Question 1

How to handle async operations with Rambda?

Accepted Answer

Use R.pipeAsync for building asynchronous transformation chains, which works similarly to R.pipe but handles promises, allowing you to integrate async functions seamlessly in your data pipelines. The README mentions R.pipeAsync for correct type inference.

Question 2

Rambda vs Ramda: which is better for TypeScript?

Accepted Answer

Rambda is superior for TypeScript projects due to its focus on type safety in pipe chains, offering better inference than Ramda. However, Ramda has a broader API, so choose Rambda if type correctness is critical and you can work with its curated method set.

Question 3

How to migrate from Ramda to Rambda?

Accepted Answer

Since Rambda v10 is not a drop-in replacement, migrate by updating function calls to use curried syntax within pipe chains and checking for missing methods. The README provides a link to the older version's documentation for reference during transition.

Question 4

Does Rambda support tree-shaking for smaller bundles?

Accepted Answer

Yes, Rambda is modular, allowing tree-shaking to include only the functions you import, which helps optimize bundle sizes. Use ES6 imports like `import { pipe, filter } from 'rambda'` to leverage this.

Question 5

How to use dot notation with nested paths in Rambda?

Accepted Answer

Use R.path with string dot notation (e.g., 'a.b') for simple paths, but for numeric indices or complex arrays, you must use array notation (e.g., ['a', '1', 'b']) as specified in the README to avoid type issues.

Question 6

What are the performance trade-offs of using Rambda?

Accepted Answer

Rambda prioritizes type safety and immutability, which can introduce slight overhead compared to imperative code, but it's optimized for pipe chains. The curated method set helps keep performance predictable, though extensive chaining might impact speed in hot paths.

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