def test_load_exact(self, mocker):
ts = generate_timestamp()
versioned_hdfs = SparkDataSet(
filepath="hdfs://{}".format(HDFS_PREFIX), version=Version(ts, None)
)
get_spark = mocker.patch.object(versioned_hdfs, "_get_spark")
versioned_hdfs.load()
get_spark.return_value.read.load.assert_called_once_with(
"hdfs://{fn}/{f}/{v}/{f}".format(fn=FOLDER_NAME, f=FILENAME, v=ts),
"parquet",
)
def test_run_load_versions(self, tmp_path, dummy_context, dummy_dataframe,
mocker):
class DummyContext(KedroContext):
project_name = "bob"
package_name = "bob"
project_version = kedro_version
def _get_pipelines(self) -> Dict[str, Pipeline]:
return {
"__default__": Pipeline([node(identity, "cars", "boats")])
}
mocker.patch("logging.config.dictConfig")
dummy_context = DummyContext(str(tmp_path))
filepath = (dummy_context.project_path / "cars.csv").as_posix()
old_save_version = generate_timestamp()
old_df = pd.DataFrame({"col1": [0, 0], "col2": [0, 0], "col3": [0, 0]})
old_csv_data_set = CSVDataSet(
filepath=filepath,
save_args={"sep": ","},
version=Version(None, old_save_version),
)
old_csv_data_set.save(old_df)
sleep(0.5)
new_save_version = generate_timestamp()
new_csv_data_set = CSVDataSet(
filepath=filepath,
save_args={"sep": ","},
version=Version(None, new_save_version),
)
new_csv_data_set.save(dummy_dataframe)
load_versions = {"cars": old_save_version}
dummy_context.run(load_versions=load_versions)
assert not dummy_context.catalog.load("boats").equals(dummy_dataframe)
assert dummy_context.catalog.load("boats").equals(old_df)
def test_load_exact(self, mocker):
ts = generate_timestamp()
ds_s3 = SparkDataSet(
filepath="s3a://{}/{}".format(BUCKET_NAME, FILENAME),
version=Version(ts, None),
credentials=AWS_CREDENTIALS,
)
get_spark = mocker.patch.object(ds_s3, "_get_spark")
ds_s3.load()
get_spark.return_value.read.load.assert_called_once_with(
"s3a://{b}/{f}/{v}/{f}".format(b=BUCKET_NAME, f=FILENAME, v=ts),
"parquet")
def test_from_sane_config_versioned(self, sane_config, dummy_dataframe):
"""Test load and save of versioned data sets from config"""
sane_config["catalog"]["boats"]["versioned"] = True
version = generate_timestamp()
journal = Journal({"run_id": "fake-id", "project_path": "fake-path"})
catalog = DataCatalog.from_config(**sane_config,
load_versions={"boats": version},
save_version=version,
journal=journal)
assert catalog._journal == journal # pylint: disable=protected-access
catalog.save("boats", dummy_dataframe)
path = Path(sane_config["catalog"]["boats"]["filepath"])
path = path / version / path.name
assert path.is_file()
reloaded_df = catalog.load("boats")
assert_frame_equal(reloaded_df, dummy_dataframe)
def test_multiple_loads(self, versioned_image_dataset, image_object,
filepath_png):
"""Test that if a new version is created mid-run, by an
external system, it won't be loaded in the current run."""
versioned_image_dataset.save(image_object)
v1 = versioned_image_dataset.resolve_load_version()
# force-drop a newer version into the same location
v_new = generate_timestamp()
ImageDataSet(filepath=filepath_png,
version=Version(v_new, v_new)).save(image_object)
v2 = versioned_image_dataset.resolve_load_version()
assert v2 == v1 # v2 should not be v_new!
ds_new = ImageDataSet(filepath=filepath_png,
version=Version(None, None))
assert (ds_new.resolve_load_version() == v_new
) # new version is discoverable by a new instance
def test_multiple_loads(self, versioned_csv_data_set, dummy_dataframe,
filepath_csv):
"""Test that if a new version is created mid-run, by an
external system, it won't be loaded in the current run."""
versioned_csv_data_set.save(dummy_dataframe)
versioned_csv_data_set.load()
v1 = versioned_csv_data_set.resolve_load_version()
# force-drop a newer version into the same location
v_new = generate_timestamp()
CSVDataSet(filepath=filepath_csv,
version=Version(v_new, v_new)).save(dummy_dataframe)
versioned_csv_data_set.load()
v2 = versioned_csv_data_set.resolve_load_version()
assert v2 == v1 # v2 should not be v_new!
ds_new = CSVDataSet(filepath=filepath_csv, version=Version(None, None))
assert (ds_new.resolve_load_version() == v_new
) # new version is discoverable by a new instance
def create(
cls,
project_path: Union[Path, str] = None,
save_on_close: bool = True,
env: str = None,
) -> "KedroSession":
"""Create a new instance of ``KedroSession``.
Args:
project_path: Path to the project root directory.
save_on_close: Whether or not to save the session when it's closed.
env: Environment for the KedroContext.
Returns:
A new ``KedroSession`` instance.
"""
# pylint: disable=protected-access
session = cls(
project_path=project_path,
session_id=generate_timestamp(),
save_on_close=save_on_close,
)
session_data = get_static_project_data(session._project_path)
session_data["project_path"] = session._project_path
session_data["session_id"] = session.session_id
session_data.update(_describe_git(session._project_path))
ctx = click.get_current_context(silent=True)
if ctx:
session_data["cli"] = _jsonify_cli_context(ctx)
if env:
session_data["env"] = env
session._store.update(session_data)
return session
def from_config(
cls: Type,
catalog: Optional[Dict[str, Dict[str, Any]]],
credentials: Dict[str, Dict[str, Any]] = None,
load_versions: Dict[str, str] = None,
save_version: str = None,
journal: Journal = None,
) -> "DataCatalog":
"""Create a ``DataCatalog`` instance from configuration. This is a
factory method used to provide developers with a way to instantiate
``DataCatalog`` with configuration parsed from configuration files.
Args:
catalog: A dictionary whose keys are the data set names and
the values are dictionaries with the constructor arguments
for classes implementing ``AbstractDataSet``. The data set
class to be loaded is specified with the key ``type`` and their
fully qualified class name. All ``kedro.io`` data set can be
specified by their class name only, i.e. their module name
can be omitted.
credentials: A dictionary containing credentials for different
data sets. Use the ``credentials`` key in a ``AbstractDataSet``
to refer to the appropriate credentials as shown in the example
below.
load_versions: A mapping between dataset 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.
journal: Instance of Journal.
Returns:
An instantiated ``DataCatalog`` containing all specified
data sets, created and ready to use.
Raises:
DataSetError: When the method fails to create any of the data
sets from their config.
DataSetNotFoundError: When `load_versions` refers to a dataset that doesn't
exist in the catalog.
Example:
::
>>> config = {
>>> "cars": {
>>> "type": "pandas.CSVDataSet",
>>> "filepath": "cars.csv",
>>> "save_args": {
>>> "index": False
>>> }
>>> },
>>> "boats": {
>>> "type": "pandas.CSVDataSet",
>>> "filepath": "s3://aws-bucket-name/boats.csv",
>>> "credentials": "boats_credentials"
>>> "save_args": {
>>> "index": False
>>> }
>>> }
>>> }
>>>
>>> credentials = {
>>> "boats_credentials": {
>>> "client_kwargs": {
>>> "aws_access_key_id": "<your key id>",
>>> "aws_secret_access_key": "<your secret>"
>>> }
>>> }
>>> }
>>>
>>> catalog = DataCatalog.from_config(config, credentials)
>>>
>>> df = catalog.load("cars")
>>> catalog.save("boats", df)
"""
data_sets = {}
catalog = copy.deepcopy(catalog) or {}
credentials = copy.deepcopy(credentials) or {}
run_id = journal.run_id if journal else None
save_version = save_version or run_id or generate_timestamp()
load_versions = copy.deepcopy(load_versions) or {}
missing_keys = load_versions.keys() - catalog.keys()
if missing_keys:
raise DataSetNotFoundError(
f"`load_versions` keys [{', '.join(sorted(missing_keys))}] "
f"are not found in the catalog.")
layers = defaultdict(set) # type: Dict[str, Set[str]]
for ds_name, ds_config in catalog.items():
ds_layer = ds_config.pop("layer", None)
if ds_layer is not None:
layers[ds_layer].add(ds_name)
ds_config = _resolve_credentials(ds_config, credentials)
data_sets[ds_name] = AbstractDataSet.from_config(
ds_name, ds_config, load_versions.get(ds_name), save_version)
dataset_layers = layers or None
return cls(data_sets=data_sets, journal=journal, layers=dataset_layers)
def save_version(request):
return request.param or generate_timestamp()
def run( # pylint: disable=too-many-arguments,too-many-locals
self,
tags: Iterable[str] = None,
runner: AbstractRunner = None,
node_names: Iterable[str] = None,
from_nodes: Iterable[str] = None,
to_nodes: Iterable[str] = None,
from_inputs: Iterable[str] = None,
load_versions: Dict[str, str] = None,
pipeline_name: str = None,
) -> Dict[str, Any]:
"""Runs the pipeline with a specified runner.
Args:
tags: An optional list of node tags which should be used to
filter the nodes of the ``Pipeline``. If specified, only the nodes
containing *any* of these tags will be run.
runner: An optional parameter specifying the runner that you want to run
the pipeline with.
node_names: An optional list of node names which should be used to
filter the nodes of the ``Pipeline``. If specified, only the nodes
with these names will be run.
from_nodes: An optional list of node names which should be used as a
starting point of the new ``Pipeline``.
to_nodes: An optional list of node names which should be used as an
end point of the new ``Pipeline``.
from_inputs: An optional list of input datasets which should be used as a
starting point of the new ``Pipeline``.
load_versions: An optional flag to specify a particular dataset version timestamp
to load.
pipeline_name: Name of the ``Pipeline`` to execute.
Defaults to "__default__".
Raises:
KedroContextError: If the resulting ``Pipeline`` is empty
or incorrect tags are provided.
Returns:
Any node outputs that cannot be processed by the ``DataCatalog``.
These are returned in a dictionary, where the keys are defined
by the node outputs.
"""
# Report project name
logging.info("** Kedro project %s", self.project_path.name)
try:
pipeline = self._get_pipeline(name=pipeline_name)
except NotImplementedError:
common_migration_message = (
"`ProjectContext._get_pipeline(self, name)` method is expected. "
"Please refer to the 'Modular Pipelines' section of the documentation."
)
if pipeline_name:
raise KedroContextError(
"The project is not fully migrated to use multiple pipelines. "
+ common_migration_message
)
warn(
"You are using the deprecated pipeline construction mechanism. "
+ common_migration_message,
DeprecationWarning,
)
pipeline = self.pipeline
filtered_pipeline = self._filter_pipeline(
pipeline=pipeline,
tags=tags,
from_nodes=from_nodes,
to_nodes=to_nodes,
node_names=node_names,
from_inputs=from_inputs,
)
run_id = generate_timestamp()
record_data = {
"run_id": run_id,
"project_path": str(self.project_path),
"env": self.env,
"kedro_version": self.project_version,
"tags": tags,
"from_nodes": from_nodes,
"to_nodes": to_nodes,
"node_names": node_names,
"from_inputs": from_inputs,
"load_versions": load_versions,
"pipeline_name": pipeline_name,
}
journal = Journal(record_data)
catalog = self._get_catalog(
save_version=run_id, journal=journal, load_versions=load_versions
)
# Run the runner
runner = runner or SequentialRunner()
return runner.run(filtered_pipeline, catalog)
def create( # pylint: disable=too-many-arguments
cls,
package_name: str = None,
project_path: Union[Path, str] = None,
save_on_close: bool = True,
env: str = None,
extra_params: Dict[str, Any] = None,
) -> "KedroSession":
"""Create a new instance of ``KedroSession`` with the session data.
Args:
package_name: Package name for the Kedro project the session is
created for.
project_path: Path to the project root directory. Default is
current working directory Path.cwd().
save_on_close: Whether or not to save the session when it's closed.
env: Environment for the KedroContext.
extra_params: Optional dictionary containing extra project parameters
for underlying KedroContext. If specified, will update (and therefore
take precedence over) the parameters retrieved from the project
configuration.
Returns:
A new ``KedroSession`` instance.
"""
# this is to make sure that for workflows that manually create session
# without going through one of our known entrypoints, e.g. some plugins like kedro-airflow,
# the project is still properly configured. This is for backward compatibility
# and should be removed in 0.18.
if package_name is not None:
configure_project(package_name)
session = cls(
package_name=package_name,
project_path=project_path,
session_id=generate_timestamp(),
save_on_close=save_on_close,
)
# have to explicitly type session_data otherwise mypy will complain
# possibly related to this: https://github.com/python/mypy/issues/1430
session_data: Dict[str, Any] = {
"package_name": session._package_name,
"project_path": session._project_path,
"session_id": session.session_id,
**_describe_git(session._project_path),
}
ctx = click.get_current_context(silent=True)
if ctx:
session_data["cli"] = _jsonify_cli_context(ctx)
env = env or os.getenv("KEDRO_ENV")
if env:
session_data["env"] = env
if extra_params:
session_data["extra_params"] = extra_params
session._store.update(session_data)
# we need a ConfigLoader registered in order to be able to set up logging
session._setup_logging()
return session
def version():
load_version = None # use latest
save_version = generate_timestamp() # freeze save version
return Version(load_version, save_version)
def from_config(
cls: Type,
catalog: Optional[Dict[str, Dict[str, Any]]],
credentials: Dict[str, Dict[str, Any]] = None,
load_versions: Dict[str, str] = None,
save_version: str = None,
journal: Journal = None,
) -> "DataCatalog":
"""Create a ``DataCatalog`` instance from configuration. This is a
factory method used to provide developers with a way to instantiate
``DataCatalog`` with configuration parsed from configuration files.
Args:
catalog: A dictionary whose keys are the data set names and
the values are dictionaries with the constructor arguments
for classes implementing ``AbstractDataSet``. The data set
class to be loaded is specified with the key ``type`` and their
fully qualified class name. All ``kedro.io`` data set can be
specified by their class name only, i.e. their module name
can be omitted.
credentials: A dictionary containing credentials for different
data sets. Use the ``credentials`` key in a ``AbstractDataSet``
to refer to the appropriate credentials as shown in the example
below.
load_versions: A mapping between dataset 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.
journal: Instance of Journal.
Returns:
An instantiated ``DataCatalog`` containing all specified
data sets, created and ready to use.
Raises:
DataSetError: When the method fails to create any of the data
sets from their config.
Example:
::
>>> config = {
>>> "cars": {
>>> "type": "CSVLocalDataSet",
>>> "filepath": "cars.csv",
>>> "save_args": {
>>> "index": False
>>> }
>>> },
>>> "boats": {
>>> "type": "CSVS3DataSet",
>>> "filepath": "boats.csv",
>>> "bucket_name": "mck-147789798-bucket",
>>> "credentials": "boats_credentials"
>>> "save_args": {
>>> "index": False
>>> }
>>> }
>>> }
>>>
>>> credentials = {
>>> "boats_credentials": {
>>> "aws_access_key_id": "<your key id>",
>>> "aws_secret_access_key": "<your secret>"
>>> }
>>> }
>>>
>>> catalog = DataCatalog.from_config(config, credentials)
>>>
>>> df = catalog.load("cars")
>>> catalog.save("boats", df)
"""
data_sets = {}
catalog = copy.deepcopy(catalog) or {}
credentials = copy.deepcopy(credentials) or {}
run_id = journal.run_id if journal else None
save_version = save_version or run_id or generate_timestamp()
load_versions = copy.deepcopy(load_versions) or {}
missing_keys = load_versions.keys() - catalog.keys()
if missing_keys:
warn("`load_versions` keys [{}] are not found in the catalog.".
format(", ".join(sorted(missing_keys))))
for ds_name, ds_config in catalog.items():
if "type" not in ds_config:
raise DataSetError("`type` is missing from DataSet '{}' "
"catalog configuration".format(ds_name))
if CREDENTIALS_KEY in ds_config:
ds_config[CREDENTIALS_KEY] = _get_credentials(
ds_config.pop(CREDENTIALS_KEY),
credentials # credentials name
)
data_sets[ds_name] = AbstractDataSet.from_config(
ds_name, ds_config, load_versions.get(ds_name), save_version)
return cls(data_sets=data_sets, journal=journal)