Benchmark: Test filter and map

Script Preparation code:

testCase = new Array(100).fill(1).map(() => Math.random() > 0.5 ? 1 : 0)

Tests:

map then filter
testCase.map(t => t ? t : undefined).filter(t => !t)
filter tehn map
testCase.filter(t => !t).map(t => t)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map then filter
filter tehn map

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 explanation of the provided benchmark.

**What is tested?**

The benchmark tests two different approaches to executing two operations on an array: `filter` followed by `map`, and `map` followed by `filter`. The input data is generated by creating an array of 100 random elements, where each element is either 1 or 0. The `map` operation is applied to the array, which returns a new array with the results of applying the callback function to each element. The `filter` operation is then applied to this resulting array, returning a new array with only the elements that pass the filtering criteria.

**Options compared**

There are two options being compared:

1. **Filter-then-map**: This approach first filters out the zero values from the original array, and then applies the `map` operation to the remaining non-zero values.
2. **Map-then-filter**: This approach first applies the `map` operation to all elements in the original array, which returns a new array with the results of applying the callback function to each element. Then, it filters out the zero values from this resulting array.

**Pros and Cons**

**Filter-then-map:**

Pros:

* Can take advantage of more advanced filtering techniques or optimizations that only apply to non-zero values.
* May be faster for certain types of data where zeros are not significant.

Cons:

* Requires two passes over the original data, which can be slower due to the extra iteration.

**Map-then-filter:**

Pros:

* Only requires one pass over the original data, which can make it faster in some cases.
* Can take advantage of optimizations that apply to all elements, regardless of their value.

Cons:

* May require additional processing or filtering steps for zeros, which can be slower due to the extra iteration.

**Other considerations**

In general, the performance difference between these two approaches depends on the specific use case and data distribution. If the data is highly skewed towards zero values, the filter-then-map approach may be faster. However, if the data is more evenly distributed or contains a lot of non-zero values, the map-then-filter approach may be faster.

**Library usage**

The benchmark uses the `Array.prototype.map` and `Array.prototype.filter` methods, which are built-in JavaScript methods that operate on arrays. These methods use various optimizations and techniques under the hood to improve performance, such as caching and iterative algorithms.

**Special JS feature or syntax**

There is no special JavaScript feature or syntax used in this benchmark. It only uses standard JavaScript features like arrays, map, filter, and callback functions.

**Alternatives**

Other alternatives for testing these operations include:

* Using a custom loop-based implementation instead of `map` and `filter`.
* Using a different data structure, such as a linked list or tree, to test the filtering and mapping operations.
* Using a just-in-time (JIT) compiler or native code optimization to generate optimized machine code for the comparison.
* Adding additional noise or randomness to the input data to simulate real-world scenarios.

However, these alternatives would likely require more complex setup and may not provide meaningful results without proper benchmarking and analysis.

Related benchmarks:

Test filter and map (version: 0)

Comparing performance of: map then filter vs filter tehn map

Created: 4 years ago by: Guest

Jump to the latest result

map then filter

filter tehn map

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):