agefreighter 0.5.3

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

AGEFreighter

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

Apache AGE™ 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 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).

0.5.0 Release

Refactored the code to make it more readable and maintainable with the separated classes for factory model. Please note how to use the new version of the package is tottally different from the previous versions.

0.5.2 Release -AzureStorageFreighter-

• AzureStorageFreighter class is used to load data from Azure Storage into the graph database. It's totally different from other classes. The class works as follows:
  • If the argument, 'subscription_id' is not set, the class tries to find the Azure Subscription ID from your local environment using the 'az' command.
  • Creates an Azure Storage account and a blob container under the resource group where the PostgreSQL server runs in.
  • Enables the 'azure_storage' extension in the PostgreSQL server, if it's not enabled.
  • Uploads the CSV file to the blob container.
  • Creates a UDF (User Defined Function) named 'load_from_azure_storage' in the PostgreSQL server. The UDF loads data from the Azure Storage into the graph database.
  • Executes the UDF.
• The above process takes time to prepare for loading data, making it unsuitable for loading small files, but effective for loading large files. For instance, it takes under 3 seconds to load 'actorfilms.csv' after uploading.
• However, please note that it is still in the early stages of implementation, so there is room for optimization and potential issues due to insufficient testing.

0.5.3 Release -AzureStorageFreighter-

• AzureStorageFreighter class is totally refactored for better performance and scalability.
  • 0.5.2 didn't work well for large files.
  • Now, it works well for large files. Checked with a 5.4GB CSV file consisting of 10M of start vertices, 10K of end vertices, and 25M edges, it took 512 seconds to load the data into the graph database with PostgreSQL Flex, Standard_D32ds_v4 (32 vcpus, 128 GiB memory) and 512TB / 7500 iops of storage.
  • Tested data was generated with tests/generate_dummy_data.py.
  • UDF to load the data to graph is no longer used.
• However, please note that it is still in the early stages of implementation, so there is room for optimization and potential issues due to insufficient testing.

Features

• Asynchronous connection pool support for psycopg PostgreSQL driver
• 'direct_loading' option for loading data directly into the graph. If 'direct_loading' is True, the data is loaded into the graph using the 'INSERT' statement, not Cypher queries.
• 'COPY' protocol support for loading data into the graph. If 'use_copy' is True, the data is loaded into the graph using the 'COPY' protocol.

Classes

• AzureStorageFreighter
• AvroFreighter
• CosmosGremlinFreighter
• CSVFreighter
• MultiCSVFreighter
• Neo4jFreighter
• NetworkXFreighter
• ParquetFreighter
• PGFreighter

Method

All the classes have the same load() method. The method loads data into the graph database.

Arguments for each class

• common arguments

  • graph_name (str) : the name of the graph
  • chunk_size (int) : the number of rows to be loaded at once
  • direct_loading (bool) : if True, the data is loaded into the graph using the 'INSERT' statement, not Cypher queries
  • use_copy (bool) : if True, the data is loaded into the graph using the 'COPY' protocol
  • drop_graph (bool) : if True, the graph is dropped before loading the data

• AzureStorageFreighter

  • csv (str): CSV file path
  • start_v_label (str): Start Vertex Label
  • start_id (str): Start Vertex ID
  • start_props (list): Start Vertex Properties
  • edge_type (str): Edge Type
  • end_v_label (str): End Vertex Label
  • end_id (str): End Vertex ID
  • end_props (list): End Vertex Properties
  • graph_name (str): Graph Name
  • chunk_size (int): Chunk Size
  • drop_graph (bool): Drop Graph

• AvroFreighter

  • source_avro (str): The path to the Avro file.
  • start_v_label (str): The label of the start vertex.
  • start_id (str): The ID of the start vertex.
  • start_props (list): The properties of the start vertex.
  • edge_type (str): The type of the edge.
  • end_v_label (str): The label of the end vertex.
  • end_id (str): The ID of the end vertex.
  • end_props (list): The properties of the end vertex.

• CosmosGremlinFreighter

  • cosmos_gremlin_endpoint (str): The Cosmos Gremlin endpoint.
  • cosmos_gremlin_key (str): The Cosmos Gremlin key.
  • cosmos_username (str): The Cosmos username.
  • id_map (dict): The ID map.

• CSVFreighter

  • csv (str): The path to the CSV file.
  • start_v_label (str): The label of the start vertex.
  • start_id (str): The ID of the start vertex.
  • start_props (list): The properties of the start vertex.
  • edge_type (str): The type of the edge.
  • end_v_label (str): The label of the end vertex.
  • end_id (str): The ID of the end vertex.
  • end_props (list): The properties of the end vertex.

• MultiCSVFreighter

  • vertex_csvs (list): The paths to the vertex CSV files.
  • vertex_labels (list): The labels of the vertices.
  • edge_csvs (list): The paths to the edge CSV files.
  • edge_types (list): The types of the edges.

• Neo4jFreighter

  • neo4j_uri (str): The URI of the Neo4j database.
  • neo4j_user (str): The username of the Neo4j database.
  • neo4j_password (str): The password of the Neo4j database.
  • neo4j_database (str): The database of the Neo4j database.
  • id_map (dict): The ID map.

• NetworkXFreighter

  • networkx_graph (nx.Graph): The NetworkX graph.
  • id_map (dict): The ID map.

• ParquetFreighter

  • source_parquet (str): The path to the Parquet file.
  • start_v_label (str): The label of the start vertex.
  • start_id (str): The ID of the start vertex.
  • start_props (list): The properties of the start vertex.
  • edge_type (str): The type of the edge.
  • end_v_label (str): The label of the end vertex.
  • end_id (str): The ID of the end vertex.
  • end_props (list): The properties of the end vertex.

• PGFreighter

  • source_pg_con_string (str): The connection string of the source PostgreSQL database.
  • source_schema (str): The source schema.
  • source_tables (list): The source tables.
  • id_map (dict): The ID map.

Release Notes

• 0.4.0 : Added 'loadFromCosmosGremlin()' function.
• 0.4.1 : Changed base Python version to 3.9 to run on Azure Cloud Shell and Databricks 15.4ML.
• 0.4.2 : Tuning for 'loadFromCosmosGremlin()' function.
• 0.4.3 : Standardized the argument names. Enhanced the tests for each functions.
• 0.4.4 : Performance tuning.
• 0.4.5 : Simplified 'loadFromNeo4j'.
• 0.4.6 : Added 'loadFromAvro()' function.
• 0.5.0 : Refactored the code to make it more readable and maintainable with the separated classes for factory model. Introduced concurrent.futures for better performance.
• 0.5.1 : Improved the usage
• 0.5.2 : Added AzureStorageFreighter class, fixed a bug in ParquetFreighter class (THX! Reported from my co-worker, Srikanth-san)
• 0.5.3 : Refactored AzureStorageFreighter class for better performance and scalability.

Install

pip install agefreighter

Prerequisites

• over Python 3.9
• This module runs on psycopg and psycopg_pool
• 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.

CREATE EXTENSION IF NOT EXISTS age CASCADE;

Usage

import asyncio
import os
from agefreighter import Factory
import logging

log = logging.getLogger(__name__)
logging.basicConfig(
    level=logging.DEBUG,
    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)


async def main():
    class_name = "CSVFreighter"
    instance = Factory.create_instance(class_name)

    await instance.connect(
        dsn=os.environ["PG_CONNECTION_STRING"],
        max_connections=64,
    )
    await instance.load(
        graph_name="AgeTester",
        start_v_label="Actor",
        start_id="ActorID",
        start_props=["Actor"],
        edge_type="ACTED_IN",
        end_v_label="Film",
        end_id="FilmID",
        end_props=["Film", "Year", "Votes", "Rating"],
        csv="./actorfilms.csv",
        drop_graph=True,
    )


if __name__ == "__main__":
    import asyncio

    asyncio.run(main())

See, tests/agefreightertester.py for more details.

Test & Samples

export PG_CONNECTION_STRING="host=your_host.postgres.database.azure.com port=5432 dbname=postgres user=account password=your_password"
cd tests/
python3.9 agefreightertester.py

For more information about Apache AGE

• 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