Запись в Postgres с использованием DBWriter

Для записи данных в Postgres используйте DBWriter.

Warning

Пожалуйста, учитывайте типы данных Postgres

Warning

Всегда рекомендуется создавать таблицу явно с помощью Postgres.execute вместо того, чтобы полагаться на автоматическую генерацию DDL в Spark.

Это связано с тем, что генератор DDL в Spark может создавать столбцы с другой точностью и типами, чем ожидается, что приводит к потере точности или другим проблемам.

Примеры

```python
    from onetl.connection import Postgres
    from onetl.db import DBWriter

    postgres = Postgres(...)

    df = ...  # здесь данные

    writer = DBWriter(
        connection=postgres,
        target="schema.table",
        options=Postgres.WriteOptions(if_exists="append"),
    )

    writer.run(df)
```

Опции

Метод выше принимает Postgres.WriteOptions

onetl.connection.db_connection.postgres.options.PostgresWriteOptions

Bases: JDBCWriteOptions

Source code in onetl/connection/db_connection/postgres/options.py

class PostgresWriteOptions(JDBCWriteOptions):
    __doc__ = JDBCWriteOptions.__doc__.replace("SomeDB", "Postgres")  # type: ignore[assignment, union-attr]

batchsize = 20000 class-attribute instance-attribute

How many rows can be inserted per round trip.

Tuning this option can influence performance of writing.

.. warning::

Default value is different from Spark.

Spark uses quite small value ``1000``, which is absolutely not usable
in BigData world.

Thus we've overridden default value with ``20_000``,
which should increase writing performance.

You can increase it even more, up to ``50_000``,
but it depends on your database load and number of columns in the row.
Higher values does not increase performance.

.. versionchanged:: 0.4.0 Changed default value from 1000 to 20_000

if_exists = Field(default=(JDBCTableExistBehavior.APPEND), alias=(avoid_alias('mode'))) class-attribute instance-attribute

Behavior of writing data into existing table.

Possible values

• append (default) Adds new rows into existing table.

  .. dropdown:: Behavior in details

  * Table does not exist
      Table is created using options provided by user
      (``createTableOptions``, ``createTableColumnTypes``, etc).
  
  * Table exists
      Data is appended to a table. Table has the same DDL as before writing data
  
      .. warning::
  
          This mode does not check whether table already contains
          rows from dataframe, so duplicated rows can be created.
  
          Also Spark does not support passing custom options to
          insert statement, like ``ON CONFLICT``, so don't try to
          implement deduplication using unique indexes or constraints.
  
          Instead, write to staging table and perform deduplication
          using :obj:`~execute` method.
  

• replace_entire_table Table is dropped and then created, or truncated.

  .. dropdown:: Behavior in details

  * Table does not exist
      Table is created using options provided by user
      (``createTableOptions``, ``createTableColumnTypes``, etc).
  
  * Table exists
      Table content is replaced with dataframe content.
  
      After writing completed, target table could either have the same DDL as
      before writing data (``truncate=True``), or can be recreated (``truncate=False``
      or source does not support truncation).
  

• ignore Ignores the write operation if the table already exists.

  .. dropdown:: Behavior in details

  * Table does not exist
      Table is created using options provided by user
      (``createTableOptions``, ``createTableColumnTypes``, etc).
  
  * Table exists
      The write operation is ignored, and no data is written to the table.
  

• error Raises an error if the table already exists.

  .. dropdown:: Behavior in details

  * Table does not exist
      Table is created using options provided by user
      (``createTableOptions``, ``createTableColumnTypes``, etc).
  
  * Table exists
      An error is raised, and no data is written to the table.
  

.. versionchanged:: 0.9.0 Renamed mode → if_exists

isolation_level = Field(default='READ_UNCOMMITTED', alias='isolationLevel') class-attribute instance-attribute

The transaction isolation level, which applies to current connection.

Possible values

• NONE (as string, not Python's None)
• READ_COMMITTED
• READ_UNCOMMITTED
• REPEATABLE_READ
• SERIALIZABLE

Values correspond to transaction isolation levels defined by JDBC standard. Please refer the documentation for java.sql.Connection <https://docs.oracle.com/javase/8/docs/api/java/sql/Connection.html>_.

query_timeout = Field(default=None, alias='queryTimeout') class-attribute instance-attribute

The number of seconds the driver will wait for a statement to execute. Zero means there is no limit.

This option depends on driver implementation, some drivers can check the timeout of each query instead of an entire JDBC batch.

parse(options) classmethod

If a parameter inherited from the ReadOptions class was passed, then it will be returned unchanged. If a Dict object was passed it will be converted to ReadOptions.

Otherwise, an exception will be raised

Source code in onetl/impl/generic_options.py

@classmethod
def parse(
    cls: type[T],
    options: GenericOptions | dict | None,
) -> T:
    """
    If a parameter inherited from the ReadOptions class was passed, then it will be returned unchanged.
    If a Dict object was passed it will be converted to ReadOptions.

    Otherwise, an exception will be raised
    """

    if not options:
        return cls()

    if isinstance(options, dict):
        return cls.parse_obj(options)

    if not isinstance(options, cls):
        raise TypeError(
            f"{options.__class__.__name__} is not a {cls.__name__} instance",
        )

    return options