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map and filter vs reduce v2
(version: 0)
test which one is faster using filter then map or reduce v2
Comparing performance of:
filter then map v2 vs reduce v2
Created:
3 years ago
by:
Guest
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Tests:
filter then map v2
const counters = [ {name: 'Average DL RB Quantity', id: 11, checked: false} , {name: 'Average UL RB Quantity', id: 10, checked: false} , {name: 'Average User Number', id: 6, checked: true} , {name: 'Data Volume (TB)', id: 21, checked: false} , {name: 'Downlink Traffic Volume', id: 4, checked: false} , {name: 'E-RAB Setup Success Rate', id: 15, checked: false} , {name: 'FL- DL Throughput Per User', id: 9, checked: false} , {name: 'FL- UL Throughput Per User', id: 8, checked: true} , {name: 'Inter-Frequency Handover Out Success Rate', id: 13, checked: false} , {name: 'Intra-Frequency Handover Out Success Rate', id: 12, checked: true} , {name: 'L.RRC.ConnReq.AttBB', id: 1, checked: false} , {name: 'L.RRC.ConnReq.Succ', id: 2, checked: true} , {name: 'Maximum User Number', id: 7, checked: false} , {name: 'RRC Setup Success Rate', id: 14, checked: true} , {name: 'S1-based handover success rate', id: 19, checked: false} , {name: 'Service Drop Rate', id: 16, checked: false} , {name: 'Success rate of S1-based handover preparations', id: 17, checked: false} , {name: 'Success rate of X2-based handover preparations', id: 18, checked: false} , {name: 'Uplink Resource Utilization Rate', id: 3, checked: false} , {name: 'Uplink Traffic Volume', id: 5, checked: false} , {name: 'X2-based handover success rate', id: 20, checked:false}] counters .filter((counter) => counter.checked) .map((counter) => counter.id)
reduce v2
const counters = [ {name: 'Average DL RB Quantity', id: 11, checked: false} , {name: 'Average UL RB Quantity', id: 10, checked: false} , {name: 'Average User Number', id: 6, checked: true} , {name: 'Data Volume (TB)', id: 21, checked: false} , {name: 'Downlink Traffic Volume', id: 4, checked: false} , {name: 'E-RAB Setup Success Rate', id: 15, checked: false} , {name: 'FL- DL Throughput Per User', id: 9, checked: false} , {name: 'FL- UL Throughput Per User', id: 8, checked: true} , {name: 'Inter-Frequency Handover Out Success Rate', id: 13, checked: false} , {name: 'Intra-Frequency Handover Out Success Rate', id: 12, checked: true} , {name: 'L.RRC.ConnReq.AttBB', id: 1, checked: false} , {name: 'L.RRC.ConnReq.Succ', id: 2, checked: true} , {name: 'Maximum User Number', id: 7, checked: false} , {name: 'RRC Setup Success Rate', id: 14, checked: true} , {name: 'S1-based handover success rate', id: 19, checked: false} , {name: 'Service Drop Rate', id: 16, checked: false} , {name: 'Success rate of S1-based handover preparations', id: 17, checked: false} , {name: 'Success rate of X2-based handover preparations', id: 18, checked: false} , {name: 'Uplink Resource Utilization Rate', id: 3, checked: false} , {name: 'Uplink Traffic Volume', id: 5, checked: false} , {name: 'X2-based handover success rate', id: 20, checked:false}] counters.reduce( (previousState, nextCounter) => nextCounter.checked ? [...previousState, nextCounter.id] : [...previousState], [], )
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filter then map v2
reduce v2
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's dive into the explanation of the provided benchmark. **Benchmark Purpose:** The benchmark measures which approach is faster between using `filter`, `map`, and `reduce` in JavaScript. The test case filters an array of counters and then either maps each counter to its ID or reduces the array to only include checked counters. **Options Compared:** 1. **Filter + Map**: This option uses `filter` to filter out unchecked counters, followed by `map` to transform each remaining counter into its ID. * Pros: + Easy to read and understand + No need to worry about reducing an array of IDs back into a single value * Cons: + Can be slower than other approaches since it involves two separate operations (filtering and mapping) 2. **Reduce**: This option uses `reduce` to iterate over the counters, accumulating only the checked counter IDs. * Pros: + Can be faster for large arrays since it reduces the amount of data being processed * Cons: + Requires understanding how `reduce` works and how to use it effectively 3. **Other Options**: There are likely other approaches that could be used, such as using `forEach`, `forEachArr`, or even using a simple loop. **Library Usage:** The benchmark does not appear to use any external libraries or frameworks beyond the standard JavaScript features being tested. **Special JS Feature/Syntax:** This benchmark does not seem to rely on any special JavaScript features or syntax. However, it's worth noting that some modern browsers may have additional performance optimizations or quirks when running benchmarks like this. **Considerations:** * The benchmark is designed to measure the execution speed of different approaches, so the test cases should be representative of real-world usage. * The test cases could benefit from more randomness in the input data to simulate real-world scenarios. * Consider adding more test cases or variations to account for edge cases and other potential performance optimizations. **Alternative Approaches:** * Using `forEach` instead of `filter` and then mapping each counter's ID * Using `reduceRight` instead of `reduce` to accumulate the IDs in reverse order * Using a simple loop with an index variable instead of `map` or `reduce` * Utilizing browser-specific performance optimizations, such as WebAssembly or native modules
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