Working with Engines and Connections

This section details direct usage of the Engine, Connection, and related objects. Its important to note that when using the SQLAlchemy ORM, these objects are not generally accessed; instead, the Session object is used as the interface to the database. However, for applications that are built around direct usage of textual SQL statements and/or SQL expression constructs without involvement by the ORM’s higher level management services, the Engine and Connection are king (and queen?) - read on.

Basic Usage

Recall from Engine Configuration that an Engine is created via the create_engine() call:

  1. engine = create_engine("mysql+mysqldb://scott:tiger@localhost/test")

The typical usage of create_engine() is once per particular database URL, held globally for the lifetime of a single application process. A single Engine manages many individual DBAPI connections on behalf of the process and is intended to be called upon in a concurrent fashion. The Engine is not synonymous to the DBAPI connect() function, which represents just one connection resource - the Engine is most efficient when created just once at the module level of an application, not per-object or per-function call.

tip

When using an Engine with multiple Python processes, such as when using os.fork or Python multiprocessing, it’s important that the engine is initialized per process. See Using Connection Pools with Multiprocessing or os.fork() for details.

The most basic function of the Engine is to provide access to a Connection, which can then invoke SQL statements. To emit a textual statement to the database looks like:

  1. from sqlalchemy import text
  3. with engine.connect() as connection:
  4.     result = connection.execute(text("select username from users"))
  5.     for row in result:
  6.         print("username:", row["username"])

Above, the Engine.connect() method returns a Connection object, and by using it in a Python context manager (e.g. the with: statement) the Connection.close() method is automatically invoked at the end of the block. The Connection, is a proxy object for an actual DBAPI connection. The DBAPI connection is retrieved from the connection pool at the point at which Connection is created.

The object returned is known as CursorResult, which references a DBAPI cursor and provides methods for fetching rows similar to that of the DBAPI cursor. The DBAPI cursor will be closed by the CursorResult when all of its result rows (if any) are exhausted. A CursorResult that returns no rows, such as that of an UPDATE statement (without any returned rows), releases cursor resources immediately upon construction.

When the Connection is closed at the end of the with: block, the referenced DBAPI connection is released to the connection pool. From the perspective of the database itself, the connection pool will not actually “close” the connection assuming the pool has room to store this connection for the next use. When the connection is returned to the pool for re-use, the pooling mechanism issues a rollback() call on the DBAPI connection so that any transactional state or locks are removed (this is known as Reset On Return), and the connection is ready for its next use.

Our example above illustrated the execution of a textual SQL string, which should be invoked by using the text() construct to indicate that we’d like to use textual SQL. The Connection.execute() method can of course accommodate more than that; see Working with Data in the SQLAlchemy Unified Tutorial for a tutorial.

Using Transactions

Note

This section describes how to use transactions when working directly with Engine and Connection objects. When using the SQLAlchemy ORM, the public API for transaction control is via the Session object, which makes usage of the Transaction object internally. See Managing Transactions for further information.

Commit As You Go

The Connection object always emits SQL statements within the context of a transaction block. The first time the Connection.execute() method is called to execute a SQL statement, this transaction is begun automatically, using a behavior known as autobegin. The transaction remains in place for the scope of the Connection object until the Connection.commit() or Connection.rollback() methods are called. Subsequent to the transaction ending, the Connection waits for the Connection.execute() method to be called again, at which point it autobegins again.

This calling style is referred towards as commit as you go, and is illustrated in the example below:

  1. with engine.connect() as connection:
  2.     connection.execute(some_table.insert(), {"x": 7, "y": "this is some data"})
  3.     connection.execute(
  4.         some_other_table.insert(), {"q": 8, "p": "this is some more data"}
  5.     )
  7.     connection.commit()  # commit the transaction

the Python DBAPI is where autobegin actually happens

The design of “commit as you go” is intended to be complementary to the design of the DBAPI, which is the underlying database interface that SQLAlchemy interacts with. In the DBAPI, the connection object does not assume changes to the database will be automatically committed, instead requiring in the default case that the connection.commit() method is called in order to commit changes to the database. It should be noted that the DBAPI itself does not have a begin() method at all. All Python DBAPIs implement “autobegin” as the primary means of managing transactions, and handle the job of emitting a statement like BEGIN on the connection when SQL statements are first emitted. SQLAlchemy’s API is basically re-stating this behavior in terms of higher level Python objects.

In “commit as you go” style, we can call upon Connection.commit() and Connection.rollback() methods freely within an ongoing sequence of other statements emitted using Connection.execute(); each time the transaction is ended, and a new statement is emitted, a new transaction begins implicitly:

  1. with engine.connect() as connection:
  2.     connection.execute("<some statement>")
  3.     connection.commit()  # commits "some statement"
  5.     # new transaction starts
  6.     connection.execute("<some other statement>")
  7.     connection.rollback()  # rolls back "some other statement"
  9.     # new transaction starts
  10.     connection.execute("<a third statement>")
  11.     connection.commit()  # commits "a third statement"

New in version 2.0: “commit as you go” style is a new feature of SQLAlchemy 2.0. It is also available in SQLAlchemy 1.4’s “transitional” mode when using a “future” style engine.

Begin Once

The Connection object provides a more explicit transaction management style referred towards as begin once. In contrast to “commit as you go”, “begin once” allows the start point of the transaction to be stated explicitly, and allows that the transaction itself may be framed out as a context manager block so that the end of the transaction is instead implicit. To use “begin once”, the Connection.begin() method is used, which returns a Transaction object which represents the DBAPI transaction. This object also supports explicit management via its own Transaction.commit() and Transaction.rollback() methods, but as a preferred practice also supports the context manager interface, where it will commit itself when the block ends normally and emit a rollback if an exception is raised, before propagating the exception outwards. Below illustrates the form of a “begin once” block:

  1. with engine.connect() as connection:
  2.     with connection.begin():
  3.         connection.execute(some_table.insert(), {"x": 7, "y": "this is some data"})
  4.         connection.execute(
  5.             some_other_table.insert(), {"q": 8, "p": "this is some more data"}
  6.         )
  8.     # transaction is committed

Connect and Begin Once from the Engine

A convenient shorthand form for the above “begin once” block is to use the Engine.begin() method at the level of the originating Engine object, rather than performing the two separate steps of Engine.connect() and Connection.begin(); the Engine.begin() method returns a special context manager that internally maintains both the context manager for the Connection as well as the context manager for the Transaction normally returned by the Connection.begin() method:

  1. with engine.begin() as connection:
  2.     connection.execute(some_table.insert(), {"x": 7, "y": "this is some data"})
  3.     connection.execute(
  4.         some_other_table.insert(), {"q": 8, "p": "this is some more data"}
  5.     )
  7. # transaction is committed, and Connection is released to the connection
  8. # pool

Tip

Within the Engine.begin() block, we can call upon the Connection.commit() or Connection.rollback() methods, which will end the transaction normally demarcated by the block ahead of time. However, if we do so, no further SQL operations may be emitted on the Connection until the block ends:

  1. >>> from sqlalchemy import create_engine
  2. >>> e = create_engine("sqlite://", echo=True)
  3. >>> with e.begin() as conn:
  4. ...     conn.commit()
  5. ...     conn.begin()
  6. 2021-11-08 09:49:07,517 INFO sqlalchemy.engine.Engine BEGIN (implicit)
  7. 2021-11-08 09:49:07,517 INFO sqlalchemy.engine.Engine COMMIT
  8. Traceback (most recent call last):
  9. ...
  10. sqlalchemy.exc.InvalidRequestError: Can't operate on closed transaction inside
  11. context manager.  Please complete the context manager before emitting
  12. further commands.

Mixing Styles

The “commit as you go” and “begin once” styles can be freely mixed within a single Engine.connect() block, provided that the call to Connection.begin() does not conflict with the “autobegin” behavior. To accomplish this, Connection.begin() should only be called either before any SQL statements have been emitted, or directly after a previous call to Connection.commit() or Connection.rollback():

  1. with engine.connect() as connection:
  2.     with connection.begin():
  3.         # run statements in a "begin once" block
  4.         connection.execute(some_table.insert(), {"x": 7, "y": "this is some data"})
  6.     # transaction is committed
  8.     # run a new statement outside of a block. The connection
  9.     # autobegins
  10.     connection.execute(
  11.         some_other_table.insert(), {"q": 8, "p": "this is some more data"}
  12.     )
  14.     # commit explicitly
  15.     connection.commit()
  17.     # can use a "begin once" block here
  18.     with connection.begin():
  19.         # run more statements
  20.         connection.execute(...)

When developing code that uses “begin once”, the library will raise InvalidRequestError if a transaction was already “autobegun”.

Setting Transaction Isolation Levels including DBAPI Autocommit

Most DBAPIs support the concept of configurable transaction isolation levels. These are traditionally the four levels “READ UNCOMMITTED”, “READ COMMITTED”, “REPEATABLE READ” and “SERIALIZABLE”. These are usually applied to a DBAPI connection before it begins a new transaction, noting that most DBAPIs will begin this transaction implicitly when SQL statements are first emitted.

DBAPIs that support isolation levels also usually support the concept of true “autocommit”, which means that the DBAPI connection itself will be placed into a non-transactional autocommit mode. This usually means that the typical DBAPI behavior of emitting “BEGIN” to the database automatically no longer occurs, but it may also include other directives. SQLAlchemy treats the concept of “autocommit” like any other isolation level; in that it is an isolation level that loses not only “read committed” but also loses atomicity.

Tip

It is important to note, as will be discussed further in the section below at Understanding the DBAPI-Level Autocommit Isolation Level, that “autocommit” isolation level like any other isolation level does not affect the “transactional” behavior of the Connection object, which continues to call upon DBAPI .commit() and .rollback() methods (they just have no effect under autocommit), and for which the .begin() method assumes the DBAPI will start a transaction implicitly (which means that SQLAlchemy’s “begin” does not change autocommit mode).

SQLAlchemy dialects should support these isolation levels as well as autocommit to as great a degree as possible.

Setting Isolation Level or DBAPI Autocommit for a Connection

For an individual Connection object that’s acquired from Engine.connect(), the isolation level can be set for the duration of that Connection object using the Connection.execution_options() method. The parameter is known as Connection.execution_options.isolation_level and the values are strings which are typically a subset of the following names:

  1. # possible values for Connection.execution_options(isolation_level="<value>")
  3. "AUTOCOMMIT"
  4. "READ COMMITTED"
  5. "READ UNCOMMITTED"
  6. "REPEATABLE READ"
  7. "SERIALIZABLE"

Not every DBAPI supports every value; if an unsupported value is used for a certain backend, an error is raised.

For example, to force REPEATABLE READ on a specific connection, then begin a transaction:

  1. with engine.connect().execution_options(
  2.     isolation_level="REPEATABLE READ"
  3. ) as connection:
  4.     with connection.begin():
  5.         connection.execute("<statement>")

Tip

The return value of the Connection.execution_options() method is the same Connection object upon which the method was called, meaning, it modifies the state of the Connection object in place. This is a new behavior as of SQLAlchemy 2.0. This behavior does not apply to the Engine.execution_options() method; that method still returns a copy of the Engine and as described below may be used to construct multiple Engine objects with different execution options, which nonetheless share the same dialect and connection pool.

Note

The Connection.execution_options.isolation_level parameter necessarily does not apply to statement level options, such as that of Executable.execution_options(), and will be rejected if set at this level. This because the option must be set on a DBAPI connection on a per-transaction basis.

Setting Isolation Level or DBAPI Autocommit for an Engine

The Connection.execution_options.isolation_level option may also be set engine wide, as is often preferable. This may be achieved by passing the create_engine.isolation_level parameter to create_engine():

  1. from sqlalchemy import create_engine
  3. eng = create_engine(
  4.     "postgresql://scott:tiger@localhost/test", isolation_level="REPEATABLE READ"
  5. )

With the above setting, each new DBAPI connection the moment it’s created will be set to use a "REPEATABLE READ" isolation level setting for all subsequent operations.

Maintaining Multiple Isolation Levels for a Single Engine

The isolation level may also be set per engine, with a potentially greater level of flexibility, using either the create_engine.execution_options parameter to create_engine() or the Engine.execution_options() method, the latter of which will create a copy of the Engine that shares the dialect and connection pool of the original engine, but has its own per-connection isolation level setting:

  1. from sqlalchemy import create_engine
  3. eng = create_engine(
  4.     "postgresql+psycopg2://scott:tiger@localhost/test",
  5.     execution_options={"isolation_level": "REPEATABLE READ"},
  6. )

With the above setting, the DBAPI connection will be set to use a "REPEATABLE READ" isolation level setting for each new transaction begun; but the connection as pooled will be reset to the original isolation level that was present when the connection first occurred. At the level of create_engine(), the end effect is not any different from using the create_engine.isolation_level parameter.

However, an application that frequently chooses to run operations within different isolation levels may wish to create multiple “sub-engines” of a lead Engine, each of which will be configured to a different isolation level. One such use case is an application that has operations that break into “transactional” and “read-only” operations, a separate Engine that makes use of "AUTOCOMMIT" may be separated off from the main engine:

  1. from sqlalchemy import create_engine
  3. eng = create_engine("postgresql+psycopg2://scott:tiger@localhost/test")
  5. autocommit_engine = eng.execution_options(isolation_level="AUTOCOMMIT")

Above, the Engine.execution_options() method creates a shallow copy of the original Engine. Both eng and autocommit_engine share the same dialect and connection pool. However, the “AUTOCOMMIT” mode will be set upon connections when they are acquired from the autocommit_engine.

The isolation level setting, regardless of which one it is, is unconditionally reverted when a connection is returned to the connection pool.

See also

SQLite Transaction Isolation

PostgreSQL Transaction Isolation

MySQL Transaction Isolation

SQL Server Transaction Isolation

Oracle Transaction Isolation

Setting Transaction Isolation Levels / DBAPI AUTOCOMMIT - for the ORM

Using DBAPI Autocommit Allows for a Readonly Version of Transparent Reconnect - a recipe that uses DBAPI autocommit to transparently reconnect to the database for read-only operations

Understanding the DBAPI-Level Autocommit Isolation Level

In the parent section, we introduced the concept of the Connection.execution_options.isolation_level parameter and how it can be used to set database isolation levels, including DBAPI-level “autocommit” which is treated by SQLAlchemy as another transaction isolation level. In this section we will attempt to clarify the implications of this approach.

If we wanted to check out a Connection object and use it “autocommit” mode, we would proceed as follows:

  1. with engine.connect() as connection:
  2.     connection.execution_options(isolation_level="AUTOCOMMIT")
  3.     connection.execute("<statement>")
  4.     connection.execute("<statement>")

Above illustrates normal usage of “DBAPI autocommit” mode. There is no need to make use of methods such as Connection.begin() or Connection.commit(), as all statements are committed to the database immediately. When the block ends, the Connection object will revert the “autocommit” isolation level, and the DBAPI connection is released to the connection pool where the DBAPI connection.rollback() method will normally be invoked, but as the above statements were already committed, this rollback has no change on the state of the database.

It is important to note that “autocommit” mode persists even when the Connection.begin() method is called; the DBAPI will not emit any BEGIN to the database, nor will it emit COMMIT when Connection.commit() is called. This usage is also not an error scenario, as it is expected that the “autocommit” isolation level may be applied to code that otherwise was written assuming a transactional context; the “isolation level” is, after all, a configurational detail of the transaction itself just like any other isolation level.

In the example below, statements remain autocommitting regardless of SQLAlchemy-level transaction blocks:

  1. with engine.connect() as connection:
  2.     connection = connection.execution_options(isolation_level="AUTOCOMMIT")
  4.     # this begin() does not affect the DBAPI connection, isolation stays at AUTOCOMMIT
  5.     with connection.begin() as trans:
  6.         connection.execute("<statement>")
  7.         connection.execute("<statement>")

When we run a block like the above with logging turned on, the logging will attempt to indicate that while a DBAPI level .commit() is called, it probably will have no effect due to autocommit mode:

  1. INFO sqlalchemy.engine.Engine BEGIN (implicit)
  2. ...
  3. INFO sqlalchemy.engine.Engine COMMIT using DBAPI connection.commit(), DBAPI should ignore due to autocommit mode

At the same time, even though we are using “DBAPI autocommit”, SQLAlchemy’s transactional semantics, that is, the in-Python behavior of Connection.begin() as well as the behavior of “autobegin”, remain in place, even though these don’t impact the DBAPI connection itself. To illustrate, the code below will raise an error, as Connection.begin() is being called after autobegin has already occurred:

  1. with engine.connect() as connection:
  2.     connection = connection.execution_options(isolation_level="AUTOCOMMIT")
  4.     # "transaction" is autobegin (but has no effect due to autocommit)
  5.     connection.execute("<statement>")
  7.     # this will raise; "transaction" is already begun
  8.     with connection.begin() as trans:
  9.         connection.execute("<statement>")

The above example also demonstrates the same theme that the “autocommit” isolation level is a configurational detail of the underlying database transaction, and is independent of the begin/commit behavior of the SQLAlchemy Connection object. The “autocommit” mode will not interact with Connection.begin() in any way and the Connection does not consult this status when performing its own state changes with regards to the transaction (with the exception of suggesting within engine logging that these blocks are not actually committing). The rationale for this design is to maintain a completely consistent usage pattern with the Connection where DBAPI-autocommit mode can be changed independently without indicating any code changes elsewhere.

Changing Between Isolation Levels

TL;DR;

prefer to use individual Connection objects each with just one isolation level, rather than switching isolation on a single Connection. The code will be easier to read and less error prone.

Isolation level settings, including autocommit mode, are reset automatically when the connection is released back to the connection pool. Therefore it is preferable to avoid trying to switch isolation levels on a single Connection object as this leads to excess verbosity.

To illustrate how to use “autocommit” in an ad-hoc mode within the scope of a single Connection checkout, the Connection.execution_options.isolation_level parameter must be re-applied with the previous isolation level. The previous section illustrated an attempt to call Connection.begin() in order to start a transaction while autocommit was taking place; we can rewrite that example to actually do so by first reverting the isolation level before we call upon Connection.begin():

  1. # if we wanted to flip autocommit on and off on a single connection/
  2. # which... we usually don't.
  4. with engine.connect() as connection:
  6.     connection.execution_options(isolation_level="AUTOCOMMIT")
  8.     # run statement(s) in autocommit mode
  9.     connection.execute("<statement>")
  11.     # "commit" the autobegun "transaction"
  12.     connection.commit()
  14.     # switch to default isolation level
  15.     connection.execution_options(isolation_level=connection.default_isolation_level)
  17.     # use a begin block
  18.     with connection.begin() as trans:
  19.         connection.execute("<statement>")

Above, to manually revert the isolation level we made use of Connection.default_isolation_level to restore the default isolation level (assuming that’s what we want here). However, it’s probably a better idea to work with the architecture of of the Connection which already handles resetting of isolation level automatically upon checkin. The preferred way to write the above is to use two blocks

  1. # use an autocommit block
  2. with engine.connect().execution_options(isolation_level="AUTOCOMMIT") as connection:
  4.     # run statement in autocommit mode
  5.     connection.execute("<statement>")
  7. # use a regular block
  8. with engine.begin() as connection:
  9.     connection.execute("<statement>")

To sum up:

  1. “DBAPI level autocommit” isolation level is entirely independent of the Connection object’s notion of “begin” and “commit”

  2. use individual Connection checkouts per isolation level. Avoid trying to change back and forth between “autocommit” on a single connection checkout; let the engine do the work of restoring default isolation levels

Using Server Side Cursors (a.k.a. stream results)

Some backends feature explicit support for the concept of “server side cursors” versus “client side cursors”. A client side cursor here means that the database driver fully fetches all rows from a result set into memory before returning from a statement execution. Drivers such as those of PostgreSQL and MySQL/MariaDB generally use client side cursors by default. A server side cursor, by contrast, indicates that result rows remain pending within the database server’s state as result rows are consumed by the client. The drivers for Oracle generally use a “server side” model, for example, and the SQLite dialect, while not using a real “client / server” architecture, still uses an unbuffered result fetching approach that will leave result rows outside of process memory before they are consumed.

What we really mean is “buffered” vs. “unbuffered” results

Server side cursors also imply a wider set of features with relational databases, such as the ability to “scroll” a cursor forwards and backwards. SQLAlchemy does not include any explicit support for these behaviors; within SQLAlchemy itself, the general term “server side cursors” should be considered to mean “unbuffered results” and “client side cursors” means “result rows are buffered into memory before the first row is returned”. To work with a richer “server side cursor” featureset specific to a certain DBAPI driver, see the section Working with the DBAPI cursor directly.

From this basic architecture it follows that a “server side cursor” is more memory efficient when fetching very large result sets, while at the same time may introduce more complexity in the client/server communication process and be less efficient for small result sets (typically less than 10000 rows).

For those dialects that have conditional support for buffered or unbuffered results, there are usually caveats to the use of the “unbuffered”, or server side cursor mode. When using the psycopg2 dialect for example, an error is raised if a server side cursor is used with any kind of DML or DDL statement. When using MySQL drivers with a server side cursor, the DBAPI connection is in a more fragile state and does not recover as gracefully from error conditions nor will it allow a rollback to proceed until the cursor is fully closed.

For this reason, SQLAlchemy’s dialects will always default to the less error prone version of a cursor, which means for PostgreSQL and MySQL dialects it defaults to a buffered, “client side” cursor where the full set of results is pulled into memory before any fetch methods are called from the cursor. This mode of operation is appropriate in the vast majority of cases; unbuffered cursors are not generally useful except in the uncommon case of an application fetching a very large number of rows in chunks, where the processing of these rows can be complete before more rows are fetched.

For database drivers that provide client and server side cursor options, the Connection.execution_options.stream_results and Connection.execution_options.yield_per execution options provide access to “server side cursors” on a per-Connection or per-statement basis. Similar options exist when using an ORM Session as well.

Streaming with a fixed buffer via yield_per

As individual row-fetch operations with fully unbuffered server side cursors are typically more expensive than fetching batches of rows at once, The Connection.execution_options.yield_per execution option configures a Connection or statement to make use of server-side cursors as are available, while at the same time configuring a fixed-size buffer of rows that will retrieve rows from the server in batches as they are consumed. This parameter may be to a positive integer value using the Connection.execution_options() method on Connection or on a statement using the Executable.execution_options() method.

New in version 1.4.40: Connection.execution_options.yield_per as a Core-only option is new as of SQLAlchemy 1.4.40; for prior 1.4 versions, use Connection.execution_options.stream_results directly in combination with Result.yield_per().

Using this option is equivalent to manually setting the Connection.execution_options.stream_results option, described in the next section, and then invoking the Result.yield_per() method on the Result object with the given integer value. In both cases, the effect this combination has includes:

  • server side cursors mode is selected for the given backend, if available and not already the default behavior for that backend

  • as result rows are fetched, they will be buffered in batches, where the size of each batch up until the last batch will be equal to the integer argument passed to the Connection.execution_options.yield_per option or the Result.yield_per() method; the last batch is then sized against the remaining rows fewer than this size

  • The default partition size used by the Result.partitions() method, if used, will be made equal to this integer size as well.

These three behaviors are illustrated in the example below:

  1. with engine.connect() as conn:
  2.     with conn.execution_options(yield_per=100).execute(
  3.         text("select * from table")
  4.     ) as result:
  5.         for partition in result.partitions():
  6.             # partition is an iterable that will be at most 100 items
  7.             for row in partition:
  8.                 print(f"{row}")

The above example illustrates the combination of yield_per=100 along with using the Result.partitions() method to run processing on rows in batches that match the size fetched from the server. The use of Result.partitions() is optional, and if the Result is iterated directly, a new batch of rows will be buffered for each 100 rows fetched. Calling a method such as Result.all() should not be used, as this will fully fetch all remaining rows at once and defeat the purpose of using yield_per.

Tip

The Result object may be used as a context manager as illustrated above. When iterating with a server-side cursor, this is the best way to ensure the Result object is closed, even if exceptions are raised within the iteration process.

The Connection.execution_options.yield_per option is portable to the ORM as well, used by a Session to fetch ORM objects, where it also limits the amount of ORM objects generated at once. See the section Fetching Large Result Sets with Yield Per - in the ORM Querying Guide for further background on using Connection.execution_options.yield_per with the ORM.

New in version 1.4.40: Added Connection.execution_options.yield_per as a Core level execution option to conveniently set streaming results, buffer size, and partition size all at once in a manner that is transferrable to that of the ORM’s similar use case.

Streaming with a dynamically growing buffer using stream_results

To enable server side cursors without a specific partition size, the Connection.execution_options.stream_results option may be used, which like Connection.execution_options.yield_per may be called on the Connection object or the statement object.

When a Result object delivered using the Connection.execution_options.stream_results option is iterated directly, rows are fetched internally using a default buffering scheme that buffers first a small set of rows, then a larger and larger buffer on each fetch up to a pre-configured limit of 1000 rows. The maximum size of this buffer can be affected using the Connection.execution_options.max_row_buffer execution option:

  1. with engine.connect() as conn:
  2.     with conn.execution_options(stream_results=True, max_row_buffer=100).execute(
  3.         text("select * from table")
  4.     ) as result:
  6.         for row in result:
  7.             print(f"{row}")

While the Connection.execution_options.stream_results option may be combined with use of the Result.partitions() method, a specific partition size should be passed to Result.partitions() so that the entire result is not fetched. It is usually more straightforward to use the Connection.execution_options.yield_per option when setting up to use the Result.partitions() method.

See also

Fetching Large Result Sets with Yield Per - in the ORM Querying Guide

Result.partitions()

Result.yield_per()

Translation of Schema Names

To support multi-tenancy applications that distribute common sets of tables into multiple schemas, the Connection.execution_options.schema_translate_map execution option may be used to repurpose a set of Table objects to render under different schema names without any changes.

Given a table:

  1. user_table = Table(
  2.     "user",
  3.     metadata_obj,
  4.     Column("id", Integer, primary_key=True),
  5.     Column("name", String(50)),
  6. )

The “schema” of this Table as defined by the Table.schema attribute is None. The Connection.execution_options.schema_translate_map can specify that all Table objects with a schema of None would instead render the schema as user_schema_one:

  1. connection = engine.connect().execution_options(
  2.     schema_translate_map={None: "user_schema_one"}
  3. )
  5. result = connection.execute(user_table.select())

The above code will invoke SQL on the database of the form:

  1. SELECT user_schema_one.user.id, user_schema_one.user.name FROM
  2. user_schema_one.user

That is, the schema name is substituted with our translated name. The map can specify any number of target->destination schemas:

  1. connection = engine.connect().execution_options(
  2.     schema_translate_map={
  3.         None: "user_schema_one",  # no schema name -> "user_schema_one"
  4.         "special": "special_schema",  # schema="special" becomes "special_schema"
  5.         "public": None,  # Table objects with schema="public" will render with no schema
  6.     }
  7. )

The Connection.execution_options.schema_translate_map parameter affects all DDL and SQL constructs generated from the SQL expression language, as derived from the Table or Sequence objects. It does not impact literal string SQL used via the text() construct nor via plain strings passed to Connection.execute().

The feature takes effect only in those cases where the name of the schema is derived directly from that of a Table or Sequence; it does not impact methods where a string schema name is passed directly. By this pattern, it takes effect within the “can create” / “can drop” checks performed by methods such as MetaData.create_all() or MetaData.drop_all() are called, and it takes effect when using table reflection given a Table object. However it does not affect the operations present on the Inspector object, as the schema name is passed to these methods explicitly.

Tip

To use the schema translation feature with the ORM Session, set this option at the level of the Engine, then pass that engine to the Session. The Session uses a new Connection for each transaction:

  1. schema_engine = engine.execution_options(schema_translate_map={...})
  3. session = Session(schema_engine)
  5. ...

Warning

When using the ORM Session without extensions, the schema translate feature is only supported as a single schema translate map per Session. It will not work if different schema translate maps are given on a per-statement basis, as the ORM Session does not take current schema translate values into account for individual objects.

To use a single Session with multiple schema_translate_map configurations, the Horizontal Sharding extension may be used. See the example at Horizontal Sharding.

New in version 1.1.

SQL Compilation Caching

New in version 1.4: SQLAlchemy now has a transparent query caching system that substantially lowers the Python computational overhead involved in converting SQL statement constructs into SQL strings across both Core and ORM. See the introduction at Transparent SQL Compilation Caching added to All DQL, DML Statements in Core, ORM.

SQLAlchemy includes a comprehensive caching system for the SQL compiler as well as its ORM variants. This caching system is transparent within the Engine and provides that the SQL compilation process for a given Core or ORM SQL statement, as well as related computations which assemble result-fetching mechanics for that statement, will only occur once for that statement object and all others with the identical structure, for the duration that the particular structure remains within the engine’s “compiled cache”. By “statement objects that have the identical structure”, this generally corresponds to a SQL statement that is constructed within a function and is built each time that function runs:

  1. def run_my_statement(connection, parameter):
  2.     stmt = select(table)
  3.     stmt = stmt.where(table.c.col == parameter)
  4.     stmt = stmt.order_by(table.c.id)
  5.     return connection.execute(stmt)

The above statement will generate SQL resembling SELECT id, col FROM table WHERE col = :col ORDER BY id, noting that while the value of parameter is a plain Python object such as a string or an integer, the string SQL form of the statement does not include this value as it uses bound parameters. Subsequent invocations of the above run_my_statement() function will use a cached compilation construct within the scope of the connection.execute() call for enhanced performance.

Note

it is important to note that the SQL compilation cache is caching the SQL string that is passed to the database only, and not the data returned by a query. It is in no way a data cache and does not impact the results returned for a particular SQL statement nor does it imply any memory use linked to fetching of result rows.

While SQLAlchemy has had a rudimentary statement cache since the early 1.x series, and additionally has featured the “Baked Query” extension for the ORM, both of these systems required a high degree of special API use in order for the cache to be effective. The new cache as of 1.4 is instead completely automatic and requires no change in programming style to be effective.

The cache is automatically used without any configurational changes and no special steps are needed in order to enable it. The following sections detail the configuration and advanced usage patterns for the cache.

Configuration

The cache itself is a dictionary-like object called an LRUCache, which is an internal SQLAlchemy dictionary subclass that tracks the usage of particular keys and features a periodic “pruning” step which removes the least recently used items when the size of the cache reaches a certain threshold. The size of this cache defaults to 500 and may be configured using the create_engine.query_cache_size parameter:

  1. engine = create_engine(
  2.     "postgresql+psycopg2://scott:tiger@localhost/test", query_cache_size=1200
  3. )

The size of the cache can grow to be a factor of 150% of the size given, before it’s pruned back down to the target size. A cache of size 1200 above can therefore grow to be 1800 elements in size at which point it will be pruned to 1200.

The sizing of the cache is based on a single entry per unique SQL statement rendered, per engine. SQL statements generated from both the Core and the ORM are treated equally. DDL statements will usually not be cached. In order to determine what the cache is doing, engine logging will include details about the cache’s behavior, described in the next section.

Estimating Cache Performance Using Logging

The above cache size of 1200 is actually fairly large. For small applications, a size of 100 is likely sufficient. To estimate the optimal size of the cache, assuming enough memory is present on the target host, the size of the cache should be based on the number of unique SQL strings that may be rendered for the target engine in use. The most expedient way to see this is to use SQL echoing, which is most directly enabled by using the create_engine.echo flag, or by using Python logging; see the section Configuring Logging for background on logging configuration.

As an example, we will examine the logging produced by the following program:

  1. from sqlalchemy import Column
  2. from sqlalchemy import create_engine
  3. from sqlalchemy import ForeignKey
  4. from sqlalchemy import Integer
  5. from sqlalchemy import select
  6. from sqlalchemy import String
  7. from sqlalchemy.ext.declarative import declarative_base
  8. from sqlalchemy.orm import relationship
  9. from sqlalchemy.orm import Session
  11. Base = declarative_base()
  14. class A(Base):
  15.     __tablename__ = "a"
  17.     id = Column(Integer, primary_key=True)
  18.     data = Column(String)
  19.     bs = relationship("B")
  22. class B(Base):
  23.     __tablename__ = "b"
  24.     id = Column(Integer, primary_key=True)
  25.     a_id = Column(ForeignKey("a.id"))
  26.     data = Column(String)
  29. e = create_engine("sqlite://", echo=True)
  30. Base.metadata.create_all(e)
  32. s = Session(e)
  34. s.add_all([A(bs=[B(), B(), B()]), A(bs=[B(), B(), B()]), A(bs=[B(), B(), B()])])
  35. s.commit()
  37. for a_rec in s.scalars(select(A)):
  38.     print(a_rec.bs)

When run, each SQL statement that’s logged will include a bracketed cache statistics badge to the left of the parameters passed. The four types of message we may see are summarized as follows:

  • [raw sql] - the driver or the end-user emitted raw SQL using Connection.exec_driver_sql() - caching does not apply

  • [no key] - the statement object is a DDL statement that is not cached, or the statement object contains uncacheable elements such as user-defined constructs or arbitrarily large VALUES clauses.

  • [generated in Xs] - the statement was a cache miss and had to be compiled, then stored in the cache. it took X seconds to produce the compiled construct. The number X will be in the small fractional seconds.

  • [cached since Xs ago] - the statement was a cache hit and did not have to be recompiled. The statement has been stored in the cache since X seconds ago. The number X will be proportional to how long the application has been running and how long the statement has been cached, so for example would be 86400 for a 24 hour period.

Each badge is described in more detail below.

The first statements we see for the above program will be the SQLite dialect checking for the existence of the “a” and “b” tables:

  1. INFO sqlalchemy.engine.Engine PRAGMA temp.table_info("a")
  2. INFO sqlalchemy.engine.Engine [raw sql] ()
  3. INFO sqlalchemy.engine.Engine PRAGMA main.table_info("b")
  4. INFO sqlalchemy.engine.Engine [raw sql] ()

For the above two SQLite PRAGMA statements, the badge reads [raw sql], which indicates the driver is sending a Python string directly to the database using Connection.exec_driver_sql(). Caching does not apply to such statements because they already exist in string form, and there is nothing known about what kinds of result rows will be returned since SQLAlchemy does not parse SQL strings ahead of time.

The next statements we see are the CREATE TABLE statements:

  1. INFO sqlalchemy.engine.Engine
  2. CREATE TABLE a (
  3.   id INTEGER NOT NULL,
  4.   data VARCHAR,
  5.   PRIMARY KEY (id)
  6. )
  8. INFO sqlalchemy.engine.Engine [no key 0.00007s] ()
  9. INFO sqlalchemy.engine.Engine
  10. CREATE TABLE b (
  11.   id INTEGER NOT NULL,
  12.   a_id INTEGER,
  13.   data VARCHAR,
  14.   PRIMARY KEY (id),
  15.   FOREIGN KEY(a_id) REFERENCES a (id)
  16. )
  18. INFO sqlalchemy.engine.Engine [no key 0.00006s] ()

For each of these statements, the badge reads [no key 0.00006s]. This indicates that these two particular statements, caching did not occur because the DDL-oriented CreateTable construct did not produce a cache key. DDL constructs generally do not participate in caching because they are not typically subject to being repeated a second time and DDL is also a database configurational step where performance is not as critical.

The [no key] badge is important for one other reason, as it can be produced for SQL statements that are cacheable except for some particular sub-construct that is not currently cacheable. Examples of this include custom user-defined SQL elements that don’t define caching parameters, as well as some constructs that generate arbitrarily long and non-reproducible SQL strings, the main examples being the Values construct as well as when using “multivalued inserts” with the Insert.values() method.

So far our cache is still empty. The next statements will be cached however, a segment looks like:

  1. .. sourcecode:: sql

INFO sqlalchemy.engine.Engine INSERT INTO a (data) VALUES (?) INFO sqlalchemy.engine.Engine [generated in 0.00011s] (None,) INFO sqlalchemy.engine.Engine INSERT INTO a (data) VALUES (?) INFO sqlalchemy.engine.Engine [cached since 0.0003533s ago] (None,) INFO sqlalchemy.engine.Engine INSERT INTO a (data) VALUES (?) INFO sqlalchemy.engine.Engine [cached since 0.0005326s ago] (None,) INFO sqlalchemy.engine.Engine INSERT INTO b (a_id, data) VALUES (?, ?) INFO sqlalchemy.engine.Engine [generated in 0.00010s] (1, None) INFO sqlalchemy.engine.Engine INSERT INTO b (a_id, data) VALUES (?, ?) INFO sqlalchemy.engine.Engine [cached since 0.0003232s ago] (1, None) INFO sqlalchemy.engine.Engine INSERT INTO b (a_id, data) VALUES (?, ?) INFO sqlalchemy.engine.Engine [cached since 0.0004887s ago] (1, None)

Above, we see essentially two unique SQL strings; "INSERT INTO a (data) VALUES (?)" and "INSERT INTO b (a_id, data) VALUES (?, ?)". Since SQLAlchemy uses bound parameters for all literal values, even though these statements are repeated many times for different objects, because the parameters are separate, the actual SQL string stays the same.

Note

the above two statements are generated by the ORM unit of work process, and in fact will be caching these in a separate cache that is local to each mapper. However the mechanics and terminology are the same. The section Disabling or using an alternate dictionary to cache some (or all) statements below will describe how user-facing code can also use an alternate caching container on a per-statement basis.

The caching badge we see for the first occurrence of each of these two statements is [generated in 0.00011s]. This indicates that the statement was not in the cache, was compiled into a String in .00011s and was then cached. When we see the [generated] badge, we know that this means there was a cache miss. This is to be expected for the first occurrence of a particular statement. However, if lots of new [generated] badges are observed for a long-running application that is generally using the same series of SQL statements over and over, this may be a sign that the create_engine.query_cache_size parameter is too small. When a statement that was cached is then evicted from the cache due to the LRU cache pruning lesser used items, it will display the [generated] badge when it is next used.

The caching badge that we then see for the subsequent occurrences of each of these two statements looks like [cached since 0.0003533s ago]. This indicates that the statement was found in the cache, and was originally placed into the cache .0003533 seconds ago. It is important to note that while the [generated] and [cached since] badges refer to a number of seconds, they mean different things; in the case of [generated], the number is a rough timing of how long it took to compile the statement, and will be an extremely small amount of time. In the case of [cached since], this is the total time that a statement has been present in the cache. For an application that’s been running for six hours, this number may read [cached since 21600 seconds ago], and that’s a good thing. Seeing high numbers for “cached since” is an indication that these statements have not been subject to cache misses for a long time. Statements that frequently have a low number of “cached since” even if the application has been running a long time may indicate these statements are too frequently subject to cache misses, and that the create_engine.query_cache_size may need to be increased.

Our example program then performs some SELECTs where we can see the same pattern of “generated” then “cached”, for the SELECT of the “a” table as well as for subsequent lazy loads of the “b” table:

  1. INFO sqlalchemy.engine.Engine SELECT a.id AS a_id, a.data AS a_data
  2. FROM a
  3. INFO sqlalchemy.engine.Engine [generated in 0.00009s] ()
  4. INFO sqlalchemy.engine.Engine SELECT b.id AS b_id, b.a_id AS b_a_id, b.data AS b_data
  5. FROM b
  6. WHERE ? = b.a_id
  7. INFO sqlalchemy.engine.Engine [generated in 0.00010s] (1,)
  8. INFO sqlalchemy.engine.Engine SELECT b.id AS b_id, b.a_id AS b_a_id, b.data AS b_data
  9. FROM b
  10. WHERE ? = b.a_id
  11. INFO sqlalchemy.engine.Engine [cached since 0.0005922s ago] (2,)
  12. INFO sqlalchemy.engine.Engine SELECT b.id AS b_id, b.a_id AS b_a_id, b.data AS b_data
  13. FROM b
  14. WHERE ? = b.a_id

From our above program, a full run shows a total of four distinct SQL strings being cached. Which indicates a cache size of four would be sufficient. This is obviously an extremely small size, and the default size of 500 is fine to be left at its default.

How much memory does the cache use?

The previous section detailed some techniques to check if the create_engine.query_cache_size needs to be bigger. How do we know if the cache is not too large? The reason we may want to set create_engine.query_cache_size to not be higher than a certain number would be because we have an application that may make use of a very large number of different statements, such as an application that is building queries on the fly from a search UX, and we don’t want our host to run out of memory if for example, a hundred thousand different queries were run in the past 24 hours and they were all cached.

It is extremely difficult to measure how much memory is occupied by Python data structures, however using a process to measure growth in memory via top as a successive series of 250 new statements are added to the cache suggest a moderate Core statement takes up about 12K while a small ORM statement takes about 20K, including result-fetching structures which for the ORM will be much greater.

Disabling or using an alternate dictionary to cache some (or all) statements

The internal cache used is known as LRUCache, but this is mostly just a dictionary. Any dictionary may be used as a cache for any series of statements by using the Connection.execution_options.compiled_cache option as an execution option. Execution options may be set on a statement, on an Engine or Connection, as well as when using the ORM Session.execute() method for SQLAlchemy-2.0 style invocations. For example, to run a series of SQL statements and have them cached in a particular dictionary:

  1. my_cache = {}
  2. with engine.connect().execution_options(compiled_cache=my_cache) as conn:
  3.     conn.execute(table.select())

The SQLAlchemy ORM uses the above technique to hold onto per-mapper caches within the unit of work “flush” process that are separate from the default cache configured on the Engine, as well as for some relationship loader queries.

The cache can also be disabled with this argument by sending a value of None:

  1. # disable caching for this connection
  2. with engine.connect().execution_options(compiled_cache=None) as conn:
  3.     conn.execute(table.select())

Caching for Third Party Dialects

The caching feature requires that the dialect’s compiler produces SQL strings that are safe to reuse for many statement invocations, given a particular cache key that is keyed to that SQL string. This means that any literal values in a statement, such as the LIMIT/OFFSET values for a SELECT, can not be hardcoded in the dialect’s compilation scheme, as the compiled string will not be re-usable. SQLAlchemy supports rendered bound parameters using the BindParameter.render_literal_execute() method which can be applied to the existing Select._limit_clause and Select._offset_clause attributes by a custom compiler, which are illustrated later in this section.

As there are many third party dialects, many of which may be generating literal values from SQL statements without the benefit of the newer “literal execute” feature, SQLAlchemy as of version 1.4.5 has added an attribute to dialects known as Dialect.supports_statement_cache. This attribute is checked at runtime for its presence directly on a particular dialect’s class, even if it’s already present on a superclass, so that even a third party dialect that subclasses an existing cacheable SQLAlchemy dialect such as sqlalchemy.dialects.postgresql.PGDialect must still explicitly include this attribute for caching to be enabled. The attribute should only be enabled once the dialect has been altered as needed and tested for reusability of compiled SQL statements with differing parameters.

For all third party dialects that don’t support this attribute, the logging for such a dialect will indicate dialect does not support caching.

When a dialect has been tested against caching, and in particular the SQL compiler has been updated to not render any literal LIMIT / OFFSET within a SQL string directly, dialect authors can apply the attribute as follows:

  1. from sqlalchemy.engine.default import DefaultDialect
  4. class MyDialect(DefaultDialect):
  5.     supports_statement_cache = True

The flag needs to be applied to all subclasses of the dialect as well:

  1. class MyDBAPIForMyDialect(MyDialect):
  2.     supports_statement_cache = True

New in version 1.4.5: Added the Dialect.supports_statement_cache attribute.

The typical case for dialect modification follows.

Example: Rendering LIMIT / OFFSET with post compile parameters

As an example, suppose a dialect overrides the SQLCompiler.limit_clause() method, which produces the “LIMIT / OFFSET” clause for a SQL statement, like this:

  1. # pre 1.4 style code
  2. def limit_clause(self, select, **kw):
  3.     text = ""
  4.     if select._limit is not None:
  5.         text += " \n LIMIT %d" % (select._limit,)
  6.     if select._offset is not None:
  7.         text += " \n OFFSET %d" % (select._offset,)
  8.     return text

The above routine renders the Select._limit and Select._offset integer values as literal integers embedded in the SQL statement. This is a common requirement for databases that do not support using a bound parameter within the LIMIT/OFFSET clauses of a SELECT statement. However, rendering the integer value within the initial compilation stage is directly incompatible with caching as the limit and offset integer values of a Select object are not part of the cache key, so that many Select statements with different limit/offset values would not render with the correct value.

The correction for the above code is to move the literal integer into SQLAlchemy’s post-compile facility, which will render the literal integer outside of the initial compilation stage, but instead at execution time before the statement is sent to the DBAPI. This is accessed within the compilation stage using the BindParameter.render_literal_execute() method, in conjunction with using the Select._limit_clause and Select._offset_clause attributes, which represent the LIMIT/OFFSET as a complete SQL expression, as follows:

  1. # 1.4 cache-compatible code
  2. def limit_clause(self, select, **kw):
  3.     text = ""
  5.     limit_clause = select._limit_clause
  6.     offset_clause = select._offset_clause
  8.     if select._simple_int_clause(limit_clause):
  9.         text += " \n LIMIT %s" % (
  10.             self.process(limit_clause.render_literal_execute(), **kw)
  11.         )
  12.     elif limit_clause is not None:
  13.         # assuming the DB doesn't support SQL expressions for LIMIT.
  14.         # Otherwise render here normally
  15.         raise exc.CompileError(
  16.             "dialect 'mydialect' can only render simple integers for LIMIT"
  17.         )
  18.     if select._simple_int_clause(offset_clause):
  19.         text += " \n OFFSET %s" % (
  20.             self.process(offset_clause.render_literal_execute(), **kw)
  21.         )
  22.     elif offset_clause is not None:
  23.         # assuming the DB doesn't support SQL expressions for OFFSET.
  24.         # Otherwise render here normally
  25.         raise exc.CompileError(
  26.             "dialect 'mydialect' can only render simple integers for OFFSET"
  27.         )
  29.     return text

The approach above will generate a compiled SELECT statement that looks like:

  1. SELECT x FROM y
  2. LIMIT __[POSTCOMPILE_param_1]
  3. OFFSET __[POSTCOMPILE_param_2]

Where above, the __[POSTCOMPILE_param_1] and __[POSTCOMPILE_param_2] indicators will be populated with their corresponding integer values at statement execution time, after the SQL string has been retrieved from the cache.

After changes like the above have been made as appropriate, the Dialect.supports_statement_cache flag should be set to True. It is strongly recommended that third party dialects make use of the dialect third party test suite which will assert that operations like SELECTs with LIMIT/OFFSET are correctly rendered and cached.

See also

Why is my application slow after upgrading to 1.4 and/or 2.x? - in the Frequently Asked Questions section

Using Lambdas to add significant speed gains to statement production

Deep Alchemy

This technique is generally non-essential except in very performance intensive scenarios, and intended for experienced Python programmers. While fairly straightforward, it involves metaprogramming concepts that are not appropriate for novice Python developers. The lambda approach can be applied to at a later time to existing code with a minimal amount of effort.

Python functions, typically expressed as lambdas, may be used to generate SQL expressions which are cacheable based on the Python code location of the lambda function itself as well as the closure variables within the lambda. The rationale is to allow caching of not only the SQL string-compiled form of a SQL expression construct as is SQLAlchemy’s normal behavior when the lambda system isn’t used, but also the in-Python composition of the SQL expression construct itself, which also has some degree of Python overhead.

The lambda SQL expression feature is available as a performance enhancing feature, and is also optionally used in the with_loader_criteria() ORM option in order to provide a generic SQL fragment.

Synopsis

Lambda statements are constructed using the lambda_stmt() function, which returns an instance of StatementLambdaElement, which is itself an executable statement construct. Additional modifiers and criteria are added to the object using the Python addition operator +, or alternatively the StatementLambdaElement.add_criteria() method which allows for more options.

It is assumed that the lambda_stmt() construct is being invoked within an enclosing function or method that expects to be used many times within an application, so that subsequent executions beyond the first one can take advantage of the compiled SQL being cached. When the lambda is constructed inside of an enclosing function in Python it is then subject to also having closure variables, which are significant to the whole approach:

  1. from sqlalchemy import lambda_stmt
  4. def run_my_statement(connection, parameter):
  5.     stmt = lambda_stmt(lambda: select(table))
  6.     stmt += lambda s: s.where(table.c.col == parameter)
  7.     stmt += lambda s: s.order_by(table.c.id)
  9.     return connection.execute(stmt)
  12. with engine.connect() as conn:
  13.     result = run_my_statement(some_connection, "some parameter")

Above, the three lambda callables that are used to define the structure of a SELECT statement are invoked exactly once, and the resulting SQL string cached in the compilation cache of the engine. From that point forward, the run_my_statement() function may be invoked any number of times and the lambda callables within it will not be called, only used as cache keys to retrieve the already-compiled SQL.

Note

It is important to note that there is already SQL caching in place when the lambda system is not used. The lambda system only adds an additional layer of work reduction per SQL statement invoked by caching the building up of the SQL construct itself and also using a simpler cache key.

Quick Guidelines for Lambdas

Above all, the emphasis within the lambda SQL system is ensuring that there is never a mismatch between the cache key generated for a lambda and the SQL string it will produce. The LambdaElement and related objects will run and analyze the given lambda in order to calculate how it should be cached on each run, trying to detect any potential problems. Basic guidelines include:

  • Any kind of statement is supported - while it’s expected that select() constructs are the prime use case for lambda_stmt(), DML statements such as insert() and update() are equally usable:

    ``` def upd(id_, newname):

    1. stmt = lambda_stmt(lambda: users.update())
    2. stmt += lambda s: s.values(name=newname)
    3. stmt += lambda s: s.where(users.c.id == id_)
    4. return stmt
  1. with engine.begin() as conn:
  2.     conn.execute(upd(7, "foo"))
  3. ```

  • ORM use cases directly supported as well - the lambda_stmt() can accommodate ORM functionality completely and used directly with Session.execute():

    1. def select_user(session, name):
    2.     stmt = lambda_stmt(lambda: select(User))
    3.     stmt += lambda s: s.where(User.name == name)
    5.     row = session.execute(stmt).first()
    6.     return row

  • Bound parameters are automatically accommodated - in contrast to SQLAlchemy’s previous “baked query” system, the lambda SQL system accommodates for Python literal values which become SQL bound parameters automatically. This means that even though a given lambda runs only once, the values that become bound parameters are extracted from the closure of the lambda on every run:

    1. >>> def my_stmt(x, y):
    2. ...     stmt = lambda_stmt(lambda: select(func.max(x, y)))
    3. ...     return stmt
    4. >>> engine = create_engine("sqlite://", echo=True)
    5. >>> with engine.connect() as conn:
    6. ...     print(conn.scalar(my_stmt(5, 10)))
    7. ...     print(conn.scalar(my_stmt(12, 8)))
    8. SELECT max(?, ?) AS max_1
    9. [generated in 0.00057s] (5, 10)
    10. 10
    11. SELECT max(?, ?) AS max_1
    12. [cached since 0.002059s ago] (12, 8)
    13. 12

    Above, StatementLambdaElement extracted the values of x and y from the closure of the lambda that is generated each time my_stmt() is invoked; these were substituted into the cached SQL construct as the values of the parameters.

  • The lambda should ideally produce an identical SQL structure in all cases - Avoid using conditionals or custom callables inside of lambdas that might make it produce different SQL based on inputs; if a function might conditionally use two different SQL fragments, use two separate lambdas:

    ```

    Don’t do this:

  1. def my_stmt(parameter, thing=False):
  2.     stmt = lambda_stmt(lambda: select(table))
  3.     stmt += (
  4.         lambda s: s.where(table.c.x > parameter)
  5.         if thing
  6.         else s.where(table.c.y == parameter)
  7.     )
  8.     return stmt
  11. # **Do** do this:
  14. def my_stmt(parameter, thing=False):
  15.     stmt = lambda_stmt(lambda: select(table))
  16.     if thing:
  17.         stmt += lambda s: s.where(table.c.x > parameter)
  18.     else:
  19.         stmt += lambda s: s.where(table.c.y == parameter)
  20.     return stmt
  21. ```
  23. There are a variety of failures which can occur if the lambda does not produce a consistent SQL construct and some are not trivially detectable right now.

  • Don’t use functions inside the lambda to produce bound values - the bound value tracking approach requires that the actual value to be used in the SQL statement be locally present in the closure of the lambda. This is not possible if values are generated from other functions, and the LambdaElement should normally raise an error if this is attempted:

    1. >>> def my_stmt(x, y):
    2. ...     def get_x():
    3. ...         return x
    4. ...
    5. ...     def get_y():
    6. ...         return y
    7. ...
    8. ...     stmt = lambda_stmt(lambda: select(func.max(get_x(), get_y())))
    9. ...     return stmt
    10. >>> with engine.connect() as conn:
    11. ...     print(conn.scalar(my_stmt(5, 10)))
    12. Traceback (most recent call last):
    13.   # ...
    14. sqlalchemy.exc.InvalidRequestError: Can't invoke Python callable get_x()
    15. inside of lambda expression argument at
    16. <code object <lambda> at 0x7fed15f350e0, file "<stdin>", line 6>;
    17. lambda SQL constructs should not invoke functions from closure variables
    18. to produce literal values since the lambda SQL system normally extracts
    19. bound values without actually invoking the lambda or any functions within it.

    Above, the use of get_x() and get_y(), if they are necessary, should occur outside of the lambda and assigned to a local closure variable:

    1. >>> def my_stmt(x, y):
    2. ...     def get_x():
    3. ...         return x
    4. ...
    5. ...     def get_y():
    6. ...         return y
    7. ...
    8. ...     x_param, y_param = get_x(), get_y()
    9. ...     stmt = lambda_stmt(lambda: select(func.max(x_param, y_param)))
    10. ...     return stmt

  • Avoid referring to non-SQL constructs inside of lambdas as they are not cacheable by default - this issue refers to how the LambdaElement creates a cache key from other closure variables within the statement. In order to provide the best guarantee of an accurate cache key, all objects located in the closure of the lambda are considered to be significant, and none will be assumed to be appropriate for a cache key by default. So the following example will also raise a rather detailed error message:

    1. >>> class Foo:
    2. ...     def __init__(self, x, y):
    3. ...         self.x = x
    4. ...         self.y = y
    5. >>> def my_stmt(foo):
    6. ...     stmt = lambda_stmt(lambda: select(func.max(foo.x, foo.y)))
    7. ...     return stmt
    8. >>> with engine.connect() as conn:
    9. ...     print(conn.scalar(my_stmt(Foo(5, 10))))
    10. Traceback (most recent call last):
    11.   # ...
    12. sqlalchemy.exc.InvalidRequestError: Closure variable named 'foo' inside of
    13. lambda callable <code object <lambda> at 0x7fed15f35450, file
    14. "<stdin>", line 2> does not refer to a cacheable SQL element, and also
    15. does not appear to be serving as a SQL literal bound value based on the
    16. default SQL expression returned by the function.  This variable needs to
    17. remain outside the scope of a SQL-generating lambda so that a proper cache
    18. key may be generated from the lambda's state.  Evaluate this variable
    19. outside of the lambda, set track_on=[<elements>] to explicitly select
    20. closure elements to track, or set track_closure_variables=False to exclude
    21. closure variables from being part of the cache key.

    The above error indicates that LambdaElement will not assume that the Foo object passed in will continue to behave the same in all cases. It also won’t assume it can use Foo as part of the cache key by default; if it were to use the Foo object as part of the cache key, if there were many different Foo objects this would fill up the cache with duplicate information, and would also hold long-lasting references to all of these objects.

    The best way to resolve the above situation is to not refer to foo inside of the lambda, and refer to it outside instead:

    1. >>> def my_stmt(foo):
    2. ...     x_param, y_param = foo.x, foo.y
    3. ...     stmt = lambda_stmt(lambda: select(func.max(x_param, y_param)))
    4. ...     return stmt

    In some situations, if the SQL structure of the lambda is guaranteed to never change based on input, to pass track_closure_variables=False which will disable any tracking of closure variables other than those used for bound parameters:

    1. >>> def my_stmt(foo):
    2. ...     stmt = lambda_stmt(
    3. ...         lambda: select(func.max(foo.x, foo.y)), track_closure_variables=False
    4. ...     )
    5. ...     return stmt

    There is also the option to add objects to the element to explicitly form part of the cache key, using the track_on parameter; using this parameter allows specific values to serve as the cache key and will also prevent other closure variables from being considered. This is useful for cases where part of the SQL being constructed originates from a contextual object of some sort that may have many different values. In the example below, the first segment of the SELECT statement will disable tracking of the foo variable, whereas the second segment will explicitly track self as part of the cache key:

    1. >>> def my_stmt(self, foo):
    2. ...     stmt = lambda_stmt(
    3. ...         lambda: select(*self.column_expressions), track_closure_variables=False
    4. ...     )
    5. ...     stmt = stmt.add_criteria(lambda: self.where_criteria, track_on=[self])
    6. ...     return stmt

    Using track_on means the given objects will be stored long term in the lambda’s internal cache and will have strong references for as long as the cache doesn’t clear out those objects (an LRU scheme of 1000 entries is used by default).

Cache Key Generation

In order to understand some of the options and behaviors which occur with lambda SQL constructs, an understanding of the caching system is helpful.

SQLAlchemy’s caching system normally generates a cache key from a given SQL expression construct by producing a structure that represents all the state within the construct:

  1. >>> from sqlalchemy import select, column
  2. >>> stmt = select(column("q"))
  3. >>> cache_key = stmt._generate_cache_key()
  4. >>> print(cache_key)  # somewhat paraphrased
  5. CacheKey(key=(
  6.   '0',
  7.   <class 'sqlalchemy.sql.selectable.Select'>,
  8.   '_raw_columns',
  9.   (
  10.     (
  11.       '1',
  12.       <class 'sqlalchemy.sql.elements.ColumnClause'>,
  13.       'name',
  14.       'q',
  15.       'type',
  16.       (
  17.         <class 'sqlalchemy.sql.sqltypes.NullType'>,
  18.       ),
  19.     ),
  20.   ),
  21.   # a few more elements are here, and many more for a more
  22.   # complicated SELECT statement
  23. ),)

The above key is stored in the cache which is essentially a dictionary, and the value is a construct that among other things stores the string form of the SQL statement, in this case the phrase “SELECT q”. We can observe that even for an extremely short query the cache key is pretty verbose as it has to represent everything that may vary about what’s being rendered and potentially executed.

The lambda construction system by contrast creates a different kind of cache key:

  1. >>> from sqlalchemy import lambda_stmt
  2. >>> stmt = lambda_stmt(lambda: select(column("q")))
  3. >>> cache_key = stmt._generate_cache_key()
  4. >>> print(cache_key)
  5. CacheKey(key=(
  6.   <code object <lambda> at 0x7fed1617c710, file "<stdin>", line 1>,
  7.   <class 'sqlalchemy.sql.lambdas.StatementLambdaElement'>,
  8. ),)

Above, we see a cache key that is vastly shorter than that of the non-lambda statement, and additionally that production of the select(column("q")) construct itself was not even necessary; the Python lambda itself contains an attribute called __code__ which refers to a Python code object that within the runtime of the application is immutable and permanent.

When the lambda also includes closure variables, in the normal case that these variables refer to SQL constructs such as column objects, they become part of the cache key, or if they refer to literal values that will be bound parameters, they are placed in a separate element of the cache key:

  1. >>> def my_stmt(parameter):
  2. ...     col = column("q")
  3. ...     stmt = lambda_stmt(lambda: select(col))
  4. ...     stmt += lambda s: s.where(col == parameter)
  5. ...     return stmt

The above StatementLambdaElement includes two lambdas, both of which refer to the col closure variable, so the cache key will represent both of these segments as well as the column() object:

  1. >>> stmt = my_stmt(5)
  2. >>> key = stmt._generate_cache_key()
  3. >>> print(key)
  4. CacheKey(key=(
  5.   <code object <lambda> at 0x7f07323c50e0, file "<stdin>", line 3>,
  6.   (
  7.     '0',
  8.     <class 'sqlalchemy.sql.elements.ColumnClause'>,
  9.     'name',
  10.     'q',
  11.     'type',
  12.     (
  13.       <class 'sqlalchemy.sql.sqltypes.NullType'>,
  14.     ),
  15.   ),
  16.   <code object <lambda> at 0x7f07323c5190, file "<stdin>", line 4>,
  17.   <class 'sqlalchemy.sql.lambdas.LinkedLambdaElement'>,
  18.   (
  19.     '0',
  20.     <class 'sqlalchemy.sql.elements.ColumnClause'>,
  21.     'name',
  22.     'q',
  23.     'type',
  24.     (
  25.       <class 'sqlalchemy.sql.sqltypes.NullType'>,
  26.     ),
  27.   ),
  28.   (
  29.     '0',
  30.     <class 'sqlalchemy.sql.elements.ColumnClause'>,
  31.     'name',
  32.     'q',
  33.     'type',
  34.     (
  35.       <class 'sqlalchemy.sql.sqltypes.NullType'>,
  36.     ),
  37.   ),
  38. ),)

The second part of the cache key has retrieved the bound parameters that will be used when the statement is invoked:

  1. >>> key.bindparams
  2. [BindParameter('%(139668884281280 parameter)s', 5, type_=Integer())]

For a series of examples of “lambda” caching with performance comparisons, see the “short_selects” test suite within the Performance performance example.

“Insert Many Values” Behavior for INSERT statements

New in version 2.0: see Optimized ORM bulk insert now implemented for all backends other than MySQL for background on the change including sample performance tests

As more databases have added support for INSERT..RETURNING, SQLAlchemy has undergone a major change in how it approaches the subject of INSERT statements where there’s a need to acquire server-generated values, most importantly server-generated primary key values which allow the new row to be referenced in subsequent operations. This issue has for over a decade prevented SQLAlchemy from being able to batch large sets of rows into a small number of database round trips for the very common case where primary key values are server-generated, and historically has been the most significant performance bottleneck in the ORM.

With recent support for RETURNING added to SQLite and MariaDB, SQLAlchemy no longer needs to rely upon the single-row-only cursor.lastrowid attribute provided by the DBAPI for most backends; RETURNING may now be used for all SQLAlchemy-included backends with the exception of MySQL. The remaining performance limitation, that the cursor.executemany() DBAPI method does not allow for rows to be fetched, is resolved for most backends by foregoing the use of executemany() and instead restructuring individual INSERT statements to each accommodate a large number of rows in a single statement that is invoked using cursor.execute(). This approach originates from the psycopg2 fast execution helpers feature of the psycopg2 DBAPI, which SQLAlchemy incrementally added more and more support towards in recent release series.

Concretely, for most backends the behavior will rewrite a statement of the form:

  1. INSERT INTO a (data, x, y) VALUES (%(data)s, %(x)s, %(y)s) RETURNING a.id

into a “batched” form as:

  1. INSERT INTO a (data, x, y) VALUES
  2.     (%(data_0)s, %(x_0)s, %(y_0)s),
  3.     (%(data_1)s, %(x_1)s, %(y_1)s),
  4.     (%(data_2)s, %(x_2)s, %(y_2)s),
  5.     ...
  6.     (%(data_78)s, %(x_78)s, %(y_78)s)
  7. RETURNING a.id

It’s also important to note that the feature will invoke multiple INSERT statements using the DBAPI cursor.execute() method, within the scope of single call to the Core-level Connection.execute() method, with each statement containing up to a fixed limit of parameter sets. This limit is configurable as described below at Controlling the Batch Size. The separate calls to cursor.execute() are logged individually and also individually passed along to event listeners such as ConnectionEvents.before_cursor_execute() (see Logging and Events below).

The feature is enabled for included SQLAlchemy backends that support RETURNING as well as “multiple VALUES()” clauses within INSERT statements, and takes place for all INSERT…RETURNING statements that are used with “executemany” style execution, which occurs when passing a list of dictionaries to the Connection.execute.parameters parameter of the Connection.execute() method, as well as throughout Core and ORM for any similar method including ORM methods like Session.execute() and asyncio methods like AsyncConnection.execute() and AsyncSession.execute(). The ORM itself also makes use of the feature within the unit of work process when inserting many rows, that is, for large numbers of objects added to a Session using methods such as Session.add() and Session.add_all().

For SQLAlchemy’s included dialects, support or equivalent support is currently as follows:

  • SQLite - supported for SQLite versions 3.35 and above

  • PostgreSQL - all supported Postgresql versions (9 and above)

  • SQL Server - all supported SQL Server versions

  • MariaDB - supported for MariaDB versions 10.5 and above

  • MySQL - no support, no RETURNING feature is present

  • Oracle - supports RETURNING with executemany using native cx_Oracle / OracleDB APIs, for all supported Oracle versions 9 and above, using multi-row OUT parameters. This is not the same implementation as “executemanyvalues”, however has the same usage patterns and equivalent performance benefits.

Enabling/Disabling the feature

To disable the “insertmanyvalues” feature for a given backend for an Engine overall, pass the create_engine.use_insertmanyvalues parameter as False to create_engine():

  1. engine = create_engine(
  2.     "mariadb+mariadbconnector://scott:tiger@host/db", use_insertmanyvalues=False
  3. )

The feature can also be disabled from being used implicitly for a particular Table object by passing the Table.implicit_returning parameter as False:

  1. t = Table(
  2.     "t",
  3.     metadata,
  4.     Column("id", Integer, primary_key=True),
  5.     Column("x", Integer),
  6.     implicit_returning=False,
  7. )

The reason one might want to disable RETURNING for a specific table is to work around backend-specific limitations. For example, there is a known limitation of SQL Server that the OUTPUT inserted.<colname> feature may not work correctly for a table that has INSERT triggers established; such a table may need to include implicit_returning=False (see Triggers).

Controlling the Batch Size

A key characteristic of “insertmanyvalues” is that the size of the INSERT statement is limited on a fixed max number of “values” clauses as well as a dialect-specific fixed total number of bound parameters that may be represented in one INSERT statement at a time. When the number of parameter dictionaries given exceeds a fixed limit, or when the total number of bound parameters to be rendered in a single INSERT statement exceeds a fixed limit (the two fixed limits are separate), multiple INSERT statements will be invoked within the scope of a single Connection.execute() call, each of which accommodate for a portion of the parameter dictionaries, referred towards as a “batch”. The number of parameter dictionaries represented within each “batch” is then known as the “batch size”. For example, a batch size of 500 means that each INSERT statement emitted will INSERT at most 500 rows.

It’s potentially important to be able to adjust the batch size, as a larger batch size may be more performant for an INSERT where the value sets themselves are relatively small, and a smaller batch size may be more appropriate for an INSERT that uses very large value sets, where both the size of the rendered SQL as well as the total data size being passed in one statement may benefit from being limited to a certain size based on backend behavior and memory constraints. For this reason the batch size can be configured on a per-Engine as well as a per-statement basis. The parameter limit on the other hand is fixed based on the known characteristics of the database in use.

The batch size defaults to 1000 for most backends, with an additional per-dialect “max number of parameters” limiting factor that may reduce the batch size further on a per-statement basis. The max number of parameters varies by dialect and server version; the largest size is 32700 (chosen as a healthy distance away from PostgreSQL’s limit of 32767 and SQLite’s modern limit of 32766, while leaving room for additional parameters in the statement as well as for DBAPI quirkiness). Older versions of SQLite (prior to 3.32.0) will set this value to 999; SQL Server sets it to 2099. MariaDB has no established limit however 32700 remains as a limiting factor for SQL message size.

The value of the “batch size” can be affected Engine wide via the create_engine.insertmanyvalues_page_size parameter. Such as, to affect INSERT statements to include up to 100 parameter sets in each statement:

  1. e = create_engine("sqlite://", insertmanyvalues_page_size=100)

The batch size may also be affected on a per statement basis using the Connection.execution_options.insertmanyvalues_page_size execution option, such as per execution:

  1. with e.begin() as conn:
  2.     result = conn.execute(
  3.         table.insert().returning(table.c.id),
  4.         parameterlist,
  5.         execution_options={"insertmanyvalues_page_size": 100},
  6.     )

Or configured on the statement itself:

  1. stmt = (
  2.     table.insert()
  3.     .returning(table.c.id)
  4.     .execution_options(insertmanyvalues_page_size=100)
  5. )
  6. with e.begin() as conn:
  7.     result = conn.execute(stmt, parameterlist)

Logging and Events

The “insertmanyvalues” feature integrates fully with SQLAlchemy’s statement logging as well as cursor events such as ConnectionEvents.before_cursor_execute(). When the list of parameters is broken into separate batches, each INSERT statement is logged and passed to event handlers individually. This is a major change compared to how the psycopg2-only feature worked in previous 1.x series of SQLAlchemy, where the production of multiple INSERT statements was hidden from logging and events. Logging display will truncate the long lists of parameters for readability, and will also indicate the specific batch of each statement. The example below illustrates an excerpt of this logging:

  1. INSERT INTO a (data, x, y) VALUES (?, ?, ?), ... 795 characters truncated ...  (?, ?, ?), (?, ?, ?) RETURNING id
  2. [generated in 0.00177s (insertmanyvalues)] ('d0', 0, 0, 'd1',  ...
  3. INSERT INTO a (data, x, y) VALUES (?, ?, ?), ... 795 characters truncated ...  (?, ?, ?), (?, ?, ?) RETURNING id
  4. [insertmanyvalues batch 2 of 10] ('d100', 100, 1000, 'd101', ...
  6. ...
  8. INSERT INTO a (data, x, y) VALUES (?, ?, ?), ... 795 characters truncated ...  (?, ?, ?), (?, ?, ?) RETURNING id
  9. [insertmanyvalues batch 10 of 10] ('d900', 900, 9000, 'd901', ...

Upsert Support

The PostgreSQL, SQLite, and MariaDB dialects offer backend-specific “upsert” constructs insert(), insert() and insert(), which are each Insert constructs that have an additional method such as on_conflict_do_update()` or ``on_duplicate_key(). These constructs also support “insertmanyvalues” behaviors when they are used with RETURNING, allowing efficient upserts with RETURNING to take place.

Engine Disposal

The Engine refers to a connection pool, which means under normal circumstances, there are open database connections present while the Engine object is still resident in memory. When an Engine is garbage collected, its connection pool is no longer referred to by that Engine, and assuming none of its connections are still checked out, the pool and its connections will also be garbage collected, which has the effect of closing out the actual database connections as well. But otherwise, the Engine will hold onto open database connections assuming it uses the normally default pool implementation of QueuePool.

The Engine is intended to normally be a permanent fixture established up-front and maintained throughout the lifespan of an application. It is not intended to be created and disposed on a per-connection basis; it is instead a registry that maintains both a pool of connections as well as configurational information about the database and DBAPI in use, as well as some degree of internal caching of per-database resources.

However, there are many cases where it is desirable that all connection resources referred to by the Engine be completely closed out. It’s generally not a good idea to rely on Python garbage collection for this to occur for these cases; instead, the Engine can be explicitly disposed using the Engine.dispose() method. This disposes of the engine’s underlying connection pool and replaces it with a new one that’s empty. Provided that the Engine is discarded at this point and no longer used, all checked-in connections which it refers to will also be fully closed.

Valid use cases for calling Engine.dispose() include:

  • When a program wants to release any remaining checked-in connections held by the connection pool and expects to no longer be connected to that database at all for any future operations.

  • When a program uses multiprocessing or fork(), and an Engine object is copied to the child process, Engine.dispose() should be called so that the engine creates brand new database connections local to that fork. Database connections generally do not travel across process boundaries. Use the Engine.dispose.close parameter set to False in this case. See the section Using Connection Pools with Multiprocessing or os.fork() for more background on this use case.

  • Within test suites or multitenancy scenarios where many ad-hoc, short-lived Engine objects may be created and disposed.

Connections that are checked out are not discarded when the engine is disposed or garbage collected, as these connections are still strongly referenced elsewhere by the application. However, after Engine.dispose() is called, those connections are no longer associated with that Engine; when they are closed, they will be returned to their now-orphaned connection pool which will ultimately be garbage collected, once all connections which refer to it are also no longer referenced anywhere. Since this process is not easy to control, it is strongly recommended that Engine.dispose() is called only after all checked out connections are checked in or otherwise de-associated from their pool.

An alternative for applications that are negatively impacted by the Engine object’s use of connection pooling is to disable pooling entirely. This typically incurs only a modest performance impact upon the use of new connections, and means that when a connection is checked in, it is entirely closed out and is not held in memory. See Switching Pool Implementations for guidelines on how to disable pooling.

See also

Connection Pooling

Using Connection Pools with Multiprocessing or os.fork()

Working with Driver SQL and Raw DBAPI Connections

The introduction on using Connection.execute() made use of the text() construct in order to illustrate how textual SQL statements may be invoked. When working with SQLAlchemy, textual SQL is actually more of the exception rather than the norm, as the Core expression language and the ORM both abstract away the textual representation of SQL. However, the text() construct itself also provides some abstraction of textual SQL in that it normalizes how bound parameters are passed, as well as that it supports datatyping behavior for parameters and result set rows.

Invoking SQL strings directly to the driver

For the use case where one wants to invoke textual SQL directly passed to the underlying driver (known as the DBAPI) without any intervention from the text() construct, the Connection.exec_driver_sql() method may be used:

  1. with engine.connect() as conn:
  2.     conn.exec_driver_sql("SET param='bar'")

New in version 1.4: Added the Connection.exec_driver_sql() method.

Working with the DBAPI cursor directly

There are some cases where SQLAlchemy does not provide a genericized way at accessing some DBAPI functions, such as calling stored procedures as well as dealing with multiple result sets. In these cases, it’s just as expedient to deal with the raw DBAPI connection directly.

The most common way to access the raw DBAPI connection is to get it from an already present Connection object directly. It is present using the Connection.connection attribute:

  1. connection = engine.connect()
  2. dbapi_conn = connection.connection

The DBAPI connection here is actually a “proxied” in terms of the originating connection pool, however this is an implementation detail that in most cases can be ignored. As this DBAPI connection is still contained within the scope of an owning Connection object, it is best to make use of the Connection object for most features such as transaction control as well as calling the Connection.close() method; if these operations are performed on the DBAPI connection directly, the owning Connection will not be aware of these changes in state.

To overcome the limitations imposed by the DBAPI connection that is maintained by an owning Connection, a DBAPI connection is also available without the need to procure a Connection first, using the Engine.raw_connection() method of Engine:

  1. dbapi_conn = engine.raw_connection()

This DBAPI connection is again a “proxied” form as was the case before. The purpose of this proxying is now apparent, as when we call the .close() method of this connection, the DBAPI connection is typically not actually closed, but instead released back to the engine’s connection pool:

  1. dbapi_conn.close()

While SQLAlchemy may in the future add built-in patterns for more DBAPI use cases, there are diminishing returns as these cases tend to be rarely needed and they also vary highly dependent on the type of DBAPI in use, so in any case the direct DBAPI calling pattern is always there for those cases where it is needed.

See also

How do I get at the raw DBAPI connection when using an Engine? - includes additional details about how the DBAPI connection is accessed as well as the “driver” connection when using asyncio drivers.

Some recipes for DBAPI connection use follow.

Calling Stored Procedures and User Defined Functions

SQLAlchemy supports calling stored procedures and user defined functions several ways. Please note that all DBAPIs have different practices, so you must consult your underlying DBAPI’s documentation for specifics in relation to your particular usage. The following examples are hypothetical and may not work with your underlying DBAPI.

For stored procedures or functions with special syntactical or parameter concerns, DBAPI-level callproc may potentially be used with your DBAPI. An example of this pattern is:

  1. connection = engine.raw_connection()
  2. try:
  3.     cursor_obj = connection.cursor()
  4.     cursor_obj.callproc("my_procedure", ["x", "y", "z"])
  5.     results = list(cursor_obj.fetchall())
  6.     cursor_obj.close()
  7.     connection.commit()
  8. finally:
  9.     connection.close()

Note

Not all DBAPIs use callproc and overall usage details will vary. The above example is only an illustration of how it might look to use a particular DBAPI function.

Your DBAPI may not have a callproc requirement or may require a stored procedure or user defined function to be invoked with another pattern, such as normal SQLAlchemy connection usage. One example of this usage pattern is, at the time of this documentation’s writing, executing a stored procedure in the PostgreSQL database with the psycopg2 DBAPI, which should be invoked with normal connection usage:

  1. connection.execute("CALL my_procedure();")

This above example is hypothetical. The underlying database is not guaranteed to support “CALL” or “SELECT” in these situations, and the keyword may vary dependent on the function being a stored procedure or a user defined function. You should consult your underlying DBAPI and database documentation in these situations to determine the correct syntax and patterns to use.

Multiple Result Sets

Multiple result set support is available from a raw DBAPI cursor using the nextset method:

  1. connection = engine.raw_connection()
  2. try:
  3.     cursor_obj = connection.cursor()
  4.     cursor_obj.execute("select * from table1; select * from table2")
  5.     results_one = cursor_obj.fetchall()
  6.     cursor_obj.nextset()
  7.     results_two = cursor_obj.fetchall()
  8.     cursor_obj.close()
  9. finally:
  10.     connection.close()

Registering New Dialects

The create_engine() function call locates the given dialect using setuptools entrypoints. These entry points can be established for third party dialects within the setup.py script. For example, to create a new dialect “foodialect://”, the steps are as follows:

  1. Create a package called foodialect.

  2. The package should have a module containing the dialect class, which is typically a subclass of sqlalchemy.engine.default.DefaultDialect. In this example let’s say it’s called FooDialect and its module is accessed via foodialect.dialect.

  3. The entry point can be established in setup.cfg as follows:

    1. [options.entry_points]
    2. sqlalchemy.dialects =
    3.     foodialect = foodialect.dialect:FooDialect

If the dialect is providing support for a particular DBAPI on top of an existing SQLAlchemy-supported database, the name can be given including a database-qualification. For example, if FooDialect were in fact a MySQL dialect, the entry point could be established like this:

  1. [options.entry_points]
  2. sqlalchemy.dialects
  3.     mysql.foodialect = foodialect.dialect:FooDialect

The above entrypoint would then be accessed as create_engine("mysql+foodialect://").

Registering Dialects In-Process

SQLAlchemy also allows a dialect to be registered within the current process, bypassing the need for separate installation. Use the register() function as follows:

  1. from sqlalchemy.dialects import registry
  3. registry.register("mysql.foodialect", "myapp.dialect", "MyMySQLDialect")

The above will respond to create_engine("mysql+foodialect://") and load the MyMySQLDialect class from the myapp.dialect module.

Connection / Engine API

Object NameDescription

Connection

Provides high-level functionality for a wrapped DB-API connection.

CreateEnginePlugin

A set of hooks intended to augment the construction of an Engine object based on entrypoint names in a URL.

Engine

Connects a Pool and Dialect together to provide a source of database connectivity and behavior.

ExceptionContext

Encapsulate information about an error condition in progress.

NestedTransaction

Represent a ‘nested’, or SAVEPOINT transaction.

RootTransaction

Represent the “root” transaction on a Connection.

Transaction

Represent a database transaction in progress.

TwoPhaseTransaction

Represent a two-phase transaction.

class sqlalchemy.engine.Connection

Provides high-level functionality for a wrapped DB-API connection.

The Connection object is procured by calling the Engine.connect() method of the Engine object, and provides services for execution of SQL statements as well as transaction control.

The Connection object is not thread-safe. While a Connection can be shared among threads using properly synchronized access, it is still possible that the underlying DBAPI connection may not support shared access between threads. Check the DBAPI documentation for details.

Members

__init__(), begin(), begin_nested(), begin_twophase(), close(), closed, commit(), connection, default_isolation_level, detach(), exec_driver_sql(), execute(), execution_options(), get_execution_options(), get_isolation_level(), get_nested_transaction(), get_transaction(), in_nested_transaction(), in_transaction(), info, invalidate(), invalidated, rollback(), scalar(), scalars(), schema_for_object()

The Connection object represents a single DBAPI connection checked out from the connection pool. In this state, the connection pool has no affect upon the connection, including its expiration or timeout state. For the connection pool to properly manage connections, connections should be returned to the connection pool (i.e. connection.close()) whenever the connection is not in use.

Class signature

class sqlalchemy.engine.Connection (sqlalchemy.engine.interfaces.ConnectionEventsTarget, sqlalchemy.inspection.Inspectable)

  • method sqlalchemy.engine.Connection.__init__(engine: Engine, connection: Optional[PoolProxiedConnection] = None, _has_events: Optional[bool] = None, _allow_revalidate: bool = True, _allow_autobegin: bool = True)

    Construct a new Connection.

  • method sqlalchemy.engine.Connection.begin() → RootTransaction

    Begin a transaction prior to autobegin occurring.

    E.g.:

    1. with engine.connect() as conn:
    2.     with conn.begin() as trans:
    3.         conn.execute(table.insert(), {"username": "sandy"})

    The returned object is an instance of RootTransaction. This object represents the “scope” of the transaction, which completes when either the Transaction.rollback() or Transaction.commit() method is called; the object also works as a context manager as illustrated above.

    The Connection.begin() method begins a transaction that normally will be begun in any case when the connection is first used to execute a statement. The reason this method might be used would be to invoke the ConnectionEvents.begin() event at a specific time, or to organize code within the scope of a connection checkout in terms of context managed blocks, such as:

    1. with engine.connect() as conn:
    2.     with conn.begin():
    3.         conn.execute(...)
    4.         conn.execute(...)
    6.     with conn.begin():
    7.         conn.execute(...)
    8.         conn.execute(...)

    The above code is not fundamentally any different in its behavior than the following code which does not use Connection.begin(); the below style is referred towards as “commit as you go” style:

    1. with engine.connect() as conn:
    2.     conn.execute(...)
    3.     conn.execute(...)
    4.     conn.commit()
    6.     conn.execute(...)
    7.     conn.execute(...)
    8.     conn.commit()

    From a database point of view, the Connection.begin() method does not emit any SQL or change the state of the underlying DBAPI connection in any way; the Python DBAPI does not have any concept of explicit transaction begin.

    See also

    Working with Transactions and the DBAPI - in the SQLAlchemy Unified Tutorial

    Connection.begin_nested() - use a SAVEPOINT

    Connection.begin_twophase() - use a two phase /XID transaction

    Engine.begin() - context manager available from Engine

  • method sqlalchemy.engine.Connection.begin_nested() → NestedTransaction

    Begin a nested transaction (i.e. SAVEPOINT) and return a transaction handle that controls the scope of the SAVEPOINT.

    E.g.:

    1. with engine.begin() as connection:
    2.     with connection.begin_nested():
    3.         connection.execute(table.insert(), {"username": "sandy"})

    The returned object is an instance of NestedTransaction, which includes transactional methods NestedTransaction.commit() and NestedTransaction.rollback(); for a nested transaction, these methods correspond to the operations “RELEASE SAVEPOINT <name>” and “ROLLBACK TO SAVEPOINT <name>”. The name of the savepoint is local to the NestedTransaction object and is generated automatically. Like any other Transaction, the NestedTransaction may be used as a context manager as illustrated above which will “release” or “rollback” corresponding to if the operation within the block were successful or raised an exception.

    Nested transactions require SAVEPOINT support in the underlying database, else the behavior is undefined. SAVEPOINT is commonly used to run operations within a transaction that may fail, while continuing the outer transaction. E.g.:

    1. from sqlalchemy import exc
    3. with engine.begin() as connection:
    4.     trans = connection.begin_nested()
    5.     try:
    6.         connection.execute(table.insert(), {"username": "sandy"})
    7.         trans.commit()
    8.     except exc.IntegrityError:  # catch for duplicate username
    9.         trans.rollback()  # rollback to savepoint
    11.     # outer transaction continues
    12.     connection.execute( ... )

    If Connection.begin_nested() is called without first calling Connection.begin() or Engine.begin(), the Connection object will “autobegin” the outer transaction first. This outer transaction may be committed using “commit-as-you-go” style, e.g.:

    1. with engine.connect() as connection:  # begin() wasn't called
    3.     with connection.begin_nested(): will auto-"begin()" first
    4.         connection.execute( ... )
    5.     # savepoint is released
    7.     connection.execute( ... )
    9.     # explicitly commit outer transaction
    10.     connection.commit()
    12.     # can continue working with connection here

    Changed in version 2.0: Connection.begin_nested() will now participate in the connection “autobegin” behavior that is new as of 2.0 / “future” style connections in 1.4.

    See also

    Connection.begin()

    Using SAVEPOINT - ORM support for SAVEPOINT

  • method sqlalchemy.engine.Connection.begin_twophase(xid: Optional[Any] = None) → TwoPhaseTransaction

    Begin a two-phase or XA transaction and return a transaction handle.

    The returned object is an instance of TwoPhaseTransaction, which in addition to the methods provided by Transaction, also provides a TwoPhaseTransaction.prepare() method.

    • Parameters:

      xid – the two phase transaction id. If not supplied, a random id will be generated.

  1. See also
  3. [Connection.begin()](#sqlalchemy.engine.Connection.begin "sqlalchemy.engine.Connection.begin")
  5. [Connection.begin\_twophase()](#sqlalchemy.engine.Connection.begin_twophase "sqlalchemy.engine.Connection.begin_twophase")
  1.     -   **parameters** – parameters which will be bound into the statement. This may be either a dictionary of parameter names to values, or a mutable sequence (e.g. a list) of dictionaries. When a list of dictionaries is passed, the underlying statement execution will make use of the DBAPI `cursor.executemany()` method. When a single dictionary is passed, the DBAPI `cursor.execute()` method will be used.
  3.     -   **execution\_options** – optional dictionary of execution options, which will be associated with the statement execution. This dictionary can provide a subset of the options that are accepted by [Connection.execution\_options()](#sqlalchemy.engine.Connection.execution_options "sqlalchemy.engine.Connection.execution_options").
  6.     Returns:
  8.     a [Result](#sqlalchemy.engine.Result "sqlalchemy.engine.Result") object.
  1. See also
  3. [Engine.execution\_options()](#sqlalchemy.engine.Engine.execution_options "sqlalchemy.engine.Engine.execution_options")
  5. [Executable.execution\_options()]($75ae4d183452a412.md#sqlalchemy.sql.expression.Executable.execution_options "sqlalchemy.sql.expression.Executable.execution_options")
  7. [Connection.get\_execution\_options()](#sqlalchemy.engine.Connection.get_execution_options "sqlalchemy.engine.Connection.get_execution_options")
  9. [ORM Execution Options]($661bd2ffd6937693.md#orm-queryguide-execution-options) - documentation on all ORM-specific execution options

  • method sqlalchemy.engine.Connection.get_execution_options() → _ExecuteOptions

    Get the non-SQL options which will take effect during execution.

    New in version 1.3.

    See also

    Connection.execution_options()

  • method sqlalchemy.engine.Connection.get_isolation_level() → Literal[‘SERIALIZABLE’, ‘REPEATABLE READ’, ‘READ COMMITTED’, ‘READ UNCOMMITTED’, ‘AUTOCOMMIT’]

    Return the current isolation level assigned to this Connection.

    This will typically be the default isolation level as determined by the dialect, unless if the Connection.execution_options.isolation_level feature has been used to alter the isolation level on a per-Connection basis.

    This attribute will typically perform a live SQL operation in order to procure the current isolation level, so the value returned is the actual level on the underlying DBAPI connection regardless of how this state was set. Compare to the Connection.default_isolation_level accessor which returns the dialect-level setting without performing a SQL query.

    New in version 0.9.9.

    See also

    Connection.default_isolation_level - view default level

    create_engine.isolation_level - set per Engine isolation level

    Connection.execution_options.isolation_level - set per Connection isolation level

  • method sqlalchemy.engine.Connection.get_nested_transaction() → Optional[NestedTransaction]

    Return the current nested transaction in progress, if any.

    New in version 1.4.

  • method sqlalchemy.engine.Connection.get_transaction() → Optional[RootTransaction]

    Return the current root transaction in progress, if any.

    New in version 1.4.

  • method sqlalchemy.engine.Connection.in_nested_transaction() → bool

    Return True if a transaction is in progress.

  • method sqlalchemy.engine.Connection.in_transaction() → bool

    Return True if a transaction is in progress.

  • attribute sqlalchemy.engine.Connection.info

    Info dictionary associated with the underlying DBAPI connection referred to by this Connection, allowing user-defined data to be associated with the connection.

    The data here will follow along with the DBAPI connection including after it is returned to the connection pool and used again in subsequent instances of Connection.

  • method sqlalchemy.engine.Connection.invalidate(exception: Optional[BaseException] = None) → None

    Invalidate the underlying DBAPI connection associated with this Connection.

    An attempt will be made to close the underlying DBAPI connection immediately; however if this operation fails, the error is logged but not raised. The connection is then discarded whether or not close() succeeded.

    Upon the next use (where “use” typically means using the Connection.execute() method or similar), this Connection will attempt to procure a new DBAPI connection using the services of the Pool as a source of connectivity (e.g. a “reconnection”).

    If a transaction was in progress (e.g. the Connection.begin() method has been called) when Connection.invalidate() method is called, at the DBAPI level all state associated with this transaction is lost, as the DBAPI connection is closed. The Connection will not allow a reconnection to proceed until the Transaction object is ended, by calling the Transaction.rollback() method; until that point, any attempt at continuing to use the Connection will raise an InvalidRequestError. This is to prevent applications from accidentally continuing an ongoing transactional operations despite the fact that the transaction has been lost due to an invalidation.

    The Connection.invalidate() method, just like auto-invalidation, will at the connection pool level invoke the PoolEvents.invalidate() event.

    • Parameters:

      exception – an optional Exception instance that’s the reason for the invalidation. is passed along to event handlers and logging functions.

  1. See also
  3. [More on Invalidation]($ba04c3bd42280074.md#pool-connection-invalidation)

  • attribute sqlalchemy.engine.Connection.invalidated

    Return True if this connection was invalidated.

    This does not indicate whether or not the connection was invalidated at the pool level, however

  • method sqlalchemy.engine.Connection.rollback() → None

    Roll back the transaction that is currently in progress.

    This method rolls back the current transaction if one has been started. If no transaction was started, the method has no effect. If a transaction was started and the connection is in an invalidated state, the transaction is cleared using this method.

    A transaction is begun on a Connection automatically whenever a statement is first executed, or when the Connection.begin() method is called.

    Note

    The Connection.rollback() method only acts upon the primary database transaction that is linked to the Connection object. It does not operate upon a SAVEPOINT that would have been invoked from the Connection.begin_nested() method; for control of a SAVEPOINT, call NestedTransaction.rollback() on the NestedTransaction that is returned by the Connection.begin_nested() method itself.

  • method sqlalchemy.engine.Connection.scalar(statement: Executable, parameters: Optional[_CoreSingleExecuteParams] = None, *, execution_options: Optional[CoreExecuteOptionsParameter] = None) → Any

    Executes a SQL statement construct and returns a scalar object.

    This method is shorthand for invoking the Result.scalar() method after invoking the Connection.execute() method. Parameters are equivalent.

    • Returns:

      a scalar Python value representing the first column of the first row returned.

  1. New in version 1.4.24.

  • method sqlalchemy.engine.Connection.schema_for_object(obj: HasSchemaAttr) → Optional[str]

    Return the schema name for the given schema item taking into account current schema translate map.

class sqlalchemy.engine.CreateEnginePlugin

A set of hooks intended to augment the construction of an Engine object based on entrypoint names in a URL.

The purpose of CreateEnginePlugin is to allow third-party systems to apply engine, pool and dialect level event listeners without the need for the target application to be modified; instead, the plugin names can be added to the database URL. Target applications for CreateEnginePlugin include:

  • connection and SQL performance tools, e.g. which use events to track number of checkouts and/or time spent with statements

  • connectivity plugins such as proxies

A rudimentary CreateEnginePlugin that attaches a logger to an Engine object might look like:

  1. import logging
  3. from sqlalchemy.engine import CreateEnginePlugin
  4. from sqlalchemy import event
  6. class LogCursorEventsPlugin(CreateEnginePlugin):
  7.     def __init__(self, url, kwargs):
  8.         # consume the parameter "log_cursor_logging_name" from the
  9.         # URL query
  10.         logging_name = url.query.get("log_cursor_logging_name", "log_cursor")
  12.         self.log = logging.getLogger(logging_name)
  14.     def update_url(self, url):
  15.         "update the URL to one that no longer includes our parameters"
  16.         return url.difference_update_query(["log_cursor_logging_name"])
  18.     def engine_created(self, engine):
  19.         "attach an event listener after the new Engine is constructed"
  20.         event.listen(engine, "before_cursor_execute", self._log_event)
  22.     def _log_event(
  23.         self,
  24.         conn,
  25.         cursor,
  26.         statement,
  27.         parameters,
  28.         context,
  29.         executemany):
  31.         self.log.info("Plugin logged cursor event: %s", statement)

Plugins are registered using entry points in a similar way as that of dialects:

  1. entry_points={
  2.     'sqlalchemy.plugins': [
  3.         'log_cursor_plugin = myapp.plugins:LogCursorEventsPlugin'
  4.     ]

A plugin that uses the above names would be invoked from a database URL as in:

  1. from sqlalchemy import create_engine
  3. engine = create_engine(
  4.     "mysql+pymysql://scott:tiger@localhost/test?"
  5.     "plugin=log_cursor_plugin&log_cursor_logging_name=mylogger"
  6. )

The plugin URL parameter supports multiple instances, so that a URL may specify multiple plugins; they are loaded in the order stated in the URL:

  1. engine = create_engine(
  2.   "mysql+pymysql://scott:tiger@localhost/test?"
  3.   "plugin=plugin_one&plugin=plugin_twp&plugin=plugin_three")

The plugin names may also be passed directly to create_engine() using the create_engine.plugins argument:

  1. engine = create_engine(
  2.   "mysql+pymysql://scott:tiger@localhost/test",
  3.   plugins=["myplugin"])

New in version 1.2.3: plugin names can also be specified to create_engine() as a list

A plugin may consume plugin-specific arguments from the URL object as well as the kwargs dictionary, which is the dictionary of arguments passed to the create_engine() call. “Consuming” these arguments includes that they must be removed when the plugin initializes, so that the arguments are not passed along to the Dialect constructor, where they will raise an ArgumentError because they are not known by the dialect.

As of version 1.4 of SQLAlchemy, arguments should continue to be consumed from the kwargs dictionary directly, by removing the values with a method such as dict.pop. Arguments from the URL object should be consumed by implementing the CreateEnginePlugin.update_url() method, returning a new copy of the URL with plugin-specific parameters removed:

  1. class MyPlugin(CreateEnginePlugin):
  2.     def __init__(self, url, kwargs):
  3.         self.my_argument_one = url.query['my_argument_one']
  4.         self.my_argument_two = url.query['my_argument_two']
  5.         self.my_argument_three = kwargs.pop('my_argument_three', None)
  7.     def update_url(self, url):
  8.         return url.difference_update_query(
  9.             ["my_argument_one", "my_argument_two"]
  10.         )

Arguments like those illustrated above would be consumed from a create_engine() call such as:

  1. from sqlalchemy import create_engine
  3. engine = create_engine(
  4.   "mysql+pymysql://scott:tiger@localhost/test?"
  5.   "plugin=myplugin&my_argument_one=foo&my_argument_two=bar",
  6.   my_argument_three='bat'
  7. )

Changed in version 1.4: The URL object is now immutable; a CreateEnginePlugin that needs to alter the URL should implement the newly added CreateEnginePlugin.update_url() method, which is invoked after the plugin is constructed.

For migration, construct the plugin in the following way, checking for the existence of the CreateEnginePlugin.update_url() method to detect which version is running:

  1. class MyPlugin(CreateEnginePlugin):
  2.     def __init__(self, url, kwargs):
  3.         if hasattr(CreateEnginePlugin, "update_url"):
  4.             # detect the 1.4 API
  5.             self.my_argument_one = url.query['my_argument_one']
  6.             self.my_argument_two = url.query['my_argument_two']
  7.         else:
  8.             # detect the 1.3 and earlier API - mutate the
  9.             # URL directly
  10.             self.my_argument_one = url.query.pop('my_argument_one')
  11.             self.my_argument_two = url.query.pop('my_argument_two')
  13.         self.my_argument_three = kwargs.pop('my_argument_three', None)
  15.     def update_url(self, url):
  16.         # this method is only called in the 1.4 version
  17.         return url.difference_update_query(
  18.             ["my_argument_one", "my_argument_two"]
  19.         )

See also

The URL object is now immutable - overview of the URL change which also includes notes regarding CreateEnginePlugin.

When the engine creation process completes and produces the Engine object, it is again passed to the plugin via the CreateEnginePlugin.engine_created() hook. In this hook, additional changes can be made to the engine, most typically involving setup of events (e.g. those defined in Core Events).

Members

__init__(), engine_created(), handle_dialect_kwargs(), handle_pool_kwargs(), update_url()

New in version 1.1.

class sqlalchemy.engine.Engine

Connects a Pool and Dialect together to provide a source of database connectivity and behavior.

An Engine object is instantiated publicly using the create_engine() function.

See also

Engine Configuration

Working with Engines and Connections

Members

begin(), clear_compiled_cache(), connect(), dispose(), driver, engine, execution_options(), get_execution_options(), name, raw_connection(), update_execution_options()

Class signature

class sqlalchemy.engine.Engine (sqlalchemy.engine.interfaces.ConnectionEventsTarget, sqlalchemy.log.Identified, sqlalchemy.inspection.Inspectable)

  • method sqlalchemy.engine.Engine.begin() → Iterator[Connection]

    Return a context manager delivering a Connection with a Transaction established.

    E.g.:

    1. with engine.begin() as conn:
    2.     conn.execute(
    3.         text("insert into table (x, y, z) values (1, 2, 3)")
    4.     )
    5.     conn.execute(text("my_special_procedure(5)"))

    Upon successful operation, the Transaction is committed. If an error is raised, the Transaction is rolled back.

    See also

    Engine.connect() - procure a Connection from an Engine.

    Connection.begin() - start a Transaction for a particular Connection.

  • method sqlalchemy.engine.Engine.clear_compiled_cache() → None

    Clear the compiled cache associated with the dialect.

    This applies only to the built-in cache that is established via the create_engine.query_cache_size parameter. It will not impact any dictionary caches that were passed via the Connection.execution_options.query_cache parameter.

    New in version 1.4.

  • method sqlalchemy.engine.Engine.connect() → Connection

    Return a new Connection object.

    The Connection acts as a Python context manager, so the typical use of this method looks like:

    1. with engine.connect() as connection:
    2.     connection.execute(text("insert into table values ('foo')"))
    3.     connection.commit()

    Where above, after the block is completed, the connection is “closed” and its underlying DBAPI resources are returned to the connection pool. This also has the effect of rolling back any transaction that was explicitly begun or was begun via autobegin, and will emit the ConnectionEvents.rollback() event if one was started and is still in progress.

    See also

    Engine.begin()

  • method sqlalchemy.engine.Engine.dispose(close: bool = True) → None

    Dispose of the connection pool used by this Engine.

    A new connection pool is created immediately after the old one has been disposed. The previous connection pool is disposed either actively, by closing out all currently checked-in connections in that pool, or passively, by losing references to it but otherwise not closing any connections. The latter strategy is more appropriate for an initializer in a forked Python process.

    • Parameters:

      close –

      if left at its default of True, has the effect of fully closing all currently checked in database connections. Connections that are still checked out will not be closed, however they will no longer be associated with this Engine, so when they are closed individually, eventually the Pool which they are associated with will be garbage collected and they will be closed out fully, if not already closed on checkin.

      If set to False, the previous connection pool is de-referenced, and otherwise not touched in any way.

  1. New in version 1.4.33: Added the [Engine.dispose.close](#sqlalchemy.engine.Engine.dispose.params.close "sqlalchemy.engine.Engine.dispose") parameter to allow the replacement of a connection pool in a child process without interfering with the connections used by the parent process.
  3. See also
  5. [Engine Disposal](#engine-disposal)
  7. [Using Connection Pools with Multiprocessing or os.fork()]($ba04c3bd42280074.md#pooling-multiprocessing)

  • attribute sqlalchemy.engine.Engine.driver

    Driver name of the Dialect in use by this Engine.

  • attribute sqlalchemy.engine.Engine.engine

    Returns this Engine.

    Used for legacy schemes that accept Connection / Engine objects within the same variable.

  • method sqlalchemy.engine.Engine.execution_options(**opt: Any) → OptionEngine

    Return a new Engine that will provide Connection objects with the given execution options.

    The returned Engine remains related to the original Engine in that it shares the same connection pool and other state:

    • The Pool used by the new Engine is the same instance. The Engine.dispose() method will replace the connection pool instance for the parent engine as well as this one.

    • Event listeners are “cascaded” - meaning, the new Engine inherits the events of the parent, and new events can be associated with the new Engine individually.

    • The logging configuration and logging_name is copied from the parent Engine.

  1. The intent of the [Engine.execution\_options()](#sqlalchemy.engine.Engine.execution_options "sqlalchemy.engine.Engine.execution_options") method is to implement schemes where multiple [Engine](#sqlalchemy.engine.Engine "sqlalchemy.engine.Engine") objects refer to the same connection pool, but are differentiated by options that affect some execution-level behavior for each engine. One such example is breaking into separate “reader” and “writer” [Engine](#sqlalchemy.engine.Engine "sqlalchemy.engine.Engine") instances, where one [Engine](#sqlalchemy.engine.Engine "sqlalchemy.engine.Engine") has a lower [isolation level](https://docs.sqlalchemy.org/en/20/glossary.html#term-isolation-level) setting configured or is even transaction-disabled using “autocommit”. An example of this configuration is at [Maintaining Multiple Isolation Levels for a Single Engine](#dbapi-autocommit-multiple).
  3. Another example is one that uses a custom option `shard_id` which is consumed by an event to change the current schema on a database connection:
  5. ```
  6. from sqlalchemy import event
  7. from sqlalchemy.engine import Engine
  9. primary_engine = create_engine("mysql+mysqldb://")
  10. shard1 = primary_engine.execution_options(shard_id="shard1")
  11. shard2 = primary_engine.execution_options(shard_id="shard2")
  13. shards = {"default": "base", "shard_1": "db1", "shard_2": "db2"}
  15. @event.listens_for(Engine, "before_cursor_execute")
  16. def _switch_shard(conn, cursor, stmt,
  17.         params, context, executemany):
  18.     shard_id = conn.get_execution_options().get('shard_id', "default")
  19.     current_shard = conn.info.get("current_shard", None)
  21.     if current_shard != shard_id:
  22.         cursor.execute("use %s" % shards[shard_id])
  23.         conn.info["current_shard"] = shard_id
  24. ```
  26. The above recipe illustrates two [Engine](#sqlalchemy.engine.Engine "sqlalchemy.engine.Engine") objects that will each serve as factories for [Connection](#sqlalchemy.engine.Connection "sqlalchemy.engine.Connection") objects that have pre-established “shard\_id” execution options present. A [ConnectionEvents.before\_cursor\_execute()]($03a0310aaf427e31.md#sqlalchemy.events.ConnectionEvents.before_cursor_execute "sqlalchemy.events.ConnectionEvents.before_cursor_execute") event handler then interprets this execution option to emit a MySQL `use` statement to switch databases before a statement execution, while at the same time keeping track of which database we’ve established using the [Connection.info](#sqlalchemy.engine.Connection.info "sqlalchemy.engine.Connection.info") dictionary.
  28. See also
  30. [Connection.execution\_options()](#sqlalchemy.engine.Connection.execution_options "sqlalchemy.engine.Connection.execution_options") - update execution options on a [Connection](#sqlalchemy.engine.Connection "sqlalchemy.engine.Connection") object.
  32. [Engine.update\_execution\_options()](#sqlalchemy.engine.Engine.update_execution_options "sqlalchemy.engine.Engine.update_execution_options") - update the execution options for a given [Engine](#sqlalchemy.engine.Engine "sqlalchemy.engine.Engine") in place.
  34. [Engine.get\_execution\_options()](#sqlalchemy.engine.Engine.get_execution_options "sqlalchemy.engine.Engine.get_execution_options")

class sqlalchemy.engine.ExceptionContext

Encapsulate information about an error condition in progress.

Members

chained_exception, connection, cursor, dialect, engine, execution_context, invalidate_pool_on_disconnect, is_disconnect, original_exception, parameters, sqlalchemy_exception, statement

This object exists solely to be passed to the DialectEvents.handle_error() event, supporting an interface that can be extended without backwards-incompatibility.

  • attribute sqlalchemy.engine.ExceptionContext.chained_exception: Optional[BaseException]

    The exception that was returned by the previous handler in the exception chain, if any.

    If present, this exception will be the one ultimately raised by SQLAlchemy unless a subsequent handler replaces it.

    May be None.

  • attribute sqlalchemy.engine.ExceptionContext.connection: Optional[Connection]

    The Connection in use during the exception.

    This member is present, except in the case of a failure when first connecting.

    See also

    ExceptionContext.engine

  • attribute sqlalchemy.engine.ExceptionContext.cursor: Optional[DBAPICursor]

    The DBAPI cursor object.

    May be None.

  • attribute sqlalchemy.engine.ExceptionContext.dialect: Dialect

    The Dialect in use.

    This member is present for all invocations of the event hook.

    New in version 2.0.

  • attribute sqlalchemy.engine.ExceptionContext.engine: Optional[Engine]

    The Engine in use during the exception.

    This member is present in all cases except for when handling an error within the connection pool “pre-ping” process.

  • attribute sqlalchemy.engine.ExceptionContext.execution_context: Optional[ExecutionContext]

    The ExecutionContext corresponding to the execution operation in progress.

    This is present for statement execution operations, but not for operations such as transaction begin/end. It also is not present when the exception was raised before the ExecutionContext could be constructed.

    Note that the ExceptionContext.statement and ExceptionContext.parameters members may represent a different value than that of the ExecutionContext, potentially in the case where a ConnectionEvents.before_cursor_execute() event or similar modified the statement/parameters to be sent.

    May be None.

  • attribute sqlalchemy.engine.ExceptionContext.invalidate_pool_on_disconnect: bool

    Represent whether all connections in the pool should be invalidated when a “disconnect” condition is in effect.

    Setting this flag to False within the scope of the DialectEvents.handle_error() event will have the effect such that the full collection of connections in the pool will not be invalidated during a disconnect; only the current connection that is the subject of the error will actually be invalidated.

    The purpose of this flag is for custom disconnect-handling schemes where the invalidation of other connections in the pool is to be performed based on other conditions, or even on a per-connection basis.

    New in version 1.0.3.

  • attribute sqlalchemy.engine.ExceptionContext.is_disconnect: bool

    Represent whether the exception as occurred represents a “disconnect” condition.

    This flag will always be True or False within the scope of the DialectEvents.handle_error() handler.

    SQLAlchemy will defer to this flag in order to determine whether or not the connection should be invalidated subsequently. That is, by assigning to this flag, a “disconnect” event which then results in a connection and pool invalidation can be invoked or prevented by changing this flag.

    Note

    The pool “pre_ping” handler enabled using the create_engine.pool_pre_ping parameter does not consult this event before deciding if the “ping” returned false, as opposed to receiving an unhandled error. For this use case, the legacy recipe based on engine_connect() may be used. A future API allow more comprehensive customization of the “disconnect” detection mechanism across all functions.

  • attribute sqlalchemy.engine.ExceptionContext.original_exception: BaseException

    The exception object which was caught.

    This member is always present.

  • attribute sqlalchemy.engine.ExceptionContext.parameters: Optional[_DBAPIAnyExecuteParams]

    Parameter collection that was emitted directly to the DBAPI.

    May be None.

  • attribute sqlalchemy.engine.ExceptionContext.sqlalchemy_exception: Optional[StatementError]

    The sqlalchemy.exc.StatementError which wraps the original, and will be raised if exception handling is not circumvented by the event.

    May be None, as not all exception types are wrapped by SQLAlchemy. For DBAPI-level exceptions that subclass the dbapi’s Error class, this field will always be present.

  • attribute sqlalchemy.engine.ExceptionContext.statement: Optional[str]

    String SQL statement that was emitted directly to the DBAPI.

    May be None.

class sqlalchemy.engine.NestedTransaction

Represent a ‘nested’, or SAVEPOINT transaction.

The NestedTransaction object is created by calling the Connection.begin_nested() method of Connection.

When using NestedTransaction, the semantics of “begin” / “commit” / “rollback” are as follows:

  • the “begin” operation corresponds to the “BEGIN SAVEPOINT” command, where the savepoint is given an explicit name that is part of the state of this object.

  • The NestedTransaction.commit() method corresponds to a “RELEASE SAVEPOINT” operation, using the savepoint identifier associated with this NestedTransaction.

  • The NestedTransaction.rollback() method corresponds to a “ROLLBACK TO SAVEPOINT” operation, using the savepoint identifier associated with this NestedTransaction.

The rationale for mimicking the semantics of an outer transaction in terms of savepoints so that code may deal with a “savepoint” transaction and an “outer” transaction in an agnostic way.

See also

Using SAVEPOINT - ORM version of the SAVEPOINT API.

Members

close(), commit(), rollback()

Class signature

class sqlalchemy.engine.NestedTransaction (sqlalchemy.engine.Transaction)

class sqlalchemy.engine.RootTransaction

Represent the “root” transaction on a Connection.

This corresponds to the current “BEGIN/COMMIT/ROLLBACK” that’s occurring for the Connection. The RootTransaction is created by calling upon the Connection.begin() method, and remains associated with the Connection throughout its active span. The current RootTransaction in use is accessible via the Connection.get_transaction method of Connection.

In 2.0 style use, the Connection also employs “autobegin” behavior that will create a new RootTransaction whenever a connection in a non-transactional state is used to emit commands on the DBAPI connection. The scope of the RootTransaction in 2.0 style use can be controlled using the Connection.commit() and Connection.rollback() methods.

Members

close(), commit(), rollback()

Class signature

class sqlalchemy.engine.RootTransaction (sqlalchemy.engine.Transaction)

class sqlalchemy.engine.Transaction

Represent a database transaction in progress.

The Transaction object is procured by calling the Connection.begin() method of Connection:

  1. from sqlalchemy import create_engine
  2. engine = create_engine("postgresql+psycopg2://scott:tiger@localhost/test")
  3. connection = engine.connect()
  4. trans = connection.begin()
  5. connection.execute(text("insert into x (a, b) values (1, 2)"))
  6. trans.commit()

The object provides rollback() and commit() methods in order to control transaction boundaries. It also implements a context manager interface so that the Python with statement can be used with the Connection.begin() method:

  1. with connection.begin():
  2.     connection.execute(text("insert into x (a, b) values (1, 2)"))

The Transaction object is not threadsafe.

Members

close(), commit(), rollback()

See also

Connection.begin()

Connection.begin_twophase()

Connection.begin_nested()

Class signature

class sqlalchemy.engine.Transaction (sqlalchemy.engine.util.TransactionalContext)

  • method sqlalchemy.engine.Transaction.close() → None

    Close this Transaction.

    If this transaction is the base transaction in a begin/commit nesting, the transaction will rollback(). Otherwise, the method returns.

    This is used to cancel a Transaction without affecting the scope of an enclosing transaction.

  • method sqlalchemy.engine.Transaction.commit() → None

    Commit this Transaction.

    The implementation of this may vary based on the type of transaction in use:

    • For a simple database transaction (e.g. RootTransaction), it corresponds to a COMMIT.

    • For a NestedTransaction, it corresponds to a “RELEASE SAVEPOINT” operation.

    • For a TwoPhaseTransaction, DBAPI-specific methods for two phase transactions may be used.

class sqlalchemy.engine.TwoPhaseTransaction

Represent a two-phase transaction.

A new TwoPhaseTransaction object may be procured using the Connection.begin_twophase() method.

The interface is the same as that of Transaction with the addition of the prepare() method.

Members

close(), commit(), prepare(), rollback()

Class signature

class sqlalchemy.engine.TwoPhaseTransaction (sqlalchemy.engine.RootTransaction)

Result Set API

Object NameDescription

ChunkedIteratorResult

An IteratorResult that works from an iterator-producing callable.

CursorResult

A Result that is representing state from a DBAPI cursor.

FilterResult

A wrapper for a Result that returns objects other than Row objects, such as dictionaries or scalar objects.

FrozenResult

Represents a Result object in a “frozen” state suitable for caching.

IteratorResult

A Result that gets data from a Python iterator of Row objects or similar row-like data.

MappingResult

A wrapper for a Result that returns dictionary values rather than Row values.

MergedResult

A Result that is merged from any number of Result objects.

Result

Represent a set of database results.

Row

Represent a single result row.

RowMapping

A Mapping that maps column names and objects to Row values.

ScalarResult

A wrapper for a Result that returns scalar values rather than Row values.

TupleResult

A Result that’s typed as returning plain Python tuples instead of rows.

class sqlalchemy.engine.ChunkedIteratorResult

An IteratorResult that works from an iterator-producing callable.

The given chunks argument is a function that is given a number of rows to return in each chunk, or None for all rows. The function should then return an un-consumed iterator of lists, each list of the requested size.

The function can be called at any time again, in which case it should continue from the same result set but adjust the chunk size as given.

New in version 1.4.

Members

yield_per()

Class signature

class sqlalchemy.engine.ChunkedIteratorResult (sqlalchemy.engine.IteratorResult)

  • method sqlalchemy.engine.ChunkedIteratorResult.yield_per(num: int) → SelfChunkedIteratorResult

    Configure the row-fetching strategy to fetch num rows at a time.

    This impacts the underlying behavior of the result when iterating over the result object, or otherwise making use of methods such as Result.fetchone() that return one row at a time. Data from the underlying cursor or other data source will be buffered up to this many rows in memory, and the buffered collection will then be yielded out one row at a time or as many rows are requested. Each time the buffer clears, it will be refreshed to this many rows or as many rows remain if fewer remain.

    The Result.yield_per() method is generally used in conjunction with the Connection.execution_options.stream_results execution option, which will allow the database dialect in use to make use of a server side cursor, if the DBAPI supports a specific “server side cursor” mode separate from its default mode of operation.

    Tip

    Consider using the Connection.execution_options.yield_per execution option, which will simultaneously set Connection.execution_options.stream_results to ensure the use of server side cursors, as well as automatically invoke the Result.yield_per() method to establish a fixed row buffer size at once.

    The Connection.execution_options.yield_per execution option is available for ORM operations, with Session-oriented use described at Fetching Large Result Sets with Yield Per. The Core-only version which works with Connection is new as of SQLAlchemy 1.4.40.

    New in version 1.4.

    • Parameters:

      num – number of rows to fetch each time the buffer is refilled. If set to a value below 1, fetches all rows for the next buffer.

  1. See also
  3. [Using Server Side Cursors (a.k.a. stream results)](#engine-stream-results) - describes Core behavior for [Result.yield\_per()](#sqlalchemy.engine.Result.yield_per "sqlalchemy.engine.Result.yield_per")
  5. [Fetching Large Result Sets with Yield Per]($661bd2ffd6937693.md#orm-queryguide-yield-per) - in the [ORM Querying Guide]($86681c2576bdda58.md)

class sqlalchemy.engine.CursorResult

A Result that is representing state from a DBAPI cursor.

Changed in version 1.4: The CursorResult` class replaces the previous ResultProxy interface. This classes are based on the Result calling API which provides an updated usage model and calling facade for SQLAlchemy Core and SQLAlchemy ORM.

Returns database rows via the Row class, which provides additional API features and behaviors on top of the raw data returned by the DBAPI. Through the use of filters such as the Result.scalars() method, other kinds of objects may also be returned.

See also

Using SELECT Statements - introductory material for accessing CursorResult and Row objects.

Members

all(), close(), columns(), fetchall(), fetchmany(), fetchone(), first(), freeze(), inserted_primary_key, inserted_primary_key_rows, is_insert, keys(), last_inserted_params(), last_updated_params(), lastrow_has_defaults(), lastrowid, mappings(), merge(), one(), one_or_none(), partitions(), postfetch_cols(), prefetch_cols(), returned_defaults, returned_defaults_rows, returns_rows, rowcount, scalar(), scalar_one(), scalar_one_or_none(), scalars(), splice_horizontally(), splice_vertically(), supports_sane_multi_rowcount(), supports_sane_rowcount(), t, tuples(), unique(), yield_per()

Class signature

class sqlalchemy.engine.CursorResult (sqlalchemy.engine.Result)

  • method sqlalchemy.engine.CursorResult.all() → Sequence[Row[_TP]]

    inherited from the Result.all() method of Result

    Return all rows in a list.

    Closes the result set after invocation. Subsequent invocations will return an empty list.

    New in version 1.4.

    • Returns:

      a list of Row objects.

  • method sqlalchemy.engine.CursorResult.close() → Any

    Close this CursorResult.

    This closes out the underlying DBAPI cursor corresponding to the statement execution, if one is still present. Note that the DBAPI cursor is automatically released when the CursorResult exhausts all available rows. CursorResult.close() is generally an optional method except in the case when discarding a CursorResult that still has additional rows pending for fetch.

    After this method is called, it is no longer valid to call upon the fetch methods, which will raise a ResourceClosedError on subsequent use.

    See also

    Working with Engines and Connections

  • method sqlalchemy.engine.CursorResult.columns(*col_expressions: _KeyIndexType) → SelfResultInternal

    inherited from the Result.columns() method of Result

    Establish the columns that should be returned in each row.

    This method may be used to limit the columns returned as well as to reorder them. The given list of expressions are normally a series of integers or string key names. They may also be appropriate ColumnElement objects which correspond to a given statement construct.

    Changed in version 2.0: Due to a bug in 1.4, the Result.columns() method had an incorrect behavior where calling upon the method with just one index would cause the Result object to yield scalar values rather than Row objects. In version 2.0, this behavior has been corrected such that calling upon Result.columns() with a single index will produce a Result object that continues to yield Row objects, which include only a single column.

    E.g.:

    1. statement = select(table.c.x, table.c.y, table.c.z)
    2. result = connection.execute(statement)
    4. for z, y in result.columns('z', 'y'):
    5.     # ...

    Example of using the column objects from the statement itself:

    1. for z, y in result.columns(
    2.         statement.selected_columns.c.z,
    3.         statement.selected_columns.c.y
    4. ):
    5.     # ...

    New in version 1.4.

    • Parameters:

      *col_expressions – indicates columns to be returned. Elements may be integer row indexes, string column names, or appropriate ColumnElement objects corresponding to a select construct.

      Returns:

      this Result object with the modifications given.

  1. See also
  3. [Result.partitions()](#sqlalchemy.engine.Result.partitions "sqlalchemy.engine.Result.partitions")

  • method sqlalchemy.engine.CursorResult.fetchone() → Optional[Row[_TP]]

    inherited from the Result.fetchone() method of Result

    Fetch one row.

    When all rows are exhausted, returns None.

    This method is provided for backwards compatibility with SQLAlchemy 1.x.x.

    To fetch the first row of a result only, use the Result.first() method. To iterate through all rows, iterate the Result object directly.

    • Returns:

      a Row object if no filters are applied, or None if no rows remain.

  • method sqlalchemy.engine.CursorResult.first() → Optional[Row[_TP]]

    inherited from the Result.first() method of Result

    Fetch the first row or None if no row is present.

    Closes the result set and discards remaining rows.

    Note

    This method returns one row, e.g. tuple, by default. To return exactly one single scalar value, that is, the first column of the first row, use the Result.scalar() method, or combine Result.scalars() and Result.first().

    Additionally, in contrast to the behavior of the legacy ORM Query.first() method, no limit is applied to the SQL query which was invoked to produce this Result; for a DBAPI driver that buffers results in memory before yielding rows, all rows will be sent to the Python process and all but the first row will be discarded.

    See also

    ORM Query Unified with Core Select

    • Returns:

      a Row object, or None if no rows remain.

  1. See also
  3. [Result.scalar()](#sqlalchemy.engine.Result.scalar "sqlalchemy.engine.Result.scalar")
  5. [Result.one()](#sqlalchemy.engine.Result.one "sqlalchemy.engine.Result.one")

  • method sqlalchemy.engine.CursorResult.freeze() → FrozenResult[_TP]

    inherited from the Result.freeze() method of Result

    Return a callable object that will produce copies of this Result when invoked.

    The callable object returned is an instance of FrozenResult.

    This is used for result set caching. The method must be called on the result when it has been unconsumed, and calling the method will consume the result fully. When the FrozenResult is retrieved from a cache, it can be called any number of times where it will produce a new Result object each time against its stored set of rows.

    See also

    Re-Executing Statements - example usage within the ORM to implement a result-set cache.

  • attribute sqlalchemy.engine.CursorResult.inserted_primary_key

    Return the primary key for the row just inserted.

    The return value is a Row object representing a named tuple of primary key values in the order in which the primary key columns are configured in the source Table.

    Changed in version 1.4.8: - the CursorResult.inserted_primary_key value is now a named tuple via the Row class, rather than a plain tuple.

    This accessor only applies to single row insert() constructs which did not explicitly specify Insert.returning(). Support for multirow inserts, while not yet available for most backends, would be accessed using the CursorResult.inserted_primary_key_rows accessor.

    Note that primary key columns which specify a server_default clause, or otherwise do not qualify as “autoincrement” columns (see the notes at Column), and were generated using the database-side default, will appear in this list as None unless the backend supports “returning” and the insert statement executed with the “implicit returning” enabled.

    Raises InvalidRequestError if the executed statement is not a compiled expression construct or is not an insert() construct.

  • attribute sqlalchemy.engine.CursorResult.inserted_primary_key_rows

    Return the value of CursorResult.inserted_primary_key as a row contained within a list; some dialects may support a multiple row form as well.

    Note

    As indicated below, in current SQLAlchemy versions this accessor is only useful beyond what’s already supplied by CursorResult.inserted_primary_key when using the psycopg2 dialect. Future versions hope to generalize this feature to more dialects.

    This accessor is added to support dialects that offer the feature that is currently implemented by the Psycopg2 Fast Execution Helpers feature, currently only the psycopg2 dialect, which provides for many rows to be INSERTed at once while still retaining the behavior of being able to return server-generated primary key values.

    • When using the psycopg2 dialect, or other dialects that may support “fast executemany” style inserts in upcoming releases : When invoking an INSERT statement while passing a list of rows as the second argument to Connection.execute(), this accessor will then provide a list of rows, where each row contains the primary key value for each row that was INSERTed.

    • When using all other dialects / backends that don’t yet support this feature: This accessor is only useful for single row INSERT statements, and returns the same information as that of the CursorResult.inserted_primary_key within a single-element list. When an INSERT statement is executed in conjunction with a list of rows to be INSERTed, the list will contain one row per row inserted in the statement, however it will contain None for any server-generated values.

  1. Future releases of SQLAlchemy will further generalize the “fast execution helper” feature of psycopg2 to suit other dialects, thus allowing this accessor to be of more general use.
  3. New in version 1.4.
  5. See also
  7. [CursorResult.inserted\_primary\_key](#sqlalchemy.engine.CursorResult.inserted_primary_key "sqlalchemy.engine.CursorResult.inserted_primary_key")

  • attribute sqlalchemy.engine.CursorResult.is_insert

    True if this CursorResult is the result of a executing an expression language compiled insert() construct.

    When True, this implies that the inserted_primary_key attribute is accessible, assuming the statement did not include a user defined “returning” construct.

  • method sqlalchemy.engine.CursorResult.keys() → RMKeyView

    inherited from the sqlalchemy.engine._WithKeys.keys method of sqlalchemy.engine._WithKeys

    Return an iterable view which yields the string keys that would be represented by each Row.

    The keys can represent the labels of the columns returned by a core statement or the names of the orm classes returned by an orm execution.

    The view also can be tested for key containment using the Python in operator, which will test both for the string keys represented in the view, as well as for alternate keys such as column objects.

    Changed in version 1.4: a key view object is returned rather than a plain list.

  • method sqlalchemy.engine.CursorResult.last_inserted_params()

    Return the collection of inserted parameters from this execution.

    Raises InvalidRequestError if the executed statement is not a compiled expression construct or is not an insert() construct.

  • method sqlalchemy.engine.CursorResult.last_updated_params()

    Return the collection of updated parameters from this execution.

    Raises InvalidRequestError if the executed statement is not a compiled expression construct or is not an update() construct.

  • method sqlalchemy.engine.CursorResult.lastrow_has_defaults()

    Return lastrow_has_defaults() from the underlying ExecutionContext.

    See ExecutionContext for details.

  • attribute sqlalchemy.engine.CursorResult.lastrowid

    Return the ‘lastrowid’ accessor on the DBAPI cursor.

    This is a DBAPI specific method and is only functional for those backends which support it, for statements where it is appropriate. It’s behavior is not consistent across backends.

    Usage of this method is normally unnecessary when using insert() expression constructs; the CursorResult.inserted_primary_key attribute provides a tuple of primary key values for a newly inserted row, regardless of database backend.

  • method sqlalchemy.engine.CursorResult.mappings() → MappingResult

    inherited from the Result.mappings() method of Result

    Apply a mappings filter to returned rows, returning an instance of MappingResult.

    When this filter is applied, fetching rows will return RowMapping objects instead of Row objects.

    New in version 1.4.

  1. See also
  3. [Result.first()](#sqlalchemy.engine.Result.first "sqlalchemy.engine.Result.first")
  5. [Result.one\_or\_none()](#sqlalchemy.engine.Result.one_or_none "sqlalchemy.engine.Result.one_or_none")
  7. [Result.scalar\_one()](#sqlalchemy.engine.Result.scalar_one "sqlalchemy.engine.Result.scalar_one")
  1. See also
  3. [Result.first()](#sqlalchemy.engine.Result.first "sqlalchemy.engine.Result.first")
  5. [Result.one()](#sqlalchemy.engine.Result.one "sqlalchemy.engine.Result.one")

  • method sqlalchemy.engine.CursorResult.partitions(size: Optional[int] = None) → Iterator[Sequence[Row[_TP]]]

    inherited from the Result.partitions() method of Result

    Iterate through sub-lists of rows of the size given.

    Each list will be of the size given, excluding the last list to be yielded, which may have a small number of rows. No empty lists will be yielded.

    The result object is automatically closed when the iterator is fully consumed.

    Note that the backend driver will usually buffer the entire result ahead of time unless the Connection.execution_options.stream_results execution option is used indicating that the driver should not pre-buffer results, if possible. Not all drivers support this option and the option is silently ignored for those who do not.

    When using the ORM, the Result.partitions() method is typically more effective from a memory perspective when it is combined with use of the yield_per execution option, which instructs both the DBAPI driver to use server side cursors, if available, as well as instructs the ORM loading internals to only build a certain amount of ORM objects from a result at a time before yielding them out.

    New in version 1.4.

    • Parameters:

      size – indicate the maximum number of rows to be present in each list yielded. If None, makes use of the value set by the Result.yield_per(), method, if it were called, or the Connection.execution_options.yield_per execution option, which is equivalent in this regard. If yield_per weren’t set, it makes use of the Result.fetchmany() default, which may be backend specific and not well defined.

      Returns:

      iterator of lists

  1. See also
  3. [Using Server Side Cursors (a.k.a. stream results)](#engine-stream-results)
  5. [Fetching Large Result Sets with Yield Per]($661bd2ffd6937693.md#orm-queryguide-yield-per) - in the [ORM Querying Guide]($86681c2576bdda58.md)

  • method sqlalchemy.engine.CursorResult.postfetch_cols()

    Return postfetch_cols() from the underlying ExecutionContext.

    See ExecutionContext for details.

    Raises InvalidRequestError if the executed statement is not a compiled expression construct or is not an insert() or update() construct.

  • method sqlalchemy.engine.CursorResult.prefetch_cols()

    Return prefetch_cols() from the underlying ExecutionContext.

    See ExecutionContext for details.

    Raises InvalidRequestError if the executed statement is not a compiled expression construct or is not an insert() or update() construct.

  • attribute sqlalchemy.engine.CursorResult.returned_defaults

    Return the values of default columns that were fetched using the ValuesBase.return_defaults() feature.

    The value is an instance of Row, or None if ValuesBase.return_defaults() was not used or if the backend does not support RETURNING.

    New in version 0.9.0.

    See also

    ValuesBase.return_defaults()

  • attribute sqlalchemy.engine.CursorResult.returned_defaults_rows

    Return a list of rows each containing the values of default columns that were fetched using the ValuesBase.return_defaults() feature.

    The return value is a list of Row objects.

    New in version 1.4.

  • attribute sqlalchemy.engine.CursorResult.returns_rows

    True if this CursorResult returns zero or more rows.

    I.e. if it is legal to call the methods CursorResult.fetchone(), CursorResult.fetchmany() CursorResult.fetchall().

    Overall, the value of CursorResult.returns_rows should always be synonymous with whether or not the DBAPI cursor had a .description attribute, indicating the presence of result columns, noting that a cursor that returns zero rows still has a .description if a row-returning statement was emitted.

    This attribute should be True for all results that are against SELECT statements, as well as for DML statements INSERT/UPDATE/DELETE that use RETURNING. For INSERT/UPDATE/DELETE statements that were not using RETURNING, the value will usually be False, however there are some dialect-specific exceptions to this, such as when using the MSSQL / pyodbc dialect a SELECT is emitted inline in order to retrieve an inserted primary key value.

  • attribute sqlalchemy.engine.CursorResult.rowcount

    Return the ‘rowcount’ for this result.

    The ‘rowcount’ reports the number of rows matched by the WHERE criterion of an UPDATE or DELETE statement.

    Note

    Notes regarding CursorResult.rowcount:

    • This attribute returns the number of rows matched, which is not necessarily the same as the number of rows that were actually modified - an UPDATE statement, for example, may have no net change on a given row if the SET values given are the same as those present in the row already. Such a row would be matched but not modified. On backends that feature both styles, such as MySQL, rowcount is configured by default to return the match count in all cases.

    • CursorResult.rowcount is only useful in conjunction with an UPDATE or DELETE statement. Contrary to what the Python DBAPI says, it does not return the number of rows available from the results of a SELECT statement as DBAPIs cannot support this functionality when rows are unbuffered.

    • CursorResult.rowcount may not be fully implemented by all dialects. In particular, most DBAPIs do not support an aggregate rowcount result from an executemany call. The CursorResult.supports_sane_rowcount() and CursorResult.supports_sane_multi_rowcount() methods will report from the dialect if each usage is known to be supported.

    • Statements that use RETURNING may not return a correct rowcount.

  1. See also
  3. [Getting Affected Row Count from UPDATE, DELETE]($a04339624cb33e15.md#tutorial-update-delete-rowcount) - in the [SQLAlchemy Unified Tutorial]($4406c4fa3e52f66b.md#unified-tutorial)

  • method sqlalchemy.engine.CursorResult.scalar() → Any

    inherited from the Result.scalar() method of Result

    Fetch the first column of the first row, and close the result set.

    Returns None if there are no rows to fetch.

    No validation is performed to test if additional rows remain.

    After calling this method, the object is fully closed, e.g. the CursorResult.close() method will have been called.

    • Returns:

      a Python scalar value, or None if no rows remain.

  • method sqlalchemy.engine.CursorResult.splice_horizontally(other)

    Return a new CursorResult that “horizontally splices” together the rows of this CursorResult with that of another CursorResult.

    Tip

    This method is for the benefit of the SQLAlchemy ORM and is not intended for general use.

    “horizontally splices” means that for each row in the first and second result sets, a new row that concatenates the two rows together is produced, which then becomes the new row. The incoming CursorResult must have the identical number of rows. It is typically expected that the two result sets come from the same sort order as well, as the result rows are spliced together based on their position in the result.

    The expected use case here is so that multiple INSERT..RETURNING statements against different tables can produce a single result that looks like a JOIN of those two tables.

    E.g.:

    1. r1 = connection.execute(
    2.     users.insert().returning(users.c.user_name, users.c.user_id),
    3.     user_values
    4. )
    6. r2 = connection.execute(
    7.     addresses.insert().returning(
    8.         addresses.c.address_id,
    9.         addresses.c.address,
    10.         addresses.c.user_id,
    11.     ),
    12.     address_values
    13. )
    15. rows = r1.splice_horizontally(r2).all()
    16. assert (
    17.     rows ==
    18.     [
    19.         ("john", 1, 1, "foo@bar.com", 1),
    20.         ("jack", 2, 2, "bar@bat.com", 2),
    21.     ]
    22. )

    New in version 2.0.

    See also

    CursorResult.splice_vertically()

  • method sqlalchemy.engine.CursorResult.splice_vertically(other)

    Return a new CursorResult that “vertically splices”, i.e. “extends”, the rows of this CursorResult with that of another CursorResult.

    Tip

    This method is for the benefit of the SQLAlchemy ORM and is not intended for general use.

    “vertically splices” means the rows of the given result are appended to the rows of this cursor result. The incoming CursorResult must have rows that represent the identical list of columns in the identical order as they are in this CursorResult.

    New in version 2.0.

    See also

    CursorResult.splice_horizontally()

  • method sqlalchemy.engine.CursorResult.supports_sane_multi_rowcount()

    Return supports_sane_multi_rowcount from the dialect.

    See CursorResult.rowcount for background.

  • method sqlalchemy.engine.CursorResult.supports_sane_rowcount()

    Return supports_sane_rowcount from the dialect.

    See CursorResult.rowcount for background.

  • attribute sqlalchemy.engine.CursorResult.t

    inherited from the Result.t attribute of Result

    Apply a “typed tuple” typing filter to returned rows.

    The Result.t attribute is a synonym for calling the Result.tuples() method.

    New in version 2.0.

  • method sqlalchemy.engine.CursorResult.tuples() → TupleResult[_TP]

    inherited from the Result.tuples() method of Result

    Apply a “typed tuple” typing filter to returned rows.

    This method returns the same Result object at runtime, however annotates as returning a TupleResult object that will indicate to PEP 484 typing tools that plain typed Tuple instances are returned rather than rows. This allows tuple unpacking and __getitem__ access of Row objects to by typed, for those cases where the statement invoked itself included typing information.

    New in version 2.0.

  1. See also
  3. [Result.t](#sqlalchemy.engine.Result.t "sqlalchemy.engine.Result.t") - shorter synonym
  5. [Row.t](#sqlalchemy.engine.Row.t "sqlalchemy.engine.Row.t") - [Row](#sqlalchemy.engine.Row "sqlalchemy.engine.Row") version

  • method sqlalchemy.engine.CursorResult.unique(strategy: Optional[Callable[[Any], Any]] = None) → SelfResult

    inherited from the Result.unique() method of Result

    Apply unique filtering to the objects returned by this Result.

    When this filter is applied with no arguments, the rows or objects returned will filtered such that each row is returned uniquely. The algorithm used to determine this uniqueness is by default the Python hashing identity of the whole tuple. In some cases a specialized per-entity hashing scheme may be used, such as when using the ORM, a scheme is applied which works against the primary key identity of returned objects.

    The unique filter is applied after all other filters, which means if the columns returned have been refined using a method such as the Result.columns() or Result.scalars() method, the uniquing is applied to only the column or columns returned. This occurs regardless of the order in which these methods have been called upon the Result object.

    The unique filter also changes the calculus used for methods like Result.fetchmany() and Result.partitions(). When using Result.unique(), these methods will continue to yield the number of rows or objects requested, after uniquing has been applied. However, this necessarily impacts the buffering behavior of the underlying cursor or datasource, such that multiple underlying calls to cursor.fetchmany() may be necessary in order to accumulate enough objects in order to provide a unique collection of the requested size.

    • Parameters:

      strategy – a callable that will be applied to rows or objects being iterated, which should return an object that represents the unique value of the row. A Python set() is used to store these identities. If not passed, a default uniqueness strategy is used which may have been assembled by the source of this Result object.

  • method sqlalchemy.engine.CursorResult.yield_per(num: int) → SelfCursorResult

    Configure the row-fetching strategy to fetch num rows at a time.

    This impacts the underlying behavior of the result when iterating over the result object, or otherwise making use of methods such as Result.fetchone() that return one row at a time. Data from the underlying cursor or other data source will be buffered up to this many rows in memory, and the buffered collection will then be yielded out one row at a time or as many rows are requested. Each time the buffer clears, it will be refreshed to this many rows or as many rows remain if fewer remain.

    The Result.yield_per() method is generally used in conjunction with the Connection.execution_options.stream_results execution option, which will allow the database dialect in use to make use of a server side cursor, if the DBAPI supports a specific “server side cursor” mode separate from its default mode of operation.

    Tip

    Consider using the Connection.execution_options.yield_per execution option, which will simultaneously set Connection.execution_options.stream_results to ensure the use of server side cursors, as well as automatically invoke the Result.yield_per() method to establish a fixed row buffer size at once.

    The Connection.execution_options.yield_per execution option is available for ORM operations, with Session-oriented use described at Fetching Large Result Sets with Yield Per. The Core-only version which works with Connection is new as of SQLAlchemy 1.4.40.

    New in version 1.4.

    • Parameters:

      num – number of rows to fetch each time the buffer is refilled. If set to a value below 1, fetches all rows for the next buffer.

  1. See also
  3. [Using Server Side Cursors (a.k.a. stream results)](#engine-stream-results) - describes Core behavior for [Result.yield\_per()](#sqlalchemy.engine.Result.yield_per "sqlalchemy.engine.Result.yield_per")
  5. [Fetching Large Result Sets with Yield Per]($661bd2ffd6937693.md#orm-queryguide-yield-per) - in the [ORM Querying Guide]($86681c2576bdda58.md)

class sqlalchemy.engine.FilterResult

A wrapper for a Result that returns objects other than Row objects, such as dictionaries or scalar objects.

FilterResult is the common base for additional result APIs including MappingResult, ScalarResult and AsyncResult.

Members

close(), closed, yield_per()

Class signature

class sqlalchemy.engine.FilterResult (sqlalchemy.engine.ResultInternal)

class sqlalchemy.engine.FrozenResult

Represents a Result object in a “frozen” state suitable for caching.

The FrozenResult object is returned from the Result.freeze() method of any Result object.

A new iterable Result object is generated from a fixed set of data each time the FrozenResult is invoked as a callable:

  1. result = connection.execute(query)
  3. frozen = result.freeze()
  5. unfrozen_result_one = frozen()
  7. for row in unfrozen_result_one:
  8.     print(row)
  10. unfrozen_result_two = frozen()
  11. rows = unfrozen_result_two.all()
  13. # ... etc

New in version 1.4.

See also

Re-Executing Statements - example usage within the ORM to implement a result-set cache.

merge_frozen_result() - ORM function to merge a frozen result back into a Session.

Class signature

class sqlalchemy.engine.FrozenResult (typing.Generic)

class sqlalchemy.engine.IteratorResult

A Result that gets data from a Python iterator of Row objects or similar row-like data.

New in version 1.4.

Members

closed

Class signature

class sqlalchemy.engine.IteratorResult (sqlalchemy.engine.Result)

class sqlalchemy.engine.MergedResult

A Result that is merged from any number of Result objects.

Returned by the Result.merge() method.

New in version 1.4.

Class signature

class sqlalchemy.engine.MergedResult (sqlalchemy.engine.IteratorResult)

class sqlalchemy.engine.Result

Represent a set of database results.

New in version 1.4: The Result object provides a completely updated usage model and calling facade for SQLAlchemy Core and SQLAlchemy ORM. In Core, it forms the basis of the CursorResult object which replaces the previous ResultProxy interface. When using the ORM, a higher level object called ChunkedIteratorResult is normally used.

Note

In SQLAlchemy 1.4 and above, this object is used for ORM results returned by Session.execute(), which can yield instances of ORM mapped objects either individually or within tuple-like rows. Note that the Result object does not deduplicate instances or rows automatically as is the case with the legacy Query object. For in-Python de-duplication of instances or rows, use the Result.unique() modifier method.

See also

Fetching Rows - in the SQLAlchemy Unified Tutorial

Members

all(), close(), closed, columns(), fetchall(), fetchmany(), fetchone(), first(), freeze(), keys(), mappings(), merge(), one(), one_or_none(), partitions(), scalar(), scalar_one(), scalar_one_or_none(), scalars(), t, tuples(), unique(), yield_per()

Class signature

class sqlalchemy.engine.Result (sqlalchemy.engine._WithKeys, sqlalchemy.engine.ResultInternal)

  • method sqlalchemy.engine.Result.all() → Sequence[Row[_TP]]

    Return all rows in a list.

    Closes the result set after invocation. Subsequent invocations will return an empty list.

    New in version 1.4.

    • Returns:

      a list of Row objects.

  • method sqlalchemy.engine.Result.close() → None

    close this Result.

    The behavior of this method is implementation specific, and is not implemented by default. The method should generally end the resources in use by the result object and also cause any subsequent iteration or row fetching to raise ResourceClosedError.

    New in version 1.4.27: - .close() was previously not generally available for all Result classes, instead only being available on the CursorResult returned for Core statement executions. As most other result objects, namely the ones used by the ORM, are proxying a CursorResult in any case, this allows the underlying cursor result to be closed from the outside facade for the case when the ORM query is using the yield_per execution option where it does not immediately exhaust and autoclose the database cursor.

  • attribute sqlalchemy.engine.Result.closed

    return True if this Result reports .closed

    New in version 1.4.43.

  • method sqlalchemy.engine.Result.columns(*col_expressions: _KeyIndexType) → SelfResultInternal

    Establish the columns that should be returned in each row.

    This method may be used to limit the columns returned as well as to reorder them. The given list of expressions are normally a series of integers or string key names. They may also be appropriate ColumnElement objects which correspond to a given statement construct.

    Changed in version 2.0: Due to a bug in 1.4, the Result.columns() method had an incorrect behavior where calling upon the method with just one index would cause the Result object to yield scalar values rather than Row objects. In version 2.0, this behavior has been corrected such that calling upon Result.columns() with a single index will produce a Result object that continues to yield Row objects, which include only a single column.

    E.g.:

    1. statement = select(table.c.x, table.c.y, table.c.z)
    2. result = connection.execute(statement)
    4. for z, y in result.columns('z', 'y'):
    5.     # ...

    Example of using the column objects from the statement itself:

    1. for z, y in result.columns(
    2.         statement.selected_columns.c.z,
    3.         statement.selected_columns.c.y
    4. ):
    5.     # ...

    New in version 1.4.

    • Parameters:

      *col_expressions – indicates columns to be returned. Elements may be integer row indexes, string column names, or appropriate ColumnElement objects corresponding to a select construct.

      Returns:

      this Result object with the modifications given.

  1. See also
  3. [Result.partitions()](#sqlalchemy.engine.Result.partitions "sqlalchemy.engine.Result.partitions")

  • method sqlalchemy.engine.Result.fetchone() → Optional[Row[_TP]]

    Fetch one row.

    When all rows are exhausted, returns None.

    This method is provided for backwards compatibility with SQLAlchemy 1.x.x.

    To fetch the first row of a result only, use the Result.first() method. To iterate through all rows, iterate the Result object directly.

    • Returns:

      a Row object if no filters are applied, or None if no rows remain.

  • method sqlalchemy.engine.Result.first() → Optional[Row[_TP]]

    Fetch the first row or None if no row is present.

    Closes the result set and discards remaining rows.

    Note

    This method returns one row, e.g. tuple, by default. To return exactly one single scalar value, that is, the first column of the first row, use the Result.scalar() method, or combine Result.scalars() and Result.first().

    Additionally, in contrast to the behavior of the legacy ORM Query.first() method, no limit is applied to the SQL query which was invoked to produce this Result; for a DBAPI driver that buffers results in memory before yielding rows, all rows will be sent to the Python process and all but the first row will be discarded.

    See also

    ORM Query Unified with Core Select

    • Returns:

      a Row object, or None if no rows remain.

  1. See also
  3. [Result.scalar()](#sqlalchemy.engine.Result.scalar "sqlalchemy.engine.Result.scalar")
  5. [Result.one()](#sqlalchemy.engine.Result.one "sqlalchemy.engine.Result.one")

  • method sqlalchemy.engine.Result.freeze() → FrozenResult[_TP]

    Return a callable object that will produce copies of this Result when invoked.

    The callable object returned is an instance of FrozenResult.

    This is used for result set caching. The method must be called on the result when it has been unconsumed, and calling the method will consume the result fully. When the FrozenResult is retrieved from a cache, it can be called any number of times where it will produce a new Result object each time against its stored set of rows.

    See also

    Re-Executing Statements - example usage within the ORM to implement a result-set cache.

  • method sqlalchemy.engine.Result.keys() → RMKeyView

    inherited from the sqlalchemy.engine._WithKeys.keys method of sqlalchemy.engine._WithKeys

    Return an iterable view which yields the string keys that would be represented by each Row.

    The keys can represent the labels of the columns returned by a core statement or the names of the orm classes returned by an orm execution.

    The view also can be tested for key containment using the Python in operator, which will test both for the string keys represented in the view, as well as for alternate keys such as column objects.

    Changed in version 1.4: a key view object is returned rather than a plain list.

  • method sqlalchemy.engine.Result.mappings() → MappingResult

    Apply a mappings filter to returned rows, returning an instance of MappingResult.

    When this filter is applied, fetching rows will return RowMapping objects instead of Row objects.

    New in version 1.4.

  1. See also
  3. [Result.first()](#sqlalchemy.engine.Result.first "sqlalchemy.engine.Result.first")
  5. [Result.one\_or\_none()](#sqlalchemy.engine.Result.one_or_none "sqlalchemy.engine.Result.one_or_none")
  7. [Result.scalar\_one()](#sqlalchemy.engine.Result.scalar_one "sqlalchemy.engine.Result.scalar_one")
  1. See also
  3. [Result.first()](#sqlalchemy.engine.Result.first "sqlalchemy.engine.Result.first")
  5. [Result.one()](#sqlalchemy.engine.Result.one "sqlalchemy.engine.Result.one")

  • method sqlalchemy.engine.Result.partitions(size: Optional[int] = None) → Iterator[Sequence[Row[_TP]]]

    Iterate through sub-lists of rows of the size given.

    Each list will be of the size given, excluding the last list to be yielded, which may have a small number of rows. No empty lists will be yielded.

    The result object is automatically closed when the iterator is fully consumed.

    Note that the backend driver will usually buffer the entire result ahead of time unless the Connection.execution_options.stream_results execution option is used indicating that the driver should not pre-buffer results, if possible. Not all drivers support this option and the option is silently ignored for those who do not.

    When using the ORM, the Result.partitions() method is typically more effective from a memory perspective when it is combined with use of the yield_per execution option, which instructs both the DBAPI driver to use server side cursors, if available, as well as instructs the ORM loading internals to only build a certain amount of ORM objects from a result at a time before yielding them out.

    New in version 1.4.

    • Parameters:

      size – indicate the maximum number of rows to be present in each list yielded. If None, makes use of the value set by the Result.yield_per(), method, if it were called, or the Connection.execution_options.yield_per execution option, which is equivalent in this regard. If yield_per weren’t set, it makes use of the Result.fetchmany() default, which may be backend specific and not well defined.

      Returns:

      iterator of lists

  1. See also
  3. [Using Server Side Cursors (a.k.a. stream results)](#engine-stream-results)
  5. [Fetching Large Result Sets with Yield Per]($661bd2ffd6937693.md#orm-queryguide-yield-per) - in the [ORM Querying Guide]($86681c2576bdda58.md)

  • method sqlalchemy.engine.Result.scalar() → Any

    Fetch the first column of the first row, and close the result set.

    Returns None if there are no rows to fetch.

    No validation is performed to test if additional rows remain.

    After calling this method, the object is fully closed, e.g. the CursorResult.close() method will have been called.

    • Returns:

      a Python scalar value, or None if no rows remain.

  • attribute sqlalchemy.engine.Result.t

    Apply a “typed tuple” typing filter to returned rows.

    The Result.t attribute is a synonym for calling the Result.tuples() method.

    New in version 2.0.

  • method sqlalchemy.engine.Result.tuples() → TupleResult[_TP]

    Apply a “typed tuple” typing filter to returned rows.

    This method returns the same Result object at runtime, however annotates as returning a TupleResult object that will indicate to PEP 484 typing tools that plain typed Tuple instances are returned rather than rows. This allows tuple unpacking and __getitem__ access of Row objects to by typed, for those cases where the statement invoked itself included typing information.

    New in version 2.0.

  1. See also
  3. [Result.t](#sqlalchemy.engine.Result.t "sqlalchemy.engine.Result.t") - shorter synonym
  5. [Row.t](#sqlalchemy.engine.Row.t "sqlalchemy.engine.Row.t") - [Row](#sqlalchemy.engine.Row "sqlalchemy.engine.Row") version

  • method sqlalchemy.engine.Result.unique(strategy: Optional[Callable[[Any], Any]] = None) → SelfResult

    Apply unique filtering to the objects returned by this Result.

    When this filter is applied with no arguments, the rows or objects returned will filtered such that each row is returned uniquely. The algorithm used to determine this uniqueness is by default the Python hashing identity of the whole tuple. In some cases a specialized per-entity hashing scheme may be used, such as when using the ORM, a scheme is applied which works against the primary key identity of returned objects.

    The unique filter is applied after all other filters, which means if the columns returned have been refined using a method such as the Result.columns() or Result.scalars() method, the uniquing is applied to only the column or columns returned. This occurs regardless of the order in which these methods have been called upon the Result object.

    The unique filter also changes the calculus used for methods like Result.fetchmany() and Result.partitions(). When using Result.unique(), these methods will continue to yield the number of rows or objects requested, after uniquing has been applied. However, this necessarily impacts the buffering behavior of the underlying cursor or datasource, such that multiple underlying calls to cursor.fetchmany() may be necessary in order to accumulate enough objects in order to provide a unique collection of the requested size.

    • Parameters:

      strategy – a callable that will be applied to rows or objects being iterated, which should return an object that represents the unique value of the row. A Python set() is used to store these identities. If not passed, a default uniqueness strategy is used which may have been assembled by the source of this Result object.

  • method sqlalchemy.engine.Result.yield_per(num: int) → SelfResult

    Configure the row-fetching strategy to fetch num rows at a time.

    This impacts the underlying behavior of the result when iterating over the result object, or otherwise making use of methods such as Result.fetchone() that return one row at a time. Data from the underlying cursor or other data source will be buffered up to this many rows in memory, and the buffered collection will then be yielded out one row at a time or as many rows are requested. Each time the buffer clears, it will be refreshed to this many rows or as many rows remain if fewer remain.

    The Result.yield_per() method is generally used in conjunction with the Connection.execution_options.stream_results execution option, which will allow the database dialect in use to make use of a server side cursor, if the DBAPI supports a specific “server side cursor” mode separate from its default mode of operation.

    Tip

    Consider using the Connection.execution_options.yield_per execution option, which will simultaneously set Connection.execution_options.stream_results to ensure the use of server side cursors, as well as automatically invoke the Result.yield_per() method to establish a fixed row buffer size at once.

    The Connection.execution_options.yield_per execution option is available for ORM operations, with Session-oriented use described at Fetching Large Result Sets with Yield Per. The Core-only version which works with Connection is new as of SQLAlchemy 1.4.40.

    New in version 1.4.

    • Parameters:

      num – number of rows to fetch each time the buffer is refilled. If set to a value below 1, fetches all rows for the next buffer.

  1. See also
  3. [Using Server Side Cursors (a.k.a. stream results)](#engine-stream-results) - describes Core behavior for [Result.yield\_per()](#sqlalchemy.engine.Result.yield_per "sqlalchemy.engine.Result.yield_per")
  5. [Fetching Large Result Sets with Yield Per]($661bd2ffd6937693.md#orm-queryguide-yield-per) - in the [ORM Querying Guide]($86681c2576bdda58.md)

class sqlalchemy.engine.ScalarResult

A wrapper for a Result that returns scalar values rather than Row values.

The ScalarResult object is acquired by calling the Result.scalars() method.

A special limitation of ScalarResult is that it has no fetchone() method; since the semantics of fetchone() are that the None value indicates no more results, this is not compatible with ScalarResult since there is no way to distinguish between None as a row value versus None as an indicator. Use next(result) to receive values individually.

Members

all(), close(), closed, fetchall(), fetchmany(), first(), one(), one_or_none(), partitions(), unique(), yield_per()

Class signature

class sqlalchemy.engine.ScalarResult (sqlalchemy.engine.FilterResult)

class sqlalchemy.engine.MappingResult

A wrapper for a Result that returns dictionary values rather than Row values.

The MappingResult object is acquired by calling the Result.mappings() method.

Members

all(), close(), closed, columns(), fetchall(), fetchmany(), fetchone(), first(), keys(), one(), one_or_none(), partitions(), unique(), yield_per()

Class signature

class sqlalchemy.engine.MappingResult (sqlalchemy.engine._WithKeys, sqlalchemy.engine.FilterResult)

class sqlalchemy.engine.Row

Represent a single result row.

The Row object represents a row of a database result. It is typically associated in the 1.x series of SQLAlchemy with the CursorResult object, however is also used by the ORM for tuple-like results as of SQLAlchemy 1.4.

The Row object seeks to act as much like a Python named tuple as possible. For mapping (i.e. dictionary) behavior on a row, such as testing for containment of keys, refer to the Row._mapping attribute.

See also

Using SELECT Statements - includes examples of selecting rows from SELECT statements.

Changed in version 1.4: Renamed RowProxy to Row. Row is no longer a “proxy” object in that it contains the final form of data within it, and now acts mostly like a named tuple. Mapping-like functionality is moved to the Row._mapping attribute. See RowProxy is no longer a “proxy”; is now called Row and behaves like an enhanced named tuple for background on this change.

Members

_asdict(), _fields, _mapping, count, index, t, tuple()

Class signature

class sqlalchemy.engine.Row (sqlalchemy.engine._py_row.BaseRow, collections.abc.Sequence, typing.Generic)

  • method sqlalchemy.engine.Row._asdict() → Dict[str, Any]

    Return a new dict which maps field names to their corresponding values.

    This method is analogous to the Python named tuple ._asdict() method, and works by applying the dict() constructor to the Row._mapping attribute.

    New in version 1.4.

    See also

    Row._mapping

  • attribute sqlalchemy.engine.Row._fields

    Return a tuple of string keys as represented by this Row.

    The keys can represent the labels of the columns returned by a core statement or the names of the orm classes returned by an orm execution.

    This attribute is analogous to the Python named tuple ._fields attribute.

    New in version 1.4.

    See also

    Row._mapping

  • attribute sqlalchemy.engine.Row._mapping

    Return a RowMapping for this Row.

    This object provides a consistent Python mapping (i.e. dictionary) interface for the data contained within the row. The Row by itself behaves like a named tuple.

    See also

    Row._fields

    New in version 1.4.

  • attribute sqlalchemy.engine.Row.count

  • attribute sqlalchemy.engine.Row.index

  • attribute sqlalchemy.engine.Row.t

    a synonym for Row.tuple

    New in version 2.0.

    See also

    Result.t()

  • method sqlalchemy.engine.Row.tuple() → _TP

    Return a ‘tuple’ form of this Row.

    At runtime, this method returns “self”; the Row object is already a named tuple. However, at the typing level, if this Row is typed, the “tuple” return type will be a PEP 484 Tuple datatype that contains typing information about individual elements, supporting typed unpacking and attribute access.

    New in version 2.0.

    See also

    Result.tuples()

class sqlalchemy.engine.RowMapping

A Mapping that maps column names and objects to Row values.

The RowMapping is available from a Row via the Row._mapping attribute, as well as from the iterable interface provided by the MappingResult object returned by the Result.mappings() method.

RowMapping supplies Python mapping (i.e. dictionary) access to the contents of the row. This includes support for testing of containment of specific keys (string column names or objects), as well as iteration of keys, values, and items:

  1. for row in result:
  2.     if 'a' in row._mapping:
  3.         print("Column 'a': %s" % row._mapping['a'])
  5.     print("Column b: %s" % row._mapping[table.c.b])

New in version 1.4: The RowMapping object replaces the mapping-like access previously provided by a database result row, which now seeks to behave mostly like a named tuple.

Members

items(), keys(), values()

Class signature

class sqlalchemy.engine.RowMapping (sqlalchemy.engine._py_row.BaseRow, collections.abc.Mapping, typing.Generic)

class sqlalchemy.engine.TupleResult

A Result that’s typed as returning plain Python tuples instead of rows.

Since Row acts like a tuple in every way already, this class is a typing only class, regular Result is still used at runtime.

Class signature

class sqlalchemy.engine.TupleResult (sqlalchemy.engine.FilterResult, sqlalchemy.util.langhelpers.TypingOnly)