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移除书签Working with Engines and Connections
Recall from Engine Configuration that an is created via the create_engine() call:
The typical usage of is once per particular database URL, held globally for the lifetime of a single application process. A single Engine manages many individual 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 function, which represents just one connection resource - the 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 for details.
The most basic function of the Engine is to provide access to a , which can then invoke SQL statements. To emit a textual statement to the database looks like:
from sqlalchemy import textwith engine.connect() as connection:result = connection.execute(text("select username from users"))for row in result:print("username:", row["username"])
Above, the Engine.connect() method returns a 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 , 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 , 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 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 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 construct to indicate that we’d like to use textual SQL. The Connection.execute() method can of course accommodate more than that; see in the SQLAlchemy Unified Tutorial for a tutorial.
Using Transactions
Note
This section describes how to use transactions when working directly with Engine and objects. When using the SQLAlchemy ORM, the public API for transaction control is via the Session object, which makes usage of the object internally. See Managing Transactions for further information.
The Connection object always emits SQL statements within the context of a transaction block. The first time the 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 or Connection.rollback() methods are called. Subsequent to the transaction ending, the 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:
with engine.connect() as connection:connection.execute(some_table.insert(), {"x": 7, "y": "this is some data"})connection.execute(some_other_table.insert(), {"q": 8, "p": "this is some more data"})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 , 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 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:
with engine.connect() as connection:connection.execute("<some statement>")connection.commit() # commits "some statement"# new transaction startsconnection.execute("<some other statement>")connection.rollback() # rolls back "some other statement"# new transaction startsconnection.execute("<a third statement>")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 method is used, which returns a Transaction object which represents the DBAPI transaction. This object also supports explicit management via its own 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:
with engine.connect() as connection:with connection.begin():connection.execute(some_table.insert(), {"x": 7, "y": "this is some data"})connection.execute(some_other_table.insert(), {"q": 8, "p": "this is some more data"})# 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 object, rather than performing the two separate steps of Engine.connect() and ; the Engine.begin() method returns a special context manager that internally maintains both the context manager for the as well as the context manager for the Transaction normally returned by the method:
with engine.begin() as connection:connection.execute(some_table.insert(), {"x": 7, "y": "this is some data"})connection.execute(some_other_table.insert(), {"q": 8, "p": "this is some more data"})# transaction is committed, and Connection is released to the connection# pool
Tip
Within the Engine.begin() block, we can call upon the 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 until the block ends:
>>> from sqlalchemy import create_engine>>> e = create_engine("sqlite://", echo=True)>>> with e.begin() as conn:... conn.commit()... conn.begin()2021-11-08 09:49:07,517 INFO sqlalchemy.engine.Engine BEGIN (implicit)2021-11-08 09:49:07,517 INFO sqlalchemy.engine.Engine COMMITTraceback (most recent call last):...sqlalchemy.exc.InvalidRequestError: Can't operate on closed transaction insidecontext manager. Please complete the context manager before emittingfurther commands.
Mixing Styles
The “commit as you go” and “begin once” styles can be freely mixed within a single block, provided that the call to Connection.begin() does not conflict with the “autobegin” behavior. To accomplish this, should only be called either before any SQL statements have been emitted, or directly after a previous call to Connection.commit() or :
with engine.connect() as connection:with connection.begin():# run statements in a "begin once" blockconnection.execute(some_table.insert(), {"x": 7, "y": "this is some data"})# transaction is committed# run a new statement outside of a block. The connection# autobeginsconnection.execute(some_other_table.insert(), {"q": 8, "p": "this is some more data"})# commit explicitlyconnection.commit()# can use a "begin once" block herewith connection.begin():# run more statementsconnection.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 , 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 , the isolation level can be set for the duration of that Connection object using the 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:
# possible values for Connection.execution_options(isolation_level="<value>")"AUTOCOMMIT""READ COMMITTED""READ UNCOMMITTED""REPEATABLE READ""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:
with engine.connect().execution_options(isolation_level="REPEATABLE READ") as connection:with connection.begin():connection.execute("<statement>")
Tip
The return value of the method is the same Connection object upon which the method was called, meaning, it modifies the state of the 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 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 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 parameter to create_engine():
from sqlalchemy import create_engineeng = create_engine("postgresql://scott:tiger@localhost/test", isolation_level="REPEATABLE READ")
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 parameter to create_engine() or the 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:
from sqlalchemy import create_engineeng = create_engine("postgresql+psycopg2://scott:tiger@localhost/test",execution_options={"isolation_level": "REPEATABLE READ"},)
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 , 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 , 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:
from sqlalchemy import create_engineeng = create_engine("postgresql+psycopg2://scott:tiger@localhost/test")autocommit_engine = eng.execution_options(isolation_level="AUTOCOMMIT")
Above, the 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
PostgreSQL Transaction Isolation
SQL Server Transaction Isolation
Setting Transaction Isolation Levels / DBAPI AUTOCOMMIT - for the ORM
- 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 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:
with engine.connect() as connection:connection.execution_options(isolation_level="AUTOCOMMIT")connection.execute("<statement>")connection.execute("<statement>")
Above illustrates normal usage of “DBAPI autocommit” mode. There is no need to make use of methods such as or Connection.commit(), as all statements are committed to the database immediately. When the block ends, the 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 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:
with engine.connect() as connection:connection = connection.execution_options(isolation_level="AUTOCOMMIT")# this begin() does not affect the DBAPI connection, isolation stays at AUTOCOMMITwith connection.begin() as trans:connection.execute("<statement>")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:
INFO sqlalchemy.engine.Engine BEGIN (implicit)...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 is being called after autobegin has already occurred:
with engine.connect() as connection:connection = connection.execution_options(isolation_level="AUTOCOMMIT")# "transaction" is autobegin (but has no effect due to autocommit)connection.execute("<statement>")# this will raise; "transaction" is already begunwith connection.begin() as trans: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 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 . 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 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 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():
# if we wanted to flip autocommit on and off on a single connection/# which... we usually don't.with engine.connect() as connection:connection.execution_options(isolation_level="AUTOCOMMIT")# run statement(s) in autocommit modeconnection.execute("<statement>")# "commit" the autobegun "transaction"connection.commit()# switch to default isolation levelconnection.execution_options(isolation_level=connection.default_isolation_level)# use a begin blockwith connection.begin() as trans:connection.execute("<statement>")
Above, to manually revert the isolation level we made use of 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
# use an autocommit blockwith engine.connect().execution_options(isolation_level="AUTOCOMMIT") as connection:# run statement in autocommit modeconnection.execute("<statement>")# use a regular blockwith engine.begin() as connection:connection.execute("<statement>")
To sum up:
“DBAPI level autocommit” isolation level is entirely independent of the object’s notion of “begin” and “commit”
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 and Connection.execution_options.yield_per execution options provide access to “server side cursors” on a per- or per-statement basis. Similar options exist when using an ORM Session as well.
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 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 or on a statement using the Executable.execution_options() method.
New in version 1.4.40: 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 .
Using this option is equivalent to manually setting the Connection.execution_options.stream_results option, described in the next section, and then invoking the 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 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 method, if used, will be made equal to this integer size as well.
These three behaviors are illustrated in the example below:
with engine.connect() as conn:with conn.execution_options(yield_per=100).execute(text("select * from table")) as result:for partition in result.partitions():# partition is an iterable that will be at most 100 itemsfor row in partition: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 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 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 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 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 for further background on using Connection.execution_options.yield_per with the ORM.
New in version 1.4.40: Added 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 option may be used, which like Connection.execution_options.yield_per may be called on the object or the statement object.
When a Result object delivered using the 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:
with engine.connect() as conn:with conn.execution_options(stream_results=True, max_row_buffer=100).execute(text("select * from table")) as result:for row in result:print(f"{row}")
While the option may be combined with use of the Result.partitions() method, a specific partition size should be passed to 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 method.
See also
Fetching Large Result Sets with Yield Per - in the
To support multi-tenancy applications that distribute common sets of tables into multiple schemas, the execution option may be used to repurpose a set of Table objects to render under different schema names without any changes.
Given a table:
user_table = Table("user",metadata_obj,Column("id", Integer, primary_key=True),Column("name", String(50)),)
The “schema” of this as defined by the Table.schema attribute is None. The can specify that all Table objects with a schema of None would instead render the schema as user_schema_one:
connection = engine.connect().execution_options(schema_translate_map={None: "user_schema_one"})result = connection.execute(user_table.select())
The above code will invoke SQL on the database of the form:
SELECT user_schema_one.user.id, user_schema_one.user.name FROMuser_schema_one.user
That is, the schema name is substituted with our translated name. The map can specify any number of target->destination schemas:
connection = engine.connect().execution_options(schema_translate_map={None: "user_schema_one", # no schema name -> "user_schema_one""special": "special_schema", # schema="special" becomes "special_schema""public": None, # Table objects with schema="public" will render with no schema})
The parameter affects all DDL and SQL constructs generated from the SQL expression language, as derived from the Table or objects. It does not impact literal string SQL used via the text() construct nor via plain strings passed to .
The feature takes effect only in those cases where the name of the schema is derived directly from that of a Table or ; 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 are called, and it takes effect when using table reflection given a Table object. However it does not affect the operations present on the 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 , then pass that engine to the Session. The uses a new Connection for each transaction:
schema_engine = engine.execution_options(schema_translate_map={...})session = Session(schema_engine)...
Warning
When using the ORM 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 with multiple schema_translate_map configurations, the Horizontal Sharding extension may be used. See the example at .
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 .
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:
def run_my_statement(connection, parameter):stmt = select(table)stmt = stmt.where(table.c.col == parameter)stmt = stmt.order_by(table.c.id)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:
engine = create_engine("postgresql+psycopg2://scott:tiger@localhost/test", query_cache_size=1200)
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 for background on logging configuration.
As an example, we will examine the logging produced by the following program:
from sqlalchemy import Columnfrom sqlalchemy import create_enginefrom sqlalchemy import ForeignKeyfrom sqlalchemy import Integerfrom sqlalchemy import selectfrom sqlalchemy import Stringfrom sqlalchemy.ext.declarative import declarative_basefrom sqlalchemy.orm import relationshipfrom sqlalchemy.orm import SessionBase = declarative_base()class A(Base):__tablename__ = "a"id = Column(Integer, primary_key=True)data = Column(String)bs = relationship("B")class B(Base):__tablename__ = "b"id = Column(Integer, primary_key=True)a_id = Column(ForeignKey("a.id"))data = Column(String)e = create_engine("sqlite://", echo=True)Base.metadata.create_all(e)s = Session(e)s.add_all([A(bs=[B(), B(), B()]), A(bs=[B(), B(), B()]), A(bs=[B(), B(), B()])])s.commit()for a_rec in s.scalars(select(A)):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:
INFO sqlalchemy.engine.Engine PRAGMA temp.table_info("a")INFO sqlalchemy.engine.Engine [raw sql] ()INFO sqlalchemy.engine.Engine PRAGMA main.table_info("b")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 . 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:
INFO sqlalchemy.engine.EngineCREATE TABLE a (id INTEGER NOT NULL,data VARCHAR,PRIMARY KEY (id))INFO sqlalchemy.engine.Engine [no key 0.00007s] ()INFO sqlalchemy.engine.EngineCREATE TABLE b (id INTEGER NOT NULL,a_id INTEGER,data VARCHAR,PRIMARY KEY (id),FOREIGN KEY(a_id) REFERENCES a (id))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 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:
.. sourcecode:: sql
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 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 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:
INFO sqlalchemy.engine.Engine SELECT a.id AS a_id, a.data AS a_dataFROM aINFO sqlalchemy.engine.Engine [generated in 0.00009s] ()INFO sqlalchemy.engine.Engine SELECT b.id AS b_id, b.a_id AS b_a_id, b.data AS b_dataFROM bWHERE ? = b.a_idINFO sqlalchemy.engine.Engine [generated in 0.00010s] (1,)INFO sqlalchemy.engine.Engine SELECT b.id AS b_id, b.a_id AS b_a_id, b.data AS b_dataFROM bWHERE ? = b.a_idINFO sqlalchemy.engine.Engine [cached since 0.0005922s ago] (2,)INFO sqlalchemy.engine.Engine SELECT b.id AS b_id, b.a_id AS b_a_id, b.data AS b_dataFROM bWHERE ? = 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 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 or Connection, as well as when using the ORM method for SQLAlchemy-2.0 style invocations. For example, to run a series of SQL statements and have them cached in a particular dictionary:
my_cache = {}with engine.connect().execution_options(compiled_cache=my_cache) as conn: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:
# disable caching for this connectionwith engine.connect().execution_options(compiled_cache=None) as conn: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 . 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:
from sqlalchemy.engine.default import DefaultDialectclass MyDialect(DefaultDialect):supports_statement_cache = True
The flag needs to be applied to all subclasses of the dialect as well:
class MyDBAPIForMyDialect(MyDialect):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:
# pre 1.4 style codedef limit_clause(self, select, **kw):text = ""if select._limit is not None:text += " \n LIMIT %d" % (select._limit,)if select._offset is not None:text += " \n OFFSET %d" % (select._offset,)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 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 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:
# 1.4 cache-compatible codedef limit_clause(self, select, **kw):text = ""limit_clause = select._limit_clauseoffset_clause = select._offset_clauseif select._simple_int_clause(limit_clause):text += " \n LIMIT %s" % (self.process(limit_clause.render_literal_execute(), **kw))elif limit_clause is not None:# assuming the DB doesn't support SQL expressions for LIMIT.# Otherwise render here normallyraise exc.CompileError("dialect 'mydialect' can only render simple integers for LIMIT")if select._simple_int_clause(offset_clause):text += " \n OFFSET %s" % (self.process(offset_clause.render_literal_execute(), **kw))elif offset_clause is not None:# assuming the DB doesn't support SQL expressions for OFFSET.# Otherwise render here normallyraise exc.CompileError("dialect 'mydialect' can only render simple integers for OFFSET")return text
The approach above will generate a compiled SELECT statement that looks like:
SELECT x FROM yLIMIT __[POSTCOMPILE_param_1]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 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 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 ORM option in order to provide a generic SQL fragment.
Synopsis
Lambda statements are constructed using the 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 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:
from sqlalchemy import lambda_stmtdef run_my_statement(connection, parameter):stmt = lambda_stmt(lambda: select(table))stmt += lambda s: s.where(table.c.col == parameter)stmt += lambda s: s.order_by(table.c.id)with engine.connect() as conn: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 constructs are the prime use case for lambda_stmt(), DML statements such as and update() are equally usable:
``` def upd(id_, newname):
stmt = lambda_stmt(lambda: users.update())stmt += lambda s: s.values(name=newname)stmt += lambda s: s.where(users.c.id == id_)return stmt
with engine.begin() as conn:conn.execute(upd(7, "foo"))```
ORM use cases directly supported as well - the can accommodate ORM functionality completely and used directly with Session.execute():
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:
>>> def my_stmt(x, y):... stmt = lambda_stmt(lambda: select(func.max(x, y)))... return stmt>>> engine = create_engine("sqlite://", echo=True)>>> with engine.connect() as conn:... print(conn.scalar(my_stmt(5, 10)))... print(conn.scalar(my_stmt(12, 8)))SELECT max(?, ?) AS max_1[generated in 0.00057s] (5, 10)10SELECT max(?, ?) AS max_1[cached since 0.002059s ago] (12, 8)12
Above, extracted the values of
xandyfrom the closure of the lambda that is generated each timemy_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:
def my_stmt(parameter, thing=False):stmt = lambda_stmt(lambda: select(table))stmt += (lambda s: s.where(table.c.x > parameter)if thingelse s.where(table.c.y == parameter))return stmt# **Do** do this:def my_stmt(parameter, thing=False):stmt = lambda_stmt(lambda: select(table))if thing:stmt += lambda s: s.where(table.c.x > parameter)stmt += lambda s: s.where(table.c.y == parameter)return stmt```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 should normally raise an error if this is attempted:
>>> def my_stmt(x, y):... def get_x():... return x...... def get_y():... return y...... stmt = lambda_stmt(lambda: select(func.max(get_x(), get_y())))... return stmt>>> with engine.connect() as conn:... print(conn.scalar(my_stmt(5, 10)))Traceback (most recent call last):# ...sqlalchemy.exc.InvalidRequestError: Can't invoke Python callable get_x()inside of lambda expression argument at<code object <lambda> at 0x7fed15f350e0, file "<stdin>", line 6>;lambda SQL constructs should not invoke functions from closure variablesto produce literal values since the lambda SQL system normally extractsbound values without actually invoking the lambda or any functions within it.
Above, the use of
get_x()andget_y(), if they are necessary, should occur outside of the lambda and assigned to a local closure variable:>>> def my_stmt(x, y):... def get_x():... return x...... def get_y():... return y...... x_param, y_param = get_x(), get_y()... stmt = lambda_stmt(lambda: select(func.max(x_param, y_param)))... 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:
>>> class Foo:... def __init__(self, x, y):... self.x = x... self.y = y>>> def my_stmt(foo):... stmt = lambda_stmt(lambda: select(func.max(foo.x, foo.y)))... return stmt>>> with engine.connect() as conn:... print(conn.scalar(my_stmt(Foo(5, 10))))Traceback (most recent call last):# ...sqlalchemy.exc.InvalidRequestError: Closure variable named 'foo' inside oflambda callable <code object <lambda> at 0x7fed15f35450, file"<stdin>", line 2> does not refer to a cacheable SQL element, and alsodoes not appear to be serving as a SQL literal bound value based on thedefault SQL expression returned by the function. This variable needs toremain outside the scope of a SQL-generating lambda so that a proper cachekey may be generated from the lambda's state. Evaluate this variableoutside of the lambda, set track_on=[<elements>] to explicitly selectclosure elements to track, or set track_closure_variables=False to excludeclosure variables from being part of the cache key.
The above error indicates that will not assume that the
Fooobject passed in will continue to behave the same in all cases. It also won’t assume it can useFooas part of the cache key by default; if it were to use theFooobject as part of the cache key, if there were many differentFooobjects 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
fooinside of the lambda, and refer to it outside instead:>>> def my_stmt(foo):... x_param, y_param = foo.x, foo.y... stmt = lambda_stmt(lambda: select(func.max(x_param, y_param)))... 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=Falsewhich will disable any tracking of closure variables other than those used for bound parameters:>>> def my_stmt(foo):... stmt = lambda_stmt(... lambda: select(func.max(foo.x, foo.y)), track_closure_variables=False... )... return stmt
There is also the option to add objects to the element to explicitly form part of the cache key, using the
track_onparameter; 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 thefoovariable, whereas the second segment will explicitly trackselfas part of the cache key:>>> def my_stmt(self, foo):... stmt = lambda_stmt(... lambda: select(*self.column_expressions), track_closure_variables=False... )... stmt = stmt.add_criteria(lambda: self.where_criteria, track_on=[self])... return stmt
Using
track_onmeans 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:
>>> from sqlalchemy import select, column>>> stmt = select(column("q"))>>> cache_key = stmt._generate_cache_key()>>> print(cache_key) # somewhat paraphrasedCacheKey(key=('0',<class 'sqlalchemy.sql.selectable.Select'>,'_raw_columns',(('1',<class 'sqlalchemy.sql.elements.ColumnClause'>,'name','q','type',(<class 'sqlalchemy.sql.sqltypes.NullType'>,),),),# a few more elements are here, and many more for a more# complicated SELECT statement),)
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:
>>> from sqlalchemy import lambda_stmt>>> stmt = lambda_stmt(lambda: select(column("q")))>>> cache_key = stmt._generate_cache_key()>>> print(cache_key)CacheKey(key=(<code object <lambda> at 0x7fed1617c710, file "<stdin>", line 1>,<class 'sqlalchemy.sql.lambdas.StatementLambdaElement'>,),)
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:
>>> def my_stmt(parameter):... col = column("q")... stmt = lambda_stmt(lambda: select(col))... stmt += lambda s: s.where(col == parameter)... return stmt
The above 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:
>>> stmt = my_stmt(5)>>> key = stmt._generate_cache_key()>>> print(key)CacheKey(key=(<code object <lambda> at 0x7f07323c50e0, file "<stdin>", line 3>,('0',<class 'sqlalchemy.sql.elements.ColumnClause'>,'name','q','type',(<class 'sqlalchemy.sql.sqltypes.NullType'>,),),<code object <lambda> at 0x7f07323c5190, file "<stdin>", line 4>,<class 'sqlalchemy.sql.lambdas.LinkedLambdaElement'>,('0',<class 'sqlalchemy.sql.elements.ColumnClause'>,'name','q','type',(<class 'sqlalchemy.sql.sqltypes.NullType'>,),),('0',<class 'sqlalchemy.sql.elements.ColumnClause'>,'name','q','type',(<class 'sqlalchemy.sql.sqltypes.NullType'>,),),),)
The second part of the cache key has retrieved the bound parameters that will be used when the statement is invoked:
>>> key.bindparams[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 attribute provided by the DBAPI for most backends; RETURNING may now be used for all 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 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:
INSERT INTO a (data, x, y) VALUES (%(data)s, %(x)s, %(y)s) RETURNING a.id
into a “batched” form as:
INSERT INTO a (data, x, y) VALUES(%(data_0)s, %(x_0)s, %(y_0)s),(%(data_1)s, %(x_1)s, %(y_1)s),(%(data_2)s, %(x_2)s, %(y_2)s),...(%(data_78)s, %(x_78)s, %(y_78)s)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 . The separate calls to cursor.execute() are logged individually and also individually passed along to event listeners such as ConnectionEvents.before_cursor_execute() (see 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 method, as well as throughout Core and ORM for any similar method including ORM methods like Session.execute() and asyncio methods like and AsyncSession.execute(). The ORM itself also makes use of the feature within the process when inserting many rows, that is, for large numbers of objects added to a Session using methods such as 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.
To disable the “insertmanyvalues” feature for a given backend for an Engine overall, pass the parameter as False to create_engine():
engine = create_engine("mariadb+mariadbconnector://scott:tiger@host/db", use_insertmanyvalues=False)
The feature can also be disabled from being used implicitly for a particular object by passing the Table.implicit_returning parameter as False:
t = Table("t",metadata,Column("id", Integer, primary_key=True),Column("x", Integer),implicit_returning=False,)
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 ).
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 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 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:
e = create_engine("sqlite://", insertmanyvalues_page_size=100)
The batch size may also be affected on a per statement basis using the execution option, such as per execution:
with e.begin() as conn:result = conn.execute(table.insert().returning(table.c.id),parameterlist,execution_options={"insertmanyvalues_page_size": 100},)
Or configured on the statement itself:
stmt = (table.insert().returning(table.c.id).execution_options(insertmanyvalues_page_size=100))with e.begin() as conn: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 . 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:
INSERT INTO a (data, x, y) VALUES (?, ?, ?), ... 795 characters truncated ... (?, ?, ?), (?, ?, ?) RETURNING id[generated in 0.00177s (insertmanyvalues)] ('d0', 0, 0, 'd1', ...INSERT INTO a (data, x, y) VALUES (?, ?, ?), ... 795 characters truncated ... (?, ?, ?), (?, ?, ?) RETURNING id[insertmanyvalues batch 2 of 10] ('d100', 100, 1000, 'd101', ......INSERT INTO a (data, x, y) VALUES (?, ?, ?), ... 795 characters truncated ... (?, ?, ?), (?, ?, ?) RETURNING id[insertmanyvalues batch 10 of 10] ('d900', 900, 9000, 'd901', ...
Upsert Support
The PostgreSQL, SQLite, and MariaDB dialects offer backend-specific “upsert” constructs , insert() and , 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 object is still resident in memory. When an Engine is garbage collected, its connection pool is no longer referred to by that , 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 .
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 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 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 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, 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 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 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 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 for guidelines on how to disable pooling.
See also
The introduction on using 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 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 ) without any intervention from the text() construct, the method may be used:
with engine.connect() as conn: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 object directly. It is present using the Connection.connection attribute:
connection = engine.connect()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 object, it is best to make use of the Connection object for most features such as transaction control as well as calling the 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 , a DBAPI connection is also available without the need to procure a Connection first, using the method of Engine:
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 back to the engine’s connection pool:
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:
connection = engine.raw_connection()try:cursor_obj = connection.cursor()cursor_obj.callproc("my_procedure", ["x", "y", "z"])results = list(cursor_obj.fetchall())cursor_obj.close()connection.commit()finally: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:
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:
connection = engine.raw_connection()try:cursor_obj = connection.cursor()cursor_obj.execute("select * from table1; select * from table2")results_one = cursor_obj.fetchall()cursor_obj.nextset()results_two = cursor_obj.fetchall()cursor_obj.close()finally: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:
Create a package called
foodialect.The package should have a module containing the dialect class, which is typically a subclass of . In this example let’s say it’s called
FooDialectand its module is accessed viafoodialect.dialect.The entry point can be established in
setup.cfgas follows:[options.entry_points]sqlalchemy.dialects =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:
[options.entry_points]sqlalchemy.dialectsmysql.foodialect = foodialect.dialect:FooDialect
The above entrypoint would then be accessed as create_engine("mysql+foodialect://").
SQLAlchemy also allows a dialect to be registered within the current process, bypassing the need for separate installation. Use the register() function as follows:
from sqlalchemy.dialects import registryregistry.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
class sqlalchemy.engine.Connection
Provides high-level functionality for a wrapped DB-API connection.
The Connection object is procured by calling the 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
, begin(), , begin_twophase(), , closed, , connection, , detach(), , execute(), , get_execution_options(), , get_nested_transaction(), , in_nested_transaction(), , info, , invalidated, , scalar(), , 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.interfaces.ConnectionEventsTarget, sqlalchemy.inspection.Inspectable)
method sqlalchemy.engine.Connection.__init__(engine: , connection: Optional[PoolProxiedConnection] = None, _has_events: Optional[bool] = None, _allow_revalidate: bool = True, _allow_autobegin: bool = True)
Construct a new Connection.
method begin() → RootTransaction
Begin a transaction prior to autobegin occurring.
E.g.:
with engine.connect() as conn:with conn.begin() as trans:conn.execute(table.insert(), {"username": "sandy"})
The returned object is an instance of . This object represents the “scope” of the transaction, which completes when either the Transaction.rollback() or 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 event at a specific time, or to organize code within the scope of a connection checkout in terms of context managed blocks, such as:
with engine.connect() as conn:with conn.begin():conn.execute(...)conn.execute(...)with conn.begin():conn.execute(...)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:
with engine.connect() as conn:conn.execute(...)conn.execute(...)conn.commit()conn.execute(...)conn.execute(...)conn.commit()
From a database point of view, the 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
Connection.begin_nested() - use a SAVEPOINT
- use a two phase /XID transaction
Engine.begin() - context manager available from
method sqlalchemy.engine.Connection.begin_nested() →
Begin a nested transaction (i.e. SAVEPOINT) and return a transaction handle that controls the scope of the SAVEPOINT.
E.g.:
with engine.begin() as connection:with connection.begin_nested():connection.execute(table.insert(), {"username": "sandy"})
The returned object is an instance of NestedTransaction, which includes transactional methods 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 object and is generated automatically. Like any other Transaction, the 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.:
from sqlalchemy import excwith engine.begin() as connection:trans = connection.begin_nested()try:connection.execute(table.insert(), {"username": "sandy"})trans.commit()except exc.IntegrityError: # catch for duplicate usernametrans.rollback() # rollback to savepoint# outer transaction continuesconnection.execute( ... )
If Connection.begin_nested() is called without first calling or Engine.begin(), the object will “autobegin” the outer transaction first. This outer transaction may be committed using “commit-as-you-go” style, e.g.:
with engine.connect() as connection: # begin() wasn't calledwith connection.begin_nested(): will auto-"begin()" firstconnection.execute( ... )# savepoint is releasedconnection.execute( ... )# explicitly commit outer transactionconnection.commit()# 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
Using SAVEPOINT - ORM support for SAVEPOINT
method 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 , which in addition to the methods provided by Transaction, also provides a method.
Parameters:
xid – the two phase transaction id. If not supplied, a random id will be generated.
See also[Connection.begin()](#sqlalchemy.engine.Connection.begin "sqlalchemy.engine.Connection.begin")[Connection.begin\_twophase()](#sqlalchemy.engine.Connection.begin_twophase "sqlalchemy.engine.Connection.begin_twophase")
method sqlalchemy.engine.Connection.close() → None
Close this .
This results in a release of the underlying database resources, that is, the DBAPI connection referenced internally. The DBAPI connection is typically restored back to the connection-holding Pool referenced by the that produced this Connection. Any transactional state present on the DBAPI connection is also unconditionally released via the DBAPI connection’s
rollback()method, regardless of any object that may be outstanding with regards to this Connection.This has the effect of also calling if any transaction is in place.
After Connection.close() is called, the is permanently in a closed state, and will allow no further operations.
attribute sqlalchemy.engine.Connection.closed
Return True if this connection is closed.
method commit() → None
Commit the transaction that is currently in progress.
This method commits the current transaction if one has been started. If no transaction was started, the method has no effect, assuming the connection is in a non-invalidated state.
A transaction is begun on a Connection automatically whenever a statement is first executed, or when the method is called.
Note
The Connection.commit() method only acts upon the primary database transaction that is linked to the 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 on the NestedTransaction that is returned by the method itself.
attribute sqlalchemy.engine.Connection.connection
The underlying DB-API connection managed by this Connection.
This is a SQLAlchemy connection-pool proxied connection which then has the attribute
_ConnectionFairy.dbapi_connectionthat refers to the actual driver connection.See also
attribute sqlalchemy.engine.Connection.default_isolation_level
The default isolation level assigned to this .
This is the isolation level setting that the Connection has when first procured via the method. This level stays in place until the Connection.execution_options.isolation_level is used to change the setting on a per- basis.
Unlike Connection.get_isolation_level(), this attribute is set ahead of time from the first connection procured by the dialect, so SQL query is not invoked when this accessor is called.
New in version 0.9.9.
See also
- view current level
create_engine.isolation_level - set per isolation level
Connection.execution_options.isolation_level - set per isolation level
method sqlalchemy.engine.Connection.detach() → None
Detach the underlying DB-API connection from its connection pool.
E.g.:
with engine.connect() as conn:conn.detach()conn.execute(text("SET search_path TO schema1, schema2"))# work with connection# connection is fully closed (since we used "with:", can# also call .close())
This instance will remain usable. When closed (or exited from a context manager context as above), the DB-API connection will be literally closed and not returned to its originating pool.
This method can be used to insulate the rest of an application from a modified state on a connection (such as a transaction isolation level or similar).
method sqlalchemy.engine.Connection.exec_driver_sql(statement: str, parameters: Optional[_DBAPIAnyExecuteParams] = None, execution_options: Optional[CoreExecuteOptionsParameter] = None) → [Any]
Executes a SQL statement construct and returns a CursorResult.
Parameters:
statement – The statement str to be executed. Bound parameters must use the underlying DBAPI’s paramstyle, such as “qmark”, “pyformat”, “format”, etc.
parameters –
represent bound parameter values to be used in the execution. The format is one of: a dictionary of named parameters, a tuple of positional parameters, or a list containing either dictionaries or tuples for multiple-execute support.
E.g. multiple dictionaries:
conn.exec_driver_sql("INSERT INTO table (id, value) VALUES (%(id)s, %(value)s)",[{"id":1, "value":"v1"}, {"id":2, "value":"v2"}])
Single dictionary:
conn.exec_driver_sql("INSERT INTO table (id, value) VALUES (%(id)s, %(value)s)",dict(id=1, value="v1"))
Single tuple:
conn.exec_driver_sql("INSERT INTO table (id, value) VALUES (?, ?)",(1, 'v1'))
Note
The method does not participate in the ConnectionEvents.before_execute() and events. To intercept calls to Connection.exec_driver_sql(), use and ConnectionEvents.after_cursor_execute().
See also
method sqlalchemy.engine.Connection.execute(statement: , parameters: Optional[_CoreAnyExecuteParams] = None, *, execution_options: Optional[CoreExecuteOptionsParameter] = None) → CursorResult[Any]
Executes a SQL statement construct and returns a .
Parameters:
statement –
The statement to be executed. This is always an object that is in both the ClauseElement and hierarchies, including:
, Update,
TextClause and
DDL and objects which inherit from
method sqlalchemy.engine.Connection.execution_options(**opt: Any) →
Set non-SQL options for the connection which take effect during execution.
This method modifies this Connection in-place; the return value is the same object upon which the method is called. Note that this is in contrast to the behavior of the
execution_optionsmethods on other objects such as Engine.execution_options() and . The rationale is that many such execution options necessarily modify the state of the base DBAPI connection in any case so there is no feasible means of keeping the effect of such an option localized to a “sub” connection.Changed in version 2.0: The Connection.execution_options() method, in contrast to other objects with this method, modifies the connection in-place without creating copy of it.
As discussed elsewhere, the method accepts any arbitrary parameters including user defined names. All parameters given are consumable in a number of ways including by using the Connection.get_execution_options() method. See the examples at and Engine.execution_options().
The keywords that are currently recognized by SQLAlchemy itself include all those listed under , as well as others that are specific to Connection.
Parameters:
compiled_cache –
Available on: , Engine.
A dictionary where objects will be cached when the Connection compiles a clause expression into a object. This dictionary will supersede the statement cache that may be configured on the Engine itself. If set to None, caching is disabled, even if the engine has a configured cache size.
Note that the ORM makes use of its own “compiled” caches for some operations, including flush operations. The caching used by the ORM internally supersedes a cache dictionary specified here.
logging_token –
Available on: , Engine, .
Adds the specified string token surrounded by brackets in log messages logged by the connection, i.e. the logging that’s enabled either via the create_engine.echo flag or via the
logging.getLogger("sqlalchemy.engine")logger. This allows a per-connection or per-sub-engine token to be available which is useful for debugging concurrent connection scenarios.New in version 1.4.0b2.
See also
- usage example
create_engine.logging_name - adds a name to the name used by the Python logger object itself.
isolation_level –
Available on: , Engine.
Set the transaction isolation level for the lifespan of this object. Valid values include those string values accepted by the create_engine.isolation_level parameter passed to . These levels are semi-database specific; see individual dialect documentation for valid levels.
The isolation level option applies the isolation level by emitting statements on the DBAPI connection, and necessarily affects the original Connection object overall. The isolation level will remain at the given setting until explicitly changed, or when the DBAPI connection itself is released to the connection pool, i.e. the method is called, at which time an event handler will emit additional statements on the DBAPI connection in order to revert the isolation level change.
Note
The
isolation_levelexecution option may only be established before the Connection.begin() method is called, as well as before any SQL statements are emitted which would otherwise trigger “autobegin”, or directly after a call to or Connection.rollback(). A database cannot change the isolation level on a transaction in progress.Note
The
isolation_levelexecution option is implicitly reset if the is invalidated, e.g. via the Connection.invalidate() method, or if a disconnection error occurs. The new connection produced after the invalidation will not have the selected isolation level re-applied to it automatically.See also
Connection.get_isolation_level() - view current level
no_parameters –
Available on: , Executable.
When
True, if the final parameter list or dictionary is totally empty, will invoke the statement on the cursor ascursor.execute(statement), not passing the parameter collection at all. Some DBAPIs such as psycopg2 and mysql-python consider percent signs as significant only when parameters are present; this option allows code to generate SQL containing percent signs (and possibly other characters) that is neutral regarding whether it’s executed by the DBAPI or piped into a script that’s later invoked by command line tools.stream_results –
Available on: , Executable.
Indicate to the dialect that results should be “streamed” and not pre-buffered, if possible. For backends such as PostgreSQL, MySQL and MariaDB, this indicates the use of a “server side cursor” as opposed to a client side cursor. Other backends such as that of Oracle may already use server side cursors by default.
The usage of is usually combined with setting a fixed number of rows to to be fetched in batches, to allow for efficient iteration of database rows while at the same time not loading all result rows into memory at once; this can be configured on a Result object using the method, after execution has returned a new Result. If is not used, the Connection.execution_options.stream_results mode of operation will instead use a dynamically sized buffer which buffers sets of rows at a time, growing on each batch based on a fixed growth size up until a limit which may be configured using the parameter.
When using the ORM to fetch ORM mapped objects from a result, Result.yield_per() should always be used with , so that the ORM does not fetch all rows into new ORM objects at once.
For typical use, the Connection.execution_options.yield_per execution option should be preferred, which sets up both and Result.yield_per() at once. This option is supported both at a core level by as well as by the ORM
Session; the latter is described at Fetching Large Result Sets with Yield Per.See also
- background on Connection.execution_options.stream_results
Connection.execution_options.yield_per
- in the ORM Querying Guide describing the ORM version of
yield_permax_row_buffer –
Available on: , Executable. Sets a maximum buffer size to use when the execution option is used on a backend that supports server side cursors. The default value if not specified is 1000.
See also
yield_per –
Available on: Connection, . Integer value applied which will set the Connection.execution_options.stream_results execution option and invoke automatically at once. Allows equivalent functionality as is present when using this parameter with the ORM.
New in version 1.4.40.
See also
Using Server Side Cursors (a.k.a. stream results) - background and examples on using server side cursors with Core.
- in the ORM Querying Guide describing the ORM version of
yield_perinsertmanyvalues_page_size –
Available on: , Engine. Number of rows to format into an INSERT statement when the statement uses “insertmanyvalues” mode, which is a paged form of bulk insert that is used for many backends when using execution typically in conjunction with RETURNING. Defaults to 1000. May also be modified on a per-engine basis using the create_engine.insertmanyvalues_page_size parameter.
New in version 2.0.
See also
schema_translate_map –
Available on: Connection, , Executable.
A dictionary mapping schema names to schema names, that will be applied to the element of each Table encountered when SQL or DDL expression elements are compiled into strings; the resulting schema name will be converted based on presence in the map of the original name.
New in version 1.1.
See also
See also[Engine.execution\_options()](#sqlalchemy.engine.Engine.execution_options "sqlalchemy.engine.Engine.execution_options")[Executable.execution\_options()]($75ae4d183452a412.md#sqlalchemy.sql.expression.Executable.execution_options "sqlalchemy.sql.expression.Executable.execution_options")[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
method sqlalchemy.engine.Connection.get_isolation_level() → Literal[‘SERIALIZABLE’, ‘REPEATABLE READ’, ‘READ COMMITTED’, ‘READ UNCOMMITTED’, ‘AUTOCOMMIT’]
Return the current isolation level assigned to this .
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- 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
- view default level
create_engine.isolation_level - set per isolation level
Connection.execution_options.isolation_level - set per isolation level
method sqlalchemy.engine.Connection.get_nested_transaction() → Optional[]
Return the current nested transaction in progress, if any.
New in version 1.4.
method sqlalchemy.engine.Connection.get_transaction() → Optional[]
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 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 , 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 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 method or similar), this Connection will attempt to procure a new DBAPI connection using the services of the 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 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 object is ended, by calling the Transaction.rollback() method; until that point, any attempt at continuing to use the 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 method, just like auto-invalidation, will at the connection pool level invoke the PoolEvents.invalidate() event.
Parameters:
exception – an optional
Exceptioninstance that’s the reason for the invalidation. is passed along to event handlers and logging functions.
See also[More on Invalidation]($ba04c3bd42280074.md#pool-connection-invalidation)
attribute 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 automatically whenever a statement is first executed, or when the Connection.begin() method is called.
Note
The 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 method; for control of a SAVEPOINT, call NestedTransaction.rollback() on the that is returned by the Connection.begin_nested() method itself.
method 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 method after invoking the Connection.execute() method. Parameters are equivalent.
Returns:
a scalar Python value representing the first column of the first row returned.
method scalars(statement: Executable, parameters: Optional[_CoreSingleExecuteParams] = None, *, execution_options: Optional[CoreExecuteOptionsParameter] = None) → [Any]
Executes and returns a scalar result set, which yields scalar values from the first column of each row.
This method is equivalent to calling Connection.execute() to receive a object, then invoking the Result.scalars() method to produce a instance.
Returns:
New in version 1.4.24.
method schema_for_object(obj: HasSchemaAttr) → Optional[str]
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 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 that attaches a logger to an Engine object might look like:
import loggingfrom sqlalchemy.engine import CreateEnginePluginfrom sqlalchemy import eventclass LogCursorEventsPlugin(CreateEnginePlugin):def __init__(self, url, kwargs):# consume the parameter "log_cursor_logging_name" from the# URL querylogging_name = url.query.get("log_cursor_logging_name", "log_cursor")self.log = logging.getLogger(logging_name)def update_url(self, url):"update the URL to one that no longer includes our parameters"return url.difference_update_query(["log_cursor_logging_name"])def engine_created(self, engine):"attach an event listener after the new Engine is constructed"event.listen(engine, "before_cursor_execute", self._log_event)def _log_event(self,conn,cursor,statement,parameters,context,executemany):self.log.info("Plugin logged cursor event: %s", statement)
Plugins are registered using entry points in a similar way as that of dialects:
entry_points={'sqlalchemy.plugins': ['log_cursor_plugin = myapp.plugins:LogCursorEventsPlugin']
A plugin that uses the above names would be invoked from a database URL as in:
from sqlalchemy import create_engineengine = create_engine("mysql+pymysql://scott:tiger@localhost/test?""plugin=log_cursor_plugin&log_cursor_logging_name=mylogger")
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:
engine = create_engine("mysql+pymysql://scott:tiger@localhost/test?""plugin=plugin_one&plugin=plugin_twp&plugin=plugin_three")
The plugin names may also be passed directly to using the create_engine.plugins argument:
engine = create_engine("mysql+pymysql://scott:tiger@localhost/test",plugins=["myplugin"])
New in version 1.2.3: plugin names can also be specified to 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 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 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 method, returning a new copy of the URL with plugin-specific parameters removed:
class MyPlugin(CreateEnginePlugin):def __init__(self, url, kwargs):self.my_argument_one = url.query['my_argument_one']self.my_argument_two = url.query['my_argument_two']self.my_argument_three = kwargs.pop('my_argument_three', None)def update_url(self, url):return url.difference_update_query(["my_argument_one", "my_argument_two"])
Arguments like those illustrated above would be consumed from a call such as:
from sqlalchemy import create_engineengine = create_engine("mysql+pymysql://scott:tiger@localhost/test?""plugin=myplugin&my_argument_one=foo&my_argument_two=bar",)
Changed in version 1.4: The URL object is now immutable; a that needs to alter the URL should implement the newly added 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:
class MyPlugin(CreateEnginePlugin):def __init__(self, url, kwargs):if hasattr(CreateEnginePlugin, "update_url"):# detect the 1.4 APIself.my_argument_one = url.query['my_argument_one']self.my_argument_two = url.query['my_argument_two']else:# detect the 1.3 and earlier API - mutate the# URL directlyself.my_argument_one = url.query.pop('my_argument_one')self.my_argument_two = url.query.pop('my_argument_two')self.my_argument_three = kwargs.pop('my_argument_three', None)def update_url(self, url):# this method is only called in the 1.4 versionreturn url.difference_update_query(["my_argument_one", "my_argument_two"])
See also
- overview of the URL change which also includes notes regarding .
When the engine creation process completes and produces the Engine object, it is again passed to the plugin via the 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
, engine_created(), , handle_pool_kwargs(),
New in version 1.1.
method sqlalchemy.engine.CreateEnginePlugin.__init__(url: , kwargs: Dict[str, Any])
Construct a new CreateEnginePlugin.
The plugin object is instantiated individually for each call to . A single
Enginewill be passed to the CreateEnginePlugin.engine_created() method corresponding to this URL.Parameters:
url –
the object. The plugin may inspect the URL for arguments. Arguments used by the plugin should be removed, by returning an updated from the CreateEnginePlugin.update_url() method.
Changed in version 1.4: The object is now immutable, so a CreateEnginePlugin that needs to alter the object should implement the CreateEnginePlugin.update_url() method.
kwargs – The keyword arguments passed to .
method sqlalchemy.engine.CreateEnginePlugin.engine_created(engine: ) → None
Receive the Engine object when it is fully constructed.
The plugin may make additional changes to the engine, such as registering engine or connection pool events.
method handle_dialect_kwargs(dialect_cls: Type[Dialect], dialect_args: Dict[str, Any]) → None
parse and modify dialect kwargs
method handle_pool_kwargs(pool_cls: Type[Pool], pool_args: Dict[str, Any]) → None
parse and modify pool kwargs
method update_url(url: URL) →
Update the URL.
A new should be returned. This method is typically used to consume configuration arguments from the URL which must be removed, as they will not be recognized by the dialect. The method is available to remove these arguments. See the docstring at CreateEnginePlugin for an example.
New in version 1.4.
class sqlalchemy.engine.Engine
Connects a and Dialect together to provide a source of database connectivity and behavior.
An object is instantiated publicly using the create_engine() function.
See also
Working with Engines and Connections
Members
, clear_compiled_cache(), , dispose(), , engine, , get_execution_options(), , raw_connection(),
Class signature
class sqlalchemy.engine.Engine (sqlalchemy.engine.interfaces.ConnectionEventsTarget, , sqlalchemy.inspection.Inspectable)
method sqlalchemy.engine.Engine.begin() → Iterator[]
Return a context manager delivering a Connection with a established.
E.g.:
with engine.begin() as conn:conn.execute(text("insert into table (x, y, z) values (1, 2, 3)"))conn.execute(text("my_special_procedure(5)"))
Upon successful operation, the Transaction is committed. If an error is raised, the is rolled back.
See also
Engine.connect() - procure a from an Engine.
- start a Transaction for a particular .
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_sizeparameter. It will not impact any dictionary caches that were passed via the parameter.New in version 1.4.
method sqlalchemy.engine.Engine.connect() →
Return a new Connection object.
The acts as a Python context manager, so the typical use of this method looks like:
with engine.connect() as connection:connection.execute(text("insert into table values ('foo')"))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
method sqlalchemy.engine.Engine.dispose(close: bool = True) → None
Dispose of the connection pool used by this .
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 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.
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.See also[Engine Disposal](#engine-disposal)[Using Connection Pools with Multiprocessing or os.fork()]($ba04c3bd42280074.md#pooling-multiprocessing)
attribute sqlalchemy.engine.Engine.driver
Driver name of the in use by this Engine.
attribute engine
Returns this Engine.
Used for legacy schemes that accept / Engine objects within the same variable.
method execution_options(**opt: Any) → OptionEngine
Return a new Engine that will provide objects with the given execution options.
The returned Engine remains related to the original in that it shares the same connection pool and other state:
The Pool used by the new 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 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 .
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).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:```from sqlalchemy import eventfrom sqlalchemy.engine import Engineprimary_engine = create_engine("mysql+mysqldb://")shard1 = primary_engine.execution_options(shard_id="shard1")shard2 = primary_engine.execution_options(shard_id="shard2")shards = {"default": "base", "shard_1": "db1", "shard_2": "db2"}@event.listens_for(Engine, "before_cursor_execute")def _switch_shard(conn, cursor, stmt,params, context, executemany):shard_id = conn.get_execution_options().get('shard_id', "default")current_shard = conn.info.get("current_shard", None)if current_shard != shard_id:cursor.execute("use %s" % shards[shard_id])conn.info["current_shard"] = shard_id```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.See also[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.[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.[Engine.get\_execution\_options()](#sqlalchemy.engine.Engine.get_execution_options "sqlalchemy.engine.Engine.get_execution_options")
method sqlalchemy.engine.Engine.get_execution_options() → _ExecuteOptions
Get the non-SQL options which will take effect during execution.
See also
attribute sqlalchemy.engine.Engine.name
String name of the in use by this Engine.
method raw_connection() → PoolProxiedConnection
Return a “raw” DBAPI connection from the connection pool.
The returned object is a proxied version of the DBAPI connection object used by the underlying driver in use. The object will have all the same behavior as the real DBAPI connection, except that its
close()method will result in the connection being returned to the pool, rather than being closed for real.This method provides direct DBAPI connection access for special situations when the API provided by is not needed. When a Connection object is already present, the DBAPI connection is available using the accessor.
See also
method update_execution_options(**opt: Any) → None
Update the default execution_options dictionary of this Engine.
The given keys/values in **opt are added to the default execution options that will be used for all connections. The initial contents of this dictionary can be sent via the
execution_optionsparameter to .See also
class sqlalchemy.engine.ExceptionContext
Encapsulate information about an error condition in progress.
Members
chained_exception, , cursor, , engine, , invalidate_pool_on_disconnect, , original_exception, , sqlalchemy_exception,
This object exists solely to be passed to the DialectEvents.handle_error() event, supporting an interface that can be extended without backwards-incompatibility.
attribute 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[]
The Connection in use during the exception.
This member is present, except in the case of a failure when first connecting.
See also
attribute sqlalchemy.engine.ExceptionContext.cursor: Optional[]
The DBAPI cursor object.
May be None.
attribute sqlalchemy.engine.ExceptionContext.dialect:
The Dialect in use.
This member is present for all invocations of the event hook.
New in version 2.0.
attribute engine: Optional[Engine]
The 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[]
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 could be constructed.
Note that the ExceptionContext.statement and members may represent a different value than that of the ExecutionContext, potentially in the case where a 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 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 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 . 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 parameters: Optional[_DBAPIAnyExecuteParams]
Parameter collection that was emitted directly to the DBAPI.
May be None.
attribute sqlalchemy.engine.ExceptionContext.sqlalchemy_exception: Optional[]
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 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 method of Connection.
When using , 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 .
The NestedTransaction.rollback() method corresponds to a “ROLLBACK TO SAVEPOINT” operation, using the savepoint identifier associated with this .
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
, commit(),
Class signature
class sqlalchemy.engine.NestedTransaction ()
method sqlalchemy.engine.NestedTransaction.close() → None
inherited from the method of Transaction
Close this .
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.NestedTransaction.commit() → None
inherited from the method of Transaction
Commit this .
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 , it corresponds to a “RELEASE SAVEPOINT” operation.
For a TwoPhaseTransaction, DBAPI-specific methods for two phase transactions may be used.
method rollback() → None
inherited from the Transaction.rollback() method of
Roll back this Transaction.
The implementation of this may vary based on the type of transaction in use:
For a simple database transaction (e.g. ), it corresponds to a ROLLBACK.
For a NestedTransaction, it corresponds to a “ROLLBACK TO SAVEPOINT” operation.
For a , DBAPI-specific methods for two phase transactions may be used.
class sqlalchemy.engine.RootTransaction
Represent the “root” transaction on a Connection.
This corresponds to the current “BEGIN/COMMIT/ROLLBACK” that’s occurring for the . The RootTransaction is created by calling upon the method, and remains associated with the Connection throughout its active span. The current in use is accessible via the Connection.get_transaction method of .
In 2.0 style use, the 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 in 2.0 style use can be controlled using the Connection.commit() and methods.
Members
Class signature
class (sqlalchemy.engine.Transaction)
method close() → None
inherited from the Transaction.close() method of
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 commit() → None
inherited from the Transaction.commit() method of
Commit this Transaction.
The implementation of this may vary based on the type of transaction in use:
For a simple database transaction (e.g. ), it corresponds to a COMMIT.
For a NestedTransaction, it corresponds to a “RELEASE SAVEPOINT” operation.
For a , DBAPI-specific methods for two phase transactions may be used.
method sqlalchemy.engine.RootTransaction.rollback() → None
inherited from the method of Transaction
Roll back this .
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 ROLLBACK.
For a , it corresponds to a “ROLLBACK TO SAVEPOINT” operation.
For a TwoPhaseTransaction, DBAPI-specific methods for two phase transactions may be used.
class sqlalchemy.engine.Transaction
Represent a database transaction in progress.
The object is procured by calling the Connection.begin() method of :
from sqlalchemy import create_engineengine = create_engine("postgresql+psycopg2://scott:tiger@localhost/test")connection = engine.connect()trans = connection.begin()connection.execute(text("insert into x (a, b) values (1, 2)"))trans.commit()
The object provides rollback() and 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:
with connection.begin():connection.execute(text("insert into x (a, b) values (1, 2)"))
The Transaction object is not threadsafe.
Members
, commit(),
See also
Class signature
class (sqlalchemy.engine.util.TransactionalContext)
method sqlalchemy.engine.Transaction.close() → None
Close this .
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 .
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 , it corresponds to a “RELEASE SAVEPOINT” operation.
For a TwoPhaseTransaction, DBAPI-specific methods for two phase transactions may be used.
method rollback() → None
Roll back this Transaction.
The implementation of this may vary based on the type of transaction in use:
For a simple database transaction (e.g. ), it corresponds to a ROLLBACK.
For a NestedTransaction, it corresponds to a “ROLLBACK TO SAVEPOINT” operation.
For a , 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 method.
The interface is the same as that of Transaction with the addition of the method.
Members
Class signature
class sqlalchemy.engine.TwoPhaseTransaction ()
method sqlalchemy.engine.TwoPhaseTransaction.close() → None
inherited from the method of Transaction
Close this .
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.TwoPhaseTransaction.commit() → None
inherited from the method of Transaction
Commit this .
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 , it corresponds to a “RELEASE SAVEPOINT” operation.
For a TwoPhaseTransaction, DBAPI-specific methods for two phase transactions may be used.
method prepare() → None
Prepare this TwoPhaseTransaction.
After a PREPARE, the transaction can be committed.
method rollback() → None
inherited from the Transaction.rollback() method of
Roll back this Transaction.
The implementation of this may vary based on the type of transaction in use:
For a simple database transaction (e.g. ), it corresponds to a ROLLBACK.
For a NestedTransaction, it corresponds to a “ROLLBACK TO SAVEPOINT” operation.
For a , DBAPI-specific methods for two phase transactions may be used.
Result Set API
| Object Name | Description |
|---|---|
An IteratorResult that works from an iterator-producing callable. | |
A Result that is representing state from a DBAPI cursor. | |
A wrapper for a that returns objects other than Row objects, such as dictionaries or scalar objects. | |
Represents a Result object in a “frozen” state suitable for caching. | |
A Result that gets data from a Python iterator of objects or similar row-like data. | |
A wrapper for a that returns dictionary values rather than Row values. | |
A Result that is merged from any number of objects. | |
Represent a set of database results. | |
Represent a single result row. | |
A | |
A wrapper for a that returns scalar values rather than Row values. | |
A Result that’s typed as returning plain Python tuples instead of rows. |
class sqlalchemy.engine.ChunkedIteratorResult
An 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
Class signature
class (sqlalchemy.engine.IteratorResult)
method yield_per(num: int) → SelfChunkedIteratorResult
Configure the row-fetching strategy to fetch
numrows 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 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 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 method to establish a fixed row buffer size at once.
The Connection.execution_options.yield_per execution option is available for ORM operations, with -oriented use described at Fetching Large Result Sets with Yield Per. The Core-only version which works with 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.
See also[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")[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 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
- introductory material for accessing CursorResult and objects.
Members
all(), , columns(), , fetchmany(), , first(), , inserted_primary_key, , is_insert, , last_inserted_params(), , lastrow_has_defaults(), , mappings(), , one(), , partitions(), , prefetch_cols(), , returned_defaults_rows, , rowcount, , scalar_one(), , scalars(), , splice_vertically(), , supports_sane_rowcount(), , tuples(), , yield_per()
Class signature
class (sqlalchemy.engine.Result)
method all() → Sequence[Row[_TP]]
inherited from the 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 objects.
method sqlalchemy.engine.CursorResult.close() → Any
Close this .
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. 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 on subsequent use.
See also
method columns(*col_expressions: _KeyIndexType) → SelfResultInternal
inherited from the Result.columns() method of
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 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 objects. In version 2.0, this behavior has been corrected such that calling upon Result.columns() with a single index will produce a object that continues to yield Row objects, which include only a single column.
E.g.:
statement = select(table.c.x, table.c.y, table.c.z)result = connection.execute(statement)for z, y in result.columns('z', 'y'):# ...
Example of using the column objects from the statement itself:
for z, y in result.columns(statement.selected_columns.c.z,statement.selected_columns.c.y):# ...
New in version 1.4.
Parameters:
*col_expressions – indicates columns to be returned. Elements may be integer row indexes, string column names, or appropriate objects corresponding to a select construct.
Returns:
this Result object with the modifications given.
method fetchall() → Sequence[Row[_TP]]
inherited from the method of Result
A synonym for the method.
method sqlalchemy.engine.CursorResult.fetchmany(size: Optional[int] = None) → Sequence[[_TP]]
inherited from the Result.fetchmany() method of
Fetch many rows.
When all rows are exhausted, returns an empty list.
This method is provided for backwards compatibility with SQLAlchemy 1.x.x.
To fetch rows in groups, use the Result.partitions() method.
Returns:
a list of objects.
See also[Result.partitions()](#sqlalchemy.engine.Result.partitions "sqlalchemy.engine.Result.partitions")
method sqlalchemy.engine.CursorResult.fetchone() → Optional[[_TP]]
inherited from the Result.fetchone() method of
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 object directly.
Returns:
a Row object if no filters are applied, or
Noneif no rows remain.
method first() → Optional[Row[_TP]]
inherited from the method of Result
Fetch the first row or
Noneif 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 method, or combine Result.scalars() and .
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 ; 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 object, or None if no rows remain.
See also[Result.scalar()](#sqlalchemy.engine.Result.scalar "sqlalchemy.engine.Result.scalar")[Result.one()](#sqlalchemy.engine.Result.one "sqlalchemy.engine.Result.one")
method sqlalchemy.engine.CursorResult.freeze() → [_TP]
inherited from the Result.freeze() method of
Return a callable object that will produce copies of this Result when invoked.
The callable object returned is an instance of .
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 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 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 .
Changed in version 1.4.8: - the CursorResult.inserted_primary_key value is now a named tuple via the class, rather than a plain tuple.
This accessor only applies to single row insert() constructs which did not explicitly specify . 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 ), and were generated using the database-side default, will appear in this list as
Noneunless 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 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 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 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 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
Nonefor any server-generated values.
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.New in version 1.4.See also[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 is the result of a executing an expression language compiled insert() construct.
When True, this implies that the 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.keysmethod ofsqlalchemy.engine._WithKeysReturn an iterable view which yields the string keys that would be represented by each .
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
inoperator, 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 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 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 .See ExecutionContext for details.
attribute 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 mappings() → MappingResult
inherited from the method of Result
Apply a mappings filter to returned rows, returning an instance of .
When this filter is applied, fetching rows will return RowMapping objects instead of objects.
New in version 1.4.
Returns:
a new MappingResult filtering object referring to this object.
method sqlalchemy.engine.CursorResult.merge(*others: [Any]) → MergedResult[Any]
Merge this with other compatible result objects.
The object returned is an instance of MergedResult, which will be composed of iterators from the given result objects.
The new result will use the metadata from this result object. The subsequent result objects must be against an identical set of result / cursor metadata, otherwise the behavior is undefined.
method one() → Row[_TP]
inherited from the method of Result
Return exactly one row or raise an exception.
Raises if the result returns no rows, or MultipleResultsFound if multiple rows would be returned.
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 method, or combine Result.scalars() and .
New in version 1.4.
Returns:
The first Row.
Raises:
See also[Result.first()](#sqlalchemy.engine.Result.first "sqlalchemy.engine.Result.first")[Result.one\_or\_none()](#sqlalchemy.engine.Result.one_or_none "sqlalchemy.engine.Result.one_or_none")[Result.scalar\_one()](#sqlalchemy.engine.Result.scalar_one "sqlalchemy.engine.Result.scalar_one")
method one_or_none() → Optional[Row[_TP]]
inherited from the method of Result
Return at most one result or raise an exception.
Returns
Noneif the result has no rows. Raises if multiple rows are returned.New in version 1.4.
Returns:
The first Row or
Noneif no row is available.Raises:
See also[Result.first()](#sqlalchemy.engine.Result.first "sqlalchemy.engine.Result.first")[Result.one()](#sqlalchemy.engine.Result.one "sqlalchemy.engine.Result.one")
method sqlalchemy.engine.CursorResult.partitions(size: Optional[int] = None) → Iterator[Sequence[[_TP]]]
inherited from the Result.partitions() method of
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 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 , 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 default, which may be backend specific and not well defined.
Returns:
iterator of lists
See also[Using Server Side Cursors (a.k.a. stream results)](#engine-stream-results)[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 .See ExecutionContext for details.
Raises 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 .See ExecutionContext for details.
Raises 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 , or
NoneifValuesBase.return_defaults()was not used or if the backend does not support RETURNING.New in version 0.9.0.
See also
ValuesBase.return_defaults()-
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 returns_rows
True if this CursorResult returns zero or more rows.
I.e. if it is legal to call the methods , CursorResult.fetchmany() .
Overall, the value of CursorResult.returns_rows should always be synonymous with whether or not the DBAPI cursor had a
.descriptionattribute, indicating the presence of result columns, noting that a cursor that returns zero rows still has a.descriptionif 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 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.
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 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.
See also[Getting Affected Row Count from UPDATE, DELETE]($a04339624cb33e15.md#tutorial-update-delete-rowcount) - in the [SQLAlchemy Unified Tutorial]($4406c4fa3e52f66b.md#unified-tutorial)
method scalar() → Any
inherited from the Result.scalar() method of
Fetch the first column of the first row, and close the result set.
Returns
Noneif 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
Noneif no rows remain.
method scalar_one() → Any
inherited from the Result.scalar_one() method of
Return exactly one scalar result or raise an exception.
This is equivalent to calling Result.scalars() and then .
See also
method sqlalchemy.engine.CursorResult.scalar_one_or_none() → Optional[Any]
inherited from the method of Result
Return exactly one scalar result or
None.This is equivalent to calling and then Result.one_or_none().
See also
method scalars(index: _KeyIndexType = 0) → ScalarResult[Any]
inherited from the method of Result
Return a filtering object which will return single elements rather than Row objects.
E.g.:
>>> result = conn.execute(text("select int_id from table"))>>> result.scalars().all()[1, 2, 3]
When results are fetched from the filtering object, the single column-row that would be returned by the Result is instead returned as the column’s value.
New in version 1.4.
Parameters:
index – integer or row key indicating the column to be fetched from each row, defaults to
0indicating the first column.Returns:
a new filtering object referring to this Result object.
method splice_horizontally(other)
Return a new CursorResult that “horizontally splices” together the rows of this 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 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.:
r1 = connection.execute(users.insert().returning(users.c.user_name, users.c.user_id),user_values)r2 = connection.execute(addresses.insert().returning(addresses.c.address_id,addresses.c.address,addresses.c.user_id,),address_values)rows = r1.splice_horizontally(r2).all()assert (rows ==[("john", 1, 1, "foo@bar.com", 1),("jack", 2, 2, "bar@bat.com", 2),])
New in version 2.0.
See also
method splice_vertically(other)
Return a new CursorResult that “vertically splices”, i.e. “extends”, the rows of this 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 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
method sqlalchemy.engine.CursorResult.supports_sane_multi_rowcount()
Return
supports_sane_multi_rowcountfrom the dialect.See for background.
method sqlalchemy.engine.CursorResult.supports_sane_rowcount()
Return
supports_sane_rowcountfrom the dialect.See for background.
attribute sqlalchemy.engine.CursorResult.t
inherited from the attribute of Result
Apply a “typed tuple” typing filter to returned rows.
The attribute is a synonym for calling the Result.tuples() method.
New in version 2.0.
method tuples() → TupleResult[_TP]
inherited from the method of Result
Apply a “typed tuple” typing filter to returned rows.
This method returns the same object at runtime, however annotates as returning a TupleResult object that will indicate to typing tools that plain typed
Tupleinstances 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.
Returns:
the type at typing time.
See also[Result.t](#sqlalchemy.engine.Result.t "sqlalchemy.engine.Result.t") - shorter synonym[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 method of Result
Apply unique filtering to the objects returned by this .
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 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 and Result.partitions(). When using , 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 yield_per(num: int) → SelfCursorResult
Configure the row-fetching strategy to fetch
numrows 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 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 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 method to establish a fixed row buffer size at once.
The Connection.execution_options.yield_per execution option is available for ORM operations, with -oriented use described at Fetching Large Result Sets with Yield Per. The Core-only version which works with 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.
See also[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")[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 objects, such as dictionaries or scalar objects.
FilterResult is the common base for additional result APIs including , ScalarResult and AsyncResult.
Members
, closed,
Class signature
class sqlalchemy.engine.FilterResult (sqlalchemy.engine.ResultInternal)
method close() → None
Close this FilterResult.
New in version 1.4.43.
attribute closed
Return
Trueif the underlying Result reports closedNew in version 1.4.43.
method yield_per(num: int) → SelfFilterResult
Configure the row-fetching strategy to fetch
numrows at a time.The FilterResult.yield_per() method is a pass through to the method. See that method’s documentation for usage notes.
New in version 1.4.40: - added FilterResult.yield_per() so that the method is available on all result set implementations
See also
- describes Core behavior for Result.yield_per()
- in the ORM Querying Guide
class sqlalchemy.engine.FrozenResult
Represents a object in a “frozen” state suitable for caching.
The FrozenResult object is returned from the method of any Result object.
A new iterable object is generated from a fixed set of data each time the FrozenResult is invoked as a callable:
result = connection.execute(query)frozen = result.freeze()unfrozen_result_one = frozen()for row in unfrozen_result_one:print(row)unfrozen_result_two = frozen()rows = unfrozen_result_two.all()# ... etc
New in version 1.4.
See also
- 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 (typing.Generic)
class sqlalchemy.engine.IteratorResult
A Result that gets data from a Python iterator of objects or similar row-like data.
New in version 1.4.
Members
Class signature
class (sqlalchemy.engine.Result)
attribute closed
Return
Trueif this IteratorResult has been closedNew in version 1.4.43.
class sqlalchemy.engine.MergedResult
A that is merged from any number of Result objects.
Returned by the method.
New in version 1.4.
Class signature
class sqlalchemy.engine.MergedResult ()
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 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 , 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 object. For in-Python de-duplication of instances or rows, use the Result.unique() modifier method.
See also
- in the SQLAlchemy Unified Tutorial
Members
, close(), , columns(), , fetchmany(), , first(), , keys(), , merge(), , one_or_none(), , scalar(), , scalar_one_or_none(), , t, , unique(),
Class signature
class sqlalchemy.engine.Result (sqlalchemy.engine._WithKeys, sqlalchemy.engine.ResultInternal)
method 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 objects.
method sqlalchemy.engine.Result.close() → None
close this .
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 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 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 theyield_perexecution option where it does not immediately exhaust and autoclose the database cursor.attribute sqlalchemy.engine.Result.closed
return
Trueif this reports .closedNew 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 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 object to yield scalar values rather than Row objects. In version 2.0, this behavior has been corrected such that calling upon with a single index will produce a Result object that continues to yield objects, which include only a single column.
E.g.:
statement = select(table.c.x, table.c.y, table.c.z)result = connection.execute(statement)for z, y in result.columns('z', 'y'):# ...
Example of using the column objects from the statement itself:
for z, y in result.columns(statement.selected_columns.c.z,statement.selected_columns.c.y):# ...
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 object with the modifications given.
method sqlalchemy.engine.Result.fetchall() → Sequence[[_TP]]
A synonym for the Result.all() method.
method fetchmany(size: Optional[int] = None) → Sequence[Row[_TP]]
Fetch many rows.
When all rows are exhausted, returns an empty list.
This method is provided for backwards compatibility with SQLAlchemy 1.x.x.
To fetch rows in groups, use the method.
Returns:
a list of Row objects.
See also[Result.partitions()](#sqlalchemy.engine.Result.partitions "sqlalchemy.engine.Result.partitions")
method 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 method. To iterate through all rows, iterate the Result object directly.
Returns:
a object if no filters are applied, or
Noneif no rows remain.
method sqlalchemy.engine.Result.first() → Optional[[_TP]]
Fetch the first row or
Noneif 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 and Result.first().
Additionally, in contrast to the behavior of the legacy ORM 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
Returns:
a Row object, or None if no rows remain.
See also[Result.scalar()](#sqlalchemy.engine.Result.scalar "sqlalchemy.engine.Result.scalar")[Result.one()](#sqlalchemy.engine.Result.one "sqlalchemy.engine.Result.one")
method freeze() → FrozenResult[_TP]
Return a callable object that will produce copies of this 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 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
- example usage within the ORM to implement a result-set cache.
method sqlalchemy.engine.Result.keys() → RMKeyView
inherited from the
sqlalchemy.engine._WithKeys.keysmethod ofsqlalchemy.engine._WithKeysReturn an iterable view which yields the string keys that would be represented by each .
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
inoperator, 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() →
Apply a mappings filter to returned rows, returning an instance of MappingResult.
When this filter is applied, fetching rows will return objects instead of Row objects.
New in version 1.4.
Returns:
a new filtering object referring to this Result object.
method merge(*others: Result[Any]) → [_TP]
Merge this Result with other compatible result objects.
The object returned is an instance of , which will be composed of iterators from the given result objects.
The new result will use the metadata from this result object. The subsequent result objects must be against an identical set of result / cursor metadata, otherwise the behavior is undefined.
method sqlalchemy.engine.Result.one() → [_TP]
Return exactly one row or raise an exception.
Raises NoResultFound if the result returns no rows, or if multiple rows would be returned.
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_one() method, or combine and Result.one().
New in version 1.4.
Returns:
The first .
Raises:
See also[Result.first()](#sqlalchemy.engine.Result.first "sqlalchemy.engine.Result.first")[Result.one\_or\_none()](#sqlalchemy.engine.Result.one_or_none "sqlalchemy.engine.Result.one_or_none")[Result.scalar\_one()](#sqlalchemy.engine.Result.scalar_one "sqlalchemy.engine.Result.scalar_one")
method sqlalchemy.engine.Result.one_or_none() → Optional[[_TP]]
Return at most one result or raise an exception.
Returns
Noneif the result has no rows. Raises MultipleResultsFound if multiple rows are returned.New in version 1.4.
Returns:
The first or
Noneif no row is available.Raises:
See also[Result.first()](#sqlalchemy.engine.Result.first "sqlalchemy.engine.Result.first")[Result.one()](#sqlalchemy.engine.Result.one "sqlalchemy.engine.Result.one")
method 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 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 , 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 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
See also[Using Server Side Cursors (a.k.a. stream results)](#engine-stream-results)[Fetching Large Result Sets with Yield Per]($661bd2ffd6937693.md#orm-queryguide-yield-per) - in the [ORM Querying Guide]($86681c2576bdda58.md)
method scalar() → Any
Fetch the first column of the first row, and close the result set.
Returns
Noneif 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
Noneif no rows remain.
method scalar_one() → Any
Return exactly one scalar result or raise an exception.
This is equivalent to calling Result.scalars() and then .
See also
method sqlalchemy.engine.Result.scalar_one_or_none() → Optional[Any]
Return exactly one scalar result or
None.This is equivalent to calling and then Result.one_or_none().
See also
method scalars(index: _KeyIndexType = 0) → ScalarResult[Any]
Return a filtering object which will return single elements rather than Row objects.
E.g.:
>>> result = conn.execute(text("select int_id from table"))>>> result.scalars().all()[1, 2, 3]
When results are fetched from the filtering object, the single column-row that would be returned by the Result is instead returned as the column’s value.
New in version 1.4.
Parameters:
index – integer or row key indicating the column to be fetched from each row, defaults to
0indicating the first column.Returns:
a new filtering object referring to this Result object.
attribute t
Apply a “typed tuple” typing filter to returned rows.
The Result.t attribute is a synonym for calling the method.
New in version 2.0.
method sqlalchemy.engine.Result.tuples() → [_TP]
Apply a “typed tuple” typing filter to returned rows.
This method returns the same Result object at runtime, however annotates as returning a object that will indicate to PEP 484 typing tools that plain typed
Tupleinstances are returned rather than rows. This allows tuple unpacking and__getitem__access of objects to by typed, for those cases where the statement invoked itself included typing information.New in version 2.0.
Returns:
the TupleResult type at typing time.
See also[Result.t](#sqlalchemy.engine.Result.t "sqlalchemy.engine.Result.t") - shorter synonym[Row.t](#sqlalchemy.engine.Row.t "sqlalchemy.engine.Row.t") - [Row](#sqlalchemy.engine.Row "sqlalchemy.engine.Row") version
method 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 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 object.
The unique filter also changes the calculus used for methods like Result.fetchmany() and . 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 object.
method sqlalchemy.engine.Result.yield_per(num: int) → SelfResult
Configure the row-fetching strategy to fetch
numrows 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 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 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 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 execution option is available for ORM operations, with Session-oriented use described at . 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.
See also[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")[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 that returns scalar values rather than Row values.
The object is acquired by calling the Result.scalars() method.
A special limitation of 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
, close(), , fetchall(), , first(), , one_or_none(), , unique(),
Class signature
class sqlalchemy.engine.ScalarResult ()
method sqlalchemy.engine.ScalarResult.all() → Sequence[_R]
Return all scalar values in a list.
Equivalent to except that scalar values, rather than Row objects, are returned.
method close() → None
inherited from the FilterResult.close() method of
Close this FilterResult.
New in version 1.4.43.
attribute closed
inherited from the FilterResult.closed attribute of
Return
Trueif the underlying Result reports closedNew in version 1.4.43.
method fetchall() → Sequence[_R]
A synonym for the ScalarResult.all() method.
method fetchmany(size: Optional[int] = None) → Sequence[_R]
Fetch many objects.
Equivalent to Result.fetchmany() except that scalar values, rather than objects, are returned.
method sqlalchemy.engine.ScalarResult.first() → Optional[_R]
Fetch the first object or
Noneif no object is present.Equivalent to except that scalar values, rather than Row objects, are returned.
method one() → _R
Return exactly one object or raise an exception.
Equivalent to Result.one() except that scalar values, rather than objects, are returned.
method sqlalchemy.engine.ScalarResult.one_or_none() → Optional[_R]
Return at most one object or raise an exception.
Equivalent to except that scalar values, rather than Row objects, are returned.
method partitions(size: Optional[int] = None) → Iterator[Sequence[_R]]
Iterate through sub-lists of elements of the size given.
Equivalent to Result.partitions() except that scalar values, rather than objects, are returned.
method sqlalchemy.engine.ScalarResult.unique(strategy: Optional[Callable[[Any], Any]] = None) → SelfScalarResult
Apply unique filtering to the objects returned by this .
See Result.unique() for usage details.
method yield_per(num: int) → SelfFilterResult
inherited from the FilterResult.yield_per() method of
Configure the row-fetching strategy to fetch
numrows at a time.The FilterResult.yield_per() method is a pass through to the method. See that method’s documentation for usage notes.
New in version 1.4.40: - added FilterResult.yield_per() so that the method is available on all result set implementations
See also
- describes Core behavior for Result.yield_per()
- in the ORM Querying Guide
class sqlalchemy.engine.MappingResult
A wrapper for a that returns dictionary values rather than Row values.
The object is acquired by calling the Result.mappings() method.
Members
, close(), , columns(), , fetchmany(), , first(), , one(), , partitions(), , yield_per()
Class signature
class (sqlalchemy.engine._WithKeys, sqlalchemy.engine.FilterResult)
method all() → Sequence[RowMapping]
Return all scalar values in a list.
Equivalent to except that RowMapping values, rather than objects, are returned.
method sqlalchemy.engine.MappingResult.close() → None
inherited from the method of FilterResult
Close this .
New in version 1.4.43.
attribute sqlalchemy.engine.MappingResult.closed
inherited from the attribute of FilterResult
Return
Trueif the underlying reports closedNew in version 1.4.43.
method sqlalchemy.engine.MappingResult.columns(*col_expressions: _KeyIndexType) → SelfMappingResult
Establish the columns that should be returned in each row.
method fetchall() → Sequence[RowMapping]
A synonym for the method.
method sqlalchemy.engine.MappingResult.fetchmany(size: Optional[int] = None) → Sequence[]
Fetch many objects.
Equivalent to Result.fetchmany() except that values, rather than Row objects, are returned.
method fetchone() → Optional[RowMapping]
Fetch one object.
Equivalent to except that RowMapping values, rather than objects, are returned.
method sqlalchemy.engine.MappingResult.first() → Optional[]
Fetch the first object or
Noneif no object is present.Equivalent to Result.first() except that values, rather than Row objects, are returned.
method keys() → RMKeyView
inherited from the
sqlalchemy.engine._WithKeys.keysmethod ofsqlalchemy.engine._WithKeysReturn 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
inoperator, 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 one() → RowMapping
Return exactly one object or raise an exception.
Equivalent to except that RowMapping values, rather than objects, are returned.
method sqlalchemy.engine.MappingResult.one_or_none() → Optional[]
Return at most one object or raise an exception.
Equivalent to Result.one_or_none() except that values, rather than Row objects, are returned.
method partitions(size: Optional[int] = None) → Iterator[Sequence[RowMapping]]
Iterate through sub-lists of elements of the size given.
Equivalent to except that RowMapping values, rather than objects, are returned.
method sqlalchemy.engine.MappingResult.unique(strategy: Optional[Callable[[Any], Any]] = None) → SelfMappingResult
Apply unique filtering to the objects returned by this .
See Result.unique() for usage details.
method yield_per(num: int) → SelfFilterResult
inherited from the FilterResult.yield_per() method of
Configure the row-fetching strategy to fetch
numrows at a time.The FilterResult.yield_per() method is a pass through to the method. See that method’s documentation for usage notes.
New in version 1.4.40: - added FilterResult.yield_per() so that the method is available on all result set implementations
See also
- describes Core behavior for Result.yield_per()
- in the ORM Querying Guide
class sqlalchemy.engine.Row
Represent a single result row.
The 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 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
- includes examples of selecting rows from SELECT statements.
Changed in version 1.4: Renamed RowProxy to 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 for background on this change.
Members
_asdict(), , _mapping, , index, , tuple()
Class signature
class (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 thedict()constructor to the attribute.New in version 1.4.
See also
attribute _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
._fieldsattribute.New in version 1.4.
See also
attribute sqlalchemy.engine.Row._mapping
Return a for this Row.
This object provides a consistent Python mapping (i.e. dictionary) interface for the data contained within the row. The by itself behaves like a named tuple.
See also
New in version 1.4.
attribute count
attribute sqlalchemy.engine.Row.index
attribute t
a synonym for Row.tuple
New in version 2.0.
See also
Result.t()method tuple() → _TP
Return a ‘tuple’ form of this Row.
At runtime, this method returns “self”; the object is already a named tuple. However, at the typing level, if this Row is typed, the “tuple” return type will be a
Tupledatatype that contains typing information about individual elements, supporting typed unpacking and attribute access.New in version 2.0.
See also
class sqlalchemy.engine.RowMapping
A Mapping that maps column names and objects to values.
The RowMapping is available from a via the Row._mapping attribute, as well as from the iterable interface provided by the object returned by the Result.mappings() method.
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:
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
, keys(),
Class signature
class sqlalchemy.engine.RowMapping (sqlalchemy.engine._py_row.BaseRow, collections.abc.Mapping, )
method items() → ROMappingItemsView
Return a view of key/value tuples for the elements in the underlying Row.
method values() → ROMappingKeysValuesView
Return a view of values for the values represented in the underlying Row.
class sqlalchemy.engine.TupleResult
A 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 is still used at runtime.
Class signature
class sqlalchemy.engine.TupleResult (, sqlalchemy.util.langhelpers.TypingOnly)
