def __init__(self, ix, postlimit, blocklimit, name=None):
"""
:param ix: the Index object in which to write the new segment.
:param postlimit: the maximum size for a run in the posting pool.
:param blocklimit: the maximum number of postings in a posting block.
:param name: the name of the segment.
"""
self.index = ix
self.schema = ix.schema
self.storage = storage = ix.storage
self.name = name or ix._next_segment_name()
self.max_doc = 0
self.pool = postpool.PostingPool(postlimit)
# Create mappings of field numbers to the position of that field in the
# lists of scorable and stored fields. For example, consider a schema
# with fields (A, B, C, D, E, F). If B, D, and E are scorable, then the
# list of scorable fields is (B, D, E). The _scorable_to_pos dictionary
# would then map B -> 0, D -> 1, and E -> 2.
self._scorable_to_pos = dict((fnum, i)
for i, fnum
in enumerate(self.schema.scorable_fields()))
self._stored_to_pos = dict((fnum, i)
for i, fnum
in enumerate(self.schema.stored_fields()))
# Create a temporary segment object just so we can access its
# *_filename attributes (so if we want to change the naming convention,
# we only have to do it in one place).
tempseg = Segment(self.name, 0, 0, None)
self.termtable = create_terms(storage, tempseg)
self.docslist = create_storedfields(storage, tempseg)
self.doclengths = None
if self.schema.scorable_fields():
self.doclengths = create_doclengths(storage, tempseg, len(self._scorable_to_pos))
postfile = storage.create_file(tempseg.posts_filename)
self.postwriter = FilePostingWriter(postfile, blocklimit=blocklimit)
self.vectortable = None
if self.schema.has_vectored_fields():
# Table associating document fields with (postoffset, postcount)
self.vectortable = create_vectors(storage, tempseg)
vpostfile = storage.create_file(tempseg.vectorposts_filename)
self.vpostwriter = FilePostingWriter(vpostfile, stringids=True)
# Keep track of the total number of tokens (across all docs)
# in each field
self.field_length_totals = defaultdict(int)
def __init__(self, ix, postlimit, blocklimit, name=None):
"""
:param ix: the Index object in which to write the new segment.
:param postlimit: the maximum size for a run in the posting pool.
:param blocklimit: the maximum number of postings in a posting block.
:param name: the name of the segment.
"""
self.index = ix
self.schema = ix.schema
self.storage = storage = ix.storage
self.name = name or ix._next_segment_name()
self.max_doc = 0
self.pool = postpool.PostingPool(postlimit)
# Create mappings of field numbers to the position of that field in the
# lists of scorable and stored fields. For example, consider a schema
# with fields (A, B, C, D, E, F). If B, D, and E are scorable, then the
# list of scorable fields is (B, D, E). The _scorable_to_pos dictionary
# would then map B -> 0, D -> 1, and E -> 2.
self._scorable_to_pos = dict(
(fnum, i) for i, fnum in enumerate(self.schema.scorable_fields()))
self._stored_to_pos = dict(
(fnum, i) for i, fnum in enumerate(self.schema.stored_fields()))
# Create a temporary segment object just so we can access its
# *_filename attributes (so if we want to change the naming convention,
# we only have to do it in one place).
tempseg = Segment(self.name, 0, 0, None)
self.termtable = create_terms(storage, tempseg)
self.docslist = create_storedfields(storage, tempseg)
self.doclengths = None
if self.schema.scorable_fields():
self.doclengths = create_doclengths(storage, tempseg,
len(self._scorable_to_pos))
postfile = storage.create_file(tempseg.posts_filename)
self.postwriter = FilePostingWriter(postfile, blocklimit=blocklimit)
self.vectortable = None
if self.schema.has_vectored_fields():
# Table associating document fields with (postoffset, postcount)
self.vectortable = create_vectors(storage, tempseg)
vpostfile = storage.create_file(tempseg.vectorposts_filename)
self.vpostwriter = FilePostingWriter(vpostfile, stringids=True)
# Keep track of the total number of tokens (across all docs)
# in each field
self.field_length_totals = defaultdict(int)
class SegmentWriter(object):
"""Do not instantiate this object directly; it is created by the
IndexWriter object.
Handles the actual writing of new documents to the index: writes stored
fields, handles the posting pool, and writes out the term index.
"""
def __init__(self, ix, postlimit, blocklimit, name=None):
"""
:param ix: the Index object in which to write the new segment.
:param postlimit: the maximum size for a run in the posting pool.
:param blocklimit: the maximum number of postings in a posting block.
:param name: the name of the segment.
"""
self.index = ix
self.schema = ix.schema
self.storage = storage = ix.storage
self.name = name or ix._next_segment_name()
self.max_doc = 0
self.pool = postpool.PostingPool(postlimit)
# Create mappings of field numbers to the position of that field in the
# lists of scorable and stored fields. For example, consider a schema
# with fields (A, B, C, D, E, F). If B, D, and E are scorable, then the
# list of scorable fields is (B, D, E). The _scorable_to_pos dictionary
# would then map B -> 0, D -> 1, and E -> 2.
self._scorable_to_pos = dict(
(fnum, i) for i, fnum in enumerate(self.schema.scorable_fields()))
self._stored_to_pos = dict(
(fnum, i) for i, fnum in enumerate(self.schema.stored_fields()))
# Create a temporary segment object just so we can access its
# *_filename attributes (so if we want to change the naming convention,
# we only have to do it in one place).
tempseg = Segment(self.name, 0, 0, None)
self.termtable = create_terms(storage, tempseg)
self.docslist = create_storedfields(storage, tempseg)
self.doclengths = None
if self.schema.scorable_fields():
self.doclengths = create_doclengths(storage, tempseg,
len(self._scorable_to_pos))
postfile = storage.create_file(tempseg.posts_filename)
self.postwriter = FilePostingWriter(postfile, blocklimit=blocklimit)
self.vectortable = None
if self.schema.has_vectored_fields():
# Table associating document fields with (postoffset, postcount)
self.vectortable = create_vectors(storage, tempseg)
vpostfile = storage.create_file(tempseg.vectorposts_filename)
self.vpostwriter = FilePostingWriter(vpostfile, stringids=True)
# Keep track of the total number of tokens (across all docs)
# in each field
self.field_length_totals = defaultdict(int)
def segment(self):
"""Returns an index.Segment object for the segment being written."""
return Segment(self.name, self.max_doc, dict(self.field_length_totals))
def _close_all(self):
self.termtable.close()
self.postwriter.close()
self.docslist.close()
if self.doclengths:
self.doclengths.close()
if self.vectortable:
self.vectortable.close()
self.vpostwriter.close()
def close(self):
"""Finishes writing the segment (flushes the posting pool out to disk)
and closes all open files.
"""
self._flush_pool()
self._close_all()
def add_reader(self, reader):
"""Adds the contents of another segment to this one. This is used to
merge existing segments into the new one before deleting them.
:param ix: The index.Index object containing the segment to merge.
:param segment: The index.Segment object to merge into this one.
"""
start_doc = self.max_doc
has_deletions = reader.has_deletions()
if has_deletions:
doc_map = {}
schema = self.schema
name2num = schema.name_to_number
stored_to_pos = self._stored_to_pos
def storedkeyhelper(item):
return stored_to_pos[name2num(item[0])]
# Merge document info
docnum = 0
vectored_fieldnums = schema.vectored_fields()
for docnum in xrange(reader.doc_count_all()):
if not reader.is_deleted(docnum):
# Copy the stored fields and field lengths from the reader
# into this segment
storeditems = reader.stored_fields(docnum).items()
storedvalues = [
v for k, v in sorted(storeditems, key=storedkeyhelper)
]
self._add_doc_data(storedvalues,
reader.doc_field_lengths(docnum))
if has_deletions:
doc_map[docnum] = self.max_doc
# Copy term vectors
for fieldnum in vectored_fieldnums:
if reader.has_vector(docnum, fieldnum):
self._add_vector(
fieldnum,
reader.vector(docnum, fieldnum).items())
self.max_doc += 1
# Add field length totals
for fieldnum in schema.scorable_fields():
self.field_length_totals[fieldnum] += reader.field_length(fieldnum)
# Merge terms
current_fieldnum = None
decoder = None
for fieldnum, text, _, _ in reader:
if fieldnum != current_fieldnum:
current_fieldnum = fieldnum
decoder = schema[fieldnum].format.decode_frequency
postreader = reader.postings(fieldnum, text)
for docnum, valuestring in postreader.all_items():
if has_deletions:
newdoc = doc_map[docnum]
else:
newdoc = start_doc + docnum
# TODO: Is there a faster way to do this?
freq = decoder(valuestring)
self.pool.add_posting(fieldnum, text, newdoc, freq,
valuestring)
def add_document(self, fields):
scorable_to_pos = self._scorable_to_pos
stored_to_pos = self._stored_to_pos
schema = self.schema
# Sort the keys by their order in the schema
fieldnames = [
name for name in fields.keys() if not name.startswith("_")
]
fieldnames.sort(key=schema.name_to_number)
# Check if the caller gave us a bogus field
for name in fieldnames:
if name not in schema:
raise UnknownFieldError("There is no field named %r" % name)
# Create an array of counters to record the length of each field
fieldlengths = array(DOCLENGTH_TYPE, [0] * len(scorable_to_pos))
# Create a list (initially a list of Nones) in which we will put stored
# field values as we get them. Why isn't this an empty list that we
# append to? Because if the caller doesn't supply a value for a stored
# field, we don't want to have a list in the wrong order/of the wrong
# length.
storedvalues = [None] * len(stored_to_pos)
for name in fieldnames:
value = fields.get(name)
if value:
fieldnum = schema.name_to_number(name)
field = schema.field_by_number(fieldnum)
# If the field is indexed, add the words in the value to the
# index
if field.indexed:
# Count of all terms in the value
count = 0
# Count of UNIQUE terms in the value
unique = 0
# TODO: Method for adding progressive field values, ie
# setting start_pos/start_char?
for w, freq, valuestring in field.index(value):
#assert w != ""
self.pool.add_posting(fieldnum, w, self.max_doc, freq,
valuestring)
count += freq
unique += 1
if field.scorable:
# Add the term count to the total for this field
self.field_length_totals[fieldnum] += count
# Set the term count to the per-document field length
pos = scorable_to_pos[fieldnum]
fieldlengths[pos] = min(count, DOCLENGTH_LIMIT)
# If the field is vectored, add the words in the value to the
# vector table
vector = field.vector
if vector:
# TODO: Method for adding progressive field values, ie
# setting start_pos/start_char?
vlist = sorted((w, valuestring) for w, freq, valuestring in
vector.word_values(value, mode="index"))
self._add_vector(fieldnum, vlist)
# If the field is stored, put the value in storedvalues
if field.stored:
# Caller can override the stored value by including a key
# _stored_<fieldname>
storedname = "_stored_" + name
if storedname in fields:
stored_value = fields[storedname]
else:
stored_value = value
storedvalues[stored_to_pos[fieldnum]] = stored_value
self._add_doc_data(storedvalues, fieldlengths)
self.max_doc += 1
def _add_terms(self):
pass
def _add_doc_data(self, storedvalues, fieldlengths):
self.docslist.append(storedvalues)
if self.doclengths:
self.doclengths.append(fieldlengths)
def _add_vector(self, fieldnum, vlist):
vpostwriter = self.vpostwriter
vformat = self.schema[fieldnum].vector
offset = vpostwriter.start(vformat)
for text, valuestring in vlist:
assert isinstance(text, unicode), "%r is not unicode" % text
vpostwriter.write(text, valuestring)
vpostwriter.finish()
self.vectortable.add((self.max_doc, fieldnum), offset)
def _flush_pool(self):
# This method pulls postings out of the posting pool (built up as
# documents are added) and writes them to the posting file. Each time
# it encounters a posting for a new term, it writes the previous term
# to the term index (by waiting to write the term entry, we can easily
# count the document frequency and sum the terms by looking at the
# postings).
termtable = self.termtable
postwriter = self.postwriter
schema = self.schema
current_fieldnum = None # Field number of the current term
current_text = None # Text of the current term
first = True
current_freq = 0
offset = None
# Loop through the postings in the pool. Postings always come out of
# the pool in (field number, lexical) order.
for fieldnum, text, docnum, freq, valuestring in self.pool:
# Is this the first time through, or is this a new term?
if first or fieldnum > current_fieldnum or text > current_text:
if first:
first = False
else:
# This is a new term, so finish the postings and add the
# term to the term table
postcount = postwriter.finish()
termtable.add((current_fieldnum, current_text),
(current_freq, offset, postcount))
# Reset the post writer and the term variables
current_fieldnum = fieldnum
current_text = text
current_freq = 0
offset = postwriter.start(schema[fieldnum].format)
elif (fieldnum < current_fieldnum
or (fieldnum == current_fieldnum and text < current_text)):
# This should never happen!
raise Exception(
"Postings are out of order: %s:%s .. %s:%s" %
(current_fieldnum, current_text, fieldnum, text))
# Write a posting for this occurrence of the current term
current_freq += freq
postwriter.write(docnum, valuestring)
# If there are still "uncommitted" postings at the end, finish them off
if not first:
postcount = postwriter.finish()
termtable.add((current_fieldnum, current_text),
(current_freq, offset, postcount))
class SegmentWriter(object):
"""Do not instantiate this object directly; it is created by the
IndexWriter object.
Handles the actual writing of new documents to the index: writes stored
fields, handles the posting pool, and writes out the term index.
"""
def __init__(self, ix, postlimit, blocklimit, name=None):
"""
:param ix: the Index object in which to write the new segment.
:param postlimit: the maximum size for a run in the posting pool.
:param blocklimit: the maximum number of postings in a posting block.
:param name: the name of the segment.
"""
self.index = ix
self.schema = ix.schema
self.storage = storage = ix.storage
self.name = name or ix._next_segment_name()
self.max_doc = 0
self.pool = postpool.PostingPool(postlimit)
# Create mappings of field numbers to the position of that field in the
# lists of scorable and stored fields. For example, consider a schema
# with fields (A, B, C, D, E, F). If B, D, and E are scorable, then the
# list of scorable fields is (B, D, E). The _scorable_to_pos dictionary
# would then map B -> 0, D -> 1, and E -> 2.
self._scorable_to_pos = dict((fnum, i)
for i, fnum
in enumerate(self.schema.scorable_fields()))
self._stored_to_pos = dict((fnum, i)
for i, fnum
in enumerate(self.schema.stored_fields()))
# Create a temporary segment object just so we can access its
# *_filename attributes (so if we want to change the naming convention,
# we only have to do it in one place).
tempseg = Segment(self.name, 0, 0, None)
self.termtable = create_terms(storage, tempseg)
self.docslist = create_storedfields(storage, tempseg)
self.doclengths = None
if self.schema.scorable_fields():
self.doclengths = create_doclengths(storage, tempseg, len(self._scorable_to_pos))
postfile = storage.create_file(tempseg.posts_filename)
self.postwriter = FilePostingWriter(postfile, blocklimit=blocklimit)
self.vectortable = None
if self.schema.has_vectored_fields():
# Table associating document fields with (postoffset, postcount)
self.vectortable = create_vectors(storage, tempseg)
vpostfile = storage.create_file(tempseg.vectorposts_filename)
self.vpostwriter = FilePostingWriter(vpostfile, stringids=True)
# Keep track of the total number of tokens (across all docs)
# in each field
self.field_length_totals = defaultdict(int)
def segment(self):
"""Returns an index.Segment object for the segment being written."""
return Segment(self.name, self.max_doc, dict(self.field_length_totals))
def _close_all(self):
self.termtable.close()
self.postwriter.close()
self.docslist.close()
if self.doclengths:
self.doclengths.close()
if self.vectortable:
self.vectortable.close()
self.vpostwriter.close()
def close(self):
"""Finishes writing the segment (flushes the posting pool out to disk)
and closes all open files.
"""
self._flush_pool()
self._close_all()
def add_reader(self, reader):
"""Adds the contents of another segment to this one. This is used to
merge existing segments into the new one before deleting them.
:param ix: The index.Index object containing the segment to merge.
:param segment: The index.Segment object to merge into this one.
"""
start_doc = self.max_doc
has_deletions = reader.has_deletions()
if has_deletions:
doc_map = {}
schema = self.schema
name2num = schema.name_to_number
stored_to_pos = self._stored_to_pos
def storedkeyhelper(item):
return stored_to_pos[name2num(item[0])]
# Merge document info
docnum = 0
vectored_fieldnums = schema.vectored_fields()
for docnum in xrange(reader.doc_count_all()):
if not reader.is_deleted(docnum):
# Copy the stored fields and field lengths from the reader
# into this segment
storeditems = reader.stored_fields(docnum).items()
storedvalues = [v for k, v
in sorted(storeditems, key=storedkeyhelper)]
self._add_doc_data(storedvalues,
reader.doc_field_lengths(docnum))
if has_deletions:
doc_map[docnum] = self.max_doc
# Copy term vectors
for fieldnum in vectored_fieldnums:
if reader.has_vector(docnum, fieldnum):
self._add_vector(fieldnum,
reader.vector(docnum, fieldnum).items())
self.max_doc += 1
# Add field length totals
for fieldnum in schema.scorable_fields():
self.field_length_totals[fieldnum] += reader.field_length(fieldnum)
# Merge terms
current_fieldnum = None
decoder = None
for fieldnum, text, _, _ in reader:
if fieldnum != current_fieldnum:
current_fieldnum = fieldnum
decoder = schema[fieldnum].format.decode_frequency
postreader = reader.postings(fieldnum, text)
for docnum, valuestring in postreader.all_items():
if has_deletions:
newdoc = doc_map[docnum]
else:
newdoc = start_doc + docnum
# TODO: Is there a faster way to do this?
freq = decoder(valuestring)
self.pool.add_posting(fieldnum, text, newdoc, freq, valuestring)
def add_document(self, fields):
scorable_to_pos = self._scorable_to_pos
stored_to_pos = self._stored_to_pos
schema = self.schema
# Sort the keys by their order in the schema
fieldnames = [name for name in fields.keys()
if not name.startswith("_")]
fieldnames.sort(key=schema.name_to_number)
# Check if the caller gave us a bogus field
for name in fieldnames:
if name not in schema:
raise UnknownFieldError("There is no field named %r" % name)
# Create an array of counters to record the length of each field
fieldlengths = array(DOCLENGTH_TYPE, [0] * len(scorable_to_pos))
# Create a list (initially a list of Nones) in which we will put stored
# field values as we get them. Why isn't this an empty list that we
# append to? Because if the caller doesn't supply a value for a stored
# field, we don't want to have a list in the wrong order/of the wrong
# length.
storedvalues = [None] * len(stored_to_pos)
for name in fieldnames:
value = fields.get(name)
if value:
fieldnum = schema.name_to_number(name)
field = schema.field_by_number(fieldnum)
# If the field is indexed, add the words in the value to the
# index
if field.indexed:
# Count of all terms in the value
count = 0
# Count of UNIQUE terms in the value
unique = 0
# TODO: Method for adding progressive field values, ie
# setting start_pos/start_char?
for w, freq, valuestring in field.index(value):
#assert w != ""
self.pool.add_posting(fieldnum, w, self.max_doc, freq,
valuestring)
count += freq
unique += 1
if field.scorable:
# Add the term count to the total for this field
self.field_length_totals[fieldnum] += count
# Set the term count to the per-document field length
pos = scorable_to_pos[fieldnum]
fieldlengths[pos] = min(count, DOCLENGTH_LIMIT)
# If the field is vectored, add the words in the value to the
# vector table
vector = field.vector
if vector:
# TODO: Method for adding progressive field values, ie
# setting start_pos/start_char?
vlist = sorted((w, valuestring) for w, freq, valuestring
in vector.word_values(value, mode="index"))
self._add_vector(fieldnum, vlist)
# If the field is stored, put the value in storedvalues
if field.stored:
# Caller can override the stored value by including a key
# _stored_<fieldname>
storedname = "_stored_" + name
if storedname in fields:
stored_value = fields[storedname]
else :
stored_value = value
storedvalues[stored_to_pos[fieldnum]] = stored_value
self._add_doc_data(storedvalues, fieldlengths)
self.max_doc += 1
def _add_terms(self):
pass
def _add_doc_data(self, storedvalues, fieldlengths):
self.docslist.append(storedvalues)
if self.doclengths:
self.doclengths.append(fieldlengths)
def _add_vector(self, fieldnum, vlist):
vpostwriter = self.vpostwriter
vformat = self.schema[fieldnum].vector
offset = vpostwriter.start(vformat)
for text, valuestring in vlist:
assert isinstance(text, unicode), "%r is not unicode" % text
vpostwriter.write(text, valuestring)
vpostwriter.finish()
self.vectortable.add((self.max_doc, fieldnum), offset)
def _flush_pool(self):
# This method pulls postings out of the posting pool (built up as
# documents are added) and writes them to the posting file. Each time
# it encounters a posting for a new term, it writes the previous term
# to the term index (by waiting to write the term entry, we can easily
# count the document frequency and sum the terms by looking at the
# postings).
termtable = self.termtable
postwriter = self.postwriter
schema = self.schema
current_fieldnum = None # Field number of the current term
current_text = None # Text of the current term
first = True
current_freq = 0
offset = None
# Loop through the postings in the pool. Postings always come out of
# the pool in (field number, lexical) order.
for fieldnum, text, docnum, freq, valuestring in self.pool:
# Is this the first time through, or is this a new term?
if first or fieldnum > current_fieldnum or text > current_text:
if first:
first = False
else:
# This is a new term, so finish the postings and add the
# term to the term table
postcount = postwriter.finish()
termtable.add((current_fieldnum, current_text),
(current_freq, offset, postcount))
# Reset the post writer and the term variables
current_fieldnum = fieldnum
current_text = text
current_freq = 0
offset = postwriter.start(schema[fieldnum].format)
elif (fieldnum < current_fieldnum
or (fieldnum == current_fieldnum and text < current_text)):
# This should never happen!
raise Exception("Postings are out of order: %s:%s .. %s:%s" %
(current_fieldnum, current_text, fieldnum, text))
# Write a posting for this occurrence of the current term
current_freq += freq
postwriter.write(docnum, valuestring)
# If there are still "uncommitted" postings at the end, finish them off
if not first:
postcount = postwriter.finish()
termtable.add((current_fieldnum, current_text),
(current_freq, offset, postcount))
