Benchmark: JSON parse size - MeasureThat.net

Script Preparation code:

data = Array(10).fill(Array(100000).fill(0).map(Math.random));
singleLarge = JSON.stringify(data);
manySmall = data.map(JSON.stringify);

Tests:

Stringify: Single Large
JSON.stringify(data)
Stringify: Many Small
data.forEach(JSON.stringify)
Parse: Single Large
JSON.parse(singleLarge);
Parse: Many Small
manySmall.map(JSON.parse)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Stringify: Single Large
Stringify: Many Small
Parse: Single Large
Parse: Many Small

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 4 months ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.127 Safari/537.36 OPR/60.3.3004.55692

Browser/OS: Opera 60 on Linux

View result in a separate tab

Test name	Executions per second
Stringify: Single Large	0.5 Ops/sec
Stringify: Many Small	0.6 Ops/sec
Parse: Single Large	0.9 Ops/sec
Parse: Many Small	0.9 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON and explain what is tested, compared, and analyzed in the benchmark.

**Benchmark Definition**

The provided `Script Preparation Code` defines two test cases:

1. `JSON.stringify(data)`: This code creates an array of 10 objects, each with 100,000 random elements (0). The resulting data is then stringified using `JSON.stringify()`.
2. `data.forEach(JSON.stringify)`: This code iterates over the same array as above and applies `JSON.stringify()` to each element.

**Tested Options**

The benchmark tests four options:

1. **Single Large**: `JSON.stringify(data)`
2. **Many Small**: `data.forEach(JSON.stringify)`
3. **Single Large (Parse)**: `JSON.parse(singleLarge)`
4. **Many Small (Parse)**: `manySmall.map(JSON.parse)`

**Comparison and Analysis**

The benchmark compares the performance of these four options:

* **Option 1: Single Large** vs. **Option 2: Many Small**
	+ **Pros**: This comparison tests the impact of iterating over a large dataset versus applying a function to each element.
	+ **Cons**: This comparison may not accurately represent real-world scenarios, where data is often processed in chunks or iterated over multiple times.
* **Option 1 (Single Large)** vs. **Option 3: Single Large (Parse)**
	+ **Pros**: This comparison tests the parsing overhead of `JSON.parse()` on a single large string.
	+ **Cons**: This comparison may not accurately represent scenarios where data is parsed in chunks or iterated over multiple times.
* **Option 1 (Single Large)** vs. **Option 4: Many Small**
	+ **Pros**: This comparison tests the parsing overhead of `JSON.parse()` on a single large string versus iterating over multiple small strings.
	+ **Cons**: This comparison may not accurately represent scenarios where data is parsed in chunks or iterated over multiple times.

**Library and Special Features**

The benchmark uses the built-in `Array` and `Math` objects, which are part of the JavaScript standard library. No special libraries or features are used.

**Other Considerations**

* The benchmark creates a large dataset using `Array(10).fill(Array(100000).fill(0).map(Math.random))`, which generates 1 million random elements.
* The `JSON.stringify()` function is called repeatedly, which may lead to performance improvements over time due to its optimization and caching.
* The benchmark measures the execution rate of each option, which can be influenced by factors like hardware, software, and network conditions.

**Alternative Approaches**

If you're interested in exploring alternative approaches, consider:

1. **Using `JSON.parse()` with a streaming parser**: Instead of parsing the entire dataset at once, use a streaming parser to parse data as it's generated.
2. **Implementing custom parsing logic**: Depending on your specific use case, implementing custom parsing logic may be more efficient than relying on built-in functions like `JSON.stringify()` and `JSON.parse()`.
3. **Processing data in chunks**: Instead of processing the entire dataset at once, divide it into smaller chunks and process each chunk separately.
4. **Using parallel processing or concurrency**: If possible, use multiple threads or processes to execute the same code simultaneously, which can improve performance by taking advantage of multi-core processors.

Keep in mind that these alternative approaches may require additional development effort and testing to ensure optimal performance and accuracy.

Related benchmarks:

JSON parse size (version: 0)

Comparing performance of: Stringify: Single Large vs Stringify: Many Small vs Parse: Single Large vs Parse: Many Small

Created: 3 years ago by: Registered User

Jump to the latest result

Stringify: Single Large

Stringify: Many Small

Parse: Single Large

Parse: Many Small

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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