Metadata-Version: 2.3
Name: agefreighter
Version: 0.1.0
Summary: AgeFreighter is a Python package that helps you to create a graph database using Azure Database for PostgreSQL.
Project-URL: Homepage, https://github.com/rioriost/agefreighter
Project-URL: Issues, https://github.com/rioriost/agefreighter/issues
Author-email: Rio Fujita <rifujita@microsoft.com>
License: MIT License
        
        Copyright (c) 2024 Rio Fujita
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
Requires-Python: >=3.11
Requires-Dist: asyncio>=3.4.3
Requires-Dist: numpy>=2.1.3
Requires-Dist: pandas>=2.2.3
Requires-Dist: psycopg-pool>=3.2.3
Requires-Dist: psycopg>=3.2.3
Requires-Dist: typing-extensions>=4.12.2
Requires-Dist: typing>=3.10.0.0
Description-Content-Type: text/markdown

# AGEFreighter

a Python package that helps you to create a graph database using Azure Database for PostgreSQL.

[Apache AGE™](https://age.apache.org/) is a PostgreSQL Graph database compatible with PostgreSQL's distributed assets and leverages graph data structures to analyze and use relationships and patterns in data.

[Azure Database for PostgreSQL](https://azure.microsoft.com/en-us/services/postgresql/) is a managed database service that is based on the open-source Postgres database engine.

[Introducing support for Graph data in Azure Database for PostgreSQL (Preview)](https://techcommunity.microsoft.com/blog/adforpostgresql/introducing-support-for-graph-data-in-azure-database-for-postgresql-preview/4275628).

### Features
* Asynchronous connection pool support for psycopg PostgreSQL driver
* 'direct_load' option for loading data directly into the graph for better performance
* 'COPY' protocol support for loading data into the graph for much better performance

### Install

```bash
pip install agefreighter
```

### Prerequisites
* over Python 3.11
* This module runs on [psycopg](https://www.psycopg.org/) and [psycopg_pool](https://www.psycopg.org/)
* Enable the Apache AGE extension in your Azure Database for PostgreSQL instance. Login Azure Portal, go to 'server parameters' blade, and check 'AGE" on within 'azure.extensions' and 'shared_preload_libraries' parameters. See, above blog post for more information.
* Load the AGE extension in your PostgreSQL database.

```sql
CREATE EXTENSION IF NOT EXISTS age CASCADE;
```

### Usage

```python
import os

import asyncio
from agefreighter import AgeFreighter

# file downloaded from https://www.kaggle.com/datasets/darinhawley/imdb-films-by-actor-for-10k-actors
# actorfilms.csv: Actor,ActorID,Film,Year,Votes,Rating,FilmID
# # of actors: 9,623, # of films: 44,456, # of edges: 191,873
async def test_loadFromSingleCSV(af: AgeFreighter, chunk_size: int = 96, direct_loading: bool = False) -> None:
    await af.loadFromSingleCSV(
        graph_name="actorfilms",
        csv="actorfilms.csv",
        start_vertex_type="Actor",
        start_id="ActorID",
        start_properties=["Actor"],
        edge_label="ACTED_IN",
        end_vertex_type="Film",
        end_id="FilmID",
        end_properties=["Film", "Year", "Votes", "Rating"],
        chunk_size=chunk_size,
        direct_loading = direct_loading,
        drop_graph = True
    )

# cities.csv: id,name,state_id,state_code,country_id,country_code,latitude,longitude
# continents.csv: id,name,iso3,iso2,numeric_code,phone_code,capital,currency,currency_symbol,tld,native,region,subregion,latitude,longitude,emoji,emojiU
# edges.csv: start_id,start_vertex_type,end_id,end_vertex_type
# # of countries: 53, # of cities: 72,485, # of edges: 72,485
async def test_loadFromCSVs(af: AgeFreighter, chunk_size: int = 96, direct_loading: bool = False) -> None:
    await af.loadFromCSVs(
        graph_name="cities_countries",
        vertex_csvs=["countries.csv", "cities.csv"],
        vertex_labels=["Country", "City"],
        edge_csvs=["edges.csv"],
        edge_labels=["has_city"],
        chunk_size=chunk_size,
        direct_loading = direct_loading,
        drop_graph = True
    )

async def test_copyFromSingleCSV(af: AgeFreighter, chunk_size: int = 96) -> None:
    start_time = time.time()
    await af.copyFromSingleCSV(
        graph_name="actorfilms",
        csv="actorfilms.csv",
        start_vertex_type="Actor",
        start_id="ActorID",
        start_properties=["Actor"],
        edge_label="ACTED_IN",
        end_vertex_type="Film",
        end_id="FilmID",
        end_properties=["Film", "Year", "Votes", "Rating"],
        chunk_size=chunk_size,
        drop_graph = True
    )

async def test_copyFromCSVs(af: AgeFreighter, chunk_size: int = 96) -> None:
    start_time = time.time()
    await af.copyFromCSVs(
        graph_name="cities_countries",
        vertex_csvs=["countries.csv", "cities.csv"],
        vertex_labels=["Country", "City"],
        edge_csvs=["edges.csv"],
        edge_labels=["has_city"],
        chunk_size=chunk_size,
        drop_graph = True
    )

async def main() -> None:
    # export PG_CONNECTION_STRING="host=your_server.postgres.database.azure.com port=5432 dbname=postgres user=account password=your_password"
    try:
        connection_string = os.environ["PG_CONNECTION_STRING"]
    except KeyError:
        print("Please set the environment variable PG_CONNECTION_STRING")
        return

    af = await AgeFreighter.connect(dsn = connection_string, max_connections = 64)
    try:
        # Strongly reccomended to define chunk_size with your data and server before loading large amount of data
        # Especially, the number of properties in the vertex affects the complecity of the query
        # Due to asynchronous nature of the library, the duration for loading data is not linear to the number of rows
        #
        # Addition to the chunk_size, max_wal_size and checkpoint_timeout in the postgresql.conf should be considered
        chunk_size = 64
        await test_loadFromSingleCSV(af, chunk_size = chunk_size, direct_loading = False)
        await asyncio.sleep(10)
        await test_loadFromSingleCSV(af, chunk_size = chunk_size, direct_loading = True)
        await asyncio.sleep(10)
        await test_copyFromSingleCSV(af, chunk_size = chunk_size)
        await asyncio.sleep(10)

        await test_loadFromCSVs(af, chunk_size = chunk_size, direct_loading = False)
        await asyncio.sleep(10)
        await test_loadFromCSVs(af, chunk_size = chunk_size, direct_loading = True)
        await asyncio.sleep(10)
        await test_copyFromCSVs(af, chunk_size = chunk_size)
        await asyncio.sleep(10)

    finally:
        await af.pool.close()

if __name__ == "__main__":
    asyncio.run(main())
```

### Test & Samples
```
export PG_CONNECTION_STRING="host=your_server.postgres.database.azure.com port=5432 dbname=postgres user=account password=your_password"
python3 tests/test_agefreighter.py
```

### For more information about [Apache AGE](https://age.apache.org/)
* Apache AGE : https://age.apache.org/
* GitHub : https://github.com/apache/age
* Document : https://age.apache.org/age-manual/master/index.html

### License
MIT License
