Benchmark: FieldeasReduce6 - MeasureThat.net

HTML Preparation code:


<script src=''></script>

Script Preparation code:


 var testData = {
keys: ["ID", "ID_CONTACT", "ID_CHAT", "DATE", "TEXT", "BLOB", "$HDN_BIN_BLOB", "HAS_BLOB", "BLOB_TYPE", "BLOB_DURATION", "BLOB_SIZE", "$HDN_DateTime", "$HDN_XCoord", "$HDN_YCoord", "$HDN_ZCoord", "$PK", "$HDN_IDState", "$HDN_DescState", "$HDN_NumBlob"],
values: []
};
  
  for (var i = 0; i < 1000; i++) {
    testData.values.push([
    "ID", 
"ID_CONTACT" + i, 
"ID_CHAT" + i, 
"DATE" + i, 
"TEXT" + i, 
"BLOB" + i, 
"$HDN_BIN_BLOB" + i, 
"HAS_BLOB" + i, 
"BLOB_TYPE" + i, 
"BLOB_DURATION" + i, 
"BLOB_SIZE" + i, 
"$HDN_DateTime" + i, 
"$HDN_XCoord" + i, 
"$HDN_YCoord" + i, 
"$HDN_ZCoord" + i, 
"$PK" + i, 
"$HDN_IDState" + i, 
"$HDN_DescState" + i, 
"$HDN_NumBlob" + i
    ]);
  }

Tests:

reduce
var customReduce = data => { var keys = data.keys; return data.values.map(x => { return x.reduce((acc, curr, i) => { acc[keys[i]] = curr; return acc; }, {}) }) } customReduce(testData)
reduce2
var customReduce2 = data => data.values.map(x => x.reduce((acc, curr, i) => { acc[data.keys[i]] = curr; return acc; }, {}) ) customReduce2(testData)
reduce3
var customReduce3=e=>{var r=e.keys;return e.values.map(e=>e.reduce((e,u,a)=>(e[r[a]]=u,e),{}))}; customReduce3(testData)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
reduce2
reduce3

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

Let's dive into the explanation of the provided benchmark.

**Benchmark Overview**

The benchmark measures the performance of JavaScript arrays with custom reduce functions. The test data consists of an object `testData` with 1000 key-value pairs, where each value is an array of objects with various properties.

**Test Cases**

There are three individual test cases:

1. **"reduce"`**: This test case uses a custom reduce function that takes the `data` object as an argument. The function iterates over the `values` array and applies the reduce operation to each element, using the corresponding key from the `keys` array as the accumulator index.
2. **"reduce2"`**: Similar to the previous test case, but with a slight difference in the reduce function implementation. Instead of directly accessing the accumulator index using an expression like `data.keys[i]`, this version uses a hardcoded array reference (`[...]`).
3. **"reduce3"`**: This test case also uses a custom reduce function, but with a different syntax and variable naming convention. The function is defined as an arrow function (`e => { ... }`) and uses a similar approach to the previous two test cases.

**Performance Comparison**

The benchmark measures the execution frequency of each test case, which represents the performance difference between them. Here's a brief summary:

* **"reduce"`**: This implementation appears to be the most efficient, with an average of 1557.34 executions per second.
* **"reduce2"`: This version is slightly slower than the first one, averaging around 1471.67 executions per second.
* **"reduce3"`: The third implementation is the slowest, averaging approximately 1370.96 executions per second.

**Pros and Cons of Each Implementation**

Here's a brief analysis of each test case:

* **"reduce"`**: Pros:
	+ Cleaner syntax
	+ More intuitive code structure
Cons:
	+ Possible minor performance overhead due to function call overhead (although negligible in this case)
* **"reduce2"`: This implementation has some issues with readability and maintainability, as the hardcoded array reference makes it harder to understand the logic.
Pros:
	+ May be faster than the first implementation due to reduced function call overhead
Cons:
	+ Poorly written code that's hard to read and maintain
* **"reduce3"`: This implementation uses a different syntax and variable naming convention, which might make it less readable for some developers. However, the actual performance difference is negligible.

**Other Considerations**

The benchmark doesn't account for other factors that might affect performance, such as:

* Memory allocation and deallocation
* Garbage collection overhead
* CPU cache behavior

These factors can significantly impact performance in certain scenarios, but they are not considered in this specific benchmark.

**Alternatives**

If you're looking to improve the performance of your JavaScript arrays with custom reduce functions, consider exploring other optimizations, such as:

* Using `for...of` loops instead of `for...in` loops
* Minimizing function call overhead using techniques like caching or memoization
* Optimizing array allocations and deallocations using data structures like buffers or pools

Related benchmarks:

FieldeasReduce6 (version: 3)

Comparing performance of: reduce vs reduce2 vs reduce3

Created: 5 years ago by: Registered User

Jump to the latest result

reduce

reduce2

reduce3

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):