caiconghui commented on a change in pull request #4479:
URL: https://github.com/apache/incubator-doris/pull/4479#discussion_r479602472
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File path: docs/zh-CN/administrator-guide/query_cache.md
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+---
+{
+ "title": "QUERY CACHE",
+ "language": "zh-CN"
+}
+---
+
+<!--
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+or more contributor license agreements. See the NOTICE file
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+
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+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+# QUERY CACHE
+
+
+
+## 一 需求
+
+虽然在数据库层也做了对应的缓存,但这种数据库层次的缓存一般针对的是查询内容,而且粒度也太小,一般只有表中数据没有变更的时候,数据库对应的cache才发挥了作用。但这并不能减少业务系统对数据库产生的增、删、查、改的庞大IO压力。所以数据库缓存技术在此诞生,实现热点数据的高速缓存,提高应用的响应速度,极大缓解后端数据库的压力
+
+- 高并发场景
+ Doris可以较好的支持高并发,但单台服务器无法承载太高的QPS
+
+- 复杂图表的看板
+ 复杂的Dashboard或者大屏类应用,数据来自多张表,每个页面有数十个查询,虽然每个查询只有数十毫秒,但是总体查询时间会在数秒
+
+- 趋势分析
+ 给定日期范围的查询,指标按日显示,比如查询最近7天内的用户数的趋势,这类查询数据量大,查询范围广,查询时间往往需要数十秒
+
+- 用户重复查询
+ 如果产品没有防重刷机制,用户因手误或其他原因重复刷新页面,导致提交大量的重复的SQL
+
+
+
+以上四种场景,在应用层的解决方案,把查询结果放到Redis中,周期性的更新缓存或者用户手工刷新缓存,但是这个方案有如下问题:
Review comment:
```suggestion
以上四种场景,一种在应用层的解决方案是把查询结果放到Redis中,周期性地更新缓存或者用户手动刷新缓存,但是这个方案有如下问题:
```
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