
- **Naming Convention**: Metric names should follow specific conventions, such as using snake case and avoiding special characters.

## 2. Prometheus Monitoring Principles and Configuration

### 2.1 Prometheus Metric System and Data Model

Prometheus uses a time-series database (TSDB) to store monitoring data, with a key-value data model where the key is the metric name and the value is a set of time series data points, each containing a timestamp and the metric value.

The Prometheus metric system adheres to the following naming conventions:

- Metric name: Composed of subsystem, metric name, and labels, separated by dots, e.g., `node_cpu_usage{instance="***.***.*.*"}`
- Labels: In key-value pair form, used to describe the dimensions and attributes of metrics, e.g., the `instance` label indicates the instance to which the metric belongs.

### 2.2 Prometheus Configuration and Deployment

#### Prometheus Configuration

The Prometheus configuration file is located at `/etc/prometheus/prometheus.yml`, and the main configuration items include:

- `scrape_configs`: Defines a list of targets to be monitored, including target addresses, ports, and collection intervals.
- `rule_files`: Specifies Prometheus rule files for defining alert rules and data processing rules.

#### Prometheus Deployment

There are various ways to deploy Prometheus, with common methods including:

- Docker image: `docker run -p 9090:9090 prom/prometheus`
- Binary installation: Download the Prometheus binary package, unpack it, and run `./prometheus` to start the service.
- Kubernetes deployment: Use Kubernetes Helm Chart to deploy Prometheus.

### 2.3 Crawler Monitoring Metric Definition and Collection

#### Crawler Monitoring Metric Definition

For crawler monitoring, the following key metrics need to be defined:

| Metric Name | Description |
|---|---|
| `http_requests_total` | Total number of HTTP requests |
| `http_request_duration_seconds` | Duration of HTTP requests (seconds) |
| `http_request_status_code` | HTTP request status codes |
| `http_request_errors_total` | Total number of HTTP request errors |
| `page_load_time_seconds` | Page load time (seconds) |

#### Crawler Monitoring Metric Collection

Use Prometheus client libraries (e.g., Python's `prometheus_client`) to collect crawler monitoring metrics and expose them to the Prometheus service via HTTP interfaces.

import prometheus_client

# Define metrics
http_requests_total = prometheus_client.Counter('http_requests_total', 'Total number of HTTP requests')
http_request_duration_seconds = prometheus_client.Histogram('http_request_duration_seconds', 'HTTP request duration in seconds')

# Collect metrics
def process_request(ur