参考:
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"
}
]
}
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<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"
}
}
]
}
}
}
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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 ]
}
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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 ]
}
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<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
PUTdevicelog_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
}
}
}
}
}
}
}
View Code
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}]}
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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"
}
}
}
}
}
}
}
}
View Code
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"
}
}}
]
}
}
}
}
}
View Code
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 mapResult =hit.getSourceAsMap();
esDocumentList.add(mapResult);
}
}
System.out.println("11");
}
}catch (Exception ex){
System.out.println(ex);
}
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