/usr/lib/python2.7/dist-packages/whoosh/reading.py is in python-whoosh 2.5.7-3.
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All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY MATT CHAPUT ``AS IS'' AND ANY EXPRESS OR
# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
# EVENT SHALL MATT CHAPUT OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
# OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# The views and conclusions contained in the software and documentation are
# those of the authors and should not be interpreted as representing official
# policies, either expressed or implied, of Matt Chaput.
"""This module contains classes that allow reading from an index.
"""
from math import log
from bisect import bisect_left, bisect_right
from heapq import heapify, heapreplace, heappop, nlargest
from whoosh import columns, scoring
from whoosh.automata import fst
from whoosh.compat import abstractmethod
from whoosh.compat import xrange, zip_, next, iteritems
from whoosh.filedb.filestore import OverlayStorage
from whoosh.matching import MultiMatcher
from whoosh.support.levenshtein import distance
from whoosh.system import emptybytes
# Exceptions
class ReaderClosed(Exception):
"""Exception raised when you try to do some operation on a closed searcher
(or a Results object derived from a searcher that has since been closed).
"""
message = "Operation on a closed reader"
class TermNotFound(Exception):
pass
class NoGraphError(Exception):
pass
# Term Info base class
class TermInfo(object):
"""Represents a set of statistics about a term. This object is returned by
:meth:`IndexReader.term_info`. These statistics may be useful for
optimizations and scoring algorithms.
"""
def __init__(self, weight=0, df=0, minlength=None,
maxlength=0, maxweight=0, minid=None, maxid=0):
self._weight = weight
self._df = df
self._minlength = minlength
self._maxlength = maxlength
self._maxweight = maxweight
self._minid = minid
self._maxid = maxid
def add_posting(self, docnum, weight, length=None):
if self._minid is None:
self._minid = docnum
self._maxid = docnum
self._weight += weight
self._df += 1
self._maxweight = max(self._maxweight, weight)
if length is not None:
if self._minlength is None:
self._minlength = length
else:
self._minlength = min(self._minlength, length)
self._maxlength = max(self._maxlength, length)
def weight(self):
"""Returns the total frequency of the term across all documents.
"""
return self._weight
def doc_frequency(self):
"""Returns the number of documents the term appears in.
"""
return self._df
def min_length(self):
"""Returns the length of the shortest field value the term appears
in.
"""
return self._minlength
def max_length(self):
"""Returns the length of the longest field value the term appears
in.
"""
return self._maxlength
def max_weight(self):
"""Returns the number of times the term appears in the document in
which it appears the most.
"""
return self._maxweight
def min_id(self):
"""Returns the lowest document ID this term appears in.
"""
return self._minid
def max_id(self):
"""Returns the highest document ID this term appears in.
"""
return self._maxid
# Reader base class
class IndexReader(object):
"""Do not instantiate this object directly. Instead use Index.reader().
"""
def __enter__(self):
return self
def __exit__(self, *args):
self.close()
@abstractmethod
def __contains__(self, term):
"""Returns True if the given term tuple (fieldname, text) is
in this reader.
"""
raise NotImplementedError
def codec(self):
"""Returns the :class:`whoosh.codec.base.Codec` object used to read
this reader's segment. If this reader is not atomic
(``reader.is_atomic() == True``), returns None.
"""
return None
def segment(self):
"""Returns the :class:`whoosh.index.Segment` object used by this reader.
If this reader is not atomic (``reader.is_atomic() == True``), returns
None.
"""
return None
def storage(self):
"""Returns the :class:`whoosh.filedb.filestore.Storage` object used by
this reader to read its files. If the reader is not atomic,
(``reader.is_atomic() == True``), returns None.
"""
return None
def is_atomic(self):
return True
def _text_to_bytes(self, fieldname, text):
if fieldname not in self.schema:
raise TermNotFound((fieldname, text))
return self.schema[fieldname].to_bytes(text)
def close(self):
"""Closes the open files associated with this reader.
"""
pass
def generation(self):
"""Returns the generation of the index being read, or -1 if the backend
is not versioned.
"""
return None
@abstractmethod
def indexed_field_names(self):
"""Returns an iterable of strings representing the names of the indexed
fields. This may include additional names not explicitly listed in the
Schema if you use "glob" fields.
"""
raise NotImplementedError
@abstractmethod
def all_terms(self):
"""Yields (fieldname, text) tuples for every term in the index.
"""
raise NotImplementedError
def terms_from(self, fieldname, prefix):
"""Yields (fieldname, text) tuples for every term in the index starting
at the given prefix.
"""
# The default implementation just scans the whole list of terms
for fname, text in self.all_terms():
if fname < fieldname or text < prefix:
continue
yield (fname, text)
@abstractmethod
def term_info(self, fieldname, text):
"""Returns a :class:`TermInfo` object allowing access to various
statistics about the given term.
"""
raise NotImplementedError
def expand_prefix(self, fieldname, prefix):
"""Yields terms in the given field that start with the given prefix.
"""
for fn, text in self.terms_from(fieldname, prefix):
if fn != fieldname or not text.startswith(prefix):
return
yield text
def lexicon(self, fieldname):
"""Yields all bytestrings in the given field.
"""
for fn, btext in self.terms_from(fieldname, emptybytes):
if fn != fieldname:
return
yield btext
def field_terms(self, fieldname):
"""Yields all term values (converted from on-disk bytes) in the given
field.
"""
from_bytes = self.schema[fieldname].from_bytes
for btext in self.lexicon(fieldname):
yield from_bytes(btext)
def __iter__(self):
"""Yields ((fieldname, text), terminfo) tuples for each term in the
reader, in lexical order.
"""
term_info = self.term_info
for term in self.all_terms():
yield (term, term_info(*term))
def iter_from(self, fieldname, text):
"""Yields ((fieldname, text), terminfo) tuples for all terms in the
reader, starting at the given term.
"""
term_info = self.term_info
text = self._text_to_bytes(fieldname, text)
for term in self.terms_from(fieldname, text):
yield (term, term_info(*term))
def iter_field(self, fieldname, prefix=''):
"""Yields (text, terminfo) tuples for all terms in the given field.
"""
prefix = self._text_to_bytes(fieldname, prefix)
for (fn, text), terminfo in self.iter_from(fieldname, prefix):
if fn != fieldname:
return
yield text, terminfo
def iter_prefix(self, fieldname, prefix):
"""Yields (text, terminfo) tuples for all terms in the given field with
a certain prefix.
"""
prefix = self._text_to_bytes(fieldname, prefix)
for text, terminfo in self.iter_field(fieldname, prefix):
if not text.startswith(prefix):
return
yield (text, terminfo)
@abstractmethod
def has_deletions(self):
"""Returns True if the underlying index/segment has deleted
documents.
"""
raise NotImplementedError
def all_doc_ids(self):
"""Returns an iterator of all (undeleted) document IDs in the reader.
"""
is_deleted = self.is_deleted
return (docnum for docnum in xrange(self.doc_count_all())
if not is_deleted(docnum))
def iter_docs(self):
"""Yields a series of ``(docnum, stored_fields_dict)``
tuples for the undeleted documents in the reader.
"""
for docnum in self.all_doc_ids():
yield docnum, self.stored_fields(docnum)
@abstractmethod
def is_deleted(self, docnum):
"""Returns True if the given document number is marked deleted.
"""
raise NotImplementedError
@abstractmethod
def stored_fields(self, docnum):
"""Returns the stored fields for the given document number.
:param numerickeys: use field numbers as the dictionary keys instead of
field names.
"""
raise NotImplementedError
def all_stored_fields(self):
"""Yields the stored fields for all documents (including deleted
documents).
"""
for docnum in xrange(self.doc_count_all()):
yield self.stored_fields(docnum)
@abstractmethod
def doc_count_all(self):
"""Returns the total number of documents, DELETED OR UNDELETED,
in this reader.
"""
raise NotImplementedError
@abstractmethod
def doc_count(self):
"""Returns the total number of UNDELETED documents in this reader.
"""
return self.doc_count_all() - self.deleted_count()
@abstractmethod
def frequency(self, fieldname, text):
"""Returns the total number of instances of the given term in the
collection.
"""
raise NotImplementedError
@abstractmethod
def doc_frequency(self, fieldname, text):
"""Returns how many documents the given term appears in.
"""
raise NotImplementedError
@abstractmethod
def field_length(self, fieldname):
"""Returns the total number of terms in the given field. This is used
by some scoring algorithms.
"""
raise NotImplementedError
@abstractmethod
def min_field_length(self, fieldname):
"""Returns the minimum length of the field across all documents. This
is used by some scoring algorithms.
"""
raise NotImplementedError
@abstractmethod
def max_field_length(self, fieldname):
"""Returns the minimum length of the field across all documents. This
is used by some scoring algorithms.
"""
raise NotImplementedError
@abstractmethod
def doc_field_length(self, docnum, fieldname, default=0):
"""Returns the number of terms in the given field in the given
document. This is used by some scoring algorithms.
"""
raise NotImplementedError
def first_id(self, fieldname, text):
"""Returns the first ID in the posting list for the given term. This
may be optimized in certain backends.
"""
text = self._text_to_bytes(fieldname, text)
p = self.postings(fieldname, text)
if p.is_active():
return p.id()
raise TermNotFound((fieldname, text))
def iter_postings(self):
"""Low-level method, yields all postings in the reader as
``(fieldname, text, docnum, weight, valuestring)`` tuples.
"""
for fieldname, btext in self.all_terms():
m = self.postings(fieldname, btext)
while m.is_active():
yield (fieldname, btext, m.id(), m.weight(), m.value())
m.next()
@abstractmethod
def postings(self, fieldname, text):
"""Returns a :class:`~whoosh.matching.Matcher` for the postings of the
given term.
>>> pr = reader.postings("content", "render")
>>> pr.skip_to(10)
>>> pr.id
12
:param fieldname: the field name or field number of the term.
:param text: the text of the term.
:rtype: :class:`whoosh.matching.Matcher`
"""
raise NotImplementedError
@abstractmethod
def has_vector(self, docnum, fieldname):
"""Returns True if the given document has a term vector for the given
field.
"""
raise NotImplementedError
@abstractmethod
def vector(self, docnum, fieldname, format_=None):
"""Returns a :class:`~whoosh.matching.Matcher` object for the
given term vector.
>>> docnum = searcher.document_number(path=u'/a/b/c')
>>> v = searcher.vector(docnum, "content")
>>> v.all_as("frequency")
[(u"apple", 3), (u"bear", 2), (u"cab", 2)]
:param docnum: the document number of the document for which you want
the term vector.
:param fieldname: the field name or field number of the field for which
you want the term vector.
:rtype: :class:`whoosh.matching.Matcher`
"""
raise NotImplementedError
def vector_as(self, astype, docnum, fieldname):
"""Returns an iterator of (termtext, value) pairs for the terms in the
given term vector. This is a convenient shortcut to calling vector()
and using the Matcher object when all you want are the terms and/or
values.
>>> docnum = searcher.document_number(path=u'/a/b/c')
>>> searcher.vector_as("frequency", docnum, "content")
[(u"apple", 3), (u"bear", 2), (u"cab", 2)]
:param docnum: the document number of the document for which you want
the term vector.
:param fieldname: the field name or field number of the field for which
you want the term vector.
:param astype: a string containing the name of the format you want the
term vector's data in, for example "weights".
"""
vec = self.vector(docnum, fieldname)
if astype == "weight":
while vec.is_active():
yield (vec.id(), vec.weight())
vec.next()
else:
format_ = self.schema[fieldname].format
decoder = format_.decoder(astype)
while vec.is_active():
yield (vec.id(), decoder(vec.value()))
vec.next()
def has_word_graph(self, fieldname):
"""Returns True if the given field has a "word graph" associated with
it, allowing suggestions for correcting mis-typed words and fast fuzzy
term searching.
"""
return False
def word_graph(self, fieldname):
"""Returns the root :class:`whoosh.fst.Node` for the given
field, if the field has a stored word graph (otherwise raises an
exception). You can check whether a field has a word graph using
:meth:`IndexReader.has_word_graph`.
"""
raise KeyError
def corrector(self, fieldname):
"""Returns a :class:`whoosh.spelling.Corrector` object that suggests
corrections based on the terms in the given field.
"""
from whoosh.spelling import ReaderCorrector
return ReaderCorrector(self, fieldname)
def terms_within(self, fieldname, text, maxdist, prefix=0):
"""Returns a generator of words in the given field within ``maxdist``
Damerau-Levenshtein edit distance of the given text.
Important: the terms are returned in **no particular order**. The only
criterion is that they are within ``maxdist`` edits of ``text``. You
may want to run this method multiple times with increasing ``maxdist``
values to ensure you get the closest matches first. You may also have
additional information (such as term frequency or an acoustic matching
algorithm) you can use to rank terms with the same edit distance.
:param maxdist: the maximum edit distance.
:param prefix: require suggestions to share a prefix of this length
with the given word. This is often justifiable since most
misspellings do not involve the first letter of the word.
Using a prefix dramatically decreases the time it takes to generate
the list of words.
:param seen: an optional set object. Words that appear in the set will
not be yielded.
"""
fieldobj = self.schema[fieldname]
for btext in self.expand_prefix(fieldname, text[:prefix]):
word = fieldobj.from_bytes(btext)
k = distance(word, text, limit=maxdist)
if k <= maxdist:
yield word
def most_frequent_terms(self, fieldname, number=5, prefix=''):
"""Returns the top 'number' most frequent terms in the given field as a
list of (frequency, text) tuples.
"""
gen = ((terminfo.weight(), text) for text, terminfo
in self.iter_prefix(fieldname, prefix))
return nlargest(number, gen)
def most_distinctive_terms(self, fieldname, number=5, prefix=''):
"""Returns the top 'number' terms with the highest `tf*idf` scores as
a list of (score, text) tuples.
"""
N = float(self.doc_count())
gen = ((terminfo.weight() * log(N / terminfo.doc_frequency()), text)
for text, terminfo in self.iter_prefix(fieldname, prefix))
return nlargest(number, gen)
def leaf_readers(self):
"""Returns a list of (IndexReader, docbase) pairs for the child readers
of this reader if it is a composite reader. If this is not a composite
reader, it returns `[(self, 0)]`.
"""
return [(self, 0)]
def supports_caches(self):
return False
def has_column(self, fieldname):
return False
def column_reader(self, fieldname, column=None, reverse=False,
translate=False):
"""
:param fieldname: the name of the field for which to get a reader.
:param column: if passed, use this Column object instead of the one
associated with the field in the Schema.
:param reverse: if passed, reverses the order of keys returned by the
reader's ``sort_key()`` method. If the column type is not
reversible, this will raise a ``NotImplementedError``.
:param translate: if True, wrap the reader to call the field's
``from_bytes()`` method on the returned values.
:return: a :class:`whoosh.columns.ColumnReader` object.
"""
raise NotImplementedError
# Segment-based reader
class SegmentReader(IndexReader):
def __init__(self, storage, schema, segment, generation=None, codec=None):
self.schema = schema
self.is_closed = False
self._segment = segment
self._segid = self._segment.segment_id()
self._gen = generation
# self.files is a storage object from which to load the segment files.
# This is different from the general storage (which will be used for
# caches) if the segment is in a compound file.
if segment.is_compound():
# Open the compound file as a storage object
files = segment.open_compound_file(storage)
# Use an overlay here instead of just the compound storage, in rare
# circumstances a segment file may be added after the segment is
# written
self._storage = OverlayStorage(files, storage)
else:
self._storage = storage
# Get subreaders from codec
self._codec = codec if codec else segment.codec()
self._terms = self._codec.terms_reader(self._storage, segment)
self._perdoc = self._codec.per_document_reader(self._storage, segment)
self._graph = None # Lazy open with self._get_graph()
def _get_graph(self):
if not self._graph:
self._graph = self._codec.graph_reader(self._storage, self._segment)
return self._graph
def codec(self):
return self._codec
def segment(self):
return self._segment
def storage(self):
return self._storage
def has_deletions(self):
if self.is_closed:
raise ReaderClosed
return self._perdoc.has_deletions()
def doc_count(self):
if self.is_closed:
raise ReaderClosed
return self._perdoc.doc_count()
def doc_count_all(self):
if self.is_closed:
raise ReaderClosed
return self._perdoc.doc_count_all()
def is_deleted(self, docnum):
if self.is_closed:
raise ReaderClosed
return self._perdoc.is_deleted(docnum)
def generation(self):
return self._gen
def __repr__(self):
return "%s(%r, %r)" % (self.__class__.__name__, self._storage,
self._segment)
def __contains__(self, term):
if self.is_closed:
raise ReaderClosed
fieldname, text = term
if fieldname not in self.schema:
return False
text = self._text_to_bytes(fieldname, text)
return (fieldname, text) in self._terms
def close(self):
if self.is_closed:
raise ReaderClosed("Reader already closed")
self._terms.close()
self._perdoc.close()
if self._graph:
self._graph.close()
# It's possible some weird codec that doesn't use storage might have
# passed None instead of a storage object
if self._storage:
self._storage.close()
self.is_closed = True
def stored_fields(self, docnum):
if self.is_closed:
raise ReaderClosed
assert docnum >= 0
schema = self.schema
sfs = self._perdoc.stored_fields(docnum)
# Double-check with schema to filter out removed fields
return dict(item for item in iteritems(sfs) if item[0] in schema)
# Delegate doc methods to the per-doc reader
def all_doc_ids(self):
if self.is_closed:
raise ReaderClosed
return self._perdoc.all_doc_ids()
def iter_docs(self):
if self.is_closed:
raise ReaderClosed
return self._perdoc.iter_docs()
def all_stored_fields(self):
if self.is_closed:
raise ReaderClosed
return self._perdoc.all_stored_fields()
def field_length(self, fieldname):
if self.is_closed:
raise ReaderClosed
return self._perdoc.field_length(fieldname)
def min_field_length(self, fieldname):
if self.is_closed:
raise ReaderClosed
return self._perdoc.min_field_length(fieldname)
def max_field_length(self, fieldname):
if self.is_closed:
raise ReaderClosed
return self._perdoc.max_field_length(fieldname)
def doc_field_length(self, docnum, fieldname, default=0):
if self.is_closed:
raise ReaderClosed
return self._perdoc.doc_field_length(docnum, fieldname, default)
def has_vector(self, docnum, fieldname):
if self.is_closed:
raise ReaderClosed
return self._perdoc.has_vector(docnum, fieldname)
#
def _test_field(self, fieldname):
if self.is_closed:
raise ReaderClosed
if fieldname not in self.schema:
raise TermNotFound("No field %r" % fieldname)
if self.schema[fieldname].format is None:
raise TermNotFound("Field %r is not indexed" % fieldname)
def indexed_field_names(self):
return self._terms.indexed_field_names()
def all_terms(self):
if self.is_closed:
raise ReaderClosed
schema = self.schema
return ((fieldname, text) for fieldname, text in self._terms.terms()
if fieldname in schema)
def terms_from(self, fieldname, prefix):
self._test_field(fieldname)
prefix = self._text_to_bytes(fieldname, prefix)
schema = self.schema
return ((fname, text) for fname, text
in self._terms.terms_from(fieldname, prefix)
if fname in schema)
def term_info(self, fieldname, text):
self._test_field(fieldname)
text = self._text_to_bytes(fieldname, text)
try:
return self._terms.term_info(fieldname, text)
except KeyError:
raise TermNotFound("%s:%r" % (fieldname, text))
def expand_prefix(self, fieldname, prefix):
self._test_field(fieldname)
prefix = self._text_to_bytes(fieldname, prefix)
return IndexReader.expand_prefix(self, fieldname, prefix)
def lexicon(self, fieldname):
self._test_field(fieldname)
return IndexReader.lexicon(self, fieldname)
def __iter__(self):
if self.is_closed:
raise ReaderClosed
schema = self.schema
return ((term, terminfo) for term, terminfo in self._terms.items()
if term[0] in schema)
def iter_from(self, fieldname, text):
self._test_field(fieldname)
schema = self.schema
text = self._text_to_bytes(fieldname, text)
for term, terminfo in self._terms.items_from(fieldname, text):
if term[0] not in schema:
continue
yield (term, terminfo)
def frequency(self, fieldname, text):
self._test_field(fieldname)
text = self._text_to_bytes(fieldname, text)
try:
return self._terms.frequency(fieldname, text)
except KeyError:
return 0
def doc_frequency(self, fieldname, text):
self._test_field(fieldname)
text = self._text_to_bytes(fieldname, text)
try:
return self._terms.doc_frequency(fieldname, text)
except KeyError:
return 0
def postings(self, fieldname, text, scorer=None):
from whoosh.matching.wrappers import FilterMatcher
if self.is_closed:
raise ReaderClosed
if fieldname not in self.schema:
raise TermNotFound("No field %r" % fieldname)
text = self._text_to_bytes(fieldname, text)
format_ = self.schema[fieldname].format
matcher = self._terms.matcher(fieldname, text, format_, scorer=scorer)
deleted = frozenset(self._perdoc.deleted_docs())
if deleted:
matcher = FilterMatcher(matcher, deleted, exclude=True)
return matcher
def vector(self, docnum, fieldname, format_=None):
if self.is_closed:
raise ReaderClosed
if fieldname not in self.schema:
raise TermNotFound("No field %r" % fieldname)
vformat = format_ or self.schema[fieldname].vector
if not vformat:
raise Exception("No vectors are stored for field %r" % fieldname)
return self._perdoc.vector(docnum, fieldname, vformat)
# Graph methods
def has_word_graph(self, fieldname):
if self.is_closed:
raise ReaderClosed
if fieldname not in self.schema:
return False
if not self.schema[fieldname].spelling:
return False
try:
gr = self._get_graph()
except NoGraphError:
return False
return gr.has_root(fieldname)
def word_graph(self, fieldname):
if self.is_closed:
raise ReaderClosed
if not self.has_word_graph(fieldname):
raise KeyError("No word graph for field %r" % fieldname)
gr = self._get_graph()
return fst.Node(gr, gr.root(fieldname))
def terms_within(self, fieldname, text, maxdist, prefix=0):
if self.is_closed:
raise ReaderClosed
if not self.has_word_graph(fieldname):
# This reader doesn't have a graph stored, use the slow method
return IndexReader.terms_within(self, fieldname, text, maxdist,
prefix=prefix)
gr = self._get_graph()
return fst.within(gr, text, k=maxdist, prefix=prefix,
address=self._graph.root(fieldname))
# Column methods
def has_column(self, fieldname):
if self.is_closed:
raise ReaderClosed
coltype = self.schema[fieldname].column_type
return coltype and self._perdoc.has_column(fieldname)
def column_reader(self, fieldname, column=None, reverse=False,
translate=True):
if self.is_closed:
raise ReaderClosed
fieldobj = self.schema[fieldname]
column = column or fieldobj.column_type
if not column:
raise Exception("No column for field %r in %r"
% (fieldname, self))
if self._perdoc.has_column(fieldname):
creader = self._perdoc.column_reader(fieldname, column)
if reverse:
creader.set_reverse()
else:
# This segment doesn't have a column file for this field, so create
# a fake column reader that always returns the default value.
default = column.default_value(reverse)
creader = columns.EmptyColumnReader(default, self.doc_count_all())
if translate:
# Wrap the column in a Translator to give the caller
# nice values instead of sortable representations
fcv = fieldobj.from_column_value
creader = columns.TranslatingColumnReader(creader, fcv)
return creader
# Fake IndexReader class for empty indexes
class EmptyReader(IndexReader):
def __init__(self, schema):
self.schema = schema
def __contains__(self, term):
return False
def __iter__(self):
return iter([])
def indexed_field_names(self):
return []
def all_terms(self):
return iter([])
def term_info(self, fieldname, text):
raise TermNotFound((fieldname, text))
def iter_from(self, fieldname, text):
return iter([])
def iter_field(self, fieldname, prefix=''):
return iter([])
def iter_prefix(self, fieldname, prefix=''):
return iter([])
def lexicon(self, fieldname):
return iter([])
def has_deletions(self):
return False
def is_deleted(self, docnum):
return False
def stored_fields(self, docnum):
raise KeyError("No document number %s" % docnum)
def all_stored_fields(self):
return iter([])
def doc_count_all(self):
return 0
def doc_count(self):
return 0
def frequency(self, fieldname, text):
return 0
def doc_frequency(self, fieldname, text):
return 0
def field_length(self, fieldname):
return 0
def min_field_length(self, fieldname):
return 0
def max_field_length(self, fieldname):
return 0
def doc_field_length(self, docnum, fieldname, default=0):
return default
def postings(self, fieldname, text, scorer=None):
raise TermNotFound("%s:%r" % (fieldname, text))
def has_vector(self, docnum, fieldname):
return False
def vector(self, docnum, fieldname, format_=None):
raise KeyError("No document number %s" % docnum)
def most_frequent_terms(self, fieldname, number=5, prefix=''):
return iter([])
def most_distinctive_terms(self, fieldname, number=5, prefix=None):
return iter([])
# Multisegment reader class
class MultiReader(IndexReader):
"""Do not instantiate this object directly. Instead use Index.reader().
"""
def __init__(self, readers, generation=None):
self.readers = readers
self._gen = generation
self.schema = None
if readers:
self.schema = readers[0].schema
self.doc_offsets = []
self.base = 0
for r in self.readers:
self.doc_offsets.append(self.base)
self.base += r.doc_count_all()
self.is_closed = False
def _document_segment(self, docnum):
return max(0, bisect_right(self.doc_offsets, docnum) - 1)
def _segment_and_docnum(self, docnum):
segmentnum = self._document_segment(docnum)
offset = self.doc_offsets[segmentnum]
return segmentnum, docnum - offset
def is_atomic(self):
return False
def leaf_readers(self):
return zip_(self.readers, self.doc_offsets)
def add_reader(self, reader):
self.readers.append(reader)
self.doc_offsets.append(self.base)
self.base += reader.doc_count_all()
def close(self):
for d in self.readers:
d.close()
self.is_closed = True
def generation(self):
return self._gen
def format(self, fieldname):
for r in self.readers:
fmt = r.format(fieldname)
if fmt is not None:
return fmt
def vector_format(self, fieldname):
for r in self.readers:
vfmt = r.vector_format(fieldname)
if vfmt is not None:
return vfmt
# Term methods
def __contains__(self, term):
return any(r.__contains__(term) for r in self.readers)
def _merge_terms(self, iterlist):
# Merge-sorts terms coming from a list of term iterators.
# Create a map so we can look up each iterator by its id() value
itermap = {}
for it in iterlist:
itermap[id(it)] = it
# Fill in the list with the head term from each iterator.
current = []
for it in iterlist:
try:
term = next(it)
except StopIteration:
continue
current.append((term, id(it)))
# Number of active iterators
active = len(current)
# If only one iterator is active, just yield from it and return
if active == 1:
term, itid = current[0]
it = itermap[itid]
yield term
for term in it:
yield term
return
# Otherwise, do a streaming heap sort of the terms from the iterators
heapify(current)
while active:
# Peek at the first term in the sorted list
term = current[0][0]
# Re-iterate on all items in the list that have that term
while active and current[0][0] == term:
it = itermap[current[0][1]]
try:
nextterm = next(it)
heapreplace(current, (nextterm, id(it)))
except StopIteration:
heappop(current)
active -= 1
# Yield the term
yield term
def indexed_field_names(self):
names = set()
for r in self.reader():
names.update(r.indexed_field_names())
return iter(names)
def all_terms(self):
return self._merge_terms([r.all_terms() for r in self.readers])
def terms_from(self, fieldname, prefix):
return self._merge_terms([r.terms_from(fieldname, prefix)
for r in self.readers])
def term_info(self, fieldname, text):
term = (fieldname, text)
# Get the term infos for the sub-readers containing the term
tis = [(r.term_info(fieldname, text), offset) for r, offset
in zip_(self.readers, self.doc_offsets) if term in r]
# If only one reader had the term, return its terminfo with the offset
# added
if not tis:
raise TermNotFound(term)
elif len(tis) == 1:
ti, offset = tis[0]
ti._minid += offset
ti._maxid += offset
return ti
# Combine the various statistics
w = sum(ti.weight() for ti, _ in tis)
df = sum(ti.doc_frequency() for ti, _ in tis)
ml = min(ti.min_length() for ti, _ in tis)
xl = max(ti.max_length() for ti, _ in tis)
xw = max(ti.max_weight() for ti, _ in tis)
# For min and max ID, we need to add the doc offsets
mid = min(ti.min_id() + offset for ti, offset in tis)
xid = max(ti.max_id() + offset for ti, offset in tis)
return TermInfo(w, df, ml, xl, xw, mid, xid)
def frequency(self, fieldname, text):
return sum(r.frequency(fieldname, text) for r in self.readers)
def doc_frequency(self, fieldname, text):
return sum(r.doc_frequency(fieldname, text) for r in self.readers)
def postings(self, fieldname, text):
# This method does not add a scorer; for that, use Searcher.postings()
postreaders = []
docoffsets = []
term = (fieldname, text)
for i, r in enumerate(self.readers):
if term in r:
offset = self.doc_offsets[i]
pr = r.postings(fieldname, text)
postreaders.append(pr)
docoffsets.append(offset)
if not postreaders:
raise TermNotFound(fieldname, text)
return MultiMatcher(postreaders, docoffsets)
def first_id(self, fieldname, text):
for i, r in enumerate(self.readers):
try:
id = r.first_id(fieldname, text)
except (KeyError, TermNotFound):
pass
else:
if id is None:
raise TermNotFound((fieldname, text))
else:
return self.doc_offsets[i] + id
raise TermNotFound((fieldname, text))
# Deletion methods
def has_deletions(self):
return any(r.has_deletions() for r in self.readers)
def is_deleted(self, docnum):
segmentnum, segmentdoc = self._segment_and_docnum(docnum)
return self.readers[segmentnum].is_deleted(segmentdoc)
def stored_fields(self, docnum):
segmentnum, segmentdoc = self._segment_and_docnum(docnum)
return self.readers[segmentnum].stored_fields(segmentdoc)
# Per doc methods
def all_stored_fields(self):
for reader in self.readers:
for result in reader.all_stored_fields():
yield result
def doc_count_all(self):
return sum(dr.doc_count_all() for dr in self.readers)
def doc_count(self):
return sum(dr.doc_count() for dr in self.readers)
def field_length(self, fieldname):
return sum(dr.field_length(fieldname) for dr in self.readers)
def min_field_length(self, fieldname):
return min(r.min_field_length(fieldname) for r in self.readers)
def max_field_length(self, fieldname):
return max(r.max_field_length(fieldname) for r in self.readers)
def doc_field_length(self, docnum, fieldname, default=0):
segmentnum, segmentdoc = self._segment_and_docnum(docnum)
reader = self.readers[segmentnum]
return reader.doc_field_length(segmentdoc, fieldname, default=default)
def has_vector(self, docnum, fieldname):
segmentnum, segmentdoc = self._segment_and_docnum(docnum)
return self.readers[segmentnum].has_vector(segmentdoc, fieldname)
def vector(self, docnum, fieldname, format_=None):
segmentnum, segmentdoc = self._segment_and_docnum(docnum)
return self.readers[segmentnum].vector(segmentdoc, fieldname)
def vector_as(self, astype, docnum, fieldname):
segmentnum, segmentdoc = self._segment_and_docnum(docnum)
return self.readers[segmentnum].vector_as(astype, segmentdoc,
fieldname)
# Graph methods
def has_word_graph(self, fieldname):
return any(r.has_word_graph(fieldname) for r in self.readers)
def word_graph(self, fieldname):
from whoosh.automata.fst import UnionNode
from whoosh.util import make_binary_tree
if not self.has_word_graph(fieldname):
raise Exception("No word graph for field %r" % fieldname)
graphs = [r.word_graph(fieldname) for r in self.readers
if r.has_word_graph(fieldname)]
if len(graphs) == 0:
raise KeyError("No readers have graph for %r" % fieldname)
if len(graphs) == 1:
return graphs[0]
return make_binary_tree(UnionNode, graphs)
def terms_within(self, fieldname, text, maxdist, prefix=0):
tset = set()
for r in self.readers:
tset.update(r.terms_within(fieldname, text, maxdist,
prefix=prefix))
return tset
# Column methods
def has_column(self, fieldname):
return any(r.has_column(fieldname) for r in self.readers)
def column_reader(self, fieldname, column=None, reverse=False,
translate=True):
column = column or self.schema[fieldname].column_type
if not column:
raise Exception("Field %r has no column type" % (fieldname,))
creaders = []
for r in self.readers:
cr = r.column_reader(fieldname, column=column, reverse=reverse,
translate=translate)
creaders.append(cr)
return columns.MultiColumnReader(creaders)
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