Benchmark: Compare 4xMAP/REDUCE to 1xCOMPLEX REDUCE

Tests:

4xMAP/REDUCE
const projectJobs = []; for (var i = 0; i < 10000; i++) { projectJobs.push({ availablePositions: 1, jobStage: { length: 1 } }); } const teamTotalPosition = projectJobs.map(projectJob => projectJob.availablePositions).reduce(function (previous, current) {return previous + current;}, 0); const teamFilled = projectJobs.map(projectJob => projectJob.jobStage.length).reduce(function (previous, current) {return previous + current;}, 0);
1xCOMPLEX REDUCE
const projectJobs = []; for (var i = 0; i < 10000; i++) { projectJobs.push({ availablePositions: 1, jobStage: { length: 1 } }); } const initialAccumulator = { teamTotalPosition: 0, teamFilled: 0, }; const {teamTotalPosition, teamFilled} = projectJobs.reduce(function (accumulator, pj) { return { teamTotalPosition: accumulator.teamTotalPosition + pj.availablePositions, teamFilled: accumulator.teamFilled + (pj.jobStage?.length || 0), }; }, initialAccumulator);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
4xMAP/REDUCE
1xCOMPLEX REDUCE

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

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Let's break down the provided benchmark and its test cases.

**Benchmark Overview**

The benchmark is designed to compare two approaches for aggregating data: using `map` followed by `reduce`, and using `reduce` with an accumulator object.

**Options Compared**

There are two options being compared:

1. **4xMAP/REDUCE**: This approach uses the `map` function to transform each element in the array, and then uses the `reduce` function to aggregate the results.
2. **1xCOMPLEX REDUCE**: This approach also uses the `reduce` function, but with a more complex accumulator object that accumulates two values: `teamTotalPosition` and `teamFilled`.

**Pros and Cons of Each Approach**

* **4xMAP/REDUCE**
	+ Pros:
		- Easier to read and understand for developers familiar with `map` and `reduce`.
		- Can be more efficient for large datasets since it only iterates over the array once.
	+ Cons:
		- May have higher overhead due to the intermediate `map` operation.
* **1xCOMPLEX REDUCE**
	+ Pros:
		- Can be more efficient in terms of memory usage since it avoids creating an additional array with mapped values.
	+ Cons:
		- More complex and harder to understand for developers unfamiliar with accumulator objects.

**Library Used**

None of the test cases explicitly mention using any libraries, but they do use built-in JavaScript functions like `map` and `reduce`.

**Special JS Feature/Syntax**

There is no special JavaScript feature or syntax being tested in this benchmark. The focus is solely on the aggregation approach.

**Other Alternatives**

If the developers want to explore alternative approaches, they could consider:

* Using other aggregation methods like `forEach`, `every`, or `some`.
* Implementing a custom accumulator object or function.
* Using libraries like Lodash (which provides functions for reducing arrays) or Ramda (which provides a functional programming approach).

It's worth noting that the benchmark seems to be focused on comparing the performance of these two specific aggregation approaches, so exploring other alternatives might not be necessary.

Related benchmarks:

Compare 4xMAP/REDUCE to 1xCOMPLEX REDUCE (version: 0)

Comparing performance of: 4xMAP/REDUCE vs 1xCOMPLEX REDUCE

Created: 4 years ago by: Guest

Jump to the latest result

4xMAP/REDUCE

1xCOMPLEX REDUCE

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):