Benchmark: Hello World - MeasureThat.net

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Script Preparation code:

var types = {
  "all": [
    "GDPR",
    "HIPAA",
    "NIST 800-53 Rev4",
    "NIST CSF",
    "PCI DSS v3.2",
    "RIS",
    "SOC 2"
  ],
  "gcp": [
    "CIS v1.0.0 (GCP)",
    "Eddie",
    "HIPAA",
    "ISO 27001:2013",
    "Marc's Compliance Standard",
    "NIST 800-53 Rev4",
    "PCI DSS v3.2",
    "RIS",
    "SOC 2"
  ],
  "aws": [
    "CIS v1.2.0 (AWS)",
    "CS Custom Standard",
    "ChrisZ Compliance",
    "Eddie",
    "Francisco's Test",
    "GDPR",
    "HIPAA",
    "ISO 27001:2013",
    "Marc's Compliance Standard",
    "My standard",
    "NIST 800-53 Rev4",
    "NIST CSF",
    "PCI DSS v3.2",
    "RIS",
    "SOC 2",
    "compliance test"
  ],
  "azure": [
    "CIS v1.0 (Azure)",
    "GDPR",
    "HIPAA",
    "ISO 27001:2013",
    "NIST 800-53 Rev4",
    "PCI DSS v3.2",
    "RIS",
    "SOC 2"
  ]
};

Tests:

reduce then map
_.reduce(_.omit(types, 'all'), (result, standards, cloud) => { standards.map(s => { const standard = result[ s ]; if (!standard) { result[ s ] = [ cloud ]; } else { standard.push(cloud); } }); return result; }, {})
reudce then pick
var standards = [ ...new Set(Object.values(types)) ]; var typesWithoutAll = _.omit(types, 'all'); standards.reduce((result, standard) => { result[ standard ] = _.pickBy(typesWithoutAll, standards => standards.includes(standard)); return result; }, {});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce then map
reudce then pick

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.

Let's break down the provided benchmark definition and test cases.

**Benchmark Definition:**

The benchmark definition provides two scripts that define a mapping of data structures to compute. The first script uses the `reduce` method, which applies a function to each element in an array (in this case, an object) and reduces it to a single output value. The second script uses the `pickBy` function from the Lodash library to create a new object with filtered properties.

**Options Compared:**

The benchmark compares two approaches:

1. Using the `reduce` method without `map`.
2. Using the `reduce` method with `map`.

**Pros and Cons of Each Approach:**

**Approach 1: `reduce` method without `map`:**

Pros:

* Faster execution time, as it avoids the overhead of the `map` function.
* May be more efficient for large datasets, since it only requires a single pass through the data.

Cons:

* Less readable code, as the logic is tightly coupled with the iteration order.
* May require more complex error handling and edge case management.

**Approach 2: `reduce` method with `map`:**

Pros:

* More readable code, as the logic is decoupled from the iteration order.
* Easier to maintain and debug, since the code is more modular and reusable.
* Can be more flexible, as it allows for easier testing and iteration.

Cons:

* Slower execution time, due to the additional overhead of the `map` function.
* May require more memory allocation, depending on the size of the dataset.

**Library:**

The benchmark uses the Lodash library, which provides a `pickBy` function that helps filter properties in an object. The `pickBy` function takes two arguments:

* An object to filter (in this case, `typesWithoutAll`)
* A callback function that takes each property value as an argument and returns `true` if it should be included in the output.

**Special JavaScript Feature or Syntax:**

The benchmark uses the `reduce` method, which is a built-in JavaScript method that applies a function to each element in an array (or object) and reduces it to a single output value. The `map` method is also used, but its usage is more explicit due to the addition of the `map` argument.

**Other Considerations:**

* Performance: The benchmark measures the execution speed of both approaches. It's likely that Approach 1 will be faster due to the reduced overhead of the `map` function.
* Code Readability and Maintainability: Approach 2 is more readable and maintainable, as it decouples the logic from the iteration order.
* Memory Usage: The benchmark does not explicitly measure memory usage, but it's likely that Approach 1 will require less memory allocation due to the reduced overhead of the `map` function.

**Alternatives:**

Other approaches could be considered, such as:

* Using a different filtering library (e.g., Moment.js)
* Implementing a custom filtering algorithm
* Using a different data structure (e.g., arrays instead of objects)

However, these alternatives would likely deviate from the original intent of the benchmark and may not provide comparable results.

In summary, the benchmark compares two approaches to filtering data in JavaScript: using the `reduce` method without `map` versus using the `reduce` method with `map`. The first approach is faster but less readable, while the second approach is more readable and maintainable.

Related benchmarks:

Hello World (version: 0)

Hello world

Comparing performance of: reduce then map vs reudce then pick

Created: 7 years ago by: Guest

Jump to the latest result

reduce then map

reudce then pick

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):