Benchmark: array-group-test

Script Preparation code:

var groups = [['E', 'E_T'], ['T', 'T_T']];

var elements = [
  { t: 'E', cls: 'E' },
  { t: 'EU', cls: 'E_T' },
  { t: 'EC', cls: 'E_T' },
  { t: 'B', cls: 'B' },
  { t: 'T', cls: 'T' },
  { t: 'TU', cls: 'T_T' },
];

Tests:

A (2 cycles)
const temp = {}; for (const cGroup of groups) { const key = cGroup.join(); for (const el of elements) { if (!cGroup.includes(el.cls)) { continue; } if (!temp[key]) { temp[key] = [el]; } else { temp[key].push(el); } } } const res = []; for (const cGroup of Object.values(temp)) { res.push(cGroup); }
B (1 cycle + map)
const keyToIdx = {}; const res = []; for (const cGroup of groups) { const key = cGroup.join(); for (const el of elements) { if (!cGroup.includes(el.cls)) { continue; } const groupIdx = keyToIdx[key]; if (groupIdx > -1) { res[groupIdx].push(el); } else { keyToIdx[key] = (res.push([el]) - 1); } } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
A (2 cycles)
B (1 cycle + map)

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36

Browser/OS: Chrome 114 on Windows

View result in a separate tab

Test name	Executions per second
A (2 cycles)	2280629.2 Ops/sec
B (1 cycle + map)	3140915.5 Ops/sec

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

LLMs can make mistakes. Check important info.

**Overview of the Benchmark**

The provided JSON represents a JavaScript microbenchmark test case called "array-group-test". The goal of this benchmark is to measure the performance of different approaches for grouping and processing arrays with similar class names.

**Script Preparation Code**

The script preparation code defines two groups: `E` (for elements with class `E`) and `T` (for elements with class `T`). It also defines an array `elements` containing various elements with their respective classes.

**Benchmark Definition**

There are two benchmark definitions:

1. **A (2 cycles)**

This benchmark definition uses a dictionary-like data structure (`temp`) to group the elements by key (generated by joining the class groups). For each group, it iterates through the `elements` array and pushes the element into the corresponding group in the `temp` object.

The benchmark consists of two loops: one for generating keys from the class groups and another for iterating through the `elements` array.

**B (1 cycle + map)**

This benchmark definition uses a similar approach, but with a few differences. It defines an external data structure (`keyToIdx`) to keep track of the group indices for each key. This allows it to avoid repeated lookups and push operations.

The main difference between this approach and the previous one is that `B` uses a single loop to iterate through the `elements` array, which potentially reduces the number of iterations compared to the two-loop approach in `A`.

**Options Compared**

Two options are being compared:

1. **Option A (2 cycles)**: This approach uses a dictionary-like data structure to group elements by key and iterates through the array twice.
2. **Option B (1 cycle + map)**: This approach uses an external data structure to keep track of group indices and iterates through the array once.

**Pros and Cons**

Here's a brief summary of the pros and cons of each option:

**Option A (2 cycles)**

Pros:

* Easy to understand and implement
* Uses a simple dictionary-like data structure
* Iterates through the array only twice

Cons:

* Potential performance issues due to repeated lookups and push operations

**Option B (1 cycle + map)**

Pros:

* Potentially reduces number of iterations compared to Option A
* Uses an external data structure to avoid repeated lookups and push operations

Cons:

* Requires additional memory for the `keyToIdx` object
* May be more complex to understand and implement due to the use of a separate data structure

**Other Considerations**

One other consideration is the potential performance impact of using an external data structure like `keyToIdx`. This could lead to additional overhead due to memory allocation and deallocation.

Another aspect to consider is the size of the input array `elements`. If the array is very large, the performance difference between the two options may be negligible.

**Libraries and Special JS Features**

The provided benchmark code does not rely on any specific libraries. However, it uses some JavaScript features like:

* Array destructuring (e.g., `const key = cGroup.join();`)
* Destructuring assignment (e.g., `temp[key] = [el];`)
* Template literals (e.g., `const key = cGroup.join();`)

These features are widely supported in modern browsers and are not specific to any particular library.

**Alternatives**

If you're interested in exploring alternative approaches, here are a few options:

1. **Using `Array.prototype.reduce()`**: Instead of using an external data structure like `temp`, you could use the `reduce()` method to accumulate elements into groups.
2. **Using `Map` data structures**: You could use `Map` objects instead of dictionaries or external data structures to group elements by key.
3. **Using parallel processing**: If performance is a top priority, you could explore using parallel processing techniques like Web Workers or `Promise.all()` to execute the benchmark in multiple threads.

Keep in mind that these alternatives may introduce additional complexity and overhead, so it's essential to carefully evaluate their trade-offs before adopting them.

Related benchmarks:

array-group-test (version: 0)

Comparing performance of: A (2 cycles) vs B (1 cycle + map)

Created: one year ago by: Guest

Jump to the latest result

A (2 cycles)

B (1 cycle + map)

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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