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SQLAlchemy: Rename leftover occurrences of Object to ObjectType
The new symbol to represent CrateDB's `OBJECT` column type is now `ObjectType`. Before, it was just called `Object`, or sometimes `Craty`, which is both either a bit ambiguous, or a bit too fancy.
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‎CHANGES.txt‎

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@@ -5,6 +5,9 @@ Changes for crate
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Unreleased
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==========
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- SQLAlchemy: Rename leftover occurrences of ``Object``. The new symbol to represent
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CrateDB's ``OBJECT`` column type is now ``ObjectType``.
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2023/07/06 0.32.0
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=================

‎docs/by-example/sqlalchemy/working-with-types.rst‎

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@@ -7,7 +7,7 @@ SQLAlchemy: Working with special CrateDB types
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This section of the documentation shows how to work with special data types
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from the CrateDB SQLAlchemy dialect. Currently, these are:
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- Container types ``Object`` and ``ObjectArray``.
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- Container types ``ObjectType`` and ``ObjectArray``.
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- Geospatial types ``Geopoint`` and ``Geoshape``.
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@@ -33,7 +33,7 @@ Import the relevant symbols:
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... except ImportError:
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... from sqlalchemy.ext.declarative import declarative_base
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>>> from uuid import uuid4
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>>> from crate.client.sqlalchemy.types import Object, ObjectArray
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>>> from crate.client.sqlalchemy.types import ObjectType, ObjectArray
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>>> from crate.client.sqlalchemy.types import Geopoint, Geoshape
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Establish a connection to the database, see also :ref:`sa:engines_toplevel`
@@ -53,9 +53,9 @@ Introduction to container types
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In a document oriented database, it is a common pattern to store objects within
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a single field. For such cases, the CrateDB SQLAlchemy dialect provides the
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``Object`` and ``ObjectArray`` types.
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``ObjectType`` and ``ObjectArray`` types.
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The ``Object`` type effectively implements a dictionary- or map-like type. The
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The ``ObjectType`` type effectively implements a dictionary- or map-like type. The
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``ObjectArray`` type maps to a Python list of dictionaries.
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For exercising those features, let's define a schema using SQLAlchemy's
@@ -69,15 +69,15 @@ For exercising those features, let's define a schema using SQLAlchemy's
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... id = sa.Column(sa.String, primary_key=True, default=gen_key)
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... name = sa.Column(sa.String)
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... quote = sa.Column(sa.String)
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... details = sa.Column(Object)
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... details = sa.Column(ObjectType)
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... more_details = sa.Column(ObjectArray)
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In CrateDB's SQL dialect, those container types map to :ref:`crate-reference:type-object`
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and :ref:`crate-reference:type-array`.
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``Object``
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==========
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``ObjectType``
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==============
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Let's add two records which have additional items within the ``details`` field.
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Note that item keys have not been defined in the DDL schema, effectively
@@ -113,7 +113,7 @@ A subsequent select query will see all the records:
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[('Arthur Dent', 'male'), ('Tricia McMillan', 'female')]
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It is also possible to just select a part of the document, even inside the
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``Object`` type:
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``ObjectType`` type:
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>>> sorted(session.query(Character.details['gender']).all())
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[('female',), ('male',)]
@@ -129,7 +129,7 @@ Update dictionary
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-----------------
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The SQLAlchemy CrateDB dialect supports change tracking deep down the nested
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levels of a ``Object`` type field. For example, the following query will only
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levels of a ``ObjectType`` type field. For example, the following query will only
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update the ``gender`` key. The ``species`` key which is on the same level will
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be left untouched.
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@@ -170,7 +170,7 @@ Refresh and query "characters" table:
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``ObjectArray``
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===============
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Note that opposed to the ``Object`` type, the ``ObjectArray`` type isn't smart
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Note that opposed to the ``ObjectType`` type, the ``ObjectArray`` type isn't smart
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and doesn't have intelligent change tracking. Therefore, the generated
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``UPDATE`` statement will affect the whole list:
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‎docs/sqlalchemy.rst‎

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@@ -201,7 +201,7 @@ system <sa:orm_declarative_mapping>`:
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... name = sa.Column(sa.String, crate_index=False)
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... name_normalized = sa.Column(sa.String, sa.Computed("lower(name)"))
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... quote = sa.Column(sa.String, nullable=False)
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... details = sa.Column(types.Object)
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... details = sa.Column(types.ObjectType)
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... more_details = sa.Column(types.ObjectArray)
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... name_ft = sa.Column(sa.String)
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... quote_ft = sa.Column(sa.String)
@@ -224,7 +224,7 @@ In this example, we:
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- Disable indexing of the ``name`` column using ``crate_index=False``
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- Define a computed column ``name_normalized`` (based on ``name``) that
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translates into a generated column
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- Use the `Object`_ extension type for the ``details`` column
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- Use the `ObjectType`_ extension type for the ``details`` column
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- Use the `ObjectArray`_ extension type for the ``more_details`` column
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- Set up the ``name_ft`` and ``quote_ft`` fulltext indexes, but exclude them from
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the mapping (so SQLAlchemy doesn't try to update them as if they were columns)
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The appendix has a full :ref:`data types reference <data-types-sqlalchemy>`.
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.. _object:
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.. _objecttype:
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``Object``
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..........
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``ObjectType``
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..............
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Objects are a common, and useful, data type when using CrateDB, so the CrateDB
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SQLAlchemy dialect provides a custom ``Object`` type extension for working with
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.. NOTE::
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Behind the scenes, if you update an ``Object`` property and ``commit`` that
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Behind the scenes, if you update an ``ObjectType`` property, and ``commit`` that
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change, the :ref:`UPDATE <crate-reference:dml-updating-data>` statement sent
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to CrateDB will only include the data necessary to update the changed
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sub-columns.
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``ObjectArray``
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...............
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In addition to the `Object`_ type, the CrateDB SQLAlchemy dialect also provides
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In addition to the `ObjectType`_ type, the CrateDB SQLAlchemy dialect also provides
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an ``ObjectArray`` type, which is structured as a :class:`py:list` of
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:class:`dictionaries <py:dict>`.
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.. CAUTION::
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Behind the scenes, if you update an ``ObjectArray`` and ``commit`` that
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Behind the scenes, if you update an ``ObjectArray``, and ``commit`` that
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change, the :ref:`UPDATE <crate-reference:dml-updating-data>` statement
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sent to CrateDB will include all of the ``ObjectArray`` data.
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@@ -468,12 +469,12 @@ Here's what a regular select might look like:
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[('Arthur Dent', 'male'), ('Tricia McMillan', 'female')]
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You can also select a portion of each record, and this even works inside
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`Object`_ columns:
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`ObjectType`_ columns:
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>>> sorted(session.query(Character.details['gender']).all())
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[('female',), ('male',)]
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You can also filter on attributes inside the `Object`_ column:
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You can also filter on attributes inside the `ObjectType`_ column:
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>>> query = session.query(Character.name)
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>>> query.filter(Character.details['gender'] == 'male').all()

‎src/crate/client/sqlalchemy/dialect.py‎

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@@ -33,15 +33,15 @@
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)
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from crate.client.exceptions import TimezoneUnawareException
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from .sa_version import SA_VERSION, SA_1_4, SA_2_0
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from .types import Object, ObjectArray
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from .types import ObjectType, ObjectArray
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TYPES_MAP = {
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"boolean": sqltypes.Boolean,
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"short": sqltypes.SmallInteger,
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"smallint": sqltypes.SmallInteger,
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"timestamp": sqltypes.TIMESTAMP,
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"timestamp with time zone": sqltypes.TIMESTAMP,
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"object": Object,
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"object": ObjectType,
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"integer": sqltypes.Integer,
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"long": sqltypes.NUMERIC,
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"bigint": sqltypes.NUMERIC,

‎src/crate/client/sqlalchemy/tests/create_table_test.py‎

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@@ -25,7 +25,7 @@
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except ImportError:
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from sqlalchemy.ext.declarative import declarative_base
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from crate.client.sqlalchemy.types import Object, ObjectArray, Geopoint
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from crate.client.sqlalchemy.types import ObjectType, ObjectArray, Geopoint
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from crate.client.cursor import Cursor
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from unittest import TestCase
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class DummyTable(self.Base):
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__tablename__ = 'dummy'
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pk = sa.Column(sa.String, primary_key=True)
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obj_col = sa.Column(Object)
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obj_col = sa.Column(ObjectType)
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self.Base.metadata.create_all(bind=self.engine)
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fake_cursor.execute.assert_called_with(
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('\nCREATE TABLE dummy (\n\tpk STRING NOT NULL, \n\tobj_col OBJECT, '

‎src/crate/client/sqlalchemy/tests/dialect_test.py‎

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from crate.client.cursor import Cursor
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from crate.client.sqlalchemy import SA_VERSION
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from crate.client.sqlalchemy.sa_version import SA_1_4, SA_2_0
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from crate.client.sqlalchemy.types import Object
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from crate.client.sqlalchemy.types import ObjectType
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from sqlalchemy import inspect
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from sqlalchemy.orm import Session
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try:
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name = sa.Column(sa.String, primary_key=True)
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age = sa.Column(sa.Integer, primary_key=True)
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obj = sa.Column(Object)
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obj = sa.Column(ObjectType)
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ts = sa.Column(sa.DateTime, onupdate=datetime.utcnow)
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self.session = Session(bind=self.engine)

‎src/crate/client/sqlalchemy/tests/query_caching.py‎

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except ImportError:
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from sqlalchemy.ext.declarative import declarative_base
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from crate.client.sqlalchemy.types import Object, ObjectArray
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from crate.client.sqlalchemy.types import ObjectType, ObjectArray
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class SqlAlchemyQueryCompilationCaching(TestCase):
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__tablename__ = 'characters'
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name = sa.Column(sa.String, primary_key=True)
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age = sa.Column(sa.Integer)
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data = sa.Column(Object)
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data = sa.Column(ObjectType)
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data_list = sa.Column(ObjectArray)
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return Character

‎src/crate/client/sqlalchemy/tests/update_test.py‎

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from unittest import TestCase
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from unittest.mock import patch, MagicMock
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from crate.client.sqlalchemy.types import Object
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from crate.client.sqlalchemy.types import ObjectType
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import sqlalchemy as sa
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from sqlalchemy.orm import Session
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name = sa.Column(sa.String, primary_key=True)
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age = sa.Column(sa.Integer)
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obj = sa.Column(Object)
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obj = sa.Column(ObjectType)
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ts = sa.Column(sa.DateTime, onupdate=datetime.utcnow)
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self.character = Character

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