Data adaptation configuration#
The adaptation system is at the core of Psycopg and allows to customise the way Python objects are converted to PostgreSQL when a query is performed and how PostgreSQL values are converted to Python objects when query results are returned.
For a high-level view of the conversion of types between Python and PostgreSQL please look at Passing parameters to SQL queries. Using the objects described in this page is useful if you intend to customise the adaptation rules.
Every context object derived from another context inherits its adapters mapping: cursors created from a connection inherit the connection’s configuration.
By default, connections obtain an adapters map from the global map exposed as
psycopg.adapters: changing the content of this object will affect every connection created afterwards. You may specify a different template adapters map using the
AdaptersMapinstances, and contain the mapping from Python types and
Dumperclasses, and from PostgreSQL OIDs to
Loaderclasses. Changing this mapping (e.g. writing and registering your own adapters, or using a different configuration of builtin adapters) affects how types are converted between Python and PostgreSQL.
Dumpers (objects implementing the
Dumperprotocol) are the objects used to perform the conversion from a Python object to a bytes sequence in a format understood by PostgreSQL. The string returned shouldn’t be quoted: the value will be passed to the database using functions such as
PQexecParams()so quoting and quotes escaping is not necessary. The dumper usually also suggests to the server what type to use, via its
Loaders (objects implementing the
Loaderprotocol) are the objects used to perform the opposite operation: reading a bytes sequence from PostgreSQL and creating a Python object out of it.
Dumpers and loaders are instantiated on demand by a
Transformerobject when a query is executed.
Changing adapters in a context only affects that context and its children objects created afterwards; the objects already created are not affected. For instance, changing the global context will only change newly created connections, not the ones already existing.
Writing a custom adapter: XML#
Psycopg doesn’t provide adapters for the XML data type, because there are just
too many ways of handling XML in Python. Creating a loader to parse the
PostgreSQL xml type to
ElementTree is very simple, using the
psycopg.adapt.Loader base class and implementing the
>>> import xml.etree.ElementTree as ET >>> from psycopg.adapt import Loader >>> # Create a class implementing the `load()` method. >>> class XmlLoader(Loader): ... def load(self, data): ... return ET.fromstring(data) >>> # Register the loader on the adapters of a context. >>> conn.adapters.register_loader("xml", XmlLoader) >>> # Now just query the database returning XML data. >>> cur = conn.execute( ... """select XMLPARSE (DOCUMENT '<?xml version="1.0"?> ... <book><title>Manual</title><chapter>...</chapter></book>') ... """) >>> elem = cur.fetchone() >>> elem <Element 'book' at 0x7ffb55142ef0>
The opposite operation, converting Python objects to PostgreSQL, is performed
by dumpers. The
psycopg.adapt.Dumper base class makes it easy to implement one:
you only need to implement the
>>> from psycopg.adapt import Dumper >>> class XmlDumper(Dumper): ... # Setting an OID is not necessary but can be helpful ... oid = psycopg.adapters.types["xml"].oid ... ... def dump(self, elem): ... return ET.tostring(elem) >>> # Register the dumper on the adapters of a context >>> conn.adapters.register_dumper(ET.Element, XmlDumper) >>> # Now, in that context, it is possible to use ET.Element objects as parameters >>> conn.execute("SELECT xpath('//title/text()', %s)", [elem]).fetchone() ['Manual']
Note that it is possible to use a
TypesRegistry, exposed by
AdaptContext, to obtain information on builtin types, or
extension types if they have been registered on that context using the
Example: PostgreSQL numeric to Python float#
numeric values are converted to Python
Decimal instances, because both the types allow fixed-precision
arithmetic and are not subject to rounding.
Sometimes, however, you may want to perform floating-point math on
numeric values, and
Decimal may get in the way (maybe because it is
slower, or maybe because mixing
Decimal values causes Python
If you are fine with the potential loss of precision and you simply want to
numeric values as Python
float, you can register on
numeric the same
Loader class used to load
float8 values. Because the PostgreSQL textual
representation of both floats and decimal is the same, the two loaders are
conn = psycopg.connect() conn.execute("SELECT 123.45").fetchone() # Decimal('123.45') conn.adapters.register_loader("numeric", psycopg.types.numeric.FloatLoader) conn.execute("SELECT 123.45").fetchone() # 123.45
In this example the customised adaptation takes effect only on the connection
conn and on any cursor created from it, not on other connections.
Example: handling infinity date#
Suppose you want to work with the “infinity” date which is available in PostgreSQL but not handled by Python:
>>> conn.execute("SELECT 'infinity'::date").fetchone() Traceback (most recent call last): ... DataError: date too large (after year 10K): 'infinity'
One possibility would be to store Python’s
datetime.date.max as PostgreSQL
infinity. For this, let’s create a subclass for the dumper and the loader and
register them in the working scope (globally or just on a connection or
from datetime import date # Subclass existing adapters so that the base case is handled normally. from psycopg.types.datetime import DateLoader, DateDumper class InfDateDumper(DateDumper): def dump(self, obj): if obj == date.max: return b"infinity" elif obj == date.min: return b"-infinity" else: return super().dump(obj) class InfDateLoader(DateLoader): def load(self, data): if data == b"infinity": return date.max elif data == b"-infinity": return date.min else: return super().load(data) # The new classes can be registered globally, on a connection, on a cursor cur.adapters.register_dumper(date, InfDateDumper) cur.adapters.register_loader("date", InfDateLoader) cur.execute("SELECT %s::text, %s::text", [date(2020, 12, 31), date.max]).fetchone() # ('2020-12-31', 'infinity') cur.execute("SELECT '2020-12-31'::date, 'infinity'::date").fetchone() # (datetime.date(2020, 12, 31), datetime.date(9999, 12, 31))
Dumpers and loaders life cycle#
Registering dumpers and loaders will instruct Psycopg to use them in the queries to follow, in the context where they have been registered.
When a query is performed on a
Transformer object is created as a local context to manage
adaptation during the query, instantiating the required dumpers and loaders
and dispatching the values to perform the wanted conversions from Python to
Postgres and back.
Transformercopies the adapters configuration from the
Cursor, thus inheriting all the changes made to the global
psycopg.adaptersconfiguration, the current
For every Python type passed as query argument, the
Transformerwill instantiate a
Dumper. Usually all the objects of the same type will be converted by the same dumper instance.
According to the placeholder used (
%t), Psycopg may pick a binary or a text dumper. When using the
AUTO” format, if the same type has both a text and a binary dumper registered, the last one registered by
register_dumper()will be used.
Sometimes, just looking at the Python type is not enough to decide the best PostgreSQL type to use (for instance the PostgreSQL type of a Python list depends on the objects it contains, whether to use an
bigintdepends on the number size…) In these cases the mechanism provided by
upgrade()is used to create more specific dumpers.
The query is executed. Upon successful request, the result is received as a
For every OID returned by the query, the
Transformerwill instantiate a
Loader. All the values with the same OID will be converted by the same loader instance.
Recursive types (e.g. Python lists, PostgreSQL arrays and composite types) will use the same adaptation rules.
As a consequence it is possible to perform certain choices only once per query (e.g. looking up the connection encoding) and then call a fast-path operation for each value to convert.
Querying will fail if a Python object for which there isn’t a
registered (for the right
Format) is used as query parameter.
If the query returns a data type whose OID doesn’t have a
value will be returned as a string (or bytes string for binary types).