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Cribbing mapping from haystack.

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Samuel Clay 2014-04-11 16:04:30 -07:00
parent 073098750c
commit c11d0f3331
1 changed files with 60 additions and 70 deletions
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130
apps/search/models.py
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@ -118,83 +118,73 @@ class SearchFeed:
@classmethod
def create_elasticsearch_mapping(cls):
cls.ES.indices.delete_index_if_exists("%s-index" % cls.name)
settings = {
"index" : {
"analysis": {
"filter": {
"nGram_filter": {
"type": "nGram",
"min_gram": 2,
"max_gram": 20,
"token_chars": [
"letter",
"digit",
"punctuation",
"symbol"
]
}
},
"analyzer": {
"nGram_analyzer": {
"type": "custom",
"tokenizer": "whitespace",
"filter": [
"lowercase",
"asciifolding",
"nGram_filter"
]
"analysis": {
"analyzer": {
"edgengram_analyzer": {
"filter": ["edgengram"],
"tokenizer": "lowercase",
"type": "custom"
},
"ngram_analyzer": {
"filter": ["ngram"],
"tokenizer": "lowercase",
"type": "custom"
}
},
"whitespace_analyzer": {
"type": "custom",
"tokenizer": "whitespace",
"filter": [
"lowercase",
"asciifolding"
]
"filter": {
"edgengram": {
"max_gram": "15",
"min_gram": "2",
"type": "edgeNGram"
},
"ngram": {
"max_gram": "15",
"min_gram": "3",
"type": "nGram"
}
},
"tokenizer": {
"edgengram_tokenizer": {
"max_gram": "15",
"min_gram": "2",
"side": "front",
"type": "edgeNGram"
},
"ngram_tokenizer": {
"max_gram": "15",
"min_gram": "3",
"type": "nGram"
}
}
}
}
}
}
}
}
cls.ES.indices.create_index("%s-index" % cls.name, settings)
mapping = {
'address': {
'boost': 3.0,
'index': 'analyzed',
'store': 'yes',
'type': 'string',
"term_vector" : "with_positions_offsets",
"index_analyzer": "nGram_analyzer",
"search_analyzer": "whitespace_analyzer"
mapping = {
"address": {
"analyzer": "edgengram_analyzer",
"store": True,
"term_vector": "with_positions_offsets",
"type": "string"
},
'title': {
'boost': 2.0,
'index': 'analyzed',
'store': 'yes',
'type': 'string',
"term_vector" : "with_positions_offsets",
"feed_id": {
"store": True,
"type": "string"
},
'link': {
'boost': 1.0,
'index': 'analyzed',
'store': 'yes',
'type': 'string',
"term_vector" : "with_positions_offsets",
"index_analyzer": "nGram_analyzer",
"search_analyzer": "whitespace_analyzer"
},
'num_subscribers': {
'boost': 1.0,
'index': 'not_analyzed',
'store': 'yes',
'type': 'integer',
},
'feed_id': {
'store': 'yes',
'type': 'integer',
"num_subscribers": {
"index": "analyzed",
"store": True,
"type": "long"
},
"title": {
"analyzer": "edgengram_analyzer",
"store": True,
"term_vector": "with_positions_offsets",
"type": "string"
}
}
cls.ES.indices.put_mapping("%s-type" % cls.name, {'properties': mapping}, ["%s-index" % cls.name])
@ -215,21 +205,21 @@ class SearchFeed:
cls.ES.indices.refresh()
logging.info("~FGSearch ~FCfeeds~FG by address: ~SB%s" % text)
q = MatchQuery('address.partial', text, type='phrase')
q = MatchQuery('address', text)
print q.serialize(), cls.index_name(), cls.type_name()
results = cls.ES.search(query=q, sort="num_subscribers:desc", size=5,
doc_types=[cls.type_name()])
if not results.total:
logging.info("~FGSearch ~FCfeeds~FG by title: ~SB%s" % text)
q = PrefixQuery('title', text)
q = MatchQuery('title', text, operator="and")
print q.serialize()
results = cls.ES.search(query=q, sort="num_subscribers:desc", size=5,
doc_types=[cls.type_name()])
if not results.total:
logging.info("~FGSearch ~FCfeeds~FG by link: ~SB%s" % text)
q = PrefixQuery('link.partial', text)
q = MatchQuery('link', text)
print q.serialize()
results = cls.ES.search(query=q, sort="num_subscribers:desc", size=5,
doc_types=[cls.type_name()])