Benchmark: Get Unique - MeasureThat.net

Script Preparation code:

policiesList=[];
for(i=0; i<1000; i++) policiesList.push({createdBy: String(Number(i)/1000)});

var filtering = (value, index, self) => self.indexOf(value) === index;
var mapping = policy => policy.createdBy;

var reducing= (acc, val) => acc.add(val.createdBy)

Tests:

map-filter
policiesList.map(mapping).filter(filtering);
reduce
policiesList.reduce(reducing, new Set());

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map-filter
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):

LLMs can make mistakes. Check important info.

I'll break down the benchmark and explain what's being tested, compared, and their pros and cons.

**Benchmark Overview**

The benchmark is designed to measure the performance of two JavaScript functions: `map` and `filter`. The test creates an array of 1000 objects with a unique property (`createdBy`) and then applies these functions to the data.

**Options Compared**

There are two options being compared:

1. **Map-Filter**: This option uses the `map` function to transform each object in the `policiesList` into its `createdBy` property, and then uses the `filter` function to remove objects that don't match a certain condition (in this case, no condition is specified, so all objects are filtered out).
2. **Reduce**: This option uses the `reduce` function to combine the `createdBy` properties of all objects in the `policiesList` into a single value.

**Pros and Cons**

### Map-Filter

Pros:

* Easy to understand and implement
* Can be used for various purposes beyond just removing elements (e.g., transforming data)

Cons:

* Inefficient due to the unnecessary creation of an intermediate array
* May not be suitable for large datasets, as it can lead to performance issues

### Reduce

Pros:

* Efficient, as it avoids creating an intermediate array
* Suitable for reducing multiple values into a single value (e.g., summing or multiplying)

Cons:

* Can be more difficult to understand and implement for those unfamiliar with the `reduce` function
* May not be suitable for all use cases, such as when transforming data

**Library Usage**

There is no explicit library usage in this benchmark. However, the `Set` object used in the "Reduce" test case implies that the language supports Set data structures.

**Special JS Features/Syntax**

This benchmark does not explicitly use any special JavaScript features or syntax beyond the standard functions (e.g., `map`, `filter`, `reduce`). However, it's worth noting that modern JavaScript engines have introduced various features and optimizations, such as:

* SIMD (Single Instruction, Multiple Data) instructions
* Just-In-Time (JIT) compilation

These features can impact performance, but they are not explicitly used in this benchmark.

**Alternatives**

Other alternatives to measure the performance of `map`, `filter`, and `reduce` functions include:

1. **NativeArray or TypedArrays**: Instead of using JavaScript arrays, you could use native arrays or typed arrays (e.g., Int32Array) to compare performance.
2. **C++ or Rust benchmarks**: You could write a benchmark in C++ or Rust, which would likely provide more accurate results due to the language's focus on performance and lack of overhead.
3. **Benchmarking frameworks**: There are various benchmarking frameworks available for JavaScript, such as Benchmark.js, jsperf, or micro-benchmark.

Keep in mind that each alternative has its own trade-offs and requirements, and the choice ultimately depends on your specific use case and goals.

Related benchmarks:

Get Unique (version: 0)

Comparing performance of: map-filter vs reduce

Created: 3 years ago by: Guest

Jump to the latest result

map-filter

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):