API Reference

DeltaTable

class deltalake.table.DeltaTable(table_uri, version=None, storage_options=None)

Create a DeltaTable instance.

Parameters

table_uri (str) –
version (Optional[int]) –
storage_options (Optional[Dict[str, str]]) –

file_paths()

Get the list of files with an absolute path.

Returns: list of the .parquet files with an absolute URI referenced for the current version of the DeltaTable
Return type: List[str]

file_uris()

Get the list of files with an absolute path.

Returns: list of the .parquet files with an absolute URI referenced for the current version of the DeltaTable
Return type: List[str]

files()

Get the .parquet files of the DeltaTable.

Returns: list of the .parquet files referenced for the current version of the DeltaTable
Return type: List[str]

files_by_partitions(partition_filters)

Get the files that match a given list of partitions filters. Partitions which do not match the filter predicate will be removed from scanned data. Predicates are expressed in disjunctive normal form (DNF), like [(“x”, “=”, “a”), …]. DNF allows arbitrary boolean logical combinations of single partition predicates. The innermost tuples each describe a single partition predicate. The list of inner predicates is interpreted as a conjunction (AND), forming a more selective and multiple partition predicates. Each tuple has format: (key, op, value) and compares the key with the value. The supported op are: =, !=, in, and not in. If the op is in or not in, the value must be a collection such as a list, a set or a tuple. The supported type for value is str. Use empty string ‘’ for Null partition value.

Examples: (“x”, “=”, “a”) (“x”, “!=”, “a”) (“y”, “in”, [“a”, “b”, “c”]) (“z”, “not in”, [“a”,”b”])

Parameters: partition_filters (List[Tuple[str, str, Any]]) – the partition filters that will be used for getting the matched files
Returns: list of the .parquet files after applying the partition filters referenced for the current version of the DeltaTable.
Return type: List[str]

classmethod from_data_catalog(data_catalog, database_name, table_name, data_catalog_id=None, version=None)

Create the Delta Table from a Data Catalog.

Parameters

data_catalog (deltalake.data_catalog.DataCatalog) – the Catalog to use for getting the storage location of the Delta Table
database_name (str) – the database name inside the Data Catalog
table_name (str) – the table name inside the Data Catalog
data_catalog_id (Optional[str]) – the identifier of the Data Catalog
version (Optional[int]) – version of the DeltaTable

Return type

deltalake.table.DeltaTable

history(limit=None)

Run the history command on the DeltaTable. The operations are returned in reverse chronological order.

Parameters: limit (Optional[int]) – the commit info limit to return
Returns: list of the commit infos registered in the transaction log
Return type: List[Dict[str, Any]]

load_version(version)

Load a DeltaTable with a specified version.

Parameters: version (int) – the identifier of the version of the DeltaTable to load
Return type: None

load_with_datetime(datetime_string)

Time travel Delta table to the latest version that’s created at or before provided datetime_string argument. The datetime_string argument should be an RFC 3339 and ISO 8601 date and time string.

Examples: 2018-01-26T18:30:09Z 2018-12-19T16:39:57-08:00 2018-01-26T18:30:09.453+00:00

Parameters: datetime_string (str) – the identifier of the datetime point of the DeltaTable to load
Return type: None

metadata()

Get the current metadata of the DeltaTable.

Returns: the current Metadata registered in the transaction log
Return type: deltalake.table.Metadata

pyarrow_schema()

Get the current schema of the DeltaTable with the Parquet PyArrow format.

Returns: the current Schema with the Parquet PyArrow format
Return type: pyarrow.lib.Schema

schema()

Get the current schema of the DeltaTable.

Returns: the current Schema registered in the transaction log
Return type: deltalake.schema.Schema

to_pandas(partitions=None, columns=None, filesystem=None)

Build a pandas dataframe using data from the DeltaTable.

Parameters

partitions (Optional[List[Tuple[str, str, Any]]]) – A list of partition filters, see help(DeltaTable.files_by_partitions) for filter syntax
columns (Optional[List[str]]) – The columns to project. This can be a list of column names to include (order and duplicates will be preserved)
filesystem (Optional[Union[str, pyarrow._fs.FileSystem]]) – A concrete implementation of the Pyarrow FileSystem or a fsspec-compatible interface. If None, the first file path will be used to determine the right FileSystem

Returns

a pandas dataframe

Return type

pandas.DataFrame

to_pyarrow_dataset(partitions=None, filesystem=None, parquet_read_options=None)

Build a PyArrow Dataset using data from the DeltaTable.

Parameters

partitions (Optional[List[Tuple[str, str, Any]]]) – A list of partition filters, see help(DeltaTable.files_by_partitions) for filter syntax
filesystem (Optional[Union[str, pyarrow._fs.FileSystem]]) – A concrete implementation of the Pyarrow FileSystem or a fsspec-compatible interface. If None, the first file path will be used to determine the right FileSystem
parquet_read_options (Optional[pyarrow._dataset_parquet.ParquetReadOptions]) – Optional read options for Parquet. Use this to handle INT96 to timestamp conversion for edge cases like 0001-01-01 or 9999-12-31 More info: https://arrow.apache.org/docs/python/generated/pyarrow.dataset.ParquetReadOptions.html

Returns

the PyArrow dataset in PyArrow

Return type

pyarrow._dataset.Dataset

to_pyarrow_table(partitions=None, columns=None, filesystem=None)

Build a PyArrow Table using data from the DeltaTable.

Parameters

partitions (Optional[List[Tuple[str, str, Any]]]) – A list of partition filters, see help(DeltaTable.files_by_partitions) for filter syntax
columns (Optional[List[str]]) – The columns to project. This can be a list of column names to include (order and duplicates will be preserved)
filesystem (Optional[Union[str, pyarrow._fs.FileSystem]]) – A concrete implementation of the Pyarrow FileSystem or a fsspec-compatible interface. If None, the first file path will be used to determine the right FileSystem

Returns

the PyArrow table

Return type

pyarrow.lib.Table

update_incremental()

Updates the DeltaTable to the latest version by incrementally applying newer versions.

Return type: None

vacuum(retention_hours=None, dry_run=True, enforce_retention_duration=True)

Run the Vacuum command on the Delta Table: list and delete files no longer referenced by the Delta table and are older than the retention threshold.

Parameters

retention_hours (Optional[int]) – the retention threshold in hours, if none then the value from configuration.deletedFileRetentionDuration is used or default of 1 week otherwise.
dry_run (bool) – when activated, list only the files, delete otherwise
enforce_retention_duration (bool) – when disabled, accepts retention hours smaller than the value from configuration.deletedFileRetentionDuration.

Returns

the list of files no longer referenced by the Delta Table and are older than the retention threshold.

Return type

List[str]

version()

Get the version of the DeltaTable.

Returns: The current version of the DeltaTable
Return type: int

class deltalake.table.Metadata(table)

Create a Metadata instance.

Parameters: table (RawDeltaTable) –

property configuration: Dict[str, str]: Return the DeltaTable properties.

property created_time: int: Return The time when this metadata action is created, in milliseconds since the Unix epoch of the DeltaTable.

property description: str: Return the user-provided description of the DeltaTable.

property id: int: Return the unique identifier of the DeltaTable.

property name: str: Return the user-provided identifier of the DeltaTable.

property partition_columns: List[str]: Return an array containing the names of the partitioned columns of the DeltaTable.

class deltalake.table.ProtocolVersions(min_reader_version, min_writer_version)

Parameters

min_reader_version (int) –
min_writer_version (int) –

min_reader_version: int: Alias for field number 0

min_writer_version: int: Alias for field number 1

Writing DeltaTables

deltalake.write_deltalake(table_or_uri, data, *, schema=None, partition_by=None, filesystem=None, mode='error', file_options=None, max_open_files=1024, max_rows_per_file=0, min_rows_per_group=0, max_rows_per_group=1048576, name=None, description=None, configuration=None, overwrite_schema=False)

Write to a Delta Lake table (Experimental)

If the table does not already exist, it will be created.

This function only supports protocol version 1 currently. If an attempting to write to an existing table with a higher min_writer_version, this function will throw DeltaTableProtocolError.

Note that this function does NOT register this table in a data catalog.

Parameters

table_or_uri (Union[str, deltalake.table.DeltaTable]) – URI of a table or a DeltaTable object.
data (Union[pandas.core.frame.DataFrame, pyarrow.lib.Table, pyarrow.lib.RecordBatch, Iterable[pyarrow.lib.RecordBatch], pyarrow.lib.RecordBatchReader]) – Data to write. If passing iterable, the schema must also be given.
schema (Optional[pyarrow.lib.Schema]) – Optional schema to write.
partition_by (Optional[List[str]]) – List of columns to partition the table by. Only required when creating a new table.
filesystem (Optional[pyarrow._fs.FileSystem]) – Optional filesystem to pass to PyArrow. If not provided will be inferred from uri.
mode (Literal['error', 'append', 'overwrite', 'ignore']) – How to handle existing data. Default is to error if table already exists. If ‘append’, will add new data. If ‘overwrite’, will replace table with new data. If ‘ignore’, will not write anything if table already exists.
file_options (Optional[pyarrow._dataset_parquet.ParquetFileWriteOptions]) – Optional write options for Parquet (ParquetFileWriteOptions). Can be provided with defaults using ParquetFileWriteOptions().make_write_options(). Please refer to https://github.com/apache/arrow/blob/master/python/pyarrow/_dataset_parquet.pyx#L492-L533 for the list of available options
max_open_files (int) – Limits the maximum number of files that can be left open while writing. If an attempt is made to open too many files then the least recently used file will be closed. If this setting is set too low you may end up fragmenting your data into many small files.
max_rows_per_file (int) – Maximum number of rows per file. If greater than 0 then this will limit how many rows are placed in any single file. Otherwise there will be no limit and one file will be created in each output directory unless files need to be closed to respect max_open_files
min_rows_per_group (int) – Minimum number of rows per group. When the value is set, the dataset writer will batch incoming data and only write the row groups to the disk when sufficient rows have accumulated.
max_rows_per_group (int) – Maximum number of rows per group. If the value is set, then the dataset writer may split up large incoming batches into multiple row groups. If this value is set, then min_rows_per_group should also be set.
name (Optional[str]) – User-provided identifier for this table.
description (Optional[str]) – User-provided description for this table.
configuration (Optional[Mapping[str, Optional[str]]]) – A map containing configuration options for the metadata action.
overwrite_schema (bool) – If True, allows updating the schema of the table.

Return type

None

DeltaSchema

class deltalake.schema.ArrayType(element_type, contains_null)

Concrete class for array data types.

Parameters

element_type (deltalake.schema.DataType) –
contains_null (bool) –

class deltalake.schema.DataType(type)

Base class of all Delta data types.

Parameters: type (str) –
Return type: None

classmethod from_dict(json_dict)

Generate a DataType from a DataType in json format.

Parameters: json_dict (Dict[str, Any]) – the data type in json format
Returns: the Delta DataType
Return type: deltalake.schema.DataType

class deltalake.schema.Field(name, type, nullable, metadata=None)

Create a DeltaTable Field instance.

Parameters

name (str) –
type (deltalake.schema.DataType) –
nullable (bool) –
metadata (Optional[Dict[str, str]]) –

Return type

None

class deltalake.schema.MapType(key_type, value_type, value_contains_null)

Concrete class for map data types.

Parameters

key_type (deltalake.schema.DataType) –
value_type (deltalake.schema.DataType) –
value_contains_null (bool) –

class deltalake.schema.Schema(fields, json_value)

Create a DeltaTable Schema instance.

Parameters

fields (List[deltalake.schema.Field]) –
json_value (Dict[str, Any]) –

Return type

None

classmethod from_json(json_data)

Generate a DeltaTable Schema from a json format.

Parameters: json_data (str) – the schema in json format
Returns: the DeltaTable schema
Return type: deltalake.schema.Schema

class deltalake.schema.StructType(fields)

Concrete class for struct data types.

Parameters: fields (List[deltalake.schema.Field]) –

deltalake.schema.pyarrow_datatype_from_dict(json_dict)

Create a DataType in PyArrow format from a Schema json format.

Parameters: json_dict (Dict[str, Any]) – the DataType in json format
Returns: the DataType in PyArrow format
Return type: pyarrow.lib.DataType

deltalake.schema.pyarrow_field_from_dict(field)

Create a Field in PyArrow format from a Field in json format. :param field: the field in json format :return: the Field in PyArrow format

Parameters: field (Dict[str, Any]) –
Return type: pyarrow.lib.Field

deltalake.schema.pyarrow_schema_from_json(json_data)

Create a Schema in PyArrow format from a Schema in json format.

Parameters: json_data (str) – the field in json format
Returns: the Schema in PyArrow format
Return type: pyarrow.lib.Schema

DataCatalog

class deltalake.data_catalog.DataCatalog(value): List of the Data Catalogs

DeltaStorageHandler

class deltalake.fs.DeltaStorageHandler(table_uri)

DeltaStorageHander is a concrete implementations of a PyArrow FileSystemHandler.

Parameters: table_uri (str) –
Return type: None

copy_file(src, dest)

Copy a file.

If the destination exists and is a directory, an error is returned. Otherwise, it is replaced.

Parameters

src (str) – The path of the file to be copied from.
dest (str) – The destination path where the file is copied to.

Return type

None

create_dir(path, *, recursive=True)

Create a directory and subdirectories.

This function succeeds if the directory already exists.

Parameters

path (str) – The path of the new directory.
recursive (bool) – Create nested directories as well.

Return type

None

delete_dir(path)

Delete a directory and its contents, recursively.

Parameters: path (str) – The path of the directory to be deleted.
Return type: None

delete_dir_contents(path)

Delete a directory’s contents, recursively.

Like delete_dir, but doesn’t delete the directory itself.

Parameters: path (str) – The path of the directory to be deleted.
Return type: None

delete_file(path)

Delete a file.

Parameters: path (str) – The path of the file to be deleted.
Return type: None

delete_root_dir_contents()

Delete a directory’s contents, recursively.

Like delete_dir_contents, but for the root directory (path is empty or “/”)

Return type: None

get_file_info(paths)

Get info for the given files.

Parameters: paths (List[str]) – List of file paths
Returns: list of file info objects
Return type: List[pyarrow._fs.FileInfo]

get_file_info_selector(selector)

Get info for the files defined by FileSelector.

Parameters: selector (pyarrow._fs.FileSelector) – FileSelector object
Returns: list of file info objects
Return type: List[pyarrow._fs.FileInfo]

get_type_name()

The filesystem’s type name.

Returns: The filesystem’s type name.
Return type: str

move(src, dest)

Move / rename a file or directory.

If the destination exists: - if it is a non-empty directory, an error is returned - otherwise, if it has the same type as the source, it is replaced - otherwise, behavior is unspecified (implementation-dependent).

Parameters

src (str) – The path of the file or the directory to be moved.
dest (str) – The destination path where the file or directory is moved to.

Return type

None

normalize_path(path)

Normalize filesystem path.

Parameters: path (str) – the path to normalize
Returns: the normalized path
Return type: str

open_append_stream(path, metadata=None)

DEPRECATED: Open an output stream for appending.

If the target doesn’t exist, a new empty file is created.

Parameters

path (str) – The source to open for writing.
metadata (Optional[Dict[str, Any]]) – If not None, a mapping of string keys to string values.

Returns

NativeFile

Return type

pyarrow.lib.NativeFile

open_input_file(path)

Open an input file for random access reading.

Parameters

source – The source to open for reading.
path (str) –

Returns

NativeFile

Return type

pyarrow.lib.NativeFile

open_input_stream(path)

Open an input stream for sequential reading.

Parameters

source – The source to open for reading.
path (str) –

Returns

NativeFile

Return type

pyarrow.lib.NativeFile

open_output_stream(path, metadata=None)

Open an output stream for sequential writing.

If the target already exists, existing data is truncated.

Parameters

path (str) – The source to open for writing.
metadata (Optional[Dict[str, Any]]) – If not None, a mapping of string keys to string values.

Returns

NativeFile

Return type

pyarrow.lib.NativeFile