+hy dZddlmZddlZddlZddlmZddl m Z ddl m Z ddl m Zdd l mZdd l mZd d l mZd d l mZd dl mZd dl mZd dlmZd dlmZd dlmZd dlmZd dlmZd dlmZd dlmZd dlmZd dlm Z d dlmZ!d dl"m#Z#d dl$m%Z%d dl$m&Z&d dl$m'Z'd dl$m(Z(d dl$m)Z)d dl$m*Z*d d l$m+Z+d d!l$m,Z,d d"l$m-Z-d d#l$m.Z.d d$l$m/Z/ej`d%ejbZ2ej`d&ejbejfzZ4e5gd'Z6d(Z7d)Z8d*Z9Gd+d,e jtZ;Gd-d.e jxZ=Gd/d0e j|Z?e?Z@Gd1d2e j|ZAeAZBGd3d4e j|ZCeCZDGd5d6e j|ZEeEZFGd7d8e j|ZGGd9d:e j|ZHGd;de jZJGd?d@e jZKGdAdBe je jZNeNZOGdCdDe j|ZPePZQGdEdFe j|ZeZRGdGdHe j|ZSGdIdJe je jZUGdKdLejZWe je je jeNe jeUe jjeje jejiZ\idMe jdNejdOejdPejdQejdRejdSejdTejdUejdVejdWe*dXe%dYe-dZe/d[e'd\e jd]e jid^e.d_e+d`e)dae,dbe?dceAddedeePdfePdgeCdheEdieGdjeHdkeIdle=dmeJdneJeJeKeKe(eKe;e&eNeSdo ZfGdpdqejZhGdrdsejZjGdtduejZlGdvdwejZnGdxdyejZpGdzd{ejZrGd|d}ejZsGd~dejZuGddejZwGddejZxGddejZzy)a .. dialect:: postgresql :name: PostgreSQL :full_support: 9.6, 10, 11, 12, 13, 14 :normal_support: 9.6+ :best_effort: 8+ .. _postgresql_sequences: Sequences/SERIAL/IDENTITY ------------------------- PostgreSQL supports sequences, and SQLAlchemy uses these as the default means of creating new primary key values for integer-based primary key columns. When creating tables, SQLAlchemy will issue the ``SERIAL`` datatype for integer-based primary key columns, which generates a sequence and server side default corresponding to the column. To specify a specific named sequence to be used for primary key generation, use the :func:`~sqlalchemy.schema.Sequence` construct:: Table('sometable', metadata, Column('id', Integer, Sequence('some_id_seq'), primary_key=True) ) When SQLAlchemy issues a single INSERT statement, to fulfill the contract of having the "last insert identifier" available, a RETURNING clause is added to the INSERT statement which specifies the primary key columns should be returned after the statement completes. The RETURNING functionality only takes place if PostgreSQL 8.2 or later is in use. As a fallback approach, the sequence, whether specified explicitly or implicitly via ``SERIAL``, is executed independently beforehand, the returned value to be used in the subsequent insert. Note that when an :func:`~sqlalchemy.sql.expression.insert()` construct is executed using "executemany" semantics, the "last inserted identifier" functionality does not apply; no RETURNING clause is emitted nor is the sequence pre-executed in this case. To force the usage of RETURNING by default off, specify the flag ``implicit_returning=False`` to :func:`_sa.create_engine`. PostgreSQL 10 and above IDENTITY columns ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ PostgreSQL 10 and above have a new IDENTITY feature that supersedes the use of SERIAL. The :class:`_schema.Identity` construct in a :class:`_schema.Column` can be used to control its behavior:: from sqlalchemy import Table, Column, MetaData, Integer, Computed metadata = MetaData() data = Table( "data", metadata, Column( 'id', Integer, Identity(start=42, cycle=True), primary_key=True ), Column('data', String) ) The CREATE TABLE for the above :class:`_schema.Table` object would be: .. sourcecode:: sql CREATE TABLE data ( id INTEGER GENERATED BY DEFAULT AS IDENTITY (START WITH 42 CYCLE), data VARCHAR, PRIMARY KEY (id) ) .. versionchanged:: 1.4 Added :class:`_schema.Identity` construct in a :class:`_schema.Column` to specify the option of an autoincrementing column. .. note:: Previous versions of SQLAlchemy did not have built-in support for rendering of IDENTITY, and could use the following compilation hook to replace occurrences of SERIAL with IDENTITY:: from sqlalchemy.schema import CreateColumn from sqlalchemy.ext.compiler import compiles @compiles(CreateColumn, 'postgresql') def use_identity(element, compiler, **kw): text = compiler.visit_create_column(element, **kw) text = text.replace( "SERIAL", "INT GENERATED BY DEFAULT AS IDENTITY" ) return text Using the above, a table such as:: t = Table( 't', m, Column('id', Integer, primary_key=True), Column('data', String) ) Will generate on the backing database as:: CREATE TABLE t ( id INT GENERATED BY DEFAULT AS IDENTITY, data VARCHAR, PRIMARY KEY (id) ) .. _postgresql_ss_cursors: Server Side Cursors ------------------- Server-side cursor support is available for the psycopg2, asyncpg dialects and may also be available in others. Server side cursors are enabled on a per-statement basis by using the :paramref:`.Connection.execution_options.stream_results` connection execution option:: with engine.connect() as conn: result = conn.execution_options(stream_results=True).execute(text("select * from table")) Note that some kinds of SQL statements may not be supported with server side cursors; generally, only SQL statements that return rows should be used with this option. .. deprecated:: 1.4 The dialect-level server_side_cursors flag is deprecated and will be removed in a future release. Please use the :paramref:`_engine.Connection.stream_results` execution option for unbuffered cursor support. .. seealso:: :ref:`engine_stream_results` .. _postgresql_isolation_level: Transaction Isolation Level --------------------------- Most SQLAlchemy dialects support setting of transaction isolation level using the :paramref:`_sa.create_engine.isolation_level` parameter at the :func:`_sa.create_engine` level, and at the :class:`_engine.Connection` level via the :paramref:`.Connection.execution_options.isolation_level` parameter. For PostgreSQL dialects, this feature works either by making use of the DBAPI-specific features, such as psycopg2's isolation level flags which will embed the isolation level setting inline with the ``"BEGIN"`` statement, or for DBAPIs with no direct support by emitting ``SET SESSION CHARACTERISTICS AS TRANSACTION ISOLATION LEVEL `` ahead of the ``"BEGIN"`` statement emitted by the DBAPI. For the special AUTOCOMMIT isolation level, DBAPI-specific techniques are used which is typically an ``.autocommit`` flag on the DBAPI connection object. To set isolation level using :func:`_sa.create_engine`:: engine = create_engine( "postgresql+pg8000://scott:tiger@localhost/test", isolation_level = "REPEATABLE READ" ) To set using per-connection execution options:: with engine.connect() as conn: conn = conn.execution_options( isolation_level="REPEATABLE READ" ) with conn.begin(): # ... work with transaction There are also more options for isolation level configurations, such as "sub-engine" objects linked to a main :class:`_engine.Engine` which each apply different isolation level settings. See the discussion at :ref:`dbapi_autocommit` for background. Valid values for ``isolation_level`` on most PostgreSQL dialects include: * ``READ COMMITTED`` * ``READ UNCOMMITTED`` * ``REPEATABLE READ`` * ``SERIALIZABLE`` * ``AUTOCOMMIT`` .. seealso:: :ref:`dbapi_autocommit` :ref:`postgresql_readonly_deferrable` :ref:`psycopg2_isolation_level` :ref:`pg8000_isolation_level` .. _postgresql_readonly_deferrable: Setting READ ONLY / DEFERRABLE ------------------------------ Most PostgreSQL dialects support setting the "READ ONLY" and "DEFERRABLE" characteristics of the transaction, which is in addition to the isolation level setting. These two attributes can be established either in conjunction with or independently of the isolation level by passing the ``postgresql_readonly`` and ``postgresql_deferrable`` flags with :meth:`_engine.Connection.execution_options`. The example below illustrates passing the ``"SERIALIZABLE"`` isolation level at the same time as setting "READ ONLY" and "DEFERRABLE":: with engine.connect() as conn: conn = conn.execution_options( isolation_level="SERIALIZABLE", postgresql_readonly=True, postgresql_deferrable=True ) with conn.begin(): # ... work with transaction Note that some DBAPIs such as asyncpg only support "readonly" with SERIALIZABLE isolation. .. versionadded:: 1.4 added support for the ``postgresql_readonly`` and ``postgresql_deferrable`` execution options. .. _postgresql_reset_on_return: Temporary Table / Resource Reset for Connection Pooling ------------------------------------------------------- The :class:`.QueuePool` connection pool implementation used by the SQLAlchemy :class:`_sa.Engine` object includes :ref:`reset on return ` behavior that will invoke the DBAPI ``.rollback()`` method when connections are returned to the pool. While this rollback will clear out the immediate state used by the previous transaction, it does not cover a wider range of session-level state, including temporary tables as well as other server state such as prepared statement handles and statement caches. The PostgreSQL database includes a variety of commands which may be used to reset this state, including ``DISCARD``, ``RESET``, ``DEALLOCATE``, and ``UNLISTEN``. To install one or more of these commands as the means of performing reset-on-return, the :meth:`.PoolEvents.reset` event hook may be used, as demonstrated in the example below (**requires SQLAlchemy 1.4.43 or greater**). The implementation will end transactions in progress as well as discard temporary tables using the ``CLOSE``, ``RESET`` and ``DISCARD`` commands; see the PostgreSQL documentation for background on what each of these statements do. The :paramref:`_sa.create_engine.pool_reset_on_return` parameter is set to ``None`` so that the custom scheme can replace the default behavior completely. The custom hook implementation calls ``.rollback()`` in any case, as it's usually important that the DBAPI's own tracking of commit/rollback will remain consistent with the state of the transaction:: from sqlalchemy import create_engine from sqlalchemy import event postgresql_engine = create_engine( "postgresql+pyscopg2://scott:tiger@hostname/dbname", # disable default reset-on-return scheme pool_reset_on_return=None, ) @event.listens_for(postgresql_engine, "reset") def _reset_postgresql(dbapi_connection, connection_record, reset_state): dbapi_connection.execute("CLOSE ALL") dbapi_connection.execute("RESET ALL") dbapi_connection.execute("DISCARD TEMP") # so that the DBAPI itself knows that the connection has been # reset dbapi_connection.rollback() .. versionchanged:: 1.4.43 Ensured the :meth:`.PoolEvents.reset` event is invoked for all "reset" occurrences, so that it's appropriate as a place for custom "reset" handlers. Previous schemes which use the :meth:`.PoolEvents.checkin` handler remain usable as well. .. seealso:: :ref:`pool_reset_on_return` - in the :ref:`pooling_toplevel` documentation .. _postgresql_alternate_search_path: Setting Alternate Search Paths on Connect ------------------------------------------ The PostgreSQL ``search_path`` variable refers to the list of schema names that will be implicitly referred towards when a particular table or other object is referenced in a SQL statement. As detailed in the next section :ref:`postgresql_schema_reflection`, SQLAlchemy is generally organized around the concept of keeping this variable at its default value of ``public``, however, in order to have it set to any arbitrary name or names when connections are used automatically, the "SET SESSION search_path" command may be invoked for all connections in a pool using the following event handler, as discussed at :ref:`schema_set_default_connections`:: from sqlalchemy import event from sqlalchemy import create_engine engine = create_engine("postgresql+psycopg2://scott:tiger@host/dbname") @event.listens_for(engine, "connect", insert=True) def set_search_path(dbapi_connection, connection_record): existing_autocommit = dbapi_connection.autocommit dbapi_connection.autocommit = True cursor = dbapi_connection.cursor() cursor.execute("SET SESSION search_path='%s'" % schema_name) cursor.close() dbapi_connection.autocommit = existing_autocommit The reason the recipe is complicated by use of the ``.autocommit`` DBAPI attribute is so that when the ``SET SESSION search_path`` directive is invoked, it is invoked outside of the scope of any transaction and therefore will not be reverted when the DBAPI connection has a rollback. .. seealso:: :ref:`schema_set_default_connections` - in the :ref:`metadata_toplevel` documentation .. _postgresql_schema_reflection: Remote-Schema Table Introspection and PostgreSQL search_path ------------------------------------------------------------ .. admonition:: Section Best Practices Summarized keep the ``search_path`` variable set to its default of ``public``, without any other schema names. For other schema names, name these explicitly within :class:`_schema.Table` definitions. Alternatively, the ``postgresql_ignore_search_path`` option will cause all reflected :class:`_schema.Table` objects to have a :attr:`_schema.Table.schema` attribute set up. The PostgreSQL dialect can reflect tables from any schema, as outlined in :ref:`metadata_reflection_schemas`. With regards to tables which these :class:`_schema.Table` objects refer to via foreign key constraint, a decision must be made as to how the ``.schema`` is represented in those remote tables, in the case where that remote schema name is also a member of the current `PostgreSQL search path `_. By default, the PostgreSQL dialect mimics the behavior encouraged by PostgreSQL's own ``pg_get_constraintdef()`` builtin procedure. This function returns a sample definition for a particular foreign key constraint, omitting the referenced schema name from that definition when the name is also in the PostgreSQL schema search path. The interaction below illustrates this behavior:: test=> CREATE TABLE test_schema.referred(id INTEGER PRIMARY KEY); CREATE TABLE test=> CREATE TABLE referring( test(> id INTEGER PRIMARY KEY, test(> referred_id INTEGER REFERENCES test_schema.referred(id)); CREATE TABLE test=> SET search_path TO public, test_schema; test=> SELECT pg_catalog.pg_get_constraintdef(r.oid, true) FROM test-> pg_catalog.pg_class c JOIN pg_catalog.pg_namespace n test-> ON n.oid = c.relnamespace test-> JOIN pg_catalog.pg_constraint r ON c.oid = r.conrelid test-> WHERE c.relname='referring' AND r.contype = 'f' test-> ; pg_get_constraintdef --------------------------------------------------- FOREIGN KEY (referred_id) REFERENCES referred(id) (1 row) Above, we created a table ``referred`` as a member of the remote schema ``test_schema``, however when we added ``test_schema`` to the PG ``search_path`` and then asked ``pg_get_constraintdef()`` for the ``FOREIGN KEY`` syntax, ``test_schema`` was not included in the output of the function. On the other hand, if we set the search path back to the typical default of ``public``:: test=> SET search_path TO public; SET The same query against ``pg_get_constraintdef()`` now returns the fully schema-qualified name for us:: test=> SELECT pg_catalog.pg_get_constraintdef(r.oid, true) FROM test-> pg_catalog.pg_class c JOIN pg_catalog.pg_namespace n test-> ON n.oid = c.relnamespace test-> JOIN pg_catalog.pg_constraint r ON c.oid = r.conrelid test-> WHERE c.relname='referring' AND r.contype = 'f'; pg_get_constraintdef --------------------------------------------------------------- FOREIGN KEY (referred_id) REFERENCES test_schema.referred(id) (1 row) SQLAlchemy will by default use the return value of ``pg_get_constraintdef()`` in order to determine the remote schema name. That is, if our ``search_path`` were set to include ``test_schema``, and we invoked a table reflection process as follows:: >>> from sqlalchemy import Table, MetaData, create_engine, text >>> engine = create_engine("postgresql://scott:tiger@localhost/test") >>> with engine.connect() as conn: ... conn.execute(text("SET search_path TO test_schema, public")) ... metadata_obj = MetaData() ... referring = Table('referring', metadata_obj, ... autoload_with=conn) ... The above process would deliver to the :attr:`_schema.MetaData.tables` collection ``referred`` table named **without** the schema:: >>> metadata_obj.tables['referred'].schema is None True To alter the behavior of reflection such that the referred schema is maintained regardless of the ``search_path`` setting, use the ``postgresql_ignore_search_path`` option, which can be specified as a dialect-specific argument to both :class:`_schema.Table` as well as :meth:`_schema.MetaData.reflect`:: >>> with engine.connect() as conn: ... conn.execute(text("SET search_path TO test_schema, public")) ... metadata_obj = MetaData() ... referring = Table('referring', metadata_obj, ... autoload_with=conn, ... postgresql_ignore_search_path=True) ... We will now have ``test_schema.referred`` stored as schema-qualified:: >>> metadata_obj.tables['test_schema.referred'].schema 'test_schema' .. sidebar:: Best Practices for PostgreSQL Schema reflection The description of PostgreSQL schema reflection behavior is complex, and is the product of many years of dealing with widely varied use cases and user preferences. But in fact, there's no need to understand any of it if you just stick to the simplest use pattern: leave the ``search_path`` set to its default of ``public`` only, never refer to the name ``public`` as an explicit schema name otherwise, and refer to all other schema names explicitly when building up a :class:`_schema.Table` object. The options described here are only for those users who can't, or prefer not to, stay within these guidelines. Note that **in all cases**, the "default" schema is always reflected as ``None``. The "default" schema on PostgreSQL is that which is returned by the PostgreSQL ``current_schema()`` function. On a typical PostgreSQL installation, this is the name ``public``. So a table that refers to another which is in the ``public`` (i.e. default) schema will always have the ``.schema`` attribute set to ``None``. .. seealso:: :ref:`reflection_schema_qualified_interaction` - discussion of the issue from a backend-agnostic perspective `The Schema Search Path `_ - on the PostgreSQL website. INSERT/UPDATE...RETURNING ------------------------- The dialect supports PG 8.2's ``INSERT..RETURNING``, ``UPDATE..RETURNING`` and ``DELETE..RETURNING`` syntaxes. ``INSERT..RETURNING`` is used by default for single-row INSERT statements in order to fetch newly generated primary key identifiers. To specify an explicit ``RETURNING`` clause, use the :meth:`._UpdateBase.returning` method on a per-statement basis:: # INSERT..RETURNING result = table.insert().returning(table.c.col1, table.c.col2).\ values(name='foo') print(result.fetchall()) # UPDATE..RETURNING result = table.update().returning(table.c.col1, table.c.col2).\ where(table.c.name=='foo').values(name='bar') print(result.fetchall()) # DELETE..RETURNING result = table.delete().returning(table.c.col1, table.c.col2).\ where(table.c.name=='foo') print(result.fetchall()) .. _postgresql_insert_on_conflict: INSERT...ON CONFLICT (Upsert) ------------------------------ Starting with version 9.5, PostgreSQL allows "upserts" (update or insert) of rows into a table via the ``ON CONFLICT`` clause of the ``INSERT`` statement. A candidate row will only be inserted if that row does not violate any unique constraints. In the case of a unique constraint violation, a secondary action can occur which can be either "DO UPDATE", indicating that the data in the target row should be updated, or "DO NOTHING", which indicates to silently skip this row. Conflicts are determined using existing unique constraints and indexes. These constraints may be identified either using their name as stated in DDL, or they may be inferred by stating the columns and conditions that comprise the indexes. SQLAlchemy provides ``ON CONFLICT`` support via the PostgreSQL-specific :func:`_postgresql.insert()` function, which provides the generative methods :meth:`_postgresql.Insert.on_conflict_do_update` and :meth:`~.postgresql.Insert.on_conflict_do_nothing`: .. sourcecode:: pycon+sql >>> from sqlalchemy.dialects.postgresql import insert >>> insert_stmt = insert(my_table).values( ... id='some_existing_id', ... data='inserted value') >>> do_nothing_stmt = insert_stmt.on_conflict_do_nothing( ... index_elements=['id'] ... ) >>> print(do_nothing_stmt) {opensql}INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s) ON CONFLICT (id) DO NOTHING {stop} >>> do_update_stmt = insert_stmt.on_conflict_do_update( ... constraint='pk_my_table', ... set_=dict(data='updated value') ... ) >>> print(do_update_stmt) {opensql}INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s) ON CONFLICT ON CONSTRAINT pk_my_table DO UPDATE SET data = %(param_1)s .. versionadded:: 1.1 .. seealso:: `INSERT .. ON CONFLICT `_ - in the PostgreSQL documentation. Specifying the Target ^^^^^^^^^^^^^^^^^^^^^ Both methods supply the "target" of the conflict using either the named constraint or by column inference: * The :paramref:`_postgresql.Insert.on_conflict_do_update.index_elements` argument specifies a sequence containing string column names, :class:`_schema.Column` objects, and/or SQL expression elements, which would identify a unique index: .. sourcecode:: pycon+sql >>> do_update_stmt = insert_stmt.on_conflict_do_update( ... index_elements=['id'], ... set_=dict(data='updated value') ... ) >>> print(do_update_stmt) {opensql}INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s) ON CONFLICT (id) DO UPDATE SET data = %(param_1)s {stop} >>> do_update_stmt = insert_stmt.on_conflict_do_update( ... index_elements=[my_table.c.id], ... set_=dict(data='updated value') ... ) >>> print(do_update_stmt) {opensql}INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s) ON CONFLICT (id) DO UPDATE SET data = %(param_1)s * When using :paramref:`_postgresql.Insert.on_conflict_do_update.index_elements` to infer an index, a partial index can be inferred by also specifying the use the :paramref:`_postgresql.Insert.on_conflict_do_update.index_where` parameter: .. sourcecode:: pycon+sql >>> stmt = insert(my_table).values(user_email='a@b.com', data='inserted data') >>> stmt = stmt.on_conflict_do_update( ... index_elements=[my_table.c.user_email], ... index_where=my_table.c.user_email.like('%@gmail.com'), ... set_=dict(data=stmt.excluded.data) ... ) >>> print(stmt) {opensql}INSERT INTO my_table (data, user_email) VALUES (%(data)s, %(user_email)s) ON CONFLICT (user_email) WHERE user_email LIKE %(user_email_1)s DO UPDATE SET data = excluded.data * The :paramref:`_postgresql.Insert.on_conflict_do_update.constraint` argument is used to specify an index directly rather than inferring it. This can be the name of a UNIQUE constraint, a PRIMARY KEY constraint, or an INDEX: .. sourcecode:: pycon+sql >>> do_update_stmt = insert_stmt.on_conflict_do_update( ... constraint='my_table_idx_1', ... set_=dict(data='updated value') ... ) >>> print(do_update_stmt) {opensql}INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s) ON CONFLICT ON CONSTRAINT my_table_idx_1 DO UPDATE SET data = %(param_1)s {stop} >>> do_update_stmt = insert_stmt.on_conflict_do_update( ... constraint='my_table_pk', ... set_=dict(data='updated value') ... ) >>> print(do_update_stmt) {opensql}INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s) ON CONFLICT ON CONSTRAINT my_table_pk DO UPDATE SET data = %(param_1)s {stop} * The :paramref:`_postgresql.Insert.on_conflict_do_update.constraint` argument may also refer to a SQLAlchemy construct representing a constraint, e.g. :class:`.UniqueConstraint`, :class:`.PrimaryKeyConstraint`, :class:`.Index`, or :class:`.ExcludeConstraint`. In this use, if the constraint has a name, it is used directly. Otherwise, if the constraint is unnamed, then inference will be used, where the expressions and optional WHERE clause of the constraint will be spelled out in the construct. This use is especially convenient to refer to the named or unnamed primary key of a :class:`_schema.Table` using the :attr:`_schema.Table.primary_key` attribute: .. sourcecode:: pycon+sql >>> do_update_stmt = insert_stmt.on_conflict_do_update( ... constraint=my_table.primary_key, ... set_=dict(data='updated value') ... ) >>> print(do_update_stmt) {opensql}INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s) ON CONFLICT (id) DO UPDATE SET data = %(param_1)s The SET Clause ^^^^^^^^^^^^^^^ ``ON CONFLICT...DO UPDATE`` is used to perform an update of the already existing row, using any combination of new values as well as values from the proposed insertion. These values are specified using the :paramref:`_postgresql.Insert.on_conflict_do_update.set_` parameter. This parameter accepts a dictionary which consists of direct values for UPDATE: .. sourcecode:: pycon+sql >>> stmt = insert(my_table).values(id='some_id', data='inserted value') >>> do_update_stmt = stmt.on_conflict_do_update( ... index_elements=['id'], ... set_=dict(data='updated value') ... ) >>> print(do_update_stmt) {opensql}INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s) ON CONFLICT (id) DO UPDATE SET data = %(param_1)s .. warning:: The :meth:`_expression.Insert.on_conflict_do_update` method does **not** take into account Python-side default UPDATE values or generation functions, e.g. those specified using :paramref:`_schema.Column.onupdate`. These values will not be exercised for an ON CONFLICT style of UPDATE, unless they are manually specified in the :paramref:`_postgresql.Insert.on_conflict_do_update.set_` dictionary. Updating using the Excluded INSERT Values ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ In order to refer to the proposed insertion row, the special alias :attr:`~.postgresql.Insert.excluded` is available as an attribute on the :class:`_postgresql.Insert` object; this object is a :class:`_expression.ColumnCollection` which alias contains all columns of the target table: .. sourcecode:: pycon+sql >>> stmt = insert(my_table).values( ... id='some_id', ... data='inserted value', ... author='jlh' ... ) >>> do_update_stmt = stmt.on_conflict_do_update( ... index_elements=['id'], ... set_=dict(data='updated value', author=stmt.excluded.author) ... ) >>> print(do_update_stmt) {opensql}INSERT INTO my_table (id, data, author) VALUES (%(id)s, %(data)s, %(author)s) ON CONFLICT (id) DO UPDATE SET data = %(param_1)s, author = excluded.author Additional WHERE Criteria ^^^^^^^^^^^^^^^^^^^^^^^^^ The :meth:`_expression.Insert.on_conflict_do_update` method also accepts a WHERE clause using the :paramref:`_postgresql.Insert.on_conflict_do_update.where` parameter, which will limit those rows which receive an UPDATE: .. sourcecode:: pycon+sql >>> stmt = insert(my_table).values( ... id='some_id', ... data='inserted value', ... author='jlh' ... ) >>> on_update_stmt = stmt.on_conflict_do_update( ... index_elements=['id'], ... set_=dict(data='updated value', author=stmt.excluded.author), ... where=(my_table.c.status == 2) ... ) >>> print(on_update_stmt) {opensql}INSERT INTO my_table (id, data, author) VALUES (%(id)s, %(data)s, %(author)s) ON CONFLICT (id) DO UPDATE SET data = %(param_1)s, author = excluded.author WHERE my_table.status = %(status_1)s Skipping Rows with DO NOTHING ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ``ON CONFLICT`` may be used to skip inserting a row entirely if any conflict with a unique or exclusion constraint occurs; below this is illustrated using the :meth:`~.postgresql.Insert.on_conflict_do_nothing` method: .. sourcecode:: pycon+sql >>> stmt = insert(my_table).values(id='some_id', data='inserted value') >>> stmt = stmt.on_conflict_do_nothing(index_elements=['id']) >>> print(stmt) {opensql}INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s) ON CONFLICT (id) DO NOTHING If ``DO NOTHING`` is used without specifying any columns or constraint, it has the effect of skipping the INSERT for any unique or exclusion constraint violation which occurs: .. sourcecode:: pycon+sql >>> stmt = insert(my_table).values(id='some_id', data='inserted value') >>> stmt = stmt.on_conflict_do_nothing() >>> print(stmt) {opensql}INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s) ON CONFLICT DO NOTHING .. _postgresql_match: Full Text Search ---------------- SQLAlchemy makes available the PostgreSQL ``@@`` operator via the :meth:`_expression.ColumnElement.match` method on any textual column expression. On the PostgreSQL dialect, an expression like the following:: select(sometable.c.text.match("search string")) will emit to the database:: SELECT text @@ to_tsquery('search string') FROM table Various other PostgreSQL text search functions such as ``to_tsquery()``, ``to_tsvector()``, and ``plainto_tsquery()`` are available by explicitly using the standard SQLAlchemy :data:`.func` construct. For example:: select(func.to_tsvector('fat cats ate rats').match('cat & rat')) Emits the equivalent of:: SELECT to_tsvector('fat cats ate rats') @@ to_tsquery('cat & rat') The :class:`_postgresql.TSVECTOR` type can provide for explicit CAST:: from sqlalchemy.dialects.postgresql import TSVECTOR from sqlalchemy import select, cast select(cast("some text", TSVECTOR)) produces a statement equivalent to:: SELECT CAST('some text' AS TSVECTOR) AS anon_1 .. tip:: It's important to remember that text searching in PostgreSQL is powerful but complicated, and SQLAlchemy users are advised to reference the PostgreSQL documentation regarding `Full Text Search `_. There are important differences between ``to_tsquery`` and ``plainto_tsquery``, the most significant of which is that ``to_tsquery`` expects specially formatted "querytext" that is written to PostgreSQL's own specification, while ``plainto_tsquery`` expects unformatted text that is transformed into ``to_tsquery`` compatible querytext. This means the input to ``.match()`` under PostgreSQL may be incompatible with the input to ``.match()`` under another database backend. SQLAlchemy users who support multiple backends are advised to carefully implement their usage of ``.match()`` to work around these constraints. Full Text Searches in PostgreSQL are influenced by a combination of: the PostgreSQL setting of ``default_text_search_config``, the ``regconfig`` used to build the GIN/GiST indexes, and the ``regconfig`` optionally passed in during a query. When performing a Full Text Search against a column that has a GIN or GiST index that is already pre-computed (which is common on full text searches) one may need to explicitly pass in a particular PostgreSQL ``regconfig`` value to ensure the query-planner utilizes the index and does not re-compute the column on demand. In order to provide for this explicit query planning, or to use different search strategies, the ``match`` method accepts a ``postgresql_regconfig`` keyword argument:: select(mytable.c.id).where( mytable.c.title.match('somestring', postgresql_regconfig='english') ) Emits the equivalent of:: SELECT mytable.id FROM mytable WHERE mytable.title @@ to_tsquery('english', 'somestring') One can also specifically pass in a `'regconfig'` value to the ``to_tsvector()`` command as the initial argument:: select(mytable.c.id).where( func.to_tsvector('english', mytable.c.title )\ .match('somestring', postgresql_regconfig='english') ) produces a statement equivalent to:: SELECT mytable.id FROM mytable WHERE to_tsvector('english', mytable.title) @@ to_tsquery('english', 'somestring') It is recommended that you use the ``EXPLAIN ANALYZE...`` tool from PostgreSQL to ensure that you are generating queries with SQLAlchemy that take full advantage of any indexes you may have created for full text search. .. seealso:: `Full Text Search `_ - in the PostgreSQL documentation FROM ONLY ... ------------- The dialect supports PostgreSQL's ONLY keyword for targeting only a particular table in an inheritance hierarchy. This can be used to produce the ``SELECT ... FROM ONLY``, ``UPDATE ONLY ...``, and ``DELETE FROM ONLY ...`` syntaxes. It uses SQLAlchemy's hints mechanism:: # SELECT ... FROM ONLY ... result = table.select().with_hint(table, 'ONLY', 'postgresql') print(result.fetchall()) # UPDATE ONLY ... table.update(values=dict(foo='bar')).with_hint('ONLY', dialect_name='postgresql') # DELETE FROM ONLY ... table.delete().with_hint('ONLY', dialect_name='postgresql') .. _postgresql_indexes: PostgreSQL-Specific Index Options --------------------------------- Several extensions to the :class:`.Index` construct are available, specific to the PostgreSQL dialect. Covering Indexes ^^^^^^^^^^^^^^^^ The ``postgresql_include`` option renders INCLUDE(colname) for the given string names:: Index("my_index", table.c.x, postgresql_include=['y']) would render the index as ``CREATE INDEX my_index ON table (x) INCLUDE (y)`` Note that this feature requires PostgreSQL 11 or later. .. versionadded:: 1.4 .. _postgresql_partial_indexes: Partial Indexes ^^^^^^^^^^^^^^^ Partial indexes add criterion to the index definition so that the index is applied to a subset of rows. These can be specified on :class:`.Index` using the ``postgresql_where`` keyword argument:: Index('my_index', my_table.c.id, postgresql_where=my_table.c.value > 10) .. _postgresql_operator_classes: Operator Classes ^^^^^^^^^^^^^^^^ PostgreSQL allows the specification of an *operator class* for each column of an index (see https://www.postgresql.org/docs/current/interactive/indexes-opclass.html). The :class:`.Index` construct allows these to be specified via the ``postgresql_ops`` keyword argument:: Index( 'my_index', my_table.c.id, my_table.c.data, postgresql_ops={ 'data': 'text_pattern_ops', 'id': 'int4_ops' }) Note that the keys in the ``postgresql_ops`` dictionaries are the "key" name of the :class:`_schema.Column`, i.e. the name used to access it from the ``.c`` collection of :class:`_schema.Table`, which can be configured to be different than the actual name of the column as expressed in the database. If ``postgresql_ops`` is to be used against a complex SQL expression such as a function call, then to apply to the column it must be given a label that is identified in the dictionary by name, e.g.:: Index( 'my_index', my_table.c.id, func.lower(my_table.c.data).label('data_lower'), postgresql_ops={ 'data_lower': 'text_pattern_ops', 'id': 'int4_ops' }) Operator classes are also supported by the :class:`_postgresql.ExcludeConstraint` construct using the :paramref:`_postgresql.ExcludeConstraint.ops` parameter. See that parameter for details. .. versionadded:: 1.3.21 added support for operator classes with :class:`_postgresql.ExcludeConstraint`. Index Types ^^^^^^^^^^^ PostgreSQL provides several index types: B-Tree, Hash, GiST, and GIN, as well as the ability for users to create their own (see https://www.postgresql.org/docs/current/static/indexes-types.html). These can be specified on :class:`.Index` using the ``postgresql_using`` keyword argument:: Index('my_index', my_table.c.data, postgresql_using='gin') The value passed to the keyword argument will be simply passed through to the underlying CREATE INDEX command, so it *must* be a valid index type for your version of PostgreSQL. .. _postgresql_index_storage: Index Storage Parameters ^^^^^^^^^^^^^^^^^^^^^^^^ PostgreSQL allows storage parameters to be set on indexes. The storage parameters available depend on the index method used by the index. Storage parameters can be specified on :class:`.Index` using the ``postgresql_with`` keyword argument:: Index('my_index', my_table.c.data, postgresql_with={"fillfactor": 50}) .. versionadded:: 1.0.6 PostgreSQL allows to define the tablespace in which to create the index. The tablespace can be specified on :class:`.Index` using the ``postgresql_tablespace`` keyword argument:: Index('my_index', my_table.c.data, postgresql_tablespace='my_tablespace') .. versionadded:: 1.1 Note that the same option is available on :class:`_schema.Table` as well. .. _postgresql_index_concurrently: Indexes with CONCURRENTLY ^^^^^^^^^^^^^^^^^^^^^^^^^ The PostgreSQL index option CONCURRENTLY is supported by passing the flag ``postgresql_concurrently`` to the :class:`.Index` construct:: tbl = Table('testtbl', m, Column('data', Integer)) idx1 = Index('test_idx1', tbl.c.data, postgresql_concurrently=True) The above index construct will render DDL for CREATE INDEX, assuming PostgreSQL 8.2 or higher is detected or for a connection-less dialect, as:: CREATE INDEX CONCURRENTLY test_idx1 ON testtbl (data) For DROP INDEX, assuming PostgreSQL 9.2 or higher is detected or for a connection-less dialect, it will emit:: DROP INDEX CONCURRENTLY test_idx1 .. versionadded:: 1.1 support for CONCURRENTLY on DROP INDEX. The CONCURRENTLY keyword is now only emitted if a high enough version of PostgreSQL is detected on the connection (or for a connection-less dialect). When using CONCURRENTLY, the PostgreSQL database requires that the statement be invoked outside of a transaction block. The Python DBAPI enforces that even for a single statement, a transaction is present, so to use this construct, the DBAPI's "autocommit" mode must be used:: metadata = MetaData() table = Table( "foo", metadata, Column("id", String)) index = Index( "foo_idx", table.c.id, postgresql_concurrently=True) with engine.connect() as conn: with conn.execution_options(isolation_level='AUTOCOMMIT'): table.create(conn) .. seealso:: :ref:`postgresql_isolation_level` .. _postgresql_index_reflection: PostgreSQL Index Reflection --------------------------- The PostgreSQL database creates a UNIQUE INDEX implicitly whenever the UNIQUE CONSTRAINT construct is used. When inspecting a table using :class:`_reflection.Inspector`, the :meth:`_reflection.Inspector.get_indexes` and the :meth:`_reflection.Inspector.get_unique_constraints` will report on these two constructs distinctly; in the case of the index, the key ``duplicates_constraint`` will be present in the index entry if it is detected as mirroring a constraint. When performing reflection using ``Table(..., autoload_with=engine)``, the UNIQUE INDEX is **not** returned in :attr:`_schema.Table.indexes` when it is detected as mirroring a :class:`.UniqueConstraint` in the :attr:`_schema.Table.constraints` collection . .. versionchanged:: 1.0.0 - :class:`_schema.Table` reflection now includes :class:`.UniqueConstraint` objects present in the :attr:`_schema.Table.constraints` collection; the PostgreSQL backend will no longer include a "mirrored" :class:`.Index` construct in :attr:`_schema.Table.indexes` if it is detected as corresponding to a unique constraint. Special Reflection Options -------------------------- The :class:`_reflection.Inspector` used for the PostgreSQL backend is an instance of :class:`.PGInspector`, which offers additional methods:: from sqlalchemy import create_engine, inspect engine = create_engine("postgresql+psycopg2://localhost/test") insp = inspect(engine) # will be a PGInspector print(insp.get_enums()) .. autoclass:: PGInspector :members: .. _postgresql_table_options: PostgreSQL Table Options ------------------------ Several options for CREATE TABLE are supported directly by the PostgreSQL dialect in conjunction with the :class:`_schema.Table` construct: * ``TABLESPACE``:: Table("some_table", metadata, ..., postgresql_tablespace='some_tablespace') The above option is also available on the :class:`.Index` construct. * ``ON COMMIT``:: Table("some_table", metadata, ..., postgresql_on_commit='PRESERVE ROWS') * ``WITH OIDS``:: Table("some_table", metadata, ..., postgresql_with_oids=True) * ``WITHOUT OIDS``:: Table("some_table", metadata, ..., postgresql_with_oids=False) * ``INHERITS``:: Table("some_table", metadata, ..., postgresql_inherits="some_supertable") Table("some_table", metadata, ..., postgresql_inherits=("t1", "t2", ...)) .. versionadded:: 1.0.0 * ``PARTITION BY``:: Table("some_table", metadata, ..., postgresql_partition_by='LIST (part_column)') .. versionadded:: 1.2.6 .. seealso:: `PostgreSQL CREATE TABLE options `_ - in the PostgreSQL documentation. .. _postgresql_constraint_options: PostgreSQL Constraint Options ----------------------------- The following option(s) are supported by the PostgreSQL dialect in conjunction with selected constraint constructs: * ``NOT VALID``: This option applies towards CHECK and FOREIGN KEY constraints when the constraint is being added to an existing table via ALTER TABLE, and has the effect that existing rows are not scanned during the ALTER operation against the constraint being added. When using a SQL migration tool such as `Alembic `_ that renders ALTER TABLE constructs, the ``postgresql_not_valid`` argument may be specified as an additional keyword argument within the operation that creates the constraint, as in the following Alembic example:: def update(): op.create_foreign_key( "fk_user_address", "address", "user", ["user_id"], ["id"], postgresql_not_valid=True ) The keyword is ultimately accepted directly by the :class:`_schema.CheckConstraint`, :class:`_schema.ForeignKeyConstraint` and :class:`_schema.ForeignKey` constructs; when using a tool like Alembic, dialect-specific keyword arguments are passed through to these constructs from the migration operation directives:: CheckConstraint("some_field IS NOT NULL", postgresql_not_valid=True) ForeignKeyConstraint(["some_id"], ["some_table.some_id"], postgresql_not_valid=True) .. versionadded:: 1.4.32 .. seealso:: `PostgreSQL ALTER TABLE options `_ - in the PostgreSQL documentation. .. _postgresql_table_valued_overview: Table values, Table and Column valued functions, Row and Tuple objects ----------------------------------------------------------------------- PostgreSQL makes great use of modern SQL forms such as table-valued functions, tables and rows as values. These constructs are commonly used as part of PostgreSQL's support for complex datatypes such as JSON, ARRAY, and other datatypes. SQLAlchemy's SQL expression language has native support for most table-valued and row-valued forms. .. _postgresql_table_valued: Table-Valued Functions ^^^^^^^^^^^^^^^^^^^^^^^ Many PostgreSQL built-in functions are intended to be used in the FROM clause of a SELECT statement, and are capable of returning table rows or sets of table rows. A large portion of PostgreSQL's JSON functions for example such as ``json_array_elements()``, ``json_object_keys()``, ``json_each_text()``, ``json_each()``, ``json_to_record()``, ``json_populate_recordset()`` use such forms. These classes of SQL function calling forms in SQLAlchemy are available using the :meth:`_functions.FunctionElement.table_valued` method in conjunction with :class:`_functions.Function` objects generated from the :data:`_sql.func` namespace. Examples from PostgreSQL's reference documentation follow below: * ``json_each()``:: >>> from sqlalchemy import select, func >>> stmt = select(func.json_each('{"a":"foo", "b":"bar"}').table_valued("key", "value")) >>> print(stmt) SELECT anon_1.key, anon_1.value FROM json_each(:json_each_1) AS anon_1 * ``json_populate_record()``:: >>> from sqlalchemy import select, func, literal_column >>> stmt = select( ... func.json_populate_record( ... literal_column("null::myrowtype"), ... '{"a":1,"b":2}' ... ).table_valued("a", "b", name="x") ... ) >>> print(stmt) SELECT x.a, x.b FROM json_populate_record(null::myrowtype, :json_populate_record_1) AS x * ``json_to_record()`` - this form uses a PostgreSQL specific form of derived columns in the alias, where we may make use of :func:`_sql.column` elements with types to produce them. The :meth:`_functions.FunctionElement.table_valued` method produces a :class:`_sql.TableValuedAlias` construct, and the method :meth:`_sql.TableValuedAlias.render_derived` method sets up the derived columns specification:: >>> from sqlalchemy import select, func, column, Integer, Text >>> stmt = select( ... func.json_to_record('{"a":1,"b":[1,2,3],"c":"bar"}').table_valued( ... column("a", Integer), column("b", Text), column("d", Text), ... ).render_derived(name="x", with_types=True) ... ) >>> print(stmt) SELECT x.a, x.b, x.d FROM json_to_record(:json_to_record_1) AS x(a INTEGER, b TEXT, d TEXT) * ``WITH ORDINALITY`` - part of the SQL standard, ``WITH ORDINALITY`` adds an ordinal counter to the output of a function and is accepted by a limited set of PostgreSQL functions including ``unnest()`` and ``generate_series()``. The :meth:`_functions.FunctionElement.table_valued` method accepts a keyword parameter ``with_ordinality`` for this purpose, which accepts the string name that will be applied to the "ordinality" column:: >>> from sqlalchemy import select, func >>> stmt = select( ... func.generate_series(4, 1, -1). ... table_valued("value", with_ordinality="ordinality"). ... render_derived() ... ) >>> print(stmt) SELECT anon_1.value, anon_1.ordinality FROM generate_series(:generate_series_1, :generate_series_2, :generate_series_3) WITH ORDINALITY AS anon_1(value, ordinality) .. versionadded:: 1.4.0b2 .. seealso:: :ref:`tutorial_functions_table_valued` - in the :ref:`unified_tutorial` .. _postgresql_column_valued: Column Valued Functions ^^^^^^^^^^^^^^^^^^^^^^^ Similar to the table valued function, a column valued function is present in the FROM clause, but delivers itself to the columns clause as a single scalar value. PostgreSQL functions such as ``json_array_elements()``, ``unnest()`` and ``generate_series()`` may use this form. Column valued functions are available using the :meth:`_functions.FunctionElement.column_valued` method of :class:`_functions.FunctionElement`: * ``json_array_elements()``:: >>> from sqlalchemy import select, func >>> stmt = select(func.json_array_elements('["one", "two"]').column_valued("x")) >>> print(stmt) SELECT x FROM json_array_elements(:json_array_elements_1) AS x * ``unnest()`` - in order to generate a PostgreSQL ARRAY literal, the :func:`_postgresql.array` construct may be used:: >>> from sqlalchemy.dialects.postgresql import array >>> from sqlalchemy import select, func >>> stmt = select(func.unnest(array([1, 2])).column_valued()) >>> print(stmt) SELECT anon_1 FROM unnest(ARRAY[%(param_1)s, %(param_2)s]) AS anon_1 The function can of course be used against an existing table-bound column that's of type :class:`_types.ARRAY`:: >>> from sqlalchemy import table, column, ARRAY, Integer >>> from sqlalchemy import select, func >>> t = table("t", column('value', ARRAY(Integer))) >>> stmt = select(func.unnest(t.c.value).column_valued("unnested_value")) >>> print(stmt) SELECT unnested_value FROM unnest(t.value) AS unnested_value .. seealso:: :ref:`tutorial_functions_column_valued` - in the :ref:`unified_tutorial` Row Types ^^^^^^^^^ Built-in support for rendering a ``ROW`` may be approximated using ``func.ROW`` with the :attr:`_sa.func` namespace, or by using the :func:`_sql.tuple_` construct:: >>> from sqlalchemy import table, column, func, tuple_ >>> t = table("t", column("id"), column("fk")) >>> stmt = t.select().where( ... tuple_(t.c.id, t.c.fk) > (1,2) ... ).where( ... func.ROW(t.c.id, t.c.fk) < func.ROW(3, 7) ... ) >>> print(stmt) SELECT t.id, t.fk FROM t WHERE (t.id, t.fk) > (:param_1, :param_2) AND ROW(t.id, t.fk) < ROW(:ROW_1, :ROW_2) .. seealso:: `PostgreSQL Row Constructors `_ `PostgreSQL Row Constructor Comparison `_ Table Types passed to Functions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ PostgreSQL supports passing a table as an argument to a function, which it refers towards as a "record" type. SQLAlchemy :class:`_sql.FromClause` objects such as :class:`_schema.Table` support this special form using the :meth:`_sql.FromClause.table_valued` method, which is comparable to the :meth:`_functions.FunctionElement.table_valued` method except that the collection of columns is already established by that of the :class:`_sql.FromClause` itself:: >>> from sqlalchemy import table, column, func, select >>> a = table( "a", column("id"), column("x"), column("y")) >>> stmt = select(func.row_to_json(a.table_valued())) >>> print(stmt) SELECT row_to_json(a) AS row_to_json_1 FROM a .. versionadded:: 1.4.0b2 ARRAY Types ----------- The PostgreSQL dialect supports arrays, both as multidimensional column types as well as array literals: * :class:`_postgresql.ARRAY` - ARRAY datatype * :class:`_postgresql.array` - array literal * :func:`_postgresql.array_agg` - ARRAY_AGG SQL function * :class:`_postgresql.aggregate_order_by` - helper for PG's ORDER BY aggregate function syntax. JSON Types ---------- The PostgreSQL dialect supports both JSON and JSONB datatypes, including psycopg2's native support and support for all of PostgreSQL's special operators: * :class:`_postgresql.JSON` * :class:`_postgresql.JSONB` HSTORE Type ----------- The PostgreSQL HSTORE type as well as hstore literals are supported: * :class:`_postgresql.HSTORE` - HSTORE datatype * :class:`_postgresql.hstore` - hstore literal ENUM Types ---------- PostgreSQL has an independently creatable TYPE structure which is used to implement an enumerated type. This approach introduces significant complexity on the SQLAlchemy side in terms of when this type should be CREATED and DROPPED. The type object is also an independently reflectable entity. The following sections should be consulted: * :class:`_postgresql.ENUM` - DDL and typing support for ENUM. * :meth:`.PGInspector.get_enums` - retrieve a listing of current ENUM types * :meth:`.postgresql.ENUM.create` , :meth:`.postgresql.ENUM.drop` - individual CREATE and DROP commands for ENUM. .. _postgresql_array_of_enum: Using ENUM with ARRAY ^^^^^^^^^^^^^^^^^^^^^ The combination of ENUM and ARRAY is not directly supported by backend DBAPIs at this time. Prior to SQLAlchemy 1.3.17, a special workaround was needed in order to allow this combination to work, described below. .. versionchanged:: 1.3.17 The combination of ENUM and ARRAY is now directly handled by SQLAlchemy's implementation without any workarounds needed. .. sourcecode:: python from sqlalchemy import TypeDecorator from sqlalchemy.dialects.postgresql import ARRAY class ArrayOfEnum(TypeDecorator): impl = ARRAY def bind_expression(self, bindvalue): return sa.cast(bindvalue, self) def result_processor(self, dialect, coltype): super_rp = super(ArrayOfEnum, self).result_processor( dialect, coltype) def handle_raw_string(value): inner = re.match(r"^{(.*)}$", value).group(1) return inner.split(",") if inner else [] def process(value): if value is None: return None return super_rp(handle_raw_string(value)) return process E.g.:: Table( 'mydata', metadata, Column('id', Integer, primary_key=True), Column('data', ArrayOfEnum(ENUM('a', 'b, 'c', name='myenum'))) ) This type is not included as a built-in type as it would be incompatible with a DBAPI that suddenly decides to support ARRAY of ENUM directly in a new version. .. _postgresql_array_of_json: Using JSON/JSONB with ARRAY ^^^^^^^^^^^^^^^^^^^^^^^^^^^ Similar to using ENUM, prior to SQLAlchemy 1.3.17, for an ARRAY of JSON/JSONB we need to render the appropriate CAST. Current psycopg2 drivers accommodate the result set correctly without any special steps. .. versionchanged:: 1.3.17 The combination of JSON/JSONB and ARRAY is now directly handled by SQLAlchemy's implementation without any workarounds needed. .. sourcecode:: python class CastingArray(ARRAY): def bind_expression(self, bindvalue): return sa.cast(bindvalue, self) E.g.:: Table( 'mydata', metadata, Column('id', Integer, primary_key=True), Column('data', CastingArray(JSONB)) ) ) defaultdictN)UUID)array)dml)hstore)json)ranges)excschema)sql)util)characteristics)default) reflection) coercions)compiler)elements) expression)roles)sqltypes)DDLBase)BIGINT)BOOLEAN)CHAR)DATE)FLOAT)INTEGER)NUMERIC)REAL)SMALLINT)TEXT)VARCHARz ^(?:btree|hash|gist|gin|[\w_]+)$zs\s*(?:UPDATE|INSERT|CREATE|DELETE|DROP|ALTER|GRANT|REVOKE|IMPORT FOREIGN SCHEMA|REFRESH MATERIALIZED VIEW|TRUNCATE))fallanalyseanalyzeandanyrasasc asymmetricbothcasecastcheckcollatecolumn constraintcreatecurrent_catalog current_date current_role current_timecurrent_timestamp current_userr deferrabledescdistinctdoelseendexceptfalsefetchforforeignfromgrantgrouphavingin initially intersectintoleadinglimit localtimelocaltimestampnewnotnullofoffoffsetoldononlyororderplacingprimary references returningselect session_usersome symmetrictablethentotrailingtrueunionuniqueuserusingvariadicwhenwherewindowwith authorizationbetweenbinarycrosscurrent_schemafreezefullilikeinnerisisnulljoinleftlikenaturalnotnullouteroveroverlapsrightsimilarverbose)ii)iiii)iiiceZdZdZy)BYTEAN__name__ __module__ __qualname____visit_name__V/var/www/html/venv/lib/python3.12/site-packages/sqlalchemy/dialects/postgresql/base.pyrrusNrrceZdZdZy)DOUBLE_PRECISIONNrrrrrry'NrrceZdZdZy)INETNrrrrrr}NrrceZdZdZy)CIDRNrrrrrrrrrceZdZdZy)MACADDRNrrrrrrsNrrceZdZdZy)MACADDR8NrrrrrrsNrrceZdZdZdZy)MONEYaProvide the PostgreSQL MONEY type. Depending on driver, result rows using this type may return a string value which includes currency symbols. For this reason, it may be preferable to provide conversion to a numerically-based currency datatype using :class:`_types.TypeDecorator`:: import re import decimal from sqlalchemy import TypeDecorator class NumericMoney(TypeDecorator): impl = MONEY def process_result_value(self, value: Any, dialect: Any) -> None: if value is not None: # adjust this for the currency and numeric m = re.match(r"\$([\d.]+)", value) if m: value = decimal.Decimal(m.group(1)) return value Alternatively, the conversion may be applied as a CAST using the :meth:`_types.TypeDecorator.column_expression` method as follows:: import decimal from sqlalchemy import cast from sqlalchemy import TypeDecorator class NumericMoney(TypeDecorator): impl = MONEY def column_expression(self, column: Any): return cast(column, Numeric()) .. versionadded:: 1.2 Nrrr__doc__rrrrrrs&PNrrceZdZdZdZy)OIDzCProvide the PostgreSQL OID type. .. versionadded:: 0.9.5 Nrrrrrrs NrrceZdZdZdZy)REGCLASSzHProvide the PostgreSQL REGCLASS type. .. versionadded:: 1.2.7 Nrrrrrrs  Nrrc(eZdZdZdZdfd ZxZS) TIMESTAMPz&Provide the PostgreSQL TIMESTAMP type.c<tt| |||_y)zConstruct a TIMESTAMP. :param timezone: boolean value if timezone present, default False :param precision: optional integer precision value .. versionadded:: 1.4 timezoneN)superr__init__ precisionselfrr __class__s rrzTIMESTAMP.__init__s i'':"rFNrrrrrr __classcell__rs@rrrs0 N # #rrc(eZdZdZdZdfd ZxZS)TIMEzPostgreSQL TIME type.c<tt| |||_y)zConstruct a TIME. :param timezone: boolean value if timezone present, default False :param precision: optional integer precision value .. versionadded:: 1.4 rN)rrrrrs rrz TIME.__init__s dD"H"5"rrrrs@rrrsN # #rrc^eZdZdZdZdZd dZedZe dZ d dZ e dZ d Z y) INTERVALzPostgreSQL INTERVAL type.TNc ||_||_y)a Construct an INTERVAL. :param precision: optional integer precision value :param fields: string fields specifier. allows storage of fields to be limited, such as ``"YEAR"``, ``"MONTH"``, ``"DAY TO HOUR"``, etc. .. versionadded:: 1.2 N)rfields)rrrs rrzINTERVAL.__init__ s# rc .t|jS)Nr)rsecond_precision)clsintervalkws radapt_emulated_to_nativez!INTERVAL.adapt_emulated_to_natives(";";<.processcs$ NN51E rrrdialectrs rbind_processorzUUID.bind_processor` <<  Nrc&|jrd}|Sy)Nc | t|}|Sr) _python_UUIDrs rrz&UUID.result_processor..processos$(/E rr)rrcoltypers rresult_processorzUUID.result_processorlrrc.|jrd}|Sd}|S)Nc|d|z}|S)Nz '%s'::UUIDrrs rrz'UUID.literal_processor..process{s$(50E rc|d|z}|S)N'%s'rrs rrz'UUID.literal_processor..processs$"UNE rrrs rliteral_processorzUUID.literal_processorxs! <<  N  Nrc2|jrtStSr)rrstrrs rrzUUID.python_types#|||44rr) rrrrrrrrrrrrrrs@rrr=s= N F  $55rrceZdZdZdZy)TSVECTORaThe :class:`_postgresql.TSVECTOR` type implements the PostgreSQL text search type TSVECTOR. It can be used to do full text queries on natural language documents. .. versionadded:: 0.9.0 .. seealso:: :ref:`postgresql_match` Nrrrrrrs  NrrceZdZdZdZfdZedZddZddZ Gdde Z Gd d e Z d Z dd Zdd ZddZddZxZS)ENUMa PostgreSQL ENUM type. This is a subclass of :class:`_types.Enum` which includes support for PG's ``CREATE TYPE`` and ``DROP TYPE``. When the builtin type :class:`_types.Enum` is used and the :paramref:`.Enum.native_enum` flag is left at its default of True, the PostgreSQL backend will use a :class:`_postgresql.ENUM` type as the implementation, so the special create/drop rules will be used. The create/drop behavior of ENUM is necessarily intricate, due to the awkward relationship the ENUM type has in relationship to the parent table, in that it may be "owned" by just a single table, or may be shared among many tables. When using :class:`_types.Enum` or :class:`_postgresql.ENUM` in an "inline" fashion, the ``CREATE TYPE`` and ``DROP TYPE`` is emitted corresponding to when the :meth:`_schema.Table.create` and :meth:`_schema.Table.drop` methods are called:: table = Table('sometable', metadata, Column('some_enum', ENUM('a', 'b', 'c', name='myenum')) ) table.create(engine) # will emit CREATE ENUM and CREATE TABLE table.drop(engine) # will emit DROP TABLE and DROP ENUM To use a common enumerated type between multiple tables, the best practice is to declare the :class:`_types.Enum` or :class:`_postgresql.ENUM` independently, and associate it with the :class:`_schema.MetaData` object itself:: my_enum = ENUM('a', 'b', 'c', name='myenum', metadata=metadata) t1 = Table('sometable_one', metadata, Column('some_enum', myenum) ) t2 = Table('sometable_two', metadata, Column('some_enum', myenum) ) When this pattern is used, care must still be taken at the level of individual table creates. Emitting CREATE TABLE without also specifying ``checkfirst=True`` will still cause issues:: t1.create(engine) # will fail: no such type 'myenum' If we specify ``checkfirst=True``, the individual table-level create operation will check for the ``ENUM`` and create if not exists:: # will check if enum exists, and emit CREATE TYPE if not t1.create(engine, checkfirst=True) When using a metadata-level ENUM type, the type will always be created and dropped if either the metadata-wide create/drop is called:: metadata.create_all(engine) # will emit CREATE TYPE metadata.drop_all(engine) # will emit DROP TYPE The type can also be created and dropped directly:: my_enum.create(engine) my_enum.drop(engine) .. versionchanged:: 1.0.0 The PostgreSQL :class:`_postgresql.ENUM` type now behaves more strictly with regards to CREATE/DROP. A metadata-level ENUM type will only be created and dropped at the metadata level, not the table level, with the exception of ``table.create(checkfirst=True)``. 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Valid isolation levels for z are rz=SET SESSION CHARACTERISTICS AS TRANSACTION ISOLATION LEVEL %sCOMMIT) r_isolation_lookupr ArgumentErrorr rcursorr0close)rr$levelrs rrzPGDialect.set_isolation_level s c3' .. .##$))TYYt/E/E%FH  ""$ !#( )  x  rc|j}|jd|jd}|j|j S)Nz show transaction isolation levelr)rr0fetchonerr)rr$rvals rget_isolation_levelzPGDialect.get_isolation_level sC""$9:oo" yy{rctrNotImplementedErrorrr$rs rrjzPGDialect.set_readonly !##rctrrrr$s rrszPGDialect.get_readonly rrctrrrs rr{zPGDialect.set_deferrable rrctrrrs rrzPGDialect.get_deferrable rrc:|j|jyr)do_beginr$rr$xids rdo_begin_twophasezPGDialect.do_begin_twophase s j++,rc,|jd|zy)NzPREPARE TRANSACTION '%s')rrs rdo_prepare_twophasezPGDialect.do_prepare_twophase s""#=#CDrc|rT|r|jd|jd|z|jd|j|jy|j|jy)NROLLBACKzROLLBACK PREPARED '%s'BEGIN)r do_rollbackr$rr$r is_preparedrecovers rdo_rollback_twophasezPGDialect.do_rollback_twophase sd  **:6  & &'?#'E F  & &w /   Z22 3   Z22 3rc|rT|r|jd|jd|z|jd|j|jy|j|jy)NrzCOMMIT PREPARED '%s'r)rrr$ do_commitrs rdo_commit_twophasezPGDialect.do_commit_twophase s` **:6  & &'='C D  & &w /   Z22 3 NN:00 1rcz|jtjd}|Dcgc]}|d c}Scc}w)Nz!SELECT gid FROM pg_prepared_xactsr)r0rri)rr$ resultsetrows rdo_recover_twophasezPGDialect.do_recover_twophase s9&& HH8 9 #,,3A,,,s 8c@|jdjS)Nzselect current_schema())rrrs r_get_default_schema_namez"PGDialect._get_default_schema_name s))*CDKKMMrc *d}|jtj|jtjdt j |jtj}t|jS)Nz=select nspname from pg_namespace where lower(nspname)=:schemarr#) r0rri bindparams bindparamrrrrUnicodeboolfirst)rr$rqueryrs r has_schemazPGDialect.has_schema sq N ## HHUO & & NN<6<<>2"**  FLLN##rc t|j||k|jtjdj tj dt j|tj}n|jtjdj tj dt j|tjtj dt j|tj}t|jS)Nzselect relname from pg_class c join pg_namespace n on n.oid=c.relnamespace where pg_catalog.pg_table_is_visible(c.oid) and relname=:namer r#ztselect relname from pg_class c join pg_namespace n on n.oid=c.relnamespace where n.nspname=:schema and relname=:namer) _ensure_has_table_connectionr0rrirrrrrrrr)rr$r:rrs r has_tablezPGDialect.has_tables ))*5 >''( *MMz2&.. 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The idea for using this method is that it can be fetched one time and cached for subsequent calls. Nzn.nspname = :schemaz%pg_catalog.pg_table_is_visible(c.oid)a  SELECT c.oid FROM pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_namespace n ON n.oid = c.relnamespace WHERE (%s) AND c.relname = :table_name AND c.relkind in ('r', 'v', 'm', 'f', 'p') )r:)rurr#)r:r)rrrrirrrrIntegerrr0r'rr NoSuchTableError) rr$r:rr table_oidschema_where_clauserrrs rr9zPGDialect.get_table_oidss  "7 "I  " " ^^J/  ^^F+F HHUO & &(2B2B & C II(**I +  S]]88;K;KLMA   q$*V"L MHHJ  &&z2 2rc |jtjdjtj }|Dcgc]\}|c}Scc}w)NzOSELECT nspname FROM pg_namespace WHERE nspname NOT LIKE 'pg_%' ORDER BY nspname)r)r0rrirrr)rr$rresultr s rget_schema_nameszPGDialect.get_schema_namessO## HH# gh..g/  #)))))s Ac |jtjdjtj t ||n |j}|Dcgc]\}|c}Scc}w)NzSELECT c.relname FROM pg_class c JOIN pg_namespace n ON n.oid = c.relnamespace WHERE n.nspname = :schema AND c.relkind in ('r', 'p')relnamer r0rrirrrr'r>rr$rrrr s rget_table_nameszPGDialect.get_table_namessl## HHH gh..g/ %--   #))))) A.c |jtjdjtj t ||n |j}|Dcgc]\}|c}Scc}w)Nz|SELECT c.relname FROM pg_class c JOIN pg_namespace n ON n.oid = c.relnamespace WHERE n.nspname = :schema AND c.relkind = 'f'rr rrs rrDz"PGDialect._get_foreign_table_namessl## HH@ gh..g/ %--   #)))))rc ddd} tj|Dcgc]}|| }}|s td|j t j ddjd |Dzjtj t||n |j }|D cgc]\} | c} Scc}w#t$rtd|dwxYwcc} w) Nr=rrIzinclude zU unknown, needs to be a sequence containing one or both of 'plain' and 'materialized'zZempty include, needs to be a sequence containing one or both of 'plain' and 'materialized'z~SELECT c.relname FROM pg_class c JOIN pg_namespace n ON n.oid = c.relnamespace WHERE n.nspname = :schema AND c.relkind IN (%s)rc3&K|] }d|z ywr%r)relems rrz+PGDialect.get_view_names..s=tVd]=r&rr ) rto_listKeyError ValueErrorr0rrirrrrr'r>) rr$rrr include_kindrkindsrr s rrGzPGDialect.get_view_namess "%c:  .2ll7.CD\!_DED <  ## HHB99=u==?  gh..g/ %--   #))))3E ?FI  0*s!C B?C1 C ?CCc "|s |j}|jtjdj tj dt j|tj}|Dcgc]}|d c}Scc}w)NzrSELECT relname FROM pg_class c join pg_namespace n on n.oid=c.relnamespace where relkind='S' and n.nspname=:schemarr#r) r>r0rrirrrrrr)rr$rrrrs rget_sequence_nameszPGDialect.get_sequence_namess{--F## HH$ j NN6*"**  #))3A)))s= B c |jtjdjtj t ||n |j|}|S)NzSELECT pg_get_viewdef(c.oid) view_def FROM pg_class c JOIN pg_namespace n ON n.oid = c.relnamespace WHERE n.nspname = :schema AND c.relname = :view_name AND c.relkind IN ('v', 'm'))view_def)r view_name)rrrirrrr'r>)rr$rrrrs rget_view_definitionzPGDialect.get_view_definitions]$$ HH.  gx//g0 %--#    rc |j||||jd}|jdk\rdnd}|jdk\rd}nd}d |d |d }tj|j tj d tj jtjtj} |j| t|} | j} |j|} td|j|dD} g}| D]6\}}}}}}}}|j!||||| | |||| }|j#|8|S)Nr7r6) za.attgenerated as generatedzNULL as generatedra (SELECT json_build_object( 'always', a.attidentity = 'a', 'start', s.seqstart, 'increment', s.seqincrement, 'minvalue', s.seqmin, 'maxvalue', s.seqmax, 'cache', s.seqcache, 'cycle', s.seqcycle) FROM pg_catalog.pg_sequence s JOIN pg_catalog.pg_class c on s.seqrelid = c."oid" WHERE c.relkind = 'S' AND a.attidentity != '' AND s.seqrelid = pg_catalog.pg_get_serial_sequence( a.attrelid::regclass::text, a.attname )::regclass::oid ) as identity_options zNULL as identity_optionsa SELECT a.attname, pg_catalog.format_type(a.atttypid, a.atttypmod), ( SELECT pg_catalog.pg_get_expr(d.adbin, d.adrelid) FROM pg_catalog.pg_attrdef d WHERE d.adrelid = a.attrelid AND d.adnum = a.attnum AND a.atthasdef ) AS DEFAULT, a.attnotnull, a.attrelid as table_oid, pgd.description as comment, z, a. FROM pg_catalog.pg_attribute a LEFT JOIN pg_catalog.pg_description pgd ON ( pgd.objoid = a.attrelid AND pgd.objsubid = a.attnum) WHERE a.attrelid = :table_oid AND a.attnum > 0 AND NOT a.attisdropped ORDER BY a.attnum rr#)attnamerrc3NK|]}|dr|df|fn |d|df|fyw)visibler rNr)rrecs rrz(PGDialect.get_columns..WsH 9~&k^S !x=#f+.4 5 s#%*r )r9rIrrrirrrrrrr0r'fetchall _load_domainsr?_get_column_infor/)rr$r:rrrrrbSQL_COLSrrrowsdomainsr rr rdefault_rcomment column_infos r get_columnszPGDialect.get_columns s&&  Frvvl7K' ''50 *$   # #u ,H&2H.  - 2 HHX  Z k9I9IJ K WX--x7G7GW H   q$"; <zz|$$Z0 '' 3'?    (        // K NN; '- (.rc d} | d} dx} }d}n$d} tjdd|} | | \} }ttj| }| }tj d|}|r|j d}tj d |}|r@|j dr/ttjd |j d}nd }i}| d k(r0|r+|jd \}}t|t|f}nd }n| dk(rd}n| dk(rd }n| dvrd|d<|rt||d<d }n| dvrd|d<|rt||d<d }n| dk(rd|d<|r t|f}nrd }no| jdrPtjd| tj}|rt||d<|r|j d|d<d} d }n|r t|f} | |jvr|j| }n||vr/||}t}|d|d<|ds|d|d<t|d}nP||vrI||}|d} | | \} }ttj| }|xr|d}|d r|s|d }d} |r ||i|}|rn|jd!|}nX| r*tjd"|d#tj }n,tjd$| d%|d#tj }| d&vrt#|| d'v(}d}nd}d}|tj d)|}|{t%|j&tj(rd}|}d*|j d+vr@|>|j dd,|zzd*z|j d+z|j d-z}t#|||||xs| du|.}|||d/<| | |d0<|S)1NcRtjdd||jdfS)Nz\[\]$rr )r"r#endswith)attypes r_handle_array_typez6PGDialect._get_column_info.._handle_array_types*xV,% rTzno format_type()Fz\(.*\)rz \(([\d,]+)\)rz\((.*)\)\s*,\s*rrj,rw)5re)ryr{rr)rzr|r~rqrrz interval (.+)rr r rlabelsrrmrr]z3PostgreSQL format_type() returned NULL for column ''zDid not recognize type 'z ' of column ')Nr)rs)rrz(nextval\(')([^']+)('.*$)r.rz"%s"r )r rXrmr autoincrementrrlrb)r"r#rrquoted_token_parsersearchrIsplitr startswithrdI ischema_namesrrrNULLTYPEr' issubclassrr)rr rrrrr rrrrbrno_format_typeris_arrayenum_or_domain_keyrmcharlenargsr%precscale field_matchrr,domainrlr%rdschrs rrzPGDialect._get_column_infozst   !N#5 5F[H"NVVIr;7F 2&9 FH#4#;#;F#CD;))O[9 mmA&Gyyk2 DJJqM*djjm<=DD Y %mmC0 eD 3u:. ) )D y D J J!%F: &)'l{#D   "'F: &)'l{#D } $ $F9 G    z *((#3VRTTBK&)'l{##.#4#4Q#7x FD L?D+++,,V4#u,/0!%fvI'+H~F8$T(^,#w. !34)#5f#= %*4+C+CF+K%L"$:z(:)$W%Y/G t.v.G6$,,X6w?  II ''G II@FM ''G / /9 +CHGH  II>HE g44h6F6FG$(Mekk!n,  A!C<) ++a.) ++a. )'?84+?    &.K #  &.K #rc L|j||||jd}|jdkrd|jddz}nd}t j |j tj}|j|t| }|jD cgc]} | d  } } d } t j | j tj }|j|t| }|j} | | d Scc} w)Nr7r6)raq SELECT a.attname FROM pg_class t join pg_index ix on t.oid = ix.indrelid join pg_attribute a on t.oid=a.attrelid AND %s WHERE t.oid = :table_oid and ix.indisprimary = 't' ORDER BY a.attnum a.attnum ix.indkeya SELECT a.attname FROM pg_attribute a JOIN ( SELECT unnest(ix.indkey) attnum, generate_subscripts(ix.indkey, 1) ord FROM pg_index ix WHERE ix.indrelid = :table_oid AND ix.indisprimary ) k ON a.attnum=k.attnum WHERE a.attrelid = :table_oid ORDER BY k.ord )r r rz SELECT conname FROM pg_catalog.pg_constraint r WHERE r.conrelid = :table_oid AND r.contype = 'p' ORDER BY 1 )conname)constrained_columnsr ) r9rIr _pg_index_anyrrirrrr0r'rr) rr$r:rrrPK_SQLrGrrr8 PK_CONS_SQLr s rget_pk_constraintzPGDialect.get_pk_constraint1s&&  Frvvl7K'   # #f , $$K F" F HHV  $ $X-=-= $ >   q$"; <jjl+!++ HH[ ! ) )(2B2B ) C   q$"; <xxz'+T::,s- D!c |j}|j||||jd}d}tjd} t j |jtjtj} |j| t|} g} | jD]4\} }}tj| |j}|\ }}}}}}}}}}}}}| |dk(rdnd }tjd |Dcgc]}|j!|}}|r||j"k7r|}n |}n|r|j!|}n |||k(r|}|j!|}tjd |Dcgc]}|j!|}}d |fd |fd|fd|fd|ffDcic]\}}||dk7r||}}}| |||||d}| j%|7| Scc}wcc}wcc}}w)Nr7r6a SELECT r.conname, pg_catalog.pg_get_constraintdef(r.oid, true) as condef, n.nspname as conschema FROM pg_catalog.pg_constraint r, pg_namespace n, pg_class c WHERE r.conrelid = :table AND r.contype = 'f' AND c.oid = confrelid AND n.oid = c.relnamespace ORDER BY 1 a/FOREIGN KEY \((.*?)\) REFERENCES (?:(.*?)\.)?(.*?)\((.*?)\)[\s]?(MATCH (FULL|PARTIAL|SIMPLE)+)?[\s]?(ON UPDATE (CASCADE|RESTRICT|NO ACTION|SET NULL|SET DEFAULT)+)?[\s]?(ON DELETE (CASCADE|RESTRICT|NO ACTION|SET NULL|SET DEFAULT)+)?[\s]?(DEFERRABLE|NOT DEFERRABLE)?[\s]?(INITIALLY (DEFERRED|IMMEDIATE)+)?)r<condef)rf DEFERRABLETFrz\s*,\sonupdateondeleterLr<rdz NO ACTION)r r=referred_schemareferred_tablereferred_columnsoptions)r^r9rIr"compilerrirrrr0r'rr'groupsr(r,r>r/) rr$r:rrrrrFK_SQLFK_REGEXrGrfkeysr<rD conschemarr=rHrIrJrrdrFrGr<rLrr<r=rKfkey_ds rget_foreign_keyszPGDialect.get_foreign_keysds++&&  Frvvl7K'   :: 7   HHV  $ $$$X-=-= %    q$Y"7 8*+**,C ! &GVY (F+224A  # %%/<%?TU *.AB#,,Q/# # - 8 88&/O&,O #+">">"O#)(;#)%99.IN)-=> ,,Q/    ** ),!:.e$  Aq=Q+%51 G  ':#2"0$4" F LL GC !H _#.  sG2G7<G<c|jdkr(ddjfdtddDzSddS) N)rrr z OR c3.K|] }d|fzyw)z %s[%d] = %sNr)rindr compare_tos rrz*PGDialect._pg_index_any..s"(;> S# 66(srrz = ANY(r)rrrange)rrrWs ``rr>zPGDialect._pg_index_anysK  # #f ,FKK(BG2,( &)*5 5rc &|j||||jd}|jdkrSd|jdk\rdndd|jdk\rd nd d |jd k\rd nd d|jddd }nd|jdk\rdnd d}t j |j tjtj}|j|t|}td} d} |jD]} | \ } } }}}}}}}}}}|r | | k7rtjd| z| } 5| | v}| | }|||d|<|rK|j}|r||dr ||d}|d|}ng}|Dcgc]}t!|j#c}|d<|Dcgc]}t!|j#c}|d<i}t%|xsdjD]G\}}t!|j#}d} |dzr| dz } |d zs| d!z } n |d zr| d"z } | sC| ||<I|r||d#<| |d$<|| |d%<|r+t|D!cgc]}!|!jd&c}!|d'<|r |d(k7r||d)<|s|||d*<g}"| j'D] \}#&|#&d$&dD$cgc] }$&d|$ c}$d+}%|jdk\r&dD$cgc] }$&d|$ c}$|%d,<d%&vr&d%|%d%<d#&vr(t&fd-&d#j'D|%d.<d,|%vr|%d,|%j)d/id0<d'&vr&d'|%j)d/id1<d)&vr&d)|%j)d/id2<d*&vr&d*|%j)d/id*<|"j+|% |"Scc}wcc}wcc}!wcc}$wcc}$w)3Nr7r6)rz SELECT i.relname as relname, ix.indisunique, ix.indexprs, ix.indpred, a.attname, a.attnum, NULL, ix.indkeyrz ::varcharrz, zix.indoption::varcharNULLrrz i.reloptionsam, am.amname, NULL as indnkeyatts FROM pg_class t join pg_index ix on t.oid = ix.indrelid join pg_class i on i.oid = ix.indexrelid left outer join pg_attribute a on t.oid = a.attrelid and r:r;aw left outer join pg_am am on i.relam = am.oid WHERE t.relkind IN ('r', 'v', 'f', 'm') and t.oid = :table_oid and ix.indisprimary = 'f' ORDER BY t.relname, i.relname a@ SELECT i.relname as relname, ix.indisunique, ix.indexprs, a.attname, a.attnum, c.conrelid, ix.indkey::varchar, ix.indoption::varchar, i.reloptions, am.amname, pg_get_expr(ix.indpred, ix.indrelid), ) rzix.indnkeyattsa as indnkeyatts FROM pg_class t join pg_index ix on t.oid = ix.indrelid join pg_class i on i.oid = ix.indexrelid left outer join pg_attribute a on t.oid = a.attrelid and a.attnum = ANY(ix.indkey) left outer join pg_constraint c on (ix.indrelid = c.conrelid and ix.indexrelid = c.conindid and c.contype in ('p', 'u', 'x')) left outer join pg_am am on i.relam = am.oid WHERE t.relkind IN ('r', 'v', 'f', 'm', 'p') and t.oid = :table_oid and ix.indisprimary = 'f' ORDER BY t.relname, i.relname )rr r c ttSrrr'rrrz'PGDialect.get_indexes..> k$&7rz;Skipped unsupported reflection of expression-based index %sr8r*incrr)r=r) nulls_last) nulls_firstsortingrlduplicates_constraint=rKbtreeamnamepostgresql_where)r rl column_namesinclude_columnsc3DK|]\}}dd||fyw)r8r*Nr)rrridxs rrz(PGDialect.get_indexes..s3/ 5[UA/7/s column_sortingrupostgresql_includepostgresql_withpostgresql_using)r9rIrr>rrirrrr0r'rrrrr(rstrip enumerater1rr/)'rr$r:rrrIDX_SQLrGrindexes sv_idx_nameridx_namerlrrcol_numconrelididx_key idx_optionrKrhfilter_definition indnkeyattshas_idxridx_keysinc_keysr<rdcol_idx col_flags col_sortingoptionrr rentryrms' @r get_indexeszPGDialect.get_indexess&&  Frvvl7K'   # #f ,: $776A rI++v5(++v5"":{;G$G -N++w6!?"GH HHW  % %$$h.>.> &    q$"; <78 ::<S BC !{*II46>?' ')GH%E),f g&"==?8KL#9 ( 5H' 5H!H8@A1AGGIAe 8@A1AGGIAe *3%2,,.+7&GY!$IOO$5 6I"$K 4'#y0 )D 0'?:K$t+'+;;K"+6(7'.E)$"(h'5=E12'+9@Avc*A(E)$f/&,E(O$0AE,-gS Bj ! !ID#h-9| jD cgc]!\} } | | dD cgc] } | d|  c} d #c} } } Scc} wcc} } } w) Nr7r6a SELECT cons.conname as name, cons.conkey as key, a.attnum as col_num, a.attname as col_name FROM pg_catalog.pg_constraint cons join pg_attribute a on cons.conrelid = a.attrelid AND a.attnum = ANY(cons.conkey) WHERE cons.conrelid = :table_oid AND cons.contype = 'u' )col_namer c ttSrr^rrrr_z2PGDialect.get_unique_constraints..r`rr*r8)r rj)r9rIrrirrrr0r'rrr r*rrxr1)rr$r:rrr UNIQUE_SQLrGruniquesrucr rs rget_unique_constraintsz PGDialect.get_unique_constraintss&&  Frvvl7K'  HHZ ( ((2B2B ( C   q$"; <78::< 3C"BBuI&)llBvJs{{ # 3$MMO  b2e9+MaBvJqM+M N  +M s%D5DDDc |j||||jd}d}|jtj|t |}d|j iS)Nr7r6z SELECT pgd.description as table_comment FROM pg_catalog.pg_description pgd WHERE pgd.objsubid = 0 AND pgd.objoid = :table_oid r ri)r9rIr0rrir'r)rr$r:rrr COMMENT_SQLrs rget_table_commentzPGDialect.get_table_commentsh&&  Frvvl7K'      HH[ !4)#<  ##rc 6|j||||jd}d}|jtj|t |}g}|D]\} } t jd| t j} | stjd| zd} nDt jd t jjd | jd } | | d } | r| jd rddi| d<|j| |S)Nr7r6a SELECT cons.conname as name, pg_get_constraintdef(cons.oid) as src FROM pg_catalog.pg_constraint cons WHERE cons.conrelid = :table_oid AND cons.contype = 'c' r z^CHECK *\((.+)\)( NOT VALID)?$)rz)Could not parse CHECK constraint text: %rrz^[\s\n]*\((.+)\)[\s\n]*$z\1r)r rrrtTru)r9rIr0rrir'r"rdDOTALLrrrLr#rIr/)rr$r:rrr CHECK_SQLrretr srcrrrs rget_check_constraintszPGDialect.get_check_constraintss &&  Frvvl7K'      sxx 2D94M N ID#13biiA EKL**/ryy#eQWWQZ("g6EQWWQZ,7+>'( JJu - . rc|xs |j}|jsiSd}|dk7r|dz }|dz }tj|j t j t j }|dk7r|j|}|j|}g}i}|jD]}|j|jf} | |vr"|| dj|jA|j|j|jgdx|| <} |j| dj|j|j| |S) Na SELECT t.typname as "name", -- no enum defaults in 8.4 at least -- t.typdefault as "default", pg_catalog.pg_type_is_visible(t.oid) as "visible", n.nspname as "schema", e.enumlabel as "label" FROM pg_catalog.pg_type t LEFT JOIN pg_catalog.pg_namespace n ON n.oid = t.typnamespace LEFT JOIN pg_catalog.pg_enum e ON t.oid = e.enumtypid WHERE t.typtype = 'e' rzAND n.nspname = :schema z ORDER BY "schema", "name", e.oid)r labelr r!)r rr r!)r>rrrirrrrr0rrr r/rr ) rr$r SQL_ENUMSrrr  enum_by_namer,r*enum_recs rr?zPGDialect._load_enums#sX3433((I   S= 3 3I 77 HHY  ' '$$H,<,< (  S= F +A   q ! JJL 'D;; *Cl"S!(+224::>!II"kk#|| 0 S!H ::)X&--djj9 X& ' rcBd}tj|}|jdj|}i}|j D]Q}|}t j d|djd}|dr|df}n |d |df}||d |d d ||<S|S) Na SELECT t.typname as "name", pg_catalog.format_type(t.typbasetype, t.typtypmod) as "attype", not t.typnotnull as "nullable", t.typdefault as "default", pg_catalog.pg_type_is_visible(t.oid) as "visible", n.nspname as "schema" FROM pg_catalog.pg_type t LEFT JOIN pg_catalog.pg_namespace n ON n.oid = t.typnamespace WHERE t.typtype = 'd' T) future_resultz([^\(]+)rrr r rrmr)rrmr)rriexecution_optionsr0mappingsr"r'rI) rr$ SQL_DOMAINSrrrr6rr*s rrzPGDialect._load_domainsWs   HH[ !  ( (t ( < D DQ Gjjl FFYY{F8,<=CCAFF i f~'h'8!":.!),GCL &r)NNN)TFrrHr)brrrr supports_statement_cachesupports_altermax_identifier_lengthsupports_sane_rowcountrsupports_native_booleanrfsupports_sequencessequences_optional"preexecute_autoincrement_sequencespostfetch_lastrowidsupports_commentssupports_default_valuessupports_default_metavaluesupports_empty_insertsupports_multivalues_insertrcdefault_paramstyler+rrstatement_compilerr[ ddl_compilerrrr'rrQexecution_ctx_clsr4 inspectorrrrrrconnection_characteristicsrkrgrxrIndexTableCheckConstraintForeignKeyConstraintconstruct_argumentsreflection_optionsrrrrrrr?rrrrjrsr{rrrrrrrrrrr*rrcacher9rrrDrGrrrrrBrSr>rrrrr?rrrs@rrr s D#N!")-&"!%!"& $#!MH# L"M#H*ION 99"rsoN`.$ %% # BJJ:BDD A BJJ@DD2:: giV% / H (x~~(8   8   h!!   x""  *H  *Z(   x"" #""#&#8==#&%x))8+E+E%P  (    O58  O5d  x"" &I88 % %x}}I8X55 NNFLL x MM4 MM 2 2 MM5::  , fll, gnn, EJJ, U[[ , "" , "" ,  ,"",w,"",w, f,,,,  hoo!," HOO#,$ D%,&w',( U),* D+,, D-,. D/,0 D1,2 33,435,6w7,89,: U;,< 3=,>?,@(A,BC,D E,F$-"   W, ^[%%[| h H((h V F X11F R8666?*&&?D(V++(&6))&828882v 0,, 0 2,, 2z&&zr