class Book(document.DocType):
# authors = field.Nested(properties={'name': field.String(),'url':field.String(index='not_analyzed')})
authors_name = field.String()
authors_url = field.String(index='not_analyzed')
average = field.Float()
cover = field.String(index='not_analyzed')
description = field.String()
outlinks = field.String(index='not_analyzed')
ratings = field.Integer()
reviews = field.Integer()
title = field.String()
url = field.String(index='not_analyzed')
# userreviews = field.Nested(properties={'userName': field.String(), 'userReview': field.String(), 'userReviewDate': field.String(index='not_analyzed'), 'userURL': field.String(index='not_analyzed')})
userreviews_userName = field.String()
userreviews_userReview = field.String()
userreviews_userReviewDate = field.String(index='not_analyzed')
userreviews_userURL = field.String(index='not_analyzed')
def add_authors(self, authors):
self.authors_name = [author['name'] for author in authors]
self.authors_url = [author['url'] for author in authors]
def add_userreviews(self, reviews):
self.userreviews_userName = [rev['userName'] for rev in reviews]
self.userreviews_userReview = [rev['userReview'] for rev in reviews]
self.userreviews_userReviewDate = [
rev['userReviewDate'] for rev in reviews
]
self.userreviews_userURL = [rev['userURL'] for rev in reviews]
class Meta:
doc_type = 'book'
index = 'book-index'
class Book(document.DocType):
"""
This is the document type class for objects of type book for using with Elasticsearch DSL using Python
"""
authors_name = field.String()
authors_url = field.String(index='not_analyzed')
average = field.Float()
cover = field.String(index='not_analyzed')
description = field.String()
outlinks = field.String(index='not_analyzed')
ratings = field.Integer()
reviews = field.Integer()
title = field.String()
url = field.String(index='not_analyzed')
userreviews_userName = field.String()
userreviews_userReview = field.String()
userreviews_userReviewDate = field.String(index='not_analyzed')
userreviews_userURL = field.String(index='not_analyzed')
class Meta:
"""
meta data for the Book document type
"""
doc_type = 'book'
index = 'book-index'
def add_authors(self, authors):
"""
This method will add all of the authors from the authors list of dicts to Book
authors = json['authors']
"""
self.authors_name = [author['name'] for author in authors]
self.authors_url = [author['url'] for author in authors]
def add_userreviews(self, reviews):
"""
This method will add all of the user reviews from the user reviews list of dicts to Book
reviews = json['userreviews']
"""
self.userreviews_userName = [rev['userName'] for rev in reviews]
self.userreviews_userReview = [rev['userReview'] for rev in reviews]
self.userreviews_userReviewDate = [rev['userReviewDate'] for rev in reviews]
self.userreviews_userURL = [rev['userURL'] for rev in reviews]
class Dweller(Document):
value_raw = field.Keyword()
real_raw = field.Float()
def __init__(self, *args, value=None, **kw):
super().__init__(*args, **kw)
if value is not None:
self.value = value
@property
def value(self):
return self.value_raw
@value.setter
def value(self, value):
self.value_raw = str(value)
@property
def real(self):
return self.real_raw
def test_float():
f = field.Float()
assert f.deserialize('42') == 42.0
assert f.deserialize(None) is None
with pytest.raises(ValueError):
assert f.deserialize('not_a_float')
class WikiDocumentType(document.Document):
excerpt_fields = ["summary", "content"]
exclude_slugs = [
"Talk:",
"User:"******"User_talk:",
"Template_talk:",
"Project_talk:",
EXPERIMENT_TITLE_PREFIX,
]
boost = field.Float(null_value=1.0)
content = field.Text(analyzer="kuma_content",
term_vector="with_positions_offsets")
css_classnames = field.Keyword()
html_attributes = field.Keyword()
id = field.Long()
kumascript_macros = field.Keyword()
locale = field.Keyword()
modified = field.Date()
parent = field.Object(
properties={
"id": field.Long(),
"title": field.Text(analyzer="kuma_title"),
"slug": field.Keyword(),
"locale": field.Keyword(),
})
slug = field.Keyword()
summary = field.Text(analyzer="kuma_content",
term_vector="with_positions_offsets")
tags = field.Keyword()
title = field.Text(analyzer="kuma_title")
class Meta(object):
mapping = Mapping("wiki_document")
mapping.meta("_all", enabled=False)
@classmethod
def get_connection(cls, alias="default"):
return connections.get_connection(alias)
@classmethod
def get_doc_type(cls):
return cls._doc_type.name
@classmethod
def case_insensitive_keywords(cls, keywords):
"""Create a unique list of lowercased keywords."""
return sorted({keyword.lower() for keyword in keywords})
@classmethod
def from_django(cls, obj):
is_root_document = obj.slug.count("/") == 1
doc = {
"id":
obj.id,
"boost":
4.0 if is_root_document else 1.0,
"title":
obj.title,
"slug":
obj.slug,
"summary":
obj.get_summary_text(),
"locale":
obj.locale,
"modified":
obj.modified,
"content":
strip_tags(obj.get_body_html() or ""),
"tags": [o.name for o in obj.tags.all()],
"kumascript_macros":
cls.case_insensitive_keywords(obj.extract.macro_names()),
"css_classnames":
cls.case_insensitive_keywords(obj.extract.css_classnames()),
"html_attributes":
cls.case_insensitive_keywords(obj.extract.html_attributes()),
}
if obj.parent:
doc["parent"] = {
"id": obj.parent.id,
"title": obj.parent.title,
"locale": obj.parent.locale,
"slug": obj.parent.slug,
}
else:
doc["parent"] = {}
return doc
@classmethod
def get_mapping(cls):
return cls._doc_type.mapping.to_dict()
@classmethod
def get_analysis(cls):
return {
"filter": {
"kuma_word_delimiter": {
"type": "word_delimiter",
"preserve_original": True, # hi-fi -> hifi, hi-fi
"catenate_words": True, # hi-fi -> hifi
"catenate_numbers": True, # 90-210 -> 90210
}
},
"analyzer": {
"default": {
"tokenizer": "standard",
"filter": ["standard", "elision"]
},
# a custom analyzer that strips html and uses our own
# word delimiter filter and the elision filter
# (e.g. L'attribut -> attribut). The rest is the same as
# the snowball analyzer
"kuma_content": {
"type":
"custom",
"tokenizer":
"standard",
"char_filter": ["html_strip"],
"filter": [
"elision",
"kuma_word_delimiter",
"lowercase",
"standard",
"stop",
"snowball",
],
},
"kuma_title": {
"type":
"custom",
"tokenizer":
"standard",
"filter": [
"elision",
"kuma_word_delimiter",
"lowercase",
"standard",
"snowball",
],
},
},
}
@classmethod
def get_settings(cls):
return {
"mappings": cls.get_mapping(),
"settings": {
"analysis": cls.get_analysis(),
"number_of_replicas": settings.ES_DEFAULT_NUM_REPLICAS,
"number_of_shards": settings.ES_DEFAULT_NUM_SHARDS,
},
}
@classmethod
def bulk_index(cls, documents, id_field="id", es=None, index=None):
"""Index of a bunch of documents."""
es = es or cls.get_connection()
index = index or cls.get_index()
type = cls.get_doc_type()
actions = [{
"_index": index,
"_type": type,
"_id": d["id"],
"_source": d
} for d in documents]
bulk(es, actions)
@classmethod
def bulk_delete(cls, ids, es=None, index=None):
"""Index of a bunch of documents."""
es = es or cls.get_connection()
index = index or cls.get_index()
type = cls.get_doc_type()
actions = [{
"_op_type": "delete",
"_index": index,
"_type": type,
"_id": _id
} for _id in ids]
bulk(es, actions)
@classmethod
def get_index(cls):
from kuma.search.models import Index
return Index.objects.get_current().prefixed_name
@classmethod
def search(cls, **kwargs):
options = {
"using": connections.get_connection(),
"index": cls.get_index(),
"doc_type": {
cls._doc_type.name: cls.from_es
},
}
options.update(kwargs)
sq = Search(**options)
return sq
@classmethod
def get_model(cls):
from kuma.wiki.models import Document
return Document
@classmethod
def get_indexable(cls, percent=100):
"""
For this mapping type return a list of model IDs that should be
indexed with the management command, in a full reindex.
WARNING: When changing this code make sure to update the
``should_update`` method below, too!
"""
model = cls.get_model()
excludes = Q()
for exclude in cls.exclude_slugs:
excludes |= Q(slug__startswith=exclude)
qs = model.objects.filter(is_redirect=False).exclude(excludes)
percent = percent / 100
if percent < 1:
qs = qs[:int(qs.count() * percent)]
return qs.values_list("id", flat=True)
@classmethod
def should_update(cls, obj):
"""
Given a Document instance should return boolean value
whether the instance should be indexed or not.
WARNING: This *must* mirror the logic of the ``get_indexable``
method above!
"""
return (not obj.is_redirect and not obj.deleted and
not any([exclude in obj.slug
for exclude in cls.exclude_slugs]))
def get_excerpt(self):
highlighted = getattr(self.meta, "highlight", None)
if highlighted:
for excerpt_field in self.excerpt_fields:
if excerpt_field in highlighted:
return "…".join(highlighted[excerpt_field])
return self.summary
@classmethod
def reindex_all(cls, chunk_size=500, index=None, percent=100):
"""Rebuild ElasticSearch indexes.
:arg chunk_size: how many documents to bulk index as a single chunk.
:arg index: the `Index` object to reindex into. Uses the current
promoted index if none provided.
:arg percent: 1 to 100--the percentage of the db to index.
"""
from kuma.search.models import Index
from kuma.search.tasks import prepare_index, finalize_index
from kuma.wiki.tasks import index_documents
index = index or Index.objects.get_current()
# Get the list of document IDs to index.
indexable = WikiDocumentType.get_indexable(percent)
total = len(indexable)
total_chunks = int(ceil(total / chunk_size))
pre_task = prepare_index.si(index.pk)
post_task = finalize_index.si(index.pk)
if not total:
# If there's no data we still create the index and finalize it.
chain(pre_task, post_task).apply_async()
else:
index_tasks = [
index_documents.si(chunk, index.pk)
for chunk in chunked(indexable, chunk_size)
]
chord_flow(pre_task, index_tasks, post_task).apply_async()
message = _(
"Indexing %(total)d documents into %(total_chunks)d chunks of "
"size %(size)d into index %(index)s." % {
"total": total,
"total_chunks": total_chunks,
"size": chunk_size,
"index": index.prefixed_name,
})
return message
class WikiDocumentType(document.DocType):
excerpt_fields = ['summary', 'content']
exclude_slugs = [
'Talk:', 'User:'******'User_talk:', 'Template_talk:', 'Project_talk:'
]
boost = field.Float(null_value=1.0)
content = field.String(analyzer='kuma_content',
term_vector='with_positions_offsets')
css_classnames = field.String(analyzer='case_insensitive_keyword')
html_attributes = field.String(analyzer='case_insensitive_keyword')
id = field.Long()
kumascript_macros = field.String(analyzer='case_insensitive_keyword')
locale = field.String(index='not_analyzed')
modified = field.Date()
parent = field.Nested(
properties={
'id': field.Long(),
'title': field.String(analyzer='kuma_title'),
'slug': field.String(index='not_analyzed'),
'locale': field.String(index='not_analyzed'),
})
slug = field.String(index='not_analyzed')
summary = field.String(analyzer='kuma_content',
term_vector='with_positions_offsets')
tags = field.String(analyzer='case_sensitive')
title = field.String(analyzer='kuma_title', boost=1.2)
class Meta(object):
mapping = Mapping('wiki_document')
mapping.meta('_all', enalbed=False)
@classmethod
def get_connection(cls, alias='default'):
return connections.get_connection(alias)
@classmethod
def get_doc_type(cls):
return cls._doc_type.name
@classmethod
def from_django(cls, obj):
doc = {
'id': obj.id,
'title': obj.title,
'slug': obj.slug,
'summary': obj.get_summary(strip_markup=True),
'locale': obj.locale,
'modified': obj.modified,
'content': strip_tags(obj.rendered_html),
'tags': list(obj.tags.values_list('name', flat=True)),
'kumascript_macros': obj.extract_kumascript_macro_names(),
'css_classnames': obj.extract_css_classnames(),
'html_attributes': obj.extract_html_attributes(),
}
# Check if the document has a document zone attached
try:
is_zone = bool(obj.zone)
except ObjectDoesNotExist:
is_zone = False
if is_zone:
# boost all documents that are a zone
doc['boost'] = 8.0
elif obj.slug.count('/') == 1:
# a little boost if no zone but still first level
doc['boost'] = 4.0
else:
doc['boost'] = 1.0
if obj.parent:
doc['parent'] = {
'id': obj.parent.id,
'title': obj.parent.title,
'locale': obj.parent.locale,
'slug': obj.parent.slug,
}
else:
doc['parent'] = {}
return doc
@classmethod
def get_mapping(cls):
return cls._doc_type.mapping.to_dict()
@classmethod
def get_analysis(cls):
return {
'filter': {
'kuma_word_delimiter': {
'type': 'word_delimiter',
'preserve_original': True, # hi-fi -> hifi, hi-fi
'catenate_words': True, # hi-fi -> hifi
'catenate_numbers': True, # 90-210 -> 90210
}
},
'analyzer': {
'default': {
'tokenizer': 'standard',
'filter': ['standard', 'elision']
},
# a custom analyzer that strips html and uses our own
# word delimiter filter and the elision filter
# (e.g. L'attribut -> attribut). The rest is the same as
# the snowball analyzer
'kuma_content': {
'type':
'custom',
'tokenizer':
'standard',
'char_filter': ['html_strip'],
'filter': [
'elision',
'kuma_word_delimiter',
'lowercase',
'standard',
'stop',
'snowball',
],
},
'kuma_title': {
'type':
'custom',
'tokenizer':
'standard',
'filter': [
'elision',
'kuma_word_delimiter',
'lowercase',
'standard',
'snowball',
],
},
'case_sensitive': {
'type': 'custom',
'tokenizer': 'keyword'
},
'case_insensitive_keyword': {
'type': 'custom',
'tokenizer': 'keyword',
'filter': 'lowercase'
}
},
}
@classmethod
def get_settings(cls):
return {
'mappings': cls.get_mapping(),
'settings': {
'analysis': cls.get_analysis(),
'number_of_replicas': settings.ES_DEFAULT_NUM_REPLICAS,
'number_of_shards': settings.ES_DEFAULT_NUM_SHARDS,
}
}
@classmethod
def bulk_index(cls, documents, id_field='id', es=None, index=None):
"""Index of a bunch of documents."""
es = es or cls.get_connection()
index = index or cls.get_index()
type = cls.get_doc_type()
actions = [{
'_index': index,
'_type': type,
'_id': d['id'],
'_source': d
} for d in documents]
bulk(es, actions)
@classmethod
def bulk_delete(cls, ids, es=None, index=None):
"""Index of a bunch of documents."""
es = es or cls.get_connection()
index = index or cls.get_index()
type = cls.get_doc_type()
actions = [{
'_op_type': 'delete',
'_index': index,
'_type': type,
'_id': _id
} for _id in ids]
bulk(es, actions)
@classmethod
def get_index(cls):
from kuma.search.models import Index
return Index.objects.get_current().prefixed_name
@classmethod
def search(cls, **kwargs):
options = {
'using': connections.get_connection(),
'index': cls.get_index(),
'doc_type': {
cls._doc_type.name: cls.from_es
},
}
options.update(kwargs)
sq = Search(**options)
return sq
@classmethod
def get_model(cls):
from kuma.wiki.models import Document
return Document
@classmethod
def get_indexable(cls, percent=100):
"""
For this mapping type return a list of model IDs that should be
indexed with the management command, in a full reindex.
WARNING: When changing this code make sure to update the
``should_update`` method below, too!
"""
model = cls.get_model()
excludes = []
for exclude in cls.exclude_slugs:
excludes.append(Q(slug__icontains=exclude))
qs = (model.objects.filter(is_template=False,
is_redirect=False,
deleted=False).exclude(
reduce(operator.or_, excludes)))
percent = percent / 100
if percent < 1:
qs = qs[:int(qs.count() * percent)]
return qs.values_list('id', flat=True)
@classmethod
def should_update(cls, obj):
"""
Given a Document instance should return boolean value
whether the instance should be indexed or not.
WARNING: This *must* mirror the logic of the ``get_indexable``
method above!
"""
return (not obj.is_template and not obj.is_redirect and not obj.deleted
and
not any([exclude in obj.slug
for exclude in cls.exclude_slugs]))
def get_excerpt(self):
if getattr(self, 'highlight', False):
for excerpt_field in self.excerpt_fields:
if excerpt_field in self.highlight:
return u'…'.join(self.highlight[excerpt_field])
return self.summary
@classmethod
def reindex_all(cls, chunk_size=500, index=None, percent=100):
"""Rebuild ElasticSearch indexes.
:arg chunk_size: how many documents to bulk index as a single chunk.
:arg index: the `Index` object to reindex into. Uses the current
promoted index if none provided.
:arg percent: 1 to 100--the percentage of the db to index.
"""
from kuma.search.models import Index
from kuma.search.tasks import prepare_index, finalize_index
from kuma.wiki.tasks import index_documents
index = index or Index.objects.get_current()
# Get the list of document IDs to index.
indexable = WikiDocumentType.get_indexable(percent)
total = len(indexable)
total_chunks = int(ceil(total / chunk_size))
pre_task = prepare_index.si(index.pk)
post_task = finalize_index.si(index.pk)
if not total:
# If there's no data we still create the index and finalize it.
chain(pre_task, post_task).apply_async()
else:
index_tasks = [
index_documents.si(chunk, index.pk)
for chunk in chunked(indexable, chunk_size)
]
chord_flow(pre_task, index_tasks, post_task).apply_async()
message = _(
'Indexing {total} documents into {n} chunks of size {size} into '
'index {index}.'.format(total=total,
n=total_chunks,
size=chunk_size,
index=index.prefixed_name))
return message
User = get_user_model()
es_connections = Connections()
es_connections.configure(**settings.ELASTICSEARCH_DSL)
STATUS_CHOICES = [
('published', _('Published')),
('draft', _('Draft'))
]
OPENNESS_SCORE = {_type: os for _, _type, _, os in settings.SUPPORTED_CONTENT_TYPES}
signal_logger = logging.getLogger('signals')
_schema2doc_map = {
'integer': dsl_field.Float(),
'number': dsl_field.ScaledFloat(scaling_factor=100),
'string': dsl_field.Text(
analyzer=polish_analyzer,
fields={
'raw': dsl_field.Text(),
}
),
'any': dsl_field.Text(
analyzer=polish_analyzer,
fields={
'raw': dsl_field.Text(),
}
),
'boolean': dsl_field.Boolean(),
'date': dsl_field.Date(),
class WikiDocumentType(document.Document):
excerpt_fields = ['summary', 'content']
exclude_slugs = [
'Talk:', 'User:'******'User_talk:', 'Template_talk:', 'Project_talk:',
EXPERIMENT_TITLE_PREFIX
]
boost = field.Float(null_value=1.0)
content = field.Text(analyzer='kuma_content',
term_vector='with_positions_offsets')
css_classnames = field.Keyword()
html_attributes = field.Keyword()
id = field.Long()
kumascript_macros = field.Keyword()
locale = field.Keyword()
modified = field.Date()
parent = field.Object(
properties={
'id': field.Long(),
'title': field.Text(analyzer='kuma_title'),
'slug': field.Keyword(),
'locale': field.Keyword(),
})
slug = field.Keyword()
summary = field.Text(analyzer='kuma_content',
term_vector='with_positions_offsets')
tags = field.Keyword()
title = field.Text(analyzer='kuma_title')
class Meta(object):
mapping = Mapping('wiki_document')
mapping.meta('_all', enabled=False)
@classmethod
def get_connection(cls, alias='default'):
return connections.get_connection(alias)
@classmethod
def get_doc_type(cls):
return cls._doc_type.name
@classmethod
def case_insensitive_keywords(cls, keywords):
'''Create a unique list of lowercased keywords.'''
return sorted({keyword.lower() for keyword in keywords})
@classmethod
def from_django(cls, obj):
is_root_document = obj.slug.count('/') == 1
doc = {
'id':
obj.id,
'boost':
4.0 if is_root_document else 1.0,
'title':
obj.title,
'slug':
obj.slug,
'summary':
obj.get_summary_text(),
'locale':
obj.locale,
'modified':
obj.modified,
'content':
strip_tags(obj.rendered_html or ''),
'tags': [o.name for o in obj.tags.all()],
'kumascript_macros':
cls.case_insensitive_keywords(obj.extract.macro_names()),
'css_classnames':
cls.case_insensitive_keywords(obj.extract.css_classnames()),
'html_attributes':
cls.case_insensitive_keywords(obj.extract.html_attributes()),
}
if obj.parent:
doc['parent'] = {
'id': obj.parent.id,
'title': obj.parent.title,
'locale': obj.parent.locale,
'slug': obj.parent.slug,
}
else:
doc['parent'] = {}
return doc
@classmethod
def get_mapping(cls):
return cls._doc_type.mapping.to_dict()
@classmethod
def get_analysis(cls):
return {
'filter': {
'kuma_word_delimiter': {
'type': 'word_delimiter',
'preserve_original': True, # hi-fi -> hifi, hi-fi
'catenate_words': True, # hi-fi -> hifi
'catenate_numbers': True, # 90-210 -> 90210
}
},
'analyzer': {
'default': {
'tokenizer': 'standard',
'filter': ['standard', 'elision']
},
# a custom analyzer that strips html and uses our own
# word delimiter filter and the elision filter
# (e.g. L'attribut -> attribut). The rest is the same as
# the snowball analyzer
'kuma_content': {
'type':
'custom',
'tokenizer':
'standard',
'char_filter': ['html_strip'],
'filter': [
'elision',
'kuma_word_delimiter',
'lowercase',
'standard',
'stop',
'snowball',
],
},
'kuma_title': {
'type':
'custom',
'tokenizer':
'standard',
'filter': [
'elision',
'kuma_word_delimiter',
'lowercase',
'standard',
'snowball',
],
},
},
}
@classmethod
def get_settings(cls):
return {
'mappings': cls.get_mapping(),
'settings': {
'analysis': cls.get_analysis(),
'number_of_replicas': settings.ES_DEFAULT_NUM_REPLICAS,
'number_of_shards': settings.ES_DEFAULT_NUM_SHARDS,
}
}
@classmethod
def bulk_index(cls, documents, id_field='id', es=None, index=None):
"""Index of a bunch of documents."""
es = es or cls.get_connection()
index = index or cls.get_index()
type = cls.get_doc_type()
actions = [{
'_index': index,
'_type': type,
'_id': d['id'],
'_source': d
} for d in documents]
bulk(es, actions)
@classmethod
def bulk_delete(cls, ids, es=None, index=None):
"""Index of a bunch of documents."""
es = es or cls.get_connection()
index = index or cls.get_index()
type = cls.get_doc_type()
actions = [{
'_op_type': 'delete',
'_index': index,
'_type': type,
'_id': _id
} for _id in ids]
bulk(es, actions)
@classmethod
def get_index(cls):
from kuma.search.models import Index
return Index.objects.get_current().prefixed_name
@classmethod
def search(cls, **kwargs):
options = {
'using': connections.get_connection(),
'index': cls.get_index(),
'doc_type': {
cls._doc_type.name: cls.from_es
},
}
options.update(kwargs)
sq = Search(**options)
return sq
@classmethod
def get_model(cls):
from kuma.wiki.models import Document
return Document
@classmethod
def get_indexable(cls, percent=100):
"""
For this mapping type return a list of model IDs that should be
indexed with the management command, in a full reindex.
WARNING: When changing this code make sure to update the
``should_update`` method below, too!
"""
model = cls.get_model()
excludes = Q()
for exclude in cls.exclude_slugs:
excludes |= Q(slug__startswith=exclude)
qs = model.objects.filter(is_redirect=False).exclude(excludes)
percent = percent / 100
if percent < 1:
qs = qs[:int(qs.count() * percent)]
return qs.values_list('id', flat=True)
@classmethod
def should_update(cls, obj):
"""
Given a Document instance should return boolean value
whether the instance should be indexed or not.
WARNING: This *must* mirror the logic of the ``get_indexable``
method above!
"""
return (not obj.is_redirect and not obj.deleted and
not any([exclude in obj.slug
for exclude in cls.exclude_slugs]))
def get_excerpt(self):
highlighted = getattr(self.meta, 'highlight', None)
if highlighted:
for excerpt_field in self.excerpt_fields:
if excerpt_field in highlighted:
return '…'.join(highlighted[excerpt_field])
return self.summary
@classmethod
def reindex_all(cls, chunk_size=500, index=None, percent=100):
"""Rebuild ElasticSearch indexes.
:arg chunk_size: how many documents to bulk index as a single chunk.
:arg index: the `Index` object to reindex into. Uses the current
promoted index if none provided.
:arg percent: 1 to 100--the percentage of the db to index.
"""
from kuma.search.models import Index
from kuma.search.tasks import prepare_index, finalize_index
from kuma.wiki.tasks import index_documents
index = index or Index.objects.get_current()
# Get the list of document IDs to index.
indexable = WikiDocumentType.get_indexable(percent)
total = len(indexable)
total_chunks = int(ceil(total / chunk_size))
pre_task = prepare_index.si(index.pk)
post_task = finalize_index.si(index.pk)
if not total:
# If there's no data we still create the index and finalize it.
chain(pre_task, post_task).apply_async()
else:
index_tasks = [
index_documents.si(chunk, index.pk)
for chunk in chunked(indexable, chunk_size)
]
chord_flow(pre_task, index_tasks, post_task).apply_async()
message = _(
'Indexing %(total)d documents into %(total_chunks)d chunks of '
'size %(size)d into index %(index)s.' % {
'total': total,
'total_chunks': total_chunks,
'size': chunk_size,
'index': index.prefixed_name
})
return message
class ShpData(IndexedData):
_type = 'geo'
_schema2doc_map = {
'C':
dsl_field.Text(
analyzer=polish_analyzer,
fields={
'raw': dsl_field.Text(),
'keyword': dsl_field.Keyword(),
},
),
'D':
dsl_field.Date(),
'N':
dsl_field.ScaledFloat(scaling_factor=100),
'L':
dsl_field.Boolean(),
'@':
dsl_field.Date(),
'I':
dsl_field.Long(),
'+':
dsl_field.Long(),
'F':
dsl_field.Float(),
'O':
dsl_field.Double(),
}
_schema_to_api_field = {
'C': api_fields.String,
'D': api_fields.DateTime,
'N': api_fields.Number,
'L': api_fields.Boolean,
'@': api_fields.DateTime,
'I': api_fields.Number,
'+': api_fields.Number,
'F': api_fields.Number,
'O': api_fields.Number,
}
_schema_long_names = {
'C': 'string',
'D': 'datetime',
'N': 'number',
'L': 'boolean',
'@': 'datetime',
'I': 'integer',
'+': 'integer',
'F': 'number',
'O': 'number',
}
_source = None
_schema = None
_transformer = None
def __init__(self, resource, from_table_index=False):
super().__init__(resource)
self.from_table_index = from_table_index
@property
def has_geo_data(self):
return True
@property
def is_chartable(self):
fields = self.schema
return len(fields) > 1 and any(
(field.type in ('N', 'I', '+', 'F', 'O') for field in fields))
@property
def source(self):
if not self._source:
with ArchiveReader(self.resource.main_file.path) as extracted:
shp_path = next(
iter(f for f in extracted if f.endswith('.shp')))
self._source = shapefile.Reader(shp_path)
self._transformer = ShapeTransformer(extracted)
return self._source
def get_schema(self, **kwargs):
use_aliases = kwargs.get('use_aliases', False)
headers = self.reversed_headers_map
return {
'fields': [{
'name': headers[item.name] if use_aliases else item.name,
'type': self._schema_long_names[item.type],
'format': 'default'
} for item in self.schema]
}
@property
def schema(self):
if not self._schema:
self._schema = [
DBSchemaField(*_f) for _f in self.source.fields[1:]
]
return self._schema
def prepare_doc(self):
_fields = {
'shape':
dsl_field.GeoShape(),
'point':
dsl_field.GeoPoint(),
'shape_type':
dsl_field.Integer(),
'label':
dsl_field.Text(),
'resource':
dsl_field.Nested(
properties={
'id':
dsl_field.Integer(),
'title':
dsl_field.Text(analyzer=polish_analyzer,
fields={'raw': dsl_field.Keyword()})
}),
'updated_at':
dsl_field.Date(),
'row_no':
dsl_field.Long()
}
_map = {}
for idx, _f in enumerate(self.schema, 1):
if _f.type not in self._schema2doc_map:
continue
alias_name = _f.name
field_name = f'col{idx}'
_field = self._schema2doc_map[_f.type]
_map[field_name] = alias_name
_fields[field_name] = _field
_fields['Index'] = type('Index', (type, ), {'name': self.idx_name})
doc = type(self.idx_name, (Document, ), _fields)
doc._doc_type.mapping._meta['_meta'] = {'headers': _map}
return doc
def get_api_fields(self):
record_fields = {}
for f in self.schema:
field_name = self.reversed_headers_map[f.name]
field_cls = self._schema_to_api_field[f.type]
record_fields[field_name] = field_cls(is_tabular_data_field=True)
return record_fields
@staticmethod
def _get_row_id(row):
return str(
uuid.uuid5(uuid.NAMESPACE_DNS,
'+|+'.join(str(i)[:10000] for i in row)))
def _docs_iter(self, doc):
for row_no, sr in enumerate(self.source.shapeRecords(), 1):
geojson = self._transformer.transform(sr.shape)
v = {
'shape': geojson,
'updated_at': datetime.now(),
'row_no': row_no,
'resource': {
'id': self.resource.id,
'title': self.resource.title
},
}
for i, val in enumerate(sr.record, 1):
v[f'col{i}'] = val if val != b'' else None
v['shape_type'] = sr.shape.shapeType
v['point'] = median_point(geojson)
tds = self.resource.tabular_data_schema
if tds is not None and 'geo' in tds and 'label' in tds['geo']:
v['label'] = sr.record[tds['geo']['label'].get('col_name')]
d = doc(**v)
d.meta.id = self._get_row_id(sr.record)
yield d
