strictyamlx 0.2.4

An extension of StrictYAML that adds more expressive schema tools for validation.

strictyamlx

An extension of StrictYAML that adds more expressive schema tools for validation.

Installation

pip install strictyamlx

Features

DMap (Dynamic Map)

DMap allows you to validate YAML where the schema of a map depends on the value of one or more of its keys. It evaluates a "control" schema first, and uses its values to determine which Case schema applies. The final parsed object safely merges the validated control and case values.

Case matching rules:

• At most one Case can evaluate to True.
• Zero matching Case blocks is allowed; in that scenario, DMap validates with control + active overlays only.

from strictyamlx import Map, Str, Int, load, Control, Case, DMap

# Control schema evaluates the "action" key to route the document
ctrl = Control(Map({"action": Str()}))

# Blocks conditionally apply a schema based on the evaluated control value
blocks = [
    Case(
        when=lambda raw, ctrl: ctrl["action"] == "message", 
        schema=Map({"text": Str()})
    ),
    Case(
        when=lambda raw, ctrl: ctrl["action"] == "transfer", 
        schema=Map({"amount": Int(), "to": Str()})
    ),
]

# Create the schema 
schema = DMap(ctrl, blocks)

# Validation merges the control ("action") and case schema values ("text")
yaml_str = """
action: message
text: Hello!
"""
doc = load(yaml_str, schema)
assert doc.data == {"action": "message", "text": "Hello!"}

Using source in Control

Sometimes the values needed to determine the schema aren't placed at the root of the document, but nested inside another key (like "metadata"). You can use source to define where the Control values should be drawn from:

from strictyamlx import Map, Str, Int, load, Control, Case, DMap

# Evaluates the control values from the `meta` key
ctrl = Control(Map({"type": Str()}), source="meta")

blocks = [
    Case(
        when=lambda raw, ctrl: ctrl["type"] == "number", 
        schema=Map({"value": Int()})
    )
]

schema = DMap(ctrl, blocks)

yaml_str = """
meta:
  type: number
value: 42
"""
doc = load(yaml_str, schema)
assert doc.data == {"meta": {"type": "number"}, "value": 42}

Constraints

You can append constraints to Case blocks or globally on the DMap. Constraints are callables that validate the incoming data.

Case(
    when=lambda raw, ctrl: ctrl["action"] == "transfer",
    schema=Map({"amount": Int(), "from": Str(), "to": Str()}),
    constraints=[
        lambda raw, ctrl, validated: validated["amount"] > 0,
        lambda raw, ctrl, validated: validated["from"] != validated["to"]
    ]
)

Constraint callbacks can use:

• constraint(raw, ctrl, validated) — no parent context
• constraint(raw, ctrl, validated, parents=None) — parent-aware

When parents is provided to constraints, each item is a dictionary with:

• raw: ancestor raw document
• ctrl: ancestor control projection
• val: ancestor validated value

Constraints are evaluated after schema validation, so parent val is available for nested DMap constraints.

Nesting DMaps

DMaps can nested to create complex state graphs. A Case block can even have another DMap as its schema!

from strictyamlx import Map, Str, Int, Control, Case, DMap

inner_schema = DMap(
    Control(Map({"subkind": Str()})),
    [
        Case(when=lambda raw, ctrl: ctrl["subkind"] == "V1", schema=Map({"v1": Int()})),
        Case(when=lambda raw, ctrl: ctrl["subkind"] == "V2", schema=Map({"v2": Str()})),
    ]
)

schema = DMap(
    Control(Map({"kind": Str()})),
    [
        Case(when=lambda raw, ctrl: ctrl["kind"] == "complex", schema=inner_schema),
        Case(when=lambda raw, ctrl: ctrl["kind"] == "simple", schema=Map({"value": Str()})),
    ]
)

Parent context in nested when

Nested when clauses can inspect ancestor context with an optional parents argument:

schema = DMap(
    control=Control(Map({"type": Str()})),
    blocks=[
        Case(
            when=lambda raw, ctrl: ctrl["type"] == "parent",
            schema=Map({
                "child": DMap(
                    control=Control(Map({"subtype": Str()})),
                    blocks=[
                        Case(
                            # Access parent control from the immediate parent.
                            when=lambda raw, ctrl, parents=None: (
                                parents
                                and parents[-1]["ctrl"]["type"] == "parent"
                                and ctrl["subtype"] == "V1"
                            ),
                            schema=Map({"v1": Int()}),
                        )
                    ],
                )
            }),
        )
    ],
)

For when, each parents item contains:

• raw
• ctrl

Parent context in nested constraints

Nested constraints can also read ancestor validated values:

def child_constraint(raw, ctrl, val, parents=None):
    if not parents:
        return False
    parent_val = parents[-1]["val"]
    return parent_val["type"] == "parent" and val["value"] > 0

schema = DMap(
    control=Control(Map({"type": Str()})),
    blocks=[
        Case(
            when=lambda raw, ctrl: ctrl["type"] == "parent",
            schema=Map({
                "child": DMap(
                    control=Control(Map({"subtype": Str()})),
                    blocks=[
                        Case(
                            when=lambda raw, ctrl: ctrl["subtype"] == "child_type",
                            schema=Map({"value": Int()}),
                            constraints=[child_constraint],
                        )
                    ],
                )
            }),
        )
    ],
)

Overlay Blocks

Overlay blocks allow you to conditionally apply schema fragments on top of your base Case schema. Unlike Case blocks (where only one can match), multiple Overlay blocks can be active at the same time.

Overlays are useful for features like debug flags, optional configurations (like TLS or metrics), or environment-specific settings that should only appear when certain conditions are met.

Precedence: Case > Overlay (in order) > Control. This means fields in the Case schema override overlays, and overlays override the control schema.

from strictyamlx import Map, Str, Int, Bool, Enum, Optional, Seq, load, Control, Case, DMap, Overlay

schema = DMap(
    control=Control(
        validator=Enum(["simple", "advanced"]),
        source=("meta", "mode"),
    ),
    blocks=[
        # Base Case 1: Simple Mode
        Case(
            when=lambda raw, ctrl: ctrl == "simple",
            schema=Map({
                "meta": Map({"mode": Enum(["simple"])}),
                "service": Map({"name": Str(), "port": Int()}),
            }),
        ),
        # Base Case 2: Advanced Mode
        Case(
            when=lambda raw, ctrl: ctrl == "advanced",
            schema=Map({
                "meta": Map({"mode": Enum(["advanced"])}),
                "service": Map({"name": Str(), "ports": Seq(Int())}),
                Optional("workers"): Int(),
            }),
        ),
        # Overlay: Debug (applied if 'debug' is true)
        Overlay(
            when=lambda raw, ctrl: raw.get("debug") is True,
            schema=Map({
                Optional("debug"): Bool(),
                Optional("log_level"): Enum(["debug", "info", "warn", "error"]),
            }),
        ),
        # Overlay: TLS (applied if 'tls' key exists)
        Overlay(
            when=lambda raw, ctrl: "tls" in raw,
            schema=Map({
                "tls": Map({
                    "enabled": Bool(),
                    Optional("cert_file"): Str(),
                    Optional("key_file"): Str(),
                })
            }),
        ),
    ],
)

# Example 1: Simple mode with Debug overlay
yaml_simple_debug = """
meta:
  mode: simple
service:
  name: api
  port: 8080
debug: true
log_level: debug
"""
doc = load(yaml_simple_debug, schema)
assert doc.data["log_level"] == "debug"

# Example 2: Advanced mode with TLS overlay
yaml_advanced_tls = """
meta:
  mode: advanced
service:
  name: api
  ports: [8080, 8081]
workers: 4
tls:
  enabled: true
  cert_file: /etc/certs/server.crt
"""
doc = load(yaml_advanced_tls, schema)
assert doc.data["tls"]["enabled"] is True

ForwardRef

ForwardRef defines recursive or mutually dependent schemas, letting you use a schema component before it is fully defined.

from strictyamlx import Map, Str, Optional, Seq, load, ForwardRef

# 1. Create the reference
tree = ForwardRef()

# 2. Define the schema recursively and assign it using .set()
tree.set(Map({"name": Str(), Optional("children"): Seq(tree)}))

# 3. Validation handles recursive resolution automatically
yaml_str = """
name: root
children:
  - name: child
    children:
      - name: grandchild
"""
doc = load(yaml_str, tree)

DMaps with ForwardRef

You can use a ForwardRef inside DMap case schemas to build recursive dynamic behavior:

from strictyamlx import Map, Str, Int, Optional, load, Control, Case, DMap, ForwardRef

ref = ForwardRef()

schema = DMap(
    Control(Map({"type": Str()})),
    [
        Case(
            when=lambda raw, ctrl: ctrl["type"] == "node", 
            schema=Map({"value": Int(), Optional("child"): ref})
        ),
        Case(
            when=lambda raw, ctrl: ctrl["type"] == "leaf", 
            schema=Map({"value": Int()})
        ),
    ]
)

# Reference points to the DMap itself
ref.set(schema)

yaml_str = """
type: node
value: 1
child:
  type: leaf
  value: 2
"""
doc = load(yaml_str, schema)