- Features
- Screenshots
- Quick Start
- Installation
- Usage
- Docker Image
- Configuration
- Metrics Meta
- Scale and HA Setup
- Contribute
This Prometheus exporter collects metrics from the CloudMonitor API of Alibaba Cloud. It can help you:
- integrate CloudMonitor to your Monitoring System.
- leverage the power of PromQL, Alertmanager and Grafana(see Screenshots).
- analyze metrics however you want.
- save money. Api invocation is far cheaper than other services provided by CloudMonitor.
This project also provides an out-of-box solution for full-stack monitoring of Alibaba Cloud, including dashboards, alerting and diagnosing.
A docker-compose stack is provided to launch the entire monitoring stack with Aliyun-Exporter, Prometheus, Grafana and Alertmanager.
Pre-requisites: docker 1.17+
git clone [email protected]:aylei/aliyun-exporter.git
cd docker-compose
ALIYUN_ACCESS_ID=YOUR_ACCESS_ID ALIYUN_ACCESS_SECRET=YOUR_ACCESS_KEY docker-compose upInvestigate dashboards in localhost:3000 (the default credential for Grafana is admin:admin).
For more details, see Docker Compose.
Python 3.5+ is required.
pip3 install aliyun-exporterConfig your credential and interested metrics:
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: 300Run the exporter:
> aliyun-exporter -p 9525 -c aliyun-exporter.ymlThe default port is 9525, default config file location is ./aliyun-exporter.yml.
Visit metrics in localhost:9525/metrics
Install
docker pull aylei/aliyun-exporter:0.3.1To run the container, external configuration file is required:
docker run -p 9525:9525 -v $(pwd)/aliyun-exporter.yml:$(pwd)/aliyun-exporter.yml aylei/aliyun-exporter:0.3.1 -c $(pwd)/aliyun-exporter.ymlrate_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
- redisNotes:
- Find your target metrics using Metrics Meta
- CloudMonitor API has an rate limit, tuning the
rate_limitconfiguration if the requests are rejected. - CloudMonitor API also has an monthly quota for invocations (AFAIK, 5,000,000 invocations / month for free). Plan your usage in advance.
Given that you have 50 metrics to scrape with 60s scrape interval, about 2,160,000 requests will be sent by the exporter for 30 days.
Some metrics are not included in the Cloud Monitor API. For these metrics, we keep the configuration abstraction consistent by defining special projects.
Special Projects:
rds_performance: RDS performance metrics, available metric names: Performance parameter table
An example configuration file of special project is provided as
special-projects.yml
Note: special projects invokes different API with ordinary metrics, so it will not consume your Cloud Monitor API invocation quota. But the API of special projects could be slow, so it is recommended to separate special projects into a standalone exporter instance.
aliyun-exporter shipped with a simple site hosting the metrics meta from the CloudMonitor API. You can visit the metric meta in localhost:9525 after launching the exporter.
host:portwill host all the available monitor projectshost:port/projects/{project}will host the metrics meta of a certain projecthost:port/yaml/{project}will host a config YAML of the project's metrics
you can easily navigate in this pages by hyperlink.
From 0.3.1, we provide a docker-compose stack to help users building monitoring stack from scratch. The stack contains:
- aliyun-exporter (this project): Retrieving metrics (and instance information) from Alibaba Cloud.
- Prometheus: Metric storage and alerting calculation.
- Alertmanager: Alert routing and notifying.
- Grafana: Dashboards.
- prometheus-webhook-dingtalk: DingTalk (a.k.a. DingDing) notification integrating.
Here's a detailed launch guide:
# 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 -dAfter launching, you can access:
- grafana: http://localhost:3000
- prometheus: http://localhost:9090
- alertmanager: http://localhost:9093
You may customize the configuration of this components by editing the configuration files in ./docker-compose/{component}
Request success summary and failure summary are exposed in cloudmonitor_request_latency_seconds and cloudmonitor_failed_request_latency_seconds.
Each Project-Metric pair will have a corresponding metric named aliyun_{project}_{metric}_up, which indicates whether this metric are successfully scraped.
The CloudMonitor API could be slow if you have large amount of resources. You can separate metrics over multiple exporter instances to scale.
For HA setup, simply duplicate your deployments: 2 * prometheus, and 2 * exporter for each prometheus.
HA Setup will double your requests, which may run out your quota.
Feel free to open issues and pull requests, any feedback will be highly appreciated!
Please check the help wanted label to find issues that are good for getting started.
Besides, contributing to new alert rules, new dashboards is also welcomed!
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