Benchmark: Sparse Array Vs. Object, Map, Set, Number Indexes 2

Script Preparation code:

var iterations =100000;
var step = 100;
var obj = {};
var arr = [];
var map = new Map();
var set = new Set();
var setLengthArr = new Array(iterations);
var typedArray = new Uint32Array(iterations);

Tests:

Object
for(let i=0;i<iterations;i+=step){ obj[i]= i; obj[i+1]=obj[i]+1; }
Array
for(let i=0;i<iterations;i+=step){ arr[i]=i; arr[i+1]= arr[i]+1; }
Set Length Array
for(let i=0;i<iterations;i+=step){ setLengthArr[i]=i; setLengthArr[i+1] = setLengthArr[i]+1; }
TypedArray
for(let i=0;i<iterations;i+=step){ typedArray[i]=i; typedArray[i+1]= typedArray[i]+1; }
map
for(let i=0;i<iterations;i+=step){ map.set(i,i); map.set(i+1,map.get(i)+1) }
set
for(let i=0;i<iterations;i+=step){ set.add(i); set.add(set.has(i) && i) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Object
Array
Set Length Array
TypedArray
map
set

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested, compared, and the pros/cons of each approach.

**Benchmark Overview**

The benchmark compares the performance of different data structures in JavaScript:

1. Object
2. Array
3. Set Length Array (a custom array implementation)
4. TypedArray (a typed array implementation)
5. Map
6. Set

Each test case has a simple loop that increments values, demonstrating basic arithmetic operations.

**Data Structures Compared**

Here's a brief explanation of each data structure and their purpose:

1. **Object**: A generic JavaScript object, which is an unordered collection of key-value pairs.
2. **Array**: A fixed-size, homogeneous collection of values.
3. **Set Length Array (SLA)**: A custom array implementation that uses a set to keep track of its length, allowing for dynamic resizing.
4. **TypedArray**: A typed array implementation that provides more efficient memory access and manipulation compared to regular arrays.
5. **Map**: An ordered collection of key-value pairs, similar to objects but with additional methods for iterating and searching.
6. **Set**: A mathematical set data structure, which is an unordered collection of unique values.

**Performance Comparison**

The benchmark measures the performance of each data structure by executing a series of loops, incrementing values, and counting the number of executions per second. The results are stored in the `ExecutionsPerSecond` field for each test case.

**Pros/Cons of Each Approach**

Here's a brief analysis of the pros and cons of each data structure:

1. **Object**:
	* Pros: Easy to implement, flexible, and widely supported.
	* Cons: Slow for large datasets due to frequent property lookups.
2. **Array**:
	* Pros: Fast for large datasets, efficient memory access.
	* Cons: Fixed-size, less flexible than objects or maps.
3. **Set Length Array (SLA)**:
	* Pros: Dynamic resizing, potentially more efficient than arrays.
	* Cons: Custom implementation required, may have performance overhead.
4. **TypedArray**:
	* Pros: Efficient memory access, faster arithmetic operations.
	* Cons: Limited compatibility with older browsers or environments.
5. **Map**:
	* Pros: Fast lookups, ordered iteration, and additional methods.
	* Cons: May be slower than arrays for simple key-value pairs.
6. **Set**:
	* Pros: Fast membership testing, potentially more efficient than objects.
	* Cons: Limited iteration methods, may not be suitable for complex data structures.

**Other Considerations**

When choosing a data structure, consider the following factors:

1. Data size and growth rate
2. Performance requirements (speed, memory usage)
3. Complexity of operations (iterations, lookups, insertions)
4. Compatibility with older browsers or environments
5. Development convenience and flexibility

Keep in mind that this benchmark only compares simple arithmetic operations and may not reflect real-world scenarios where data structures are used for more complex tasks.

**Alternatives**

If you're looking for alternative data structures or libraries, consider the following:

1. **ArrayBuffer**: A low-level, typed array implementation with direct access to memory.
2. **Int8Array**, **Uint8Array**, etc.: Specialized typed arrays for specific data types (integers, unsigned integers).
3. **WeakMap** and **WeakSet**: Set-like data structures that use garbage collection to manage entries.

These alternatives may offer improved performance or additional features depending on your specific use case.

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36

Browser/OS: Chrome 113 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Object	2669.0 Ops/sec
Array	2659.1 Ops/sec
Set Length Array	3956.0 Ops/sec
TypedArray	3803.2 Ops/sec
map	3364.9 Ops/sec
set	3646.3 Ops/sec

Related benchmarks:

Sparse Array Vs. Object, Map, Set, Number Indexes 2 (version: 0)

Comparing performance of: Object vs Array vs Set Length Array vs TypedArray vs map vs set

Created: one year ago by: Guest

Jump to the latest result

Object

Array

Set Length Array

TypedArray

map

set

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):