# lbson - Fast BSON Library for Python
[](https://badge.fury.io/py/lbson-py)
[](https://pypi.org/project/lbson-py/)
[](https://opensource.org/licenses/MIT)
[](https://github.com/Soju06/lbson/actions/workflows/ci.yml)
A high-performance BSON (Binary JSON) encoding and decoding library for Python, built with C++ for maximum speed. This library enables you to work with BSON data without requiring MongoDB drivers, making it perfect for standalone applications, data processing pipelines, and microservices.
## ✨ Key Features
- **🚀 High Performance**: C++ implementation with Python bindings using pybind11
- **🔧 Zero Dependencies**: No MongoDB driver required - works standalone
- **🎯 Multiple Modes**: Support for Python native, JSON, and Extended JSON decoding modes
- **🛡️ Safe by Default**: Built-in circular reference detection and configurable limits
- **📦 Complete BSON Support**: All standard BSON types including ObjectId, DateTime, Binary, UUID, Regex
- **⚡ Memory Efficient**: Streaming operations with minimal memory footprint
## 🚀 Quick Start
### Installation
```bash
pip install lbson-py
```
### Basic Usage
```python
import lbson
from datetime import datetime
import uuid
# Encode Python objects to BSON
data = {
"name": "John Doe",
"age": 30,
"email": "john@example.com",
"active": True,
"created_at": datetime.now(),
"user_id": uuid.uuid4(),
"scores": [85, 92, 78, 96],
"metadata": {
"source": "api",
"version": "1.2.3"
}
}
# Encode to BSON bytes
bson_data = lbson.encode(data)
print(f"Encoded size: {len(bson_data)} bytes")
# Decode back to Python objects
decoded_data = lbson.decode(bson_data)
print(decoded_data)
```
## 📚 Comprehensive Guide
### Encoding Options
The `encode()` function supports various options for controlling the encoding behavior:
```python
import lbson
data = {"name": "Alice", "values": [1, 2, 3]}
# Basic encoding
bson_data = lbson.encode(data)
# With options
bson_data = lbson.encode(
data,
sort_keys=True, # Sort dictionary keys
check_circular=True, # Detect circular references (default)
allow_nan=True, # Allow NaN values (default)
skipkeys=False, # Skip unsupported key types
max_depth=100, # Maximum nesting depth
max_size=1024*1024 # Maximum document size (1MB)
)
```
### Decoding Modes
Choose the decoding mode that best fits your use case:
#### Python Mode (Default)
Preserves Python types and provides the most accurate representation:
```python
from datetime import datetime
import uuid
data = {
"timestamp": datetime.now(),
"user_id": uuid.uuid4(),
"count": 42
}
bson_data = lbson.encode(data)
result = lbson.decode(bson_data, mode="python")
print(type(result["timestamp"])) # <class 'datetime.datetime'>
print(type(result["user_id"])) # <class 'uuid.UUID'>
```
#### JSON Mode
Converts all types to JSON-compatible format:
```python
result = lbson.decode(bson_data, mode="json")
print(type(result["timestamp"])) # <class 'str'>
print(type(result["user_id"])) # <class 'str'>
```
#### Extended JSON Mode
Uses MongoDB's Extended JSON format for type preservation:
```python
result = lbson.decode(bson_data, mode="extended_json")
print(result["timestamp"]) # {"$date": "2023-12-07T15:30:45.123Z"}
print(result["user_id"]) # {"$uuid": "550e8400-e29b-41d4-a716-446655440000"}
```
### Supported Data Types
lbson supports all standard BSON types:
| Python Type | BSON Type | Notes |
|-------------|-----------|--------|
| `dict` | Document | Nested objects supported |
| `list`, `tuple` | Array | Converts tuples to arrays |
| `str` | String | UTF-8 encoded |
| `bytes` | Binary | Raw binary data |
| `int` | Int32/Int64 | Automatic size detection |
| `float` | Double | IEEE 754 double precision |
| `bool` | Boolean | True/False values |
| `None` | Null | Python None |
| `str` | ObjectId | MongoDB ObjectId |
| `datetime.datetime` | DateTime | UTC timestamps |
| `uuid.UUID` | Binary | UUID subtype |
| `re.Pattern` | Regex | Compiled regex patterns |
### Advanced Examples
#### Working with Binary Data
```python
import lbson
# Binary data
binary_data = {
"file_content": b"Hello, World!",
"checksum": bytes.fromhex("deadbeef"),
"metadata": {
"size": 13,
"type": "text/plain"
}
}
bson_data = lbson.encode(binary_data)
decoded = lbson.decode(bson_data)
```
#### Handling Large Documents
```python
import lbson
# Large document with size and depth limits
large_data = {
"users": [{"id": i, "name": f"User {i}"} for i in range(1000)]
}
try:
bson_data = lbson.encode(
large_data,
max_size=512*1024, # 512KB limit
max_depth=10 # Maximum nesting depth
)
except ValueError as e:
print(f"Document too large: {e}")
```
### Performance Tips
1. **Disable circular checking** for trusted data:
```python
bson_data = lbson.encode(data, check_circular=False)
```
2. **Use appropriate decoding modes**:
- Use `"python"` mode for Python-to-Python serialization
- Use `"json"` mode when you need JSON compatibility
- Use `"extended_json"` for MongoDB compatibility
## 🔧 API Reference
### `lbson.encode(obj, **options) -> bytes`
Encode a Python object to BSON bytes.
**Parameters:**
- `obj` (Any): The Python object to encode
- `skipkeys` (bool): Skip unsupported key types (default: False)
- `check_circular` (bool): Enable circular reference detection (default: True)
- `allow_nan` (bool): Allow NaN/Infinity values (default: True)
- `sort_keys` (bool): Sort dictionary keys (default: False)
- `max_depth` (int|None): Maximum recursion depth (default: None)
- `max_size` (int|None): Maximum document size in bytes (default: None)
**Returns:** BSON-encoded bytes
**Raises:**
- `TypeError`: Unsupported object type
- `ValueError`: Circular reference or invalid value
- `MemoryError`: Document exceeds size limits
### `lbson.decode(data, **options) -> dict`
Decode BSON bytes to a Python object.
**Parameters:**
- `data` (bytes): BSON data to decode
- `mode` (str): Decoding mode - "python", "json", or "extended_json" (default: "python")
- `max_depth` (int|None): Maximum recursion depth (default: None)
**Returns:** Decoded Python dictionary
**Raises:**
- `ValueError`: Malformed BSON data or depth exceeded
- `TypeError`: Invalid input type
## 🏗️ Building from Source
### Prerequisites
- Python 3.9+
- CMake 3.15+
- C++20 compatible compiler
- pybind11
### Build Instructions
```bash
# Clone the repository
git clone https://github.com/Soju06/lbson.git
cd python-bson
# Install lbson
make install
```
### Development Setup
```bash
# Install development build dependencies
make build
# Run tests
make test
# Run benchmarks
make benchmark
```
## 📊 Performance
<img src="benchmarks/images/roundtrip_avg_throughput.png">
| Operation | Benchmark | lbson (ops/s) | PyMongo (ops/s) | bson (ops/s) | lbson vs PyMongo | lbson vs bson |
|-------------|---------------------------------|-----------------|-------------------|----------------|--------------------|-----------------|
| roundtrip | encode_decode_10kb_array_heavy | 12472 | 6153 | 370 | 2.03× faster | 33.71× faster |
| roundtrip | encode_decode_1mb_array_heavy | 194 | 96 | 6 | 2.02× faster | 32.33× faster |
| roundtrip | encode_decode_100kb_array_heavy | 1904 | 962 | 58 | 1.98× faster | 32.83× faster |
| roundtrip | encode_decode_1kb_array_heavy | 48360 | 25224 | 1493 | 1.92× faster | 32.39× faster |
| roundtrip | encode_decode_10mb_array_heavy | 17 | 9 | 1 | 1.89× faster | 17.00× faster |
<details>
<summary>Benchmark Details</summary>
<img src="benchmarks/images/encode_avg_throughput.png">
<img src="benchmarks/images/decode_avg_throughput.png">
| Operation | Benchmark | lbson (ops/s) | PyMongo (ops/s) | bson (ops/s) | lbson vs PyMongo | lbson vs bson |
|-------------|---------------------------------|-----------------|-------------------|----------------|--------------------|-----------------|
| decode | decode_100kb_array_heavy | 3612 | 3093 | 159 | 1.17× faster | 22.72× faster |
| decode | decode_100kb_flat | 4963 | 8171 | 751 | 0.61× faster | 6.61× faster |
| decode | decode_100kb_nested | 12671 | 14105 | 1559 | 0.90× faster | 8.13× faster |
| decode | decode_10kb_array_heavy | 22837 | 19378 | 1011 | 1.18× faster | 22.59× faster |
| decode | decode_10kb_flat | 35846 | 53960 | 4224 | 0.66× faster | 8.49× faster |
| decode | decode_10kb_nested | 39423 | 41799 | 3855 | 0.94× faster | 10.23× faster |
| decode | decode_10mb_array_heavy | 33 | 30 | 2 | 1.10× faster | 16.50× faster |
| decode | decode_10mb_flat | 35 | 55 | 8 | 0.64× faster | 4.38× faster |
| decode | decode_10mb_nested | 594 | 602 | 414 | 0.99× faster | 1.43× faster |
| decode | decode_1kb_array_heavy | 90415 | 80836 | 4072 | 1.12× faster | 22.20× faster |
| decode | decode_1kb_flat | 153838 | 236909 | 20080 | 0.65× faster | 7.66× faster |
| decode | decode_1kb_nested | 374800 | 488637 | 64522 | 0.77× faster | 5.81× faster |
| decode | decode_1mb_array_heavy | 385 | 337 | 15 | 1.14× faster | 25.67× faster |
| decode | decode_1mb_flat | 488 | 797 | 80 | 0.61× faster | 6.10× faster |
| decode | decode_1mb_nested | 4904 | 5343 | 1126 | 0.92× faster | 4.36× faster |
| encode | encode_100kb_array_heavy | 4286 | 1389 | 91 | 3.09× faster | 47.10× faster |
| encode | encode_100kb_flat | 18709 | 6848 | 513 | 2.73× faster | 36.47× faster |
| encode | encode_100kb_nested | 36471 | 13399 | 985 | 2.72× faster | 37.03× faster |
| encode | encode_10kb_array_heavy | 28458 | 9045 | 585 | 3.15× faster | 48.65× faster |
| encode | encode_10kb_flat | 95217 | 38317 | 2837 | 2.48× faster | 33.56× faster |
| encode | encode_10kb_nested | 93763 | 36864 | 2678 | 2.54× faster | 35.01× faster |
| encode | encode_10mb_array_heavy | 36 | 13 | 1 | 2.77× faster | 36.00× faster |
| encode | encode_10mb_flat | 170 | 68 | 5 | 2.50× faster | 34.00× faster |
| encode | encode_10mb_nested | 465 | 372 | 85 | 1.25× faster | 5.47× faster |
| encode | encode_1kb_array_heavy | 106657 | 37554 | 2434 | 2.84× faster | 43.82× faster |
| encode | encode_1kb_flat | 297390 | 163006 | 13583 | 1.82× faster | 21.89× faster |
| encode | encode_1kb_nested | 481591 | 398013 | 43375 | 1.21× faster | 11.10× faster |
| encode | encode_1mb_array_heavy | 404 | 136 | 9 | 2.97× faster | 44.89× faster |
| encode | encode_1mb_flat | 2043 | 732 | 55 | 2.79× faster | 37.15× faster |
| encode | encode_1mb_nested | 13130 | 6431 | 525 | 2.04× faster | 25.01× faster |
| roundtrip | encode_decode_100kb_array_heavy | 1904 | 962 | 58 | 1.98× faster | 32.83× faster |
| roundtrip | encode_decode_100kb_flat | 3889 | 3694 | 305 | 1.05× faster | 12.75× faster |
| roundtrip | encode_decode_100kb_nested | 9141 | 6732 | 591 | 1.36× faster | 15.47× faster |
| roundtrip | encode_decode_10kb_array_heavy | 12472 | 6153 | 370 | 2.03× faster | 33.71× faster |
| roundtrip | encode_decode_10kb_flat | 25533 | 21864 | 1662 | 1.17× faster | 15.36× faster |
| roundtrip | encode_decode_10kb_nested | 27376 | 19352 | 1537 | 1.41× faster | 17.81× faster |
| roundtrip | encode_decode_10mb_array_heavy | 17 | 9 | 1 | 1.89× faster | 17.00× faster |
| roundtrip | encode_decode_10mb_flat | 28 | 30 | 3 | 0.93× faster | 9.33× faster |
| roundtrip | encode_decode_10mb_nested | 242 | 185 | 60 | 1.31× faster | 4.03× faster |
| roundtrip | encode_decode_1kb_array_heavy | 48360 | 25224 | 1493 | 1.92× faster | 32.39× faster |
| roundtrip | encode_decode_1kb_flat | 97414 | 94199 | 7550 | 1.03× faster | 12.90× faster |
| roundtrip | encode_decode_1kb_nested | 207828 | 211679 | 22397 | 0.98× faster | 9.28× faster |
| roundtrip | encode_decode_1mb_array_heavy | 194 | 96 | 6 | 2.02× faster | 32.33× faster |
| roundtrip | encode_decode_1mb_flat | 390 | 374 | 33 | 1.04× faster | 11.82× faster |
| roundtrip | encode_decode_1mb_nested | 3532 | 2610 | 347 | 1.35× faster | 10.18× faster |
</details>
## 📚 Related Projects
- [pymongo](https://github.com/mongodb/mongo-python-driver) - Official MongoDB Python driver
- [bson](https://pypi.org/project/bson/) - Pure Python BSON implementation
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"description": "# lbson - Fast BSON Library for Python\n\n[](https://badge.fury.io/py/lbson-py)\n[](https://pypi.org/project/lbson-py/)\n[](https://opensource.org/licenses/MIT)\n[](https://github.com/Soju06/lbson/actions/workflows/ci.yml)\n\nA high-performance BSON (Binary JSON) encoding and decoding library for Python, built with C++ for maximum speed. This library enables you to work with BSON data without requiring MongoDB drivers, making it perfect for standalone applications, data processing pipelines, and microservices.\n\n## \u2728 Key Features\n\n- **\ud83d\ude80 High Performance**: C++ implementation with Python bindings using pybind11\n- **\ud83d\udd27 Zero Dependencies**: No MongoDB driver required - works standalone\n- **\ud83c\udfaf Multiple Modes**: Support for Python native, JSON, and Extended JSON decoding modes\n- **\ud83d\udee1\ufe0f Safe by Default**: Built-in circular reference detection and configurable limits\n- **\ud83d\udce6 Complete BSON Support**: All standard BSON types including ObjectId, DateTime, Binary, UUID, Regex\n- **\u26a1 Memory Efficient**: Streaming operations with minimal memory footprint\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n\n```bash\npip install lbson-py\n```\n\n### Basic Usage\n\n```python\nimport lbson\nfrom datetime import datetime\nimport uuid\n\n# Encode Python objects to BSON\ndata = {\n \"name\": \"John Doe\",\n \"age\": 30,\n \"email\": \"john@example.com\",\n \"active\": True,\n \"created_at\": datetime.now(),\n \"user_id\": uuid.uuid4(),\n \"scores\": [85, 92, 78, 96],\n \"metadata\": {\n \"source\": \"api\",\n \"version\": \"1.2.3\"\n }\n}\n\n# Encode to BSON bytes\nbson_data = lbson.encode(data)\nprint(f\"Encoded size: {len(bson_data)} bytes\")\n\n# Decode back to Python objects\ndecoded_data = lbson.decode(bson_data)\nprint(decoded_data)\n```\n\n## \ud83d\udcda Comprehensive Guide\n\n### Encoding Options\n\nThe `encode()` function supports various options for controlling the encoding behavior:\n\n```python\nimport lbson\n\ndata = {\"name\": \"Alice\", \"values\": [1, 2, 3]}\n\n# Basic encoding\nbson_data = lbson.encode(data)\n\n# With options\nbson_data = lbson.encode(\n data,\n sort_keys=True, # Sort dictionary keys\n check_circular=True, # Detect circular references (default)\n allow_nan=True, # Allow NaN values (default)\n skipkeys=False, # Skip unsupported key types\n max_depth=100, # Maximum nesting depth\n max_size=1024*1024 # Maximum document size (1MB)\n)\n```\n\n### Decoding Modes\n\nChoose the decoding mode that best fits your use case:\n\n#### Python Mode (Default)\nPreserves Python types and provides the most accurate representation:\n\n```python\nfrom datetime import datetime\nimport uuid\n\ndata = {\n \"timestamp\": datetime.now(),\n \"user_id\": uuid.uuid4(),\n \"count\": 42\n}\n\nbson_data = lbson.encode(data)\nresult = lbson.decode(bson_data, mode=\"python\")\n\nprint(type(result[\"timestamp\"])) # <class 'datetime.datetime'>\nprint(type(result[\"user_id\"])) # <class 'uuid.UUID'>\n```\n\n#### JSON Mode\nConverts all types to JSON-compatible format:\n\n```python\nresult = lbson.decode(bson_data, mode=\"json\")\n\nprint(type(result[\"timestamp\"])) # <class 'str'>\nprint(type(result[\"user_id\"])) # <class 'str'>\n```\n\n#### Extended JSON Mode\nUses MongoDB's Extended JSON format for type preservation:\n\n```python\nresult = lbson.decode(bson_data, mode=\"extended_json\")\n\nprint(result[\"timestamp\"]) # {\"$date\": \"2023-12-07T15:30:45.123Z\"}\nprint(result[\"user_id\"]) # {\"$uuid\": \"550e8400-e29b-41d4-a716-446655440000\"}\n```\n\n### Supported Data Types\n\nlbson supports all standard BSON types:\n\n| Python Type | BSON Type | Notes |\n|-------------|-----------|--------|\n| `dict` | Document | Nested objects supported |\n| `list`, `tuple` | Array | Converts tuples to arrays |\n| `str` | String | UTF-8 encoded |\n| `bytes` | Binary | Raw binary data |\n| `int` | Int32/Int64 | Automatic size detection |\n| `float` | Double | IEEE 754 double precision |\n| `bool` | Boolean | True/False values |\n| `None` | Null | Python None |\n| `str` | ObjectId | MongoDB ObjectId |\n| `datetime.datetime` | DateTime | UTC timestamps |\n| `uuid.UUID` | Binary | UUID subtype |\n| `re.Pattern` | Regex | Compiled regex patterns |\n\n### Advanced Examples\n\n#### Working with Binary Data\n\n```python\nimport lbson\n\n# Binary data\nbinary_data = {\n \"file_content\": b\"Hello, World!\",\n \"checksum\": bytes.fromhex(\"deadbeef\"),\n \"metadata\": {\n \"size\": 13,\n \"type\": \"text/plain\"\n }\n}\n\nbson_data = lbson.encode(binary_data)\ndecoded = lbson.decode(bson_data)\n```\n\n#### Handling Large Documents\n\n```python\nimport lbson\n\n# Large document with size and depth limits\nlarge_data = {\n \"users\": [{\"id\": i, \"name\": f\"User {i}\"} for i in range(1000)]\n}\n\ntry:\n bson_data = lbson.encode(\n large_data,\n max_size=512*1024, # 512KB limit\n max_depth=10 # Maximum nesting depth\n )\nexcept ValueError as e:\n print(f\"Document too large: {e}\")\n```\n\n### Performance Tips\n\n1. **Disable circular checking** for trusted data:\n ```python\n bson_data = lbson.encode(data, check_circular=False)\n ```\n\n2. **Use appropriate decoding modes**:\n - Use `\"python\"` mode for Python-to-Python serialization\n - Use `\"json\"` mode when you need JSON compatibility\n - Use `\"extended_json\"` for MongoDB compatibility\n\n## \ud83d\udd27 API Reference\n\n### `lbson.encode(obj, **options) -> bytes`\n\nEncode a Python object to BSON bytes.\n\n**Parameters:**\n- `obj` (Any): The Python object to encode\n- `skipkeys` (bool): Skip unsupported key types (default: False)\n- `check_circular` (bool): Enable circular reference detection (default: True)\n- `allow_nan` (bool): Allow NaN/Infinity values (default: True)\n- `sort_keys` (bool): Sort dictionary keys (default: False)\n- `max_depth` (int|None): Maximum recursion depth (default: None)\n- `max_size` (int|None): Maximum document size in bytes (default: None)\n\n**Returns:** BSON-encoded bytes\n\n**Raises:**\n- `TypeError`: Unsupported object type\n- `ValueError`: Circular reference or invalid value\n- `MemoryError`: Document exceeds size limits\n\n### `lbson.decode(data, **options) -> dict`\n\nDecode BSON bytes to a Python object.\n\n**Parameters:**\n- `data` (bytes): BSON data to decode\n- `mode` (str): Decoding mode - \"python\", \"json\", or \"extended_json\" (default: \"python\")\n- `max_depth` (int|None): Maximum recursion depth (default: None)\n\n**Returns:** Decoded Python dictionary\n\n**Raises:**\n- `ValueError`: Malformed BSON data or depth exceeded\n- `TypeError`: Invalid input type\n\n## \ud83c\udfd7\ufe0f Building from Source\n\n### Prerequisites\n\n- Python 3.9+\n- CMake 3.15+\n- C++20 compatible compiler\n- pybind11\n\n### Build Instructions\n\n```bash\n# Clone the repository\ngit clone https://github.com/Soju06/lbson.git\ncd python-bson\n\n# Install lbson\nmake install\n```\n\n### Development Setup\n\n```bash\n# Install development build dependencies\nmake build\n\n# Run tests\nmake test\n\n# Run benchmarks\nmake benchmark\n```\n\n## \ud83d\udcca Performance\n\n<img src=\"benchmarks/images/roundtrip_avg_throughput.png\">\n\n| Operation | Benchmark | lbson (ops/s) | PyMongo (ops/s) | bson (ops/s) | lbson vs PyMongo | lbson vs bson |\n|-------------|---------------------------------|-----------------|-------------------|----------------|--------------------|-----------------|\n| roundtrip | encode_decode_10kb_array_heavy | 12472 | 6153 | 370 | 2.03\u00d7 faster | 33.71\u00d7 faster |\n| roundtrip | encode_decode_1mb_array_heavy | 194 | 96 | 6 | 2.02\u00d7 faster | 32.33\u00d7 faster |\n| roundtrip | encode_decode_100kb_array_heavy | 1904 | 962 | 58 | 1.98\u00d7 faster | 32.83\u00d7 faster |\n| roundtrip | encode_decode_1kb_array_heavy | 48360 | 25224 | 1493 | 1.92\u00d7 faster | 32.39\u00d7 faster |\n| roundtrip | encode_decode_10mb_array_heavy | 17 | 9 | 1 | 1.89\u00d7 faster | 17.00\u00d7 faster |\n\n<details>\n<summary>Benchmark Details</summary>\n\n<img src=\"benchmarks/images/encode_avg_throughput.png\">\n<img src=\"benchmarks/images/decode_avg_throughput.png\">\n\n| Operation | Benchmark | lbson (ops/s) | PyMongo (ops/s) | bson (ops/s) | lbson vs PyMongo | lbson vs bson |\n|-------------|---------------------------------|-----------------|-------------------|----------------|--------------------|-----------------|\n| decode | decode_100kb_array_heavy | 3612 | 3093 | 159 | 1.17\u00d7 faster | 22.72\u00d7 faster |\n| decode | decode_100kb_flat | 4963 | 8171 | 751 | 0.61\u00d7 faster | 6.61\u00d7 faster |\n| decode | decode_100kb_nested | 12671 | 14105 | 1559 | 0.90\u00d7 faster | 8.13\u00d7 faster |\n| decode | decode_10kb_array_heavy | 22837 | 19378 | 1011 | 1.18\u00d7 faster | 22.59\u00d7 faster |\n| decode | decode_10kb_flat | 35846 | 53960 | 4224 | 0.66\u00d7 faster | 8.49\u00d7 faster |\n| decode | decode_10kb_nested | 39423 | 41799 | 3855 | 0.94\u00d7 faster | 10.23\u00d7 faster |\n| decode | decode_10mb_array_heavy | 33 | 30 | 2 | 1.10\u00d7 faster | 16.50\u00d7 faster |\n| decode | decode_10mb_flat | 35 | 55 | 8 | 0.64\u00d7 faster | 4.38\u00d7 faster |\n| decode | decode_10mb_nested | 594 | 602 | 414 | 0.99\u00d7 faster | 1.43\u00d7 faster |\n| decode | decode_1kb_array_heavy | 90415 | 80836 | 4072 | 1.12\u00d7 faster | 22.20\u00d7 faster |\n| decode | decode_1kb_flat | 153838 | 236909 | 20080 | 0.65\u00d7 faster | 7.66\u00d7 faster |\n| decode | decode_1kb_nested | 374800 | 488637 | 64522 | 0.77\u00d7 faster | 5.81\u00d7 faster |\n| decode | decode_1mb_array_heavy | 385 | 337 | 15 | 1.14\u00d7 faster | 25.67\u00d7 faster |\n| decode | decode_1mb_flat | 488 | 797 | 80 | 0.61\u00d7 faster | 6.10\u00d7 faster |\n| decode | decode_1mb_nested | 4904 | 5343 | 1126 | 0.92\u00d7 faster | 4.36\u00d7 faster |\n| encode | encode_100kb_array_heavy | 4286 | 1389 | 91 | 3.09\u00d7 faster | 47.10\u00d7 faster |\n| encode | encode_100kb_flat | 18709 | 6848 | 513 | 2.73\u00d7 faster | 36.47\u00d7 faster |\n| encode | encode_100kb_nested | 36471 | 13399 | 985 | 2.72\u00d7 faster | 37.03\u00d7 faster |\n| encode | encode_10kb_array_heavy | 28458 | 9045 | 585 | 3.15\u00d7 faster | 48.65\u00d7 faster |\n| encode | encode_10kb_flat | 95217 | 38317 | 2837 | 2.48\u00d7 faster | 33.56\u00d7 faster |\n| encode | encode_10kb_nested | 93763 | 36864 | 2678 | 2.54\u00d7 faster | 35.01\u00d7 faster |\n| encode | encode_10mb_array_heavy | 36 | 13 | 1 | 2.77\u00d7 faster | 36.00\u00d7 faster |\n| encode | encode_10mb_flat | 170 | 68 | 5 | 2.50\u00d7 faster | 34.00\u00d7 faster |\n| encode | encode_10mb_nested | 465 | 372 | 85 | 1.25\u00d7 faster | 5.47\u00d7 faster |\n| encode | encode_1kb_array_heavy | 106657 | 37554 | 2434 | 2.84\u00d7 faster | 43.82\u00d7 faster |\n| encode | encode_1kb_flat | 297390 | 163006 | 13583 | 1.82\u00d7 faster | 21.89\u00d7 faster |\n| encode | encode_1kb_nested | 481591 | 398013 | 43375 | 1.21\u00d7 faster | 11.10\u00d7 faster |\n| encode | encode_1mb_array_heavy | 404 | 136 | 9 | 2.97\u00d7 faster | 44.89\u00d7 faster |\n| encode | encode_1mb_flat | 2043 | 732 | 55 | 2.79\u00d7 faster | 37.15\u00d7 faster |\n| encode | encode_1mb_nested | 13130 | 6431 | 525 | 2.04\u00d7 faster | 25.01\u00d7 faster |\n| roundtrip | encode_decode_100kb_array_heavy | 1904 | 962 | 58 | 1.98\u00d7 faster | 32.83\u00d7 faster |\n| roundtrip | encode_decode_100kb_flat | 3889 | 3694 | 305 | 1.05\u00d7 faster | 12.75\u00d7 faster |\n| roundtrip | encode_decode_100kb_nested | 9141 | 6732 | 591 | 1.36\u00d7 faster | 15.47\u00d7 faster |\n| roundtrip | encode_decode_10kb_array_heavy | 12472 | 6153 | 370 | 2.03\u00d7 faster | 33.71\u00d7 faster |\n| roundtrip | encode_decode_10kb_flat | 25533 | 21864 | 1662 | 1.17\u00d7 faster | 15.36\u00d7 faster |\n| roundtrip | encode_decode_10kb_nested | 27376 | 19352 | 1537 | 1.41\u00d7 faster | 17.81\u00d7 faster |\n| roundtrip | encode_decode_10mb_array_heavy | 17 | 9 | 1 | 1.89\u00d7 faster | 17.00\u00d7 faster |\n| roundtrip | encode_decode_10mb_flat | 28 | 30 | 3 | 0.93\u00d7 faster | 9.33\u00d7 faster |\n| roundtrip | encode_decode_10mb_nested | 242 | 185 | 60 | 1.31\u00d7 faster | 4.03\u00d7 faster |\n| roundtrip | encode_decode_1kb_array_heavy | 48360 | 25224 | 1493 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