### Run Aliyun Exporter

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Start the exporter process with default or custom configuration and port settings.

```bash
# Run with default port 9525 and default config file ./aliyun-exporter.yml
aliyun-exporter

# Run with custom port and config file
aliyun-exporter -p 9525 -c /path/to/aliyun-exporter.yml

# Access metrics at http://localhost:9525/metrics
# Browse available projects at http://localhost:9525/
```

--------------------------------

### Run the Exporter

Source: https://github.com/aylei/aliyun-exporter/blob/master/README.md

Start the exporter process specifying the port and configuration file path.

```bash
> aliyun-exporter -p 9525 -c aliyun-exporter.yml
```

--------------------------------

### Install Aliyun Exporter

Source: https://github.com/aylei/aliyun-exporter/blob/master/README.md

Install the package via pip. Requires Python 3.5+.

```bash
pip3 install aliyun-exporter
```

--------------------------------

### Launch Monitoring Stack with Docker Compose

Source: https://github.com/aylei/aliyun-exporter/blob/master/README.md

Use docker-compose to start the exporter along with Prometheus, Grafana, and Alertmanager.

```bash
git clone git@github.com:aylei/aliyun-exporter.git
cd docker-compose
ALIYUN_ACCESS_ID=YOUR_ACCESS_ID ALIYUN_ACCESS_SECRET=YOUR_ACCESS_KEY docker-compose up
```

--------------------------------

### Docker Compose Setup for Aliyun Exporter Stack

Source: https://github.com/aylei/aliyun-exporter/blob/master/README.md

Use this command to set up and launch the Aliyun Exporter along with Prometheus, Alertmanager, Grafana, and DingTalk notification integration using Docker Compose. Ensure you configure your public IP, DingTalk token, and Aliyun credentials before running.

```bash
# config prometheus external host
export PROMETHEUS_HOST=YOUR_PUBLIC_IP_OR_HOSTNAME
# config dingtalk robot token
export DINGTALK_TOKEN=YOUR_DINGTALK_ROBOT_TOEKN
# config aliyun-exporter credential
export ALIYUN_REGION=YOUR_REGION
export ALIYUN_ACCESS_ID=YOUR_ID
export ALIYUN_ACCESS_SECRET=YOUR_SECRET
docker-compose up -d
```

--------------------------------

### ECS CPU Utilization Metric

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Example of a gauge metric for ECS CPU utilization, including instance and user IDs.

```text
# TYPE aliyun_acs_ecs_dashboard_CPUUtilization gauge
aliyun_acs_ecs_dashboard_CPUUtilization{instanceId="i-abc123",userId="12345"} 25.5
```

--------------------------------

### Get Metrics Meta for Specific Project

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Use curl to retrieve metadata for metrics within a specific CloudMonitor project.

```bash
# Get metrics meta for a specific project
curl http://localhost:9525/projects/acs_ecs_dashboard
```

--------------------------------

### Initialize and Use InfoProvider

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Configures the Alibaba Cloud client and retrieves resource metrics. Results are cached for one hour by default.

```python
from aliyun_exporter.info_provider import InfoProvider
from aliyunsdkcore.client import AcsClient

# Initialize Alibaba Cloud client
client = AcsClient(
    ak='your_access_key_id',
    secret='your_access_key_secret',
    region_id='cn-hangzhou'
)

# Create info provider (results cached for 1 hour)
provider = InfoProvider(client)

# Get resource metrics (returns GaugeMetricFamily)
# Supported resources: 'ecs', 'rds', 'redis', 'slb', 'mongodb'
ecs_info = provider.get_metrics('ecs')
rds_info = provider.get_metrics('rds')
redis_info = provider.get_metrics('redis')
slb_info = provider.get_metrics('slb')
mongodb_info = provider.get_metrics('mongodb')

# Metrics are exposed with resource metadata as labels:
# aliyun_meta_ecs_info{InstanceId="i-xxx",HostName="server1",VpcAttributes="10.0.0.1"} 1.0
# aliyun_meta_rds_info{DBInstanceId="rm-xxx",DBInstanceDescription="prod-db"} 1.0
```

--------------------------------

### Deploy with Docker

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Run the exporter as a Docker container, mounting the configuration file and passing credentials via environment variables.

```bash
# Pull the Docker image
docker pull aylei/aliyun-exporter:0.3.1

# Run with configuration file mounted
docker run -p 9525:9525 \
  -v $(pwd)/aliyun-exporter.yml:/etc/aliyun-exporter/aliyun-exporter.yml \
  -e ALIYUN_ACCESS_ID=your_access_key_id \
  -e ALIYUN_ACCESS_SECRET=your_access_key_secret \
  -e ALIYUN_REGION=cn-hangzhou \
  aylei/aliyun-exporter:0.3.1 \
  -c /etc/aliyun-exporter/aliyun-exporter.yml
```

--------------------------------

### Deploy Full Monitoring Stack

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Launch a complete monitoring stack including Prometheus, Grafana, and Alertmanager using Docker Compose.

```bash
# Clone the repository
git clone git@github.com:aylei/aliyun-exporter.git
cd aliyun-exporter/docker-compose

# Set required environment variables
export PROMETHEUS_HOST=your_public_ip_or_hostname
export DINGTALK_TOKEN=your_dingtalk_robot_token
export ALIYUN_ACCESS_ID=your_access_key_id
export ALIYUN_ACCESS_SECRET=your_access_key_secret
export ALIYUN_REGION=cn-hangzhou

# Launch the stack
docker-compose up -d

# Access services:
# - Grafana: http://localhost:3000 (admin:admin)
# - Prometheus: http://localhost:9090
# - Alertmanager: http://localhost:9093
# - Exporter: http://localhost:9525
```

--------------------------------

### Full Configuration Schema

Source: https://github.com/aylei/aliyun-exporter/blob/master/README.md

Detailed configuration structure including rate limiting, credentials, and metric specifications.

```yaml
rate_limit: 5 # request rate limit per second. default: 10
credential:
  access_key_id: <YOUR_ACCESS_KEY_ID> # required
  access_key_secret: <YOUR_ACCESS_KEY_SECRET> # required
  region_id: <REGION_ID> # default: 'cn-hangzhou'
  
metrics: # required, metrics specifications
  acs_cdn: # required, Project Name of CloudMonitor
  - name: QPS # required, Metric Name of CloudMonitor, belongs to a certain Project
    rename: qps # rename the related prometheus metric. default: same as the 'name'
    period: 60 # query period. default: 60
    measure: Average # measure field in the response. default: Average

info_metrics:
  - ecs
  - rds
  - redis
```

--------------------------------

### Discover Available Monitor Projects

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Use curl to query the exporter's web UI for a list of all available monitor projects.

```bash
# Browse all available monitor projects
curl http://localhost:9525/
```

--------------------------------

### Configure Credentials via Environment Variables

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Set credentials using environment variables to override settings in the configuration file.

```bash
# Set credentials via environment variables
export ALIYUN_ACCESS_ID=your_access_key_id
export ALIYUN_ACCESS_SECRET=your_access_key_secret
export ALIYUN_REGION=cn-hangzhou

# Run the exporter (credentials from env vars)
aliyun-exporter -c aliyun-exporter.yml
```

--------------------------------

### Python CollectorConfig Initialization

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Python code demonstrating how to initialize the CollectorConfig class, load credentials and metrics from a YAML file, and register the AliyunCollector with Prometheus.

```python
from aliyun_exporter.collector import CollectorConfig, AliyunCollector
from prometheus_client.core import REGISTRY
import yaml

# Load configuration from YAML file
with open('aliyun-exporter.yml', 'r') as f:
    cfg = yaml.load(f, Loader=yaml.FullLoader)

# Create collector config (credentials can come from env vars)
# Environment variables: ALIYUN_ACCESS_ID, ALIYUN_ACCESS_SECRET, ALIYUN_REGION
config = CollectorConfig(
    credential=cfg.get('credential'),
    metrics=cfg.get('metrics'),
    info_metrics=cfg.get('info_metrics'),
    rate_limit=cfg.get('rate_limit', 10)
)

# Register collector with Prometheus
collector = AliyunCollector(config)
REGISTRY.register(collector)
```

--------------------------------

### Pull and Run Docker Image

Source: https://github.com/aylei/aliyun-exporter/blob/master/README.md

Commands to pull the official Docker image and run it with a local configuration file mounted.

```bash
docker pull aylei/aliyun-exporter:0.3.1
```

```bash
docker run -p 9525:9525 -v $(pwd)/aliyun-exporter.yml:$(pwd)/aliyun-exporter.yml aylei/aliyun-exporter:0.3.1 -c $(pwd)/aliyun-exporter.yml
```

--------------------------------

### Configure Aliyun Exporter

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Define credentials, rate limits, and specific CloudMonitor metrics to collect in a YAML configuration file.

```yaml
# aliyun-exporter.yml
credential:
  access_key_id: <YOUR_ACCESS_KEY_ID>
  access_key_secret: <YOUR_ACCESS_KEY_SECRET>
  region_id: cn-hangzhou

rate_limit: 5  # Request rate limit per second, default: 10

metrics:
  acs_ecs_dashboard:  # Project name from CloudMonitor
  - name: CPUUtilization
    period: 60        # Query period in seconds, default: 60
    measure: Average  # Measure field: Average, Maximum, Minimum
  - name: DiskReadBPS
    period: 60
  - name: memory_usedutilization
    rename: mem_usage  # Rename metric in Prometheus output
    period: 15

  acs_rds_dashboard:
  - name: CpuUsage
    period: 60
  - name: MemoryUsage
    period: 60
  - name: DiskUsage
    period: 60
  - name: ConnectionUsage
    period: 60

  acs_kvstore:  # Redis
  - name: CpuUsage
    period: 60
  - name: MemoryUsage
    period: 60

  acs_slb_dashboard:
  - name: Qps
    period: 60
  - name: StatusCode5xx
    period: 60
  - name: Rt
    period: 60

  acs_mongodb:
  - name: CPUUtilization
    period: 300
  - name: QPS
    period: 300

  acs_cdn:
  - name: QPS

# Resource information metrics (exposes instance metadata as labels)
info_metrics:
  - ecs
  - rds
  - redis
  - slb
  - mongodb
```

--------------------------------

### Generate YAML Configuration Template

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Use curl to generate a YAML configuration template for all metrics within a specified project.

```bash
# Generate YAML configuration template for a project
curl http://localhost:9525/yaml/acs_ecs_dashboard
# Output: Ready-to-use YAML config for all metrics in the project
```

--------------------------------

### Configure Aliyun Exporter Metrics

Source: https://github.com/aylei/aliyun-exporter/blob/master/aliyun_exporter/templates/yaml.html

Defines the structure for Aliyun Exporter metrics configuration. Use this template to specify metrics for a given project and iterate through a list of metrics, setting their names and periods.

```yaml
metrics:
  {{ project }}:
{%- for metric in metrics %}
  - name: {{ metric\[\'Metric\' ] | formatmetric }}
    period: {{ metric\[\'Periods' ] | formatperiod}}
{%- endfor -%}

```

--------------------------------

### Configure Exporter Credentials and Metrics

Source: https://github.com/aylei/aliyun-exporter/blob/master/README.md

Define the Alibaba Cloud credentials and the specific metrics to collect in a YAML configuration file.

```yaml
credential:
  access_key_id: <YOUR_ACCESS_KEY_ID>
  access_key_secret: <YOUR_ACCESS_KEY_SECRET>
  region_id: <REGION_ID>

metrics:
  acs_cdn:
  - name: QPS
  acs_mongodb:
  - name: CPUUtilization
    period: 300
```

--------------------------------

### Configure Prometheus Scrape

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Add the Aliyun Exporter as a target in the Prometheus configuration file.

```yaml
# prometheus.yml
global:
  scrape_interval: 30s
  evaluation_interval: 30s

scrape_configs:
  - job_name: 'aliyun-exporter'
    scrape_interval: 30s
    scrape_timeout: 30s
    static_configs:
      - targets: ['aliyun-exporter:9525']
```

--------------------------------

### Fetch Metrics

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Verify metric output by querying the exporter's metrics endpoint.

```bash
# Fetch metrics
curl http://localhost:9525/metrics

# Example output:
# HELP aliyun_acs_ecs_dashboard_CPUUtilization
```

--------------------------------

### RDS Performance Metrics Configuration

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

YAML configuration for the 'rds_performance' special project, enabling collection of detailed RDS MySQL metrics.

```yaml
# special_projects.yml
credential:
  access_key_id: <YOUR_ACCESS_KEY_ID>
  access_key_secret: <YOUR_ACCESS_KEY_SECRET>
  region_id: cn-hangzhou

metrics:
  rds_performance:
  - name: MySQL_QPSTPS
  - name: MySQL_NetworkTraffic
  - name: MySQL_Sessions
  - name: MySQL_InnoDBBufferRatio
  - name: MySQL_InnoDBDataReadWriten
  - name: MySQL_InnoDBLogRequests
  - name: MySQL_InnoDBLogWrites
  - name: MySQL_TempDiskTableCreates
  - name: MySQL_MyISAMKeyBufferRatio
  - name: MySQL_COMDML
  - name: MySQL_RowDML
  - name: MySQL_MemCpuUsage
  - name: MySQL_IOPS
  - name: MySQL_DetailedSpaceUsage
  - name: MySQL_CPS

# Required: info_metrics must include rds for rds_performance to work
info_metrics:
  - rds
```

--------------------------------

### ECS Info Metric

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

A gauge metric providing metadata for ECS instances, with instance ID, hostname, and VPC attributes.

```text
# Resource info metrics (always value 1.0, metadata in labels)
# HELP aliyun_meta_ecs_info
# TYPE aliyun_meta_ecs_info gauge
aliyun_meta_ecs_info{InstanceId="i-abc123",HostName="my-server",VpcAttributes="10.0.0.5"} 1.0
```

--------------------------------

### CloudMonitor Request Latency Metric

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

A summary metric measuring the latency of CloudMonitor requests, categorized by project.

```text
# Exporter telemetry
# HELP cloudmonitor_request_latency_seconds CloudMonitor request latency
# TYPE cloudmonitor_request_latency_seconds summary
cloudmonitor_request_latency_seconds{project="acs_ecs_dashboard",quantile="0.5"} 0.125
```

--------------------------------

### Prometheus Alert: Critical RDS CPU Pressure

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Prometheus alerting rule to detect critical CPU pressure on RDS instances. It aggregates CPU usage and joins with RDS instance metadata.

```yaml
- name: rds
  rules:
  - alert: rds_cpu_pressure:critical
    expr: |-
      sum(aliyun_acs_rds_dashboard_CpuUsage
        * on (instanceId) group_left(DBInstanceDescription,ZoneId)
        label_replace(aliyun_meta_rds_info, "instanceId", "$1", "DBInstanceId", "(.*)"))
      without (instance, userId, job) > 95
    labels:
      severity: 0
    for: 5m
    annotations:
      summary: 'RDS {{ $labels.DBInstanceDescription }} under critical cpu pressure > 95%'
```

--------------------------------

### Prometheus Alert: Critical SLB 5xx Percent

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Prometheus alerting rule to detect a critical percentage of 5xx errors for SLB instances. It calculates the ratio of 5xx responses to total QPS.

```yaml
- name: slb
  rules:
  - alert: slb_5xx_percent:critical
    expr: |
      sum(aliyun_acs_slb_dashboard_StatusCode5xx) by (vip, port) /
      sum(aliyun_acs_slb_dashboard_Qps) by (vip, port) > 0.1
    labels:
      severity: 0
    for: 5m
    annotations:
      summary: 'SLB {{ $labels.vip }}:{{ $labels.port }} 5xx percent > 10%'
```

--------------------------------

### Prometheus Alert: High ECS CPU Pressure

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Prometheus alerting rule to detect high CPU pressure on ECS instances. It correlates CPU utilization with instance metadata.

```yaml
# rules.yml
groups:
- name: ecs
  rules:
  - alert: ecs_cpu_pressure:high
    expr: |-
      (aliyun_acs_ecs_dashboard_CPUUtilization > 95)
      * on (instanceId) group_left(VpcAttributes,HostName,InnerIpAddress)
      label_replace(aliyun_meta_ecs_info,"instanceId","$1","InstanceId","(.*)")
    labels:
      severity: 1
    for: 5m
    annotations:
      summary: 'ECS {{ $labels.HostName }} cpu usage > 95%'
```

--------------------------------

### ECS CPU Utilization Up Metric

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

A gauge metric indicating the success of fetching the ECS CPU utilization metric.

```text
# HELP aliyun_acs_ecs_dashboard_CPUUtilization_up Did the aliyun_acs_ecs_dashboard_CPUUtilization fetch succeed.
# TYPE aliyun_acs_ecs_dashboard_CPUUtilization_up gauge
aliyun_acs_ecs_dashboard_CPUUtilization_up 1
```

--------------------------------

### Prometheus Alert: High ECS Memory Pressure

Source: https://context7.com/aylei/aliyun-exporter/llms.txt

Prometheus alerting rule to detect high memory pressure on ECS instances. It correlates memory utilization with instance metadata.

```yaml
  - alert: ecs_memory_pressure:high
    expr: |-
      (aliyun_acs_ecs_dashboard_memory_usedutilization > 95)
      * on (instanceId) group_left(VpcAttributes,HostName,InnerIpAddress)
      label_replace(aliyun_meta_ecs_info,"instanceId","$1","InstanceId","(.*)")
    labels:
      severity: 1
    for: 5m
    annotations:
      summary: 'ECS {{ $labels.HostName }} memory usage > 95%'
```

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