elasticsearch分页获取数据

￡神魔★判官ぃ 2023-06-15 06:43 106阅读 0赞

提到elasticsearch分页，可能首先想到的是类似mysql的那种处理方式，传入分页起始值以及每页数据量，es确实提供了类似的处理策略，代码如下：

@Test
    	public void searchFromSize() throws IOException{
    		SearchRequest searchRequest = new SearchRequest("sub_bank1120");
    		SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    		searchSourceBuilder.query(QueryBuilders.matchAllQuery());
            //每页10个数据
    		searchSourceBuilder.size(10);
            //起始位置从10开始
    		searchSourceBuilder.from(10);
    		searchRequest.source(searchSourceBuilder);
    		SearchResponse searchResponse = highLevelClient.search(searchRequest, RequestOptions.DEFAULT); 
    		SearchHit[] searchHits = searchResponse.getHits().getHits();
    	    for(SearchHit s:searchHits){
    	    	println(s.getSourceAsString());
    	    }
    	}

但是上述方式有一个严重的缺陷：from和size不能太大，两者之和不能超过index.max\_result\_window，超过该值就会报

org.elasticsearch.client.ResponseException异常

Result window is too large, from + size must be less than or equal to: \[10000\] but was \[11010\]

为什么会使用index.max\_result\_window来限制搜索深度，因为这需要耗费大量内存，比如from为10000，es会按照一定的顺序从每个分片读取10010个数据，然后取出每个分片中排序前10的数据返回给协调节点，协调节点会将从所有分片节点返回的10条数据再次进行统一排序处理，以此来返回全局排序前10的数据，如果有类似的需要可以使用scroll以及search after来实现超大分页问题，

scroll分页示例代码可以参考：[https://www.elastic.co/guide/en/elasticsearch/client/java-rest/6.8/java-rest-high-search-scroll.html][https_www.elastic.co_guide_en_elasticsearch_client_java-rest_6.8_java-rest-high-search-scroll.html]

search after示例可以参考下面代码：

/**
    	 * search after
    	 * @throws IOException
    	 */
    	@Test
    	public void searchAfter() throws IOException{
    		SearchRequest searchRequest = new SearchRequest("sub_bank1031");
    		SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    		searchSourceBuilder.query(QueryBuilders.matchQuery("cityId", "511000"));
    		searchSourceBuilder.size(2);
            //id动态映射为text类型，排序不能使用分词的字段，所以这里选择了id的keyword多字段属性
    		searchSourceBuilder.sort(new FieldSortBuilder("id.keyword").order(SortOrder.ASC));
    		//
    		searchRequest.source(searchSourceBuilder);
    		SearchResponse searchResponse = highLevelClient.search(searchRequest, RequestOptions.DEFAULT); 
    		SearchHit[] searchHits = searchResponse.getHits().getHits();
    		if(searchHits.length >0){
    		    for(SearchHit s:searchHits){
    		    	println(s.getSourceAsString());
    		    }
    		    JSONObject json = JSON.parseObject(searchHits[searchHits.length-1].getSourceAsString());
    		    String id = json.getString("id");
    		    searchSourceBuilder.searchAfter(new Object[]{id});
    		    searchRequest.source(searchSourceBuilder);
    		    searchResponse = highLevelClient.search(searchRequest, RequestOptions.DEFAULT); 
    		    for(SearchHit s:searchHits){
    		    	println(s.getSourceAsString());
    		    }		    
    		}
    	}

下图为索引映射部分截图：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pvaG4xMzM3_size_16_color_FFFFFF_t_70][]

PS：

search after与scroll相比简单些，而且无状态。

[https_www.elastic.co_guide_en_elasticsearch_client_java-rest_6.8_java-rest-high-search-scroll.html]: https://www.elastic.co/guide/en/elasticsearch/client/java-rest/6.8/java-rest-high-search-scroll.html
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pvaG4xMzM3_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20191120170128477.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pvaG4xMzM3,size_16,color_FFFFFF,t_70