Playhouse, extensions to Peewee

Peewee comes with numerous extension modules which are collected under the playhouse namespace. Despite the silly name, there are some very useful extensions, particularly those that expose vendor-specific database features like the SQLite Extensions and Postgresql Extensions extensions.

Below you will find a loosely organized listing of the various modules that make up the playhouse.

Database drivers / vendor-specific database functionality

• SQLite Extensions (on its own page)
• SqliteQ
• Sqlite User-Defined Functions
• apsw, an advanced sqlite driver
• Sqlcipher backend
• Postgresql Extensions

High-level features

• Fields
• Shortcuts
• Hybrid Attributes
• Signal support
• DataSet

Database management and framework integration

• pwiz, a model generator
• Schema Migrations
• Connection pool
• Reflection
• Database URL
• Test Utils
• Flask Utils

SqliteQ

The playhouse.sqliteq module provides a subclass of SqliteExtDatabase, that will serialize concurrent writes to a SQLite database. SqliteQueueDatabase can be used as a drop-in replacement for the regular SqliteDatabase if you want simple read and write access to a SQLite database from multiple threads.

SQLite only allows one connection to write to the database at any given time. As a result, if you have a multi-threaded application (like a web-server, for example) that needs to write to the database, you may see occasional errors when one or more of the threads attempting to write cannot acquire the lock.

SqliteQueueDatabase is designed to simplify things by sending all write queries through a single, long-lived connection. The benefit is that you get the appearance of multiple threads writing to the database without conflicts or timeouts. The downside, however, is that you cannot issue write transactions that encompass multiple queries – all writes run in autocommit mode, essentially.

Note

The module gets its name from the fact that all write queries get put into a thread-safe queue. A single worker thread listens to the queue and executes all queries that are sent to it.

Transactions

Because all queries are serialized and executed by a single worker thread, it is possible for transactional SQL from separate threads to be executed out-of-order. In the example below, the transaction started by thread “B” is rolled back by thread “A” (with bad consequences!):

• Thread A: UPDATE transplants SET organ=’liver’, …;
• Thread B: BEGIN TRANSACTION;
• Thread B: UPDATE life_support_system SET timer += 60 …;
• Thread A: ROLLBACK; – Oh no….

Since there is a potential for queries from separate transactions to be interleaved, the transaction() and atomic() methods are disabled on SqliteQueueDatabase.

For cases when you wish to temporarily write to the database from a different thread, you can use the pause() and unpause() methods. These methods block the caller until the writer thread is finished with its current workload. The writer then disconnects and the caller takes over until unpause is called.

The stop(), start(), and is_stopped() methods can also be used to control the writer thread.

Note

Take a look at SQLite’s isolation documentation for more information about how SQLite handles concurrent connections.

Code sample

Creating a database instance does not require any special handling. The SqliteQueueDatabase accepts some special parameters which you should be aware of, though. If you are using gevent, you must specify use_gevent=True when instantiating your database – this way Peewee will know to use the appropriate objects for handling queueing, thread creation, and locking.

from playhouse.sqliteq import SqliteQueueDatabase
db = SqliteQueueDatabase(
    'my_app.db',
    use_gevent=False,  # Use the standard library "threading" module.
    autostart=False,  # The worker thread now must be started manually.
    queue_max_size=64,  # Max. # of pending writes that can accumulate.
    results_timeout=5.0)  # Max. time to wait for query to be executed.

If autostart=False, as in the above example, you will need to call start() to bring up the worker threads that will do the actual write query execution.

@app.before_first_request
def _start_worker_threads():
    db.start()

If you plan on performing SELECT queries or generally wanting to access the database, you will need to call connect() and close() as you would with any other database instance.

When your application is ready to terminate, use the stop() method to shut down the worker thread. If there was a backlog of work, then this method will block until all pending work is finished (though no new work is allowed).

import atexit
@atexit.register
def _stop_worker_threads():
    db.stop()

Lastly, the is_stopped() method can be used to determine whether the database writer is up and running.

Sqlite User-Defined Functions

The sqlite_udf playhouse module contains a number of user-defined functions, aggregates, and table-valued functions, which you may find useful. The functions are grouped in collections and you can register these user-defined extensions individually, by collection, or register everything.

Scalar functions are functions which take a number of parameters and return a single value. For example, converting a string to upper-case, or calculating the MD5 hex digest.

Aggregate functions are like scalar functions that operate on multiple rows of data, producing a single result. For example, calculating the sum of a list of integers, or finding the smallest value in a particular column.

Table-valued functions are simply functions that can return multiple rows of data. For example, a regular-expression search function that returns all the matches in a given string, or a function that accepts two dates and generates all the intervening days.

Note

To use table-valued functions, you will need to build the playhouse._sqlite_ext C extension.

Registering user-defined functions:

db = SqliteDatabase('my_app.db')
# Register *all* functions.
register_all(db)
# Alternatively, you can register individual groups. This will just
# register the DATE and MATH groups of functions.
register_groups(db, 'DATE', 'MATH')
# If you only wish to register, say, the aggregate functions for a
# particular group or groups, you can:
register_aggregate_groups(db, 'DATE')

Using a library function (“hostname”):

# Assume we have a model, Link, that contains lots of arbitrary URLs.
# We want to discover the most common hosts that have been linked.
query = (Link
         .select(fn.hostname(Link.url).alias('host'), fn.COUNT(Link.id))
         .group_by(fn.hostname(Link.url))
         .order_by(fn.COUNT(Link.id).desc())
         .tuples())
# Print the hostname along with number of links associated with it.
for host, count in query:
    print('%s: %s' % (host, count))

Functions, listed by collection name

Scalar functions are indicated by (f), aggregate functions by (a), and table-valued functions by (t).

CONTROL_FLOW

if_then_else(cond, truthy[, falsey=None])

Simple ternary-type operator, where, depending on the truthiness of the cond parameter, either the truthy or falsey value will be returned.

DATE

strip_tz(date_str)

The time is not adjusted in any way, the timezone is simply removed.

humandelta(nseconds[, glue=’, ‘])

Example, 86471 -> “1 day, 1 minute, 11 seconds”

mintdiff(datetime_value)

Aggregate function that computes the minimum difference between any two datetimes.

avgtdiff(datetime_value)

Aggregate function that computes the average difference between consecutive values in the list.

duration(datetime_value)

Aggregate function that computes the duration from the smallest to the largest value in the list, returned in seconds.

date_series(start, stop[, step_seconds=86400])

Table-value function that returns rows consisting of the date/+time values encountered iterating from start to stop, step_seconds at a time.

Additionally, if start does not have a time component and step_seconds is greater-than-or-equal-to one day (86400 seconds), the values returned will be dates. Conversely, if start does not have a date component, values will be returned as times. Otherwise values are returned as datetimes.

Example:

SELECT * FROM date_series('2017-01-28', '2017-02-02');
value
-----
2017-01-28
2017-01-29
2017-01-30
2017-01-31
2017-02-01
2017-02-02

FILE

file_ext(filename)

file_read(filename)

HELPER

gzip(data[, compression=9])

gunzip(data)

hostname(url)

toggle(key)

Toggle a key between True/False state. Example:

>>> toggle('my-key')
True
>>> toggle('my-key')
False
>>> toggle('my-key')
True

setting(key[, value=None])

Store/retrieve a setting in memory and persist during lifetime of application. To get the current value, only specify the key. To set a new value, call with key and new value.

clear_toggles()

Clears all state associated with the toggle() function.

clear_settings()

Clears all state associated with the setting() function.

MATH

randomrange(start[, stop=None[, step=None]])

Return a random integer between [start, end).

gauss_distribution(mean, sigma)

sqrt(n)

Calculate the square root of n.

tonumber(s)

mode(val)

Aggregate function which calculates mode of values.

minrange(val)

Aggregate function which calculates the minimal distance between two numbers in the sequence.

avgrange(val)

Aggregate function which calculates the average distance between two consecutive numbers in the sequence.

range(val)

Aggregate function which returns range of values observed.

median(val)

Aggregate function which calculates the middle value in a sequence.

Note

Only available if you compiled the _sqlite_udf extension.

STRING

substr_count(haystack, needle)

Returns number of times needle appears in haystack.

strip_chars(haystack, chars)

Strips any characters in chars from beginning and end of haystack.

damerau_levenshtein_dist(s1, s2)

Computes the edit distance from s1 to s2 using the damerau variant of the levenshtein algorithm.

Note

Only available if you compiled the _sqlite_udf extension.

levenshtein_dist(s1, s2)

Computes the edit distance from s1 to s2 using the levenshtein algorithm.

Note

Only available if you compiled the _sqlite_udf extension.

str_dist(s1, s2)

Computes the edit distance from s1 to s2 using the standard library SequenceMatcher’s algorithm.

Note

Only available if you compiled the _sqlite_udf extension.

regex_search(regex, search_string)

Table-value function that searches a string for substrings that match the provided regex. Returns rows for each match found.

Example:

SELECT * FROM regex_search('\w+', 'extract words, ignore! symbols');
value
-----
extract
words
ignore
symbols

apsw, an advanced sqlite driver

The apsw_ext module contains a database class suitable for use with the apsw sqlite driver.

APSW Project page: https://github.com/rogerbinns/apsw

APSW is a really neat library that provides a thin wrapper on top of SQLite’s C interface, making it possible to use all of SQLite’s advanced features.

Here are just a few reasons to use APSW, taken from the documentation:

• APSW gives all functionality of SQLite, including virtual tables, virtual file system, blob i/o, backups and file control.
• Connections can be shared across threads without any additional locking.
• Transactions are managed explicitly by your code.
• APSW can handle nested transactions.
• Unicode is handled correctly.
• APSW is faster.

For more information on the differences between apsw and pysqlite, check the apsw docs.

How to use the APSWDatabase

from apsw_ext import *
db = APSWDatabase(':memory:')
class BaseModel(Model):
    class Meta:
        database = db
class SomeModel(BaseModel):
    col1 = CharField()
    col2 = DateTimeField()

apsw_ext API notes

APSWDatabase extends the SqliteExtDatabase and inherits its advanced features.

class APSWDatabase(database, \*connect_kwargs*)

• register_module(mod_name, mod_inst)

  Provides a way of globally registering a module. For more information, see the documentation on virtual tables.

  Parameters:	mod_name (string) – name to use for module mod_inst (object) – an object implementing the Virtual Table interface

• unregister_module(mod_name)

  Unregister a module.

  Parameters:	mod_name (string) – name to use for module

Note

Be sure to use the Field subclasses defined in the apsw_ext module, as they will properly handle adapting the data types for storage.

For example, instead of using peewee.DateTimeField, be sure you are importing and using playhouse.apsw_ext.DateTimeField.

Sqlcipher backend

• Although this extention’s code is short, it has not been properly peer-reviewed yet and may have introduced vulnerabilities.
• The code contains minimum values for passphrase length and kdf_iter, as well as a default value for the later. Do not regard these numbers as advice. Consult the docs at http://sqlcipher.net/sqlcipher-api/ and security experts.

Also note that this code relies on pysqlcipher and sqlcipher, and the code there might have vulnerabilities as well, but since these are widely used crypto modules, we can expect “short zero days” there.

sqlcipher_ext API notes

class SqlCipherDatabase(database, passphrase, kdf_iter=64000, \*kwargs*)

Subclass of SqliteDatabase that stores the database encrypted. Instead of the standard sqlite3 backend, it uses pysqlcipher: a python wrapper for sqlcipher, which – in turn – is an encrypted wrapper around sqlite3, so the API is identical to SqliteDatabase’s, except for object construction parameters:

• If the database file doesn’t exist, it will be created with encryption by a key derived from passhprase with kdf_iter PBKDF2 iterations.
• When trying to open an existing database, passhprase and kdf_iter should be identical to the ones used when it was created.

Notes:

• [Hopefully] there’s no way to tell whether the passphrase is wrong or the file is corrupt. In both cases – the first time we try to acces the database – a DatabaseError error is raised, with the exact message: "file is encrypted or is not a database".

  As mentioned above, this only happens when you access the databse, so if you need to know right away whether the passphrase was correct, you can trigger this check by calling [e.g.] get_tables() (see example below).

• Most applications can expect failed attempts to open the database (common case: prompting the user for passphrase), so the database can’t be hardwired into the Meta of model classes. To defer initialization, pass None in to the database.

Example:

db = SqlCipherDatabase(None)
class BaseModel(Model):
    """Parent for all app's models"""
    class Meta:
        # We won't have a valid db until user enters passhrase.
        database = db
# Derive our model subclasses
class Person(BaseModel):
    name = TextField(primary_key=True)
right_passphrase = False
while not right_passphrase:
    db.init(
        'testsqlcipher.db',
        passphrase=get_passphrase_from_user())
    try:  # Actually execute a query against the db to test passphrase.
        db.get_tables()
    except DatabaseError as exc:
        # This error indicates the password was wrong.
        if exc.args[0] == 'file is encrypted or is not a database':
            tell_user_the_passphrase_was_wrong()
            db.init(None)  # Reset the db.
        else:
            raise exc
    else:
        # The password was correct.
        right_passphrase = True

See also: a slightly more elaborate example.

Postgresql Extensions

The postgresql extensions module provides a number of “postgres-only” functions, currently:

• hstore support
• json support, including jsonb for Postgres 9.4.
• server-side cursors
• full-text search
• ArrayField field type, for storing arrays.
• HStoreField field type, for storing key/value pairs.
• IntervalField field type, for storing timedelta objects.
• JSONField field type, for storing JSON data.
• BinaryJSONField field type for the jsonb JSON data type.
• TSVectorField field type, for storing full-text search data.
• DateTimeTZ field type, a timezone-aware datetime field.

In the future I would like to add support for more of postgresql’s features. If there is a particular feature you would like to see added, please open a Github issue.

Warning

In order to start using the features described below, you will need to use the extension PostgresqlExtDatabase class instead of PostgresqlDatabase.

The code below will assume you are using the following database and base model:

from playhouse.postgres_ext import *
ext_db = PostgresqlExtDatabase('peewee_test', user='postgres')
class BaseExtModel(Model):
    class Meta:
        database = ext_db

hstore support

Postgresql hstore is an embedded key/value store. With hstore, you can store arbitrary key/value pairs in your database alongside structured relational data.

To use hstore, you need to specify an additional parameter when instantiating your PostgresqlExtDatabase:

# Specify "register_hstore=True":
db = PostgresqlExtDatabase('my_db', register_hstore=True)

Currently the postgres_ext module supports the following operations:

• Store and retrieve arbitrary dictionaries
• Filter by key(s) or partial dictionary
• Update/add one or more keys to an existing dictionary
• Delete one or more keys from an existing dictionary
• Select keys, values, or zip keys and values
• Retrieve a slice of keys/values
• Test for the existence of a key
• Test that a key has a non-NULL value

Using hstore

To start with, you will need to import the custom database class and the hstore functions from playhouse.postgres_ext (see above code snippet). Then, it is as simple as adding a HStoreField to your model:

class House(BaseExtModel):
    address = CharField()
    features = HStoreField()

You can now store arbitrary key/value pairs on House instances:

>>> h = House.create(
...     address='123 Main St',
...     features={'garage': '2 cars', 'bath': '2 bath'})
...
>>> h_from_db = House.get(House.id == h.id)
>>> h_from_db.features
{'bath': '2 bath', 'garage': '2 cars'}

You can filter by individual key, multiple keys or partial dictionary:

>>> query = House.select()
>>> garage = query.where(House.features.contains('garage'))
>>> garage_and_bath = query.where(House.features.contains(['garage', 'bath']))
>>> twocar = query.where(House.features.contains({'garage': '2 cars'}))

Suppose you want to do an atomic update to the house:

>>> new_features = House.features.update({'bath': '2.5 bath', 'sqft': '1100'})
>>> query = House.update(features=new_features)
>>> query.where(House.id == h.id).execute()
1
>>> h = House.get(House.id == h.id)
>>> h.features
{'bath': '2.5 bath', 'garage': '2 cars', 'sqft': '1100'}

Or, alternatively an atomic delete:

>>> query = House.update(features=House.features.delete('bath'))
>>> query.where(House.id == h.id).execute()
1
>>> h = House.get(House.id == h.id)
>>> h.features
{'garage': '2 cars', 'sqft': '1100'}

Multiple keys can be deleted at the same time:

>>> query = House.update(features=House.features.delete('garage', 'sqft'))

You can select just keys, just values, or zip the two:

>>> for h in House.select(House.address, House.features.keys().alias('keys')):
...     print(h.address, h.keys)
123 Main St [u'bath', u'garage']
>>> for h in House.select(House.address, House.features.values().alias('vals')):
...     print(h.address, h.vals)
123 Main St [u'2 bath', u'2 cars']
>>> for h in House.select(House.address, House.features.items().alias('mtx')):
...     print(h.address, h.mtx)
123 Main St [[u'bath', u'2 bath'], [u'garage', u'2 cars']]

You can retrieve a slice of data, for example, all the garage data:

>>> query = House.select(House.address, House.features.slice('garage').alias('garage_data'))
>>> for house in query:
...     print(house.address, house.garage_data)
123 Main St {'garage': '2 cars'}

You can check for the existence of a key and filter rows accordingly:

>>> has_garage = House.features.exists('garage')
>>> for house in House.select(House.address, has_garage.alias('has_garage')):
...     print(house.address, house.has_garage)
123 Main St True
>>> for house in House.select().where(House.features.exists('garage')):
...     print(house.address, house.features['garage'])  # <-- just houses w/garage data
123 Main St 2 cars

Interval support

Postgres supports durations through the INTERVAL data-type (docs).

class IntervalField([null=False[, …]])

Field class capable of storing Python datetime.timedelta instances.

Example:

from datetime import timedelta
from playhouse.postgres_ext import *
db = PostgresqlExtDatabase('my_db')
class Event(Model):
    location = CharField()
    duration = IntervalField()
    start_time = DateTimeField()
    class Meta:
        database = db
    @classmethod
    def get_long_meetings(cls):
        return cls.select().where(cls.duration > timedelta(hours=1))

JSON Support

peewee has basic support for Postgres’ native JSON data type, in the form of JSONField. As of version 2.4.7, peewee also supports the Postgres 9.4 binary json jsonb type, via BinaryJSONField.

Warning

Postgres supports a JSON data type natively as of 9.2 (full support in 9.3). In order to use this functionality you must be using the correct version of Postgres with psycopg2 version 2.5 or greater.

To use BinaryJSONField, which has many performance and querying advantages, you must have Postgres 9.4 or later.

Note

You must be sure your database is an instance of PostgresqlExtDatabase in order to use the JSONField.

Here is an example of how you might declare a model with a JSON field:

import json
import urllib2
from playhouse.postgres_ext import *
db = PostgresqlExtDatabase('my_database')
class APIResponse(Model):
    url = CharField()
    response = JSONField()
    class Meta:
        database = db
    @classmethod
    def request(cls, url):
        fh = urllib2.urlopen(url)
        return cls.create(url=url, response=json.loads(fh.read()))
APIResponse.create_table()
# Store a JSON response.
offense = APIResponse.request('http://crime-api.com/api/offense/')
booking = APIResponse.request('http://crime-api.com/api/booking/')
# Query a JSON data structure using a nested key lookup:
offense_responses = APIResponse.select().where(
    APIResponse.response['meta']['model'] == 'offense')
# Retrieve a sub-key for each APIResponse. By calling .as_json(), the
# data at the sub-key will be returned as Python objects (dicts, lists,
# etc) instead of serialized JSON.
q = (APIResponse
     .select(
       APIResponse.data['booking']['person'].as_json().alias('person'))
     .where(APIResponse.data['meta']['model'] == 'booking'))
for result in q:
    print(result.person['name'], result.person['dob'])

The BinaryJSONField works the same and supports the same operations as the regular JSONField, but provides several additional operations for testing containment. Using the binary json field, you can test whether your JSON data contains other partial JSON structures (contains(), contains_any(), contains_all()), or whether it is a subset of a larger JSON document (contained_by()).

For more examples, see the JSONField and BinaryJSONField API documents below.

Server-side cursors

When psycopg2 executes a query, normally all results are fetched and returned to the client by the backend. This can cause your application to use a lot of memory when making large queries. Using server-side cursors, results are returned a little at a time (by default 2000 records). For the definitive reference, please see the psycopg2 documentation.

Note

To use server-side (or named) cursors, you must be using PostgresqlExtDatabase.

To execute a query using a server-side cursor, simply wrap your select query using the ServerSide() helper:

large_query = PageView.select()  # Build query normally.
# Iterate over large query inside a transaction.
for page_view in ServerSide(large_query):
    # do some interesting analysis here.
    pass
# Server-side resources are released.

If you would like all SELECT queries to automatically use a server-side cursor, you can specify this when creating your PostgresqlExtDatabase:

from postgres_ext import PostgresqlExtDatabase
ss_db = PostgresqlExtDatabase('my_db', server_side_cursors=True)

Note

Server-side cursors live only as long as the transaction, so for this reason peewee will not automatically call commit() after executing a SELECT query. If you do not commit after you are done iterating, you will not release the server-side resources until the connection is closed (or the transaction is committed later). Furthermore, since peewee will by default cache rows returned by the cursor, you should always call .iterator() when iterating over a large query.

If you are using the ServerSide() helper, the transaction and call to iterator() will be handled transparently.

Full-text search

Postgresql provides sophisticated full-text search using special data-types (tsvector and tsquery). Documents should be stored or converted to the tsvector type, and search queries should be converted to tsquery.

For simple cases, you can simply use the Match() function, which will automatically perform the appropriate conversions, and requires no schema changes:

def blog_search(query):
    return Blog.select().where(
        (Blog.status == Blog.STATUS_PUBLISHED) &
        Match(Blog.content, query))

The Match() function will automatically convert the left-hand operand to a tsvector, and the right-hand operand to a tsquery. For better performance, it is recommended you create a GIN index on the column you plan to search:

CREATE INDEX blog_full_text_search ON blog USING gin(to_tsvector(content));

Alternatively, you can use the TSVectorField to maintain a dedicated column for storing tsvector data:

class Blog(Model):
    content = TextField()
    search_content = TSVectorField()

You will need to explicitly convert the incoming text data to tsvector when inserting or updating the search_content field:

content = 'Excellent blog post about peewee ORM.'
blog_entry = Blog.create(
    content=content,
    search_content=fn.to_tsvector(content))

Note

If you are using the TSVectorField, it will automatically be created with a GIN index.

postgres_ext API notes

class PostgresqlExtDatabase(database[, server_side_cursors=False[, register_hstore=False[, …]]])

Identical to PostgresqlDatabase but required in order to support:

• Server-side cursors
• ArrayField
• DateTimeTZField
• JSONField
• BinaryJSONField
• HStoreField
• TSVectorField

If you wish to use the HStore extension, you must specify register_hstore=True.

If using server_side_cursors, also be sure to wrap your queries with ServerSide().

ServerSide(select_query)

Wrap the given select query in a transaction, and call it’s iterator() method to avoid caching row instances. In order for the server-side resources to be released, be sure to exhaust the generator (iterate over all the rows).

Usage:

large_query = PageView.select()
for page_view in ServerSide(large_query):
    # Do something interesting.
    pass
# At this point server side resources are released.

class ArrayField([field_class=IntegerField[, dimensions=1]])

Field capable of storing arrays of the provided field_class.

Note

By default ArrayField will use a GIN index. To disable this, initialize the field with index=False.

You can store and retrieve lists (or lists-of-lists):

class BlogPost(BaseModel):
    content = TextField()
    tags = ArrayField(CharField)
post = BlogPost(content='awesome', tags=['foo', 'bar', 'baz'])

Additionally, you can use the __getitem__ API to query values or slices in the database:

# Get the first tag on a given blog post.
first_tag = (BlogPost
             .select(BlogPost.tags[0].alias('first_tag'))
             .where(BlogPost.id == 1)
             .dicts()
             .get())
# first_tag = {'first_tag': 'foo'}

Get a slice of values:

# Get the first two tags.
two_tags = (BlogPost
            .select(BlogPost.tags[:2].alias('two'))
            .dicts()
            .get())
# two_tags = {'two': ['foo', 'bar']}

• contains(\items*)

  Parameters:	items – One or more items that must be in the given array field.

  # Get all blog posts that are tagged with both "python" and "django".
  Blog.select().where(Blog.tags.contains('python', 'django'))

• contains_any(\items*)

  Parameters:	items – One or more items to search for in the given array field.

  Like contains(), except will match rows where the array contains any of the given items.

  # Get all blog posts that are tagged with "flask" and/or "django".
  Blog.select().where(Blog.tags.contains_any('flask', 'django'))

class DateTimeTZField(\args, **kwargs*)

A timezone-aware subclass of DateTimeField.

class HStoreField(\args, **kwargs*)

A field for storing and retrieving arbitrary key/value pairs. For details on usage, see hstore support.

Attention

To use the HStoreField you will need to be sure the hstore extension is registered with the connection. To accomplish this, instantiate the PostgresqlExtDatabase with register_hstore=True.

Note

By default HStoreField will use a GiST index. To disable this, initialize the field with index=False.

• keys()

  Returns the keys for a given row.

  >>> for h in House.select(House.address, House.features.keys().alias('keys')):
  ...     print(h.address, h.keys)
  123 Main St [u'bath', u'garage']

• values()

  Return the values for a given row.

  >>> for h in House.select(House.address, House.features.values().alias('vals')):
  ...     print(h.address, h.vals)
  123 Main St [u'2 bath', u'2 cars']

• items()

  Like python’s dict, return the keys and values in a list-of-lists:

  >>> for h in House.select(House.address, House.features.items().alias('mtx')):
  ...     print(h.address, h.mtx)
  123 Main St [[u'bath', u'2 bath'], [u'garage', u'2 cars']]

• slice(\args*)

  Return a slice of data given a list of keys.

  >>> for h in House.select(House.address, House.features.slice('garage').alias('garage_data')):
  ...     print(h.address, h.garage_data)
  123 Main St {'garage': '2 cars'}

• exists(key)

  Query for whether the given key exists.

  >>> for h in House.select(House.address, House.features.exists('garage').alias('has_garage')):
  ...     print(h.address, h.has_garage)
  123 Main St True
  >>> for h in House.select().where(House.features.exists('garage')):
  ...     print(h.address, h.features['garage']) # <-- just houses w/garage data
  123 Main St 2 cars

• defined(key)

  Query for whether the given key has a value associated with it.

• update(\*data*)

  Perform an atomic update to the keys/values for a given row or rows.

  >>> query = House.update(features=House.features.update(
  ...     sqft=2000,
  ...     year_built=2012))
  >>> query.where(House.id == 1).execute()

• delete(\keys*)

  Delete the provided keys for a given row or rows.

  Note

  We will use an UPDATE query.

  >>> query = House.update(features=House.features.delete(
  ...     'sqft', 'year_built'))
  >>> query.where(House.id == 1).execute()

• contains(value)

  Parameters:	value – Either a dict, a list of keys, or a single key.

  Query rows for the existence of either:

  • a partial dictionary.
  • a list of keys.
  • a single key.

  >>> query = House.select()
  >>> has_garage = query.where(House.features.contains('garage'))
  >>> garage_bath = query.where(House.features.contains(['garage', 'bath']))
  >>> twocar = query.where(House.features.contains({'garage': '2 cars'}))

• contains_any(\keys*)

  Parameters:	keys – One or more keys to search for.

  Query rows for the existince of any key.

class JSONField(dumps=None, \args, **kwargs*)

Field class suitable for storing and querying arbitrary JSON. When using this on a model, set the field’s value to a Python object (either a dict or a list). When you retrieve your value from the database it will be returned as a Python data structure.

Note

You must be using Postgres 9.2 / psycopg2 2.5 or greater.

Note

If you are using Postgres 9.4, strongly consider using the BinaryJSONField instead as it offers better performance and more powerful querying options.

Example model declaration:

db = PostgresqlExtDatabase('my_db')
class APIResponse(Model):
    url = CharField()
    response = JSONField()
    class Meta:
        database = db

Example of storing JSON data:

url = 'http://foo.com/api/resource/'
resp = json.loads(urllib2.urlopen(url).read())
APIResponse.create(url=url, response=resp)
APIResponse.create(url='http://foo.com/baz/', response={'key': 'value'})

To query, use Python’s [] operators to specify nested key or array lookups:

APIResponse.select().where(
    APIResponse.response['key1']['nested-key'] == 'some-value')

To illustrate the use of the [] operators, imagine we have the following data stored in an APIResponse:

{
  "foo": {
    "bar": ["i1", "i2", "i3"],
    "baz": {
      "huey": "mickey",
      "peewee": "nugget"
    }
  }
}

Here are the results of a few queries:

def get_data(expression):
    # Helper function to just retrieve the results of a
    # particular expression.
    query = (APIResponse
             .select(expression.alias('my_data'))
             .dicts()
             .get())
    return query['my_data']
# Accessing the foo -> bar subkey will return a JSON
# representation of the list.
get_data(APIResponse.data['foo']['bar'])
# '["i1", "i2", "i3"]'
# In order to retrieve this list as a Python list,
# we will call .as_json() on the expression.
get_data(APIResponse.data['foo']['bar'].as_json())
# ['i1', 'i2', 'i3']
# Similarly, accessing the foo -> baz subkey will
# return a JSON representation of the dictionary.
get_data(APIResponse.data['foo']['baz'])
# '{"huey": "mickey", "peewee": "nugget"}'
# Again, calling .as_json() will return an actual
# python dictionary.
get_data(APIResponse.data['foo']['baz'].as_json())
# {'huey': 'mickey', 'peewee': 'nugget'}
# When dealing with simple values, either way works as
# you expect.
get_data(APIResponse.data['foo']['bar'][0])
# 'i1'
# Calling .as_json() when the result is a simple value
# will return the same thing as the previous example.
get_data(APIResponse.data['foo']['bar'][0].as_json())
# 'i1'

class BinaryJSONField(dumps=None, \args, **kwargs*)

Store and query arbitrary JSON documents. Data should be stored using normal Python dict and list objects, and when data is returned from the database, it will be returned using dict and list as well.

For examples of basic query operations, see the above code samples for JSONField. The example queries below will use the same APIResponse model described above.

Note

By default BinaryJSONField will use a GiST index. To disable this, initialize the field with index=False.

Note

You must be using Postgres 9.4 / psycopg2 2.5 or newer. If you are using Postgres 9.2 or 9.3, you can use the regular JSONField instead.

• contains(other)

  Test whether the given JSON data contains the given JSON fragment or key.

  Example:

  search_fragment = {
      'foo': {'bar': ['i2']}
  }
  query = (APIResponse
           .select()
           .where(APIResponse.data.contains(search_fragment)))
  # If we're searching for a list, the list items do not need to
  # be ordered in a particular way:
  query = (APIResponse
           .select()
           .where(APIResponse.data.contains({
               'foo': {'bar': ['i2', 'i1']}})))

  We can pass in simple keys as well. To find APIResponses that contain the key foo at the top-level:

  APIResponse.select().where(APIResponse.data.contains('foo'))

  We can also search sub-keys using square-brackets:

  APIResponse.select().where(
      APIResponse.data['foo']['bar'].contains(['i2', 'i1']))

• contains_any(\items*)

  Search for the presence of one or more of the given items.

  APIResponse.select().where(
      APIResponse.data.contains_any('foo', 'baz', 'nugget'))

  Like contains(), we can also search sub-keys:

  APIResponse.select().where(
      APIResponse.data['foo']['bar'].contains_any('i2', 'ix'))

• contains_all(\items*)

  Search for the presence of all of the given items.

  APIResponse.select().where(
      APIResponse.data.contains_all('foo'))

  Like contains_any(), we can also search sub-keys:

  APIResponse.select().where(
      APIResponse.data['foo']['bar'].contains_all('i1', 'i2', 'i3'))

• contained_by(other)

  Test whether the given JSON document is contained by (is a subset of) the given JSON document. This method is the inverse of contains().

  big_doc = {
      'foo': {
          'bar': ['i1', 'i2', 'i3'],
          'baz': {
              'huey': 'mickey',
              'peewee': 'nugget',
          }
      },
      'other_key': ['nugget', 'bear', 'kitten'],
  }
  APIResponse.select().where(
      APIResponse.data.contained_by(big_doc))

Match(field, query)

Generate a full-text search expression, automatically converting the left-hand operand to a tsvector, and the right-hand operand to a tsquery.

Example:

def blog_search(query):
    return Blog.select().where(
        (Blog.status == Blog.STATUS_PUBLISHED) &
        Match(Blog.content, query))

class TSVectorField

Field type suitable for storing tsvector data. This field will automatically be created with a GIN index for improved search performance.

Note

Data stored in this field will still need to be manually converted to the tsvector type.

Note

By default TSVectorField will use a GIN index. To disable this, initialize the field with index=False.

Example usage:

class Blog(Model):
    content = TextField()
    search_content = TSVectorField()
content = 'this is a sample blog entry.'
blog_entry = Blog.create(
    content=content,
    search_content=fn.to_tsvector(content))  # Note `to_tsvector()`.

DataSet

The dataset module contains a high-level API for working with databases modeled after the popular project of the same name. The aims of the dataset module are to provide:

• A simplified API for working with relational data, along the lines of working with JSON.
• An easy way to export relational data as JSON or CSV.
• An easy way to import JSON or CSV data into a relational database.

A minimal data-loading script might look like this:

from playhouse.dataset import DataSet
db = DataSet('sqlite:///:memory:')
table = db['sometable']
table.insert(name='Huey', age=3)
table.insert(name='Mickey', age=5, gender='male')
huey = table.find_one(name='Huey')
print huey
# {'age': 3, 'gender': None, 'id': 1, 'name': 'Huey'}
for obj in table:
    print obj
# {'age': 3, 'gender': None, 'id': 1, 'name': 'Huey'}
# {'age': 5, 'gender': 'male', 'id': 2, 'name': 'Mickey'}

You can export or import data using freeze() and thaw():

# Export table content to the `users.json` file.
db.freeze(table.all(), format='json', filename='users.json')
# Import data from a CSV file into a new table. Columns will be automatically
# created for each field in the CSV file.
new_table = db['stats']
new_table.thaw(format='csv', filename='monthly_stats.csv')

Getting started

DataSet objects are initialized by passing in a database URL of the format dialect://user:password@host/dbname. See the Database URL section for examples of connecting to various databases.

# Create an in-memory SQLite database.
db = DataSet('sqlite:///:memory:')

Storing data

To store data, we must first obtain a reference to a table. If the table does not exist, it will be created automatically:

# Get a table reference, creating the table if it does not exist.
table = db['users']

We can now insert() new rows into the table. If the columns do not exist, they will be created automatically:

table.insert(name='Huey', age=3, color='white')
table.insert(name='Mickey', age=5, gender='male')

To update existing entries in the table, pass in a dictionary containing the new values and filter conditions. The list of columns to use as filters is specified in the columns argument. If no filter columns are specified, then all rows will be updated.

# Update the gender for "Huey".
table.update(name='Huey', gender='male', columns=['name'])
# Update all records. If the column does not exist, it will be created.
table.update(favorite_orm='peewee')

Importing data

To import data from an external source, such as a JSON or CSV file, you can use the thaw() method. By default, new columns will be created for any attributes encountered. If you wish to only populate columns that are already defined on a table, you can pass in strict=True.

# Load data from a JSON file containing a list of objects.
table = dataset['stock_prices']
table.thaw(filename='stocks.json', format='json')
table.all()[:3]
# Might print...
[{'id': 1, 'ticker': 'GOOG', 'price': 703},
 {'id': 2, 'ticker': 'AAPL', 'price': 109},
 {'id': 3, 'ticker': 'AMZN', 'price': 300}]

Using transactions

DataSet supports nesting transactions using a simple context manager.

table = db['users']
with db.transaction() as txn:
    table.insert(name='Charlie')
    with db.transaction() as nested_txn:
        # Set Charlie's favorite ORM to Django.
        table.update(name='Charlie', favorite_orm='django', columns=['name'])
        # jk/lol
        nested_txn.rollback()

Inspecting the database

You can use the tables() method to list the tables in the current database:

>>> print db.tables
['sometable', 'user']

And for a given table, you can print the columns:

>>> table = db['user']
>>> print table.columns
['id', 'age', 'name', 'gender', 'favorite_orm']

We can also find out how many rows are in a table:

>>> print len(db['user'])
3

Reading data

To retrieve all rows, you can use the all() method:

# Retrieve all the users.
users = db['user'].all()
# We can iterate over all rows without calling `.all()`
for user in db['user']:
    print user['name']

Specific objects can be retrieved using find() and find_one().

# Find all the users who like peewee.
peewee_users = db['user'].find(favorite_orm='peewee')
# Find Huey.
huey = db['user'].find_one(name='Huey')

Exporting data

To export data, use the freeze() method, passing in the query you wish to export:

peewee_users = db['user'].find(favorite_orm='peewee')
db.freeze(peewee_users, format='json', filename='peewee_users.json')

API

class DataSet(url)

The DataSet class provides a high-level API for working with relational databases.

• tables

  Return a list of tables stored in the database. This list is computed dynamically each time it is accessed.

• __getitem__(table_name)

  Provide a Table reference to the specified table. If the table does not exist, it will be created.

• query(sql[, params=None[, commit=True]])

  Parameters:	sql (str) – A SQL query. params (list) – Optional parameters for the query. commit (bool) – Whether the query should be committed upon execution.
  Returns:	A database cursor.

  Execute the provided query against the database.

• transaction()

  Create a context manager representing a new transaction (or savepoint).

• freeze(query[, format=’csv’[, filename=None[, file_obj=None[, \*kwargs*]]]])

  Parameters:	query – A SelectQuery, generated using all() or ~Table.find. format – Output format. By default, csv and json are supported. filename – Filename to write output to. file_obj – File-like object to write output to. kwargs – Arbitrary parameters for export-specific functionality.

• thaw(table[, format=’csv’[, filename=None[, file_obj=None[, strict=False[, \*kwargs*]]]]])

  Parameters:	table (str) – The name of the table to load data into. format – Input format. By default, csv and json are supported. filename – Filename to read data from. file_obj – File-like object to read data from. strict (bool) – Whether to store values for columns that do not already exist on the table. kwargs – Arbitrary parameters for import-specific functionality.

• connect()

  Open a connection to the underlying database. If a connection is not opened explicitly, one will be opened the first time a query is executed.

• close()

  Close the connection to the underlying database.

class Table(dataset, name, model_class)

Provides a high-level API for working with rows in a given table.

• columns

  Return a list of columns in the given table.

• model_class

  A dynamically-created Model class.

• create_index(columns[, unique=False])

  Create an index on the given columns:

  # Create a unique index on the `username` column.
  db['users'].create_index(['username'], unique=True)

• insert(\*data*)

  Insert the given data dictionary into the table, creating new columns as needed.

• update(columns=None, conjunction=None, \*data*)

  Update the table using the provided data. If one or more columns are specified in the columns parameter, then those columns’ values in the data dictionary will be used to determine which rows to update.

  # Update all rows.
  db['users'].update(favorite_orm='peewee')
  # Only update Huey's record, setting his age to 3.
  db['users'].update(name='Huey', age=3, columns=['name'])

• find(\*query*)

  Query the table for rows matching the specified equality conditions. If no query is specified, then all rows are returned.

  peewee_users = db['users'].find(favorite_orm='peewee')

• find_one(\*query*)

  Return a single row matching the specified equality conditions. If no matching row is found then None will be returned.

  huey = db['users'].find_one(name='Huey')

• all()

  Return all rows in the given table.

• delete(\*query*)

  Delete all rows matching the given equality conditions. If no query is provided, then all rows will be deleted.

  # Adios, Django!
  db['users'].delete(favorite_orm='Django')
  # Delete all the secret messages.
  db['secret_messages'].delete()

• freeze([format=’csv’[, filename=None[, file_obj=None[, \*kwargs*]]]])

  Parameters:	format – Output format. By default, csv and json are supported. filename – Filename to write output to. file_obj – File-like object to write output to. kwargs – Arbitrary parameters for export-specific functionality.

• thaw([format=’csv’[, filename=None[, file_obj=None[, strict=False[, \*kwargs*]]]]])

  Parameters:	format – Input format. By default, csv and json are supported. filename – Filename to read data from. file_obj – File-like object to read data from. strict (bool) – Whether to store values for columns that do not already exist on the table. kwargs – Arbitrary parameters for import-specific functionality.

Fields

These fields can be found in the playhouse.fields module.

class CompressedField([compression_level=6[, algorithm=’zlib’[, \*kwargs*]]])

Stores compressed data using the specified algorithm. This field extends BlobField, transparently storing a compressed representation of the data in the database.

Hybrid Attributes

Hybrid attributes encapsulate functionality that operates at both the Python and SQL levels. The idea for hybrid attributes comes from a feature of the same name in SQLAlchemy. Consider the following example:

class Interval(Model):
    start = IntegerField()
    end = IntegerField()
    @hybrid_property
    def length(self):
        return self.end - self.start
    @hybrid_method
    def contains(self, point):
        return (self.start <= point) & (point < self.end)

The hybrid attribute gets its name from the fact that the length attribute will behave differently depending on whether it is accessed via the Interval class or an Interval instance.

If accessed via an instance, then it behaves just as you would expect.

If accessed via the Interval.length class attribute, however, the length calculation will be expressed as a SQL expression. For example:

query = Interval.select().where(Interval.length > 5)

This query will be equivalent to the following SQL:

SELECT "t1"."id", "t1"."start", "t1"."end"
FROM "interval" AS t1
WHERE (("t1"."end" - "t1"."start") > 5)

The playhouse.hybrid module also contains a decorator for implementing hybrid methods which can accept parameters. As with hybrid properties, when accessed via a model instance, then the function executes normally as-written. When the hybrid method is called on the class, however, it will generate a SQL expression.

Example:

query = Interval.select().where(Interval.contains(2))

This query is equivalent to the following SQL:

SELECT "t1"."id", "t1"."start", "t1"."end"
FROM "interval" AS t1
WHERE (("t1"."start" <= 2) AND (2 < "t1"."end"))

There is an additional API for situations where the python implementation differs slightly from the SQL implementation. Let’s add a radius method to the Interval model. Because this method calculates an absolute value, we will use the Python abs() function for the instance portion and the fn.ABS() SQL function for the class portion.

class Interval(Model):
    start = IntegerField()
    end = IntegerField()
    @hybrid_property
    def length(self):
        return self.end - self.start
    @hybrid_property
    def radius(self):
        return abs(self.length) / 2
    @radius.expression
    def radius(cls):
        return fn.ABS(cls.length) / 2

What is neat is that both the radius implementations refer to the length hybrid attribute! When accessed via an Interval instance, the radius calculation will be executed in Python. When invoked via an Interval class, we will get the appropriate SQL.

Example:

query = Interval.select().where(Interval.radius < 3)

This query is equivalent to the following SQL:

SELECT "t1"."id", "t1"."start", "t1"."end"
FROM "interval" AS t1
WHERE ((abs("t1"."end" - "t1"."start") / 2) < 3)

Pretty neat, right? Thanks for the cool idea, SQLAlchemy!

Hybrid API

class hybrid_method(func[, expr=None])

Method decorator that allows the definition of a Python object method with both instance-level and class-level behavior.

Example:

class Interval(Model):
    start = IntegerField()
    end = IntegerField()
    @hybrid_method
    def contains(self, point):
        return (self.start <= point) & (point < self.end)

When called with an Interval instance, the contains method will behave as you would expect. When called as a classmethod, though, a SQL expression will be generated:

query = Interval.select().where(Interval.contains(2))

Would generate the following SQL:

SELECT "t1"."id", "t1"."start", "t1"."end"
FROM "interval" AS t1
WHERE (("t1"."start" <= 2) AND (2 < "t1"."end"))

• expression(expr)

  Method decorator for specifying the SQL-expression producing method.

class hybrid_property(fget[, fset=None[, fdel=None[, expr=None]]])

Method decorator that allows the definition of a Python object property with both instance-level and class-level behavior.

Examples:

class Interval(Model):
    start = IntegerField()
    end = IntegerField()
    @hybrid_property
    def length(self):
        return self.end - self.start
    @hybrid_property
    def radius(self):
        return abs(self.length) / 2
    @radius.expression
    def radius(cls):
        return fn.ABS(cls.length) / 2

When accessed on an Interval instance, the length and radius properties will behave as you would expect. When accessed as class attributes, though, a SQL expression will be generated instead:

query = (Interval
         .select()
         .where(
             (Interval.length > 6) &
             (Interval.radius >= 3)))

Would generate the following SQL:

SELECT "t1"."id", "t1"."start", "t1"."end"
FROM "interval" AS t1
WHERE (
    (("t1"."end" - "t1"."start") > 6) AND
    ((abs("t1"."end" - "t1"."start") / 2) >= 3)
)

Shortcuts

This module contains helper functions for expressing things that would otherwise be somewhat verbose or cumbersome using peewee’s APIs. There are also helpers for serializing models to dictionaries and vice-versa.

model_to_dict(model[, recurse=True[, backrefs=False[, only=None[, exclude=None[, extra_attrs=None[, fields_from_query=None[, max_depth=None[, manytomany=False]]]]]]]])

Convert a model instance (and optionally any related instances) to a dictionary.

Examples:

>>> user = User.create(username='charlie')
>>> model_to_dict(user)
{'id': 1, 'username': 'charlie'}
>>> model_to_dict(user, backrefs=True)
{'id': 1, 'tweets': [], 'username': 'charlie'}
>>> t1 = Tweet.create(user=user, message='tweet-1')
>>> t2 = Tweet.create(user=user, message='tweet-2')
>>> model_to_dict(user, backrefs=True)
{
  'id': 1,
  'tweets': [
    {'id': 1, 'message': 'tweet-1'},
    {'id': 2, 'message': 'tweet-2'},
  ],
  'username': 'charlie'
}
>>> model_to_dict(t1)
{
  'id': 1,
  'message': 'tweet-1',
  'user': {
    'id': 1,
    'username': 'charlie'
  }
}
>>> model_to_dict(t2, recurse=False)
{'id': 1, 'message': 'tweet-2', 'user': 1}

dict_to_model(model_class, data[, ignore_unknown=False])

Convert a dictionary of data to a model instance, creating related instances where appropriate.

Examples:

>>> user_data = {'id': 1, 'username': 'charlie'}
>>> user = dict_to_model(User, user_data)
>>> user
<__main__.User at 0x7fea8fa4d490>
>>> user.username
'charlie'
>>> note_data = {'id': 2, 'text': 'note text', 'user': user_data}
>>> note = dict_to_model(Note, note_data)
>>> note.text
'note text'
>>> note.user.username
'charlie'
>>> user_with_notes = {
...     'id': 1,
...     'username': 'charlie',
...     'notes': [{'id': 1, 'text': 'note-1'}, {'id': 2, 'text': 'note-2'}]}
>>> user = dict_to_model(User, user_with_notes)
>>> user.notes[0].text
'note-1'
>>> user.notes[0].user.username
'charlie'

update_model_from_dict(instance, data[, ignore_unknown=False])

Update a model instance with the given data dictionary.

Signal support

Models with hooks for signals (a-la django) are provided in playhouse.signals. To use the signals, you will need all of your project’s models to be a subclass of playhouse.signals.Model, which overrides the necessary methods to provide support for the various signals.

from playhouse.signals import Model, post_save
class MyModel(Model):
    data = IntegerField()
@post_save(sender=MyModel)
def on_save_handler(model_class, instance, created):
    put_data_in_cache(instance.data)

Warning

For what I hope are obvious reasons, Peewee signals do not work when you use the Model.insert(), Model.update(), or Model.delete() methods. These methods generate queries that execute beyond the scope of the ORM, and the ORM does not know about which model instances might or might not be affected when the query executes.

Signals work by hooking into the higher-level peewee APIs like Model.save() and Model.delete_instance(), where the affected model instance is known ahead of time.

The following signals are provided:

pre_save

Called immediately before an object is saved to the database. Provides an additional keyword argument created, indicating whether the model is being saved for the first time or updated.

post_save

Called immediately after an object is saved to the database. Provides an additional keyword argument created, indicating whether the model is being saved for the first time or updated.

pre_delete

Called immediately before an object is deleted from the database when Model.delete_instance() is used.

post_delete

Called immediately after an object is deleted from the database when Model.delete_instance() is used.

pre_init

Called when a model class is first instantiated

Connecting handlers

Whenever a signal is dispatched, it will call any handlers that have been registered. This allows totally separate code to respond to events like model save and delete.

The Signal class provides a connect() method, which takes a callback function and two optional parameters for “sender” and “name”. If specified, the “sender” parameter should be a single model class and allows your callback to only receive signals from that one model class. The “name” parameter is used as a convenient alias in the event you wish to unregister your signal handler.

Example usage:

from playhouse.signals import *
def post_save_handler(sender, instance, created):
    print '%s was just saved' % instance
# our handler will only be called when we save instances of SomeModel
post_save.connect(post_save_handler, sender=SomeModel)

All signal handlers accept as their first two arguments sender and instance, where sender is the model class and instance is the actual model being acted upon.

If you’d like, you can also use a decorator to connect signal handlers. This is functionally equivalent to the above example:

@post_save(sender=SomeModel)
def post_save_handler(sender, instance, created):
    print '%s was just saved' % instance

Signal API

class Signal

Stores a list of receivers (callbacks) and calls them when the “send” method is invoked.

• connect(receiver[, sender=None[, name=None]])

  Parameters:	receiver (callable) – a callable that takes at least two parameters, a “sender”, which is the Model subclass that triggered the signal, and an “instance”, which is the actual model instance. sender (Model) – if specified, only instances of this model class will trigger the receiver callback. name (string) – a short alias

  Add the receiver to the internal list of receivers, which will be called whenever the signal is sent.

  from playhouse.signals import post_save
  from project.handlers import cache_buster
  post_save.connect(cache_buster, name='project.cache_buster')

• disconnect([receiver=None[, name=None]])

  Parameters:	receiver (callable) – the callback to disconnect name (string) – a short alias

  Disconnect the given receiver (or the receiver with the given name alias) so that it no longer is called. Either the receiver or the name must be provided.

  post_save.disconnect(name='project.cache_buster')

• send(instance, \args, **kwargs*)

  Parameters:	instance – a model instance

  Iterates over the receivers and will call them in the order in which they were connected. If the receiver specified a sender, it will only be called if the instance is an instance of the sender.

pwiz, a model generator

pwiz is a little script that ships with peewee and is capable of introspecting an existing database and generating model code suitable for interacting with the underlying data. If you have a database already, pwiz can give you a nice boost by generating skeleton code with correct column affinities and foreign keys.

If you install peewee using setup.py install, pwiz will be installed as a “script” and you can just run:

python -m pwiz -e postgresql -u postgres my_postgres_db

This will print a bunch of models to standard output. So you can do this:

python -m pwiz -e postgresql my_postgres_db > mymodels.py
python # <-- fire up an interactive shell

>>> from mymodels import Blog, Entry, Tag, Whatever
>>> print [blog.name for blog in Blog.select()]