diff --git a/best-practices/high-concurrency-best-practices.md b/best-practices/high-concurrency-best-practices.md
index bbca43b1a38d9..4cd6b74b6c338 100644
--- a/best-practices/high-concurrency-best-practices.md
+++ b/best-practices/high-concurrency-best-practices.md
@@ -35,7 +35,7 @@ For a distributed database, it is important to make full use of the capacity of
 
 To address the above challenges, it is necessary to start with the data segmentation and scheduling principle of TiDB. Refer to [Scheduling](https://pingcap.com/blog/2017-07-20-tidbinternal3/) for more details.
 
-TiDB splits data into Regions, each representing a range of data with a size limit of 96M by default. Each Region has multiple replicas, and each group of replicas is called a Raft Group. In a Raft Group, the Region Leader executes the read and write tasks within the data range. The Region Leader is automatically scheduled by the Placement Driver (PD) component to different physical nodes evenly to distribute the read and write pressure.
+TiDB splits data into Regions, each representing a range of data with a size limit of 96M by default. Each Region has multiple replicas, and each group of replicas is called a Raft Group. In a Raft Group, the Region Leader executes the read and write tasks (TiDB supports [Follower-Read](/follower-read.md)) within the data range. The Region Leader is automatically scheduled by the Placement Driver (PD) component to different physical nodes evenly to distribute the read and write pressure.
 
 ![TiDB Data Overview](/media/best-practices/tidb-data-overview.png)