DSL：Elasticsearch嵌套式对象Nested
参考：
es中索引对象包含数组子节点时， 查询和聚合的统计需要注意的问题。
https://es.xiaoleilu.com/402_Nested/30_Nested_objects.html
嵌套对象
事实上在Elasticsearch中，创建丶删除丶修改一个文档是是原子性的，因此我们可以在一个文档中储存密切关联的实体。
举例来说，我们可以在一个文档中储存一笔订单及其所有内容，或是储存一个Blog文章及其所有回应，藉由传递一个comments阵列：
PUT /my_index/blogpost/1 { "title": "Nest eggs", "body": "Making your money work...", "tags": [ "cash", "shares" ], "comments": [ { "name": "John Smith", "comment": "Great article", "age": 28, "stars": 4, "date": "2014-09-01" }, { "name": "Alice White", "comment": "More like this please", "age": 31, "stars": 5, "date": "2014-10-22" } ] }
<1> 如果我们依靠动态映射，comments栏位会被自动建立为一个object栏位。
因为所有内容都在同一个文档中，使搜寻时并不需要连接(join)blog文章与回应，因此搜寻表现更加优异。
问题在於以上的文档可能会如下所示的匹配一个搜寻：
GET my_index/_search { "query": { "bool": { "must": [ { "match": { "comments.age": 31 } }, { "match": { "comments.name": "John Smith" } } ] } } }
Alice是31岁，而不是28岁！
造成跨对象配对的原因如同我们在对象阵列中所讨论到，在于我们优美结构的JSON文档在索引中被扁平化为下方的 键-值 形式：
{ "title": [ eggs, nest ], "body": [ making, money, work, your ], "tags": [ cash, shares ], "comments.name": [ alice, john, smith, white ], "comments.comment": [ article, great, like, more, please, this ], "comments.age": [ 28, 31 ], "comments.stars": [ 4, 5 ], "comments.date": [ 2014-09-01, 2014-10-22 ] }
Alice与31 以及 John与2014-09-01 之间的关联已经无法挽回的消失了。 当object类型的栏位用于储存单一对象是非常有用的。 从搜寻的角度来看，对於排序一个对象阵列来说关联是不需要的东西。
这是嵌套对象被设计来解决的问题。 藉由映射commments栏位为nested类型而不是object类型， 每个嵌套对象会被索引为一个隐藏分割文档，例如：
{ <1>
"comments.name": [ john, smith ],
"comments.comment": [ article, great ],
"comments.age": [ 28 ],
"comments.stars": [ 4 ],
"comments.date": [ 2014-09-01 ]
}
{ <2>
"comments.name": [ alice, white ],
"comments.comment": [ like, more, please, this ],
"comments.age": [ 31 ],
"comments.stars": [ 5 ],
"comments.date": [ 2014-10-22 ]
}
{ <3>
"title": [ eggs, nest ],
"body": [ making, money, work, your ],
"tags": [ cash, shares ]
}
<1> 第一个嵌套对象
<2> 第二个嵌套对象
<3> 根或是父文档
藉由分别索引每个嵌套对象，对象的栏位中保持了其关联。 我们的查询可以只在同一个嵌套对象都匹配时才回应。
不仅如此，因嵌套对象都被索引了，连接嵌套对象至根文档的查询速度非常快--几乎与查询单一文档一样快。
这些额外的嵌套对象被隐藏起来，我们无法直接访问他们。 为了要新增丶修改或移除一个嵌套对象，我们必须重新索引整个文档。 要牢记搜寻要求的结果并不是只有嵌套对象，而是整个文档。
示例
包含嵌套对象的文档创建
创建映射
PUT devicelog_22 { "mappings" : { "log" : { "properties" : { "Items" : { "type":"nested", "properties" : { "name" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } }, "unit" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } }, "value" : { "type" : "double" } } }, "OperationDateTime" : { "type" : "date", "format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||epoch_millis", "ignore_malformed":false }, "systemId" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } } } } } }
批量插入数据
POST /devicelog_22/log/_bulk {"index":{"_id":1}} {"systemId":"001","OperationDateTime":"2020-03-02 10:34:03","Items":[{"name":"k1","value": 11},{"name":"k2","value": 12},{"dd":"k3","value":33}]} {"index":{"_id":2}} {"systemId":"001","OperationDateTime":"2020-03-02 11:34:03","Items":[{"name":"k1","value": 22},{"name":"k2","value": 33},{"dd":"k3","value":44}]} {"index":{"_id":3}} {"systemId":"001","OperationDateTime":"2020-03-02 16:34:03","Items":[{"name":"k1","value": 45.3},{"name":"k2","value": 89.333},{"dd":"k3","value":18.33}]} {"index":{"_id":4}} {"systemId":"001","OperationDateTime":"2020-03-03 15:34:03","Items":[{"name":"k1","value": 45.3},{"name":"k2","value": 89.333},{"dd":"k3","value":18.33}]} {"index":{"_id":5}} {"systemId":"001","OperationDateTime":"2020-03-03 18:34:03","Items":[{"name":"k1","value": 222.3},{"name":"k2","value": 33.333},{"dd":"k3","value":55.33}]}
聚合分析
GET devicelog_22/_search { "query": { "match_all": {} }, "aggs": { "item.name": { "nested": { "path": "Items" }, "aggs": { "terms": { "terms": { "field": "Items.name.keyword", "size": 10 }, "aggs": { "sum-aggs": { "sum": { "field": "Items.value" } } } } } } } }
对于嵌套对象的过滤查询
GET devicelog_22/_search { "query": { "nested": { "path": "Items", "query": { "bool": { "must": [ {"term": { "Items.dd": { "value": "k3" } }} ] } } } } }
java查询
@Test public void test2(){ try { SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder(); QueryBuilder queryBuilderHave=QueryBuilders.termQuery("Items.name","k1"); NestedQueryBuilder nestedQueryBuilder=QueryBuilders.nestedQuery("Items",queryBuilderHave, ScoreMode.None); QueryBuilder queryBuilder=QueryBuilders.boolQuery().must(nestedQueryBuilder); TopHitsAggregationBuilder aggregation1= AggregationBuilders.topHits("details").size(1). sort(SortBuilders.fieldSort("OperationDateTime").order(SortOrder.DESC)).fetchSource(true); DateHistogramAggregationBuilder aggregation= AggregationBuilders.dateHistogram("agg"). keyed(true).format("yyyy-MM-dd").field("OperationDateTime").dateHistogramInterval(DateHistogramInterval.DAY). timeZone(DateTimeZone.forOffsetHours(8)).subAggregation(aggregation1); searchSourceBuilder.size(0).fetchSource(false). timeout(new TimeValue(1000, TimeUnit.SECONDS)).query(queryBuilder).aggregation(aggregation); SearchRequest searchRequest=new SearchRequest(); searchRequest.source(searchSourceBuilder); SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT); if(searchResponse.status()== RestStatus.OK){ Histogram histogram= searchResponse.getAggregations().get("agg"); EsDocumentList esDocumentList=new EsDocumentList(); for (Histogram.Bucket entry : histogram.getBuckets()){ TopHits topHits=entry.getAggregations().get("details"); for (SearchHit hit : topHits.getHits().getHits()) { Map<String, Object> mapResult = hit.getSourceAsMap(); esDocumentList.add(mapResult); } } System.out.println("11"); } }catch (Exception ex){ System.out.println(ex); }
