Elasticsearch
版本：7.0以上
基本操作
查询所有索引： get _cat/indices
查询某个索引的字段 ：get gs-recommend-v2/_mappings
新建索引
例如：
put /gs-recommend-v2
{
"mappings": {
"properties": {
"_update": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"child_count": {
"type": "float"
},
"collection_count": {
"type": "float"
},
"comment_rating": {
"type": "float"
},
"commentcount": {
"type": "float"
},
"commentcount_within_1year": {
"type": "float"
},
"districtid": {
"type": "keyword"
},
"districtpath": {
"type": "text"
},
"officialpoiid": {
"type": "keyword"
},
"parentdistrictid": {
"type": "keyword"
},
"poiscore": {
"type": "float"
},
"price": {
"type": "float"
},
"recallscore": {
"type": "float"
},
"share_count": {
"type": "float"
},
"sight_rank": {
"type": "float"
},
"sortno": {
"type": "float"
},
"t_score_no_add": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"total_picnum": {
"type": "float"
},
"totalsales": {
"type": "float"
},
"uv_section": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"uvsum": {
"type": "float"
},
"feature": {
"type": "dense_vector",
"dims": 13
}
}
}
}检索数据
全局检索
POST gs-recommend-v2/_search
{
"query": {
"match_all": {}
}
}条件检索
POST gs-recommend-v2/_search
{
"query": {
"match": {"poiid": 112}
}
}向量检索
POST gs-recommend-v2/_search
{
"query": {
"script_score": {
"query": {
"match_all": {}
},
"script": {
"source": "cosineSimilarity(params.queryVector,doc['feature'])+1.0",
"params": {
"queryVector": [
0,
4.7418923,
12781,
330,
37332,
30236,
5,
3,
1549.6129,
13,
0,
0,
42
]
}
}
}
}
}删除索引和数据
删除索引：Delete 索引名称1，索引名称2
根据匹配条件删除数据
POST 索引名称/_delete_by_query
{
"query":{
"term":{
"_id":111
}
}
}删除所有数据
POST /testindex/_delete_by_query?pretty
{
"query": {
"match_all": {
}
}
}Python操作ES
安装elasticsearch包：pip install elasticsearch
from elasticsearch import Elasticsearch
index_name = 'testindex'
http_auth = ("username", "password")
es = Elasticsearch("access url", http_auth=http_auth)
# 批量插入数据
actions = []
action = {
"_index": index_name,
"_id": "111",
"_source": {
"poiid": 112,
"feature": [0,4.7418923,12781,330,37332,30236,5,3,1549.6129,13,0,0,42]
}
}
actions.append(action)
a = helpers.bulk(es, actions)
# 查询数据，和ES语法一样
query_json = {
"query": {
"match": {"poiid": 112}
}
}
res = es.search(index=index_name, body=query_json)
hits = res['hits']['hits']
for hit in hits:
print(str(hit['_id']) + '\t' + str(hit['_score']))查询ES里的所有数据
body = {
"_source": ["_id"],
"query": {
"match_all": {}}
}
query = es.search(index=index_name, body=body, scroll='5m', size=10000)
results = query['hits']['hits'] # es查询出的结果第一页
total = query['hits']['total'] # es查询出的结果总量
print(len(results), len(total))
scroll_id = query['_scroll_id'] # 游标用于输出es查询出的所有结果
espoiid_list = [doc["_id"] for doc in results]
for i in range(100):
# scroll参数必须指定否则会报错
query = es.scroll(scroll_id=scroll_id, scroll='5m')
query_scroll = query['hits']['hits']
results += query_scroll
espoiid_list += [doc["_id"] for doc in query_scroll]
# print(query_scroll)
scroll_id = query['_scroll_id']
print(len(results))
print(len(results))
espoiid_list = set(espoiid_list)删除满足条件的数据
body = {
"query": {
"terms": {
"_id": diff_poiid
}
}
}
es.delete_by_query(index=index_name, body=body)Java操作ES
@Autowired
private ElasticsearchQConfig elasticsearchQConfig;
private List<PoiRecsysPoifeaturesV2> getPoiFeatureFromEs(String districtId) throws IOException {
EsConfig.Query query = elasticsearchQConfig.getConfig().getQuery();
SearchRequest searchRequest = new SearchRequest(query.getIndexName());
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
BoolQueryBuilder boolQuery = QueryBuilders.boolQuery();
boolQuery.must(QueryBuilders.rangeQuery("t_score_no_add").gte(query.getScoreNoAdd()));
boolQuery.filter(QueryBuilders.rangeQuery("total_picnum").gt(query.getTotalPicture()));
if (!districtId.isEmpty()){
boolQuery.filter(QueryBuilders.matchQuery("districtpath", districtId));
}
// if (districtId > 0) {
// boolQuery.filter(QueryBuilders.matchQuery("districtpath", districtId));
// }
searchSourceBuilder.query(boolQuery)
.sort("recallscore", SortOrder.DESC)
.size(query.getSize());
searchRequest.source(searchSourceBuilder);
SearchResponse searchResponse = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
SearchHits hits = searchResponse.getHits();
org.elasticsearch.search.SearchHit[] searchHits = hits.getHits();
List<PoiRecsysPoifeaturesV2> res = new ArrayList<>(searchHits.length);
for (org.elasticsearch.search.SearchHit hit : searchHits) {
res.add(JSON.parseObject(hit.getSourceAsString(), PoiRecsysPoifeaturesV2.class));
}
return res;
}BoolQuery 的用法与特性
BoolQuery 作为一个主要的Query结构一般用于整个Query的根。Query的结果会根据score默认排序
searchSourceBuilder.query(QueryBuilders.boolQueryBuilder());
searchRequest.source(searchSourceBuilder);BoolQuery 拥有4个不同的Query类型。
// 必须匹配，匹配会增加score。MatchQuery 这里表示"名字"必须包含“大卫”或者“David”其中一个, 多个匹配会增加其结果的score。
boolQueryBuilder.must(QueryBuilders.matchQuery("名字", "大卫 David"))
// 每一个query类型都可以添加多个子Query，其关系是“AND”。并且可以是BoolQuery
boolQueryBuilder.must(QueryBuilders.boolQuery())
// 必须匹配，匹配不影响score。TermsQuery 这里表示"id"必须等于“123”或者“321”
boolQueryBuilder.filter(QueryBuilders.termsQuery("id", "123"， "321"))
// 非必须匹配，匹配增加score。
boolQueryBuilder.should()
// 设定最少匹配“should”的数量，如果没有规定“must”/“filter”默认值是1，有的话默认值是0
boolQueryBuilder.minimalShouldMatch(1)
// 必须不包含，不影响score。
boolQueryBuilder.must_not()默认的排序是按照score来进行的。我们也可以自己添加用来排序的键。
// 按日期的排序，默认是正序
searchSourceBuilder.sort(SortBuilders.fieldSort("日期"))
// 按score排序，默认是倒序。当多个sort被提供的时候，按其先后顺序优先排列
searchSourceBuilder.sort(SortBuilders.scoreSort())当搜索请求中包含sort时，搜索结果中会包含sortValues。例如以上sort的结果中，每一个hit会有如下Array
result.getHits().getHits()[0].getSortValues();
// ["2020-01-01", 2.2]参考资料
【最佳实践】阿里云 Elasticsearch 向量检索4步搭建“以图搜图”搜索引擎
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