Merge branch 'main' into develop

RELEASE.md

@@ -1,8 +1,11 @@
 # Upcoming Release
 
 ## Major features and improvements
+* Implemented `KedroDataCatalog.to_config()` method that converts the catalog instance into a configuration format suitable for serialization.
+
 ## Bug fixes and other changes
 * Added validation to ensure dataset versions consistency across catalog.
+
 ## Breaking changes to the API
 ## Documentation changes
 ## Community contributions

kedro/framework/cli/catalog.py

@@ -28,6 +28,7 @@ def _create_session(package_name: str, **kwargs: Any) -> KedroSession:
 
 
 def is_parameter(dataset_name: str) -> bool:
+    # TODO: when breaking change move it to kedro/io/core.py
     """Check if dataset is a parameter."""
     return dataset_name.startswith("params:") or dataset_name == "parameters"
 

kedro/io/catalog_config_resolver.py

@@ -30,7 +30,7 @@ def __init__(
         self._dataset_patterns, self._default_pattern = self._extract_patterns(
             config, credentials
         )
-        self._resolved_configs = self._resolve_config_credentials(config, credentials)
+        self._resolved_configs = self.resolve_credentials(config, credentials)
 
     @property
     def config(self) -> dict[str, dict[str, Any]]:
@@ -237,8 +237,9 @@ def _extract_patterns(
 
         return sorted_patterns, user_default
 
-    def _resolve_config_credentials(
-        self,
+    @classmethod
+    def resolve_credentials(
+        cls,
         config: dict[str, dict[str, Any]] | None,
         credentials: dict[str, dict[str, Any]] | None,
     ) -> dict[str, dict[str, Any]]:
@@ -254,13 +255,55 @@ def _resolve_config_credentials(
                     "\nHint: If this catalog entry is intended for variable interpolation, "
                     "make sure that the key is preceded by an underscore."
                 )
-            if not self.is_pattern(ds_name):
-                resolved_configs[ds_name] = self._resolve_credentials(
+            if not cls.is_pattern(ds_name):
+                resolved_configs[ds_name] = cls._resolve_credentials(
                     ds_config, credentials
                 )
 
         return resolved_configs
 
+    @staticmethod
+    def unresolve_credentials(
+        cred_name: str, ds_config: dict[str, dict[str, Any]] | None
+    ) -> tuple[dict[str, dict[str, Any]], dict[str, dict[str, Any]]]:
+        """Extracts and replaces credentials in a dataset configuration with
+        references, ensuring separation of credentials from the dataset configuration.
+
+        Credentials are searched for recursively in the dataset configuration.
+        The first occurrence of the `CREDENTIALS_KEY` is replaced with a generated
+        reference key.
+
+        Args:
+            cred_name: A unique identifier for the credentials being unresolved.
+                This is used to generate a reference key for the credentials.
+            ds_config: The dataset configuration containing potential credentials
+                under the key `CREDENTIALS_KEY`.
+
+        Returns:
+            A tuple containing:
+                ds_config_copy : A deep copy of the original dataset
+                    configuration with credentials replaced by reference keys.
+                credentials: A dictionary mapping generated reference keys to the original credentials.
+        """
+        ds_config_copy = copy.deepcopy(ds_config) or {}
+        credentials: dict[str, Any] = {}
+        credentials_ref = f"{cred_name}_{CREDENTIALS_KEY}"
+
+        def unresolve(config: Any) -> None:
+            # We don't expect credentials key appears more than once within the same dataset config,
+            # So once we found the key first time we unresolve it and stop iterating after
+            for key, val in config.items():
+                if key == CREDENTIALS_KEY and config[key]:
+                    credentials[credentials_ref] = config[key]
+                    config[key] = credentials_ref
+                    return
+                if isinstance(val, dict):
+                    unresolve(val)
+
+        unresolve(ds_config_copy)
+
+        return ds_config_copy, credentials
+
     def resolve_pattern(self, ds_name: str) -> dict[str, Any]:
         """Resolve dataset patterns and return resolved configurations based on the existing patterns."""
         matched_pattern = self.match_pattern(ds_name)

kedro/io/core.py

@@ -15,6 +15,7 @@
 from datetime import datetime, timezone
 from functools import partial, wraps
 from glob import iglob
+from inspect import getcallargs
 from operator import attrgetter
 from pathlib import Path, PurePath, PurePosixPath
 from typing import (
@@ -57,6 +58,7 @@
     "s3a",
     "s3n",
 )
+TYPE_KEY = "type"
 
 
 class DatasetError(Exception):
@@ -210,6 +212,59 @@ def from_config(
             ) from err
         return dataset
 
+    def to_config(self) -> dict[str, Any]:
+        """Converts the dataset instance into a dictionary-based configuration for
+        serialization. Ensures that any subclass-specific details are handled, with
+        additional logic for versioning and caching implemented for `CachedDataset`.
+
+        Adds a key for the dataset's type using its module and class name and
+        includes the initialization arguments.
+
+        For `CachedDataset` it extracts the underlying dataset's configuration,
+        handles the `versioned` flag and removes unnecessary metadata. It also
+        ensures the embedded dataset's configuration is appropriately flattened
+        or transformed.
+
+        If the dataset has a version key, it sets the `versioned` flag in the
+        configuration.
+
+        Removes the `metadata` key from the configuration if present.
+
+        Returns:
+            A dictionary containing the dataset's type and initialization arguments.
+        """
+        return_config: dict[str, Any] = {
+            f"{TYPE_KEY}": f"{type(self).__module__}.{type(self).__name__}"
+        }
+
+        if self._init_args:  # type: ignore[attr-defined]
+            self._init_args.pop("self", None)  # type: ignore[attr-defined]
+            return_config.update(self._init_args)  # type: ignore[attr-defined]
+
+        if type(self).__name__ == "CachedDataset":
+            cached_ds = return_config.pop("dataset")
+            cached_ds_return_config: dict[str, Any] = {}
+            if isinstance(cached_ds, dict):
+                cached_ds_return_config = cached_ds
+            elif isinstance(cached_ds, AbstractDataset):
+                cached_ds_return_config = cached_ds.to_config()
+            if VERSIONED_FLAG_KEY in cached_ds_return_config:
+                return_config[VERSIONED_FLAG_KEY] = cached_ds_return_config.pop(
+                    VERSIONED_FLAG_KEY
+                )
+            # Pop metadata from configuration
+            cached_ds_return_config.pop("metadata", None)
+            return_config["dataset"] = cached_ds_return_config
+
+        # Set `versioned` key if version present in the dataset
+        if return_config.pop(VERSION_KEY, None):
+            return_config[VERSIONED_FLAG_KEY] = True
+
+        # Pop metadata from configuration
+        return_config.pop("metadata", None)
+
+        return return_config
+
     @property
     def _logger(self) -> logging.Logger:
         return logging.getLogger(__name__)
@@ -290,11 +345,32 @@ def save(self: Self, data: _DI) -> None:
         return save
 
     def __init_subclass__(cls, **kwargs: Any) -> None:
-        """Decorate the `load` and `save` methods provided by the class.
+        """Customizes the behavior of subclasses of AbstractDataset during
+        their creation. This method is automatically invoked when a subclass
+        of AbstractDataset is defined.
 
+        Decorates the `load` and `save` methods provided by the class.
         If `_load` or `_save` are defined, alias them as a prerequisite.
-
         """
+
+        # Save the original __init__ method of the subclass
+        init_func: Callable = cls.__init__
+
+        @wraps(init_func)
+        def new_init(self, *args, **kwargs) -> None:  # type: ignore[no-untyped-def]
+            """Executes the original __init__, then save the arguments used
+            to initialize the instance.
+            """
+            # Call the original __init__ method
+            init_func(self, *args, **kwargs)
+            # Capture and save the arguments passed to the original __init__
+            self._init_args = getcallargs(init_func, self, *args, **kwargs)
+
+        # Replace the subclass's __init__ with the new_init
+        # A hook for subclasses to capture initialization arguments and save them
+        # in the AbstractDataset._init_args field
+        cls.__init__ = new_init  # type: ignore[method-assign]
+
         super().__init_subclass__(**kwargs)
 
         if hasattr(cls, "_load") and not cls._load.__qualname__.startswith("Abstract"):
@@ -493,14 +569,14 @@ def parse_dataset_definition(
     config = copy.deepcopy(config)
 
     # TODO: remove when removing old catalog as moved to KedroDataCatalog
-    if "type" not in config:
+    if TYPE_KEY not in config:
         raise DatasetError(
             "'type' is missing from dataset catalog configuration."
             "\nHint: If this catalog entry is intended for variable interpolation, "
             "make sure that the top level key is preceded by an underscore."
         )
 
-    dataset_type = config.pop("type")
+    dataset_type = config.pop(TYPE_KEY)
     class_obj = None
     if isinstance(dataset_type, str):
         if len(dataset_type.strip(".")) != len(dataset_type):

kedro/io/kedro_data_catalog.py

@@ -18,6 +18,7 @@
 
 from kedro.io.catalog_config_resolver import CatalogConfigResolver, Patterns
 from kedro.io.core import (
+    TYPE_KEY,
     AbstractDataset,
     AbstractVersionedDataset,
     CatalogProtocol,
@@ -28,7 +29,7 @@
     _validate_versions,
     generate_timestamp,
 )
-from kedro.io.memory_dataset import MemoryDataset
+from kedro.io.memory_dataset import MemoryDataset, _is_memory_dataset
 from kedro.utils import _format_rich, _has_rich_handler
 
 
@@ -97,7 +98,7 @@ def __init__(
             >>> catalog = KedroDataCatalog(datasets={"cars": cars})
         """
         self._config_resolver = config_resolver or CatalogConfigResolver()
-        self._datasets = datasets or {}
+        self._datasets: dict[str, AbstractDataset] = datasets or {}
         self._lazy_datasets: dict[str, _LazyDataset] = {}
         self._load_versions, self._save_version = _validate_versions(
             datasets, load_versions or {}, save_version
@@ -369,6 +370,74 @@ class to be loaded is specified with the key ``type`` and their
             config_resolver=config_resolver,
         )
 
+    def to_config(
+        self,
+    ) -> tuple[
+        dict[str, dict[str, Any]],
+        dict[str, dict[str, Any]],
+        dict[str, str | None],
+        str | None,
+    ]:
+        """Converts the `KedroDataCatalog` instance into a configuration format suitable for
+        serialization. This includes datasets, credentials, and versioning information.
+
+        This method is only applicable to catalogs that contain datasets initialized with static, primitive
+        parameters. For example, it will work fine if one passes credentials as dictionary to
+        `GBQQueryDataset` but not as `google.auth.credentials.Credentials` object. See
+        https://github.com/kedro-org/kedro-plugins/issues/950 for the details.
+
+        Returns:
+            A tuple containing:
+                catalog: A dictionary mapping dataset names to their unresolved configurations,
+                    excluding in-memory datasets.
+                credentials: A dictionary of unresolved credentials extracted from dataset configurations.
+                load_versions: A dictionary mapping dataset names to specific versions to be loaded,
+                    or `None` if no version is set.
+                save_version: A global version identifier for saving datasets, or `None` if not specified.
+        Example:
+        ::
+
+            >>> from kedro.io import KedroDataCatalog
+            >>> from kedro_datasets.pandas import CSVDataset
+            >>>
+            >>> cars = CSVDataset(
+            >>>     filepath="cars.csv",
+            >>>     load_args=None,
+            >>>     save_args={"index": False}
+            >>> )
+            >>> catalog = KedroDataCatalog(datasets={'cars': cars})
+            >>>
+            >>> config, credentials, load_versions, save_version = catalog.to_config()
+            >>>
+            >>> new_catalog = KedroDataCatalog.from_config(config, credentials, load_versions, save_version)
+        """
+        catalog: dict[str, dict[str, Any]] = {}
+        credentials: dict[str, dict[str, Any]] = {}
+        load_versions: dict[str, str | None] = {}
+
+        for ds_name, ds in self._lazy_datasets.items():
+            if _is_memory_dataset(ds.config.get(TYPE_KEY, "")):
+                continue
+            unresolved_config, unresolved_credentials = (
+                self._config_resolver.unresolve_credentials(ds_name, ds.config)
+            )
+            catalog[ds_name] = unresolved_config
+            credentials.update(unresolved_credentials)
+            load_versions[ds_name] = self._load_versions.get(ds_name, None)
+
+        for ds_name, ds in self._datasets.items():  # type: ignore[assignment]
+            if _is_memory_dataset(ds):  # type: ignore[arg-type]
+                continue
+            resolved_config = ds.to_config()  # type: ignore[attr-defined]
+            unresolved_config, unresolved_credentials = (
+                self._config_resolver.unresolve_credentials(ds_name, resolved_config)
+            )
+            catalog[ds_name] = unresolved_config
+            credentials.update(unresolved_credentials)
+            load_versions[ds_name] = self._load_versions.get(ds_name, None)
+
+        return catalog, credentials, load_versions, self._save_version
+
     @staticmethod
     def _validate_dataset_config(ds_name: str, ds_config: Any) -> None:
         if not isinstance(ds_config, dict):

kedro/io/memory_dataset.py

@@ -140,3 +140,13 @@ def _copy_with_mode(data: Any, copy_mode: str) -> Any:
         )
 
     return copied_data
+
+
+def _is_memory_dataset(ds_or_type: AbstractDataset | str) -> bool:
+    """Check if dataset or str type provided is a MemoryDataset."""
+    if isinstance(ds_or_type, MemoryDataset):
+        return True
+    if isinstance(ds_or_type, str):
+        return ds_or_type in {"MemoryDataset", "kedro.io.memory_dataset.MemoryDataset"}
+
+    return False

tests/io/conftest.py

@@ -73,6 +73,52 @@ def correct_config(filepath):
     }
 
 
+@pytest.fixture
+def correct_config_versioned(filepath):
+    return {
+        "catalog": {
+            "boats": {
+                "type": "pandas.CSVDataset",
+                "filepath": filepath,
+                "versioned": True,
+            },
+            "cars": {
+                "type": "pandas.CSVDataset",
+                "filepath": "s3://test_bucket/test_file.csv",
+                "credentials": "cars_credentials",
+            },
+            "cars_ibis": {
+                "type": "ibis.FileDataset",
+                "filepath": "cars_ibis.csv",
+                "file_format": "csv",
+                "table_name": "cars",
+                "connection": {"backend": "duckdb", "database": "company.db"},
+                "load_args": {"sep": ",", "nullstr": "#NA"},
+                "save_args": {"sep": ",", "nullstr": "#NA"},
+            },
+            "cached_ds": {
+                "type": "kedro.io.cached_dataset.CachedDataset",
+                "versioned": True,
+                "dataset": {
+                    "type": "pandas.CSVDataset",
+                    "filepath": "cached_ds.csv",
+                    "credentials": "cached_ds_credentials",
+                },
+                "copy_mode": None,
+            },
+            "parameters": {
+                "type": "kedro.io.memory_dataset.MemoryDataset",
+                "data": [4, 5, 6],
+                "copy_mode": None,
+            },
+        },
+        "credentials": {
+            "cars_credentials": {"key": "FAKE_ACCESS_KEY", "secret": "FAKE_SECRET_KEY"},
+            "cached_ds_credentials": {"key": "KEY", "secret": "SECRET"},
+        },
+    }
+
+
 @pytest.fixture
 def correct_config_with_nested_creds(correct_config):
     correct_config["catalog"]["cars"]["credentials"] = {