pyrs-yaml

A high-performance Python YAML library with perfect round-trip support, built with Rust and PyO3.

Features

YAML 1.2 compliant - Uses saphyr-parser for full YAML 1.2 support
Perfect Round-Trip - Preserves comments, anchors, tags, chomping, scalar styles, and flow/block formatting
In-Place Editing - Edit parsed documents via JSONPath-style paths (doc.set("$.a.b", v)) or the Node tree API, without losing formatting
High Performance - Rust backend, see benchmarks
NumPy ndarray support - safe_dump() / safe_dumps() / from_dict() / dump_file() serialize numpy.ndarray of any dimension (0-D through N-D) with zero-copy Rust dispatch
JSON Schema validation - YamlDocument.validate(schema) validates parsed documents against JSON Schema; YamlValidateError for failures
Async I/O - safe_dumps_async / safe_dump_async / safe_loads_async / safe_load_async via asyncio.run_in_executor
Incremental re-parse - doc.source() + doc.reparse() for re-parsing stored YAML in-place with different options
JSON serialization - doc.to_json() exports documents to standard JSON
Duplicate keys - allow_duplicate_keys=True opts into last-value-wins; YamlDuplicateKeyError otherwise
Custom tag handlers - register_tag with priority-based chaining, YamlTagSkip, remove_tag/clear_tag_handlers
Pydantic models - parse_as(Model, yaml) validates parsed YAML against Pydantic v2 models
Custom AST - Extensible AST for advanced YAML manipulation
PyYAML Compatible - Drop-in replacement with safe_load/safe_dump API

Installation

pip install pyrs-yaml

Quick Start

import pyrs_yaml

# Parse YAML
doc = pyrs_yaml.parse("key: value")
print(doc.to_yaml())  # key: value

# PyYAML compatible API
data = pyrs_yaml.safe_load("key: value")
print(data)  # {'key': 'value'}

# Round-trip preserves comments
original = "# Comment\nkey: value  # inline\n"
doc = pyrs_yaml.parse(original)
assert doc.to_yaml() == original  # True

# Edit in place without losing formatting
doc.set("$.key", "edited")  # key: edited  # inline
doc.set("$.new", 1)  # add a new key
print(doc.to_yaml())

JSON Schema validation

doc = pyrs_yaml.parse("name: Alice\nage: 30")
doc.validate({"type": "object", "properties": {"name": {"type": "string"}}})
# None — validation passed

# Invalid — raises YamlValidateError
doc.validate({"type": "object", "required": ["email"]})
# pyrs_yaml.YamlValidateError: "Email" is a required property

Async serialization

import asyncio
import pyrs_yaml


async def main():
    yaml = await pyrs_yaml.safe_dumps_async({"a": 1})
    data = await pyrs_yaml.safe_loads_async(yaml)
    print(data)  # {'a': 1}


asyncio.run(main())

Incremental re-parse

doc = pyrs_yaml.parse("x: on")
print(doc.get("x"))  # "on" (core schema: string)

doc.reparse(schema="yaml1.1")
print(doc.get("x"))  # True (yaml1.1 schema: bool)

JSON export

doc = pyrs_yaml.parse("a: 1\nb: hello")
json_str = doc.to_json()  # '{"a": 1, "b": "hello"}'

NumPy ndarray support

import numpy as np
import pyrs_yaml

# 1-D array
arr = np.array([1, 2, 3], dtype="int32")
yaml_str = pyrs_yaml.safe_dump(arr)
print(yaml_str)
# - 1
# - 2
# - 3

# 2-D matrix
matrix = np.array([[1, 2], [3, 4]], dtype="float64")
yaml_str = pyrs_yaml.safe_dump(matrix)
print(yaml_str)
# -
#   - 1.0
#   - 2.0
# -
#   - 3.0
#   - 4.0

# Round-trip
loaded = pyrs_yaml.safe_load(yaml_str)
assert loaded == [[1.0, 2.0], [3.0, 4.0]]

Duplicate keys

Duplicate mapping keys raise YamlDuplicateKeyError by default:

pyrs_yaml.parse("key: first\nkey: second")
# pyrs_yaml.YamlDuplicateKeyError: duplicate key: key

Pass allow_duplicate_keys=True to keep the last value instead:

doc = pyrs_yaml.parse("key: first\nkey: second", allow_duplicate_keys=True)
doc.get("key")  # "second"

The flag is available on parse, safe_load, safe_loads, parse_file, parse_all_docs, and YAML(allow_duplicate_keys=True). In round-trip mode, serializing a document with allowed duplicate keys emits the last occurrence.

Serialization options

to_yaml_with_options() controls indentation and line wrapping:

yaml_str = doc.to_yaml_with_options(
    indent_size=2,  # legacy base indent (used when the per-type options are omitted)
    width=80,  # line-wrap width; 0 disables wrapping
    indent_mapping=4,  # indent per block-mapping level
    indent_sequence=2,  # indent per block-sequence level
    indent_offset=0,  # base offset applied to the whole document
)

indent_mapping / indent_sequence / indent_offset default to indent_size / 0 when omitted, so indent_size=4 still indents everything by 4.

Tag handlers

import pyrs_yaml


# Decorator form
@pyrs_yaml.register_tag("!custom")
def custom_handler(node):
    return f"custom:{node}"


# Imperative form
pyrs_yaml.register_tag("!custom", lambda node: node.upper())

doc = pyrs_yaml.parse("name: !custom value")
doc.get("name")  # "custom:value"

Multiple handlers per tag run in ascending priority order; raising YamlTagSkip passes control to the next handler.
A handler must return a string — anything else raises YamlTagError.
remove_tag("!custom") and clear_tag_handlers() unregister handlers.

Pydantic models

Parse YAML directly into a Pydantic v2 model:

from pydantic import BaseModel
import pyrs_yaml


class Config(BaseModel):
    name: str
    age: int


cfg = pyrs_yaml.parse_as(Config, "name: Alice\nage: 30")
cfg.name  # "Alice"

parse_as raises TypeError for non-BaseModel targets and propagates Pydantic's ValidationError when the YAML does not match the model.

Features Supported

Feature	Support
YAML 1.2	Full
Comments (standalone + inline)	Preserved
Anchors (`&`) and aliases (`*`)	Preserved
Tags (`!!str`, `!!int`, etc.)	Preserved
Chomping (`\|-`, `\|+`, `>-`, `>+`)	Preserved
Complex keys (sequence/mapping as key)	Supported
Escape sequences (`\n`, `\t`, `\uXXXX`)	Supported
Flow collections (`{}`, `[]`)	Preserved
Block scalars (`\|`, `>`)	Preserved
Merge keys (`<<: *alias`)	Resolved (opt-out via `resolve_merges=False`)
NumPy ndarray	Full (0-D through N-D)
JSON Schema validation	Full
Async I/O	Full
Incremental re-parse	Full
JSON export	Full
Duplicate keys	Configurable (`YamlDuplicateKeyError` / last-wins)
Custom tag handlers	Priority-chained `register_tag`
Pydantic models	`parse_as()` validation

API Reference

Core Functions

# Parse YAML string (accepts str or bytes)
doc = pyrs_yaml.parse(yaml_str)
doc = pyrs_yaml.parse(yaml_bytes)

# Parse with options
doc = pyrs_yaml.parse(yaml_str, resolve_merges=False)

# Parse YAML file
doc = pyrs_yaml.parse_file("config.yaml")

# Parse multiple YAML documents
docs = pyrs_yaml.parse_all_docs(yaml_str)

# Convert to YAML string (with options)
yaml_str = doc.to_yaml()
yaml_str = doc.to_yaml_with_options(indent_size=4, explicit_start=True, sort_keys=True)

# Get value by key (with default)
value = doc.get("key")
value = doc.get("missing_key", "default")

# Get root type
doc.root_type()  # "mapping", "sequence", "scalar", "null"

# Check containment and length
"key" in doc
len(doc)

# Iterate
for key in doc:
    print(key, doc[key])

PyYAML Compatible API

# Load YAML to dict
data = pyrs_yaml.safe_load(yaml_str)

# Load multiple documents
docs = pyrs_yaml.safe_loads(yaml_str)

# Dump dict to YAML
yaml_str = pyrs_yaml.safe_dump(data)

# Convert dict to YAML
yaml_str = pyrs_yaml.from_dict(data)

# Convert JSON to YAML
yaml_str = pyrs_yaml.from_json(json_str)

# Dump to file
pyrs_yaml.dump_file(data, "output.yaml")

# Extract YAML frontmatter from markdown
frontmatter, content = pyrs_yaml.read_markdown("post.md")
frontmatter, content = pyrs_yaml.read_markdown_str(markdown_text)

# i18n language management
pyrs_yaml.set_language("zh-CN")
pyrs_yaml.get_language()  # "zh-CN"
pyrs_yaml.list_languages()  # ["en", "zh-CN"]
pyrs_yaml.detect_language()  # auto-detect from environment
pyrs_yaml.negotiate_language(["zh-CN", "en"], "en")  # "zh-CN"

Performance

Criterion benchmarks in benches/yaml_bench.rs (Rust) + pytest-codspeed in tests/test_benchmark_crosslib.py (Python):

Operation	Time
Parse (small, ~2 keys)	~1.7 µs
Parse (medium, ~30 keys)	~12 µs
Parse (large, ~60 keys)	~38 µs
Serialize (small)	~4.4 µs
Serialize (medium)	~4.7 µs
Serialize (large)	~5.5 µs
Roundtrip (small)	~5.9 µs
Roundtrip (large)	~45 µs

Development

# Install dependencies
uv sync

# Build Python extension
maturin develop --release

# Run tests
cargo test
pytest tests/

# Run benchmarks (Rust)
cargo bench

# Run benchmarks (Python)
uv run pytest tests/test_benchmark_crosslib.py tests/test_benchmark_api.py --codspeed

# Performance sanity checks
uv run pytest tests/test_performance.py -v

# Run clippy
cargo clippy -- -D warnings

# Format code
cargo fmt

License

Licensed under either of:

at your option.

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