Microservices Engine:Kubernetes jobs

Resource type reference

For script types (Shell-Script, Python-Script, Php-Script, and Node.js-Script), you write scripts directly in the console. No manual image build is required. For Job-YAML and Pod-YAML, you define the full specification in YAML.

After you complete the configuration, click Run once in the Operation column on the Tasks page. The corresponding Pod starts in the cluster. To view the Pod logs, click Historical records in the Operation column.

The following sections show the configuration and results for each resource type.

Shell script

Set resource to Shell-Script. The default image is BusyBox. You can change the image.

A Pod named schedulerx-shell-{JobId} starts in the cluster.

Python script

Set resource to Python-Script. The default image is Python. You can change the image.

A Pod named schedulerx-python-{JobId} starts in the cluster.

PHP script

Set resource to Php-Script. The default image is php:7.4-cli. You can change the image.

A Pod named schedulerx-php-{JobId} starts in the cluster.

Node.js script

Set resource to Node.js-Script. The default image is node:16. You can change the image.

A Pod named schedulerx-node-{JobId} starts in the cluster.

Job-YAML

Set resource to Job-YAML to schedule a native Kubernetes Job. Define the Job specification in YAML.

A Pod and the Job start in the cluster.

Pod-YAML

Set resource to Pod-YAML to run a native Kubernetes Pod. Define the Pod specification in YAML.

The native Kubernetes Pod starts in the cluster.

Edit scripts online without rebuilding images

Native Kubernetes Jobs require you to package scripts into images and configure commands in YAML files. Every script change requires an image rebuild and redeployment. 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 this workflow. The console supports online editing of Shell, Python, PHP, and Node.js scripts. Save your changes and SchedulerX runs the updated script in a new Pod on the next execution. No image rebuild, no YAML editing, no redeployment.

This approach also hides container details, which makes Kubernetes job management accessible to developers unfamiliar with container services.

Orchestrate jobs visually with drag-and-drop

In the Kubernetes ecosystem, Argo Workflows is a popular workflow orchestration tool that relies on YAML definitions. For example:

# 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 replaces this YAML with a visual drag-and-drop interface.

The visual interface also shows real-time execution progress, making it straightforward to identify blocked steps and troubleshoot failures.

Monitor jobs and receive alerts

SchedulerX provides built-in alerting for scheduled Pods and Jobs.

Alert channels: SMS, phone call, email, and webhook (DingTalk, WeCom, and Lark).

Alert policies: alert on failure and alert on execution timeout.

Collect logs automatically

SchedulerX collects Pod logs automatically. You do not need to activate additional log services such as Simple Log Service. When a Pod fails, view the failure details and troubleshoot directly from the SchedulerX console.

Use the built-in monitoring dashboard

SchedulerX includes a built-in monitoring dashboard for job execution metrics. No need to activate Managed Service for Prometheus separately.

Mix online and offline job deployments

SchedulerX supports both Java and Kubernetes jobs, enabling mixed deployment of online and offline workloads:

• Online jobs that require high real-time performance (such as order processing) run in-process alongside other online services for seamless integration.

• Offline jobs that consume heavy resources but tolerate latency (such as financial report generation) run in separate Pods.

This mixed deployment model provides flexible scheduling across diverse business requirements.