Benchmark: JSON parse nested vs flat

Script Preparation code:

var a = '{"p1.p1":"wartość_1","p1.p2":"wartość_2","p1.p2.p3":"wartość_3","p1.p2.p4":"wartość_4","p1.p2.p3.p5":"wartość_5","p1.p2.p3.p6":"wartość_6","p1.p2.p3.p4.p7":"wartość_7","p1.p2.p3.p4.p8":"wartość_8","p1.p2.p3.p4.p5.p9":"wartość_9","p1.p2.p3.p4.p5.p10":"wartość_10","p1.p2.p3.p4.p5.d1.p11":"wartość_11","p1.p2.p3.p4.p5.d1.p12":"wartość_12","p1.p2.p3.p4.p5.d1.d2.p13":"wartość_13","p1.p2.p3.p4.p5.d1.d2.p14":"wartość_14","p1.p2.p3.p4.p5.d1.d2.d3.p15":"wartość_15","p1.p2.p3.p4.p5.d1.d2.d3.p16":"wartość_16","p1.p2.p3.p4.p5.d1.d2.d3.d4.p17":"wartość_17","p1.p2.p3.p4.p5.d1.d2.d3.d4.p18":"wartość_18","p1.p2.p3.p4.p5.d1.d2.d3.d4.d5.p19":"wartość_19","p1.p2.p3.p4.p5.d1.d2.d3.d4.d5.p20":"wartość_20"}';

var b = '{"poziom_1":{"pole_1":"wartość_1","pole_2":"wartość_2","poziom_2":{"pole_3":"wartość_3","pole_4":"wartość_4","poziom_3":{"pole_5":"wartość_5","pole_6":"wartość_6","poziom_4":{"pole_7":"wartość_7","pole_8":"wartość_8","poziom_5":{"pole_9":"wartość_9","pole_10":"wartość_10","dane_1":{"pole_11":"wartość_11","pole_12":"wartość_12","dane_2":{"pole_13":"wartość_13","pole_14":"wartość_14","dane_3":{"pole_15":"wartość_15","pole_16":"wartość_16","dane_4":{"pole_17":"wartość_17","pole_18":"wartość_18","dane_5":{"pole_19":"wartość_19","pole_20":"wartość_20"}}}}}}}}}}}';

Tests:

JSON parse nested
JSON.parse(b)
JSON parse flat
JSON.parse(a);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
JSON parse nested
JSON parse flat

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 5 days ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36

Browser/OS: Chrome 131 on Windows

View result in a separate tab

Test name	Executions per second
JSON parse nested	494800.5 Ops/sec
JSON parse flat	753650.3 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Definition**

The benchmark measures the performance difference between parsing two types of JSON data: nested and flat. The benchmark definition consists of two test cases:

1. `JSON.parse(b)`: This test case parses a deeply nested JSON object, represented by the string `b`. The JSON has multiple levels of nesting, making it a complex data structure.
2. `JSON.parse(a)`: This test case parses a flat JSON object, represented by the string `a`. The JSON has no nesting, making it a simple data structure.

**Options Compared**

The benchmark compares two options:

1. **Nested JSON parsing**: Parsing the deeply nested JSON object `b`.
2. **Flat JSON parsing**: Parsing the flat JSON object `a`.

**Pros and Cons of Each Approach**

* **Nested JSON parsing (JSON.parse(b))**:
	+ Pros: May be more representative of real-world data structures, which often involve nesting.
	+ Cons: Can be slower due to the increased complexity of the data structure.
* **Flat JSON parsing (JSON.parse(a))**:
	+ Pros: Typically faster than nested parsing since it involves less computational overhead.
	+ Cons: May not accurately represent real-world data structures.

**Library Used**

The benchmark uses the built-in `JSON` object in JavaScript, which provides a way to parse JSON data. The `JSON.parse()` method is used to parse both the nested and flat JSON objects.

**Special JS Feature/Syntax (None)**

There are no special JavaScript features or syntax used in this benchmark.

**Other Considerations**

* The benchmark measures the number of executions per second, which indicates the performance of each test case.
* The benchmark uses a mobile device (iPhone) to simulate real-world usage, as indicated by the `DevicePlatform` and `OperatingSystem` fields.

**Alternative Approaches**

Some alternative approaches could include:

1. Using a different JSON parsing library, such as UJSON or jsonparse, which might offer better performance.
2. Adding more complexity to the flat JSON object, making it more comparable to the nested structure.
3. Using a different metric, such as throughput or latency, to evaluate the performance of each test case.

However, these alternatives would require significant changes to the benchmark definition and implementation, which may not be necessary given the current design.

Related benchmarks:

JSON parse nested vs flat (version: 0)

Comparing performance of: JSON parse nested vs JSON parse flat

Created: one year ago by: Guest

Jump to the latest result

JSON parse nested

JSON parse flat

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):