How to implement the custom elastic telescopic _docker of Docker application

Summary: Introduction Now many customers are concerned about the application of automatic elasticity, some customers also have their own monitoring framework, and want to be able to integrate with the Aliyun container services. The Aliyun Container Service provides a service resilient scaling trigger, and can be integrated with the monitoring framework to implement customized services for automatic elasticity scaling. The Aliyun Container service automatically collects the monitoring data of the container and can send the monitoring data to the three-party monitoring framework through integration. With the monitoring ... Brief introduction

There are many customers who are concerned with the automatic elasticity of the application, and some have their own monitoring framework, and want to integrate with the Aliyun container service. The Aliyun Container Service provides a service resilient scaling trigger, and can be integrated with the monitoring framework to implement customized services for automatic elasticity scaling.
The Aliyun Container service automatically collects the monitoring data of the container and can send the monitoring data to the three-party monitoring framework through integration. With the monitoring data, we can define our own alarm rules in the monitoring framework, and call the Aliyun container service-provided triggers to enlarge or shrink the container when the indicator alarm occurs. The following is a influxdb,kapacitor to introduce how to implement a custom elastic scaling with a trigger and a monitoring framework integration. Generating scaling triggers for services

In the Aliyun container Service Application list, click on the application for automatic expansion control and click "Create TRIGGER" after entering

Then select "Resource Scaling" and select the service name to be scaled

The generated triggers are as follows:

You need to add a parameter &type=scale_out&step=2 when calling sale out. Deploy INFLUXDB and monitor

Including Monitoring time series database INFLUXDB, monitoring alarm frame kapacitor, and interface display Grafana. We can use a template to deploy:

Version: ' 2 '

services:
  influxdb:
    image:influxdb:0.13
    ports:
     -"8083:8083"
     -"8,086:8,086"
    container_name: "Influxdb"
    Labels:
        aliyun.monitoring.addon.influxdb: "http://influxdb:8086"

  Grafana:
    image:grafana/grafana:3.0.3-1463994644
    ports:
      -"3000:3000"
    Links:
      -Influxdb

  kapacitor:
    image:kapacitor:0.13
    ports:
      -"9092:9092"
    volumes:
            -/etc/acs/:/etc/ acs/
    Environment:
      -kapacitor_influxdb_0_urls_0=http://influxdb:8086
    command:kapacitord-config/ Etc/kapacitor/kapacitor.conf

In the template, the label "Aliyun.monitoring.addon.influxdb" is used to specify that the monitoring data in the Aliyun container service be sent to the influxdb.

Add a template to the Aliyun container service and create an application from the template:

Deployment of good applications:

Configure the Kapacitor alert rule to create an alert rule file. Configure the alert rule in Kapacitor and when the alarm is called the expansion trigger URL.

Through the Web remote terminal or Docker exec into the Kapacitor container, add alarm rules, such as our CPU indicators set alarm rules, create/etc/acs/cpu.tick files, the contents are as follows:

Stream Select just the CPU measurement from our Exa                                                                                                                              
    Mple database.                                                                                                                                                                                    
        |from () . Measurement (' Docker_con                                                                                                                                                   
    Tainer_cpu ')                                                                                                                                            
    |groupby (' Aliyun.cluster ', ' aliyun.service.id ')                           |alert ()                                                                                                                          . Crit (Lambda: "Aliyun.cluster" = "xxxxx" and "aliyun.service.id" =                                                                                                                                                                                                                = ' xxxxx ' and usage_percent ' > 70) . Post (' Https://cs.console.aliyun.com/hook/trigger?triggerUr                                                                                                               
L=yzm5nmrindg YywrhodqwmdjhnjrmnwvhZjcxzju1ztllfg5naw54lwrlzmf1bhr8cmvkzxbsb3l8mthtzhfxbxjknxj                                                                                                                
        Kahw=&secret=xxx&&type=scale_out&step=1 ') . log ('/tmp/alerts                                                                                                                                                                                                                                              . Log ')

The CPU indicator "DOCKER_CONTAINER_CPU" is aggregated by cluster and service, then the service is expanded when usage_percent>70.
Similarly, we can also add an alarm rule with a reduced volume. Define alert rules and enable the following commands in the Kapacitor container to define and enable alert rules.

Kapacitor define Cpu_alert-type stream  -tick CPU_ALERT.TICK-DBRP telegraf.default;
Kapacitor Enable Cpu_alert

In this way, when the CPU usage exceeds 70%, the expansion trigger is automatically invoked to enlarge the container.

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