Inspection and Reflection

This section shows you how to inspect the schema of a database using CrateDB’s SQLAlchemy integration.


The CrateDB SQLAlchemy integration provides different ways to inspect the database.

  1. The runtime inspection API allows you to get an Inspector instance that can be used to fetch schema names, table names and other information.

  2. Reflection capabilities allow you to create Table instances from existing tables to inspect their columns and constraints.

  3. A CrateDialect allows you to get connection information and it contains low level function to check the existence of schemas and tables.

All approaches require an Engine instance, which you can create like this:

>>> import sqlalchemy as sa
>>> engine = sa.create_engine(f"crate://{crate_host}")

This effectively establishes a connection to the database, see also Engine Configuration and Connect.


The SQLAlchemy inspector is a low level interface which provides a backend-agnostic system of loading lists of schema, table, column, and constraint descriptions from a given database. You can create an inspector like this:

>>> inspector = sa.inspect(engine)

List all schemas:

>>> inspector.get_schema_names()
['blob', 'doc', 'information_schema', 'pg_catalog', 'sys']

List all tables:

>>> set(['characters', 'cities', 'locations']).issubset(inspector.get_table_names())
>>> set(['checks', 'cluster', 'jobs', 'jobs_log']).issubset(inspector.get_table_names(schema='sys'))

List all views:

>>> inspector.get_view_names()

Get default schema name:

>>> inspector.default_schema_name

Schema-supported reflection

A Table object can load its own schema information from the corresponding table in the database. This process is called reflection, see Reflecting Database Objects.

In the most simple case you need only specify the table name, a MetaData object, and the autoload_with argument.

Create a SQLAlchemy table object:

>>> meta = sa.MetaData()
>>> table = sa.Table(
...     "characters", meta,
...     autoload_with=engine)

Reflect column data types from the table metadata:

>>> table.columns.get('name')
Column('name', String(), table=<characters>)
>>> table.primary_key
PrimaryKeyConstraint(Column('id', String(), table=<characters>, primary_key=True...


After initializing the dialect instance with a connection instance,

>>> from sqlalchemy_cratedb.dialect import CrateDialect
>>> dialect = CrateDialect()
>>> connection = engine.connect()
>>> dialect.initialize(connection)

the database server version and default schema name can be inquired.

>>> dialect.server_version_info >= (1, 0, 0)

Check if a schema exists:

>>> dialect.has_schema(connection, 'doc')

Check if a table exists:

>>> dialect.has_table(connection, 'locations')