ElasticSearch Nested类型全文检索、聚合查询

Nested类型全文检索

创建索引

PUT /products1
{"mappings": {"properties": {"fulltext": {"type": "text"},"name": {"type": "text","fields": {"keyword": {"type": "keyword","ignore_above": 256}}},"reviews": {"type": "nested","properties": {"rating": {"type": "integer"},"author": {"type": "text","copy_to": "fulltext"},"date": {"type": "date"}}}}}
}

以上创建索引语句中实现全文检索重点为"fulltext": { "type": "text" }和"copy_to": "fulltext"，nested类型中哪个text类型的字段需要全文检索，就在字段上加"copy_to": "fulltext"

添加数据

PUT /products1/_doc/1
{"name": "Product A","reviews": [{"rating": 5,"author": "Alice","date": "2021-01-01"},{"rating": 4,"author": "Bob","date": "2021-01-02"}]
}PUT /products1/_doc/2
{"name": "Product B","reviews": [{"rating": 1,"author": "John","date": "2021-01-03"},{"rating": 2,"author": "Mary","date": "2021-01-04"},{"rating": 3,"author": "James","date": "2021-01-05"},{"rating": 4,"author": "Elisabeth","date": "2021-01-06"},{"rating": 5,"author": "Richard","date": "2021-01-07"}]
}PUT /products1/_doc/3
{"name": "Product C","reviews": [{"rating": 1,"author": "Alex","date": "2021-01-03"},{"rating": 2,"author": "Alice","date": "2021-01-04"}]
}

执行查询

POST products1/_search
{"query": {"simple_query_string": {"query": "Alice"}}
}

结果如下，可以看到nested类型中包含Alice的数据也被检索出来了

{"took" : 2,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 2,"relation" : "eq"},"max_score" : 0.5442147,"hits" : [{"_index" : "products1","_type" : "_doc","_id" : "1","_score" : 0.5442147,"_source" : {"name" : "Product A","reviews" : [{"rating" : 5,"author" : "Alice","date" : "2021-01-01"},{"rating" : 4,"author" : "Bob","date" : "2021-01-02"}]}},{"_index" : "products1","_type" : "_doc","_id" : "3","_score" : 0.5442147,"_source" : {"name" : "Product C","reviews" : [{"rating" : 1,"author" : "Alex","date" : "2021-01-03"},{"rating" : 2,"author" : "Alice","date" : "2021-01-04"}]}}]}
}

以上可以看到实现nested类型全文检索

nested类型聚合查询

还是在上面product1索引中测试

现在，您可以对嵌套文档执行嵌套聚合。例如，让我们计算每个产品的平均评分：

GET /products1/_search
{"size": 0,"aggs": {"聚合名称": {"terms": {"field": "name.keyword"},"aggs": {"reviews": {"nested": {"path": "reviews"},"aggs": {"average_rating": {"avg": {"field": "reviews.rating"}}}}}}}
}

我们首先使用术语聚合为每个产品创建存储桶。然后，对于每个产品，我们运行嵌套聚合，以便我们可以访问嵌套文档的集合。最后，我们可以计算这些嵌套文档的指标聚合，在我们的示例中是平均评分

{"took" : 3,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 3,"relation" : "eq"},"max_score" : null,"hits" : [ ]},"aggregations" : {"products" : {"doc_count_error_upper_bound" : 0,"sum_other_doc_count" : 0,"buckets" : [{"key" : "Product A","doc_count" : 1,"reviews" : {"doc_count" : 2,"average_rating" : {"value" : 4.5}}},{"key" : "Product B","doc_count" : 1,"reviews" : {"doc_count" : 5,"average_rating" : {"value" : 3.0}}},{"key" : "Product C","doc_count" : 1,"reviews" : {"doc_count" : 2,"average_rating" : {"value" : 1.5}}}]}}
}