Current File : //opt/alt/python35/lib64/python3.5/site-packages/playhouse/sqlite_ext.py
"""
Sqlite3 extensions
==================
* Define custom aggregates, collations and functions
* Basic support for virtual tables
* Basic support for FTS3/4
* Specify isolation level in transactions
Example usage of the Full-text search:
class Document(FTSModel):
title = TextField() # type affinities are ignored in FTS
content = TextField()
Document.create_table(tokenize='porter') # use the porter stemmer
# populate the documents using normal operations.
for doc in documents:
Document.create(title=doc['title'], content=doc['content'])
# use the "match" operation for FTS queries.
matching_docs = Document.select().where(match(Document.title, 'some query'))
# to sort by best match, use the custom "rank" function.
best_docs = (Document
.select(Document, Document.rank('score'))
.where(match(Document.title, 'some query'))
.order_by(SQL('score')))
# or use the shortcut method.
best_docs = Document.match('some phrase')
"""
import glob
import inspect
import math
import os
import re
import struct
import sys
try:
import simplejson as json
except ImportError:
import json
try:
from vtfunc import TableFunction
except ImportError:
pass
from peewee import *
from peewee import EnclosedClause
from peewee import Entity
from peewee import Expression
from peewee import Node
from peewee import OP
from peewee import SqliteQueryCompiler
from peewee import _AutoPrimaryKeyField
from peewee import sqlite3 # Import the best SQLite version.
from peewee import transaction
from peewee import _sqlite_date_part
from peewee import _sqlite_date_trunc
from peewee import _sqlite_regexp
try:
from playhouse import _sqlite_ext as _c_ext
except ImportError:
_c_ext = None
if sys.version_info[0] == 3:
basestring = str
FTS_MATCHINFO_FORMAT = 'pcnalx'
FTS_MATCHINFO_FORMAT_SIMPLE = 'pcx'
FTS_VER = sqlite3.sqlite_version_info[:3] >= (3, 7, 4) and 'FTS4' or 'FTS3'
FTS5_MIN_VERSION = (3, 9, 0)
class RowIDField(_AutoPrimaryKeyField):
"""
Field used to access hidden primary key on FTS5 or any other SQLite
table that does not have a separately-defined primary key.
"""
_column_name = 'rowid'
class DocIDField(_AutoPrimaryKeyField):
"""Field used to access hidden primary key on FTS3/4 tables."""
_column_name = 'docid'
class PrimaryKeyAutoIncrementField(PrimaryKeyField):
"""
SQLite by default uses MAX(primary key) + 1 to set the ID on a new row.
Using the `AUTOINCREMENT` field, the IDs will increase monotonically
even if rows are deleted. Use this if you need to guarantee IDs are not
re-used in the event of deletion.
"""
def __ddl__(self, column_type):
ddl = super(PrimaryKeyAutoIncrementField, self).__ddl__(column_type)
return ddl + [SQL('AUTOINCREMENT')]
class JSONField(TextField):
def python_value(self, value):
if value is not None:
try:
return json.loads(value)
except (TypeError, ValueError):
return value
def db_value(self, value):
if value is not None:
return json.dumps(value)
def clean_path(self, path):
if path.startswith('[') or not path:
return '$%s' % path
return '$.%s' % path
def length(self, path=None):
if path:
return fn.json_array_length(self, self.clean_path(path))
return fn.json_array_length(self)
def extract(self, path):
return fn.json_extract(self, self.clean_path(path))
def _value_for_insertion(self, value):
if isinstance(value, (list, tuple, dict)):
return fn.json(json.dumps(value))
return value
def _insert_like(self, fn, pairs):
npairs = len(pairs)
if npairs % 2 != 0:
raise ValueError('Mismatched path and value parameters.')
accum = []
for i in range(0, npairs, 2):
accum.append(self.clean_path(pairs[i]))
accum.append(self._value_for_insertion(pairs[i + 1]))
return fn(self, *accum)
def insert(self, *pairs):
return self._insert_like(fn.json_insert, pairs)
def replace(self, *pairs):
return self._insert_like(fn.json_replace, pairs)
def set(self, *pairs):
return self._insert_like(fn.json_set, pairs)
def remove(self, *paths):
return fn.json_remove(self, *[self.clean_path(path) for path in paths])
def json_type(self, path=None):
if path:
return fn.json_type(self, self.clean_path(path))
return fn.json_type(self)
def children(self, path=None):
"""
Schema of `json_each` and `json_tree`:
key,
value,
type TEXT (object, array, string, etc),
atom (value for primitive/scalar types, NULL for array and object)
id INTEGER (unique identifier for element)
parent INTEGER (unique identifier of parent element or NULL)
fullkey TEXT (full path describing element)
path TEXT (path to the container of the current element)
json JSON hidden (1st input parameter to function)
root TEXT hidden (2nd input parameter, path at which to start)
"""
if path:
return fn.json_each(self, self.clean_path(path))
return fn.json_each(self)
def tree(self, path=None):
if path:
return fn.json_tree(self, self.clean_path(path))
return fn.json_tree(self)
class SearchField(BareField):
"""
Field class to be used with full-text search extension. Since the FTS
extensions do not support any field types besides `TEXT`, and furthermore
do not support secondary indexes, using this field will prevent you
from mistakenly creating the wrong kind of field on your FTS table.
"""
def __init__(self, unindexed=False, db_column=None, coerce=None, **_):
kwargs = {'null': True, 'db_column': db_column, 'coerce': coerce}
self._unindexed = unindexed
if unindexed:
kwargs['constraints'] = [SQL('UNINDEXED')]
super(SearchField, self).__init__(**kwargs)
def clone_base(self, **kwargs):
clone = super(SearchField, self).clone_base(**kwargs)
clone._unindexed = self._unindexed
return clone
class _VirtualFieldMixin(object):
"""
Field mixin to support virtual table attributes that may not correspond
to actual columns in the database.
"""
def add_to_class(self, model_class, name):
super(_VirtualFieldMixin, self).add_to_class(model_class, name)
model_class._meta.remove_field(name)
# Virtual field types that can be used to reference specially-created fields
# on virtual tables. These fields are exposed as attributes on the model class,
# but are not included in any `CREATE TABLE` statements or by default when
# performing an `INSERT` or `UPDATE` query.
class VirtualField(_VirtualFieldMixin, BareField): pass
class VirtualIntegerField(_VirtualFieldMixin, IntegerField): pass
class VirtualCharField(_VirtualFieldMixin, CharField): pass
class VirtualFloatField(_VirtualFieldMixin, FloatField): pass
class VirtualModel(Model):
class Meta:
virtual_table = True
extension_module = None
extension_options = {}
@classmethod
def clean_options(cls, **options):
# Called by the QueryCompiler when generating the virtual table's
# options clauses.
return options
@classmethod
def create_table(cls, fail_silently=False, **options):
# Modified to support **options, which are passed back to the
# query compiler.
if fail_silently and cls.table_exists():
return
cls._meta.database.create_table(cls, options=options)
cls._create_indexes()
class BaseFTSModel(VirtualModel):
@classmethod
def clean_options(cls, **options):
tokenize = options.get('tokenize')
content = options.get('content')
if tokenize:
# Tokenizers need to be in quoted string.
options['tokenize'] = '"%s"' % tokenize
if isinstance(content, basestring) and content == '':
# Special-case content-less full-text search tables.
options['content'] = "''"
return options
class FTSModel(BaseFTSModel):
"""
VirtualModel class for creating tables that use either the FTS3 or FTS4
search extensions. Peewee automatically determines which version of the
FTS extension is supported and will use FTS4 if possible.
Note: because FTS5 is significantly different from FTS3 and FTS4, there is
a separate model class for FTS5 virtual tables.
"""
# FTS3/4 does not support declared primary keys, but we can use the
# implicit docid.
docid = DocIDField()
class Meta:
extension_module = FTS_VER
@classmethod
def validate_model(cls):
if cls._meta.primary_key.name != 'docid':
raise ImproperlyConfigured(
'FTSModel classes must use the default `docid` primary key.')
@classmethod
def _fts_cmd(cls, cmd):
tbl = cls._meta.db_table
res = cls._meta.database.execute_sql(
"INSERT INTO %s(%s) VALUES('%s');" % (tbl, tbl, cmd))
return res.fetchone()
@classmethod
def optimize(cls):
return cls._fts_cmd('optimize')
@classmethod
def rebuild(cls):
return cls._fts_cmd('rebuild')
@classmethod
def integrity_check(cls):
return cls._fts_cmd('integrity-check')
@classmethod
def merge(cls, blocks=200, segments=8):
return cls._fts_cmd('merge=%s,%s' % (blocks, segments))
@classmethod
def automerge(cls, state=True):
return cls._fts_cmd('automerge=%s' % (state and '1' or '0'))
@classmethod
def match(cls, term):
"""
Generate a `MATCH` expression appropriate for searching this table.
"""
return match(cls.as_entity(), term)
@classmethod
def rank(cls, *weights):
return fn.fts_rank(fn.matchinfo(
cls.as_entity(), FTS_MATCHINFO_FORMAT_SIMPLE), *weights)
@classmethod
def bm25(cls, *weights):
match_info = fn.matchinfo(cls.as_entity(), FTS_MATCHINFO_FORMAT)
return fn.fts_bm25(match_info, *weights)
@classmethod
def lucene(cls, *weights):
match_info = fn.matchinfo(cls.as_entity(), FTS_MATCHINFO_FORMAT)
return fn.fts_lucene(match_info, *weights)
@classmethod
def _search(cls, term, weights, with_score, score_alias, score_fn,
explicit_ordering):
if not weights:
rank = score_fn()
elif isinstance(weights, dict):
weight_args = []
for field in cls._meta.declared_fields:
weight_args.append(
weights.get(field, weights.get(field.name, 1.0)))
rank = score_fn(*weight_args)
else:
rank = score_fn(*weights)
selection = ()
order_by = rank
if with_score:
selection = (cls, rank.alias(score_alias))
if with_score and not explicit_ordering:
order_by = SQL(score_alias)
return (cls
.select(*selection)
.where(cls.match(term))
.order_by(order_by))
@classmethod
def search(cls, term, weights=None, with_score=False, score_alias='score',
explicit_ordering=False):
"""Full-text search using selected `term`."""
return cls._search(
term,
weights,
with_score,
score_alias,
cls.rank,
explicit_ordering)
@classmethod
def search_bm25(cls, term, weights=None, with_score=False,
score_alias='score', explicit_ordering=False):
"""Full-text search for selected `term` using BM25 algorithm."""
return cls._search(
term,
weights,
with_score,
score_alias,
cls.bm25,
explicit_ordering)
@classmethod
def search_lucene(cls, term, weights=None, with_score=False,
score_alias='score', explicit_ordering=False):
"""Full-text search for selected `term` using BM25 algorithm."""
return cls._search(
term,
weights,
with_score,
score_alias,
cls.lucene,
explicit_ordering)
_alphabet = 'abcdefghijklmnopqrstuvwxyz'
_alphanum = set([
'\t', ' ', ',', '"',
chr(26), # Substitution control character.
'(', ')', '{', '}',
'*', ':', '_', '+',
]) | set('0123456789') | set(_alphabet) | set(_alphabet.upper())
_invalid_ascii = set([chr(p) for p in range(128) if chr(p) not in _alphanum])
_quote_re = re.compile('(?:[^\s"]|"(?:\\.|[^"])*")+')
class FTS5Model(BaseFTSModel):
"""
Requires SQLite >= 3.9.0.
Table options:
content: table name of external content, or empty string for "contentless"
content_rowid: column name of external content primary key
prefix: integer(s). Ex: '2' or '2 3 4'
tokenize: porter, unicode61, ascii. Ex: 'porter unicode61'
The unicode tokenizer supports the following parameters:
* remove_diacritics (1 or 0, default is 1)
* tokenchars (string of characters, e.g. '-_'
* separators (string of characters)
Parameters are passed as alternating parameter name and value, so:
{'tokenize': "unicode61 remove_diacritics 0 tokenchars '-_'"}
Content-less tables:
If you don't need the full-text content in it's original form, you can
specify a content-less table. Searches and auxiliary functions will work
as usual, but the only values returned when SELECT-ing can be rowid. Also
content-less tables do not support UPDATE or DELETE.
External content tables:
You can set up triggers to sync these, e.g.
-- Create a table. And an external content fts5 table to index it.
CREATE TABLE tbl(a INTEGER PRIMARY KEY, b);
CREATE VIRTUAL TABLE ft USING fts5(b, content='tbl', content_rowid='a');
-- Triggers to keep the FTS index up to date.
CREATE TRIGGER tbl_ai AFTER INSERT ON tbl BEGIN
INSERT INTO ft(rowid, b) VALUES (new.a, new.b);
END;
CREATE TRIGGER tbl_ad AFTER DELETE ON tbl BEGIN
INSERT INTO ft(fts_idx, rowid, b) VALUES('delete', old.a, old.b);
END;
CREATE TRIGGER tbl_au AFTER UPDATE ON tbl BEGIN
INSERT INTO ft(fts_idx, rowid, b) VALUES('delete', old.a, old.b);
INSERT INTO ft(rowid, b) VALUES (new.a, new.b);
END;
Built-in auxiliary functions:
* bm25(tbl[, weight_0, ... weight_n])
* highlight(tbl, col_idx, prefix, suffix)
* snippet(tbl, col_idx, prefix, suffix, ?, max_tokens)
"""
# FTS5 does not support declared primary keys, but we can use the
# implicit rowid.
rowid = RowIDField()
class Meta:
extension_module = 'fts5'
_error_messages = {
'field_type': ('Besides the implicit `rowid` column, all columns must '
'be instances of SearchField'),
'index': 'Secondary indexes are not supported for FTS5 models',
'pk': 'FTS5 models must use the default `rowid` primary key',
}
@classmethod
def validate_model(cls):
# Perform FTS5-specific validation and options post-processing.
if cls._meta.primary_key.name != 'rowid':
raise ImproperlyConfigured(cls._error_messages['pk'])
for field in cls._meta.fields.values():
if not isinstance(field, (SearchField, RowIDField)):
raise ImproperlyConfigured(cls._error_messages['field_type'])
if cls._meta.indexes:
raise ImproperlyConfigured(cls._error_messages['index'])
@classmethod
def fts5_installed(cls):
if sqlite3.sqlite_version_info[:3] < FTS5_MIN_VERSION:
return False
# Test in-memory DB to determine if the FTS5 extension is installed.
tmp_db = sqlite3.connect(':memory:')
try:
tmp_db.execute('CREATE VIRTUAL TABLE fts5test USING fts5 (data);')
except:
try:
sqlite3.enable_load_extension(True)
sqlite3.load_extension('fts5')
except:
return False
else:
cls._meta.database.load_extension('fts5')
finally:
tmp_db.close()
return True
@staticmethod
def validate_query(query):
"""
Simple helper function to indicate whether a search query is a
valid FTS5 query. Note: this simply looks at the characters being
used, and is not guaranteed to catch all problematic queries.
"""
tokens = _quote_re.findall(query)
for token in tokens:
if token.startswith('"') and token.endswith('"'):
continue
if set(token) & _invalid_ascii:
return False
return True
@staticmethod
def clean_query(query, replace=chr(26)):
"""
Clean a query of invalid tokens.
"""
accum = []
any_invalid = False
tokens = _quote_re.findall(query)
for token in tokens:
if token.startswith('"') and token.endswith('"'):
accum.append(token)
continue
token_set = set(token)
invalid_for_token = token_set & _invalid_ascii
if invalid_for_token:
any_invalid = True
for c in invalid_for_token:
token = token.replace(c, replace)
accum.append(token)
if any_invalid:
return ' '.join(accum)
return query
@classmethod
def match(cls, term):
"""
Generate a `MATCH` expression appropriate for searching this table.
"""
return match(cls.as_entity(), term)
@classmethod
def rank(cls, *args):
if args:
return cls.bm25(*args)
else:
return SQL('rank')
@classmethod
def bm25(cls, *weights):
return fn.bm25(cls.as_entity(), *weights)
@classmethod
def search(cls, term, weights=None, with_score=False, score_alias='score',
explicit_ordering=False):
"""Full-text search using selected `term`."""
return cls.search_bm25(
FTS5Model.clean_query(term),
weights,
with_score,
score_alias,
explicit_ordering)
@classmethod
def search_bm25(cls, term, weights=None, with_score=False,
score_alias='score', explicit_ordering=False):
"""Full-text search using selected `term`."""
if not weights:
rank = SQL('rank')
elif isinstance(weights, dict):
weight_args = []
for field in cls._meta.declared_fields:
weight_args.append(
weights.get(field, weights.get(field.name, 1.0)))
rank = fn.bm25(cls.as_entity(), *weight_args)
else:
rank = fn.bm25(cls.as_entity(), *weights)
selection = ()
order_by = rank
if with_score:
selection = (cls, rank.alias(score_alias))
if with_score and not explicit_ordering:
order_by = SQL(score_alias)
return (cls
.select(*selection)
.where(cls.match(FTS5Model.clean_query(term)))
.order_by(order_by))
@classmethod
def _fts_cmd(cls, cmd, **extra_params):
tbl = cls.as_entity()
columns = [tbl]
values = [cmd]
for key, value in extra_params.items():
columns.append(Entity(key))
values.append(value)
inner_clause = EnclosedClause(tbl)
clause = Clause(
SQL('INSERT INTO'),
cls.as_entity(),
EnclosedClause(*columns),
SQL('VALUES'),
EnclosedClause(*values))
return cls._meta.database.execute(clause)
@classmethod
def automerge(cls, level):
if not (0 <= level <= 16):
raise ValueError('level must be between 0 and 16')
return cls._fts_cmd('automerge', rank=level)
@classmethod
def merge(cls, npages):
return cls._fts_cmd('merge', rank=npages)
@classmethod
def set_pgsz(cls, pgsz):
return cls._fts_cmd('pgsz', rank=pgsz)
@classmethod
def set_rank(cls, rank_expression):
return cls._fts_cmd('rank', rank=rank_expression)
@classmethod
def delete_all(cls):
return cls._fts_cmd('delete-all')
@classmethod
def VocabModel(cls, table_type='row', table_name=None):
if table_type not in ('row', 'col'):
raise ValueError('table_type must be either "row" or "col".')
attr = '_vocab_model_%s' % table_type
if not hasattr(cls, attr):
class Meta:
database = cls._meta.database
db_table = table_name or cls._meta.db_table + '_v'
extension_module = fn.fts5vocab(
cls.as_entity(),
SQL(table_type))
attrs = {
'term': BareField(),
'doc': IntegerField(),
'cnt': IntegerField(),
'rowid': RowIDField(),
'Meta': Meta,
}
if table_type == 'col':
attrs['col'] = BareField()
class_name = '%sVocab' % cls.__name__
setattr(cls, attr, type(class_name, (VirtualModel,), attrs))
return getattr(cls, attr)
def ClosureTable(model_class, foreign_key=None):
"""Model factory for the transitive closure extension."""
if foreign_key is None:
for field_obj in model_class._meta.rel.values():
if field_obj.rel_model is model_class:
foreign_key = field_obj
break
else:
raise ValueError('Unable to find self-referential foreign key.')
primary_key = model_class._meta.primary_key
class BaseClosureTable(VirtualModel):
depth = VirtualIntegerField()
id = VirtualIntegerField()
idcolumn = VirtualIntegerField()
parentcolumn = VirtualIntegerField()
root = VirtualIntegerField()
tablename = VirtualCharField()
class Meta:
extension_module = 'transitive_closure'
@classmethod
def descendants(cls, node, depth=None, include_node=False):
query = (model_class
.select(model_class, cls.depth.alias('depth'))
.join(cls, on=(primary_key == cls.id))
.where(cls.root == node))
if depth is not None:
query = query.where(cls.depth == depth)
elif not include_node:
query = query.where(cls.depth > 0)
return query
@classmethod
def ancestors(cls, node, depth=None, include_node=False):
query = (model_class
.select(model_class, cls.depth.alias('depth'))
.join(cls, on=(primary_key == cls.root))
.where(cls.id == node))
if depth:
query = query.where(cls.depth == depth)
elif not include_node:
query = query.where(cls.depth > 0)
return query
@classmethod
def siblings(cls, node, include_node=False):
fk_value = node._data.get(foreign_key.name)
query = model_class.select().where(foreign_key == fk_value)
if not include_node:
query = query.where(primary_key != node)
return query
class Meta:
database = model_class._meta.database
extension_options = {
'tablename': model_class._meta.db_table,
'idcolumn': model_class._meta.primary_key.db_column,
'parentcolumn': foreign_key.db_column}
primary_key = False
name = '%sClosure' % model_class.__name__
return type(name, (BaseClosureTable,), {'Meta': Meta})
class SqliteExtQueryCompiler(SqliteQueryCompiler):
"""
Subclass of QueryCompiler that can be used to construct virtual tables.
"""
def _create_table(self, model_class, safe=False, options=None):
clause = super(SqliteExtQueryCompiler, self)._create_table(
model_class, safe=safe)
if issubclass(model_class, VirtualModel):
statement = 'CREATE VIRTUAL TABLE'
# If we are using a special extension, need to insert that after
# the table name node.
extension = model_class._meta.extension_module
if isinstance(extension, Node):
# If the `extension_module` attribute is a `Node` subclass,
# then we assume the VirtualModel will be responsible for
# defining not only the extension, but also the columns.
parts = clause.nodes[:2] + [SQL('USING'), extension]
clause = Clause(*parts)
else:
# The extension name is a simple string.
clause.nodes.insert(2, SQL('USING %s' % extension))
else:
statement = 'CREATE TABLE'
if safe:
statement += ' IF NOT EXISTS'
clause.nodes[0] = SQL(statement) # Overwrite the statement.
table_options = self.clean_options(model_class, clause, options)
if table_options:
columns_constraints = clause.nodes[-1]
for k, v in sorted(table_options.items()):
if isinstance(v, Field):
# Special hack here for FTS5. We want to include the
# fully-qualified column entity in most cases, but for
# FTS5 we only want the string column name.
v = v.as_entity(extension != 'fts5')
elif inspect.isclass(v) and issubclass(v, Model):
# The option points to a table name.
v = v.as_entity()
elif isinstance(v, (list, tuple)):
# Lists will be quoted and joined by commas.
v = SQL("'%s'" % ','.join(map(str, v)))
elif not isinstance(v, Node):
v = SQL(v)
option = Clause(SQL(k), v)
option.glue = '='
columns_constraints.nodes.append(option)
if getattr(model_class._meta, 'without_rowid', None):
clause.nodes.append(SQL('WITHOUT ROWID'))
return clause
def clean_options(self, model_class, clause, extra_options):
model_options = getattr(model_class._meta, 'extension_options', None)
if model_options:
options = model_class.clean_options(**model_options)
else:
options = {}
if extra_options:
options.update(model_class.clean_options(**extra_options))
return options
def create_table(self, model_class, safe=False, options=None):
return self.parse_node(self._create_table(model_class, safe, options))
@Node.extend(clone=False)
def disqualify(self):
# In the where clause, prevent the given node/expression from constraining
# an index.
return Clause('+', self, glue='')
class SqliteExtDatabase(SqliteDatabase):
"""
Database class which provides additional Sqlite-specific functionality:
* Register custom aggregates, collations and functions
* Specify a row factory
* Advanced transactions (specify isolation level)
"""
compiler_class = SqliteExtQueryCompiler
def __init__(self, database, c_extensions=True, *args, **kwargs):
super(SqliteExtDatabase, self).__init__(database, *args, **kwargs)
self._aggregates = {}
self._collations = {}
self._functions = {}
self._extensions = set([])
self._row_factory = None
if _c_ext and c_extensions:
self._using_c_extensions = True
self.register_function(_c_ext.peewee_date_part, 'date_part', 2)
self.register_function(_c_ext.peewee_date_trunc, 'date_trunc', 2)
self.register_function(_c_ext.peewee_regexp, 'regexp', 2)
self.register_function(_c_ext.peewee_rank, 'fts_rank', -1)
self.register_function(_c_ext.peewee_lucene, 'fts_lucene', -1)
self.register_function(_c_ext.peewee_bm25, 'fts_bm25', -1)
self.register_function(_c_ext.peewee_murmurhash, 'murmurhash', 1)
else:
self._using_c_extensions = False
self.register_function(_sqlite_date_part, 'date_part', 2)
self.register_function(_sqlite_date_trunc, 'date_trunc', 2)
self.register_function(_sqlite_regexp, 'regexp', 2)
self.register_function(rank, 'fts_rank', -1)
self.register_function(bm25, 'fts_bm25', -1)
@property
def using_c_extensions(self):
return self._using_c_extensions
def _add_conn_hooks(self, conn):
self._set_pragmas(conn)
self._load_aggregates(conn)
self._load_collations(conn)
self._load_functions(conn)
if self._row_factory:
conn.row_factory = self._row_factory
if self._extensions:
self._load_extensions(conn)
def _load_aggregates(self, conn):
for name, (klass, num_params) in self._aggregates.items():
conn.create_aggregate(name, num_params, klass)
def _load_collations(self, conn):
for name, fn in self._collations.items():
conn.create_collation(name, fn)
def _load_functions(self, conn):
for name, (fn, num_params) in self._functions.items():
conn.create_function(name, num_params, fn)
def _load_extensions(self, conn):
conn.enable_load_extension(True)
for extension in self._extensions:
conn.load_extension(extension)
def register_aggregate(self, klass, name=None, num_params=-1):
self._aggregates[name or klass.__name__.lower()] = (klass, num_params)
if not self.is_closed():
self._load_aggregates(self.get_conn())
def aggregate(self, name=None, num_params=-1):
def decorator(klass):
self.register_aggregate(klass, name, num_params)
return klass
return decorator
def register_collation(self, fn, name=None):
name = name or fn.__name__
def _collation(*args):
expressions = args + (SQL('collate %s' % name),)
return Clause(*expressions)
fn.collation = _collation
self._collations[name] = fn
if not self.is_closed():
self._load_collations(self.get_conn())
def collation(self, name=None):
def decorator(fn):
self.register_collation(fn, name)
return fn
return decorator
def register_function(self, fn, name=None, num_params=-1):
self._functions[name or fn.__name__] = (fn, num_params)
if not self.is_closed():
self._load_functions(self.get_conn())
def func(self, name=None, num_params=-1):
def decorator(fn):
self.register_function(fn, name, num_params)
return fn
return decorator
def load_extension(self, extension):
self._extensions.add(extension)
if not self.is_closed():
conn = self.get_conn()
conn.enable_load_extension(True)
conn.load_extension(extension)
def unregister_aggregate(self, name):
del(self._aggregates[name])
def unregister_collation(self, name):
del(self._collations[name])
def unregister_function(self, name):
del(self._functions[name])
def unload_extension(self, extension):
self._extensions.remove(extension)
def row_factory(self, fn):
self._row_factory = fn
def create_table(self, model_class, safe=False, options=None):
sql, params = self.compiler().create_table(model_class, safe, options)
return self.execute_sql(sql, params)
def create_index(self, model_class, field_name, unique=False):
if issubclass(model_class, FTSModel):
return
return super(SqliteExtDatabase, self).create_index(
model_class, field_name, unique)
def granular_transaction(self, lock_type='deferred'):
assert lock_type.lower() in ('deferred', 'immediate', 'exclusive')
return granular_transaction(self, lock_type)
class granular_transaction(transaction):
def __init__(self, db, lock_type='deferred'):
self.db = db
self.conn = self.db.get_conn()
self.lock_type = lock_type
def _begin(self):
self.db.begin(self.lock_type)
OP.MATCH = 'match'
SqliteExtDatabase.register_ops({
OP.MATCH: 'MATCH',
})
def match(lhs, rhs):
return Expression(lhs, OP.MATCH, rhs)
def _parse_match_info(buf):
# See http://sqlite.org/fts3.html#matchinfo
bufsize = len(buf) # Length in bytes.
return [struct.unpack('@I', buf[i:i+4])[0] for i in range(0, bufsize, 4)]
# Ranking implementation, which parse matchinfo.
def rank(raw_match_info, *weights):
# Handle match_info called w/default args 'pcx' - based on the example rank
# function http://sqlite.org/fts3.html#appendix_a
match_info = _parse_match_info(raw_match_info)
score = 0.0
p, c = match_info[:2]
if not weights:
weights = [1] * c
else:
weights = [0] * c
for i, weight in enumerate(weights):
weights[i] = weight
for phrase_num in range(p):
phrase_info_idx = 2 + (phrase_num * c * 3)
for col_num in range(c):
weight = weights[col_num]
if not weight:
continue
col_idx = phrase_info_idx + (col_num * 3)
x1, x2 = match_info[col_idx:col_idx + 2]
if x1 > 0:
score += weight * (float(x1) / x2)
return -score
# Okapi BM25 ranking implementation (FTS4 only).
def bm25(raw_match_info, *args):
"""
Usage:
# Format string *must* be pcnalx
# Second parameter to bm25 specifies the index of the column, on
# the table being queries.
bm25(matchinfo(document_tbl, 'pcnalx'), 1) AS rank
"""
match_info = _parse_match_info(raw_match_info)
K = 1.2
B = 0.75
score = 0.0
P_O, C_O, N_O, A_O = range(4)
term_count = match_info[P_O]
col_count = match_info[C_O]
total_docs = match_info[N_O]
L_O = A_O + col_count
X_O = L_O + col_count
if not args:
weights = [1] * col_count
else:
weights = [0] * col_count
for i, weight in enumerate(args):
weights[i] = args[i]
for i in range(term_count):
for j in range(col_count):
weight = weights[j]
if weight == 0:
continue
avg_length = float(match_info[A_O + j])
doc_length = float(match_info[L_O + j])
if avg_length == 0:
D = 0
else:
D = 1 - B + (B * (doc_length / avg_length))
x = X_O + (3 * j * (i + 1))
term_frequency = float(match_info[x])
docs_with_term = float(match_info[x + 2])
idf = max(
math.log(
(total_docs - docs_with_term + 0.5) /
(docs_with_term + 0.5)),
0)
denom = term_frequency + (K * D)
if denom == 0:
rhs = 0
else:
rhs = (term_frequency * (K + 1)) / denom
score += (idf * rhs) * weight
return -score
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