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filter,map,reduce 2
(version: 0)
filter.map vs reduce
Comparing performance of:
filter,map vs reduce spread vs reduce push
Created:
4 years ago
by:
Guest
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Tests:
filter,map
const isFeatureNode = (feature) => feature.type === 'Feature'; const newNodes=[{type:'Feature', id:1}, {type:'non', id:2}, {type:'Feature', id:3}, {type:'non', id:4}]; newNodes.filter((feature) => !isFeatureNode(feature)).map((node) => node.id);
reduce spread
const isFeatureNode = (feature) => feature.type === 'Feature'; const newNodes=[{type:'Feature', id:1}, {type:'non', id:2}, {type:'Feature', id:3}, {type:'non', id:4}]; newNodes.reduce((acc, feature) => ( !isFeatureNode(feature) ? [...acc, feature.id] : acc ), []);
reduce push
const isFeatureNode = (feature) => feature.type === 'Feature'; const newNodes=[{type:'Feature', id:1}, {type:'non', id:2}, {type:'Feature', id:3}, {type:'non', id:4}]; newNodes.reduce((acc, feature) => { if (!isFeatureNode(feature)) { acc.push(feature); } return acc; }, []);
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Suite status:
<idle, ready to run>
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Test case name
Result
filter,map
reduce spread
reduce push
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided benchmark and explain what is being tested, compared, and the pros and cons of different approaches. **Benchmark Overview** The benchmark compares three ways to process an array of objects with two types: "Feature" and "non". The goal is to filter out the "non" elements and then perform a specific operation on the remaining "Feature" elements. The three approaches are: 1. `filter` + `map` 2. `reduce` (with spread operator) 3. `reduce` (without spread operator) **Approach 1: `filter` + `map`** This approach first filters out the non-"Feature" elements using `Array.prototype.filter()`. The resulting array only contains "Feature" elements. Then, it applies a mapping function to each element in the filtered array using `Array.prototype.map()`, which returns an array of ids. **Pros:** * Easy to understand and implement * Fast execution time due to lazy evaluation (filtering before mapping) **Cons:** * Two separate methods are used, which can lead to additional overhead and memory allocation * The resulting array is created twice (once by filtering and once by mapping), which can be inefficient in terms of memory allocation **Approach 2: `reduce` (with spread operator)** This approach uses `Array.prototype.reduce()` to accumulate an array of ids from the non-"Feature" elements while filtering out those that are not "Feature". The spread operator (`...`) is used to create a new array by spreading the accumulated array. **Pros:** * Single method call, which can reduce overhead and memory allocation * No need to create intermediate arrays **Cons:** * Less intuitive for developers who are familiar with `filter` and `map` * Can lead to more complex code due to the use of a single method for multiple operations **Approach 3: `reduce` (without spread operator)** This approach uses `Array.prototype.reduce()` to filter out non-"Feature" elements while accumulating an array of ids. No spread operator is used. **Pros:** * Similar to Approach 2, with the added advantage of being more intuitive for developers familiar with `filter` and `map` **Cons:** * More complex code due to the need to manually push elements to the accumulator array **Library Usage** The benchmark uses the following libraries: * None explicitly mentioned in the provided benchmark definitions. However, it is likely that the test environment (e.g., Browser) includes standard library functions like `Array.prototype.filter()` and `Array.prototype.map()`, as well as `Array.prototype.reduce()`. **Special JS Feature or Syntax** The benchmark uses ES6+ syntax features like: * Arrow functions (`const isFeatureNode = (feature) => feature.type === 'Feature';`) * Spread operator (`...`) These features are used to make the code more concise and readable, but may not be supported in older browsers. **Other Alternatives** Other approaches could include: * Using `Array.prototype.forEach()` with a callback function that filters out non-"Feature" elements and accumulates an array of ids. * Using a library like Lodash, which provides a `filter` function for filtering arrays. However, these alternatives may not be as efficient or intuitive as the three approaches compared in this benchmark.
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