SQLAlchemy 1.3 Documentation
SQLAlchemy Core
- SQL Expression Language Tutorial
- SQL Statements and Expressions API
- Schema Definition Language
- Column and Data Types
- Engine and Connection Use
- Engine Configuration¶
- Supported Databases
- Database Urls
- Engine Creation API
- Pooling
- Custom DBAPI connect() arguments / on-connect routines
- Configuring Logging
- Working with Engines and Connections
- Connection Pooling
- Core Events
- Engine Configuration¶
- Core API Basics
Project Versions
- Previous: Engine and Connection Use
- Next: Working with Engines and Connections
- Up: Home
- On this page:
- Engine Configuration
- Supported Databases
- Database Urls
- Engine Creation API
- Pooling
- Custom DBAPI connect() arguments / on-connect routines
- Configuring Logging
Engine Configuration¶
The Engine
is the starting point for any SQLAlchemy application. It’s
“home base” for the actual database and its DBAPI, delivered to the SQLAlchemy
application through a connection pool and a Dialect
, which describes how
to talk to a specific kind of database/DBAPI combination.
The general structure can be illustrated as follows:
Where above, an Engine
references both a
Dialect
and a Pool
,
which together interpret the DBAPI’s module functions as well as the behavior
of the database.
Creating an engine is just a matter of issuing a single call,
create_engine()
:
from sqlalchemy import create_engine
engine = create_engine('postgresql://scott:tiger@localhost:5432/mydatabase')
The above engine creates a Dialect
object tailored towards
PostgreSQL, as well as a Pool
object which will establish a DBAPI
connection at localhost:5432
when a connection request is first received.
Note that the Engine
and its underlying Pool
do not
establish the first actual DBAPI connection until the Engine.connect()
method is called, or an operation which is dependent on this method such as
Engine.execute()
is invoked. In this way, Engine
and
Pool
can be said to have a lazy initialization behavior.
The Engine
, once created, can either be used directly to interact with the database,
or can be passed to a Session
object to work with the ORM. This section
covers the details of configuring an Engine
. The next section, Working with Engines and Connections,
will detail the usage API of the Engine
and similar, typically for non-ORM
applications.
Supported Databases¶
SQLAlchemy includes many Dialect
implementations for various
backends. Dialects for the most common databases are included with SQLAlchemy; a handful
of others require an additional install of a separate dialect.
See the section Dialects for information on the various backends available.
Database Urls¶
The create_engine()
function produces an Engine
object based
on a URL. These URLs follow RFC-1738, and usually can include username, password,
hostname, database name as well as optional keyword arguments for additional configuration.
In some cases a file path is accepted, and in others a “data source name” replaces
the “host” and “database” portions. The typical form of a database URL is:
dialect+driver://username:password@host:port/database
Dialect names include the identifying name of the SQLAlchemy dialect,
a name such as sqlite
, mysql
, postgresql
, oracle
, or mssql
.
The drivername is the name of the DBAPI to be used to connect to
the database using all lowercase letters. If not specified, a “default” DBAPI
will be imported if available - this default is typically the most widely
known driver available for that backend.
As the URL is like any other URL, special characters such as those that may
be used in the password need to be URL encoded to be parsed correctly.. Below
is an example of a URL that includes the password "kx%jj5/g"
, where the
percent sign and slash characters are represented as %25
and %2F
,
respectively:
postgresql+pg8000://dbuser:kx%25jj5%2Fg@pghost10/appdb
The encoding for the above password can be generated using urllib.parse:
>>> import urllib.parse
>>> urllib.parse.quote_plus("kx%jj5/g")
'kx%25jj5%2Fg'
Examples for common connection styles follow below. For a full index of detailed information on all included dialects as well as links to third-party dialects, see Dialects.
PostgreSQL¶
The PostgreSQL dialect uses psycopg2 as the default DBAPI. pg8000 is also available as a pure-Python substitute:
# default
engine = create_engine('postgresql://scott:tiger@localhost/mydatabase')
# psycopg2
engine = create_engine('postgresql+psycopg2://scott:tiger@localhost/mydatabase')
# pg8000
engine = create_engine('postgresql+pg8000://scott:tiger@localhost/mydatabase')
More notes on connecting to PostgreSQL at PostgreSQL.
MySQL¶
The MySQL dialect uses mysql-python as the default DBAPI. There are many MySQL DBAPIs available, including MySQL-connector-python and OurSQL:
# default
engine = create_engine('mysql://scott:tiger@localhost/foo')
# mysqlclient (a maintained fork of MySQL-Python)
engine = create_engine('mysql+mysqldb://scott:tiger@localhost/foo')
# PyMySQL
engine = create_engine('mysql+pymysql://scott:tiger@localhost/foo')
More notes on connecting to MySQL at MySQL.
Oracle¶
The Oracle dialect uses cx_oracle as the default DBAPI:
engine = create_engine('oracle://scott:tiger@127.0.0.1:1521/sidname')
engine = create_engine('oracle+cx_oracle://scott:tiger@tnsname')
More notes on connecting to Oracle at Oracle.
Microsoft SQL Server¶
The SQL Server dialect uses pyodbc as the default DBAPI. pymssql is also available:
# pyodbc
engine = create_engine('mssql+pyodbc://scott:tiger@mydsn')
# pymssql
engine = create_engine('mssql+pymssql://scott:tiger@hostname:port/dbname')
More notes on connecting to SQL Server at Microsoft SQL Server.
SQLite¶
SQLite connects to file-based databases, using the Python built-in
module sqlite3
by default.
As SQLite connects to local files, the URL format is slightly different. The “file” portion of the URL is the filename of the database. For a relative file path, this requires three slashes:
# sqlite://<nohostname>/<path>
# where <path> is relative:
engine = create_engine('sqlite:///foo.db')
And for an absolute file path, the three slashes are followed by the absolute path:
# Unix/Mac - 4 initial slashes in total
engine = create_engine('sqlite:////absolute/path/to/foo.db')
# Windows
engine = create_engine('sqlite:///C:\\path\\to\\foo.db')
# Windows alternative using raw string
engine = create_engine(r'sqlite:///C:\path\to\foo.db')
To use a SQLite :memory:
database, specify an empty URL:
engine = create_engine('sqlite://')
More notes on connecting to SQLite at SQLite.
Others¶
See Dialects, the top-level page for all additional dialect documentation.
Engine Creation API¶
Object Name | Description |
---|---|
create_engine(*args, **kwargs) |
Create a new |
engine_from_config(configuration[, prefix], **kwargs) |
Create a new Engine instance using a configuration dictionary. |
make_url(name_or_url) |
Given a string or unicode instance, produce a new URL instance. |
Represent the components of a URL used to connect to a database. |
- function sqlalchemy.create_engine(*args, **kwargs)¶
Create a new
Engine
instance.The standard calling form is to send the URL as the first positional argument, usually a string that indicates database dialect and connection arguments:
engine = create_engine("postgresql://scott:tiger@localhost/test")
Note
Please review Database Urls for general guidelines in composing URL strings. In particular, special characters, such as those often part of passwords, must be URL encoded to be properly parsed.
Additional keyword arguments may then follow it which establish various options on the resulting
Engine
and its underlyingDialect
andPool
constructs:engine = create_engine("mysql://scott:tiger@hostname/dbname", encoding='latin1', echo=True)
The string form of the URL is
dialect[+driver]://user:password@host/dbname[?key=value..]
, wheredialect
is a database name such asmysql
,oracle
,postgresql
, etc., anddriver
the name of a DBAPI, such aspsycopg2
,pyodbc
,cx_oracle
, etc. Alternatively, the URL can be an instance ofURL
.**kwargs
takes a wide variety of options which are routed towards their appropriate components. Arguments may be specific to theEngine
, the underlyingDialect
, as well as thePool
. Specific dialects also accept keyword arguments that are unique to that dialect. Here, we describe the parameters that are common to mostcreate_engine()
usage.Once established, the newly resulting
Engine
will request a connection from the underlyingPool
onceEngine.connect()
is called, or a method which depends on it such asEngine.execute()
is invoked. ThePool
in turn will establish the first actual DBAPI connection when this request is received. Thecreate_engine()
call itself does not establish any actual DBAPI connections directly.- Parameters:
case_sensitive=True¶ – if False, result column names will match in a case-insensitive fashion, that is,
row['SomeColumn']
.connect_args¶ – a dictionary of options which will be passed directly to the DBAPI’s
connect()
method as additional keyword arguments. See the example at Custom DBAPI connect() arguments / on-connect routines.convert_unicode=False¶ –
if set to True, causes all
String
datatypes to act as though theString.convert_unicode
flag has been set toTrue
, regardless of a setting ofFalse
on an individualString
type. This has the effect of causing allString
-based columns to accommodate Python Unicode objects directly as though the datatype were theUnicode
type.Deprecated since version 1.3: The
create_engine.convert_unicode
parameter is deprecated and will be removed in a future release. All modern DBAPIs now support Python Unicode directly and this parameter is unnecessary.creator¶ –
a callable which returns a DBAPI connection. This creation function will be passed to the underlying connection pool and will be used to create all new database connections. Usage of this function causes connection parameters specified in the URL argument to be bypassed.
This hook is not as flexible as the newer
do_connect
hook which allows complete control over how a connection is made to the database, given the full set of URL arguments and state beforehand.See also
do_connect
- event hook that allows full control over DBAPI connection mechanics.echo=False¶ –
if True, the Engine will log all statements as well as a
repr()
of their parameter lists to the default log handler, which defaults tosys.stdout
for output. If set to the string"debug"
, result rows will be printed to the standard output as well. Theecho
attribute ofEngine
can be modified at any time to turn logging on and off; direct control of logging is also available using the standard Pythonlogging
module.See also
Configuring Logging - further detail on how to configure logging.
echo_pool=False¶ –
if True, the connection pool will log informational output such as when connections are invalidated as well as when connections are recycled to the default log handler, which defaults to
sys.stdout
for output. If set to the string"debug"
, the logging will include pool checkouts and checkins. Direct control of logging is also available using the standard Pythonlogging
module.See also
Configuring Logging - further detail on how to configure logging.
empty_in_strategy¶ –
The SQL compilation strategy to use when rendering an IN or NOT IN expression for
ColumnOperators.in_()
where the right-hand side is an empty set. This is a string value that may be one ofstatic
,dynamic
, ordynamic_warn
. Thestatic
strategy is the default, and an IN comparison to an empty set will generate a simple false expression “1 != 1”. Thedynamic
strategy behaves like that of SQLAlchemy 1.1 and earlier, emitting a false expression of the form “expr != expr”, which has the effect of evaluting to NULL in the case of a null expression.dynamic_warn
is the same asdynamic
, however also emits a warning when an empty set is encountered; this because the “dynamic” comparison is typically poorly performing on most databases.New in version 1.2: Added the
empty_in_strategy
setting and additionally defaulted the behavior for empty-set IN comparisons to a static boolean expression.encoding¶ –
Defaults to
utf-8
. This is the string encoding used by SQLAlchemy for string encode/decode operations which occur within SQLAlchemy, outside of the DBAPIs own encoding facilities.Note
The
encoding
parameter deals only with in-Python encoding issues that were prevalent with many DBAPIs under Python 2. Under Python 3 it is mostly unused. For DBAPIs that require client encoding configurations, such as those of MySQL and Oracle, please consult specific dialect documentation for details.All modern DBAPIs that work in Python 3 necessarily feature direct support for Python unicode strings. Under Python 2, this was not always the case. For those scenarios where the DBAPI is detected as not supporting a Python
unicode
object under Python 2, this encoding is used to determine the source/destination encoding. It is not used for those cases where the DBAPI handles unicode directly.To properly configure a system to accommodate Python
unicode
objects, the DBAPI should be configured to handle unicode to the greatest degree as is appropriate - see the notes on unicode pertaining to the specific target database in use at Dialects.Areas where string encoding may need to be accommodated outside of the DBAPI, nearly always under Python 2 only, include zero or more of:
the values passed to bound parameters, corresponding to the
Unicode
type or theString
type whenconvert_unicode
isTrue
;the values returned in result set columns corresponding to the
Unicode
type or theString
type whenconvert_unicode
isTrue
;the string SQL statement passed to the DBAPI’s
cursor.execute()
method;the string names of the keys in the bound parameter dictionary passed to the DBAPI’s
cursor.execute()
as well ascursor.setinputsizes()
methods;the string column names retrieved from the DBAPI’s
cursor.description
attribute.
When using Python 3, the DBAPI is required to support all of the above values as Python
unicode
objects, which in Python 3 are just known asstr
. In Python 2, the DBAPI does not specify unicode behavior at all, so SQLAlchemy must make decisions for each of the above values on a per-DBAPI basis - implementations are completely inconsistent in their behavior.execution_options¶ – Dictionary execution options which will be applied to all connections. See
Connection.execution_options()
hide_parameters¶ –
Boolean, when set to True, SQL statement parameters will not be displayed in INFO logging nor will they be formatted into the string representation of
StatementError
objects.New in version 1.3.8.
See also
Configuring Logging - further detail on how to configure logging.
implicit_returning=True¶ – When
True
, a RETURNING- compatible construct, if available, will be used to fetch newly generated primary key values when a single row INSERT statement is emitted with no existing returning() clause. This applies to those backends which support RETURNING or a compatible construct, including PostgreSQL, Firebird, Oracle, Microsoft SQL Server. Set this toFalse
to disable the automatic usage of RETURNING.isolation_level¶ –
this string parameter is interpreted by various dialects in order to affect the transaction isolation level of the database connection. The parameter essentially accepts some subset of these string arguments:
"SERIALIZABLE"
,"REPEATABLE READ"
,"READ COMMITTED"
,"READ UNCOMMITTED"
and"AUTOCOMMIT"
. Behavior here varies per backend, and individual dialects should be consulted directly.Note that the isolation level can also be set on a per-
Connection
basis as well, using theConnection.execution_options.isolation_level
feature.See also
Connection.default_isolation_level
- view default levelConnection.execution_options.isolation_level
- set perConnection
isolation levelPostgreSQL Transaction Isolation
Setting Transaction Isolation Levels / DBAPI AUTOCOMMIT - for the ORM
json_deserializer¶ –
for dialects that support the
JSON
datatype, this is a Python callable that will convert a JSON string to a Python object. By default, the Pythonjson.loads
function is used.Changed in version 1.3.7: The SQLite dialect renamed this from
_json_deserializer
.json_serializer¶ –
for dialects that support the
JSON
datatype, this is a Python callable that will render a given object as JSON. By default, the Pythonjson.dumps
function is used.Changed in version 1.3.7: The SQLite dialect renamed this from
_json_serializer
.label_length=None¶ –
optional integer value which limits the size of dynamically generated column labels to that many characters. If less than 6, labels are generated as “_(counter)”. If
None
, the value ofdialect.max_identifier_length
, which may be affected via thecreate_engine.max_identifier_length
parameter, is used instead. The value ofcreate_engine.label_length
may not be larger than that ofcreate_engine.max_identfier_length
.See also
listeners¶ – A list of one or more
PoolListener
objects which will receive connection pool events.logging_name¶ –
String identifier which will be used within the “name” field of logging records generated within the “sqlalchemy.engine” logger. Defaults to a hexstring of the object’s id.
See also
Configuring Logging - further detail on how to configure logging.
max_identifier_length¶ –
integer; override the max_identifier_length determined by the dialect. if
None
or zero, has no effect. This is the database’s configured maximum number of characters that may be used in a SQL identifier such as a table name, column name, or label name. All dialects determine this value automatically, however in the case of a new database version for which this value has changed but SQLAlchemy’s dialect has not been adjusted, the value may be passed here.New in version 1.3.9.
See also
max_overflow=10¶ – the number of connections to allow in connection pool “overflow”, that is connections that can be opened above and beyond the pool_size setting, which defaults to five. this is only used with
QueuePool
.module=None¶ – reference to a Python module object (the module itself, not its string name). Specifies an alternate DBAPI module to be used by the engine’s dialect. Each sub-dialect references a specific DBAPI which will be imported before first connect. This parameter causes the import to be bypassed, and the given module to be used instead. Can be used for testing of DBAPIs as well as to inject “mock” DBAPI implementations into the
Engine
.paramstyle=None¶ – The paramstyle to use when rendering bound parameters. This style defaults to the one recommended by the DBAPI itself, which is retrieved from the
.paramstyle
attribute of the DBAPI. However, most DBAPIs accept more than one paramstyle, and in particular it may be desirable to change a “named” paramstyle into a “positional” one, or vice versa. When this attribute is passed, it should be one of the values"qmark"
,"numeric"
,"named"
,"format"
or"pyformat"
, and should correspond to a parameter style known to be supported by the DBAPI in use.pool=None¶ – an already-constructed instance of
Pool
, such as aQueuePool
instance. If non-None, this pool will be used directly as the underlying connection pool for the engine, bypassing whatever connection parameters are present in the URL argument. For information on constructing connection pools manually, see Connection Pooling.poolclass=None¶ – a
Pool
subclass, which will be used to create a connection pool instance using the connection parameters given in the URL. Note this differs frompool
in that you don’t actually instantiate the pool in this case, you just indicate what type of pool to be used.pool_logging_name¶ –
String identifier which will be used within the “name” field of logging records generated within the “sqlalchemy.pool” logger. Defaults to a hexstring of the object’s id.
See also
Configuring Logging - further detail on how to configure logging.
pool_pre_ping¶ –
boolean, if True will enable the connection pool “pre-ping” feature that tests connections for liveness upon each checkout.
New in version 1.2.
See also
pool_size=5¶ – the number of connections to keep open inside the connection pool. This used with
QueuePool
as well asSingletonThreadPool
. WithQueuePool
, apool_size
setting of 0 indicates no limit; to disable pooling, setpoolclass
toNullPool
instead.pool_recycle=-1¶ –
this setting causes the pool to recycle connections after the given number of seconds has passed. It defaults to -1, or no timeout. For example, setting to 3600 means connections will be recycled after one hour. Note that MySQL in particular will disconnect automatically if no activity is detected on a connection for eight hours (although this is configurable with the MySQLDB connection itself and the server configuration as well).
See also
pool_reset_on_return='rollback'¶ –
set the
Pool.reset_on_return
parameter of the underlyingPool
object, which can be set to the values"rollback"
,"commit"
, orNone
.See also
pool_timeout=30¶ – number of seconds to wait before giving up on getting a connection from the pool. This is only used with
QueuePool
.pool_use_lifo=False¶ –
use LIFO (last-in-first-out) when retrieving connections from
QueuePool
instead of FIFO (first-in-first-out). Using LIFO, a server-side timeout scheme can reduce the number of connections used during non- peak periods of use. When planning for server-side timeouts, ensure that a recycle or pre-ping strategy is in use to gracefully handle stale connections.New in version 1.3.
plugins¶ –
string list of plugin names to load. See
CreateEnginePlugin
for background.New in version 1.2.3.
strategy='plain'¶ –
selects alternate engine implementations. Currently available are:
the
threadlocal
strategy, which is described in Using the Threadlocal Execution Strategy;the
mock
strategy, which dispatches all statement execution to a function passed as the argumentexecutor
. See example in the FAQ.
executor=None¶ – a function taking arguments
(sql, *multiparams, **params)
, to which themock
strategy will dispatch all statement execution. Used only bystrategy='mock'
.
- function sqlalchemy.engine_from_config(configuration, prefix='sqlalchemy.', **kwargs)¶
Create a new Engine instance using a configuration dictionary.
The dictionary is typically produced from a config file.
The keys of interest to
engine_from_config()
should be prefixed, e.g.sqlalchemy.url
,sqlalchemy.echo
, etc. The ‘prefix’ argument indicates the prefix to be searched for. Each matching key (after the prefix is stripped) is treated as though it were the corresponding keyword argument to acreate_engine()
call.The only required key is (assuming the default prefix)
sqlalchemy.url
, which provides the database URL.A select set of keyword arguments will be “coerced” to their expected type based on string values. The set of arguments is extensible per-dialect using the
engine_config_types
accessor.- Parameters:
configuration¶ – A dictionary (typically produced from a config file, but this is not a requirement). Items whose keys start with the value of ‘prefix’ will have that prefix stripped, and will then be passed to create_engine.
prefix¶ – Prefix to match and then strip from keys in ‘configuration’.
kwargs¶ – Each keyword argument to
engine_from_config()
itself overrides the corresponding item taken from the ‘configuration’ dictionary. Keyword arguments should not be prefixed.
- function sqlalchemy.engine.url.make_url(name_or_url)¶
Given a string or unicode instance, produce a new URL instance.
The given string is parsed according to the RFC 1738 spec. If an existing URL object is passed, just returns the object.
- class sqlalchemy.engine.url.URL(drivername, username=None, password=None, host=None, port=None, database=None, query=None)¶
Represent the components of a URL used to connect to a database.
This object is suitable to be passed directly to a
create_engine()
call. The fields of the URL are parsed from a string by themake_url()
function. The string format of the URL is an RFC-1738-style string.All initialization parameters are available as public attributes.
Members
- Parameters:
drivername¶ – the name of the database backend. This name will correspond to a module in sqlalchemy/databases or a third party plug-in.
username¶ – The user name.
password¶ – database password.
host¶ – The name of the host.
port¶ – The port number.
database¶ – The database name.
query¶ – A dictionary of options to be passed to the dialect and/or the DBAPI upon connect.
-
method
sqlalchemy.engine.url.URL.
get_dialect()¶ Return the SQLAlchemy database dialect class corresponding to this URL’s driver name.
-
method
sqlalchemy.engine.url.URL.
translate_connect_args(names=[], **kw)¶ Translate url attributes into a dictionary of connection arguments.
Returns attributes of this url (host, database, username, password, port) as a plain dictionary. The attribute names are used as the keys by default. Unset or false attributes are omitted from the final dictionary.
Pooling¶
The Engine
will ask the connection pool for a
connection when the connect()
or execute()
methods are called. The
default connection pool, QueuePool
, will open connections to the
database on an as-needed basis. As concurrent statements are executed,
QueuePool
will grow its pool of connections to a
default size of five, and will allow a default “overflow” of ten. Since the
Engine
is essentially “home base” for the
connection pool, it follows that you should keep a single
Engine
per database established within an
application, rather than creating a new one for each connection.
Note
QueuePool
is not used by default for SQLite engines. See
SQLite for details on SQLite connection pool usage.
For more information on connection pooling, see Connection Pooling.
Custom DBAPI connect() arguments / on-connect routines¶
For cases where special connection methods are needed, in the vast majority
of cases, it is most appropriate to use one of several hooks at the
create_engine()
level in order to customize this process. These
are described in the following sub-sections.
Special Keyword Arguments Passed to dbapi.connect()¶
All Python DBAPIs accept additional arguments beyond the basics of connecting. Common parameters include those to specify character set encodings and timeout values; more complex data includes special DBAPI constants and objects and SSL sub-parameters. There are two rudimentary means of passing these arguments without complexity.
Add Parameters to the URL Query string¶
Simple string values, as well as some numeric values and boolean flags, may be
often specified in the query string of the URL directly. A common example of
this is DBAPIs that accept an argument encoding
for character encodings,
such as most MySQL DBAPIs:
engine = create_engine(
"mysql+pymysql://user:pass@host/test?charset=utf8mb4"
)
The advantage of using the query string is that additional DBAPI options may be specified in configuration files in a manner that’s portable to the DBAPI specified in the URL. The specific parameters passed through at this level vary by SQLAlchemy dialect. Some dialects pass all arguments through as strings, while others will parse for specific datatypes and move parameters to different places, such as into driver-level DSNs and connect strings. As per-dialect behavior in this area currently varies, the dialect documentation should be consulted for the specific dialect in use to see if particular parameters are supported at this level.
Tip
A general technique to display the exact arguments passed to the DBAPI
for a given URL may be performed using the Dialect.create_connect_args()
method directly as follows:
>>> from sqlalchemy import create_engine
>>> engine = create_engine("mysql+pymysql://some_user:some_pass@some_host/test?charset=utf8mb4")
>>> args, kwargs = engine.dialect.create_connect_args(engine.url)
>>> args, kwargs
([], {'host': 'some_host', 'database': 'test', 'user': 'some_user', 'password': 'some_pass', 'charset': 'utf8mb4', 'client_flag': 2})
The above args, kwargs
pair is normally passed to the DBAPI as
dbapi.connect(*args, **kwargs)
.
Use the connect_args dictionary parameter¶
A more general system of passing any parameter to the dbapi.connect()
function that is guaranteed to pass all parameters at all times is the
create_engine.connect_args
dictionary parameter. This may be
used for parameters that are otherwise not handled by the dialect when added to
the query string, as well as when special sub-structures or objects must be
passed to the DBAPI. Sometimes it’s just that a particular flag must be sent as
the True
symbol and the SQLAlchemy dialect is not aware of this keyword
argument to coerce it from its string form as presented in the URL. Below
illustrates the use of a psycopg2 “connection factory” that replaces the
underlying implementation the connection:
engine = create_engine(
"postgresql://user:pass@hostname/dbname",
connect_args={"connection_factory": MyConnectionFactory}
)
Another example is the pyodbc “timeout” parameter:
engine = create_engine(
"mssql+pyodbc://user:pass@sqlsrvr?driver=ODBC+Driver+13+for+SQL+Server",
connect_args={"timeout": 30}
)
The above example also illustrates that both URL “query string” parameters as
well as create_engine.connect_args
may be used at the same
time; in the case of pyodbc, the “driver” keyword has special meaning
within the URL.
Controlling how parameters are passed to the DBAPI connect() function¶
Beyond manipulating the parameters passed to connect()
, we can further
customize how the DBAPI connect()
function itself is called using the
DialectEvents.do_connect()
event hook. This hook is passed the full
*args, **kwargs
that the dialect would send to connect()
. These
collections can then be modified in place to alter how they are used:
from sqlalchemy import event
engine = create_engine("postgresql://user:pass@hostname/dbname")
@event.listens_for(engine, "do_connect")
def receive_do_connect(dialect, conn_rec, cargs, cparams):
cparams['connection_factory'] = MyConnectionFactory
Modifying the DBAPI connection after connect, or running commands after connect¶
For a DBAPI connection that SQLAlchemy creates without issue, but where we
would like to modify the completed connection before it’s actually used, such
as for setting special flags or running certain commands, the
PoolEvents.connect()
event hook is the most appropriate hook. This
hook is called for every new connection created, before it is used by
SQLAlchemy:
from sqlalchemy import event
engine = create_engine(
"postgresql://user:pass@hostname/dbname"
)
@event.listens_for(engine, "connect")
def connect(dbapi_connection, connection_record):
cursor = dbapi_connection.cursor()
cursor.execute("SET some session variables")
cursor.close()
Fully Replacing the DBAPI connect()
function¶
Finally, the DialectEvents.do_connect()
event hook can also allow us to take
over the connection process entirely by establishing the connection
and returning it:
from sqlalchemy import event
engine = create_engine(
"postgresql://user:pass@hostname/dbname"
)
@event.listens_for(engine, "do_connect")
def receive_do_connect(dialect, conn_rec, cargs, cparams):
# return the new DBAPI connection with whatever we'd like to
# do
return psycopg2.connect(*cargs, **cparams)
The DialectEvents.do_connect()
hook supersedes the previous
create_engine.creator
hook, which remains available.
DialectEvents.do_connect()
has the distinct advantage that the
complete arguments parsed from the URL are also passed to the user-defined
function which is not the case with create_engine.creator
.
Configuring Logging¶
Python’s standard logging module is used to
implement informational and debug log output with SQLAlchemy. This allows
SQLAlchemy’s logging to integrate in a standard way with other applications
and libraries. There are also two parameters
create_engine.echo
and create_engine.echo_pool
present on create_engine()
which allow immediate logging to sys.stdout
for the purposes of local development; these parameters ultimately interact
with the regular Python loggers described below.
This section assumes familiarity with the above linked logging module. All
logging performed by SQLAlchemy exists underneath the sqlalchemy
namespace, as used by logging.getLogger('sqlalchemy')
. When logging has
been configured (i.e. such as via logging.basicConfig()
), the general
namespace of SA loggers that can be turned on is as follows:
sqlalchemy.engine
- controls SQL echoing. Set tologging.INFO
for SQL query output,logging.DEBUG
for query + result set output. These settings are equivalent toecho=True
andecho="debug"
oncreate_engine.echo
, respectively.sqlalchemy.pool
- controls connection pool logging. Set tologging.INFO
to log connection invalidation and recycle events; set tologging.DEBUG
to additionally log all pool checkins and checkouts. These settings are equivalent topool_echo=True
andpool_echo="debug"
oncreate_engine.echo_pool
, respectively.sqlalchemy.dialects
- controls custom logging for SQL dialects, to the extend that logging is used within specific dialects, which is generally minimal.sqlalchemy.orm
- controls logging of various ORM functions to the extent that logging is used within the ORM, which is generally minimal. Set tologging.INFO
to log some top-level information on mapper configurations.
For example, to log SQL queries using Python logging instead of the
echo=True
flag:
import logging
logging.basicConfig()
logging.getLogger('sqlalchemy.engine').setLevel(logging.INFO)
By default, the log level is set to logging.WARN
within the entire
sqlalchemy
namespace so that no log operations occur, even within an
application that has logging enabled otherwise.
Note
The SQLAlchemy Engine
conserves Python function call
overhead by only emitting log statements when the current logging level is
detected as logging.INFO
or logging.DEBUG
. It only checks this
level when a new connection is procured from the connection pool. Therefore
when changing the logging configuration for an already-running application,
any Connection
that’s currently active, or more commonly a
Session
object that’s active in a transaction, won’t
log any SQL according to the new configuration until a new
Connection
is procured (in the case of
Session
, this is after the current transaction ends
and a new one begins).
More on the Echo Flag¶
As mentioned previously, the create_engine.echo
and create_engine.echo_pool
parameters are a shortcut to immediate logging to sys.stdout
:
>>> from sqlalchemy import create_engine, text
>>> e = create_engine("sqlite://", echo=True, echo_pool='debug')
>>> with e.connect() as conn:
... print(conn.scalar(text("select 'hi'")))
...
2020-10-24 12:54:57,701 DEBUG sqlalchemy.pool.impl.SingletonThreadPool Created new connection <sqlite3.Connection object at 0x7f287819ac60>
2020-10-24 12:54:57,701 DEBUG sqlalchemy.pool.impl.SingletonThreadPool Connection <sqlite3.Connection object at 0x7f287819ac60> checked out from pool
2020-10-24 12:54:57,702 INFO sqlalchemy.engine.Engine select 'hi'
2020-10-24 12:54:57,702 INFO sqlalchemy.engine.Engine ()
hi
2020-10-24 12:54:57,703 DEBUG sqlalchemy.pool.impl.SingletonThreadPool Connection <sqlite3.Connection object at 0x7f287819ac60> being returned to pool
2020-10-24 12:54:57,704 DEBUG sqlalchemy.pool.impl.SingletonThreadPool Connection <sqlite3.Connection object at 0x7f287819ac60> rollback-on-return
Use of these flags is roughly equivalent to:
import logging
logging.basicConfig()
logging.getLogger("sqlalchemy.engine").setLevel(logging.INFO)
logging.getLogger("sqlalchemy.pool").setLevel(logging.DEBUG)
It’s important to note that these two flags work independently of any
existing logging configuration, and will make use of logging.basicConfig()
unconditionally. This has the effect of being configured in addition to
any existing logger configurations. Therefore, when configuring logging
explicitly, ensure all echo flags are set to False at all times, to avoid
getting duplicate log lines.
Setting the Logging Name¶
The logger name of instance such as an Engine
or
Pool
defaults to using a truncated hex identifier
string. To set this to a specific name, use the
create_engine.logging_name
and
create_engine.pool_logging_name
with
sqlalchemy.create_engine()
:
>>> from sqlalchemy import create_engine
>>> from sqlalchemy import text
>>> e = create_engine("sqlite://", echo=True, logging_name='myengine')
>>> with e.connect() as conn:
... conn.execute(text("select 'hi'"))
...
2020-10-24 12:47:04,291 INFO sqlalchemy.engine.Engine.myengine select 'hi'
2020-10-24 12:47:04,292 INFO sqlalchemy.engine.Engine.myengine ()
Hiding Parameters¶
The logging emitted by Engine
also indicates an excerpt
of the SQL parameters that are present for a particular statement. To prevent
these parameters from being logged for privacy purposes, enable the
create_engine.hide_parameters
flag:
>>> e = create_engine("sqlite://", echo=True, hide_parameters=True)
>>> with e.connect() as conn:
... conn.execute(text("select :some_private_name"), {"some_private_name": "pii"})
...
2020-10-24 12:48:32,808 INFO sqlalchemy.engine.Engine select ?
2020-10-24 12:48:32,808 INFO sqlalchemy.engine.Engine [SQL parameters hidden due to hide_parameters=True]
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