11👍
After a deep look into the code I found that the search generated by haystack was:
{
"query":{
"filtered":{
"filter":{
"fquery":{
"query":{
"query_string":{
"query": "django_ct:(csi.geoname)"
}
},
"_cache":false
}
},
"query":{
"query_string":{
"query": "name_auto:(mid)",
"default_operator":"or",
"default_field":"text",
"auto_generate_phrase_queries":true,
"analyze_wildcard":true
}
}
}
},
"from":0,
"size":6
}
Running this query in elasticsearch was given me as result the same 6 objects that haystack was showing…but If I added to the “query_string”
"analyzer": "standard"
it worked as desired. So the idea was to be able to setup a different search analyzer for the field.
Based on the @user954994 answer’s link and the explanation on this post, what I finally did to make it work was:
- I created my custom elasticsearch backend, adding a new custom analyzer based on the standard one.
- I added a custom EdgeNgramField, enabling the way to setup an specific analyzer for index (index_analyzer) and another analyzer for search (search_analyzer).
So, my new settings are:
ELASTICSEARCH_INDEX_SETTINGS = {
'settings': {
"analysis": {
"analyzer": {
"ngram_analyzer": {
"type": "custom",
"tokenizer": "lowercase",
"filter": ["haystack_ngram"]
},
"edgengram_analyzer": {
"type": "custom",
"tokenizer": "lowercase",
"filter": ["haystack_edgengram"]
},
"suggest_analyzer": {
"type":"custom",
"tokenizer":"standard",
"filter":[
"standard",
"lowercase",
"asciifolding"
]
},
},
"tokenizer": {
"haystack_ngram_tokenizer": {
"type": "nGram",
"min_gram": 3,
"max_gram": 15,
},
"haystack_edgengram_tokenizer": {
"type": "edgeNGram",
"min_gram": 2,
"max_gram": 15,
"side": "front"
}
},
"filter": {
"haystack_ngram": {
"type": "nGram",
"min_gram": 3,
"max_gram": 15
},
"haystack_edgengram": {
"type": "edgeNGram",
"min_gram": 2,
"max_gram": 15
}
}
}
}
}
My new custom build_schema method looks as follow:
def build_schema(self, fields):
content_field_name, mapping = super(ConfigurableElasticBackend,
self).build_schema(fields)
for field_name, field_class in fields.items():
field_mapping = mapping[field_class.index_fieldname]
index_analyzer = getattr(field_class, 'index_analyzer', None)
search_analyzer = getattr(field_class, 'search_analyzer', None)
field_analyzer = getattr(field_class, 'analyzer', self.DEFAULT_ANALYZER)
if field_mapping['type'] == 'string' and field_class.indexed:
if not hasattr(field_class, 'facet_for') and not field_class.field_type in('ngram', 'edge_ngram'):
field_mapping['analyzer'] = field_analyzer
if index_analyzer and search_analyzer:
field_mapping['index_analyzer'] = index_analyzer
field_mapping['search_analyzer'] = search_analyzer
del(field_mapping['analyzer'])
mapping.update({field_class.index_fieldname: field_mapping})
return (content_field_name, mapping)
And after rebuild index my mapping looks as below:
modelresult: {
_boost: {
name: "boost",
null_value: 1
},
properties: {
django_ct: {
type: "string"
},
django_id: {
type: "string"
},
name_auto: {
type: "string",
store: true,
term_vector: "with_positions_offsets",
index_analyzer: "edgengram_analyzer",
search_analyzer: "suggest_analyzer"
}
}
}
Now everything is working as expected!
UPDATE:
Bellow you’ll find the code to clarify this part:
- I created my custom elasticsearch backend, adding a new custom analyzer based on the standard one.
- I added a custom EdgeNgramField, enabling the way to setup an specific analyzer for index (index_analyzer) and another analyzer for
search (search_analyzer).
Into my app search_backends.py:
from django.conf import settings
from haystack.backends.elasticsearch_backend import ElasticsearchSearchBackend
from haystack.backends.elasticsearch_backend import ElasticsearchSearchEngine
from haystack.fields import EdgeNgramField as BaseEdgeNgramField
# Custom Backend
class CustomElasticBackend(ElasticsearchSearchBackend):
DEFAULT_ANALYZER = None
def __init__(self, connection_alias, **connection_options):
super(CustomElasticBackend, self).__init__(
connection_alias, **connection_options)
user_settings = getattr(settings, 'ELASTICSEARCH_INDEX_SETTINGS', None)
self.DEFAULT_ANALYZER = getattr(settings, 'ELASTICSEARCH_DEFAULT_ANALYZER', "snowball")
if user_settings:
setattr(self, 'DEFAULT_SETTINGS', user_settings)
def build_schema(self, fields):
content_field_name, mapping = super(CustomElasticBackend,
self).build_schema(fields)
for field_name, field_class in fields.items():
field_mapping = mapping[field_class.index_fieldname]
index_analyzer = getattr(field_class, 'index_analyzer', None)
search_analyzer = getattr(field_class, 'search_analyzer', None)
field_analyzer = getattr(field_class, 'analyzer', self.DEFAULT_ANALYZER)
if field_mapping['type'] == 'string' and field_class.indexed:
if not hasattr(field_class, 'facet_for') and not field_class.field_type in('ngram', 'edge_ngram'):
field_mapping['analyzer'] = field_analyzer
if index_analyzer and search_analyzer:
field_mapping['index_analyzer'] = index_analyzer
field_mapping['search_analyzer'] = search_analyzer
del(field_mapping['analyzer'])
mapping.update({field_class.index_fieldname: field_mapping})
return (content_field_name, mapping)
class CustomElasticSearchEngine(ElasticsearchSearchEngine):
backend = CustomElasticBackend
# Custom field
class CustomFieldMixin(object):
def __init__(self, **kwargs):
self.analyzer = kwargs.pop('analyzer', None)
self.index_analyzer = kwargs.pop('index_analyzer', None)
self.search_analyzer = kwargs.pop('search_analyzer', None)
super(CustomFieldMixin, self).__init__(**kwargs)
class CustomEdgeNgramField(CustomFieldMixin, BaseEdgeNgramField):
pass
My index definition goes something like:
class MyIndex(indexes.SearchIndex, indexes.Indexable):
text = indexes.CharField(document=True, use_template=True)
name_auto = CustomEdgeNgramField(model_attr='name', index_analyzer="edgengram_analyzer", search_analyzer="suggest_analyzer")
And finally, settings uses of course the custom backend for the haystack connection definition:
HAYSTACK_CONNECTIONS = {
'default': {
'ENGINE': 'my_app.search_backends.CustomElasticSearchEngine',
'URL': 'http://localhost:9200',
'INDEX_NAME': 'index'
},
}
1👍
Well, I had a similar problem and my strategy was make a custom backend.
The complete instructions can be found on:
http://www.wellfireinteractive.com/blog/custom-haystack-elasticsearch-backend/
It works to me !
Hope this helps.