Benchmark: so two map compare

Script Preparation code:

var primary =
[
 {recordId:123, type: "Primary" },
 {recordId:123, type: "Primary" },
 {recordId:123, type: "Primary" },
 {recordId:9393, type: "Primary" },
 {recordId:9393, type: "Primary" },
 {recordId:9393, type: "Primary" },
];

var secondary = [
 {recordId:123, type: "Secondary" },
 {recordId:123, type: "Secondary" },
 {recordId:9393, type: "Secondary" },
 {recordId:9393, type: "Secondary" },
 {recordId:9393, type: "Secondary" },
];

Tests:

one map
const missingRecords = [] const occurences = primary.reduce((acc, val) => acc.set(val.recordId, 1 + (acc.get(val.recordId) || 0)), new Map()); secondary.reduce((acc, val) => acc.set(val.recordId, (acc.get(val.recordId) || 0) - 1), occurences); for (const [skey, svalue] of occurences.entries()) { if (svalue !== 0) { missingRecords.push(skey); } }
two maps
const missingRecords = [] const primaryOccurences = primary.reduce((acc, val) => acc.set(val.recordId, 1 + (acc.get(val.recordId) || 0)), new Map()); const secondaryOccurences = secondary.reduce((acc, val) => acc.set(val.recordId, 1 + (acc.get(val.recordId) || 0)), new Map()); primaryOccurences.forEach((value, key) => { for (const [skey, svalue] of secondaryOccurences.entries()) { if (skey === key && value !== svalue) { missingRecords.push(key); } } })

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
one map
two maps

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.

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

**Benchmark Definition**

The benchmark is testing two different approaches to find missing records between two lists: `primary` and `secondary`. The goal is to identify which record IDs are present in `primary` but not in `secondary`.

**Options Compared**

There are two main options being compared:

1. **One Map**: This approach uses the `Map` data structure to store the occurrences of each record ID in both lists. It then iterates over the map to find missing records by checking if the count of an ID is greater than 0.
2. **Two Maps**: This approach creates two separate maps, one for each list, to store the occurrences of each record ID. It then iterates over one map and checks each value against the other map to find missing records.

**Pros and Cons**

**One Map:**

Pros:

* Less memory usage since only one map is used.
* Faster iteration over the map, as it's a contiguous data structure.

Cons:

* May be slower due to the need to create a temporary map for the occurrences of secondary IDs.

**Two Maps:**

Pros:

* Allows for easier comparison between the two maps, as each value can be directly compared.
* Can be faster if the iteration over one map is optimized.

Cons:

* More memory usage since two separate maps are used.
* Slower iteration due to the need to iterate over multiple values.

**Library and Special JS Features**

No specific libraries or special JavaScript features are mentioned in the benchmark definition. However, it's worth noting that the use of `Map` data structure is a modern JavaScript feature introduced in ECMAScript 2015 (ES6).

**Other Considerations**

* **Cacheability**: The benchmark results may not be representative of real-world scenarios, as cache effects can influence performance.
* **Optimizations**: The test may benefit from optimizations, such as parallel processing or SIMD instructions, which could further improve performance.

**Alternatives**

For similar benchmarks, consider testing other approaches, such as:

1. Using a more efficient data structure, like an array with indexes or a trie.
2. Implementing a custom solution using iteration and conditional statements.
3. Utilizing machine learning or deep learning techniques to optimize the detection of missing records.

Keep in mind that these alternatives may introduce additional complexity or trade-offs, such as increased memory usage or reduced cache effectiveness.

Related benchmarks:

so two map compare (version: 0)

Comparing performance of: one map vs two maps

Created: 3 years ago by: Registered User

Jump to the latest result

one map

two maps

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):