Django haystack EdgeNgramField given different results than elasticsearch

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:

1. I created my custom elasticsearch backend, adding a new custom analyzer based on the standard one.
2. 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:

1. I created my custom elasticsearch backend, adding a new custom analyzer based on the standard one.
2. 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 settingsfrom haystack.backends.elasticsearch_backend import ElasticsearchSearchBackendfrom haystack.backends.elasticsearch_backend import ElasticsearchSearchEnginefrom 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 fieldclass 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'    },}

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.