Benchmark: 111111111111111 - MeasureThat.net

Tests:

222222222222222222222
const currency = [{ usd: 5 }, { uah: 10 }, { usd: 3 }]; // {ysd:8, hrn:10} const result = currency.reduce((res, item) => { for (const [key, value] of Object.entries(item)) { !res[key] ? (res[key] = value) : (res[key] = res[key] + value); } return res; }, {});
3333333333333333333
const currency = [{ usd: 5 }, { uah: 10 }, { usd: 3 }]; // {ysd:8, hrn:10} const merge = (data) => { const res = {}; data.forEach((item) => { for (let [key, value] of Object.entries(item)) { !res[key] ? (res[key] = value) : (res[key] += value); } }); return res; };

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
222222222222222222222
3333333333333333333

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark definitions and explain what's being tested, compared, and considered.

**Benchmark Definitions**

The two benchmark definitions are identical:

```javascript
const currency = [{ usd: 5 }, { uah: 10 }, { usd: 3 }]; // {ysd:8, hrn:10}
```

This is a JavaScript object with three properties (`usd`, `uah`, and another non-existent property `ysd`), each initialized to a specific value. This object represents a "currency" data structure.

**Script Preparation Code**

The script preparation code is empty for both benchmarks. This means that the benchmark scripts are not modified before running, which allows for direct comparison of the two approaches.

**Html Preparation Code**

Again, there's no html preparation code provided. This implies that any HTML-specific overhead or configuration is not being considered in these benchmarks.

**Library and Library Purpose**

In the first test case:

```javascript
const currency = [{ usd: 5 }, { uah: 10 }, { usd: 3 }]; // {ysd:8, hrn:10}
```

No library is explicitly imported or used. However, the `Object.entries()` method and template literals are used, which are part of the standard JavaScript syntax.

In the second test case:

```javascript
const currency = [{ usd: 5 }, { uah: 10 }, { usd: 3 }]; // {ysd:8, hrn:10}

The `merge()` function is a custom-built JavaScript function using the same standard library functions (`Object.entries()`) as before.

**Special JS Feature or Syntax**

In neither benchmark are any special JavaScript features or syntax explicitly used. The tests rely solely on standard JavaScript language constructs.

**Comparison and Approaches**

There are only two approaches being compared:

1.  **Using reduce()**: In the first test case, a `reduce()` method is used to merge the currency data structure. This approach involves creating an initial value (`{}`), then iteratively adding values to it.
2.  **Using a custom merge function (merge())**: In the second test case, a custom `merge()` function is defined. This approach uses iteration over each item in the input data and adds its key-value pairs to a result object.

**Pros and Cons of Each Approach**

*   Using `reduce()`: Pros:
    *   Simple, intuitive syntax.
    *   Fast, as it's optimized for performance by the JavaScript engine.
Cons:
    *   May not be easily extensible or customizable, as it relies on standard library functions.
*   Custom merge function (`merge()`): Pros:
    *   Highly flexible and customizable, allowing developers to write their own specific logic for merging data structures.
    *   Can potentially lead to better maintainability and reusability of code if written correctly.
Cons:
    *   May be more complex and harder to understand due to custom implementation details.

**Other Considerations**

When writing benchmarks like these, it's generally a good idea to consider several factors:

*   **Language features**: Which standard JavaScript language features or libraries are being used? Are there any specific syntaxes or constructs that could impact performance?
*   **Implementation complexity**: How straightforward is the implementation of each approach? Can you make adjustments for better maintainability, reusability, or readability without significantly impacting performance?
*   **Customization and extensibility**: If using a custom solution like `merge()`, how well can it be customized to meet specific requirements?

By considering these aspects when designing benchmarks, developers can write effective tests that provide valuable insights into the performance characteristics of different approaches.

**Alternatives**

Alternative approaches for merging data structures in JavaScript could include:

*   Using an iterative approach with simple variables and conditional statements (e.g., `merge()` function above).
*   Leveraging popular libraries like Lodash (`_.merge()`) or Ramda (`R.merge()`) that provide optimized implementations.
*   Exploring different data structure types, such as arrays or Maps, which might offer better performance or easier merging capabilities in certain scenarios.

Keep in mind that the best approach depends on the specific use case and requirements.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.2 Safari/605.1.15

Browser/OS: Safari 13 on Mac OS X 10.14.6

View result in a separate tab

Test name	Executions per second
222222222222222222222	819302.8 Ops/sec
3333333333333333333	35480088.0 Ops/sec

Related benchmarks:

111111111111111 (version: 0)

Comparing performance of: 222222222222222222222 vs 3333333333333333333

Created: 3 years ago by: Guest

Jump to the latest result

222222222222222222222

3333333333333333333

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):