Java代码解决ElasticSearch的Resultwindowistoolarge

编程

调用ElasticSearch做分页查询时报错:

QueryPhaseExecutionException[Result window is too large, from + size must be less than or equal to: [10000] but was [666000]. See the scroll api for a more efficient way to request large data sets. This limit can be set by changing the [index.max_result_window] index level setting.]; }

提示用from+size方式有1万条数据查询的限制,需要更改index.max_result_window参数的值。

翻了下elasticsearch官网的文档:

index.max_result_window

The maximum value of from + size for searches to this index.Defaults to 10000.

Search requests take heap memory and time proportional to from + size and this limits that memory.

See Scroll or Search After for a more efficient alternative to raising this.

说是用传统方式(from + size)查询占用内存空间且比较消耗时间,所以做了限制。

问题是用scroll方式做后台分页根本行不通。

不说用scroll方式只能一页页的翻这种不人性化的操作。页码一多,scrollId也很难管理啊。

所以继续鼓捣传统方式的分页。

上网查了下设置max_result_window的方法,全都是用crul或者http方式改的。

后来无意间看到了一篇文档: https://blog.csdn.net/tzconn/article/details/83309516

结合之前逛elastic中文社区的时候知道这个参数是索引级别的。于是小试了一下,结果竟然可以了。

java代码如下:

public SearchResponse search(String logIndex, String logType, QueryBuilder query, 

List<AggregationBuilder> agg, int page, int size) {

page = page > 0 ? page - 1 : page;

TransportClient client = getClient();

SearchRequestBuilder searchRequestBuilder = client.prepareSearch(logIndex.split(","))

.setTypes(logType.split(","))

.setSearchType(SearchType.DFS_QUERY_THEN_FETCH)

.addSort("createTime", SortOrder.DESC);

if (agg != null && !agg.isEmpty()) {

for (int i = 0; i < agg.size(); i++) {

searchRequestBuilder.addAggregation(agg.get(i));

}

}

updateIndexs(client, logIndex, page, size);

SearchResponse searchResponse = searchRequestBuilder

.setQuery(query)

.setFrom(page * size)

.setSize(size)

.get();

return searchResponse;

}

//更新索引的max_result_window参数

private boolean updateIndexs(TransportClient client, String indices, int from, int size) {

int records = from * size + size;

if (records <= 10000) return true;

UpdateSettingsResponse indexResponse = client.admin().indices()

.prepareUpdateSettings(indices)

.setSettings(Settings.builder()

.put("index.max_result_window", records)

.build()

).get();

return indexResponse.isAcknowledged();

}

搞定。

当然这段代码不好的地方在于:

每次查询超过10000万条记录的时候,都会去更新一次index。

这对原本就偏慢的from+size查询来说,更是雪上加霜了。

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