<script src="https://cdn.jsdelivr.net/npm/immer@3.1.3/dist/immer.umd.min.js"></script><script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>data = _.range(100)const reducer = immer.produce((draft, curr) => { draft[0] = 0; });const answer = data.reduce(reducer, {})const reducer = (draft, curr) => { draft[0] = 0; return draft; };const answer = data.reduce(reducer, {})const answer = data.reduce((acc, curr) => ({ acc, 0: 0,}), {});const answer = immer.produce({}, draft => { data.reduce((acc, curr) => { acc[0] = 0; return acc }, draft) });--enable-precise-memory-info flag.
| Test case name | Result |
|---|---|
| immer | |
| Mutate | |
| Spread | |
| immer pull up |
| Test name | Executions per second |
|---|---|
| immer | 32824.3 Ops/sec |
| Mutate | 2419474.2 Ops/sec |
| Spread | 549029.3 Ops/sec |
| immer pull up | 133659.7 Ops/sec |
Let's dive into the world of JavaScript microbenchmarks.
The provided JSON represents a benchmark test case created using MeasureThat.net, which compares three different approaches for reducing an array in JavaScript: Immer's produce function, the "Mutate" approach (which is actually just modifying the original array), and the "Spread" approach (using the spread operator to create a new array).
Immer's produce function
The first test case uses Immer's produce function, which is a library that provides a safe way to update immutable data structures. In this case, it creates an reducer function that takes two arguments: draft and curr. The reducer function sets the value of draft[0] to 0 using the spread operator ({...draft, 0: 0}). Then, it reduces the data array using this reducer function with an initial value of {}.
The purpose of Immer's produce function is to provide a safe and predictable way to update immutable data structures, avoiding common pitfalls like mutating state or creating side effects. This library is useful when working with complex data structures that require immutability, such as in reactive programming or state management.
Mutate approach
The second test case uses the "Mutate" approach, which is simply modifying the original array by setting its first element to 0.
This approach is straightforward but has some drawbacks. By modifying the original array, it can lead to unexpected behavior if the array is not expected to change, and it also creates a side effect (the modification of the original data). This approach is generally discouraged in favor of more predictable and immutable approaches like Immer's produce function.
Spread approach
The third test case uses the "Spread" approach, which creates a new array using the spread operator (const answer = [...data, 0]).
This approach is simple but has some limitations. By creating a new array, it can lead to memory allocation and garbage collection overhead, especially for large datasets. Additionally, this approach does not provide any guarantees about the immutability of the data structure.
Comparison and pros/cons
| Approach | Description | Pros | Cons |
|---|---|---|---|
| Immer's produce function | Safe and predictable way to update immutable data structures | Predictable behavior, avoids side effects | Can be slower than other approaches due to additional overhead |
| Mutate approach | Simple and straightforward | Easy to implement, no additional overhead | Creates side effects, can lead to unexpected behavior if not expected |
| Spread approach | Simple and easy to understand | No additional overhead, easy to implement | Can lead to memory allocation and garbage collection overhead |
Benchmark results
The latest benchmark result shows that the "Mutate" approach is significantly faster than the other two approaches, followed by Immer's produce function. The "Spread" approach is slower due to its potential for memory allocation and garbage collection overhead.
It's worth noting that the performance differences between these approaches may vary depending on the specific use case and dataset being used.
