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Add a working, more complete example of using a catalog (docs) #1427

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e4c214b
Add a working, more complete example of using a catalog
toppyy Mar 13, 2026
b3123fc
the default schema is 'public', not 'default'
toppyy Mar 14, 2026
366696a
in-memory table instead of imaginary csv for standalone example
toppyy Mar 19, 2026
4824905
typo fix
toppyy Mar 19, 2026
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26 changes: 19 additions & 7 deletions docs/source/user-guide/data-sources.rst
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Expand Up @@ -224,25 +224,37 @@ A common technique for organizing tables is using a three level hierarchical app
supports this form of organizing using the :py:class:`~datafusion.catalog.Catalog`,
:py:class:`~datafusion.catalog.Schema`, and :py:class:`~datafusion.catalog.Table`. By default,
a :py:class:`~datafusion.context.SessionContext` comes with a single Catalog and a single Schema
with the names ``datafusion`` and ``default``, respectively.
with the names ``datafusion`` and ``public``, respectively.
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@kevinjqliu kevinjqliu Mar 18, 2026

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uv run --with datafusion python3 -c "
from datafusion import SessionContext
ctx = SessionContext()
print('Catalog names:', ctx.catalog_names())
catalog = ctx.catalog('datafusion')
print('Schema names:', catalog.names())
"

Catalog names: {'datafusion'}
Schema names: {'public'}


The default implementation uses an in-memory approach to the catalog and schema. We have support
for adding additional in-memory catalogs and schemas. This can be done like in the following
for adding additional in-memory catalogs and schemas. You can access tables registered in a schema
either through the Dataframe API or via sql commands. This can be done like in the following
example:

.. code-block:: python

import pyarrow as pa
from datafusion.catalog import Catalog, Schema
from datafusion import SessionContext

ctx = SessionContext()

my_catalog = Catalog.memory_catalog()
my_schema = Schema.memory_schema()
my_schema = Schema.memory_schema()
my_catalog.register_schema('my_schema_name', my_schema)
ctx.register_catalog_provider('my_catalog_name', my_catalog)

my_catalog.register_schema("my_schema_name", my_schema)
# Create an in-memory table
table = pa.table({
'name': ['Bulbasaur', 'Charmander', 'Squirtle'],
'type': ['Grass', 'Fire', 'Water'],
'hp': [45, 39, 44],
})
df = ctx.create_dataframe([table.to_batches()], name='pokemon')

ctx.register_catalog("my_catalog_name", my_catalog)
my_schema.register_table('pokemon', df)

You could then register tables in ``my_schema`` and access them either through the DataFrame
API or via sql commands such as ``"SELECT * from my_catalog_name.my_schema_name.my_table"``.
ctx.sql('SELECT * FROM my_catalog_name.my_schema_name.pokemon').show()

User Defined Catalog and Schema
-------------------------------
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