plateau.io.dask.delayed module

plateau.io.dask.delayed.delete_dataset__delayed(dataset_uuid=None, store=None, factory=None)[source]

Parameters:

dataset_uuid (str) – The dataset UUID
store (Callable or str or minimalkv.KeyValueStore) –
The store where we can find or store the dataset.

Can be either minimalkv.KeyValueStore, a minimalkv store url or a generic Callable producing a minimalkv.KeyValueStore
factory (plateau.core.factory.DatasetFactory) – A DatasetFactory holding the store and UUID to the source dataset.

plateau.io.dask.delayed.garbage_collect_dataset__delayed(dataset_uuid: str | None = None, store: str | KeyValueStore | Callable[[], KeyValueStore] | None = None, chunk_size: int = 100, factory=None) → List[Delayed][source]

Remove auxiliary files that are no longer tracked by the dataset.

These files include indices that are no longer referenced by the metadata as well as files in the directories of the tables that are no longer referenced. The latter is only applied to static datasets.

Parameters:

dataset_uuid (str) – The dataset UUID
store (Callable or str or minimalkv.KeyValueStore) –
The store where we can find or store the dataset.

Can be either minimalkv.KeyValueStore, a minimalkv store url or a generic Callable producing a minimalkv.KeyValueStore
factory (plateau.core.factory.DatasetFactory) – A DatasetFactory holding the store and UUID to the source dataset.
chunk_size – Number of files that should be deleted in a single job.

plateau.io.dask.delayed.read_dataset_as_delayed(dataset_uuid=None, store=None, columns=None, predicate_pushdown_to_io=True, categoricals=None, dates_as_object: bool = True, predicates=None, factory=None, dispatch_by=None)[source]

A collection of dask.delayed objects to retrieve a dataset from store where each partition is loaded as a DataFrame.

Parameters:

dataset_uuid (str) – The dataset UUID
store (Callable or str or minimalkv.KeyValueStore) –
The store where we can find or store the dataset.

Can be either minimalkv.KeyValueStore, a minimalkv store url or a generic Callable producing a minimalkv.KeyValueStore
columns – A subset of columns to be loaded.
predicate_pushdown_to_io (bool) – Push predicates through to the I/O layer, default True. Disable this if you see problems with predicate pushdown for the given file even if the file format supports it. Note that this option only hides problems in the storage layer that need to be addressed there.
categoricals – Load the provided subset of columns as a pandas.Categorical.
dates_as_object (bool) – Load pyarrow.date{32,64} columns as object columns in Pandas instead of using np.datetime64 to preserve their type. While this improves type-safety, this comes at a performance cost.
predicates (List[List[Tuple[str, str, Any]]) –
Optional list of predicates, like [[(‘x’, ‘>’, 0), …], that are used to filter the resulting DataFrame, possibly using predicate pushdown, if supported by the file format. This parameter is not compatible with filter_query.

Predicates are expressed in disjunctive normal form (DNF). This means that the innermost tuple describes a single column predicate. These inner predicates are all combined with a conjunction (AND) into a larger predicate. The most outer list then combines all predicates with a disjunction (OR). By this, we should be able to express all kinds of predicates that are possible using boolean logic.

Available operators are: ==, !=, <=, >=, <, > and in.

Filtering for missings is supported with operators ==, != and in and values np.nan and None for float and string columns respectively.

Categorical data

When using order sensitive operators on categorical data we will assume that the categories obey a lexicographical ordering. This filtering may result in less than optimal performance and may be slower than the evaluation on non-categorical data.

See also Filtering / Predicate pushdown and Efficient Querying
factory (plateau.core.factory.DatasetFactory) – A DatasetFactory holding the store and UUID to the source dataset.
dispatch_by (Optional[List[str]]) –
List of index columns to group and partition the jobs by. There will be one job created for every observed index value combination. This may result in either many very small partitions or in few very large partitions, depending on the index you are using this on.

Secondary indices

This is also useable in combination with secondary indices where the physical file layout may not be aligned with the logically requested layout. For optimal performance it is recommended to use this for columns which can benefit from predicate pushdown since the jobs will fetch their data individually and will not shuffle data in memory / over network.

plateau.io.dask.delayed.store_delayed_as_dataset(delayed_tasks: List[Delayed], store, dataset_uuid=None, metadata=None, df_serializer=None, overwrite=False, metadata_merger=None, metadata_version=4, partition_on=None, metadata_storage_format='json', table_name: str = 'table', secondary_indices=None) → Delayed[source]

Transform and store a list of dictionaries containing dataframes to a plateau dataset in store.

Parameters:

delayed_tasks – A list of delayed objects where each element returns a pandas.DataFrame.
store (Callable or str or minimalkv.KeyValueStore) –
The store where we can find or store the dataset.

Can be either minimalkv.KeyValueStore, a minimalkv store url or a generic Callable producing a minimalkv.KeyValueStore
dataset_uuid (str) – The dataset UUID
metadata (Optional[Dict]) – A dictionary used to update the dataset metadata.
df_serializer (Optional[plateau.serialization.DataFrameSerializer]) – A pandas DataFrame serialiser from plateau.serialization
overwrite (Optional[bool]) – If True, allow overwrite of an existing dataset.
metadata_merger (Optional[Callable]) – By default partition metadata is combined using the combine_metadata() function. You can supply a callable here that implements a custom merge operation on the metadata dictionaries (depending on the matches this might be more than two values).
metadata_version (Optional[int]) – The dataset metadata version
partition_on (List) – Column names by which the dataset should be partitioned by physically. These columns may later on be used as an Index to improve query performance. Partition columns need to be present in all dataset tables. Sensitive to ordering.
metadata_storage_format (str) – Optional list of datastorage format to use. Currently supported is .json & .msgpack.zstd”
table_name –
The table name of the dataset to be loaded. This creates a namespace for the partitioning like

dataset_uuid/table_name/*

This is to support legacy workflows. We recommend not to use this and use the default wherever possible.
secondary_indices (List[str]) – A list of columns for which a secondary index should be calculated.

plateau.io.dask.delayed.update_dataset_from_delayed(delayed_tasks: List[Delayed], store=None, dataset_uuid=None, delete_scope=None, metadata=None, df_serializer=None, metadata_merger=None, default_metadata_version=4, partition_on=None, sort_partitions_by=None, secondary_indices=None, factory=None, table_name='table')[source]

A dask.delayed graph to add and store a list of dictionaries containing dataframes to a plateau dataset in store. The input should be a list (or splitter pipeline) containing MetaPartition. If you want to use this pipeline step for just deleting partitions without adding new ones you have to give an empty meta partition as input ([Metapartition(None)]).

Parameters:

delayed_tasks – A list of delayed objects where each element returns a pandas.DataFrame.
store (Callable or str or minimalkv.KeyValueStore) –
The store where we can find or store the dataset.

Can be either minimalkv.KeyValueStore, a minimalkv store url or a generic Callable producing a minimalkv.KeyValueStore
dataset_uuid (str) – The dataset UUID
delete_scope (List[Dict]) – This defines which partitions are replaced with the input and therefore get deleted. It is a lists of query filters for the dataframe in the form of a dictionary, e.g.: [{‘column_1’: ‘value_1’}, {‘column_1’: ‘value_2’}]. Each query filter will be given to: func: `dataset.query and the returned partitions will be deleted. If no scope is given nothing will be deleted. For plateau.io.dask.update.update_dataset.* a delayed object resolving to a list of dicts is also accepted.
metadata (Optional[Dict]) – A dictionary used to update the dataset metadata.
df_serializer (Optional[plateau.serialization.DataFrameSerializer]) – A pandas DataFrame serialiser from plateau.serialization
metadata_merger (Optional[Callable]) – By default partition metadata is combined using the combine_metadata() function. You can supply a callable here that implements a custom merge operation on the metadata dictionaries (depending on the matches this might be more than two values).
default_metadata_version (int) – Default metadata version. (Note: Metadata version greater than 3 are only supported)
partition_on (List) – Column names by which the dataset should be partitioned by physically. These columns may later on be used as an Index to improve query performance. Partition columns need to be present in all dataset tables. Sensitive to ordering.
sort_partitions_by (str) – Provide a column after which the data should be sorted before storage to enable predicate pushdown.
secondary_indices (List[str]) – A list of columns for which a secondary index should be calculated.
factory (plateau.core.factory.DatasetFactory) – A DatasetFactory holding the store and UUID to the source dataset.
table_name –
The table name of the dataset to be loaded. This creates a namespace for the partitioning like

dataset_uuid/table_name/*

This is to support legacy workflows. We recommend not to use this and use the default wherever possible.