Microservices Engine:Kubernetes job

Online script editing

Kubernetes Jobs are commonly used for data processing or operations tasks implemented as scripts. Natively, you must package each script into an image and configure the command in a YAML file, rebuilding and redeploying the image for every change. For example:

apiVersion: batch/v1
kind: Job
metadata:
  name: hello
spec:
  template:
    spec:
      containers:
      - name: hello
        image: busybox
        command: ["sh",  "/root/hello.sh"]
      restartPolicy: Never
  backoffLimit: 4

SchedulerX eliminates the need to build images or write YAML for scripts. You can edit Shell, Python, PHP, and Node.js scripts directly in the console, and the system runs them in a Pod automatically. Updates take effect at the next scheduled run.

For example:

On the job editing page in the SchedulerX console, you can write and modify script content directly in the script editor. After you finish, click Save. The latest version of the script will automatically run at the next scheduled time.

Visual job orchestration

In the Kubernetes ecosystem, Argo is a popular solution for workflow orchestration. For example:

# The following workflow executes a diamond workflow
# 
#   A
#  / \
# B   C
#  \ /
#   D
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: dag-diamond-
spec:
  entrypoint: diamond
  templates:
  - name: diamond
    dag:
      tasks:
      - name: A
        template: echo
        arguments:
          parameters: [{name: message, value: A}]
      - name: B
        depends: "A"
        template: echo
        arguments:
          parameters: [{name: message, value: B}]
      - name: C
        depends: "A"
        template: echo
        arguments:
          parameters: [{name: message, value: C}]
      - name: D
        depends: "B && C"
        template: echo
        arguments:
          parameters: [{name: message, value: D}]

  - name: echo
    inputs:
      parameters:
      - name: message
    container:
      image: alpine:3.7
      command: [echo, "{{inputs.parameters.message}}"]

SchedulerX lets you visually orchestrate Kubernetes Jobs by using a drag-and-drop interface.

Compared to Argo, SchedulerX provides a visual workflow graph at runtime that tracks job progress, making it easy to identify bottlenecks or failures.

In the workflow instance view, each task node is color-coded by its status (for example, green for success and red for failure), allowing you to visually pinpoint the failure.

Monitoring and alerting

SchedulerX includes built-in monitoring and alerting for Pods and Jobs.

• Supported alert channels: SMS, phone call, email, and webhook (for DingTalk, WeCom, and Lark).

• Supported alert policies: alert on failure and alert on execution timeout.

Log service

SchedulerX automatically collects logs from Pod execution without requiring a separate log service. If a Pod fails, you can view the failure reason in the SchedulerX console for quick troubleshooting.

Task instance details include complete Pod execution logs with stack traces and failure reasons.

Monitoring dashboard

SchedulerX provides a built-in job monitoring dashboard, eliminating the need to set up Prometheus or a similar service.

The dashboard displays key metrics such as job execution counts, success and failure rates, and execution duration trends.

Mixed job deployment

SchedulerX supports both Java and Kubernetes job types for mixed deployment. Use in-process method invocation for latency-sensitive online jobs such as order processing, and run resource-intensive offline jobs such as report exports as scripts in separate Pods.