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# Licensed to Elasticsearch B.V. under one or more contributor
# license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright
# ownership. Elasticsearch B.V. licenses this file to you under
# the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
from typing_extensions import Self
from . import analysis
from .utils import AnyUsingType, merge
if TYPE_CHECKING:
from .document_base import DocumentMeta
from .field import Field
from .mapping_base import MappingBase
class IndexBase:
def __init__(self, name: str, mapping_class: type, using: AnyUsingType = "default"):
"""
:arg name: name of the index
:arg using: connection alias to use, defaults to ``'default'``
"""
self._name = name
self._doc_types: List["DocumentMeta"] = []
self._using = using
self._settings: Dict[str, Any] = {}
self._aliases: Dict[str, Any] = {}
self._analysis: Dict[str, Any] = {}
self._mapping_class = mapping_class
self._mapping: Optional["MappingBase"] = None
def resolve_nested(
self, field_path: str
) -> Tuple[List[str], Optional["MappingBase"]]:
for doc in self._doc_types:
nested, field = doc._doc_type.mapping.resolve_nested(field_path)
if field is not None:
return nested, field
if self._mapping:
return self._mapping.resolve_nested(field_path)
return [], None
def resolve_field(self, field_path: str) -> Optional["Field"]:
for doc in self._doc_types:
field = doc._doc_type.mapping.resolve_field(field_path)
if field is not None:
return field
if self._mapping:
return self._mapping.resolve_field(field_path)
return None
def get_or_create_mapping(self) -> "MappingBase":
if self._mapping is None:
self._mapping = self._mapping_class()
return self._mapping
def mapping(self, mapping: "MappingBase") -> None:
"""
Associate a mapping (an instance of
:class:`~elasticsearch.dsl.Mapping`) with this index.
This means that, when this index is created, it will contain the
mappings for the document type defined by those mappings.
"""
self.get_or_create_mapping().update(mapping)
def document(self, document: "DocumentMeta") -> "DocumentMeta":
"""
Associate a :class:`~elasticsearch.dsl.Document` subclass with an index.
This means that, when this index is created, it will contain the
mappings for the ``Document``. If the ``Document`` class doesn't have a
default index yet (by defining ``class Index``), this instance will be
used. Can be used as a decorator::
i = Index('blog')
@i.document
class Post(Document):
title = Text()
# create the index, including Post mappings
i.create()
# .search() will now return a Search object that will return
# properly deserialized Post instances
s = i.search()
"""
self._doc_types.append(document)
# If the document index does not have any name, that means the user
# did not set any index already to the document.
# So set this index as document index
if document._index._name is None:
document._index = self
return document
def settings(self, **kwargs: Any) -> Self:
"""
Add settings to the index::
i = Index('i')
i.settings(number_of_shards=1, number_of_replicas=0)
Multiple calls to ``settings`` will merge the keys, later overriding
the earlier.
"""
self._settings.update(kwargs)
return self
def aliases(self, **kwargs: Any) -> Self:
"""
Add aliases to the index definition::
i = Index('blog-v2')
i.aliases(blog={}, published={'filter': Q('term', published=True)})
"""
self._aliases.update(kwargs)
return self
def analyzer(self, *args: Any, **kwargs: Any) -> None:
"""
Explicitly add an analyzer to an index. Note that all custom analyzers
defined in mappings will also be created. This is useful for search analyzers.
Example::
from elasticsearch.dsl import analyzer, tokenizer
my_analyzer = analyzer('my_analyzer',
tokenizer=tokenizer('trigram', 'nGram', min_gram=3, max_gram=3),
filter=['lowercase']
)
i = Index('blog')
i.analyzer(my_analyzer)
"""
analyzer = analysis.analyzer(*args, **kwargs)
d = analyzer.get_analysis_definition()
# empty custom analyzer, probably already defined out of our control
if not d:
return
# merge the definition
merge(self._analysis, d, True)
def to_dict(self) -> Dict[str, Any]:
out = {}
if self._settings:
out["settings"] = self._settings
if self._aliases:
out["aliases"] = self._aliases
mappings = self._mapping.to_dict() if self._mapping else {}
analysis = self._mapping._collect_analysis() if self._mapping else {}
for d in self._doc_types:
mapping = d._doc_type.mapping
merge(mappings, mapping.to_dict(), True)
merge(analysis, mapping._collect_analysis(), True)
if mappings:
out["mappings"] = mappings
if analysis or self._analysis:
merge(analysis, self._analysis)
out.setdefault("settings", {})["analysis"] = analysis
return out
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