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 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。