Benchmark: Lens vs iu-object

HTML Preparation code:

<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script>

Script Preparation code:

// Initial state
var state = {
  visible: false,
  lazy: { test: true, number: 44 },
  count: 1
}

// Define reducers ahead of time

var spread = state => ({
  ...state,
  lazy: {
    ...state.lazy,
    test: !state.lazy.test
  }
})


var lens = R.lensPath(["lazy", "test"])
var ramda = state => {
  return R.over(lens, test => !test, state)
}

var iuUpdate = state => iu(state, "lazy/test", !state.lazy.test)


function iu(state, subArray, val) {
  if (!subArray) {
    console.log("No target provided")
  } else if (subArray.length < 1) {
    console.log("No target provided")
  } else if (!state) {
    console.log("No state object provided")
  } else {
    if (typeof subArray === "string") {
      subArray = subArray.split("/").filter(segment => segment)
    }
    return reducer(state, subArray, cloner(val), 0)
  }
}

function reducer(_state, subArray, val, l) {
  try {
    const key = subArray[l]
    if (l + 1 === subArray.length) {
      const replacer = isEmpty(val) ? undefined : val
      return replacer !== undefined
        ? Object.assign({}, _state, { [key]: replacer })
        : stateWithoutKey(_state, key)
    } else {
      const value = !isEmpty(_state[key]) ? _state[key] : {}
      return Object.assign({}, _state, {
        [key]: reducer(value, subArray, val, l + 1),
      })
    }
  } catch (error) {
    console.log("reducer error =>", error)
  }
}

function cloner(arg) {
  try {
    return JSON.parse(JSON.stringify(arg))
  } catch (e) {
    return null
  }
}

function isEmpty(val) {
  return (
    val === undefined ||
    val === null ||
    (typeof val === "object" && Array.isArray(val) && val.length === 0) ||
    (typeof val === "object" &&
      !Array.isArray(val) &&
      Object.keys(val).length === 0)
  )
}

function stateWithoutKey(state, key) {
  const { [key]: value, ...withoutKey } = state
  return withoutKey
}

Tests:

Spread
var data = spread(state)
Ramda lens
var data = ramda(state)
IU
var data = iuUpdate(state)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Spread
Ramda lens
IU

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 6 years ago)

User agent: Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36

Browser/OS: Chrome 69 on Windows 7

View result in a separate tab

Test name	Executions per second
Spread	834228.3 Ops/sec
Ramda lens	651885.1 Ops/sec
IU	260807.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down what's being tested in this benchmark.

**Benchmark Definition**

The benchmark is comparing the performance of three different approaches to update an object:

1. **Lens**: A functional programming library that provides a way to create and manipulate lenses, which are functions that focus on specific parts of an object.
2. **IU (Immutable Update)**: An implementation of immutable updates, which ensures that data is never mutated in place. Instead, new objects are created with the updated values.
3. **Ramda lens**: A variant of the Lens library that uses Ramda, a functional programming library.

**Benchmark Preparation Code**

The preparation code sets up an initial state object `state` and defines two functions:

1. `spread`: Spreads the state object into a new object using the spread operator (`{...state}`).
2. `ramda`: Uses the Lens library to create a lens that focuses on the `lazy.test` property of the `state` object, and then uses Ramda's `R.over` function to update the value of this property.

**IU Update Implementation**

The IU Update implementation is a custom function `iuUpdate` that takes an object `state` as input. It uses two functions:

1. `reducer`: Recursively updates the state object by creating new objects with updated values.
2. `cloner`: Creates a deep copy of an object using JSON parsing and stringification.

**Options Compared**

The benchmark is comparing the performance of these three approaches to update an object:

* **Lens**: Uses a functional programming approach to create and manipulate lenses.
* **Ramda lens**: A variant of Lens that uses Ramda for functional programming.
* **IU (Immutable Update)**: Ensures data immutability by creating new objects with updated values.

**Pros and Cons**

Here are some pros and cons of each approach:

* **Lens**:
	+ Pros: Efficient use of function composition, flexible lens creation.
	+ Cons: May require more code to set up and use lenses.
* **Ramda lens**: Similar to Lens, but uses Ramda for functional programming.
	+ Pros: Combines the benefits of Ramda with Lens.
	+ Cons: May have slightly slower performance due to Ramda overhead.
* **IU (Immutable Update)**:
	+ Pros: Ensures data immutability, easy to implement recursive updates.
	+ Cons: Creates new objects with updated values, potentially leading to higher memory usage.

**Device Platform and Browser**

The benchmark results are reported for a Chrome 69 browser on a Windows 7 desktop device. The executions per second (EPS) value indicates the number of updates performed per second.

Overall, this benchmark provides a comparison of three different approaches to update an object in JavaScript, highlighting the pros and cons of each approach.

Related benchmarks:

Lens vs iu-object (version: 0)

Comparing perf of ramda lens vs https://github.com/attack-monkey/immutable-update

Comparing performance of: Spread vs Ramda lens vs IU

Created: 6 years ago by: Guest

Jump to the latest result

Spread

Ramda lens

IU

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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