[DataCatalog2.0]: KedroDataCatalog

RELEASE.md

@@ -1,6 +1,12 @@
 # Upcoming Release
 
 ## Major features and improvements
+* Implemented `KedroDataCatalog` repeating `DataCatalog` functionality with a few API enhancements:
+  * Removed `_FrozenDatasets` and access datasets as properties;
+  * Added get dataset by name feature: dedicated function and access by key;
+  * Added iterate over the datasets feature;
+  * `add_feed_dict()` was simplified and renamed to `add_raw_data()`;
+  * Datasets' initialisation was moved out from `from_config()` method to the constructor.
 * Implemented `Protocol` abstraction for the current `DataCatalog` and adding new catalog implementations.
 * Refactored `kedro run` and `kedro catalog` commands.
 * Moved pattern resolution logic from `DataCatalog` to a separate component - `CatalogConfigResolver`. Updated `DataCatalog` to use `CatalogConfigResolver` internally.

docs/source/conf.py

@@ -134,6 +134,7 @@
         "kedro.io.core.DatasetError",
         "kedro.io.core.Version",
         "kedro.io.data_catalog.DataCatalog",
+        "kedro.io.kedro_data_catalog.KedroDataCatalog",
         "kedro.io.memory_dataset.MemoryDataset",
         "kedro.io.partitioned_dataset.PartitionedDataset",
         "kedro.pipeline.pipeline.Pipeline",
@@ -172,6 +173,7 @@
         "Patterns",
         "CatalogConfigResolver",
         "CatalogProtocol",
+        "KedroDataCatalog",
     ),
     "py:data": (
         "typing.Any",

kedro/io/__init__.py

@@ -16,6 +16,7 @@
     Version,
 )
 from .data_catalog import DataCatalog
+from .kedro_data_catalog import KedroDataCatalog
 from .lambda_dataset import LambdaDataset
 from .memory_dataset import MemoryDataset
 from .shared_memory_dataset import SharedMemoryDataset
@@ -30,6 +31,7 @@
     "DatasetAlreadyExistsError",
     "DatasetError",
     "DatasetNotFoundError",
+    "KedroDataCatalog",
     "LambdaDataset",
     "MemoryDataset",
     "SharedMemoryDataset",

kedro/io/kedro_data_catalog.py

@@ -0,0 +1,326 @@
+"""``KedroDataCatalog`` stores instances of ``AbstractDataset`` implementations to
+provide ``load`` and ``save`` capabilities from anywhere in the program. To
+use a ``KedroDataCatalog``, you need to instantiate it with a dictionary of datasets.
+Then it will act as a single point of reference for your calls, relaying load and
+save functions to the underlying datasets.
+"""
+
+from __future__ import annotations
+
+import copy
+import difflib
+import logging
+import re
+from typing import Any
+
+from kedro.io.catalog_config_resolver import CatalogConfigResolver, Patterns
+from kedro.io.core import (
+    AbstractDataset,
+    AbstractVersionedDataset,
+    DatasetAlreadyExistsError,
+    DatasetError,
+    DatasetNotFoundError,
+    Version,
+    generate_timestamp,
+)
+from kedro.io.memory_dataset import MemoryDataset
+from kedro.utils import _format_rich, _has_rich_handler
+
+
+class KedroDataCatalog:
+    def __init__(
+        self,
+        datasets: dict[str, AbstractDataset] | None = None,
+        raw_data: dict[str, Any] | None = None,
+        config_resolver: CatalogConfigResolver | None = None,
+        load_versions: dict[str, str] | None = None,
+        save_version: str | None = None,
+    ) -> None:
+        """``KedroDataCatalog`` stores instances of ``AbstractDataset``
+        implementations to provide ``load`` and ``save`` capabilities from
+        anywhere in the program. To use a ``KedroDataCatalog``, you need to
+        instantiate it with a dictionary of data sets. Then it will act as a
+        single point of reference for your calls, relaying load and save
+        functions to the underlying data sets.
+
+        Args:
+            datasets: A dictionary of data set names and data set instances.
+            raw_data: A dictionary with data to be added in memory as `MemoryDataset`` instances.
+                Keys represent dataset names and the values are raw data.
+            config_resolver: An instance of CatalogConfigResolver to resolve dataset patterns and configurations.
+            load_versions: A mapping between data set names and versions
+                to load. Has no effect on data sets without enabled versioning.
+            save_version: Version string to be used for ``save`` operations
+                by all data sets with enabled versioning. It must: a) be a
+                case-insensitive string that conforms with operating system
+                filename limitations, b) always return the latest version when
+                sorted in lexicographical order.
+        """
+        self._config_resolver = config_resolver or CatalogConfigResolver()
+        self._datasets = datasets or {}
+        self._load_versions = load_versions or {}
+        self._save_version = save_version
+
+        self._use_rich_markup = _has_rich_handler()
+
+        for ds_name, ds_config in self._config_resolver.config.items():
+            self._add_from_config(ds_name, ds_config)
+
+        if raw_data:
+            self.add_raw_data(raw_data)
+
+    @property
+    def datasets(self) -> dict[str, Any]:
+        return copy.copy(self._datasets)
+
+    @datasets.setter
+    def datasets(self, value: Any) -> None:
+        raise AttributeError(
+            "Operation not allowed! Please use KedroDataCatalog.add() instead."
+        )
+
+    @property
+    def config_resolver(self) -> CatalogConfigResolver:
+        return self._config_resolver
+
+    def __repr__(self) -> str:
+        return self._datasets.__repr__()
+
+    def __contains__(self, dataset_name: str) -> bool:
+        """Check if an item is in the catalog as a materialised dataset or pattern"""
+        return (
+            dataset_name in self._datasets
+            or self._config_resolver.match_pattern(dataset_name) is not None
+        )
+
+    def __eq__(self, other) -> bool:  # type: ignore[no-untyped-def]
+        return (self._datasets, self._config_resolver.list_patterns()) == (
+            other._datasets,
+            other.config_resolver.list_patterns(),
+        )
+
+    @property
+    def _logger(self) -> logging.Logger:
+        return logging.getLogger(__name__)
+
+    @classmethod
+    def from_config(
+        cls,
+        catalog: dict[str, dict[str, Any]] | None,
+        credentials: dict[str, dict[str, Any]] | None = None,
+        load_versions: dict[str, str] | None = None,
+        save_version: str | None = None,
+    ) -> KedroDataCatalog:
+        """Create a ``KedroDataCatalog`` instance from configuration. This is a
+        factory method used to provide developers with a way to instantiate
+        ``KedroDataCatalog`` with configuration parsed from configuration files.
+        """
+        catalog = catalog or {}
+        config_resolver = CatalogConfigResolver(catalog, credentials)
+        save_version = save_version or generate_timestamp()
+        load_versions = load_versions or {}
+
+        missing_keys = [
+            ds_name
+            for ds_name in load_versions
+            if not (
+                ds_name in config_resolver.config
+                or config_resolver.match_pattern(ds_name)
+            )
+        ]
+        if missing_keys:
+            raise DatasetNotFoundError(
+                f"'load_versions' keys [{', '.join(sorted(missing_keys))}] "
+                f"are not found in the catalog."
+            )
+
+        return cls(
+            load_versions=load_versions,
+            save_version=save_version,
+            config_resolver=config_resolver,
+        )
+
+    @staticmethod
+    def _validate_dataset_config(ds_name: str, ds_config: Any) -> None:
+        if not isinstance(ds_config, dict):
+            raise DatasetError(
+                f"Catalog entry '{ds_name}' is not a valid dataset configuration. "
+                "\nHint: If this catalog entry is intended for variable interpolation, "
+                "make sure that the key is preceded by an underscore."
+            )
+
+    def _add_from_config(self, ds_name: str, ds_config: dict[str, Any]) -> None:
+        # TODO: Add lazy loading feature to store the configuration but not to init actual dataset
+        # TODO: Initialise actual dataset when load or save
+        self._validate_dataset_config(ds_name, ds_config)
+        ds = AbstractDataset.from_config(
+            ds_name,
+            ds_config,
+            self._load_versions.get(ds_name),
+            self._save_version,
+        )
+
+        self.add(ds_name, ds)
+
+    def get_dataset(
+        self, ds_name: str, version: Version | None = None, suggest: bool = True
+    ) -> AbstractDataset:
+        ds_config = self._config_resolver.resolve_dataset_pattern(ds_name)
+
+        if ds_name not in self._datasets and ds_config:
+            self._add_from_config(ds_name, ds_config)
+
+        dataset = self._datasets.get(ds_name, None)
+
+        if dataset is None:
+            error_msg = f"Dataset '{ds_name}' not found in the catalog"
+            # Flag to turn on/off fuzzy-matching which can be time consuming and
+            # slow down plugins like `kedro-viz`
+            if suggest:
+                matches = difflib.get_close_matches(ds_name, self._datasets.keys())
+                if matches:
+                    suggestions = ", ".join(matches)
+                    error_msg += f" - did you mean one of these instead: {suggestions}"
+            raise DatasetNotFoundError(error_msg)
+
+        if version and isinstance(dataset, AbstractVersionedDataset):
+            # we only want to return a similar-looking dataset,
+            # not modify the one stored in the current catalog
+            dataset = dataset._copy(_version=version)
+
+        return dataset
+
+    def _get_dataset(
+        self, dataset_name: str, version: Version | None = None, suggest: bool = True
+    ) -> AbstractDataset:
+        # TODO: remove when removing old catalog
+        return self.get_dataset(dataset_name, version, suggest)
+
+    def add(
+        self, ds_name: str, dataset: AbstractDataset, replace: bool = False
+    ) -> None:
+        """Adds a new ``AbstractDataset`` object to the ``KedroDataCatalog``."""
+        if ds_name in self._datasets:
+            if replace:
+                self._logger.warning("Replacing dataset '%s'", ds_name)
+            else:
+                raise DatasetAlreadyExistsError(
+                    f"Dataset '{ds_name}' has already been registered"
+                )
+        self._datasets[ds_name] = dataset
+
+    def list(self, regex_search: str | None = None) -> list[str]:
+        """
+        List of all dataset names registered in the catalog.
+        This can be filtered by providing an optional regular expression
+        which will only return matching keys.
+        """
+
+        if regex_search is None:
+            return list(self._datasets.keys())
+
+        if not regex_search.strip():
+            self._logger.warning("The empty string will not match any datasets")
+            return []
+
+        try:
+            pattern = re.compile(regex_search, flags=re.IGNORECASE)
+        except re.error as exc:
+            raise SyntaxError(
+                f"Invalid regular expression provided: '{regex_search}'"
+            ) from exc
+        return [ds_name for ds_name in self._datasets if pattern.search(ds_name)]
+
+    def save(self, name: str, data: Any) -> None:
+        """Save data to a registered dataset."""
+        dataset = self.get_dataset(name)
+
+        self._logger.info(
+            "Saving data to %s (%s)...",
+            _format_rich(name, "dark_orange") if self._use_rich_markup else name,
+            type(dataset).__name__,
+            extra={"markup": True},
+        )
+
+        dataset.save(data)
+
+    def load(self, name: str, version: str | None = None) -> Any:
+        """Loads a registered dataset."""
+        load_version = Version(version, None) if version else None
+        dataset = self.get_dataset(name, version=load_version)
+
+        self._logger.info(
+            "Loading data from %s (%s)...",
+            _format_rich(name, "dark_orange") if self._use_rich_markup else name,
+            type(dataset).__name__,
+            extra={"markup": True},
+        )
+
+        return dataset.load()
+
+    def release(self, name: str) -> None:
+        """Release any cached data associated with a dataset
+        Args:
+            name: A dataset to be checked.
+        Raises:
+            DatasetNotFoundError: When a dataset with the given name
+                has not yet been registered.
+        """
+        dataset = self.get_dataset(name)
+        dataset.release()
+
+    def confirm(self, name: str) -> None:
+        """Confirm a dataset by its name.
+        Args:
+            name: Name of the dataset.
+        Raises:
+            DatasetError: When the dataset does not have `confirm` method.
+        """
+        self._logger.info("Confirming dataset '%s'", name)
+        dataset = self.get_dataset(name)
+
+        if hasattr(dataset, "confirm"):
+            dataset.confirm()
+        else:
+            raise DatasetError(f"Dataset '{name}' does not have 'confirm' method")
+
+    def add_raw_data(self, data: dict[str, Any], replace: bool = False) -> None:
+        # This method was simplified to add memory datasets only, since
+        # adding AbstractDataset can be done via add() method
+        for ds_name, ds_data in data.items():
+            self.add(ds_name, MemoryDataset(data=ds_data), replace)  # type: ignore[abstract]
+
+    def add_feed_dict(self, feed_dict: dict[str, Any], replace: bool = False) -> None:
+        # TODO: remove when removing old catalog
+        return self.add_raw_data(feed_dict, replace)
+
+    def shallow_copy(
+        self, extra_dataset_patterns: Patterns | None = None
+    ) -> KedroDataCatalog:
+        # TODO: remove when removing old catalog
+        """Returns a shallow copy of the current object.
+
+        Returns:
+            Copy of the current object.
+        """
+        if extra_dataset_patterns:
+            self._config_resolver.add_runtime_patterns(extra_dataset_patterns)
+        return self
+
+    def exists(self, name: str) -> bool:
+        """Checks whether registered dataset exists by calling its `exists()`
+        method. Raises a warning and returns False if `exists()` is not
+        implemented.
+
+        Args:
+            name: A dataset to be checked.
+
+        Returns:
+            Whether the dataset output exists.
+
+        """
+        try:
+            dataset = self._get_dataset(name)
+        except DatasetNotFoundError:
+            return False
+        return dataset.exists()