Benchmark: Test seq vs subset

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/immutable/4.3.0/immutable.min.js'></script>

Script Preparation code:

const arr = [];
for(let i=0; i < 10000; i++) {
  arr.push(['a' + i, 'a' + i]);
}
const data = Immutable.Map(arr);
var seq = data.valueSeq();
var idx = data.valueSeq().indexOf('a5000');

Tests:

cached seq
const tmpIdx = seq.indexOf('a5000'); const result = seq.find((m, i) => i > tmpIdx);
cache idx search forward
const subSet = seq.skip(idx); const result = subSet.first();
cache idx search backward
const subSet = seq.take(idx); const result = subSet.last();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
cached seq
cache idx search forward
cache idx search backward

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

I'll explain the provided benchmark in detail, focusing on the options being compared, their pros and cons, library usage, special JS features or syntax, and other considerations.

**Benchmark Overview**

The benchmark measures the performance of three different approaches to search for an element in a sequence of data:

1.  **Cached Sequence**: Reuse the original sequence to find elements after its initial creation.
2.  **Cache Index Search Forward**: Create a new subset of the sequence that starts from the index where the target element was found, and then search forward for the next element.
3.  **Cache Index Search Backward**: Create a new subset of the sequence that ends at the index where the target element was found, and then search backward for the previous element.

**Library Usage**

The benchmark uses the Immutable.js library to create immutable data structures, specifically a Map (equivalent to JavaScript's Object) to store the initial sequence of elements.

**Options Being Compared**

The three options being compared are:

*   **Cached Sequence**: Reuse the original sequence.
    *   Pros: Simple implementation, low memory usage.
    *   Cons: Slow performance due to repeated searches and potential collisions between elements.
*   **Cache Index Search Forward**: Create a new subset of the sequence starting from the index where the target element was found.
    *   Pros: Efficient search with O(1) time complexity after initial creation, low memory usage.
    *   Cons: More complex implementation, requires additional memory to store the subset.
*   **Cache Index Search Backward**: Create a new subset of the sequence ending at the index where the target element was found.
    *   Pros: Similar efficiency as forward search, but with an additional step (taking from end), which can be slow for large sequences.
    *   Cons: More complex implementation, potential memory usage issues.

**Special JS Features or Syntax**

The benchmark does not use any special JavaScript features or syntax. It only leverages the Immutable.js library to create immutable data structures.

**Other Considerations**

When choosing an approach, consider factors such as:

*   **Memory Usage**: Repeatedly creating and reusing sequences can lead to increased memory usage over time.
*   **Search Efficiency**: Opt for approaches with O(1) or near-O(1) search times for high performance.
*   **Implementation Complexity**: Balance the need for efficiency with maintainability and simplicity.

**Alternatives**

Some alternative approaches to consider:

*   **Arrays vs. Linked Lists**: For very large datasets, linked lists may offer better memory usage but at the cost of slower searches.
*   **Hash Tables**: Implementing a hash table can provide fast lookups, but it requires more memory and computational resources.

In summary, the benchmark provides insights into the performance characteristics of three different approaches to searching for elements in sequences. By understanding the trade-offs between options being compared, developers can make informed decisions when choosing the most suitable approach for their specific use cases.

Related benchmarks:

Test seq vs subset (version: 0)

Comparing performance of: cached seq vs cache idx search forward vs cache idx search backward

Created: 3 years ago by: Guest

Jump to the latest result

cached seq

cache idx search forward

cache idx search backward

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):