Elasticsearch -> Indeces(索引) -> Types(类型) -> Documents(文档) -> Fields(属性)。 Elasticsearch集群可以包含多个索引(indices)(数据库),每一个索引可以包含多个类型 (Types)(表),每一个类型包含多个文档(documents)(行),然后每个文档包含多个字段(Fields)(列)。 虽然这么
接下来创建test.yml(文件名随意),内容很好理解_index为索引名称,sql为对应语句,内容如下: dataSourceKey: defaultDS destination: example groupId: esMapping: _index: test _type: _doc _id: _id upsert: true sql: "select a.id as _id,a.name,a.address from test a" commitBatch: 3000 配置完成后...
{ "error" : { "root_cause" : [ { "type" : "resource_already_exists_exception", "reason" : "index [shopping/oTiQnf9UT--dwgsZ_2wBow] already exists", "index_uuid" : "oTiQnf9UT--dwgsZ_2wBow", "index" : "shopping" } ], "type" : "resource_already_exists_exception", "reason...
Elasticsearch报错number of documents in the index...该报错是因为lucene的文档数限制，每个分片最大支持...
20 Query = "描述" 21 } 22 }; 23 var result = client.DeleteByQuery(req); 24 //等价于 25 var result2 = client.DeleteByQuery<Company>(dq => dq.MaximumDocuments(10) 26 .Query(q => q.Match(tr => tr.Field(fd => fd.Description).Query("描述"))).Index(indexName)); 27 #endreg...
然而让人吃惊的发现, Elaticsearch不是只把符合你的查询的值加载到fielddata. 而是把index里的所document都加载到内存,甚至是不同的 _type 的document。逻辑是这样的,如果你在这个查询需要访问documents X,Y和Z, 你可能在下一次查询就需要访问别documents。而一次把所有的值都加载并保存在内存 , 比每次查询都去扫描...
在今天的练习中,我们将使用sentence-transformers/all-MiniLM-L6-v2 · Hugging Face模型来实现语义的搜素。这是一个sentence-transformers模型:它将句子和段落映射到384维密集向量空间,可用于聚类或语义搜索等任务。我可以参考文档来了解更多。 在使用这个模型做我们的搜索之前,我们先来使用几个小的例子来进行展示一下...
Any index that gets matched with this template during its creation inherits the configurations defined in this template. For example a black_friday_orders index will have the order_date field, shards will be set to 5 and the replicas set to 2. In addition to this,allindices created from thi...
即:降低评分 negative_boost (Required, float) Floating point number between 0 and 1.0 used to decrease the relevance scores of documents matching the negative query. 官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/query-dsl-boosting-query.html POST index_a/_search { "...
for n_lines in iter(lambda: tuple(islice(documents_file, BATCH_SIZE)), ()): processed += 1 if processed % INFO_UPDATE_FACTOR == 0: print("Processed {} batch of documents".format(processed)) # Create sentence embedding vectors = encode(n_lines) ...