Benchmark: test array vs map lookup 2

Script Preparation code:

// Generate an array with 100 objects
var rawData = [];
for (let i = 0; i < 5001; i++) {
  rawData.push({
    cost_per: Math.random() * 100,
    network: `network${i}`,
    metric: {
      name: `metric${i}`,
      description: `Description ${i}`,
    },
    created_at: `2023-09-01T00:00:00-07:00`,
  });
}

// Using a Map for indexing
var transformedDataMap = new Map();

for (const entry of rawData) {
  const key = JSON.stringify({
    network: entry.network,
    metricName: entry.metric.name,
    createdAt: entry.created_at,
  });
  transformedDataMap.set(key, entry.cost_per);
}

Tests:

Test using Map for indexing (5000 iterations)
const iterations = 5000; const mapLookupKey = JSON.stringify({ network: 'network50', metricName: 'metric50', createdAt: '2023-09-01T00:00:00-07:00', }); for (let i = 0; i < iterations; i++) { const mapResult = transformedDataMap.get(mapLookupKey); }
Test using Array.prototype.find (5000 iterations)
const iterations = 5000; const arrayLookupKey = { network: 'network50', metricName: 'metric50', createdAt: '2023-09-01T00:00:00-07:00', }; const arrayStartTime = new Date().getTime(); for (let i = 0; i < iterations; i++) { const arrayResult = rawData.find((entry) => { return ( entry.network === arrayLookupKey.network && entry.metric.name === arrayLookupKey.metricName && entry.created_at === arrayLookupKey.createdAt ); }); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Test using Map for indexing (5000 iterations)
Test using Array.prototype.find (5000 iterations)

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's dive into the Benchmark Definition JSON and analyze what's being tested.

**Benchmark Description**

The benchmark is designed to compare two approaches for lookup operations on an array of objects:

1. Using a `Map` data structure for indexing
2. Using the `Array.prototype.find()` method for searching in the array

**Test Case 1: Map Lookup**

The first test case uses a `Map` to store indices corresponding to specific network, metric name, and creation date combinations. The `transformedDataMap.get(mapLookupKey)` expression retrieves the cost per value associated with each key.

Pros of using a `Map`:

* Fast lookup time (O(1) on average)
* Efficient use of memory, as only relevant data is stored

Cons:

* May have higher overhead due to object creation and manipulation
* Requires manual key generation and management

**Test Case 2: Array.prototype.find()**

The second test case uses the `Array.prototype.find()` method to search for an object in the array based on specific criteria. The expression `rawData.find((entry) => { ... })` returns the first matching entry.

Pros of using `Array.prototype.find()`:

* Familiar and widely supported API
* Easy to read and maintain

Cons:

* Generally slower than `Map` lookup (O(n) in worst-case scenarios)
* May have higher overhead due to array scanning

**Library Usage**

The benchmark uses the following libraries:

* None explicitly mentioned, but it's likely that the standard JavaScript libraries are being used.

**Special JS Features/Syntax**

None are mentioned specifically. However, the use of template literals (`\r\n  network${i}\r\n`, `\r\n  metricName: 'metric${i}'\r\n`) and the `JSON.stringify()` method might be considered advanced or niche features.

**Alternatives**

Other approaches for lookup operations in arrays include:

* Using an object with multiple properties to store indices (e.g., a nested object)
* Implementing a custom search algorithm using bitwise operations
* Utilizing libraries like Lodash or Ramda, which provide optimized searching and filtering functions

Keep in mind that the choice of approach depends on the specific requirements and constraints of your use case.

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36

Browser/OS: Chrome 117 on Linux

View result in a separate tab

Test name	Executions per second
Test using Map for indexing (5000 iterations)	6549.2 Ops/sec
Test using Array.prototype.find (5000 iterations)	785.9 Ops/sec

Related benchmarks:

test array vs map lookup 2 (version: 0)

asd

Comparing performance of: Test using Map for indexing (5000 iterations) vs Test using Array.prototype.find (5000 iterations)

Created: one year ago by: Guest

Jump to the latest result

Test using Map for indexing (5000 iterations)

Test using Array.prototype.find (5000 iterations)

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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