atoti.Session.read_arrow()#

Session.read_arrow(table, /, *, table_name, keys=frozenset({}), partitioning=None, types={}, default_values={}, **kwargs)#

Read an Arrow Table into a table.

Parameters:

  • table (Table) – The Arrow Table to load.

  • table_name (str) – The name of the table to create.

  • keys (Set[str] | Sequence[str]) –

    The columns that will become keys of the table.

    If a Set is given, the keys will be ordered as the table columns.

  • partitioning (str | None) –

    The description of how the data will be split across partitions of the table.

    Default rules:

    • Only non-joined tables are automatically partitioned.

    • Tables are automatically partitioned by hashing their key columns. If there are no key columns, all the dictionarized columns are hashed.

    • Joined tables can only use a sub-partitioning of the table referencing them.

    • Automatic partitioning is done modulo the number of available cores.

    Example

    modulo4(country) splits the data across 4 partitions based on the country column’s dictionarized value.

  • types (Mapping[str, DataType]) – Types for some or all columns of the table. Types for non specified columns will be inferred from arrow DataTypes.

  • default_values (Mapping[str, bool | date | datetime | float | int | Sequence[int] | Sequence[float] | str | time | None]) – Mapping from column name to column default_value.

Return type:

Table

Example

>>> import pyarrow as pa
>>> arrow_table = pa.Table.from_arrays(
...     [
...         pa.array(["phone", "headset", "watch"]),
...         pa.array([600.0, 80.0, 250.0]),
...     ],
...     names=["Product", "Price"],
... )
>>> arrow_table
pyarrow.Table
Product: string
Price: double
----
Product: [["phone","headset","watch"]]
Price: [[600,80,250]]
>>> table = session.read_arrow(
...     arrow_table, keys={"Product"}, table_name="Arrow"
... )
>>> table.head().sort_index()
         Price
Product
headset   80.0
phone    600.0
watch    250.0