Benchmark: Dict vs. Map vs. Array ( complete iteration 4)

Script Preparation code:

var dict= {}
for (var i=0; i<50000; i++) {
  dict['abc_'+i] = i;
}

var map= new Map()
for (var i=0; i<50000; i++) {
  map.set('abc_'+i, i);
}

var arr = []
for (var i=0; i<50000; i++) {
  arr.push(i);
}

Tests:

Dictionary
let c = 0 for (let key in dict) { c += dict[key]}
Array
let c = 0 const l = arr.length; for (let i=0; i<l; i+=1) { c += arr[i]}
Map
let c = 0 for (let [key, entry] of map) { c += map.get(key)}
Array Iterator
let c = 0 for (let entry of arr) { c += entry}
Array Inverse While
let c = 0 let i = arr.length; while(i--){ c += arr[i]; }
Array ++i
let c = 0 const l = arr.length; for (let i=0; i<l; ++i) { c += arr[i]}

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Dictionary
Array
Map
Array Iterator
Array Inverse While
Array ++i

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36

Browser/OS: Chrome 131 on Windows

View result in a separate tab

Test name	Executions per second
Dictionary	156.4 Ops/sec
Array	13057.2 Ops/sec
Map	626.6 Ops/sec
Array Iterator	28208.7 Ops/sec
Array Inverse While	11366.2 Ops/sec
Array ++i	13293.5 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated 2 months ago):

LLMs can make mistakes. Check important info.

This benchmark tests the performance of iterating through three different data structures in JavaScript: a dictionary (using plain JavaScript objects), a Map, and an Array. The benchmark examines how each of these structures performs in terms of iteration speed under the same conditions, specifically with a large dataset of 50,000 entries.

### Options Compared

1. **Dictionary (JavaScript Object)**
   - **Test Case**: Iteration using a `for...in` loop.
   - **Pros**: Dictionaries (objects) are naturally versatile and flexible for key-value storage. They require minimal setup.
   - **Cons**: The `for...in` loop iterates over all enumerable properties, including those from the prototype chain unless filtered, which can lead to performance overhead. This could potentially return keys that are not part of the actual data being measured if the object is not carefully constructed.

2. **Map**
   - **Test Case**: Iteration using `for...of` with the Map iterator.
   - **Pros**: Maps maintain the insertion order of keys and have better performance characteristics for frequent additions and removals compared to objects. The iteration over its items using `map.get(key)` is straightforward and clear.
   - **Cons**: Maps generally have a higher memory overhead compared to objects and can be slower for property accesses after the initial construction, particularly with a high number of elements due to the additional features they provide.

3. **Array**
   - **Test Cases**:
     - Standard iteration using a traditional `for` loop.
     - Using an Array Iterator (`for...of`).
     - A while loop iterating backwards through the array.
     - Differences in increment styles (`i++` versus `++i`).
   - **Pros**: Arrays are optimized for indexed access and are faster for sequential access compared to both dictionaries and maps when dealing with large datasets. The performance differences among the various looping methods highlight the optimizations in JavaScript engines for different loop constructs.
   - **Cons**: Arrays are less suited for associative data storage compared to objects or Maps. Adding key-value pairs would not be as straightforward or efficient.

### Other Considerations

- **Performance Varies by Implementation**: Results can vary significantly across different JavaScript engines or browsers (e.g., Chrome, Firefox, etc.) due to optimizations or different garbage collection strategies.
- **Practical Usability**: While raw performance is important, the choice between these structures should also consider factors like readability, ease of use, and maintainability in addition to performance. They may perform differently based on specific use cases and data access patterns.
- **Scalability**: For larger data sets or more complex data operations, a data structure’s performance characteristics can significantly influence scalability.

### Alternative Approaches

Other alternatives besides the above three structures could include:

- **Typed Arrays**: These are array-like objects that provide a mechanism for reading and writing binary data in memory. They are performance-optimized for numerical data.
- **Set**: Designed for storing unique values and providing operations to check for existence without accounting for duplicates.
- **WeakMap / WeakSet**: These collections allow for garbage collection of entries when there are no other references available; useful to manage memory efficiently.
- **Immutable.js / other libraries**: Libraries that provide immutable data structures can help manage complex state without side effects, although they often come with their own performance trade-offs.

Both the choice of data structure and the specific iteration method can drastically influence performance, and engineers must balance between optimal data manipulation and maintainable code.

Related benchmarks:

Dict vs. Map vs. Array ( complete iteration 4) (version: 1)

Large dictionary - how worse is it from array with direct reference? And how does Map perform?

Comparing performance of: Dictionary vs Array vs Map vs Array Iterator vs Array Inverse While vs Array ++i

Created: 2 months ago by: Guest

Jump to the latest result

Dictionary

Array

Map

Array Iterator

Array Inverse While

Array ++i

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 2 months ago):