Best practices for UDAs

General

Overview

When working with User Defined Analytics (UDAs) in kdb Insights, following best practices ensures that your analytics are efficient, maintainable, and reliable. This section covers general guidelines that apply to UDAs.

General best practices

1. Modular design

2. Break down complex analytics into smaller, reusable components. This makes your UDAs easier to manage and test.

3. Consistent naming conventions

4. Use clear and consistent naming conventions for your variables, and UDAs. This improves readability and helps other developers understand your code.

5. Thorough documentation

6. Document your code extensively, including comments within your scripts and separate documentation that explains the purpose, inputs, and outputs of each UDA.

7. Version control

8. Implement version control to track changes and maintain different versions of your UDAs. This helps in managing updates and reverting to previous versions if necessary.

9. Testing

10. Regularly test your UDAs with a variety of datasets to ensure they handle different scenarios correctly. Use unit tests to validate individual components.

11. Error handling

12. Include comprehensive error handling in your UDAs to catch and manage exceptions, which can prevent type errors and improve reliability.

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For UDAs

Overview

Creating and managing UDAs requires specific considerations to ensure that they perform well and integrate seamlessly within kdb Insights. This section outlines best practices for working with UDAs.

Best practices

1. Define clear objectives

2. Start by clearly defining the purpose of the UDA. Understanding the problem it addresses helps in designing an effective solution.

3. Optimize for performance

4. Ensure that your UDAs are optimized for performance, particularly when handling large datasets. Avoid unnecessary computations and focus on efficient data processing.

5. Reusable components

6. Design UDAs with reusability in mind. Create functions that can be easily reused across different UDAs.

7. Consistent data handling

8. Standardize how your UDAs handle input and output data. Consistent data formats and structures make it easier to integrate UDAs into larger workflows.

9. Metadata management

10. Properly manage metadata for your UDAs, including detailed descriptions, versioning information, and author details. This helps in tracking and managing UDAs over time.

11. Security considerations

12. Ensure that your UDAs adhere to security best practices, such as validating inputs and managing access controls. This reduces the risk of security vulnerabilities.

13. Routing

14. If your environment includes multiple sets of DAPs or aggregators, ensure that the custom UDA uses distinguished parameters such as table, labels, or scope. These parameters route the request to the correct set of DAPs and aggregators.

15. For more information and examples, refer to routing.

16. Naming

17. Currently only namespaced UDAs are supported.