class RapidNHEvent(DocType):
class Meta:
index = settings.ES_INDICES['rapid']['name']
doc_type = settings.ES_INDICES['rapid']['documents'][1]['name']
dynamic = MetaField('strict')
event_date = Date()
created_date = Date()
title = Text(analyzer='english')
event_type = String(fields={'_exact': Keyword()})
location_description = Text(analyzer='english',
fields={'_exact': Keyword()})
location = GeoPoint()
main_image_url = String(fields={'_exact': Keyword()})
main_image_uuid = String(fields={'_exact': Keyword()})
datasets = Nested(
properties={
"id": String(fields={'_exact': Keyword()}),
"title": String(fields={'_exact': Keyword()}),
"doi": String(fields={'_exact': Keyword()}),
"url": String(fields={'_exact': Keyword()}),
})
def save(self, **kwargs):
# self.created_date = datetime.utcnow()
return super(RapidNHEvent, self).save(**kwargs)
class Products(DocType):
about = Text(fields={"keyword": Keyword()}, required=True)
brand = Text(fields={"keyword": Keyword()}, required=True)
care = Text(fields={"keyword": Keyword()}, required=True)
code = Text(fields={"keyword": Keyword()}, required=True)
details = Text(fields={"keyword": Keyword()}, required=True)
gender = Text(fields={"keyword": Keyword()}, required=True)
images = Text(fields={"keyword": Keyword()}, required=True)
kind = Text(fields={"keyword": Keyword()}, required=True)
link = Text(fields={"keyword": Keyword()}, required=True)
name = Text(fields={"keyword": Keyword()}, required=True)
price = Nested(
properties={
"currency": Text(fields={'keyword': Keyword()}, required=True),
"outlet": Float(required=True),
"retail": Float(required=True)
})
sessionid = Text(fields={"keyword": Keyword()}, required=True)
sessionname = Text(fields={"keyword": Keyword()}, required=True)
storename = Text(fields={"keyword": Keyword()}, required=True)
class Meta:
index = "store"
type = "products"
doc_type = "products"
class Article(DocType):
article_id = Keyword(ignore_above=256)
article_title = Text(analyzer='ik_max_word', search_analyzer='ik_max_word')
author = Keyword(ignore_above=256)
author_parsed = Keyword(ignore_above=256)
board = Keyword(ignore_above=256)
content = Text(analyzer='ik_max_word', search_analyzer='ik_max_word')
date = Keyword(ignore_above=256)
date_parsed = Date(default_timezone='Asia/Taipei')
ip = Keyword(ignore_above=256)
message_count = Nested(MessageCount)
messages = Nested(Message)
class Meta:
index = 'ptt-2018-06'
doc_type = 'article'
class CRECDoc(DocType):
"""An elasticsearch_dsl document model for CREC documents.
"""
title = Text()
title_part = Text()
date_issued = Date()
content = Text(fielddata=True)
crec_id = Text()
pdf_url = Text()
html_url = Text()
page_start = Text()
page_end = Text()
speakers = Text()
segments = Nested(
properties={
'segment_id': Text(),
'speaker': Text(),
'text': Text(),
'bioguide_id': Text()
}
)
class Meta:
index = settings.ES_CW_INDEX
class IndexedSocialIdentity(InnerObjectWrapper):
"""Contact indexed social identity model."""
name = Text()
type = Keyword()
# Abstract everything else in a map
infos = Nested()
class MentorProject(Document):
name = Keyword(required=True)
company = Text()
bio = Text()
backgroundRural = Boolean(required=True)
preferStudentUnderRep = Short(required=True) # (0-2)
preferToolExistingKnowledge = Boolean(required=True)
okExtended = Boolean(required=True)
okTimezoneDifference = Boolean(required=True)
timezone = Integer(required=True) # +- UTC
id = Keyword(required=True)
proj_description = Text(required=True)
proj_tags = Keyword(multi=True)
numStudentsSelected = Short()
listStudentsSelected = Nested(StudentVote)
track = Keyword(required=True)
class Index:
name = "mentors_index"
settings = {
"number_of_shards": 1,
"number_of_replicas": 0,
}
def add_vote(self, student_id, choice):
self.listStudentsSelected.append(
StudentVote(student_id=student_id, choice=choice))
def save(self, **kwargs):
self.numStudentsSelected = 0
return super().save(**kwargs)
class CustomType(DocType):
business = Nested(
properties={
'id': Integer(),
'name': String(),
}
)
class Comment(InnerDoc):
content = Text()
created_at = Date()
author = Object(User)
history = Nested(History)
class Meta:
dynamic = MetaField(False)
class AgentDocument(DocumentBase):
id = Keyword()
task_id = Keyword()
names = Nested(AgentNameDocument)
start_date = Date()
end_date = Date()
@classmethod
def get_model(cls):
return Agent
@classmethod
def from_obj(cls, obj):
if obj.task is None:
task_id = None
else:
task_id = str(obj.task.pk)
doc = AgentDocument(
_id=str(obj.pk),
id=str(obj.pk),
task_id=task_id,
names=[
AgentNameDocument.from_obj(name)
for name in obj.names.iterator()
],
start_date=obj.start_date,
end_date=obj.end_date,
)
return doc
class Index:
name = 'agent'
analyzers = [autocomplete_analyzer]
class SocialmediaType(DocType):
# 微博账户类型
# suggest = Completion(analyzer=ik_analyzer)
type = Keyword()
name = Keyword()
# account_domain = Keyword()
screen_name = Text(analyzer="ik_max_word")
icon = Keyword()
tweets = Integer()
followers = Integer()
following = Integer()
brief = Keyword()
# thumb_image = Keyword()
category = Keyword()
alpha = Keyword()
language = Keyword()
country = Nested(properties={
"id": Keyword(),
"name": Text(analyzer="ik_max_word"),
})
class Meta:
index = "weibo_account"
doc_type = "weibo_account"
settings = {
"number_of_shards": 5,
}
def test_mapping_can_collect_all_analyzers():
a1 = analysis.analyzer('my_analyzer1',
tokenizer='keyword',
filter=['lowercase', analysis.token_filter('my_filter1', 'stop', stopwords=['a', 'b'])],
)
a2 = analysis.analyzer('english')
a3 = analysis.analyzer('unknown_custom')
a4 = analysis.analyzer('my_analyzer2',
tokenizer=analysis.tokenizer('trigram', 'nGram', min_gram=3, max_gram=3),
filter=[analysis.token_filter('my_filter2', 'stop', stopwords=['c', 'd'])],
)
m = mapping.Mapping('article')
m.field('title', 'string', analyzer=a1,
fields={
'english': String(analyzer=a2),
'unknown': String(analyzer=a3),
}
)
m.field('comments', Nested(properties={
'author': String(analyzer=a4)
}))
assert {
'analyzer': {
'my_analyzer1': {'filter': ['lowercase', 'my_filter1'], 'tokenizer': 'keyword', 'type': 'custom'},
'my_analyzer2': {'filter': ['my_filter2'], 'tokenizer': 'trigram', 'type': 'custom'}},
'filter': {
'my_filter1': {'stopwords': ['a', 'b'], 'type': 'stop'},
'my_filter2': {'stopwords': ['c', 'd'], 'type': 'stop'},
},
'tokenizer': {
'trigram': {'max_gram': 3, 'min_gram': 3, 'type': 'nGram'},
}
} == m._collect_analysis()
class eBPF_Program_Instance_Document(Base_Document):
"""Represents an eBPF program installed in an execution environment."""
# id already defined by Elasticsearch
ebpf_program_catalog_id = Text(required=True)
exec_env_id = Text(required=True)
parameters = Nested(eBPF_Program_Instance_Parameter_Inner_Doc)
description = Text()
class Index:
"""Elasticsearch configuration."""
name = 'ebpf-program-instance'
def edit_parameter(self, parameter):
so = self.Status_Operation
id = parameter.get('id', None)
for p in self.parameters:
val = parameter.get('value', None)
ts = parameter.get('timestamp', None)
if p.id == id:
if p.value != val or p.timestamp != ts:
p.value = val
p.timestamp = ts
return so.UPDATED
return so.NOT_MODIFIED
self.parameters.append(
eBPF_Program_Instance_Parameter_Inner_Doc(**parameter))
return so.UPDATED
class GoodsSKUIndex(Document):
name = Text(analyzer=html_strip)
desc = Text(analyzer=html_strip)
types = Nested(GoodsTypeIndex)
class Index:
index = 'goodssku-index'
name = 'goodssku-index'
def add_types(self, name):
self.types.append(GoodsTypeIndex(name=name))
def save(self, **kwargs):
return super(GoodsSKUIndex, self).save(**kwargs)
def delete(self, **kwargs):
super(GoodsSKUIndex, self).delete(**kwargs)
# Simple search function, return id
def search(query="", nested_path="types"):
q1 = Q("multi_match", query=query)
q2 = Q('nested',
inner_hits={},
path=nested_path,
query=Q('multi_match', query=query))
s1 = Search().query(q1)
s2 = Search().query(q2)
response1 = s1.execute()
response2 = s2.execute()
result1 = (hit.meta.id for hit in response1 if hit.meta)
result2 = (hit.meta.id for hit in response2 if hit.meta)
ids = chain(result1, result2)
return ids
class DomainResourceRecordDoc(GenericDoc):
class Meta:
doc_type = "opendns_domain_resource_record"
first_seen = Date()
last_seen = Date()
rrs = Nested(doc_class=ResourceRecordDoc)
class Company(Document):
"""Class representing the searchable companies metadata"""
name = Text(analyzer='snowball', fields={'raw': Keyword()})
number = Keyword()
registered_address = Object(Address)
category = Keyword()
status = Keyword()
country_of_origin = Keyword()
dissolution = Date(format='dd/MM/yyyy')
incorporation = Date(format='dd/MM/yyyy')
accounts = Object(Accounts)
returns = Object(Returns)
mortgages = Object(Mortgages)
SIC_code = [
Text(analyzer='snowball', fields={'raw': Keyword()}),
Text(analyzer='snowball', fields={'raw': Keyword()}),
Text(analyzer='snowball', fields={'raw': Keyword()}),
Text(analyzer='snowball', fields={'raw': Keyword()})
]
limited_partnerships = Object(LimitedPartnerships)
URI = Keyword()
previous_name = Nested(PreviousName)
confirmation_statement = Object(Returns)
def add_address(self, care_of, po_box, line1, line2, town, county, country, post_code):
"""Change the registered address"""
self.registered_address.update(
Address(
care_of=care_of,
po_box=po_box,
line1=line1,
line2=line2,
town=town,
county=county,
country=country,
post_code=post_code))
def age(self):
"""Calculate the current age of the company"""
if self.is_dissolved():
return self.dissolution - self.incorporation
return datetime.now() - self.incorporation
def is_dissolved(self):
"""Check if the company has been dissolved"""
return self.dissolution >= self.incorporation
class Index:
"""The index that all instances of this metadata will be saved to"""
name = 'companies'
settings = {
"number_of_shards" : 1,
"mapping.ignore_malformed": True,
}
def save(self, **kwargs):
"""Saves the current item to the index"""
# self.lines = len(self.body.split())
return super(Company, self).save(**kwargs)
def test_properties_can_iterate_over_all_the_fields():
m = mapping.Mapping('testing')
m.field('f1', 'string', test_attr='f1', fields={'f2': String(test_attr='f2')})
m.field('f3', Nested(test_attr='f3', properties={
'f4': String(test_attr='f4')}))
assert set(('f1', 'f2', 'f3', 'f4')) == set(f.test_attr for f in m.properties._collect_fields())
class Alternative(Document):
content = Text()
confidence = Float()
alternate = Nested(Alternate)
class Index:
name = 'alternatives'
class Agent_Catalog_Resource_Inner_Doc(Inner_Doc):
"""Agent resource."""
id = Text(required=True)
config = Nested(Agent_Catalog_Resource_Config_Inner_Doc, required=True)
description = Text()
example = Text()
class BookmarkSearch(DocType): curator_title = Text() created_at = Date() updated_on = Date() rating = Text() description = Text() user_id = Text() work = Nested(BookmarkWorkSearch) class Meta: index = 'bookmark' class Index: name = 'bookmark' def save(self, ** kwargs): self.created_at = datetime.now() return super().save(** kwargs) def create_from_json(self, bookmark_json): BookmarkSearch.init() self.curator_title=bookmark_json['curator_title'] if "description" in bookmark_json: self.description=bookmark_json['description'] if "rating" in bookmark_json: self.rating=bookmark_json['rating'] self.user_id=bookmark_json['user_id'] self.meta.id = bookmark_json['id'] bookmark_work_search = BookmarkWorkSearch() bookmark_work_search.create_from_json(bookmark_json['work']) self.work.append( bookmark_work_search) self.save()
class HooverDoc(Document):
"""
Document with the same fields as documents in hoover-search. This document will be used for created indices.
The only difference is, that the Text field is replaced with the nested TextUnits.
"""
attachments = Boolean()
content_type = Keyword()
date = Date()
date_created = Date()
email_domains = Keyword()
filetype = Keyword()
id = Keyword()
in_reply_to = Keyword()
lang = Keyword()
md5 = Keyword()
message = Keyword()
message_id = Keyword()
path = Keyword()
path_text = Text()
path_parts = Keyword()
references = Keyword()
rev = Integer()
sha1 = Keyword()
size = Integer()
suffix = Keyword()
thread_index = Keyword()
word_count = Integer()
body = Nested(TextUnit)
def add_unit(self, content, position):
self.body.append(TextUnit(content=content, position=position))
class Model(DocType):
id = Integer()
name = String(fields={'sub': String()})
other_models = Nested(
doc_class=OtherModel,
multi=True,
properties={
'id':
Integer(),
'name':
String(fields={
'raw': String(index='not_analyzed'),
'sub': String()
})
})
class Meta:
index = 'models'
def get_term_query(self, column_name, value, default_op='should'):
tq = ElasticSearchMixin.get_match_query(value, default_op)
if column_name is None:
column_name = 'name'
return {'match': {column_name: tq}}
class GenreDocument(Document):
"""
Genre Document
"""
genre_id = Text()
name = Text(analyzer='snowball', fields={'row': Keyword()}, fielddata=True)
name_lower = Text(analyzer='snowball',
fields={'row': Keyword()},
fielddata=True)
picture = Text()
is_active = Boolean()
is_deleted = Boolean()
is_picture_deleted = Boolean()
genre_movies = Nested(properties={
'id': Text(),
'movie__id': Text(),
'movie__name': Text(),
})
class Index:
"""
Index class for MovieDocument
"""
name = ES_DOCUMENT_SET['genre']
settings = {
"number_of_shards": ES_DOCUMENT_SET['genre_index_shared'],
}
class Account(DocType):
traderId = Keyword()
cash = Float()
positions = Nested()
allValue = Float()
timestamp = Date()
tradingClose = Boolean()
def copy_for_save(self, trading_close):
account = Account()
account.cash = self.cash
account.traderId = self.traderId
account.allValue = self.allValue
account.positions = self.positions
account.timestamp = account.timestamp
account.tradingClose = trading_close
return account
def save(self, using=None, index=None, validate=True, **kwargs):
self.meta.id = "{}_{}".format(
self.traderId, self.timestamp.strftime('%Y-%m-%d %H:%M:%S'))
return super().save(using, index, validate, **kwargs)
class Meta:
doc_type = 'doc'
all = MetaField(enabled=False)
class Agent_Catalog_Action_Inner_Doc(Inner_Doc):
"""Agent action."""
config = Nested(Agent_Catalog_Action_Config_Inner_Doc, required=True)
status = Text()
description = Text()
example = Text()
class StructureUnitDocument(DocumentBase):
id = Keyword()
task_id = Keyword()
name = Text(analyzer=autocomplete_analyzer,
search_analyzer='standard',
fields={'keyword': {
'type': 'keyword'
}})
type = Keyword()
desc = Text() # python migrate_structure_unit_field.py
comment = Text()
reference_code = Text(analyzer=autocomplete_analyzer,
search_analyzer='standard',
fields={'keyword': {
'type': 'keyword'
}})
archive = Nested(InnerArchiveDocument)
start_date = Date()
end_date = Date()
@classmethod
def get_model(cls):
return StructureUnit
@classmethod
def get_index_queryset(cls):
return StructureUnit.objects.filter(structure__is_template=False)
@classmethod
def from_obj(cls, obj):
structure_set = obj.structure.tagstructure_set
archive_doc = None
if structure_set.exists():
archive = structure_set.first().get_root().tag.current_version
if archive is not None:
archive_doc = InnerArchiveDocument.from_obj(archive)
if obj.task is None:
task_id = None
else:
task_id = str(obj.task.pk)
doc = StructureUnitDocument(
_id=obj.pk,
id=obj.pk,
task_id=task_id,
name=obj.name,
type=obj.type.name,
desc=obj.description,
comment=obj.comment,
reference_code=obj.reference_code,
archive=archive_doc,
start_date=obj.start_date,
end_date=obj.end_date,
)
return doc
class Index:
name = 'structure_unit'
analyzers = [autocomplete_analyzer]
class _AggregateReportDoc(Document):
class Index:
name = "dmarc_aggregate"
xml_schema = Text()
org_name = Text()
org_email = Text()
org_extra_contact_info = Text()
report_id = Text()
date_range = Date()
date_begin = Date()
date_end = Date()
errors = Text()
published_policy = Object(_PublishedPolicy)
source_ip_address = Ip()
source_country = Text()
source_reverse_dns = Text()
source_Base_domain = Text()
message_count = Integer
disposition = Text()
dkim_aligned = Boolean()
spf_aligned = Boolean()
passed_dmarc = Boolean()
policy_overrides = Nested(_PolicyOverride)
header_from = Text()
envelope_from = Text()
envelope_to = Text()
dkim_results = Nested(_DKIMResult)
spf_results = Nested(_SPFResult)
def add_policy_override(self, type_, comment):
self.policy_overrides.append(
_PolicyOverride(type=type_, comment=comment))
def add_dkim_result(self, domain, selector, result):
self.dkim_results.append(
_DKIMResult(domain=domain, selector=selector, result=result))
def add_spf_result(self, domain, scope, result):
self.spf_results.append(
_SPFResult(domain=domain, scope=scope, result=result))
def save(self, **kwargs):
self.passed_dmarc = False
self.passed_dmarc = self.spf_aligned or self.dkim_aligned
return super().save(**kwargs)
class BlogPost(DocType):
authors = Nested(required=True,
doc_class=Author,
properties={
'name': String(required=True),
'email': String(required=True)
})
created = Date()
class Post(Document):
author = Text()
content = Text()
created_at = Date()
comments = Nested(Comment)
co = Object(Comment)
class Publication(Record):
authors = Nested(Author)
publication_date = Date(format='yyyy-MM-dd')
publisher = Text(fields={'keyword': Keyword()})
serial = Text(fields={'keyword': Keyword()})
class Meta:
index = 'publications'
class Port(Document):
services = Nested(Service)
class Index:
name = 'port'
settings = {
'number_of_shards': 2,
}
def build_mapping(cls):
"""Create elasticsearch indexed_contacts mapping object for an user."""
m = Mapping(cls.doc_type)
m.meta('_all', enabled=True)
m.field('user_id', 'keyword')
m.field('contact_id', 'keyword')
m.field('additional_name', 'text', fields={
"normalized": {"type": "text", "analyzer": "text_analyzer"}
})
# addresses
addresses = Nested(doc_class=IndexedPostalAddress, include_in_all=True,
properties={
"address_id": "keyword",
"label": "text",
"type": "keyword",
"is_primary": "boolean",
"street": "text",
"city": "text",
"postal_code": "keyword",
"country": "text",
"region": "text"
})
m.field("addresses", addresses)
m.field("avatar", "keyword")
m.field('date_insert', 'date')
m.field('date_update', 'date')
m.field('deleted', 'date')
# emails
internet_addr = Nested(doc_class=IndexedInternetAddress,
include_in_all=True,
)
internet_addr.field("address", "text", analyzer="text_analyzer",
fields={
"raw": {"type": "keyword"},
"parts": {"type": "text",
"analyzer": "email_analyzer"}
})
internet_addr.field("email_id", Keyword())
internet_addr.field("is_primary", Boolean())
internet_addr.field("label", "text", analyzer="text_analyzer")
internet_addr.field("type", Keyword())
m.field("emails", internet_addr)
m.field('family_name', "text", fields={
"normalized": {"type": "text", "analyzer": "text_analyzer"}
})
m.field('given_name', 'text', fields={
"normalized": {"type": "text", "analyzer": "text_analyzer"}
})
m.field("groups", Keyword(multi=True))
# social ids
social_ids = Nested(doc_class=IndexedSocialIdentity,
include_in_all=True,
properties={
"name": "text",
"type": "keyword",
"infos": Nested()
})
m.field("identities", social_ids)
m.field("ims", internet_addr)
m.field("infos", Nested())
m.field('name_prefix', 'keyword')
m.field('name_suffix', 'keyword')
# organizations
organizations = Nested(doc_class=IndexedOrganization,
include_in_all=True)
organizations.field("deleted", Boolean())
organizations.field("department", "text", analyzer="text_analyzer")
organizations.field("is_primary", Boolean())
organizations.field("job_description", "text")
organizations.field("label", "text", analyzer="text_analyzer")
organizations.field("name", 'text', fields={
"normalized": {"type": "text", "analyzer": "text_analyzer"}
})
organizations.field("organization_id", Keyword())
organizations.field("title", Keyword())
organizations.field("type", Keyword())
m.field("organizations", organizations)
# phones
phones = Nested(doc_class=IndexedPhone, include_in_all=True,
properties={
"is_primary": "boolean",
"number": "text",
"normalized_number": "text",
"phone_id": "keyword",
"type": "keyword",
"uri": "keyword"
})
m.field("phones", phones)
# pi
pi = Object(doc_class=PIIndexModel, include_in_all=True,
properties={
"comportment": "integer",
"context": "integer",
"date_update": "date",
"technic": "integer",
"version": "integer"
})
m.field("pi", pi)
m.field("privacy_features", Object(include_in_all=True))
m.field("public_key", Nested())
m.field("social_identities", social_ids)
m.field("tags", Keyword(multi=True))
m.field('title', 'text', analyzer="text_analyzer",
fields={
"raw": {"type": "keyword"}
})
return m
def build_mapping(cls):
"""Generate the mapping definition for indexed messages"""
m = Mapping(cls.doc_type)
m.meta('_all', enabled=True)
m.field('user_id', 'keyword')
# attachments
m.field('attachments', Nested(doc_class=IndexedMessageAttachment,
include_in_all=True,
properties={
"content_type": Keyword(),
"file_name": Keyword(),
"is_inline": Boolean(),
"size": Integer(),
"temp_id": Keyword(),
"url": Keyword(),
"mime_boundary": Keyword()
})
)
m.field('body_html', 'text', fields={
"normalized": {"type": "text", "analyzer": "text_analyzer"}
})
m.field('body_plain', 'text', fields={
"normalized": {"type": "text", "analyzer": "text_analyzer"}
})
m.field('date', 'date')
m.field('date_delete', 'date')
m.field('date_insert', 'date')
m.field('date_sort', 'date')
m.field('discussion_id', 'keyword')
# external references
m.field('external_references',
Nested(doc_class=IndexedExternalReferences,
include_in_all=True,
properties={
"ancestors_ids": Keyword(),
"message_id": Keyword(),
"parent_id": Keyword()
})
)
m.field('importance_level', 'short')
m.field('is_answered', 'boolean')
m.field('is_draft', 'boolean')
m.field('is_unread', 'boolean')
m.field('is_received', 'boolean')
m.field('message_id', 'keyword')
m.field('parent_id', 'keyword')
# participants
participants = Nested(doc_class=IndexedParticipant,
include_in_all=True)
participants.field("address", "text", analyzer="text_analyzer",
fields={
"raw": {"type": "keyword"},
"parts": {"type": "text",
"analyzer": "email_analyzer"}
})
participants.field("contact_ids", Keyword(multi=True))
participants.field("label", "text", analyzer="text_analyzer")
participants.field("protocol", Keyword())
participants.field("type", Keyword())
m.field('participants', participants)
# PI
pi = Object(doc_class=PIIndexModel, include_in_all=True,
properties={
"technic": "integer",
"comportment": "integer",
"context": "integer",
"version": "integer",
"date_update": "date"
})
m.field("pi", pi)
m.field('privacy_features', Object(include_in_all=True))
m.field('raw_msg_id', "keyword")
m.field('subject', 'text', fields={
"normalized": {"type": "text", "analyzer": "text_analyzer"}
})
m.field('tags', Keyword(multi=True))
m.field('subject', 'text')
m.field('tags', Keyword(multi=True))
m.field('protocol', 'keyword')
m.field('user_identities', Keyword(multi=True))
return m
