Toggle navigation
MeasureThat.net
Create a benchmark
Tools
Feedback
FAQ
Register
Log In
reduce vs map as;dfkajdf
(version: 0)
Comparing performance of:
reduce vs map
Created:
4 years ago
by:
Guest
Jump to the latest result
Tests:
reduce
Object.entries({foo: "bar", bar: "baz", alex: "karp"}).reduce((queryString, [key, value]) => value != null ? `${queryString}&${key}=${value}` : queryString, "")
map
Object.entries({foo: "bar", bar: "baz", alex: "karp"}) .filter(([_key, value]) => value != null) .map(([key, value]) => `${key}=${value}`) .join("&")
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
Fork
Test case name
Result
reduce
map
Fastest:
N/A
Slowest:
N/A
Latest run results:
No previous run results
This benchmark does not have any results yet. Be the first one
to run it!
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided benchmark and explain what's being tested, the options compared, their pros and cons, and other considerations. **Benchmark Overview** The benchmark compares two approaches to concatenate key-value pairs from an object into a query string: using the `reduce()` method versus using the `map()` method with filtering and joining. The input object has three properties: `foo`, `bar`, and `alex`, each with a corresponding value. **Options Compared** 1. **`reduce()`**: This approach iterates over the object's key-value pairs, applying a callback function to each pair. If the value is not null, it appends the key-value pair to the result string; otherwise, it returns the current result string. 2. **`map()` + `filter()` + `join()`**: This approach filters out null values from the object's key-value pairs, maps each pair to a string in the format `key=value`, and then joins these strings into a single query string using the `&` separator. **Pros and Cons of Each Approach** 1. **`reduce()`**: * Pros: + More concise and easier to read. + Can be more efficient since it avoids creating intermediate arrays or strings. * Cons: + May be slower for large inputs due to the overhead of iteratively appending values to the result string. 2. **`map()` + `filter()` + `join()`**: * Pros: + Can be faster for large inputs since it creates an array and then joins it, which can be more efficient than accumulating a string in place. * Cons: + More verbose and may require more memory to store the intermediate arrays and strings. **Other Considerations** * The `reduce()` method assumes that the input object has at least one key-value pair; if this is not guaranteed, additional error handling or checks should be added. * The `map()` + `filter()` + `join()` approach may require more memory to store the intermediate arrays and strings. **Library/Utility Functions** None of the provided benchmark definitions use any specific JavaScript library or utility function beyond the built-in `Object.entries()`, `Array.prototype.reduce()`, `Array.prototype.map()`, `Array.prototype.filter()`, and `String.prototype.join()` methods. **Special JS Features/Syntax** There are no special JavaScript features or syntax used in this benchmark. The examples only use standard JavaScript syntax and built-in methods. **Alternative Approaches** If you want to explore alternative approaches, here are a few options: * Using a library like Lodash (specifically the `mapValues()` function) to create a query string from an object. * Using a framework like React or Angular to generate the query string as part of a larger application. * Implementing a custom solution using bitwise operations or regex patterns to concatenate key-value pairs. Keep in mind that these alternatives might not be as concise or readable as the original `reduce()` and `map()` approaches, but they can provide different performance characteristics or use cases.
Related benchmarks:
Tim's reduce vs flatMap
flatMap vs reduce using push
flatMap vs reduce test
flat map vs reduce concat
Flatmap vs reduce with objects
Comments
Confirm delete:
Do you really want to delete benchmark?