Benchmark: FieldeasReduce2 - MeasureThat.net

HTML Preparation code:


<script src=''></script>

Script Preparation code:


 var testData = {
keys: ["ID", "ID_CONTACT", "ID_CHAT", "DATE", "TEXT", "BLOB", "$HDN_BIN_BLOB", "HAS_BLOB", "BLOB_TYPE", "BLOB_DURATION", "BLOB_SIZE", "$HDN_DateTime", "$HDN_XCoord", "$HDN_YCoord", "$HDN_ZCoord", "$PK", "$HDN_IDState", "$HDN_DescState", "$HDN_NumBlob"],
values: []
};
  
  for (var i = 0; i < 10000; i++) {
    testData.values.push([
    "ID", 
"ID_CONTACT" + i, 
"ID_CHAT" + i, 
"DATE" + i, 
"TEXT" + i, 
"BLOB" + i, 
"$HDN_BIN_BLOB" + i, 
"HAS_BLOB" + i, 
"BLOB_TYPE" + i, 
"BLOB_DURATION" + i, 
"BLOB_SIZE" + i, 
"$HDN_DateTime" + i, 
"$HDN_XCoord" + i, 
"$HDN_YCoord" + i, 
"$HDN_ZCoord" + i, 
"$PK" + i, 
"$HDN_IDState" + i, 
"$HDN_DescState" + i, 
"$HDN_NumBlob" + i
    ]);
  }

Tests:

reduce
var customReduce = data => { var keys = data.keys; return data.values.map(x => { return x.reduce((acc, curr, i) => { acc[keys[i]] = curr; return acc; }, {}) }) } customReduce(testData)
reduce2
function makeAnObject(data) { return data.values.reduce((arr, vals) => { return [ ...arr, vals.reduce((obj, v, i) => ({ ...obj, [data.keys[i]]: v }), {}) ] }, []) } makeAnObject(testData)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
reduce2

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.

Measuring JavaScript performance is a crucial aspect of software development, and MeasureThat.net provides a great platform for benchmarking various scenarios.

**Benchmark Description:**
The provided JSON represents a JavaScript microbenchmark that tests the performance of reducing an array using two different approaches:

1. `customReduce` function
2. `makeAnObject` function

In both cases, the input data is an object with a `values` property containing an array of objects, each representing a single row in a dataset.

**Options Compared:**
The benchmark compares the performance of the following two options:

1. **Array.prototype.reduce()**: This method reduces an array to a single value using a callback function.
2. **Custom reduce function (`customReduce`)**: A custom implementation of the reduce method, where each element of the input array is processed individually.
3. **`makeAnObject` function**: This function takes advantage of the spread operator (`...`) and the `reduce()` method to create an object from the input array.

**Pros and Cons:**

1. **Array.prototype.reduce()**:
	* Pros: Efficient, widely supported, and well-documented.
	* Cons: May not be suitable for large datasets or complex data structures.
2. **Custom reduce function (`customReduce`)**:
	* Pros: Allows for fine-grained control over the reduction process and can be more readable for some use cases.
	* Cons: May have performance implications due to the overhead of individual element processing.
3. **`makeAnObject` function**:
	* Pros: Utilizes modern JavaScript features (spread operator, `reduce()`) and can create an object with nested properties in a single pass.
	* Cons: May be less readable for those unfamiliar with these features.

**Library Usage:**
None of the benchmarked functions rely on external libraries.

**Special JS Features/Syntax:**
The `makeAnObject` function utilizes two special JavaScript features:

1. **Spread operator (`...`)**: This operator is used to create a new array by spreading the elements of an existing array.
2. **Template literals (`${}`)**: These are used to define string templates with dynamic values.

**Alternatives:**
If you need to benchmark other reduce-related functions or scenarios, consider exploring alternative approaches, such as:

1. Using `Array.prototype.reduceRight()` for a right-to-left reduction approach.
2. Implementing a custom reducer function using recursion or iterative methods.
3. Utilizing third-party libraries like Lodash's `reduce` method.

Keep in mind that the choice of approach depends on your specific use case, performance requirements, and personal preferences.

Related benchmarks:

FieldeasReduce2 (version: 0)

Comparing performance of: reduce vs reduce2

Created: 5 years ago by: Guest

Jump to the latest result

reduce

reduce2

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):