Benchmark: Object vs Map lookup w/ rando integer key

Script Preparation code:

window.m = new Map();
window.o = {};
for (let i = 0; i < 2000; ++i) {
  window.m.set(Math.random(100000) , i);
  window.o[Math.random(100000)] = i;
}

Tests:

Map Lookup
for (let i = 0; i < 10000; ++i) { if (window.m.get( Math.random(10000)) !== 1) { } }
Object Lookup
for (let i = 0; i < 10000; ++i) { if (window.o[Math.random(10000)] !== 1) { } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Map Lookup
Object Lookup

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's break down the provided benchmark and its components.

**Benchmark Definition**

The benchmark defines two test cases: Object Lookup and Map Lookup. Both tests aim to compare the performance of looking up values in large objects versus large maps when using integers as keys.

**Options Compared**

Two options are compared:

1. **Object Lookup**: This test case uses an object (`window.o`) to store integer values, where each key is generated randomly.
2. **Map Lookup**: This test case uses a map (`window.m`) to store the same integer values, where each key is also generated randomly.

**Pros and Cons of Each Approach**

*   **Object Lookup:**
    *   Pros:
        *   Simpler implementation
        *   Faster initial setup (since objects are generally faster than maps in JavaScript)
    *   Cons:
        *   Less efficient for large datasets due to the way JavaScript objects store keys
*   **Map Lookup:**
    *   Pros:
        *   More efficient for large datasets, as maps provide better key management and iteration
    *   Cons:
        *   More complex implementation

**Library Used**

Neither object lookup nor map lookup explicitly use a library. However, the `window` object is used, which is a built-in JavaScript object that provides access to various global variables, functions, and properties.

**Special JS Feature or Syntax**

There are no special features or syntaxes mentioned in this benchmark. The focus is on comparing the performance of different data structures (objects vs maps) for lookup operations.

**Other Considerations**

When designing benchmarks like this, consider the following:

1.  **Relevance**: Make sure the benchmark tests what you want to measure (e.g., performance, memory usage).
2.  **Consistency**: Ensure that all test cases follow the same setup and execution patterns.
3.  **Accuracy**: Use a robust method for measuring performance (e.g., using a profiling tool or a microbenchmarking library).

**Alternative Benchmarks**

Other alternatives to measure object vs map lookup performance might include:

1.  **Array Lookup:** Comparing the performance of array indexing versus map iteration.
2.  **Set Lookup:** Testing the performance of set membership testing versus map membership testing.
3.  **Large-Data Comparison:** Benchmarking the performance of large objects and maps with a specific dataset or load.

Keep in mind that different benchmarks might be more relevant depending on your specific use case, application, or industry requirements.

Related benchmarks:

Object vs Map lookup w/ rando integer key (version: 0)

Comparing the performance of lookups in large objects vs large maps when the keys are integers

Comparing performance of: Map Lookup vs Object Lookup

Created: 3 years ago by: Guest

Jump to the latest result

Map Lookup

Object Lookup

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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