Sqlalchemy timeout error

QueuePool limit of size <x> overflow <y> reached, connection timed out, timeout <z>¶

This is possibly the most common runtime error experienced, as it directly
involves the work load of the application surpassing a configured limit, one
which typically applies to nearly all SQLAlchemy applications.

The following points summarize what this error means, beginning with the
most fundamental points that most SQLAlchemy users should already be
familiar with.

• The SQLAlchemy Engine object uses a pool of connections by default — What
  this means is that when one makes use of a SQL database connection resource
  of an Engine object, and then releases that resource,
  the database connection itself remains connected to the database and
  is returned to an internal queue where it can be used again. Even though
  the code may appear to be ending its conversation with the database, in many
  cases the application will still maintain a fixed number of database connections
  that persist until the application ends or the pool is explicitly disposed.

• Because of the pool, when an application makes use of a SQL database
  connection, most typically from either making use of Engine.connect()
  or when making queries using an ORM Session, this activity
  does not necessarily establish a new connection to the database at the
  moment the connection object is acquired; it instead consults the
  connection pool for a connection, which will often retrieve an existing
  connection from the pool to be re-used. If no connections are available,
  the pool will create a new database connection, but only if the
  pool has not surpassed a configured capacity.

• The default pool used in most cases is called QueuePool. When
  you ask this pool to give you a connection and none are available, it
  will create a new connection if the total number of connections in play
  are less than a configured value. This value is equal to the
  pool size plus the max overflow. That means if you have configured
  your engine as:

  engine = create_engine("mysql://u:p@host/db", pool_size=10, max_overflow=20)

  The above Engine will allow at most 30 connections to be in
  play at any time, not including connections that were detached from the
  engine or invalidated. If a request for a new connection arrives and
  30 connections are already in use by other parts of the application,
  the connection pool will block for a fixed period of time,
  before timing out and raising this error message.

  In order to allow for a higher number of connections be in use at once,
  the pool can be adjusted using the
  create_engine.pool_size and create_engine.max_overflow
  parameters as passed to the create_engine() function. The timeout
  to wait for a connection to be available is configured using the
  create_engine.pool_timeout parameter.

• The pool can be configured to have unlimited overflow by setting
  create_engine.max_overflow to the value “-1”. With this setting,
  the pool will still maintain a fixed pool of connections, however it will
  never block upon a new connection being requested; it will instead unconditionally
  make a new connection if none are available.

  However, when running in this way, if the application has an issue where it
  is using up all available connectivity resources, it will eventually hit the
  configured limit of available connections on the database itself, which will
  again return an error. More seriously, when the application exhausts the
  database of connections, it usually will have caused a great
  amount of resources to be used up before failing, and can also interfere
  with other applications and database status mechanisms that rely upon being
  able to connect to the database.

  Given the above, the connection pool can be looked at as a safety valve
  for connection use, providing a critical layer of protection against
  a rogue application causing the entire database to become unavailable
  to all other applications. When receiving this error message, it is vastly
  preferable to repair the issue using up too many connections and/or
  configure the limits appropriately, rather than allowing for unlimited
  overflow which does not actually solve the underlying issue.

What causes an application to use up all the connections that it has available?

• The application is fielding too many concurrent requests to do work based
  on the configured value for the pool — This is the most straightforward
  cause. If you have
  an application that runs in a thread pool that allows for 30 concurrent
  threads, with one connection in use per thread, if your pool is not configured
  to allow at least 30 connections checked out at once, you will get this
  error once your application receives enough concurrent requests. Solution
  is to raise the limits on the pool or lower the number of concurrent threads.

• The application is not returning connections to the pool — This is the
  next most common reason, which is that the application is making use of the
  connection pool, but the program is failing to release these
  connections and is instead leaving them open. The connection pool as well
  as the ORM Session do have logic such that when the session and/or
  connection object is garbage collected, it results in the underlying
  connection resources being released, however this behavior cannot be relied
  upon to release resources in a timely manner.

  A common reason this can occur is that the application uses ORM sessions and
  does not call Session.close() upon them one the work involving that
  session is complete. Solution is to make sure ORM sessions if using the ORM,
  or engine-bound Connection objects if using Core, are explicitly
  closed at the end of the work being done, either via the appropriate
  .close() method, or by using one of the available context managers (e.g.
  “with:” statement) to properly release the resource.

• The application is attempting to run long-running transactions — A
  database transaction is a very expensive resource, and should never be
  left idle waiting for some event to occur. If an application is waiting
  for a user to push a button, or a result to come off of a long running job
  queue, or is holding a persistent connection open to a browser, don’t
  keep a database transaction open for the whole time. As the application
  needs to work with the database and interact with an event, open a short-lived
  transaction at that point and then close it.

• The application is deadlocking — Also a common cause of this error and
  more difficult to grasp, if an application is not able to complete its use
  of a connection either due to an application-side or database-side deadlock,
  the application can use up all the available connections which then leads to
  additional requests receiving this error. Reasons for deadlocks include:

  • Using an implicit async system such as gevent or eventlet without
    properly monkeypatching all socket libraries and drivers, or which
    has bugs in not fully covering for all monkeypatched driver methods,
    or less commonly when the async system is being used against CPU-bound
    workloads and greenlets making use of database resources are simply waiting
    too long to attend to them. Neither implicit nor explicit async
    programming frameworks are typically
    necessary or appropriate for the vast majority of relational database
    operations; if an application must use an async system for some area
    of functionality, it’s best that database-oriented business methods
    run within traditional threads that pass messages to the async part
    of the application.

  • A database side deadlock, e.g. rows are mutually deadlocked

  • Threading errors, such as mutexes in a mutual deadlock, or calling
    upon an already locked mutex in the same thread

Keep in mind an alternative to using pooling is to turn off pooling entirely.
See the section Switching Pool Implementations for background on this. However, note
that when this error message is occurring, it is always due to a bigger
problem in the application itself; the pool just helps to reveal the problem
sooner.

Can’t reconnect until invalid transaction is rolled back¶

This error condition refers to the case where a Connection was
invalidated, either due to a database disconnect detection or due to an
explicit call to Connection.invalidate(), but there is still a
transaction present that was initiated by the Connection.begin()
method. When a connection is invalidated, any Transaction
that was in progress is now in an invalid state, and must be explicitly rolled
back in order to remove it from the Connection.

InterfaceError¶

Exception raised for errors that are related to the database interface rather
than the database itself.

This error is a DBAPI Error and originates from
the database driver (DBAPI), not SQLAlchemy itself.

The InterfaceError is sometimes raised by drivers in the context
of the database connection being dropped, or not being able to connect
to the database. For tips on how to deal with this, see the section
Dealing with Disconnects.

DatabaseError¶

Exception raised for errors that are related to the database itself, and not
the interface or data being passed.

This error is a DBAPI Error and originates from
the database driver (DBAPI), not SQLAlchemy itself.

DataError¶

Exception raised for errors that are due to problems with the processed data
like division by zero, numeric value out of range, etc.

This error is a DBAPI Error and originates from
the database driver (DBAPI), not SQLAlchemy itself.

OperationalError¶

Exception raised for errors that are related to the database’s operation and
not necessarily under the control of the programmer, e.g. an unexpected
disconnect occurs, the data source name is not found, a transaction could not
be processed, a memory allocation error occurred during processing, etc.

This error is a DBAPI Error and originates from
the database driver (DBAPI), not SQLAlchemy itself.

The OperationalError is the most common (but not the only) error class used
by drivers in the context of the database connection being dropped, or not
being able to connect to the database. For tips on how to deal with this, see
the section Dealing with Disconnects.

IntegrityError¶

Exception raised when the relational integrity of the database is affected,
e.g. a foreign key check fails.

This error is a DBAPI Error and originates from
the database driver (DBAPI), not SQLAlchemy itself.

InternalError¶

Exception raised when the database encounters an internal error, e.g. the
cursor is not valid anymore, the transaction is out of sync, etc.

This error is a DBAPI Error and originates from
the database driver (DBAPI), not SQLAlchemy itself.

The InternalError is sometimes raised by drivers in the context
of the database connection being dropped, or not being able to connect
to the database. For tips on how to deal with this, see the section
Dealing with Disconnects.

ProgrammingError¶

Exception raised for programming errors, e.g. table not found or already
exists, syntax error in the SQL statement, wrong number of parameters
specified, etc.

This error is a DBAPI Error and originates from
the database driver (DBAPI), not SQLAlchemy itself.

The ProgrammingError is sometimes raised by drivers in the context
of the database connection being dropped, or not being able to connect
to the database. For tips on how to deal with this, see the section
Dealing with Disconnects.

NotSupportedError¶

Exception raised in case a method or database API was used which is not
supported by the database, e.g. requesting a .rollback() on a connection that
does not support transaction or has transactions turned off.

This error is a DBAPI Error and originates from
the database driver (DBAPI), not SQLAlchemy itself.

Object will not produce a cache key, Performance Implications¶

SQLAlchemy as of version 1.4 includes a
SQL compilation caching facility which will allow
Core and ORM SQL constructs to cache their stringified form, along with other
structural information used to fetch results from the statement, allowing the
relatively expensive string compilation process to be skipped when another
structurally equivalent construct is next used. This system
relies upon functionality that is implemented for all SQL constructs, including
objects such as Column,
select(), and TypeEngine objects, to produce a
cache key which fully represents their state to the degree that it affects
the SQL compilation process.

If the warnings in question refer to widely used objects such as
Column objects, and are shown to be affecting the majority of
SQL constructs being emitted (using the estimation techniques described at
Estimating Cache Performance Using Logging) such that caching is generally not enabled for an
application, this will negatively impact performance and can in some cases
effectively produce a performance degradation compared to prior SQLAlchemy
versions. The FAQ at Why is my application slow after upgrading to 1.4 and/or 2.x? covers this in additional detail.

Compiler StrSQLCompiler can’t render element of type <element type>¶

This error usually occurs when attempting to stringify a SQL expression
construct that includes elements which are not part of the default compilation;
in this case, the error will be against the StrSQLCompiler class.
In less common cases, it can also occur when the wrong kind of SQL expression
is used with a particular type of database backend; in those cases, other
kinds of SQL compiler classes will be named, such as SQLCompiler or
sqlalchemy.dialects.postgresql.PGCompiler. The guidance below is
more specific to the “stringification” use case but describes the general
background as well.

Normally, a Core SQL construct or ORM Query object can be stringified
directly, such as when we use print():

>>> from sqlalchemy import column
>>> print(column("x") == 5)
x = :x_1

When the above SQL expression is stringified, the StrSQLCompiler
compiler class is used, which is a special statement compiler that is invoked
when a construct is stringified without any dialect-specific information.

However, there are many constructs that are specific to some particular kind
of database dialect, for which the StrSQLCompiler doesn’t know how
to turn into a string, such as the PostgreSQL
“insert on conflict” construct:

>>> from sqlalchemy.dialects.postgresql import insert
>>> from sqlalchemy import table, column
>>> my_table = table("my_table", column("x"), column("y"))
>>> insert_stmt = insert(my_table).values(x="foo")
>>> insert_stmt = insert_stmt.on_conflict_do_nothing(index_elements=["y"])
>>> print(insert_stmt)
Traceback (most recent call last):

...

sqlalchemy.exc.UnsupportedCompilationError:
Compiler <sqlalchemy.sql.compiler.StrSQLCompiler object at 0x7f04fc17e320>
can't render element of type
<class 'sqlalchemy.dialects.postgresql.dml.OnConflictDoNothing'>

In order to stringify constructs that are specific to particular backend,
the ClauseElement.compile() method must be used, passing either an
Engine or a Dialect object which will invoke the correct
compiler. Below we use a PostgreSQL dialect:

>>> from sqlalchemy.dialects import postgresql
>>> print(insert_stmt.compile(dialect=postgresql.dialect()))
INSERT INTO my_table (x) VALUES (%(x)s) ON CONFLICT (y) DO NOTHING

For an ORM Query object, the statement can be accessed using the
Query.statement accessor:

statement = query.statement
print(statement.compile(dialect=postgresql.dialect()))

See the FAQ link below for additional detail on direct stringification /
compilation of SQL elements.

TypeError: <operator> not supported between instances of ‘ColumnProperty’ and <something>¶

This often occurs when attempting to use a column_property() or
deferred() object in the context of a SQL expression, usually within
declarative such as:

class Bar(Base):
    __tablename__ = "bar"

    id = Column(Integer, primary_key=True)
    cprop = deferred(Column(Integer))

    __table_args__ = (CheckConstraint(cprop > 5),)

Above, the cprop attribute is used inline before it has been mapped,
however this cprop attribute is not a Column,
it’s a ColumnProperty, which is an interim object and therefore
does not have the full functionality of either the Column object
or the InstrumentedAttribute object that will be mapped onto the
Bar class once the declarative process is complete.

While the ColumnProperty does have a __clause_element__() method,
which allows it to work in some column-oriented contexts, it can’t work in an
open-ended comparison context as illustrated above, since it has no Python
__eq__() method that would allow it to interpret the comparison to the
number “5” as a SQL expression and not a regular Python comparison.

The solution is to access the Column directly using the
ColumnProperty.expression attribute:

class Bar(Base):
    __tablename__ = "bar"

    id = Column(Integer, primary_key=True)
    cprop = deferred(Column(Integer))

    __table_args__ = (CheckConstraint(cprop.expression > 5),)

A value is required for bind parameter <x> (in parameter group <y>)¶

This error occurs when a statement makes use of bindparam() either
implicitly or explicitly and does not provide a value when the statement
is executed:

stmt = select(table.c.column).where(table.c.id == bindparam("my_param"))

result = conn.execute(stmt)

Above, no value has been provided for the parameter “my_param”. The correct
approach is to provide a value:

result = conn.execute(stmt, my_param=12)

When the message takes the form “a value is required for bind parameter <x>
in parameter group <y>”, the message is referring to the “executemany” style
of execution. In this case, the statement is typically an INSERT, UPDATE,
or DELETE and a list of parameters is being passed. In this format, the
statement may be generated dynamically to include parameter positions for
every parameter given in the argument list, where it will use the
first set of parameters to determine what these should be.

For example, the statement below is calculated based on the first parameter
set to require the parameters, “a”, “b”, and “c” — these names determine
the final string format of the statement which will be used for each
set of parameters in the list. As the second entry does not contain “b”,
this error is generated:

m = MetaData()
t = Table(
    't', m,
    Column('a', Integer),
    Column('b', Integer),
    Column('c', Integer)
)

e.execute(
    t.insert(), [
        {"a": 1, "b": 2, "c": 3},
        {"a": 2, "c": 4},
        {"a": 3, "b": 4, "c": 5},
    ]
)

sqlalchemy.exc.StatementError: (sqlalchemy.exc.InvalidRequestError)
A value is required for bind parameter 'b', in parameter group 1
[SQL: u'INSERT INTO t (a, b, c) VALUES (?, ?, ?)']
[parameters: [{'a': 1, 'c': 3, 'b': 2}, {'a': 2, 'c': 4}, {'a': 3, 'c': 5, 'b': 4}]]

Since “b” is required, pass it as None so that the INSERT may proceed:

e.execute(
    t.insert(),
    [
        {"a": 1, "b": 2, "c": 3},
        {"a": 2, "b": None, "c": 4},
        {"a": 3, "b": 4, "c": 5},
    ],
)

Expected FROM clause, got Select. To create a FROM clause, use the .subquery() method¶

This refers to a change made as of SQLAlchemy 1.4 where a SELECT statement as generated
by a function such as select(), but also including things like unions and textual
SELECT expressions are no longer considered to be FromClause objects and
can’t be placed directly in the FROM clause of another SELECT statement without them
being wrapped in a Subquery first. This is a major conceptual change in the
Core and the full rationale is discussed at A SELECT statement is no longer implicitly considered to be a FROM clause.

Given an example as:

m = MetaData()
t = Table("t", m, Column("a", Integer), Column("b", Integer), Column("c", Integer))
stmt = select(t)

Above, stmt represents a SELECT statement. The error is produced when we want
to use stmt directly as a FROM clause in another SELECT, such as if we
attempted to select from it:

new_stmt_1 = select(stmt)

Or if we wanted to use it in a FROM clause such as in a JOIN:

new_stmt_2 = select(some_table).select_from(some_table.join(stmt))

In previous versions of SQLAlchemy, using a SELECT inside of another SELECT
would produce a parenthesized, unnamed subquery. In most cases, this form of
SQL is not very useful as databases like MySQL and PostgreSQL require that
subqueries in FROM clauses have named aliases, which means using the
SelectBase.alias() method or as of 1.4 using the
SelectBase.subquery() method to produce this. On other databases, it
is still much clearer for the subquery to have a name to resolve any ambiguity
on future references to column names inside the subquery.

Beyond the above practical reasons, there are a lot of other SQLAlchemy-oriented
reasons the change is being made. The correct form of the above two statements
therefore requires that SelectBase.subquery() is used:

subq = stmt.subquery()

new_stmt_1 = select(subq)

new_stmt_2 = select(some_table).select_from(some_table.join(subq))

An alias is being generated automatically for raw clauseelement¶

New in version 1.4.26.

This deprecation warning refers to a very old and likely not well known pattern
that applies to the legacy Query.join() method as well as the
2.0 style Select.join() method, where a join can be stated
in terms of a relationship() but the target is the
Table or other Core selectable to which the class is mapped,
rather than an ORM entity such as a mapped class or aliased()
construct:

a1 = Address.__table__

q = (
    s.query(User)
    .join(a1, User.addresses)
    .filter(Address.email_address == "ed@foo.com")
    .all()
)

The above pattern also allows an arbitrary selectable, such as
a Core Join or Alias object,
however there is no automatic adaptation of this element, meaning the
Core element would need to be referred towards directly:

a1 = Address.__table__.alias()

q = (
    s.query(User)
    .join(a1, User.addresses)
    .filter(a1.c.email_address == "ed@foo.com")
    .all()
)

The correct way to specify a join target is always by using the mapped
class itself or an aliased object, in the latter case using the
PropComparator.of_type() modifier to set up an alias:

# normal join to relationship entity
q = s.query(User).join(User.addresses).filter(Address.email_address == "ed@foo.com")

# name Address target explicitly, not necessary but legal
q = (
    s.query(User)
    .join(Address, User.addresses)
    .filter(Address.email_address == "ed@foo.com")
)

Join to an alias:

from sqlalchemy.orm import aliased

a1 = aliased(Address)

# of_type() form; recommended
q = (
    s.query(User)
    .join(User.addresses.of_type(a1))
    .filter(a1.email_address == "ed@foo.com")
)

# target, onclause form
q = s.query(User).join(a1, User.addresses).filter(a1.email_address == "ed@foo.com")

An alias is being generated automatically due to overlapping tables¶

New in version 1.4.26.

This warning is typically generated when querying using the
Select.join() method or the legacy Query.join() method
with mappings that involve joined table inheritance. The issue is that when
joining between two joined inheritance models that share a common base table, a
proper SQL JOIN between the two entities cannot be formed without applying an
alias to one side or the other; SQLAlchemy applies an alias to the right side
of the join. For example given a joined inheritance mapping as:

class Employee(Base):
    __tablename__ = "employee"
    id = Column(Integer, primary_key=True)
    manager_id = Column(ForeignKey("manager.id"))
    name = Column(String(50))
    type = Column(String(50))

    reports_to = relationship("Manager", foreign_keys=manager_id)

    __mapper_args__ = {
        "polymorphic_identity": "employee",
        "polymorphic_on": type,
    }


class Manager(Employee):
    __tablename__ = "manager"
    id = Column(Integer, ForeignKey("employee.id"), primary_key=True)

    __mapper_args__ = {
        "polymorphic_identity": "manager",
        "inherit_condition": id == Employee.id,
    }

The above mapping includes a relationship between the Employee and
Manager classes. Since both classes make use of the “employee” database
table, from a SQL perspective this is a
self referential relationship. If we wanted to
query from both the Employee and Manager models using a join, at the
SQL level the “employee” table needs to be included twice in the query, which
means it must be aliased. When we create such a join using the SQLAlchemy
ORM, we get SQL that looks like the following:

>>> stmt = select(Employee, Manager).join(Employee.reports_to)
>>> print(stmt)
SELECT employee.id, employee.manager_id, employee.name,
employee.type, manager_1.id AS id_1, employee_1.id AS id_2,
employee_1.manager_id AS manager_id_1, employee_1.name AS name_1,
employee_1.type AS type_1
FROM employee JOIN
(employee AS employee_1 JOIN manager AS manager_1 ON manager_1.id = employee_1.id)
ON manager_1.id = employee.manager_id

Above, the SQL selects FROM the employee table, representing the
Employee entity in the query. It then joins to a right-nested join of
employee AS employee_1 JOIN manager AS manager_1, where the employee
table is stated again, except as an anonymous alias employee_1. This is the
“automatic generation of an alias” that the warning message refers towards.

When SQLAlchemy loads ORM rows that each contain an Employee and a
Manager object, the ORM must adapt rows from what above is the
employee_1 and manager_1 table aliases into those of the un-aliased
Manager class. This process is internally complex and does not accommodate
for all API features, notably when trying to use eager loading features such as
contains_eager() with more deeply nested queries than are shown
here. As the pattern is unreliable for more complex scenarios and involves
implicit decisionmaking that is difficult to anticipate and follow,
the warning is emitted and this pattern may be considered a legacy feature. The
better way to write this query is to use the same patterns that apply to any
other self-referential relationship, which is to use the aliased()
construct explicitly. For joined-inheritance and other join-oriented mappings,
it is usually desirable to add the use of the aliased.flat
parameter, which will allow a JOIN of two or more tables to be aliased by
applying an alias to the individual tables within the join, rather than
embedding the join into a new subquery:

>>> from sqlalchemy.orm import aliased
>>> manager_alias = aliased(Manager, flat=True)
>>> stmt = select(Employee, manager_alias).join(Employee.reports_to.of_type(manager_alias))
>>> print(stmt)
SELECT employee.id, employee.manager_id, employee.name,
employee.type, manager_1.id AS id_1, employee_1.id AS id_2,
employee_1.manager_id AS manager_id_1, employee_1.name AS name_1,
employee_1.type AS type_1
FROM employee JOIN
(employee AS employee_1 JOIN manager AS manager_1 ON manager_1.id = employee_1.id)
ON manager_1.id = employee.manager_id

If we then wanted to use contains_eager() to populate the
reports_to attribute, we refer to the alias:

>>> stmt = (
...     select(Employee)
...     .join(Employee.reports_to.of_type(manager_alias))
...     .options(contains_eager(Employee.reports_to.of_type(manager_alias)))
... )

Without using the explicit aliased() object, in some more nested
cases the contains_eager() option does not have enough context to
know where to get its data from, in the case that the ORM is “auto-aliasing”
in a very nested context. Therefore it’s best not to rely on this feature
and instead keep the SQL construction as explicit as possible.

Parent instance <x> is not bound to a Session; (lazy load/deferred load/refresh/etc.) operation cannot proceed¶

This is likely the most common error message when dealing with the ORM, and it
occurs as a result of the nature of a technique the ORM makes wide use of known
as lazy loading. Lazy loading is a common object-relational pattern
whereby an object that’s persisted by the ORM maintains a proxy to the database
itself, such that when various attributes upon the object are accessed, their
value may be retrieved from the database lazily. The advantage to this
approach is that objects can be retrieved from the database without having
to load all of their attributes or related data at once, and instead only that
data which is requested can be delivered at that time. The major disadvantage
is basically a mirror image of the advantage, which is that if lots of objects
are being loaded which are known to require a certain set of data in all cases,
it is wasteful to load that additional data piecemeal.

Another caveat of lazy loading beyond the usual efficiency concerns is that
in order for lazy loading to proceed, the object has to remain associated
with a Session in order to be able to retrieve its state. This error message
means that an object has become de-associated with its Session and
is being asked to lazy load data from the database.

The most common reason that objects become detached from their Session
is that the session itself was closed, typically via the Session.close()
method. The objects will then live on to be accessed further, very often
within web applications where they are delivered to a server-side templating
engine and are asked for further attributes which they cannot load.

Mitigation of this error is via these techniques:

• Try not to have detached objects; don’t close the session prematurely — Often, applications will close
  out a transaction before passing off related objects to some other system
  which then fails due to this error. Sometimes the transaction doesn’t need
  to be closed so soon; an example is the web application closes out
  the transaction before the view is rendered. This is often done in the name
  of “correctness”, but may be seen as a mis-application of “encapsulation”,
  as this term refers to code organization, not actual actions. The template that
  uses an ORM object is making use of the proxy pattern
  which keeps database logic encapsulated from the caller. If the
  Session can be held open until the lifespan of the objects are done,
  this is the best approach.

• Otherwise, load everything that’s needed up front — It is very often impossible to
  keep the transaction open, especially in more complex applications that need
  to pass objects off to other systems that can’t run in the same context
  even though they’re in the same process. In this case, the application
  should prepare to deal with detached objects,
  and should try to make appropriate use of eager loading to ensure
  that objects have what they need up front.

• And importantly, set expire_on_commit to False — When using detached objects, the
  most common reason objects need to re-load data is because they were expired
  from the last call to Session.commit(). This expiration should
  not be used when dealing with detached objects; so the
  Session.expire_on_commit parameter be set to False.
  By preventing the objects from becoming expired outside of the transaction,
  the data which was loaded will remain present and will not incur additional
  lazy loads when that data is accessed.

  Note also that Session.rollback() method unconditionally expires
  all contents in the Session and should also be avoided in
  non-error scenarios.

This Session’s transaction has been rolled back due to a previous exception during flush¶

The flush process of the Session, described at
Flushing, will roll back the database transaction if an error is
encountered, in order to maintain internal consistency. However, once this
occurs, the session’s transaction is now “inactive” and must be explicitly
rolled back by the calling application, in the same way that it would otherwise
need to be explicitly committed if a failure had not occurred.

This is a common error when using the ORM and typically applies to an
application that doesn’t yet have correct “framing” around its
Session operations. Further detail is described in the FAQ at
“This Session’s transaction has been rolled back due to a previous exception during flush.” (or similar).

For relationship <relationship>, delete-orphan cascade is normally configured only on the “one” side of a one-to-many relationship, and not on the “many” side of a many-to-one or many-to-many relationship.¶

This error arises when the “delete-orphan” cascade
is set on a many-to-one or many-to-many relationship, such as:

class A(Base):
    __tablename__ = "a"

    id = Column(Integer, primary_key=True)

    bs = relationship("B", back_populates="a")


class B(Base):
    __tablename__ = "b"
    id = Column(Integer, primary_key=True)
    a_id = Column(ForeignKey("a.id"))

    # this will emit the error message when the mapper
    # configuration step occurs
    a = relationship("A", back_populates="bs", cascade="all, delete-orphan")


configure_mappers()

Above, the “delete-orphan” setting on B.a indicates the intent that
when every B object that refers to a particular A is deleted, that the
A should then be deleted as well. That is, it expresses that the “orphan”
which is being deleted would be an A object, and it becomes an “orphan”
when every B that refers to it is deleted.

The “delete-orphan” cascade model does not support this functionality. The
“orphan” consideration is only made in terms of the deletion of a single object
which would then refer to zero or more objects that are now “orphaned” by
this single deletion, which would result in those objects being deleted as
well. In other words, it is designed only to track the creation of “orphans”
based on the removal of one and only one “parent” object per orphan, which is
the natural case in a one-to-many relationship where a deletion of the
object on the “one” side results in the subsequent deletion of the related
items on the “many” side.

The above mapping in support of this functionality would instead place the
cascade setting on the one-to-many side, which looks like:

class A(Base):
    __tablename__ = "a"

    id = Column(Integer, primary_key=True)

    bs = relationship("B", back_populates="a", cascade="all, delete-orphan")


class B(Base):
    __tablename__ = "b"
    id = Column(Integer, primary_key=True)
    a_id = Column(ForeignKey("a.id"))

    a = relationship("A", back_populates="bs")

Where the intent is expressed that when an A is deleted, all of the
B objects to which it refers are also deleted.

The error message then goes on to suggest the usage of the
relationship.single_parent flag. This flag may be used
to enforce that a relationship which is capable of having many objects
refer to a particular object will in fact have only one object referring
to it at a time. It is used for legacy or other less ideal
database schemas where the foreign key relationships suggest a “many”
collection, however in practice only one object would actually refer
to a given target object at at time. This uncommon scenario
can be demonstrated in terms of the above example as follows:

class A(Base):
    __tablename__ = "a"

    id = Column(Integer, primary_key=True)

    bs = relationship("B", back_populates="a")


class B(Base):
    __tablename__ = "b"
    id = Column(Integer, primary_key=True)
    a_id = Column(ForeignKey("a.id"))

    a = relationship(
        "A",
        back_populates="bs",
        single_parent=True,
        cascade="all, delete-orphan",
    )

The above configuration will then install a validator which will enforce
that only one B may be associated with an A at at time, within
the scope of the B.a relationship:

>>> b1 = B()
>>> b2 = B()
>>> a1 = A()
>>> b1.a = a1
>>> b2.a = a1
sqlalchemy.exc.InvalidRequestError: Instance <A at 0x7eff44359350> is
already associated with an instance of <class '__main__.B'> via its
B.a attribute, and is only allowed a single parent.

Note that this validator is of limited scope and will not prevent multiple
“parents” from being created via the other direction. For example, it will
not detect the same setting in terms of A.bs:

>>> a1.bs = [b1, b2]
>>> session.add_all([a1, b1, b2])
>>> session.commit()
INSERT INTO a DEFAULT VALUES
()
INSERT INTO b (a_id) VALUES (?)
(1,)
INSERT INTO b (a_id) VALUES (?)
(1,)

However, things will not go as expected later on, as the “delete-orphan” cascade
will continue to work in terms of a single lead object, meaning if we
delete either of the B objects, the A is deleted. The other B stays
around, where the ORM will usually be smart enough to set the foreign key attribute
to NULL, but this is usually not what’s desired:

>>> session.delete(b1)
>>> session.commit()
UPDATE b SET a_id=? WHERE b.id = ?
(None, 2)
DELETE FROM b WHERE b.id = ?
(1,)
DELETE FROM a WHERE a.id = ?
(1,)
COMMIT

For all the above examples, similar logic applies to the calculus of a
many-to-many relationship; if a many-to-many relationship sets single_parent=True
on one side, that side can use the “delete-orphan” cascade, however this is
very unlikely to be what someone actually wants as the point of a many-to-many
relationship is so that there can be many objects referring to an object
in either direction.

Overall, “delete-orphan” cascade is usually applied
on the “one” side of a one-to-many relationship so that it deletes objects
in the “many” side, and not the other way around.

Changed in version 1.3.18: The text of the “delete-orphan” error message
when used on a many-to-one or many-to-many relationship has been updated
to be more descriptive.

Instance <instance> is already associated with an instance of <instance> via its <attribute> attribute, and is only allowed a single parent.¶

This error is emitted when the relationship.single_parent flag
is used, and more than one object is assigned as the “parent” of an object at
once.

Given the following mapping:

class A(Base):
    __tablename__ = "a"

    id = Column(Integer, primary_key=True)


class B(Base):
    __tablename__ = "b"
    id = Column(Integer, primary_key=True)
    a_id = Column(ForeignKey("a.id"))

    a = relationship(
        "A",
        single_parent=True,
        cascade="all, delete-orphan",
    )

The intent indicates that no more than a single B object may refer
to a particular A object at once:

>>> b1 = B()
>>> b2 = B()
>>> a1 = A()
>>> b1.a = a1
>>> b2.a = a1
sqlalchemy.exc.InvalidRequestError: Instance <A at 0x7eff44359350> is
already associated with an instance of <class '__main__.B'> via its
B.a attribute, and is only allowed a single parent.

When this error occurs unexpectedly, it is usually because the
relationship.single_parent flag was applied in response
to the error message described at For relationship <relationship>, delete-orphan cascade is normally configured only on the “one” side of a one-to-many relationship, and not on the “many” side of a many-to-one or many-to-many relationship., and the issue is in
fact a misunderstanding of the “delete-orphan” cascade setting. See that
message for details.

relationship X will copy column Q to column P, which conflicts with relationship(s): ‘Y’¶

This warning refers to the case when two or more relationships will write data
to the same columns on flush, but the ORM does not have any means of
coordinating these relationships together. Depending on specifics, the solution
may be that two relationships need to be referred towards one another using
relationship.back_populates, or that one or more of the
relationships should be configured with relationship.viewonly
to prevent conflicting writes, or sometimes that the configuration is fully
intentional and should configure relationship.overlaps to
silence each warning.

For the typical example that’s missing
relationship.back_populates, given the following mapping:

class Parent(Base):
    __tablename__ = "parent"
    id = Column(Integer, primary_key=True)
    children = relationship("Child")


class Child(Base):
    __tablename__ = "child"
    id = Column(Integer, primary_key=True)
    parent_id = Column(ForeignKey("parent.id"))
    parent = relationship("Parent")

The above mapping will generate warnings:

SAWarning: relationship 'Child.parent' will copy column parent.id to column child.parent_id,
which conflicts with relationship(s): 'Parent.children' (copies parent.id to child.parent_id).

The relationships Child.parent and Parent.children appear to be in conflict.
The solution is to apply relationship.back_populates:

class Parent(Base):
    __tablename__ = "parent"
    id = Column(Integer, primary_key=True)
    children = relationship("Child", back_populates="parent")


class Child(Base):
    __tablename__ = "child"
    id = Column(Integer, primary_key=True)
    parent_id = Column(ForeignKey("parent.id"))
    parent = relationship("Parent", back_populates="children")

For more customized relationships where an “overlap” situation may be
intentional and cannot be resolved, the relationship.overlaps
parameter may specify the names of relationships for which the warning should
not take effect. This typically occurs for two or more relationships to the
same underlying table that include custom
relationship.primaryjoin conditions that limit the related
items in each case:

class Parent(Base):
    __tablename__ = "parent"
    id = Column(Integer, primary_key=True)
    c1 = relationship(
        "Child",
        primaryjoin="and_(Parent.id == Child.parent_id, Child.flag == 0)",
        backref="parent",
        overlaps="c2, parent",
    )
    c2 = relationship(
        "Child",
        primaryjoin="and_(Parent.id == Child.parent_id, Child.flag == 1)",
        overlaps="c1, parent",
    )


class Child(Base):
    __tablename__ = "child"
    id = Column(Integer, primary_key=True)
    parent_id = Column(ForeignKey("parent.id"))

    flag = Column(Integer)

Above, the ORM will know that the overlap between Parent.c1,
Parent.c2 and Child.parent is intentional.

Object cannot be converted to ‘persistent’ state, as this identity map is no longer valid.¶

New in version 1.4.26.

This message was added to accommodate for the case where a
Result object that would yield ORM objects is iterated after
the originating Session has been closed, or otherwise had its
Session.expunge_all() method called. When a Session
expunges all objects at once, the internal identity map used by that
Session is replaced with a new one, and the original one
discarded. An unconsumed and unbuffered Result object will
internally maintain a reference to that now-discarded identity map. Therefore,
when the Result is consumed, the objects that would be yielded
cannot be associated with that Session. This arrangement is by
design as it is generally not recommended to iterate an unbuffered
Result object outside of the transactional context in which it
was created:

# context manager creates new Session
with Session(engine) as session_obj:
    result = sess.execute(select(User).where(User.id == 7))

# context manager is closed, so session_obj above is closed, identity
# map is replaced

# iterating the result object can't associate the object with the
# Session, raises this error.
user = result.first()

The above situation typically will not occur when using the asyncio
ORM extension, as when AsyncSession returns a sync-style
Result, the results have been pre-buffered when the statement
was executed. This is to allow secondary eager loaders to invoke without needing
an additional await call.

To pre-buffer results in the above situation using the regular
Session in the same way that the asyncio extension does it,
the prebuffer_rows execution option may be used as follows:

# context manager creates new Session
with Session(engine) as session_obj:

    # result internally pre-fetches all objects
    result = sess.execute(
        select(User).where(User.id == 7), execution_options={"prebuffer_rows": True}
    )

# context manager is closed, so session_obj above is closed, identity
# map is replaced

# pre-buffered objects are returned
user = result.first()

# however they are detached from the session, which has been closed
assert inspect(user).detached
assert inspect(user).session is None

Above, the selected ORM objects are fully generated within the session_obj
block, associated with session_obj and buffered within the
Result object for iteration. Outside the block,
session_obj is closed and expunges these ORM objects. Iterating the
Result object will yield those ORM objects, however as their
originating Session has expunged them, they will be delivered in
the detached state.

AwaitRequired¶

The SQLAlchemy async mode requires an async driver to be used to connect to the db.
This error is usually raised when trying to use the async version of SQLAlchemy
with a non compatible DBAPI.

MissingGreenlet¶

A call to the async DBAPI was initiated outside the greenlet spawn
context usually setup by the SQLAlchemy AsyncIO proxy classes. Usually this
error happens when an IO was attempted in an unexpected place, without using
the provided async api. When using the ORM this may be due to a lazy loading
attempt, which is unsupported when using SQLAlchemy with AsyncIO dialects.

No Inspection Available¶

Using the inspect() function directly on an
AsyncConnection or AsyncEngine object is
not currently supported, as there is not yet an awaitable form of the
Inspector object available. Instead, the object
is used by acquiring it using the
inspect() function in such a way that it refers to the underlying
AsyncConnection.sync_connection attribute of the
AsyncConnection object; the Inspector is
then used in a “synchronous” calling style by using the
AsyncConnection.run_sync() method along with a custom function
that performs the desired operations:

async def async_main():
    async with engine.connect() as conn:
        tables = await conn.run_sync(
            lambda sync_conn: inspect(sync_conn).get_table_names()
        )

The <some function> in SQLAlchemy 2.0 will no longer <something>¶

SQLAlchemy 2.0 represents a major shift for a wide variety of key
SQLAlchemy usage patterns in both the Core and ORM components. The goal
of the 2.0 release is to make a slight readjustment in some of the most
fundamental assumptions of SQLAlchemy since its early beginnings, and
to deliver a newly streamlined usage model that is hoped to be significantly
more minimalist and consistent between the Core and ORM components, as well as
more capable.

Introduced at Migrating to SQLAlchemy 2.0, the SQLAlchemy 2.0 project includes
a comprehensive future compatibility system that’s integrated into the
1.4 series of SQLAlchemy, such that applications will have a clear,
unambiguous, and incremental upgrade path in order to migrate applications to
being fully 2.0 compatible. The RemovedIn20Warning deprecation
warning is at the base of this system to provide guidance on what behaviors in
an existing codebase will need to be modified. An overview of how to enable
this warning is at SQLAlchemy 2.0 Deprecations Mode.

Object is being merged into a Session along the backref cascade¶

This message refers to the “backref cascade” behavior of SQLAlchemy,
removed in version 2.0. This refers to the action of
an object being added into a Session as a result of another
object that’s already present in that session being associated with it.
As this behavior has been shown to be more confusing than helpful,
the relationship.cascade_backrefs and
backref.cascade_backrefs parameters were added, which can
be set to False to disable it, and in SQLAlchemy 2.0 the “cascade backrefs”
behavior has been removed entirely.

For older SQLAlchemy versions, to set
relationship.cascade_backrefs to False on a backref that
is currently configured using the relationship.backref string
parameter, the backref must be declared using the backref() function
first so that the backref.cascade_backrefs parameter may be
passed.

Alternatively, the entire “cascade backrefs” behavior can be turned off
across the board by using the Session in “future” mode,
by passing True for the Session.future parameter.

select() construct created in “legacy” mode; keyword arguments, etc.¶

The select() construct has been updated as of SQLAlchemy
1.4 to support the newer calling style that is standard in
SQLAlchemy 2.0. For backwards compatibility within
the 1.4 series, the construct accepts arguments in both the “legacy” style as well
as the “new” style.

The “new” style features that column and table expressions are passed
positionally to the select() construct only; any other
modifiers to the object must be passed using subsequent method chaining:

# this is the way to do it going forward
stmt = select(table1.c.myid).where(table1.c.myid == table2.c.otherid)

For comparison, a select() in legacy forms of SQLAlchemy,
before methods like Select.where() were even added, would like:

# this is how it was documented in original SQLAlchemy versions
# many years ago
stmt = select([table1.c.myid], whereclause=table1.c.myid == table2.c.otherid)

Or even that the “whereclause” would be passed positionally:

# this is also how it was documented in original SQLAlchemy versions
# many years ago
stmt = select([table1.c.myid], table1.c.myid == table2.c.otherid)

For some years now, the additional “whereclause” and other arguments that are
accepted have been removed from most narrative documentation, leading to a
calling style that is most familiar as the list of column arguments passed
as a list, but no further arguments:

# this is how it's been documented since around version 1.0 or so
stmt = select([table1.c.myid]).where(table1.c.myid == table2.c.otherid)

The document at select() no longer accepts varied constructor arguments, columns are passed positionally describes this change in terms
of 2.0 Migration.

A bind was located via legacy bound metadata, but since future=True is set on this Session, this bind is ignored.¶

The concept of “bound metadata” is present up until SQLAlchemy 1.4; as
of SQLAlchemy 2.0 it’s been removed.

This error refers to the MetaData.bind parameter on the
MetaData object that in turn allows objects like the ORM
Session to associate a particular mapped class with an
Engine. In SQLAlchemy 2.0, the Session must be
linked to each Engine directly. That is, instead of instantiating
the Session or sessionmaker without any arguments,
and associating the Engine with the
MetaData:

engine = create_engine("sqlite://")
Session = sessionmaker()
metadata_obj = MetaData(bind=engine)
Base = declarative_base(metadata=metadata_obj)

class MyClass(Base):
    # ...


session = Session()
session.add(MyClass())
session.commit()

The Engine must instead be associated directly with the
sessionmaker or Session. The
MetaData object should no longer be associated with any
engine:

engine = create_engine("sqlite://")
Session = sessionmaker(engine)
Base = declarative_base()

class MyClass(Base):
    # ...


session = Session()
session.add(MyClass())
session.commit()

In SQLAlchemy 1.4, this 2.0 style behavior is enabled when the
Session.future flag is set on sessionmaker
or Session.

This Compiled object is not bound to any Engine or Connection¶

This error refers to the concept of “bound metadata”, which is a legacy
SQLAlchemy pattern present only in 1.x versions. The issue occurs when one invokes
the Executable.execute() method directly off of a Core expression object
that is not associated with any Engine:

metadata_obj = MetaData()
table = Table("t", metadata_obj, Column("q", Integer))

stmt = select(table)
result = stmt.execute()  # <--- raises

What the logic is expecting is that the MetaData object has
been bound to a Engine:

engine = create_engine("mysql+pymysql://user:pass@host/db")
metadata_obj = MetaData(bind=engine)

Where above, any statement that derives from a Table which
in turn derives from that MetaData will implicitly make use of
the given Engine in order to invoke the statement.

Note that the concept of bound metadata is not present in SQLAlchemy 2.0.
The correct way to invoke statements is via
the Connection.execute() method of a Connection:

with engine.connect() as conn:
    result = conn.execute(stmt)

When using the ORM, a similar facility is available via the Session:

result = session.execute(stmt)

This connection is on an inactive transaction. Please rollback() fully before proceeding¶

This error condition was added to SQLAlchemy as of version 1.4, and does not
apply to SQLAlchemy 2.0. The error
refers to the state where a Connection is placed into a
transaction using a method like Connection.begin(), and then a
further “marker” transaction is created within that scope; the “marker”
transaction is then rolled back using Transaction.rollback() or closed
using Transaction.close(), however the outer transaction is still
present in an “inactive” state and must be rolled back.

The pattern looks like:

engine = create_engine(...)

connection = engine.connect()
transaction1 = connection.begin()

# this is a "sub" or "marker" transaction, a logical nesting
# structure based on "real" transaction transaction1
transaction2 = connection.begin()
transaction2.rollback()

# transaction1 is still present and needs explicit rollback,
# so this will raise
connection.execute(text("select 1"))

Above, transaction2 is a “marker” transaction, which indicates a logical
nesting of transactions within an outer one; while the inner transaction
can roll back the whole transaction via its rollback() method, its commit()
method has no effect except to close the scope of the “marker” transaction
itself. The call to transaction2.rollback() has the effect of
deactivating transaction1 which means it is essentially rolled back
at the database level, however is still present in order to accommodate
a consistent nesting pattern of transactions.

The correct resolution is to ensure the outer transaction is also
rolled back:

This pattern is not commonly used in Core. Within the ORM, a similar issue can
occur which is the product of the ORM’s “logical” transaction structure; this
is described in the FAQ entry at “This Session’s transaction has been rolled back due to a previous exception during flush.” (or similar).

The “subtransaction” pattern is removed in SQLAlchemy 2.0 so that this
particular programming pattern is no longer be available, preventing
this error message.