Python Script
Last updated
Last updated
The Python Script component works as a normal Python compile.
Please Note: The Python Script component can be used as a Reader, an API Data Ingestion component, a Transformation, or a writer component. The component can only return either in pandas df or a list of dictionaries.
Check out the given demonstration to understand the configuration steps involved in the Python Script.
All component configurations are classified broadly into 3 section
Basic
Metadata
Drag and drop the Python Script to the Workflow Editor.
The Python Script runner component will require an Event to pass on the data in a Pipeline Workflow.
Click the dragged Python Script component to get the component properties tabs.
It is the default tab to open for the Python Script component while configuring the component.
Invocation Type: Select an Invocation Type from the drop-down menu to confirm the running mode of the reader component. The supported invocation type for this component is Real-time.
Deployment Type: It displays the deployment type for the component. This field comes pre-selected.
Batch Size: Provide the maximum number of records to be processed in one execution cycle (Min limit for this field is 10).
Failover Event: Select a failover Event from the drop-down menu.
Container Image Version: It displays the image version for the docker container. This field comes pre-selected.
Intelligent Scaling: By selecting the Real-Time as invocation type the Intelligent Scaling option appears. By enabling this option helps the component to scale up to the max number of instances by automatically reducing the data processing.
Description: Provide description of the component.
Open the Meta Information Tab to open the fields and configure them.
Component Name: Provide a name for the Python Script component.
Please Note: The component name should be without space and special characters. Use the underscore symbol to show space in between words.
Python Script: Insert the Python script containing at least one function. The function should not have an argument, data frame argument, or custom argument.
Start Function Name: It displays all the function names used in the python script in a drop-down menu. Select one function name with which you want to start.
In Event Data Type: Provide input data type as a Data Frame or List.
External Libraries: Provide the external library name in this field. Insert multiple library names separated by commas.
Script: The user can write the script in this space. The user can also keep different versions of the script using the version control options.
It allows you to keep different versions of the script in version control systems. Anytime you need the older version of the script you can get from VCS which will replace the existing script with the committed script.
Pull script from VCS: It allows the user to pull desired committed script from the VCS.
Push script to VCS: It allow the user to commit different versions of a script to the VCS.
Please Note:
If the script in the component is same as the committed script it won't commit again. You can push any number of different scripts by giving different commit message.
The version of the committed message will be listed as V1,V2, and so on.
The user can verify the written script by using the verification icon provided for it.
A success notification message appears if the script is correct.
The user can use the Save Component icon to save the Python Script component.
Input Data: Use custom argument names as keys and provide the required value to configure the Script component.
Click the Save Component in Storage icon.
Click the Update Pipeline icon to save the Pipeline workflow. (After getting the success message).
Activate the Pipeline workflow.
The user gets notified once the Logs start loading. Open the Logs section to see the logs.
The Python Script component is ready to read the data coming from the input event, it transforms the data and returns output data.
Please Note: The below-given instructions should be followed while writing a Python script in the Data Pipeline:
The Python script needs to be written inside a valid Python function. E.g., The entire code body should be inside the proper indentation of the function (Use 4 spaces per indentation level).
The Python script should have at least one main function. Multiple functions are acceptable, and one function can call another function.
It should be written above the calling function body (if the called function is an outer function).
It should be written above the calling statement (if called function is an inner function)·
Spaces are the preferred indentation method.
Do not use "type" as the function argument as it is a predefined keyword.
The code in the core Python distribution should always use UTF-8.
Single-quoted strings and double-quoted strings are considered the same in Python.
All the packages used in the function need to import explicitly before writing the function.
The Python script should return data in the form of a data frame or list only. The form of data should be defined while writing the function.
If the user uses some Kafka event data for transformation, then the first argument of the function should be a data frame or list.
If the user needs to use some external library, the user needs to mention the library name in the external libraries field. If the user wants to use multiple external libraries, the library names should be separated by a comma.
If the user needs to pass some external input in your main function, then you can use the input data field. The key name should be the same according to the variable's name and value that is put as per the requirement.
The user can use that component as a reader, transformation, and writer.
The Custom Python Script transform component supports 3 types of scripts in the Data Pipeline.
1. As Reader Component: If you don’t have any in Event then you can use no argument function. For Example,
2. As Transformation Component: If you have data to execute some operation, then use the first argument as data or a list of dictionaries. For Example,
Here the df holds the data coming from the previous event as argument to the pram of the method.
3. Custom Argument with Data: If there is a custom argument with the data-frame i.e. the data is coming from the previous event and we have passed the custom argument to the parameter of the function. here df will hold the data from the previous event and the second param: arg range can be given in the input data section of the component.
