Benchmark: Reselect defaultMemoize

Script Preparation code:

function defaultEqualityCheck(a, b) {
  return a === b
}

function defaultMemoize_original(func, equalityCheck = defaultEqualityCheck) {
  let lastArgs = null
  let lastResult = null
  return (...args) => {
    if (
      lastArgs === null ||
      lastArgs.length !== args.length ||
      !args.every((value, index) => equalityCheck(value, lastArgs[index]))
    ) {
      lastResult = func(...args)
    }
    lastArgs = args
    return lastResult
  }
}

function defaultMemoize_optimized(func, equalityCheck = defaultEqualityCheck) {
  const isEqual = (value, index) => equalityCheck(value, lastArgs[index])
  let lastArgs = null
  let lastResult = null
  return (...args) => {
    if (
      lastArgs === null ||
      lastArgs.length !== args.length ||
      !args.every(isEqual)
    ) {
      lastResult = func(...args)
    }
    lastArgs = args
    return lastResult
  }
}

function combine(a, b) {
  return { combined: a + b };
}

var combine_original = defaultMemoize_original(combine);
var combine_optimized = defaultMemoize_optimized(combine);

var NUM_REPEAT = 50000;

Tests:

Original defaultMemoize
let result1 = 0; for (let i = 0; i < NUM_REPEAT; ++i) result1 += combine_original(1, 2).combined;
Optimized defaultMemoize
let result2 = 0; for (let i = 0; i < NUM_REPEAT; ++i) result2 += combine_optimized(1, 2).combined;
No memoization
let result3 = 0; for (let i = 0; i < NUM_REPEAT; ++i) result3 += combine(1, 2).combined;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Original defaultMemoize
Optimized defaultMemoize
No memoization

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Safari/537.36

Browser/OS: Chrome 121 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Original defaultMemoize	284.4 Ops/sec
Optimized defaultMemoize	292.6 Ops/sec
No memoization	321.6 Ops/sec

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

LLMs can make mistakes. Check important info.

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

The test cases are designed to compare the performance of three different versions of a memoization function, which is a technique used to optimize repetitive computations by caching their results.

**Memoization basics**

In this context, memoization is a technique where the result of an expensive computation (in this case, a simple addition) is cached and reused when the same inputs occur again. This avoids redundant calculations, reducing the overall execution time.

The benchmark defines three different versions of a `defaultMemoize` function:

1. **Original defaultMemoize**: This version uses a simple approach to memoization, where it checks if the arguments are the same as in the previous call and returns the cached result if they are.
2. **Optimized defaultMemoize**: This version is optimized for performance by using a constant function `isEqual` that compares values at specific indices (in this case, the first value). It still uses the same basic approach to memoization but with some optimizations.

**Test cases**

There are three test cases:

1. **Original defaultMemoize**: This test case runs a simple addition operation (`combine(1, 2)`) repeated `NUM_REPEAT` times and measures the execution time.
2. **Optimized defaultMemoize**: Similar to the first test case, but uses the optimized version of `defaultMemoize`.
3. **No memoization**: This test case also runs the same simple addition operation repeatedly, without any memoization.

**Pros and cons**

Here are some pros and cons of each approach:

* **Original defaultMemoize**:
	+ Pros: Easy to implement and understand.
	+ Cons: May have higher execution time due to repeated checks for equality.
* **Optimized defaultMemoize**:
	+ Pros: Optimized performance with fewer checks for equality.
	+ Cons: Requires a separate constant function `isEqual` which may add complexity.

Other considerations:

* The test cases use a simple addition operation (`combine(1, 2)`) to demonstrate the memoization technique. In real-world scenarios, this might not be as representative of actual usage.
* The benchmark runs on Chrome 121 on a Mac with Intel CPU, which may affect the results due to differences in hardware and browser performance.

**Alternatives**

If you were to modify or extend these benchmarks, some alternatives to consider:

* Use different types of computations (e.g., more complex functions, loops, or data structures).
* Experiment with different browsers, operating systems, or devices.
* Add more test cases to cover various edge cases or scenarios.
* Investigate other optimization techniques for memoization, such as using a cache map or a library like `lru-cache`.

These considerations can help refine the benchmark and provide a more comprehensive understanding of the performance differences between these memoization approaches.

Related benchmarks:

Reselect defaultMemoize (version: 9)

Comparing performance of: Original defaultMemoize vs Optimized defaultMemoize vs No memoization

Created: 9 years ago by: Registered User

Jump to the latest result

Original defaultMemoize

Optimized defaultMemoize

No memoization

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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