Chore: Update all imports to use sqlalchemy_cratedb, where applicable

Author: amotl

docs/advanced-querying.rst

@@ -113,7 +113,7 @@ Fulltext search in CrateDB is performed using :ref:`crate-reference:predicates_m
 The CrateDB SQLAlchemy dialect comes with a ``match`` function, which can be used to
 search on one or multiple fields.
 
-    >>> from crate.client.sqlalchemy.predicates import match
+    >>> from sqlalchemy_cratedb.predicates import match
 
     >>> session.query(Character.name) \
     ...     .filter(match(Character.name_ft, 'Arthur')) \

docs/crud.rst

@@ -32,7 +32,7 @@ Import the relevant symbols:
     ...     from sqlalchemy.orm import declarative_base
     ... except ImportError:
     ...     from sqlalchemy.ext.declarative import declarative_base
-    >>> from crate.client.sqlalchemy.types import ObjectArray
+    >>> from sqlalchemy_cratedb import ObjectArray
 
 Establish a connection to the database, see also :ref:`sa:engines_toplevel`
 and :ref:`connect`:

docs/dataframe.rst

@@ -76,7 +76,7 @@ The package provides a ``bulk_insert`` function to use the
 workload across multiple batches, using a defined chunk size.
 
     >>> import sqlalchemy as sa
-    >>> from crate.client.sqlalchemy.support import insert_bulk
+    >>> from sqlalchemy_cratedb.support import insert_bulk
     >>> from pueblo.testing.pandas import makeTimeDataFrame
     ...
     >>> # Define number of records, and chunk size.
@@ -159,7 +159,7 @@ in a batched/chunked manner, using a defined chunk size, effectively using the
 pandas implementation introduced in the previous section.
 
     >>> import dask.dataframe as dd
-    >>> from crate.client.sqlalchemy.support import insert_bulk
+    >>> from sqlalchemy_cratedb.support import insert_bulk
     >>> from pueblo.testing.pandas import makeTimeDataFrame
     ...
     >>> # Define the number of records, the number of computing partitions,

docs/inspection-reflection.rst

@@ -98,7 +98,7 @@ CrateDialect
 
 After initializing the dialect instance with a connection instance,
 
-    >>> from crate.client.sqlalchemy.dialect import CrateDialect
+    >>> from sqlalchemy_cratedb.dialect import CrateDialect
     >>> dialect = CrateDialect()
 
     >>> connection = engine.connect()

docs/overview.rst

@@ -182,8 +182,8 @@ Here is an example SQLAlchemy table definition using the :ref:`declarative
 system <sa:orm_declarative_mapping>`:
 
     >>> from sqlalchemy.ext import declarative
-    >>> from crate.client.sqlalchemy import types
     >>> from uuid import uuid4
+    >>> from sqlalchemy_cratedb import ObjectType, ObjectArray
 
     >>> def gen_key():
     ...     return str(uuid4())
@@ -201,8 +201,8 @@ system <sa:orm_declarative_mapping>`:
     ...     name = sa.Column(sa.String, crate_index=False)
     ...     name_normalized = sa.Column(sa.String, sa.Computed("lower(name)"))
     ...     quote = sa.Column(sa.String, nullable=False)
-    ...     details = sa.Column(types.ObjectType)
-    ...     more_details = sa.Column(types.ObjectArray)
+    ...     details = sa.Column(ObjectType)
+    ...     more_details = sa.Column(ObjectArray)
     ...     name_ft = sa.Column(sa.String)
     ...     quote_ft = sa.Column(sa.String)
     ...     even_more_details = sa.Column(sa.String, crate_columnstore=False)
@@ -439,12 +439,14 @@ The CrateDB SQLAlchemy dialect provides two geospatial types:
 
 To use these types, you can create columns, like so:
 
+    >>> from sqlalchemy_cratedb import Geopoint, Geoshape
+
     >>> class City(Base):
     ...
     ...    __tablename__ = 'cities'
     ...    name = sa.Column(sa.String, primary_key=True)
-    ...    coordinate = sa.Column(types.Geopoint)
-    ...    area = sa.Column(types.Geoshape)
+    ...    coordinate = sa.Column(Geopoint)
+    ...    area = sa.Column(Geoshape)
 
 A geopoint can be created in multiple ways. Firstly, you can define it as a
 :py:class:`py:tuple` of ``(longitude, latitude)``:
@@ -591,7 +593,7 @@ The CrateDB SQLAlchemy dialect provides a ``match`` function in the
 
 Here's an example use of the ``match`` function:
 
-    >>> from crate.client.sqlalchemy.predicates import match
+    >>> from sqlalchemy_cratedb.predicates import match
 
     >>> session.query(Character.name) \
     ...     .filter(match(Character.name_ft, 'Arthur')) \

docs/working-with-types.rst

@@ -33,8 +33,6 @@ Import the relevant symbols:
     ... except ImportError:
     ...     from sqlalchemy.ext.declarative import declarative_base
     >>> from uuid import uuid4
-    >>> from crate.client.sqlalchemy.types import ObjectType, ObjectArray
-    >>> from crate.client.sqlalchemy.types import Geopoint, Geoshape
 
 Establish a connection to the database, see also :ref:`sa:engines_toplevel`
 and :ref:`connect`:
@@ -61,6 +59,8 @@ The ``ObjectType`` type effectively implements a dictionary- or map-like type. T
 For exercising those features, let's define a schema using SQLAlchemy's
 :ref:`sa:orm_declarative_mapping`:
 
+    >>> from sqlalchemy_cratedb import ObjectType, ObjectArray
+
     >>> def gen_key():
     ...     return str(uuid4())
 
@@ -216,6 +216,8 @@ CrateDB's geospatial types, such as :ref:`crate-reference:type-geo_point`
 and :ref:`crate-reference:type-geo_shape`, can also be used within an
 SQLAlchemy declarative schema:
 
+    >>> from sqlalchemy_cratedb import Geopoint, Geoshape
+
     >>> class City(Base):
     ...    __tablename__ = 'cities'
     ...    name = sa.Column(sa.String, primary_key=True)