Benchmark: Points Compare - MeasureThat.net

Tests:

D
const responseToTimeseries = (response) => { const entries = response.map(({ data }) => Object.fromEntries(data)); return response.reduce((base, { data }, index) => { data.forEach(([key, point]) => { if (!base[key]) { base[key] = { x: key, points: entries.map(e => e[key] || 0), total: entries.reduce((t, v) => t += v[key] || 0, 0), }; } }) return base; }, {}) };
S
const responseToTimeseries = (response) => { // your implementation goes here const pointHash = {} class TimeSeriesPoint { constructor(x) { this.x = x this.points = [] this.total = 0 } addPoint(point = 0) { this.points.push(point) this.total += point } } // create {x: point} objects for easy accass const objectified = response.map(({data}) => Object.fromEntries(data)) console.log(objectified) // get only the list of x's and iterate over them // could get uniques by converting to a set to avoid extra iterations response.flatMap(item => item.data.map(i => i[0])) .forEach(function(key) { // when encountering a new x, add a point to it for each other data set // if it's undefined in a given set, point defaults to 0 if (!pointHash[key]) { pointHash[key] = new TimeSeriesPoint(key) objectified.forEach(function(obj) { pointHash[key].addPoint(obj[key]) }) } }) return pointHash };

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
D
S

Fastest: N/A

Slowest: N/A

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

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I'll do my best to explain the benchmark and its various components.

**Benchmark Definition**

The provided JSON represents a JavaScript microbenchmark called "Points Compare". It's a simple benchmark that tests the performance of different implementations for comparing points in a timeseries data structure. The benchmark is designed to measure how efficient two different approaches are in aggregating points by x-axis values.

**Options Compared**

There are two options compared:

1. **Option D**: This implementation uses the `Object.fromEntries()` method to create an object from each entry, and then iterates over the entries to aggregate points by x-axis values.
2. **Option S**: This implementation uses a class-based approach with custom methods (`addPoint()`) to create a timeseries data structure. It also uses a hash table (`pointHash`) to store the aggregated points.

**Pros and Cons**

* **Option D**:
	+ Pros: Simple, easy to understand, and requires minimal setup.
	+ Cons: May not be as efficient due to the overhead of `Object.fromEntries()` and iteration.
* **Option S**:
	+ Pros: More efficient, as it uses a hash table to store aggregated points, which reduces lookup times.
	+ Cons: Requires more setup and has a steeper learning curve due to the custom class-based approach.

**Library**

Neither option explicitly uses a library. However, `Object.fromEntries()` is a built-in JavaScript method introduced in ECMAScript 2015 (ES6).

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax used in either implementation that require specific knowledge of the language. Both options use standard JavaScript methods and data structures.

**Other Alternatives**

If you were to rewrite these benchmarks, you might consider using existing libraries like `lodash` or `ramda`, which provide efficient and concise implementations for common data manipulation tasks. Additionally, you could explore using specialized libraries like `ts-jsx` or `timeseries-data` that are designed specifically for working with timeseries data.

Keep in mind that these alternatives would likely change the benchmark's behavior and may not be directly comparable to the original options.

**Benchmark Preparation Code**

The preparation code for each test case is empty, which means that the benchmark starts from a blank slate. This allows you to run the tests on various JavaScript engines and environments without any pre-existing data or setup.

Overall, the "Points Compare" benchmark provides a useful example of how different approaches can be compared in terms of performance and efficiency. By understanding the pros and cons of each option, developers can choose the best approach for their specific use case.

Related benchmarks:

Points Compare (version: 0)

Comparing performance of: D vs S

Created: 5 years ago by: Guest

Jump to the latest result

D

S

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):