semvec/pypi-packages huggingface dataset:
beam-postgres 0.5.0
Light IO transforms for Postgres read/write in Apache Beam pipelines.
License: Apache-2.0
Requires Python: >=3.8
Keywords: apache beam, beam, postgres, postgresql

# beam-postgres

[![PyPI](https://img.shields.io/pypi/v/beam-postgres.svg)][pypi-project]
[![Supported Versions](https://img.shields.io/pypi/pyversions/beam-postgres.svg)][pypi-project]

Light IO transforms for Postgres read/write in Apache Beam pipelines.

## Goal

The project aims to provide highly performant and customizable transforms and is
not intended to support many different SQL database engines.

## Features

- `ReadAllFromPostgres`, `ReadFromPostgres`` and `WriteToPostgres` transforms
- Records can be mapped to tuples, dictionaries or dataclasses
- Reads and writes are in configurable batches

## Usage

Printing data from the database table:

```python
import apache_beam as beam
from psycopg.rows import dict_row

from beam_postgres.io import ReadAllFromPostgres

with beam.Pipeline() as p:
    data = p | "Reading example records from database" >> ReadAllFromPostgres(
        "host=localhost dbname=examples user=postgres password=postgres",
        "select id, data from source",
        dict_row,
    )
    data | "Writing to stdout" >> beam.Map(print)

```

Writing data to the database table:

```python
from dataclasses import dataclass

import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions

from beam_postgres.io import WriteToPostgres


@dataclass
class Example:
    data: str


with beam.Pipeline(options=PipelineOptions()) as p:
    data = p | "Reading example records" >> beam.Create(
        [
            Example("example1"),
            Example("example2"),
        ]
    )
    data | "Writing example records to database" >> WriteToPostgres(
        "host=localhost dbname=examples user=postgres password=postgres",
        "insert into sink (data) values (%(data)s)",
    )

```

See [here][examples] for more examples.

### Reading in batches

There may be situations when you have so much data that it will not fit into the
memory - then you want to read your table data in batches. You can see an
example code [here](examples/read.py#L11) (the code reads records in a batches of
1).

[pypi-project]: https://pypi.org/project/beam-postgres
[examples]: https://github.com/medzin/beam-postgres/tree/main/examples