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Chapter 7. SQLAlchemy Integration

SQLAlchemy is an open source SQL toolkit and ORM for Python.

7.1. Prerequisites
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  • Have SQLAlchemy installed installed. Data Virtualization integration was last tested with version 1.1.6.
  • Your Data Virtualization installation should already be setup for ODBC access. This allows the built-in compatibility with SQLAlchemy for PostgreSQL to be used.

7.2. Usage
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You should be able to use a SQLAlchemy engine for querying. Reflective import of most table metadata is also provided.

Sample Usage

import sqlalchemy
from sqlalchemy import create_engine, Table, MetaData
engine = create_engine("postgresql+psycopg2://user:password@host:35432/vdb")
engine.connect()
#engine is ready for queries
result = connection.execute("select * from some_table")
#reflective table import
meta = MetaData()
test = Table('public.test', meta, autoload=True, autoload_with=engine,postgresql_ignore_search_path=True)
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7.3. Limitations
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Only a subset of the PostgreSQL dialect is available. The primary intent is to allow querying through Data Virtualization. If there are additional features that are needed, please log an enhancement request.

Column metadata will not be available for tables that contain the period '.' character. Depending upon your needs, you may need import settings that use simple Data Virtualization names and not source schema qualified names.

7.4. Application compatibility
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7.4.1. Superset
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Superset is an open source data visualization and dashboard builder. It uses SQLAlchemy to access relational sources.

Once you have followed the above instructions, you may access a Data Virtualization VDB by adding a Database under the Sources menu.

The URL will be of the same form shown in the SQLAlchemy integration: postgresql+psycopg2://user:password@host:35432/vdb

Basic usage scenarios involving aggregation and all basic types have been tested. If there are additional features that are needed, please log an enhancement request

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