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Parse YAML at Rust speed. Full 1.2.2 spec, built-in linter, parallel processing. Native bindings for Python & Node.js.

fast-yaml 최신버젼 다운로드

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# fast-yaml

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**High-performance YAML 1.2.2 parser for Python and Node.js, powered by Rust.**

Drop-in replacement for PyYAML and js-yaml. Matches or beats PyYAML C on small/medium files, **2-4x faster** than pure Python, **1.2-1.4x faster** than js-yaml. Full YAML 1.2.2 Core Schema compliance, comprehensive linting, and multi-threaded parallel processing.

> [!IMPORTANT]
> **YAML 1.2.2 Compliance** — Unlike PyYAML (YAML 1.1), `fast-yaml` follows the modern YAML 1.2.2 specification. This means `yes/no/on/off` are strings, not booleans.

## Installation

```bash
# Python
pip install fastyaml-rs

# Node.js
npm install fastyaml-rs

# CLI (prebuilt binary — no Rust toolchain required)
curl -fsSL https://raw.githubusercontent.com/bug-ops/fast-yaml/main/scripts/install.sh | sh

# CLI (from source via cargo)
cargo install fast-yaml-cli
```

> [!WARNING]
> Requires Rust 1.88+, Python 3.10+ or Node.js 20+ (only for building from source — the prebuilt CLI binary has no runtime dependencies)

> [!NOTE]
> `scripts/install.sh` detects the host OS/arch/libc (including musl) and downloads the matching prebuilt `fy` binary with checksum verification from the [GitHub releases](https://github.com/bug-ops/fast-yaml/releases). Linux aarch64 musl (e.g. Alpine on ARM64) has no prebuilt binary yet — use `cargo install fast-yaml-cli` instead. Pin a version with `FASTYAML_VERSION=v0.6.5`, or change the install directory with `FASTYAML_INSTALL_DIR`.

<details>
<summary>Build from source</summary>

```bash
git clone https://github.com/bug-ops/fast-yaml.git
cd fast-yaml

# Python
uv sync && uv run maturin develop

# Node.js
cd nodejs && npm install && npm run build
```

</details>

## Architecture

fast-yaml is organized as a modular Rust workspace with clear separation of concerns:

```
fast-yaml/
├── crates/fast-yaml-core/     # Parser + Emitter + Streaming Formatter
├── crates/fast-yaml-linter/   # Linter + Diagnostic Formatters
├── crates/fast-yaml-parallel/ # Multi-threaded processing
├── python/                    # PyO3 bindings
└── nodejs/                    # NAPI-RS bindings
```

### Core Components

| Component | Location | Input | Output | Use Case |
|-----------|----------|-------|--------|----------|
| **Parser** | `fast-yaml-core` | YAML text | `Value` (DOM) | Deserialize YAML to data structures |
| **Emitter** | `fast-yaml-core` | `Value` (DOM) | YAML text | Serialize data structures to YAML |
| **Streaming Formatter** | `fast-yaml-core` | Parser events | YAML text | Format YAML without building DOM |
| **Linter** | `fast-yaml-linter` | YAML text | `Vec<Diagnostic>` | Validate YAML against rules |
| **Parallel Processor** | `fast-yaml-parallel` | YAML files/streams | `BatchResult` | Parallel processing at document and file level |

> [!TIP]
> **Parser vs Streaming Formatter**: Parser builds a full DOM (use for data manipulation), Streaming Formatter processes events directly (use for formatting/conversion).

> [!TIP]
> **Linter vs Diagnostic Formatter**: Linter validates YAML and produces diagnostics, Diagnostic Formatter renders them for display (rustc-style text, JSON, SARIF).

### Parallelism Types

| Type | API | Use Case |
|------|-----|----------|
| **Document-level** | `parse_parallel()` | Parse multi-document YAML streams |
| **File-level** | `process_files()`, `FileProcessor` | Process multiple files in parallel |
| **CLI batch mode** | `fy format -j 8 dir/` | Format directories with parallel workers |

> [!NOTE]
> All parallelism is now unified in the `fast-yaml-parallel` crate. CLI batch mode and FFI bindings use this single implementation.

## Quick Start

### Python

```python
import fast_yaml

data = fast_yaml.safe_load("""
name: fast-yaml
features: [fast, safe, yaml-1.2.2]
""")

yaml_str = fast_yaml.safe_dump(data)
```

> [!TIP]
> Migrating from PyYAML? Just change your import: `import fast_yaml as yaml`

### Node.js

```typescript
import { safeLoad, safeDump } from 'fastyaml-rs';

const data = safeLoad(`name: fast-yaml`);
const yamlStr = safeDump(data);
```

### CLI

```bash
# Single file operations
fy parse config.yaml           # Validate syntax
fy format -i config.yaml       # Format in-place (exits with error if comments detected)
fy format -i --strip-comments config.yaml  # Format and strip comments silently
fy convert json config.yaml    # YAML → JSON
fy lint config.yaml            # Lint with diagnostics

# Batch mode (directories, globs, multiple files)
fy format -i src/              # Format entire directory
fy format -i "**/*.yaml"       # Format with glob pattern
fy format -i -j 8 project/     # Parallel processing (8 workers)
fy lint --exclude "tests/**" . # Lint all except tests
```

> [!TIP]
> Batch mode activates automatically for directories, globs, or multiple files. Supports parallel processing, include/exclude patterns, and respects `.gitignore`.

> [!WARNING]
> `fy format` does **not** preserve YAML comments. If the input contains comments, the command exits with an error (exit code 1). Pass `--strip-comments` to acknowledge this and allow formatting to proceed — comments will be removed from the output.

## Features

- **High Performance** — Matches PyYAML C on small/medium files, 2-4x faster than pure Python
- **YAML 1.2.2** — Full Core Schema compliance
- **Drop-in API** — Compatible with PyYAML/js-yaml
- **Batch Processing** — Multi-file operations with parallel workers, glob patterns, .gitignore support
- **Linting** — Rich diagnostics with line/column tracking
- **Parallel** — Multi-threaded processing for large files
- **Safe** — Memory-safe Rust with minimal `unsafe` (FFI boundaries only, explicitly documented)

> [!TIP]
> Parallel processing provides 3-6x speedup on 4-8 core systems for multi-document files.

<details>
<summary>Feature details</summary>

### Linting

```python
from fast_yaml._core.lint import lint

diagnostics = lint("key: value\nkey: duplicate")
for diag in diagnostics:
    print(f"{diag.severity}: {diag.message} at line {diag.span.start.line}")
```

### Parallel Processing (Document-Level)

```python
from fast_yaml._core.parallel import parse_parallel, ParallelConfig

# Parse ONE file with MULTIPLE documents in parallel
multi_doc_yaml = "---\nfoo: 1\n---\nbar: 2\n---\nbaz: 3"
config = ParallelConfig(thread_count=4, max_input_size=100*1024*1024)
docs = parse_parallel(multi_doc_yaml, config)  # 3 documents parsed in parallel
```

> [!NOTE]
> This is document-level parallelism (parsing documents inside one file). For file-level parallelism (processing multiple files), use CLI batch mode: `fy format -i -j 8 directory/`

</details>

## Performance

> [!NOTE]
> Three separate benchmark suites: **Python API** (vs PyYAML), **Node.js API** (vs js-yaml), and **CLI Batch Mode** (vs yamlfmt).

> [!NOTE]
> Process startup overhead (~15ms for Python, ~20-25ms for Node.js) affects small file benchmarks. In long-running servers (persistent processes), speedups would be 2-4x higher.

> [!TIP]
> Batch mode is where fast-yaml excels with parallel processing. Use `-j` to specify worker count.

<details>
<summary>Benchmark results</summary>

### Python API vs PyYAML

**Parse (loading):**

| File Size | fast-yaml | PyYAML (C) | PyYAML (pure) | vs C | vs pure |
|-----------|-----------|------------|---------------|------|---------|
| Small (502B) | **15.5 ms** | 20.2 ms | 20.8 ms | **1.30x** | **1.34x** |
| Medium (44KB) | **26.3 ms** | 26.4 ms | 61.2 ms | **1.00x** | **2.33x** |
| Large (449KB) | 130.3 ms | **79.3 ms** | 429.6 ms | 0.61x | **3.30x** |

**Dump (serialization):**

| File Size | fast-yaml | PyYAML (C) | PyYAML (pure) | vs C | vs pure |
|-----------|-----------|------------|---------------|------|---------|
| Small (502B) | **15.7 ms** | 20.8 ms | 21.2 ms | **1.33x** | **1.35x** |
| Medium (44KB) | **31.6 ms** | 31.7 ms | 82.7 ms | **1.00x** | **2.62x** |
| Large (449KB) | 177.6 ms | **131.1 ms** | 653.8 ms | 0.74x | **3.68x** |

**Key findings:**
- **Small/Medium files**: fast-yaml matches or beats PyYAML C (1.0-1.3x speedup)
- **Pure Python**: fast-yaml consistently 1.3-3.7x faster across all sizes
- **Large files**: PyYAML C optimized for single large files; use fast-yaml's parallel mode for multi-document streams

Full benchmarks: [benches/comparison](benches/comparison/)

### Node.js API vs js-yaml (Apple M3 Pro, 12 cores)

**Parse (loading):**

| File Size | fast-yaml | js-yaml | Speedup |
|-----------|-----------|---------|---------|
| Small (502B) | **24.4 ms** | 28.1 ms | **1.15x** |
| Medium (44KB) | **26.2 ms** | 31.9 ms | **1.22x** |
| Large (449KB) | **40.4 ms** | 48.3 ms | **1.20x** |

**Dump (serialization):**

| File Size | fast-yaml | js-yaml | Speedup |
|-----------|-----------|---------|---------|
| Small (502B) | **24.1 ms** | 29.3 ms | **1.22x** |
| Medium (44KB) | **27.1 ms** | 34.9 ms | **1.29x** |
| Large (449KB) | **50.7 ms** | 72.1 ms | **1.42x** |

**Key findings:**
- **Consistent advantage**: fast-yaml 1.15-1.42x faster across all scenarios
- **Best performance**: Large file dump operations (1.42x speedup)
- **V8 JIT competitive**: js-yaml benefits from TurboFan optimization, reducing speedup vs pure Python
- **Real-world servers**: In persistent processes without startup overhead, expect 2-4x speedup

### CLI Single-File vs yamlfmt (Apple M3 Pro, 12 cores)

| File Size | fast-yaml | yamlfmt | Result |
|-----------|-----------|---------|--------|
| Small (502 bytes) | **1.7 ms** | 3.1 ms | **1.80x faster** ✓ |
| Medium (45 KB) | **2.5 ms** | 2.9 ms | **1.19x faster** ✓ |
| Large (460 KB) | 8.4 ms | **2.9 ms** | yamlfmt 2.88x faster |

### CLI Batch Mode vs yamlfmt

| Workload | fast-yaml (parallel) | yamlfmt (sequential) | Speedup |
|----------|---------------------|----------------------|---------|
| 50 files (26 KB) | **4.3 ms** | 10.3 ms | **2.40x faster** ✓ |
| 200 files (204 KB) | **8.0 ms** | 52.7 ms | **6.63x faster** ✓ |
| 500 files (1 MB) | **15.5 ms** | 244.7 ms | **15.77x faster** ⚡ |
| 1000 files (1 MB) | **23.4 ms** | 323.4 ms | **13.80x faster** ⚡ |

**Key takeaway:** Batch mode with parallel workers provides 6-15x speedup on multi-file operations, making it ideal for formatting entire codebases.

```bash
# Run benchmarks
bash benches/comparison/scripts/run_python_benchmark.sh  # Python API
bash benches/comparison/scripts/run_nodejs_benchmark.sh  # Node.js API
bash benches/comparison/scripts/run_batch_benchmark.sh   # CLI batch mode
```

**Test environment:** macOS 14, Apple M3 Pro (12 cores), fast-yaml 0.6.4, PyYAML 6.0.3, js-yaml 4.1.1, Node.js 25.2.1, yamlfmt 0.21.0

</details>

## YAML 1.2.2 Differences

<details>
<summary>Differences from PyYAML (YAML 1.1)</summary>

| Feature | PyYAML (YAML 1.1) | fast-yaml (YAML 1.2.2) |
|---------|-------------------|------------------------|
| `yes/no` | `True/False` | `"yes"/"no"` (strings) |
| `on/off` | `True/False` | `"on"/"off"` (strings) |
| `014` (octal) | `12` | `14` (decimal) |
| `0o14` (octal) | Error | `12` |

```python
fast_yaml.safe_load("yes")    # "yes" (string, not True!)
fast_yaml.safe_load("0o14")   # 12 (octal)
fast_yaml.safe_load("014")    # 14 (decimal, NOT octal!)
```

</details>

## API Reference

<details>
<summary>Loading YAML</summary>

```python
# Single document
data = fast_yaml.safe_load(yaml_string)

# Multiple documents
for doc in fast_yaml.safe_load_all(yaml_string):
    print(doc)

# PyYAML-compatible
data = fast_yaml.load(yaml_string, Loader=fast_yaml.SafeLoader)
```

</details>

<details>
<summary>Dumping YAML</summary>

```python
yaml_str = fast_yaml.safe_dump(data)

# With options
yaml_str = fast_yaml.dump(
    data,
    indent=2,
    width=80,
    explicit_start=True,
    sort_keys=False,
)

# Multiple documents
yaml_str = fast_yaml.safe_dump_all([doc1, doc2, doc3])
```

</details>

<details>
<summary>Type mappings</summary>

| YAML Type | Python Type |
|-----------|-------------|
| `null`, `~` | `None` |
| `true`, `false` | `bool` |
| `123`, `0x1F`, `0o17` | `int` |
| `1.23`, `.inf`, `.nan` | `float` |
| `"string"`, `'string'` | `str` |
| `[a, b, c]` | `list` |
| `{a: 1, b: 2}` | `dict` |

</details>

## Security

Input validation prevents denial-of-service attacks.

<details>
<summary>Security limits</summary>

| Limit | Default | Configurable |
|-------|---------|--------------|
| Max input size | 100 MB | Yes (up to 1GB) |
| Max documents | 100,000 | Yes (up to 10M) |
| Max threads | 128 | Yes |

</details>

## Project

<details>
<summary>Project structure</summary>

```
fast-yaml/
├── crates/
│   ├── fast-yaml-core/     # Core YAML parser/emitter
│   ├── fast-yaml-linter/   # Linting engine
│   └── fast-yaml-parallel/ # Multi-threaded processing
├── python/                 # PyO3 Python bindings
├── nodejs/                 # NAPI-RS Node.js bindings
└── Cargo.toml             # Workspace manifest
```

</details>

<details>
<summary>Technology stack</summary>

| Component | Library |
|-----------|---------|
| YAML Parser | [saphyr](https://github.com/saphyr-rs/saphyr) |
| Python Bindings | [PyO3](https://pyo3.rs/) |
| Node.js Bindings | [NAPI-RS](https://napi.rs/) |
| Parallelism | [Rayon](https://github.com/rayon-rs/rayon) |

**Rust 2024 Edition** • **Python 3.10+** • **Node.js 20+**

</details>

## Contributing

Contributions welcome! All PRs must pass CI checks:

```bash
cargo +nightly fmt --all
cargo clippy --workspace --all-targets -- -D warnings
cargo nextest run --workspace
```

## FAQ

<details>
<summary>Why not just use PyYAML?</summary>

PyYAML is excellent. Use fast-yaml when you need performance (5-10x faster), YAML 1.2.2 compliance, built-in linting, or parallel processing.

</details>

<details>
<summary>Is this a drop-in replacement?</summary>

For `safe_*` functions, yes. Just change `import yaml` to `import fast_yaml as yaml`. Note that YAML 1.2.2 has different boolean/octal handling.

</details>

<details>
<summary>When should I use parallel processing?</summary>

**Document-level parallelism** (`parse_parallel()` in Python/Node.js):
- Use for single large files with multiple `---` separated documents
- File size > 1MB with dozens/hundreds of documents
- Example: Log files, data dumps

**File-level parallelism** (CLI batch mode):
- Use for processing multiple separate files
- Example: `fy format -i -j 8 src/` for entire directories

For single-document files, use `safe_load()`.

</details>

## License

Licensed under [MIT](LICENSE-MIT) or [Apache-2.0](LICENSE-APACHE) at your option.

fast-yaml

fast-yaml 최신버젼 다운로드

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fast-yaml 최신버젼 다운로드

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