agefreighter 0.4.5

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).

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.

Functions

• 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
• 'loadFromSingleCSV()' expects a single CSV file that contains the data for the graph as a source.
  • 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.
• 'loadFromCSVs()' expects multiple CSV files, two CSV files for vertices and one CSV file for edges as sources.
  • vertex_csvs (list): The list of CSV files for vertices.
  • vertex_labels (list): The list of labels for vertices.
  • edge_csvs (list): The list of CSV files for edges.
  • edge_types (list): The list of types for edges.
• 'loadFromNetworkx()' expects a NetworkX graph object as a source.
  • networkx_graph (DiGraph): The NetworkX graph.
  • graph_name (str): The name of the graph to load the data into.
  • id_map (dict): The ID map.
• 'loadFromNeo4j()' expects a Neo4j as a source.
  • uri (str): The URI of the Neo4j server.
  • user (str): The user name of the Neo4j server.
  • password (str): The password of the Neo4j server.
  • neo4j_database (str): The name of the Neo4j database.
  • id_map (dict): The mapping of the vertex label to the vertex ID.
• 'loadFromPGSQL()' expects a PGSQL as a source.
  • src_con_string (str): The connection string of the source PostgreSQL database.
  • src_tables (list): The source tables.
  • id_map (dict): The ID map.
• 'loadFromParquet()' expects a Parquet file as a source.
  • src_parquet (str): The source 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.
• 'loadFromCosmosGremlin()' expects a Cosmos Gremlin API as a source.
  • cosmos_gremlin_endpoint (str): The endpoint of the Cosmos Gremlin API.
  • cosmos_gremlin_key (str): The key of the Cosmos Gremlin API.
  • cosmos_username (str): The username of the Cosmos Gremlin API.
  • cosmos_pkey (str): The partition key of the Cosmos Gremlin API.
  • id_map (dict): The ID map.
• Many more coming soon...

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'.

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

See, tests/test_agefreighter.py for more details.

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

• 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