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    Lindorm:Quickstart: Stream Job Operations Management Platform

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    Lindorm:Quickstart: Stream Job Operations Management Platform

    Last Updated:Mar 30, 2026

    The Stream Job Operations Management Platform lets you submit and manage stream processing jobs and their required resources. It supports job and resource isolation between workspaces and provides role-based access control.

    This guide covers the core platform modules and walks you through the end-to-end workflow for submitting a job: upload resources, create a job, add a version, publish, start, and stop.

    The Stream Job Operations Management Platform is currently in the invitational preview stage. To request access, contact Lindorm technical support (DingTalk ID: s0s3eg3).

    Prerequisites

    Before you begin, ensure that you have:

    Platform overview

    The platform is organized into the following modules:

    Module Description
    Job management Create, modify, delete, and version jobs. Organize jobs in folders. Move and copy jobs within or across workspaces. Supported job types: Stream Engine JAR, Stream Engine SQL, and Stream Engine Python.
    Dependency management Create, modify, delete, and version resources. Organize resources in folders. Move, copy, and download resources and resource folders within the same workspace or across different workspaces. View Config File resources online. Supports six resource types—see Resource types.
    Cluster management View DPI engine clusters available for creating jobs in standalone mode.
    User management View all users who can log on to the platform.
    Workspace management View, create, delete, and modify workspaces. Jobs and resources are isolated between different workspaces.
    Role management View, create, delete, and modify roles. Roles define permissions. The root user has the highest administrator permissions. Non-root users must be assigned a role in a workspace to perform operations. The role can be assigned by the root user or another authorized user in the Member Management module.
    Member management View, create, delete, and modify member information in the current workspace. Member information defines the permissions that users have in the workspace.

    Resource types

    The Dependency management module supports six resource types:

    Resource type Required file type Purpose
    Jar App .jar Main program for a Stream Engine JAR job
    Jar Library .jar A JAR dependency; can be used in any job type
    Config File .txt, .xml, .properties Configuration file read by the main program code of a Stream Engine JAR job
    Python App .py Main program for a Stream Engine Python job
    Python Library .py, or a compressed package (.tar.gz or .zip) Python dependency for a Stream Engine Python job
    Data File Compressed package (.zip or .tar.gz) containing data files such as .txt Data dependencies for a Stream Engine Python job

    Log on to the platform

    Get your username and password

    1. Log on to the Lindorm console. In the upper-left corner, select the region of the instance.

    2. On the Instances page, click the ID of the target instance, or click View Instance Details in the Actions

    3. In the left-side navigation pane, click Database Connections.

    4. Click the DPI engine tab to get the default username and password.

    Access the platform

    1. In the left-side navigation pane, click Stream Engine.

    2. Click Enter Stream Job Operations Management Platform.

    3. Enter your username and password to log on.

    Submit a job

    All three job types—Stream Engine JAR, Stream Engine SQL, and Stream Engine Python—follow the same six-step workflow. The differences are the resource types you upload and a few type-specific configuration options, which are noted in each step.

    The workflow is:

    1. Upload resources

    2. Create a job

    3. Add a job version

    4. Publish the job version

    5. Start the job

    6. Stop the job

    Step 1: Upload resources

    1. In the left-side navigation pane, click Dependency Management.

    2. Right-click the target dependency folder, select Create Resource, and upload the resources your job needs. Upload the resources that match your job type:

      Job type Resources to upload
      Stream Engine JAR Jar App (required), Jar Library, Config File
      Stream Engine SQL Jar Library (for User-Defined Functions (UDFs) and other dependencies)
      Stream Engine Python Python App (required), Python Library, Data File

    Step 2: Create a job

    1. In the left-side navigation pane, click Job Management.

    2. Select New > New Job.

    3. Fill in the required parameters.

      Job names can contain Chinese characters, uppercase and lowercase letters, hyphens (-), and underscores (_). Two consecutive spaces are not allowed.

    4. Click Submit.

    Step 3: Add a job version

    1. Click the name of the job to go to its details page.

    2. Click Add to configure the version information. The configuration options vary by job type: Stream Engine JAR jobs: Built-in resources are system-provided dependencies you can use directly without uploading. To reference a Config File in your main program, use one of the following paths: Stream Engine SQL jobs: The version editor provides auto-completion, syntax highlighting, and SQL syntax checking. Stream Engine Python jobs: Built-in resources are system-provided dependencies you can use directly without uploading.

      • Absolute path: /home/hadoop/cluster-data/lstream/user-config/ConfigFileName

      • Relative path: The Config File is treated as if it is in the resources folder of the main program JAR. Access it using a method such as getClassLoader().getResourceAsStream(ConfigFileName).

    3. Click Create Version.

    Step 4: Publish the job version

    Click image in the Actions column of the target version to publish it.

    Step 5: Start the job

    1. Click image in the Actions column of the target version to start the job.

    2. In the Start Job dialog box, choose whether to resume from an existing savepoint or checkpoint path to restore the previous run state.

    Important

    A job can have multiple versions, but only one version can be in the running state at a time.

    Step 6: Stop the job

    Click image in the Actions column of the running version to stop the job. When stopping, you can choose whether to trigger a savepoint to save the current run state so you can resume from it later.

    Delete a job version

    In the Actions column of the target version, select image > Delete.