See also
\n \nReferences: #4710
\n \n \n-[engine] [change] [performance] [py3k] \u00b6
Disabled the \u201cunicode returns\u201d check that runs on dialect startup when\n+
[engine] [performance] \u00b6
The pool \u201cpre-ping\u201d feature has been refined to not invoke for a DBAPI\n+connection that was just opened in the same checkout operation. pre ping\n+only applies to a DBAPI connection that\u2019s been checked into the pool\n+and is being checked out again.
\n+References: #4524
\n+\n+[engine] [performance] [change] [py3k] \u00b6
Disabled the \u201cunicode returns\u201d check that runs on dialect startup when\n running under Python 3, which for many years has occurred in order to test\n the current DBAPI\u2019s behavior for whether or not it returns Python Unicode\n or Py2K strings for the VARCHAR and NVARCHAR datatypes. The check still\n occurs by default under Python 2, however the mechanism to test the\n behavior will be removed in SQLAlchemy 2.0 when Python 2 support is also\n removed.
\nThis logic was very effective when it was needed, however now that Python 3\n@@ -8711,21 +8718,14 @@\n dialect flags by setting the dialect level flag returns_unicode_strings\n to one of String.RETURNS_CONDITIONAL or\n String.RETURNS_BYTES, both of which will enable Unicode conversion\n even under Python 3.
References: #5315
\n \n[engine] [performance] \u00b6
The pool \u201cpre-ping\u201d feature has been refined to not invoke for a DBAPI\n-connection that was just opened in the same checkout operation. pre ping\n-only applies to a DBAPI connection that\u2019s been checked into the pool\n-and is being checked out again.
\n-References: #4524
\n-\n-[engine] [bug] \u00b6
Revised the Connection.execution_options.schema_translate_map\n feature such that the processing of the SQL statement to receive a specific\n schema name occurs within the execution phase of the statement, rather than\n at the compile phase. This is to support the statement being efficiently\n cached. Previously, the current schema being rendered into the statement\n for a particular run would be considered as part of the cache key itself,\n meaning that for a run against hundreds of schemas, there would be hundreds\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -6298,15 +6298,21 @@\n returned by the ResultProxy is now the LegacyRow subclass, which maintains\n mapping/tuple hybrid behavior, however the base Row class now behaves more\n fully like a named tuple.\n See also\n RowProxy_is_no_longer_a_\u201cproxy\u201d;_is_now_called_Row_and_behaves_like_an_enhanced\n named_tuple\n References: #4710\n-[engine] [change] [performance] [py3k] \u00b6\n+[engine] [performance] \u00b6\n+The pool \u201cpre-ping\u201d feature has been refined to not invoke for a DBAPI\n+connection that was just opened in the same checkout operation. pre ping only\n+applies to a DBAPI connection that\u2019s been checked into the pool and is being\n+checked out again.\n+References: #4524\n+[engine] [performance] [change] [py3k] \u00b6\n Disabled the \u201cunicode returns\u201d check that runs on dialect startup when running\n under Python 3, which for many years has occurred in order to test the current\n DBAPI\u2019s behavior for whether or not it returns Python Unicode or Py2K strings\n for the VARCHAR and NVARCHAR datatypes. The check still occurs by default under\n Python 2, however the mechanism to test the behavior will be removed in\n SQLAlchemy 2.0 when Python 2 support is also removed.\n This logic was very effective when it was needed, however now that Python 3 is\n@@ -6314,20 +6320,14 @@\n datatypes. In the unlikely case that a third party DBAPI does not support this,\n the conversion logic within String is still available and the third party\n dialect may specify this in its upfront dialect flags by setting the dialect\n level flag returns_unicode_strings to one of String.RETURNS_CONDITIONAL or\n String.RETURNS_BYTES, both of which will enable Unicode conversion even under\n Python 3.\n References: #5315\n-[engine] [performance] \u00b6\n-The pool \u201cpre-ping\u201d feature has been refined to not invoke for a DBAPI\n-connection that was just opened in the same checkout operation. pre ping only\n-applies to a DBAPI connection that\u2019s been checked into the pool and is being\n-checked out again.\n-References: #4524\n [engine] [bug] \u00b6\n Revised the Connection.execution_options.schema_translate_map feature such that\n the processing of the SQL statement to receive a specific schema name occurs\n within the execution phase of the statement, rather than at the compile phase.\n This is to support the statement being efficiently cached. Previously, the\n current schema being rendered into the statement for a particular run would be\n considered as part of the cache key itself, meaning that for a run against\n"}]}, {"source1": "./usr/share/doc/python-sqlalchemy-doc/html/orm/examples.html", "source2": "./usr/share/doc/python-sqlalchemy-doc/html/orm/examples.html", "comments": ["Ordering differences only"], "unified_diff": "@@ -313,38 +313,38 @@\n where an intermediary class mediates the relationship between two\n classes that are associated in a many-to-many pattern.
Listing of files:
basic_association.py - Illustrate a many-to-many relationship between an\n \u201cOrder\u201d and a collection of \u201cItem\u201d objects, associating a purchase price\n with each via an association object called \u201cOrderItem\u201d
\nproxied_association.py - Same example as basic_association, adding in\n+usage of sqlalchemy.ext.associationproxy to make explicit references\n+to OrderItem optional.
dict_of_sets_with_default.py - An advanced association proxy example which\n illustrates nesting of association proxies to produce multi-level Python\n collections, in this case a dictionary with string keys and sets of integers\n as values, which conceal the underlying mapped classes.
\nproxied_association.py - Same example as basic_association, adding in\n-usage of sqlalchemy.ext.associationproxy to make explicit references\n-to OrderItem optional.
Examples illustrating the asyncio engine feature of SQLAlchemy.
\nListing of files:
async_orm.py - Illustrates use of the sqlalchemy.ext.asyncio.AsyncSession object\n-for asynchronous ORM use.
\n-greenlet_orm.py - Illustrates use of the sqlalchemy.ext.asyncio.AsyncSession object\n for asynchronous ORM use, including the optional run_sync() method.
\nbasic.py - Illustrates the asyncio engine / connection interface.
\nasync_orm.py - Illustrates use of the sqlalchemy.ext.asyncio.AsyncSession object\n+for asynchronous ORM use.
\n+gather_orm_statements.py - Illustrates how to run many statements concurrently using asyncio.gather()\n along many asyncio database connections, merging ORM results into a single\n AsyncSession.
HasAddresses mixin, which ensures that the\n parent class is provided with an addresses collection\n which contains Address objects.\n The discriminator_on_association.py and generic_fk.py scripts\n are modernized versions of recipes presented in the 2007 blog post\n Polymorphic Associations with SQLAlchemy.
\nListing of files:
table_per_related.py - Illustrates a generic association which persists association\n-objects within individual tables, each one generated to persist\n-those objects on behalf of a particular parent class.
\n+table_per_association.py - Illustrates a mixin which provides a generic association\n+via a individually generated association tables for each parent class.\n+The associated objects themselves are persisted in a single table\n+shared among all parents.
\ngeneric_fk.py - Illustrates a so-called \u201cgeneric foreign key\u201d, in a similar fashion\n to that of popular frameworks such as Django, ROR, etc. This\n approach bypasses standard referential integrity\n practices, in that the \u201cforeign key\u201d column is not actually\n constrained to refer to any particular table; instead,\n in-application logic is used to determine which table is referenced.
\ntable_per_association.py - Illustrates a mixin which provides a generic association\n-via a individually generated association tables for each parent class.\n-The associated objects themselves are persisted in a single table\n-shared among all parents.
\n-discriminator_on_association.py - Illustrates a mixin which provides a generic association\n using a single target table and a single association table,\n referred to by all parent tables. The association table\n contains a \u201cdiscriminator\u201d column which determines what type of\n parent object associates to each particular row in the association\n table.
\ntable_per_related.py - Illustrates a generic association which persists association\n+objects within individual tables, each one generated to persist\n+those objects on behalf of a particular parent class.
\n+Large collection example.
\nIllustrates the options to use with\n@@ -501,33 +501,33 @@\n
See also
\n \nListing of files:
single_inserts.py - In this series of tests, we\u2019re looking at a method that inserts a row\n+within a distinct transaction, and afterwards returns to essentially a\n+\u201cclosed\u201d state. This would be analogous to an API call that starts up\n+a database connection, inserts the row, commits and closes.
\n+short_selects.py - This series of tests illustrates different ways to SELECT a single\n record by primary key
\n__main__.py - Allows the examples/performance package to be run as a script.
\n+bulk_updates.py - This series of tests will illustrate different ways to UPDATE a large number\n+of rows in bulk (under construction! there\u2019s just one test at the moment)
\nlarge_resultsets.py - In this series of tests, we are looking at time to load a large number\n of very small and simple rows.
\nsingle_inserts.py - In this series of tests, we\u2019re looking at a method that inserts a row\n-within a distinct transaction, and afterwards returns to essentially a\n-\u201cclosed\u201d state. This would be analogous to an API call that starts up\n-a database connection, inserts the row, commits and closes.
\n+__main__.py - Allows the examples/performance package to be run as a script.
\nbulk_inserts.py - This series of tests illustrates different ways to INSERT a large number\n of rows in bulk.
\nbulk_updates.py - This series of tests will illustrate different ways to UPDATE a large number\n-of rows in bulk (under construction! there\u2019s just one test at the moment)
\n-This is the default form of run:
\n$ python -m examples.performance single_inserts\n@@ -669,22 +669,22 @@\n \n \n Relationship Join Conditions\u00b6
\n Examples of various relationship() configurations,\n which make use of the primaryjoin argument to compose special types\n of join conditions.
\n Listing of files:
\n-cast.py - Illustrate a relationship() that joins two columns where those\n-columns are not of the same type, and a CAST must be used on the SQL\n-side in order to match them.
\n-threeway.py - Illustrate a \u201cthree way join\u201d - where a primary table joins to a remote\n table via an association table, but then the primary table also needs\n to refer to some columns in the remote table directly.
\n cast.py - Illustrate a relationship() that joins two columns where those\n+columns are not of the same type, and a CAST must be used on the SQL\n+side in order to match them.
\n+
\n \n \n \n Space Invaders\u00b6
\n A Space Invaders game using SQLite as the state machine.
\n Originally developed in 2012. Adapted to work in Python 3.
\n@@ -814,47 +814,47 @@\n assert type(other) is SomeClass and other.id == self.id Above, if two instance of SomeClass with the same version identifier\n are updated and sent to the database for UPDATE concurrently, if the database\n isolation level allows the two UPDATE statements to proceed, one will fail\n because it no longer is against the last known version identifier.
Listing of files:
history_meta.py - Versioned mixin class and other utilities.
\n-test_versioning.py - Unit tests illustrating usage of the history_meta.py\n module functions.
history_meta.py - Versioned mixin class and other utilities.
\n+Several examples that illustrate the technique of intercepting changes\n that would be first interpreted as an UPDATE on a row, and instead turning\n it into an INSERT of a new row, leaving the previous row intact as\n a historical version.
\nCompare to the Versioning with a History Table example which writes a\n history row to a separate history table.
\nListing of files:
versioned_rows_w_versionid.py - Illustrates a method to intercept changes on objects, turning\n-an UPDATE statement on a single row into an INSERT statement, so that a new\n-row is inserted with the new data, keeping the old row intact.
\n-versioned_update_old_row.py - Illustrates the same UPDATE into INSERT technique of versioned_rows.py,\n but also emits an UPDATE on the old row to affect a change in timestamp.\n Also includes a SessionEvents.do_orm_execute() hook to limit queries\n to only the most recent version.
versioned_map.py - A variant of the versioned_rows example built around the\n+concept of a \u201cvertical table\u201d structure, like those illustrated in\n+Vertical Attribute Mapping examples.
\n+versioned_rows.py - Illustrates a method to intercept changes on objects, turning\n an UPDATE statement on a single row into an INSERT statement, so that a new\n row is inserted with the new data, keeping the old row intact.
\nversioned_map.py - A variant of the versioned_rows example built around the\n-concept of a \u201cvertical table\u201d structure, like those illustrated in\n-Vertical Attribute Mapping examples.
\n+versioned_rows_w_versionid.py - Illustrates a method to intercept changes on objects, turning\n+an UPDATE statement on a single row into an INSERT statement, so that a new\n+row is inserted with the new data, keeping the old row intact.
\nWorking examples of single-table, joined-table, and concrete-table\n inheritance as described in Mapping Class Inheritance Hierarchies.
\nListing of files:
single.py - Single-table (table-per-hierarchy) inheritance example.
\n+concrete.py - Concrete-table (table-per-class) inheritance example.
\njoined.py - Joined-table (table-per-subclass) inheritance example.
\nconcrete.py - Concrete-table (table-per-class) inheritance example.
\n+single.py - Single-table (table-per-hierarchy) inheritance example.
\nExamples illustrating modifications to SQLAlchemy\u2019s attribute management\n system.
\nListing of files:
custom_management.py - Illustrates customized class instrumentation, using\n-the sqlalchemy.ext.instrumentation extension package.
listen_for_events.py - Illustrates how to attach events to all instrumented attributes\n and listen for change events.
\nactive_column_defaults.py - Illustrates use of the AttributeEvents.init_scalar()\n event, in conjunction with Core column defaults to provide\n ORM objects that automatically produce the default value\n when an un-set attribute is accessed.
custom_management.py - Illustrates customized class instrumentation, using\n+the sqlalchemy.ext.instrumentation extension package.
A basic example of using the SQLAlchemy Sharding API.\n Sharding refers to horizontally scaling data across multiple\n@@ -961,20 +961,20 @@\n more plain-spoken alternative, the \u201cdistinct entity\u201d approach\n is a simple method of assigning objects to different tables (and potentially\n database nodes) in an explicit way - described on the wiki at\n EntityName.
\nListing of files:
separate_databases.py - Illustrates sharding using distinct SQLite databases.
\nseparate_schema_translates.py - Illustrates sharding using a single database with multiple schemas,\n-where a different \u201cschema_translates_map\u201d can be used for each shard.
\n-separate_tables.py - Illustrates sharding using a single SQLite database, that will however\n have multiple tables using a naming convention.
\nseparate_schema_translates.py - Illustrates sharding using a single database with multiple schemas,\n+where a different \u201cschema_translates_map\u201d can be used for each shard.
\n+Examples include demonstrations of the with_loader_criteria()\n option as well as the SessionEvents.do_orm_execute() hook.
As of SQLAlchemy 1.4, the Query construct is unified\n with the Select construct, so that these two objects\n are mostly the same.
Listing of files:
filter_public.py - Illustrates a global criteria applied to entities of a particular type.
\n+temporal_range.py - Illustrates a custom per-query criteria that will be applied\n to selected entities.
\nfilter_public.py - Illustrates a global criteria applied to entities of a particular type.
\n-Illustrates how to embed\n dogpile.cache\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -126,30 +126,31 @@\n Examples illustrating the usage of the \u201cassociation object\u201d pattern, where an\n intermediary class mediates the relationship between two classes that are\n associated in a many-to-many pattern.\n Listing of files:\n * basic_association.py - Illustrate a many-to-many relationship between an\n \u201cOrder\u201d and a collection of \u201cItem\u201d objects, associating a purchase price\n with each via an association object called \u201cOrderItem\u201d\n+proxied_association.py - Same example as basic_association, adding in usage of\n+sqlalchemy.ext.associationproxy to make explicit references to OrderItem\n+optional.\n dict_of_sets_with_default.py - An advanced association proxy example which\n illustrates nesting of association proxies to produce multi-level Python\n collections, in this case a dictionary with string keys and sets of integers as\n values, which conceal the underlying mapped classes.\n-proxied_association.py - Same example as basic_association, adding in usage of\n-sqlalchemy.ext.associationproxy to make explicit references to OrderItem\n-optional.\n \n **** Asyncio Integration\u00b6 ****\n Examples illustrating the asyncio engine feature of SQLAlchemy.\n Listing of files:\n- * async_orm.py - Illustrates use of the sqlalchemy.ext.asyncio.AsyncSession\n- object for asynchronous ORM use.\n-greenlet_orm.py - Illustrates use of the sqlalchemy.ext.asyncio.AsyncSession\n-object for asynchronous ORM use, including the optional run_sync() method.\n+ * greenlet_orm.py - Illustrates use of the\n+ sqlalchemy.ext.asyncio.AsyncSession object for asynchronous ORM use,\n+ including the optional run_sync() method.\n basic.py - Illustrates the asyncio engine / connection interface.\n+async_orm.py - Illustrates use of the sqlalchemy.ext.asyncio.AsyncSession\n+object for asynchronous ORM use.\n gather_orm_statements.py - Illustrates how to run many statements concurrently\n using asyncio.gather() along many asyncio database connections, merging ORM\n results into a single AsyncSession.\n \n **** Directed Graphs\u00b6 ****\n An example of persistence for a directed graph structure. The graph is stored\n as a collection of edges, each referencing both a \u201clower\u201d and an \u201cupper\u201d node\n@@ -177,31 +178,31 @@\n Supplier, both subclassing the HasAddresses mixin, which ensures that the\n parent class is provided with an addresses collection which contains Address\n objects.\n The discriminator_on_association.py and generic_fk.py scripts are modernized\n versions of recipes presented in the 2007 blog post Polymorphic_Associations\n with_SQLAlchemy.\n Listing of files:\n- * table_per_related.py - Illustrates a generic association which persists\n- association objects within individual tables, each one generated to\n- persist those objects on behalf of a particular parent class.\n+ * table_per_association.py - Illustrates a mixin which provides a generic\n+ association via a individually generated association tables for each\n+ parent class. The associated objects themselves are persisted in a single\n+ table shared among all parents.\n generic_fk.py - Illustrates a so-called \u201cgeneric foreign key\u201d, in a similar\n fashion to that of popular frameworks such as Django, ROR, etc. This approach\n bypasses standard referential integrity practices, in that the \u201cforeign key\u201d\n column is not actually constrained to refer to any particular table; instead,\n in-application logic is used to determine which table is referenced.\n-table_per_association.py - Illustrates a mixin which provides a generic\n-association via a individually generated association tables for each parent\n-class. The associated objects themselves are persisted in a single table shared\n-among all parents.\n discriminator_on_association.py - Illustrates a mixin which provides a generic\n association using a single target table and a single association table,\n referred to by all parent tables. The association table contains a\n \u201cdiscriminator\u201d column which determines what type of parent object associates\n to each particular row in the association table.\n+table_per_related.py - Illustrates a generic association which persists\n+association objects within individual tables, each one generated to persist\n+those objects on behalf of a particular parent class.\n \n **** Large Collections\u00b6 ****\n Large collection example.\n Illustrates the options to use with relationship() when the list of related\n objects is very large, including:\n * \u201cdynamic\u201d relationships which query slices of data as accessed\n * how to use ON DELETE CASCADE in conjunction with passive_deletes=True to\n@@ -262,28 +263,29 @@\n $ python -m examples.performance bulk_inserts \\\n --dburl mysql+mysqldb://scott:tiger@localhost/test \\\n --profile --num 1000\n See also\n How_can_I_profile_a_SQLAlchemy_powered_application?\n *** File Listing\u00b6 ***\n Listing of files:\n- * short_selects.py - This series of tests illustrates different ways to\n- SELECT a single record by primary key\n-__main__.py - Allows the examples/performance package to be run as a script.\n+ * single_inserts.py - In this series of tests, we\u2019re looking at a method\n+ that inserts a row within a distinct transaction, and afterwards returns\n+ to essentially a \u201cclosed\u201d state. This would be analogous to an API call\n+ that starts up a database connection, inserts the row, commits and\n+ closes.\n+short_selects.py - This series of tests illustrates different ways to SELECT a\n+single record by primary key\n+bulk_updates.py - This series of tests will illustrate different ways to UPDATE\n+a large number of rows in bulk (under construction! there\u2019s just one test at\n+the moment)\n large_resultsets.py - In this series of tests, we are looking at time to load a\n large number of very small and simple rows.\n-single_inserts.py - In this series of tests, we\u2019re looking at a method that\n-inserts a row within a distinct transaction, and afterwards returns to\n-essentially a \u201cclosed\u201d state. This would be analogous to an API call that\n-starts up a database connection, inserts the row, commits and closes.\n+__main__.py - Allows the examples/performance package to be run as a script.\n bulk_inserts.py - This series of tests illustrates different ways to INSERT a\n large number of rows in bulk.\n-bulk_updates.py - This series of tests will illustrate different ways to UPDATE\n-a large number of rows in bulk (under construction! there\u2019s just one test at\n-the moment)\n \n *** Running all tests with time\u00b6 ***\n This is the default form of run:\n $ python -m examples.performance single_inserts\n Tests to run: test_orm_commit, test_bulk_save,\n test_bulk_insert_dictionaries, test_core,\n test_core_query_caching, test_dbapi_raw_w_connect,\n@@ -424,20 +426,20 @@\n test_subqueryload : load everything, subquery eager loading. (1000 iterations);\n total time 2.977696 sec\n \n **** Relationship Join Conditions\u00b6 ****\n Examples of various relationship() configurations, which make use of the\n primaryjoin argument to compose special types of join conditions.\n Listing of files:\n- * cast.py - Illustrate a relationship() that joins two columns where those\n- columns are not of the same type, and a CAST must be used on the SQL side\n- in order to match them.\n-threeway.py - Illustrate a \u201cthree way join\u201d - where a primary table joins to a\n-remote table via an association table, but then the primary table also needs to\n-refer to some columns in the remote table directly.\n+ * threeway.py - Illustrate a \u201cthree way join\u201d - where a primary table joins\n+ to a remote table via an association table, but then the primary table\n+ also needs to refer to some columns in the remote table directly.\n+cast.py - Illustrate a relationship() that joins two columns where those\n+columns are not of the same type, and a CAST must be used on the SQL side in\n+order to match them.\n \n **** Space Invaders\u00b6 ****\n A Space Invaders game using SQLite as the state machine.\n Originally developed in 2012. Adapted to work in Python 3.\n Runs in a textual console using ASCII art.\n [orm/space_invaders.jpg]\n To run:\n@@ -543,40 +545,40 @@\n def __eq__(self, other):\n assert type(other) is SomeClass and other.id == self.id\n Above, if two instance of SomeClass with the same version identifier are\n updated and sent to the database for UPDATE concurrently, if the database\n isolation level allows the two UPDATE statements to proceed, one will fail\n because it no longer is against the last known version identifier.\n Listing of files:\n- * history_meta.py - Versioned mixin class and other utilities.\n-test_versioning.py - Unit tests illustrating usage of the history_meta.py\n-module functions.\n+ * test_versioning.py - Unit tests illustrating usage of the history_meta.py\n+ module functions.\n+history_meta.py - Versioned mixin class and other utilities.\n \n *** Versioning using Temporal Rows\u00b6 ***\n Several examples that illustrate the technique of intercepting changes that\n would be first interpreted as an UPDATE on a row, and instead turning it into\n an INSERT of a new row, leaving the previous row intact as a historical\n version.\n Compare to the Versioning_with_a_History_Table example which writes a history\n row to a separate history table.\n Listing of files:\n- * versioned_rows_w_versionid.py - Illustrates a method to intercept changes\n- on objects, turning an UPDATE statement on a single row into an INSERT\n- statement, so that a new row is inserted with the new data, keeping the\n- old row intact.\n-versioned_update_old_row.py - Illustrates the same UPDATE into INSERT technique\n-of versioned_rows.py, but also emits an UPDATE on the old row to affect a\n-change in timestamp. Also includes a SessionEvents.do_orm_execute() hook to\n-limit queries to only the most recent version.\n-versioned_rows.py - Illustrates a method to intercept changes on objects,\n-turning an UPDATE statement on a single row into an INSERT statement, so that a\n-new row is inserted with the new data, keeping the old row intact.\n+ * versioned_update_old_row.py - Illustrates the same UPDATE into INSERT\n+ technique of versioned_rows.py, but also emits an UPDATE on the old row\n+ to affect a change in timestamp. Also includes a\n+ SessionEvents.do_orm_execute() hook to limit queries to only the most\n+ recent version.\n versioned_map.py - A variant of the versioned_rows example built around the\n concept of a \u201cvertical table\u201d structure, like those illustrated in Vertical\n Attribute_Mapping examples.\n+versioned_rows.py - Illustrates a method to intercept changes on objects,\n+turning an UPDATE statement on a single row into an INSERT statement, so that a\n+new row is inserted with the new data, keeping the old row intact.\n+versioned_rows_w_versionid.py - Illustrates a method to intercept changes on\n+objects, turning an UPDATE statement on a single row into an INSERT statement,\n+so that a new row is inserted with the new data, keeping the old row intact.\n \n **** Vertical Attribute Mapping\u00b6 ****\n Illustrates \u201cvertical table\u201d mappings.\n A \u201cvertical table\u201d refers to a technique where individual attributes of an\n object are stored as distinct rows in a table. The \u201cvertical table\u201d technique\n is used to persist objects which can have a varied set of attributes, at the\n expense of simple query control and brevity. It is commonly found in content/\n@@ -605,30 +607,30 @@\n dictionary.\n \n ***** Inheritance Mapping Recipes\u00b6 *****\n **** Basic Inheritance Mappings\u00b6 ****\n Working examples of single-table, joined-table, and concrete-table inheritance\n as described in Mapping_Class_Inheritance_Hierarchies.\n Listing of files:\n- * single.py - Single-table (table-per-hierarchy) inheritance example.\n+ * concrete.py - Concrete-table (table-per-class) inheritance example.\n joined.py - Joined-table (table-per-subclass) inheritance example.\n-concrete.py - Concrete-table (table-per-class) inheritance example.\n+single.py - Single-table (table-per-hierarchy) inheritance example.\n \n ***** Special APIs\u00b6 *****\n **** Attribute Instrumentation\u00b6 ****\n Examples illustrating modifications to SQLAlchemy\u2019s attribute management\n system.\n Listing of files:\n- * custom_management.py - Illustrates customized class instrumentation,\n- using the sqlalchemy.ext.instrumentation extension package.\n-listen_for_events.py - Illustrates how to attach events to all instrumented\n-attributes and listen for change events.\n+ * listen_for_events.py - Illustrates how to attach events to all\n+ instrumented attributes and listen for change events.\n active_column_defaults.py - Illustrates use of the AttributeEvents.init_scalar\n () event, in conjunction with Core column defaults to provide ORM objects that\n automatically produce the default value when an un-set attribute is accessed.\n+custom_management.py - Illustrates customized class instrumentation, using the\n+sqlalchemy.ext.instrumentation extension package.\n \n **** Horizontal Sharding\u00b6 ****\n A basic example of using the SQLAlchemy Sharding API. Sharding refers to\n horizontally scaling data across multiple databases.\n The basic components of a \u201csharded\u201d mapping are:\n * multiple Engine instances, each assigned a \u201cshard id\u201d. These Engine\n instances may refer to different databases, or different schemas /\n@@ -652,34 +654,34 @@\n issue of organizing instances among multiple databases. For a more plain-spoken\n alternative, the \u201cdistinct entity\u201d approach is a simple method of assigning\n objects to different tables (and potentially database nodes) in an explicit way\n - described on the wiki at EntityName.\n Listing of files:\n * separate_databases.py - Illustrates sharding using distinct SQLite\n databases.\n+separate_tables.py - Illustrates sharding using a single SQLite database, that\n+will however have multiple tables using a naming convention.\n separate_schema_translates.py - Illustrates sharding using a single database\n with multiple schemas, where a different \u201cschema_translates_map\u201d can be used\n for each shard.\n-separate_tables.py - Illustrates sharding using a single SQLite database, that\n-will however have multiple tables using a naming convention.\n \n ***** Extending the ORM\u00b6 *****\n **** ORM Query Events\u00b6 ****\n Recipes which illustrate augmentation of ORM SELECT behavior as used by\n Session.execute() with 2.0_style use of select(), as well as the 1.x_style\n Query object.\n Examples include demonstrations of the with_loader_criteria() option as well as\n the SessionEvents.do_orm_execute() hook.\n As of SQLAlchemy 1.4, the Query construct is unified with the Select construct,\n so that these two objects are mostly the same.\n Listing of files:\n- * temporal_range.py - Illustrates a custom per-query criteria that will be\n- applied to selected entities.\n-filter_public.py - Illustrates a global criteria applied to entities of a\n-particular type.\n+ * filter_public.py - Illustrates a global criteria applied to entities of a\n+ particular type.\n+temporal_range.py - Illustrates a custom per-query criteria that will be\n+applied to selected entities.\n \n **** Dogpile Caching\u00b6 ****\n Illustrates how to embed dogpile.cache functionality with ORM queries, allowing\n full cache control as well as the ability to pull \u201clazy loaded\u201d attributes from\n long term cache.\n In this demo, the following techniques are illustrated:\n * Using the SessionEvents.do_orm_execute() event hook\n"}]}, {"source1": "./usr/share/doc/python-sqlalchemy-doc/html/searchindex.js", "source2": "./usr/share/doc/python-sqlalchemy-doc/html/searchindex.js", "unified_diff": null, "details": [{"source1": "js-beautify {}", "source2": "js-beautify {}", "unified_diff": "@@ -8497,20 +8497,20 @@\n \"3414\": [13, 25],\n \"alchemy2\": 13,\n \"4644\": 13,\n \"5649\": 13,\n \"get_sequence_nam\": [13, 48, 52],\n \"2056\": 13,\n \"4755\": 13,\n+ \"4524\": 13,\n \"upfront\": 13,\n \"returns_unicode_str\": [13, 48],\n \"returns_condit\": [13, 59],\n \"returns_byt\": [13, 59],\n \"5315\": 13,\n- \"4524\": 13,\n \"hundr\": [13, 21, 24, 25, 31, 76, 136, 137, 155],\n \"4645\": [13, 25],\n \"4808\": [13, 25],\n \"5004\": [13, 25],\n \"har\": [13, 25],\n \"4712\": 13,\n \"5526\": [13, 25],\n@@ -12439,17 +12439,17 @@\n \"n5\": 98,\n \"add_neighbor\": 98,\n \"higher_neighbor\": 98,\n \"directed_graph\": 98,\n \"supplier\": 98,\n \"hasaddress\": 98,\n \"generic_fk\": 98,\n- \"table_per_rel\": 98,\n- \"ror\": 98,\n \"table_per_associ\": 98,\n+ \"ror\": 98,\n+ \"table_per_rel\": 98,\n \"materialized_path\": 98,\n \"nested_set\": 98,\n \"single_insert\": 98,\n \"bulk_upd\": 98,\n \"test_orm_commit\": 98,\n \"test_bulk_insert_dictionari\": 98,\n \"test_cor\": 98,\n@@ -12504,34 +12504,34 @@\n \"sc1\": 98,\n \"sc1modifi\": 98,\n \"someclasshistori\": 98,\n \"__history_mapper__\": 98,\n \"_history_mapp\": 98,\n \"somehistoryclass\": 98,\n \"use_mapper_vers\": 98,\n- \"versioned_rows_w_versionid\": 98,\n \"versioned_update_old_row\": 98,\n \"versioned_map\": 98,\n+ \"versioned_rows_w_versionid\": 98,\n \"breviti\": 98,\n \"shrew\": 98,\n \"anim\": 98,\n \"cute\": 98,\n \"weasel\": 98,\n \"poison\": 98,\n \"animalfact\": 98,\n \"custom_manag\": 98,\n \"weather\": 98,\n \"contin\": 98,\n \"spoken\": 98,\n \"separate_databas\": 98,\n+ \"separate_t\": 98,\n \"separate_schema_transl\": 98,\n \"schema_translates_map\": 98,\n- \"separate_t\": 98,\n- \"temporal_rang\": 98,\n \"filter_publ\": 98,\n+ \"temporal_rang\": 98,\n \"demo\": 98,\n \"datafil\": 98,\n \"helloworld\": 98,\n \"local_session_cach\": 98,\n \"datamodel\": 98,\n \"postalcod\": 98,\n \"citi\": [98, 124, 133],\n"}]}]}]}]}]}