Benchmark: https://stackoverflow.com/posts/61510307 edit

Script Preparation code:

var nrows = 2000,
    nyears = [...Array(20).keys()].map(x => 2000 + x),
    nages = [...Array(20).keys()].map(x => `${18+x}`),
    npeople = [...Array(nrows / nyears.length / nages.length | 0).keys()].map(x => `Person${x}`);

var getOneRotate = (x, i) => x[(i / x.length | 0) % x.length];

var data = Array(nrows).fill(0).map((x, i) => ({
    year: getOneRotate(nyears,i),
    name: getOneRotate(npeople,i),
    age: getOneRotate(nages,i),
    value: (i / nyears.length | 0) % nyears.length
}));

const NA='NA';

function forloopObjectAssign(x) {
  let lname, lage, res=[], years = {};
for ( var i = 0 ; i < x.length ; i++ ) {
   const {year, value, name, age} = x[i];
   if ( lname != name || lage != age ) 
     res.push({name, age});
   res[ res.length-1 ][year] = value;
   
   years[year] = NA;
   lname = name;
   lage = age;
}
return res.map( x => Object.assign({...years}, x) );
}

function forloopEntries(x) {
let lname, lage, res=[], years = {};
for ( var i = 0 ; i < x.length ; i++ ) {
   const {year, value, name, age} = x[i];
   if ( lname != name|| lage != age ) 
     res.push([['name',name],['age',age]]);
   res[ res.length-1 ].push([year,value]);
   
   years[year] = NA;
   lname = name;
   lage = age;
}

return res.map( x => Object.fromEntries(Object.entries(years).concat(x)) );
}

function mapReduceSet(data) {
    years = new Set;
    result = Object
        .values(data.reduce((r, { name, year, value, ...o }) => {
            r[name] = r[name] || { name, ...o };
            r[name][year] = value;
            years.add(year);
            return r;
        }, {}))
        .map(
            (y => o => ({ ...y, ...o }))
            (Object.fromEntries([...years].map(y => [y, NaN])))
        );
    return result;
}

let yearsGenerator = {}
function* groupByName(entries) {
    let group = null;

    for (const {name, year, value, age} of entries) {
        if (group === null || group.name !== name || group.age !== age) {
            if (group !== null) {
                yield group;
            }
            
            group = {name, age};
        }

        group[year] = value;
        yearsGenerator[year] = 'NA';
    }

    if (group !== null) {
        yield group;
    }
}

function generator(entries) { return [...groupByName(entries)].map(x=>Object.assign({...yearsGenerator},x)) }

Tests:

generator
generator(data)
forloop object assign
forloopObjectAssign(data)
mapReduceSet
mapReduceSet(data)
forloopEntries
forloopEntries(data)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
generator
forloop object assign
mapReduceSet
forloopEntries

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

I'll do my best to explain what's being tested in this benchmark.

**What is being tested?**

The benchmark measures the performance of three different JavaScript functions: `forloopObjectAssign`, `mapReduceSet`, and `generator`. These functions are designed to process and transform a large dataset, which represents a merged object with various properties (year, value, name, age).

**Options compared**

Each function uses a different approach to achieve this transformation:

1. **`forloopObjectAssign`**: This function uses a traditional `for` loop to iterate over the dataset, creating an array of objects by assigning values from the original dataset to new objects.
2. **`mapReduceSet`**: This function uses the built-in `map()` and `reduce()` methods in combination with a Set data structure to process the dataset. It first creates a new object by mapping each entry in the dataset, then reduces it by merging the resulting objects into a single object.
3. **`generator`**: This function uses a generator function (a special type of function that can be used as an iterator) to process the dataset. The generator yields a sequence of values, which are then processed by mapping and reducing.

**Pros and cons**

Here's a brief summary of each approach:

*   `forloopObjectAssign`:
    *   Pros: straightforward, easy to understand
    *   Cons: can be slow for large datasets due to the overhead of creating new objects
*   `mapReduceSet`:
    *   Pros: efficient and scalable, leverages built-in methods for performance gain
    *   Cons: may require more memory due to the use of a Set data structure
*   `generator`:
    *   Pros: allows for asynchronous processing, can be more memory-efficient than creating arrays
    *   Cons: requires understanding of generator functions and iterators

**Library or special JS feature**

None of the functions directly utilize a library or a special JavaScript feature beyond the built-in methods mentioned earlier.

**Other alternatives**

To further optimize performance or improve efficiency:

*   Using `parallelism` techniques, such as using Web Workers or async/await, could potentially speed up each function by distributing the workload across multiple CPU cores.
*   Applying techniques like memoization or caching to reduce redundant computations within each function.
*   Optimizing data structure usage (e.g., switching from an object to a different data structure that's better suited for fast lookups).

These are just some ideas, and actual implementation would depend on the specific use case and requirements.

Now I hope this answers your questions.

Related benchmarks:

https://stackoverflow.com/posts/61510307 edit (version: 0)

Benchmark for transforming merged objects

Comparing performance of: generator vs forloop object assign vs mapReduceSet vs forloopEntries

Created: 6 years ago by: Guest

Jump to the latest result

generator

forloop object assign

mapReduceSet

forloopEntries

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):