Kubernetes job

Online script editing

A common use case for a Kubernetes Job is data processing or operations tasks, often implemented as scripts. The native workflow requires you to package the script into an image and configure the command in a YAML file. Each script change requires rebuilding and redeploying the image. 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

With SchedulerX, you do not need to build images or write YAML to configure scripts. You can edit scripts for Shell, Python, PHP, and Node.js directly in the console, and the system automatically runs them in a Pod. To update a script, edit and save it in the console. The updated script runs at the next scheduled time. This significantly improves the efficiency of developing and managing Kubernetes Jobs. By abstracting away container-related details, SchedulerX also makes it easier for developers who are not familiar with container services to work with Kubernetes.

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 more user-friendly experience. During runtime, it displays a visual workflow graph that tracks job progress, making it easy to identify bottlenecks or failed tasks.

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

When you schedule Pods and Jobs with SchedulerX, you can use its built-in monitoring and alerting system for status tracking and exception alerting.

• 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

When you use SchedulerX to schedule Pods and Jobs, the system automatically collects logs generated during Pod execution. You do not need to enable an additional log service. If a Pod fails, you can view and analyze the failure reason in the SchedulerX console for quick troubleshooting.

In the task instance details, you can view the complete logs generated during Pod execution, including stack traces and failure reasons, to help you quickly identify and debug issues.

Monitoring dashboard

By using SchedulerX to schedule Pods and Jobs, you can access the built-in job monitoring dashboard without needing to set up a service like Prometheus.

The monitoring dashboard displays key metrics such as job execution counts, success and failure rates, and execution duration trends, providing a global view of your jobs' operational status.

Mixed job deployment

For online jobs that require high real-time performance, such as order processing, you can invoke methods directly within the same process for efficient handling and seamless integration with online services. For offline jobs that have lower real-time requirements but consume significant resources, such as scheduled report exports, you can write a script and run it in a separate Pod. SchedulerX supports both Java and Kubernetes job types, allowing you to implement a mixed deployment of online and offline jobs.