Release: 1.2.19 | Release Date: April 15, 2019

# SQLAlchemy 1.2 Documentation

# Error Messages

This section lists descriptions and background for common error messages and warnings raised or emitted by SQLAlchemy.

SQLAlchemy normally raises errors within the context of a SQLAlchemy-specific exception class. For details on these classes, see Core Exceptions and ORM Exceptions.

SQLAlchemy errors can roughly be separated into two categories, the programming-time error and the runtime error. Programming-time errors are raised as a result of functions or methods being called with incorrect arguments, or from other configuration-oriented methods such as mapper configurations that can’t be resolved. The programming-time error is typically immediate and deterministic. The runtime error on the other hand represents a failure that occurs as a program runs in response to some condition that occurs arbitrarily, such as database connections being exhausted or some data-related issue occurring. Runtime errors are more likely to be seen in the logs of a running application as the program encounters these states in response to load and data being encountered.

Since runtime errors are not as easy to reproduce and often occur in response to some arbitrary condition as the program runs, they are more difficult to debug and also affect programs that have already been put into production.

Within this section, the goal is to try to provide background on some of the most common runtime errors as well as programming time errors.

## Connections and Transactions

### 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.

See also

Connection Pooling

Working with Engines and Connections

## DBAPI Errors

The Python database API, or DBAPI, is a specification for database drivers which can be located at Pep-249. This API specifies a set of exception classes that accommodate the full range of failure modes of the database.

SQLAlchemy does not generate these exceptions directly. Instead, they are intercepted from the database driver and wrapped by the SQLAlchemy-provided exception `DBAPIError`, however the messaging within the exception is generated by the driver, not SQLAlchemy.

### 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.

## SQL Expression Language

### 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 `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.

See also

How do I render SQL expressions as strings, possibly with bound parameters inlined?

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

This error refers to the concept of “bound metadata”, described at Connectionless Execution, Implicit Execution. 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 = MetaData()
    table = Table('t', metadata, 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 = 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 a legacy pattern and in most cases is highly discouraged. The best way to invoke the statement is to pass it to 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.exxecute(stmt)

See also

Connectionless Execution, Implicit Execution

### 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},
        ]
    )

See also

Bind Parameter Objects

Executing Multiple Statements

## Object Relational Mapping

### 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 two general techniques:

- 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.

- 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 try to make appropriate use of eager loading to ensure that objects have what they need up front. As an additional measure, special directives like the `raiseload()` option can ensure that systems don’t call upon lazy loading when its not expected.

  See also

  Relationship Loading Techniques - detailed documentation on eager loading and other relationship-oriented loading techniques

## Core Exception Classes

See Core Exceptions for Core exception classes.

## ORM Exception Classes

See ORM Exceptions for ORM exception classes.