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WeakMap vs arrays read performancesadsasd
(version: 0)
Comparing performance of:
arrays vs weakmap
Created:
4 years ago
by:
Guest
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Script Preparation code:
var keys = []; var values = []; var weakmap = new WeakMap(); for (let i = 0; i < 100000; i++) { const keyobj = { key: i }; const valobj = { val: `val_${i}` }; keys[i] = keyobj; values[i] = valobj; weakmap.set(keyobj, valobj); }
Tests:
arrays
for (let i = 0; i < 100000; i++) { const key = keys[i]; const val = values[key.key]; }
weakmap
for (let i = 0; i < 100000; i++) { const key = keys[i]; const val = weakmap.get(key); }
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
arrays
weakmap
Fastest:
N/A
Slowest:
N/A
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/136.0.0.0 Safari/537.36
Browser/OS:
Chrome 136 on Mac OS X 10.15.7
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Test name
Executions per second
arrays
5077.2 Ops/sec
weakmap
1204.5 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided JSON data and explain what is tested in each benchmark. **Benchmark Definition** The `Script Preparation Code` section creates two objects: `keys` and `values`, both of size 100,000. It then creates a WeakMap object called `weakmap` and sets 100,000 key-value pairs using the WeakMap's `set()` method. The keys are objects with a single property `key`, and the values are objects with a single property `val`. The `Html Preparation Code` section is empty. **Individual Test Cases** There are two test cases: 1. **"arrays"`** * The `Benchmark Definition` uses a traditional array-based indexing to access the values in the `keys` array. * The test accesses each value in the `values` array using the corresponding key from the `keys` array, like this: `const val = values[key.key]`. 2. **"weakmap"`** * The `Benchmark Definition` uses the WeakMap's `get()` method to access the values associated with a given key. * The test accesses each value in the `weakmap` using its corresponding key from the `keys` array, like this: `const val = weakmap.get(key)`. **Options Compared** The two test cases compare the performance of traditional array-based indexing (`arrays`) against the WeakMap's `get()` method (`weakmap`). **Pros and Cons** * **Arrays** + Pros: - Simple and well-known data structure. - No additional overhead for map operations. - Cons: - Performance may degrade with large datasets due to array lookup. - Does not take advantage of WeakMap's optimized caching mechanisms. * **WeakMap** + Pros: - Optimized for cache hits and reduced memory usage. - Can improve performance with large datasets by reducing the number of lookups. + Cons: - Requires careful key management to avoid garbage collection issues. - May not be suitable for all use cases due to its specific design. **Library** The WeakMap is a built-in JavaScript object that stores mappings between values and keys. It's designed to efficiently store and retrieve data while minimizing memory usage, making it particularly useful for caching large datasets. **Special JS Feature/Syntax** This benchmark does not explicitly use any special JavaScript features or syntax, but it does utilize the WeakMap object, which is a built-in feature in modern JavaScript. If we were to modify the benchmark to include special features or syntax, it would likely involve additional considerations such as: * Using modern JavaScript features like `async/await` or `for...of` loops. * Incorporating web API-specific optimizations (e.g., using Web Workers for parallel computation). * Experimenting with different caching strategies or data structures. **Other Alternatives** If the benchmark were to include alternative implementations, we might consider: 1. **Object-oriented approach**: Using a custom class or object that mimics the WeakMap's behavior. 2. **Using a hash table**: Implementing a simple hash table using JavaScript objects and arrays. 3. **Caching libraries**: Using specialized caching libraries like Redis or Memcached to optimize performance. These alternatives would require additional considerations, such as trade-offs in terms of simplicity, maintainability, and compatibility with different browsers or environments.
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