Settings

This page is for the Advanced configuration of the Data Pipeline. The following list of the configurations get displayed on the Settings Page:

• Logger

• Default Configuration

• System Component Status

• Data Sync

• Job BaseInfo

• List Components

Logger

This gives the info and config details of the Kafka topic that is set for logging.

Logger Configuration

Default Configuration

This page enables users to view or set the default resource configuration in Low, Medium, and High resource allocation types for pipelines and jobs.

Default Configuration Page

• To access the Default Configuration, click on the settings option on the pipeline homepage and select it from the settings page.

• There will be two tabs on this page:

  • Pipeline: The Pipeline tab opens by default.

    • This tab shows the default configuration set for Spark and Docker components in different resource allocation types (Low, Medium, High) and in different invocation types such as Real-time and Batch.

    • Users can change this default configuration as per their requirement, and the same default resource configuration will be applied to newly added components when used in the pipeline.

Default configuration for Spark component in Batch mode with Low as Resource Type

• Job: Navigate to this tab to view or set the default configuration for Jobs.

  • This tab shows the default configuration set for Spark and Docker Jobs in different resource allocation types (Low, Medium, High).

  • Users can change this default configuration as per their requirement, and the same default resource configuration will be applied to newly created jobs.

Default configuration for Spark job configured with Low resource type

System Component Status

The System Component Status page under the Settings option gives us monitoring capability on the health of the System Components.

System Component Status

The System Component Status page presents the following details for the System Components:

• Name: Name of the system component.

• Status: Pod status, indicated by green color if up and red color if not.

• Created At: Date and time when the Pod was created.

• Age: Age of the System pod.

• Version: Version of the system components.

• Restarts: Number of restarts.

• CPU: Used and requested CPU amount in cores.

• Memory: Used and requested Memory amount in MB.

Please Note: The system components should be up for proper functionality of the Pipeline module.

Data Sync

Data Sync in Settings is given to globally configure the Data Sync feature. This way the user can enable a DB connection and use the same connection in the pipeline workflow without using any extra resources.

Data Sync page under the Pipeline Settings option

Please Note: The supported drivers are:

• MongoDB

• Postgres

• MySQL

• MSSQL

• Oracle

• ClickHouse

• Snowflake

• Redshift

Creating a new Data Sync Connection:

Creating a new data sync connection

• Go to the Settings page.

• Click the Data Sync option.

• The Data Sync page opens.

• Click the Plus icon.

• The Create Data Sync Connection dialog box appears.

• Specify all the required connection details.

• Click the Save option.

Please Note:

• Please use TCP Port If you are using ClickHouse as a Driver in Data Sync.

Please Note: The configured Driver from the Settings page provided for Data Sync can be accessed inside a Data Sync component on the Pipeline Editor page while using it in a Pipeline.

• A success notification message appears on the top stating that Data Sync Setting Creating Successfully.

• Enable the action button to activate the Data Sync connection.

Activating the Data Sync Connection

Edit Setting

• Go to the Settings page.

• Click the Data Sync option.

• The DB Sync Settings page opens.

• Click the Edit Setting icon.

• The Edit Settings dialog box opens.

• Edit the connection details if required.

• Click the Update option.

Edit Data Sync

Disconnecting Data Sync

• Go to the Settings page.

• Click the Data Sync option.

• The Data Sync Settings page opens.

• Click on the Disconnect button to disconnect.

• The Disconnect Setting confirmation dialog box appears.

• Click the DISCONNECT option.

• A success notification message appears on the top when DB Sync gets disconnected.

Disconnecting Data Sync

Data Sync in a Pipeline

• All the Pipelines containing the DB Sync Event component get listed under the Pipeline using the DB Sync section.

• The user can see the following information:

  • Name: Name of the pipeline where Data Sync is used.

  • Running status: This indicates if the pipeline is active or deactivated.

  • Actions: The user can view the pipeline where Data Sync is used.

  

  Pipeline using data sync

Job BaseInfo

The Pipeline module supports the following types of jobs:

• PySpark Job

• Spark Job

• Script Executor

• Python Job

These jobs are configured in the Job BaseInfo page under the Settings menu.

Please Note: The Job BaseInfo has been created from the admin side, and the user is not supposed to create it in the settings menu.

Steps to create a new Job BaseInfo:

• To create a new Job BaseInfo, click on the Create Job BaseInfo option, as shown in the image below:

Create Job BaseInfo

• Once clicked, it will redirect to the page for creating a new Job BaseInfo and the user will be asked to fill in details in the following Tabs:

Basic Information Tab:

Basic Information Tab while Creating Job BaseInfo

1. Name: Provide a Name for the Job BaseInfo.

2. Deployment type: Select the Deployment type from the drop-down.

3. Image Name: Enter the Image name for the Job BaseInfo.

4. Version: Specify the version.

5. isExposed: This option will be automatically filled as True once the deployment type is selected

6. Job type: Select the job type from the drop-down.

Ports Tab:

Ports Tab

• Port Name: Enter the Port name.

• Port Number: Enter the Port number.

• Delete: The user can delete the port details by clicking on this option.

• Add Port: The user can add the Port by clicking on this option.

Spark component information:

• Main class: Enter the Main class for creating the Job BaseInfo.

• Main application file: Enter the main application file.

• Runtime Environment Type: Select from the drop-down. There are three options available: Scala, Python, and R.

• Now, click on the Save option to create the Job BaseInfo.

Once the Job BaseInfo is created, the user can redirect to the List Job BaseInfo page by clicking on the List Job BaseInfo icon as shown in the below image:

List Job BaseInfo Page:

This Page will show the list of all created Job BaseInfo as shown in the below image:

• The Name column represents the Name of the Job BaseInfo.

• The Status column will display the status of the Job BaseInfo.

• The Version column displays the version of the Job BaseInfo.

• The user can see the details of Job Baseinfo by clicking on the View icon. Once clicked on the View icon, the user can see the details of Job BaseInfo provided while creating that Job.

List Components

All the components being used in the pipeline are listed on this page.

Please Note: Please go through the below-given walk-through for the list components page.

List Component Page

There is a provision to create a component. At the top right corner, we have a Create Component icon. Clicking on this takes you to a different page where you can configure your component.

Creating a system Component

Check out the steps in the below-shared walk-through to configure the custom component.

Creating a custom component

Please Note: You should have your docker image created and stored in the docker repository where all other pipeline components are present. Little Help from DevOps will be required to push those images.