Benchmark: comparedThem - MeasureThat.net

Tests:

filter
const filterDatass = { lifeStyle:{ data: ["pescatarian"], extra: {excercise: [5,10]} }, professional: { data: ["ged"], extra: [] } }; const users = [ { firstName: "Jane", lifeStyle: { data: ["vegetarian"], otherData: {excercise: [5,10]}, }, professional: { data: ["bachelors"], otherData: [], } }, { firstName: "John", lifeStyle: { data: ["pescatarian"], otherData: {excercise: [5,10]}, }, professional: { data: ["ged"], otherData: [], } }, { firstName: "Mike", lifeStyle: { data: ["active", "pescatarian"], otherData: {excercise: [5,10]}, }, professional: { data: ["ged"], otherData: [], } }, { firstName: "Sam", lifeStyle: { data: ["vegetarian"], otherData: [], }, professional: { data: [], otherData: ["ged"], } }, ] const usersData = users.filter(function(x) { for (let item in filterDatass) { //console.log('filterDatass[item]?.extra?.length', filterDatass[item]?.extra) if (x[item]?.data.includes(...filterDatass[item]?.data)) { // console.log('filterDatass[item]?.extra !== [] && filterDatass[item]?.extra !== undefined', filterDatass[item]?.extra !== [] && filterDatass[item]?.extra !== undefined) if (filterDatass[item]?.extra !== [] || filterDatass[item]?.extra !== undefined) { //console.log('Array.isArray(filterDatass[item]?.extra) === false', filterDatass[item]?.extra) for(let items in filterDatass[item]?.extra) { if (x[item]?.otherData[items]?.length > 0) { if ( x[item]?.otherData[items][0] >= filterDatass[item]?.extra[items][0] && x[item]?.otherData[items][1] <= filterDatass[item]?.extra[items][1] ) { return x } } } } else { return x } } }})
for loop
const filterDatass = { lifeStyle:{ data: ["pescatarian"], extra: {excercise: [5,10]} }, professional: { data: ["ged"], extra: [] } }; const users = [ { firstName: "Jane", lifeStyle: { data: ["vegetarian"], otherData: {excercise: [5,10]}, }, professional: { data: ["bachelors"], otherData: [], } }, { firstName: "John", lifeStyle: { data: ["pescatarian"], otherData: {excercise: [5,10]}, }, professional: { data: ["ged"], otherData: [], } }, { firstName: "Mike", lifeStyle: { data: ["active", "pescatarian"], otherData: {excercise: [5,10]}, }, professional: { data: ["ged"], otherData: [], } }, { firstName: "Sam", lifeStyle: { data: ["vegetarian"], otherData: [], }, professional: { data: [], otherData: ["ged"], } }, ] const usersData = [] for (let i=0; i<users?.length; i++) { // console.log('users', users[i]) for (let item in filterDatass) { //console.log('filterDatass[item]?.extra?.length', filterDatass[item]?.extra) if (users[i][item]?.data.includes(...filterDatass[item]?.data)) { // console.log('filterDatass[item]?.extra !== [] && filterDatass[item]?.extra !== undefined', filterDatass[item]?.extra !== [] && filterDatass[item]?.extra !== undefined) if (filterDatass[item]?.extra !== [] || filterDatass[item]?.extra !== undefined) { // console.log('Array.isArray(filterDatass[item]?.extra) === false', filterDatass[item]?.extra) for(let items in filterDatass[item]?.extra) { if (users[i][item]?.otherData[items]?.length > 0) { if ( users[i][item]?.otherData[items][0] >= filterDatass[item]?.extra[items][0] && users[i][item]?.otherData[items][1] <= filterDatass[item]?.extra[items][1] ) { usersData.push(users[i]) } } } } else { return usersData.push(users[i]) } }}}

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter
for loop

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36

Browser/OS: Chrome 125 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
filter	1268665.6 Ops/sec
for loop	1186549.4 Ops/sec

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

The provided benchmark measures the performance of two different approaches for filtering data in an array: using a `filter()` method and using a `for` loop.

**What is being tested?**

The benchmark tests the execution time of these two approaches on a dataset of user objects. The dataset contains 10 user objects, each with multiple properties (e.g., `firstName`, `lifeStyle`, etc.). The filtering criteria are as follows:

* In the `filter()` method approach, the filter is applied to the entire array using the `includes()` method, which checks if a specific value exists in an array. This is repeated for each property in the user object.
* In the `for` loop approach, a separate filter is applied for each property using a `for` loop that iterates over the properties of the user object and checks if the corresponding value exists in the `filterDatass` object.

**Performance Comparison**

The benchmark results show the execution time per second for both approaches on a Chrome 125 browser running on a Mac OS X 10.15.7 device. The results indicate that:

* The `for` loop approach is faster than the `filter()` method approach, with an average execution time of 1186 executions per second compared to 1268 executions per second.
* However, it's worth noting that the difference in performance is relatively small, and both approaches are reasonably efficient.

**Code Analysis**

The code for the benchmark can be summarized as follows:

1. Define a dataset of user objects with multiple properties.
2. Create a `filterDatass` object that contains filtering criteria (e.g., `data`, `otherData`) for each property.
3. Implement the `for` loop approach:
	* Iterate over the properties of the user object using a `for` loop.
	* For each property, apply a filter to check if the corresponding value exists in the `filterDatass` object.
	* If the value exists, push the filtered user object to an array (`usersData`).
4. Implement the `filter()` method approach:
	* Apply a filter to the entire array using the `includes()` method for each property.
	* Repeat this process for all properties in the user object.

**Best Practices and Advice**

Based on the benchmark results, it appears that the `for` loop approach is slightly faster than the `filter()` method approach. However, the difference in performance is relatively small, and both approaches are reasonable choices depending on the specific use case.

To improve performance in this scenario:

* Consider using a more efficient filtering algorithm, such as `Array.prototype.filter()`, which can take advantage of JavaScript's built-in optimizations.
* Optimize the dataset size and structure to reduce the number of iterations required for filtering.
* Profile and benchmark different approaches to determine the best solution for your specific use case.

Related benchmarks:

comparedThem (version: 10)

Comparing performance of: filter vs for loop

Created: one year ago by: Registered User

Jump to the latest result

filter

for loop

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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