Benchmark: uuunnnnii - MeasureThat.net

Tests:

mina
const responseToTimeseries = (response) => { return response.reduce((base, { data }, index) => { data.forEach(([key, point]) => { const series = base[key]; if (series) { const { points, total } = series; const pointsDiff = index - points.length; // Add enough 'buffer' 0s to match the current index const buffer = pointsDiff ? new Array(pointsDiff).fill(0, 0, pointsDiff) : []; base[key] = { ...series, points: [...points, ...buffer, point], total: total + point } } else { base[key] = { // Pad as many 0s as needed to match the current index (length) // [0, point] satifies the tests, but data can be crazy sometimes 🤯 // This feels a bit more reliable points: new Array(index).fill(0, 0, index).concat(point), total: point, x: key, } } }) return base; }, {}); }; responseToTimeseries([{ data: [ [1242362340000, 20] ] }, { data: [ [1242362340000, 10], [1242362340001, 4], [1242362340002, 1] ] }, { data: [ [1242362340000, 6], [1242362340001, 9] ] }, { data: [ [1242362340000, 11], [1242362340001, 13], [1242362340002, 2] ] }, ])
sina
const responseToTimeseries = (response) => { // your implementation goes here const pointHash = {} class TimeSeriesPoint { constructor(x, point) { this.x = x this.points = [point] } addPoint(point) { this.points.push(point) } calcTotal() { return this.points.reduce((acc, curr) => acc + curr, 0) } toJson() { return {x: this.x, points: this.points, total: this.calcTotal()} } } response.flatMap(item => item.data) .forEach(([key, point]) => { if (pointHash[key]) { pointHash[key].addPoint(point) } else { pointHash[key] = new TimeSeriesPoint(key, point) } }) const plainJsonHash = {} Object.keys(pointHash) .forEach(key => { plainJsonHash[key] = pointHash[key].toJson() }) return plainJsonHash }; responseToTimeseries([{ data: [ [1242362340000, 20] ] }, { data: [ [1242362340000, 10], [1242362340001, 4], [1242362340002, 1] ] }, { data: [ [1242362340000, 6], [1242362340001, 9] ] }, { data: [ [1242362340000, 11], [1242362340001, 13], [1242362340002, 2] ] }, ])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
mina
sina

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 definition and individual test cases to explain what's being tested, compared, and their pros/cons.

**Benchmark Definition**

The provided JSON represents a JavaScript benchmark definition. It doesn't include any script preparation code or HTML preparation code, indicating that the focus is solely on the JavaScript implementation.

**Test Case 1: "mina"**

This test case uses the `responseToTimeseries` function, which appears to be an implementation of a time series aggregation algorithm. The input data is an array of objects with a `data` property containing arrays of timestamp-point pairs. The function returns a flattened object where each key is a timestamp and its corresponding value is an array of points.

Here's what's being tested:

1. Comparison of different implementation approaches for the `responseToTimeseries` function.
2. Performance variations between different browsers (in this case, Chrome 88) and devices (Desktop).

**Test Case 2: "sina"**

This test case also uses the `responseToTimeseries` function but with a different implementation approach.

Here's what's being tested:

1. Comparison of different implementation approaches for the `responseToTimeseries` function.
2. Performance variations between different browsers (in this case, Chrome 88) and devices (Desktop).

**Implementation Approaches Compared**

In both test cases, two distinct implementation approaches are being compared:

1. **Approach 1**: The first approach in each test case uses a more traditional, imperative programming style with loops and conditional statements.
2. **Approach 2**: The second approach in each test case uses a more functional programming style with classes, objects, and methods.

**Pros/Cons of Each Approach**

Pros of the Imperative Approach:

* Typically easier to understand for developers familiar with imperative programming.
* Can be faster for small inputs due to fewer function calls and object lookups.

Cons of the Imperative Approach:

* May lead to performance issues with large inputs due to increased function call overhead.
* Can result in more complex, harder-to-maintain codebases.

Pros of the Functional Approach:

* Often results in more concise, expressive code that's easier to reason about.
* Can lead to better performance for large inputs due to optimized function calls and object lookups.

Cons of the Functional Approach:

* May be less familiar to developers without prior experience with functional programming concepts.
* Can result in a steeper learning curve.

**Other Considerations**

1. **Use of JavaScript features**: Both test cases use modern JavaScript features, such as classes, objects, methods, and template literals (for string formatting).
2. **Browser and device variations**: The benchmark measures performance differences across various browsers and devices to account for platform-specific optimizations.
3. **Performance metrics**: The benchmark uses the `ExecutionsPerSecond` metric to measure performance, which represents the number of function executions per second.

Overall, this benchmark aims to compare different implementation approaches for a time series aggregation algorithm, focusing on performance variations between browsers and devices.

Related benchmarks:

uuunnnnii (version: 0)

whatever

Comparing performance of: mina vs sina

Created: 5 years ago by: Guest

Jump to the latest result

mina

sina

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):