Benchmark: map filter join vs arr.educe

Script Preparation code:

var assignments = [{
        bus: {
            number: 2
        }
    },
    {
        bus: {
            number: 3
        }
    },
]
for (i=0; i<10000; i++) {
  assignments.push({
        bus: {
            number: i
        }
    })
}

Tests:

map filter join
var tempResult = assignments.map((assignment) => assignment.bus?.number).filter((busNumber) => busNumber !== '').join(' | ');
reduce
var tempResult = assignments.reduce((acc, assignment) => { if (acc.length < 1) { return assignment.bus?.number ?? ''; } return `${acc} | ${assignment.bus?.number ?? ''}`; }, '');

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map filter join
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 benchmark and its test cases.

**Benchmark Definition**

The benchmark measures the performance of two different approaches to perform a common operation: joining strings in an array while filtering out empty values. The operation is performed on a dataset of 10,000 objects with a "bus" property containing numbers from 1 to 10,000.

**Options Compared**

Two options are compared:

1. **Map Filter Join**: This approach uses the `map` method to transform each object in the array into an array of strings (the bus number), then filters out empty values using the `filter` method, and finally joins the remaining numbers with a pipe character (`|`) to form a single string.
2. **Reduce**: This approach uses the `reduce` method to iterate over the array and accumulate the numbers in a string, joined by the pipe character.

**Pros and Cons of Each Approach**

1. **Map Filter Join**
	* Pros:
		+ Can be easier to understand and implement for developers familiar with `map` and `filter`.
	* Cons:
		+ May incur overhead due to the creation of intermediate arrays.
		+ Requires more memory allocation and garbage collection, which can impact performance in certain scenarios.
2. **Reduce**
	* Pros:
		+ Can be faster and more efficient than the map-filter-join approach, especially for large datasets, since it avoids creating intermediate arrays.
		+ Allows for a single pass through the data, reducing memory allocation and garbage collection overhead.
	* Cons:
		+ May require a deeper understanding of the `reduce` method and its accumulator parameter.
		+ Can be more challenging to debug and understand due to the complex accumulation logic.

**Library Used**

None explicitly mentioned in the benchmark definition or test cases. However, it's likely that the `map`, `filter`, and `reduce` methods are built-in JavaScript methods or part of a standard library (e.g., ECMAScript).

**Special JS Feature or Syntax**

No special JavaScript features or syntax are used in these test cases. They only utilize standard JavaScript methods and operators.

**Other Alternatives**

Some alternative approaches to joining strings in an array while filtering out empty values include:

1. **Using a custom loop**: Instead of using `map`, `filter`, or `reduce`, you could use a simple loop to iterate over the array, accumulate the numbers, and join them.
2. **Using a streaming library**: If you need to handle very large datasets, you might consider using a streaming library like `lodash` or `ramda`, which provide optimized implementations of various algorithms, including string joining and filtering.
3. **Using an optimization-specific library**: Libraries like `fast-join` or `js-fast-sorted-array` specialize in optimizing common array operations, including string joining.

Keep in mind that the choice of approach depends on the specific requirements of your project, such as performance, memory efficiency, and development complexity.

Related benchmarks:

map filter join vs arr.educe (version: 0)

Comparing performance of: map filter join vs reduce

Created: 2 years ago by: Guest

Jump to the latest result

map filter join

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):