diff --git a/tutorials/chapter-7.md b/tutorials/chapter-7.md
deleted file mode 100644
index e882064..0000000
--- a/tutorials/chapter-7.md
+++ /dev/null
@@ -1,13 +0,0 @@
-<!-- toc -->
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-
diff --git a/tutorials/cluster.md b/tutorials/cluster.md
index 91c621b..0f181c3 100644
--- a/tutorials/cluster.md
+++ b/tutorials/cluster.md
@@ -1,76 +1,52 @@
----
-id: cluster
-title: "Clustered deployment"
----
+# 集群方式部署
 
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-  ~
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-  -->
+Apache Druid 被设计部署为可扩展和容错的集群部署方式。
 
+在本文档中，我们将会设置一个示例集群，并且进行一些讨论，你可以进行那些修改来满足你的需求。
 
-Apache Druid is designed to be deployed as a scalable, fault-tolerant cluster.
+这个简单的集群包括有下面的特性：
 
-In this document, we'll set up a simple cluster and discuss how it can be further configured to meet
-your needs.
+ - 主服务器（Master Server）将会运行 Coordinator 和 Overlord 进程
+ - 2 个可扩展和容错的数据服务器将会运行 Historical 和 MiddleManager 进程
+ - 一个查询服务器（Query Server）将会运行 Broker 和 Router 进程
 
-This simple cluster will feature:
+在生产环境中，我们建议你部署多个 Master 服务器和多个 Query 服务器，服务器的高可用性（fault-tolerant）配置与你的数据特性和容错性要求息息相关。
+但是你可以使用一个主服务器（Master Server） 和 一个查询服务器（Query Server）来启动服务，随着需求的增加你可以随时增加更多的服务器节点。
 
- - A Master server to host the Coordinator and Overlord processes
- - Two scalable, fault-tolerant Data servers running Historical and MiddleManager processes
- - A query server, hosting the Druid Broker and Router processes
+## 选择硬件
 
-In production, we recommend deploying multiple Master servers and multiple Query servers in a fault-tolerant configuration based on your specific fault-tolerance needs, but you can get started quickly with one Master and one Query server and add more servers later.
+### 全新部署
 
-## Select hardware
+如果你没有已经存在的 Druid 集群，但是你希望开始在你的环境中使用集群方式部署 Druid，本文档将会使用预配置（pre-made configurations）内容来帮助你开始部署 Druid 的集群。
 
-### Fresh Deployment
+#### 主服务器（Master Server）
 
-If you do not have an existing Druid cluster, and wish to start running Druid in a clustered deployment, this guide provides an example clustered deployment with pre-made configurations.
+Coordinator 和 Overlord 进程将会负责处理 metadata 数据和在你集群中进行协调。这 2 个进程可以合并在同一个服务器上。
 
-#### Master server
+在本示例中，我们将会在 AWS [m5.2xlarge](https://aws.amazon.com/ec2/instance-types/m5/) 部署一个评估的服务器和实例。
 
-The Coordinator and Overlord processes are responsible for handling the metadata and coordination needs of your cluster. They can be colocated together on the same server.
-
-In this example, we will be deploying the equivalent of one AWS [m5.2xlarge](https://aws.amazon.com/ec2/instance-types/m5/) instance.
-
-This hardware offers:
+AWS 上面硬件的配置为：
 
 - 8 vCPUs
 - 31 GB RAM
 
-Example Master server configurations that have been sized for this hardware can be found under `conf/druid/cluster/master`.
+有关本服务器的配置信息和有关硬件大小的建议，可以在文件 `conf/druid/cluster/master` 中找到。
 
-#### Data server
+#### 数据服务器（Data server）
 
-Historicals and MiddleManagers can be colocated on the same server to handle the actual data in your cluster. These servers benefit greatly from CPU, RAM,
-and SSDs.
+Historicals 和 MiddleManagers 可以合并到同一个服务器上，这个 2 个进程在你的集群中用于处理实际的数据。通常来说越大更大的 CPU, RAM, SSDs硬盘越好更好。
 
-In this example, we will be deploying the equivalent of two AWS [i3.4xlarge](https://aws.amazon.com/ec2/instance-types/i3/) instances.
+在本示例中，我们将会在  [i3.4xlarge](https://aws.amazon.com/ec2/instance-types/i3/) 部署一个评估的服务器和实例。
 
-This hardware offers:
+AWS 上面硬件的配置为：
 
 - 16 vCPUs
 - 122 GB RAM
 - 2 * 1.9TB SSD storage
 
-Example Data server configurations that have been sized for this hardware can be found under `conf/druid/cluster/data`.
+有关本服务器的配置信息和有关硬件大小的建议，可以在文件 `conf/druid/cluster/data` 中找到。
 
-#### Query server
+#### 查询服务器（Query server）
 
 Druid Brokers accept queries and farm them out to the rest of the cluster. They also optionally maintain an
 in-memory query cache. These servers benefit greatly from CPU and RAM.
@@ -473,48 +449,7 @@ You can add more Query servers as needed based on query load. If you increase th
 Congratulations, you now have a Druid cluster! The next step is to learn about recommended ways to load data into
 Druid based on your use case. Read more about [loading data](../ingestion/index.md).
 
-## 集群部署
 
-Apache Druid旨在作为可伸缩的容错集群进行部署。
-
-在本文档中，我们将安装一个简单的集群，并讨论如何对其进行进一步配置以满足您的需求。
-
-这个简单的集群将具有以下特点：
-* 一个Master服务同时起Coordinator和Overlord进程
-* 两个可伸缩、容错的Data服务来运行Historical和MiddleManager进程
-* 一个Query服务，运行Druid Broker和Router进程
-
-在生产中，我们建议根据您的特定容错需求部署多个Master服务器和多个Query服务器，但是您可以使用一台Master服务器和一台Query服务器将服务快速运行起来，然后再添加更多服务器。
-### 选择硬件
-#### 首次部署
-
-如果您现在没有Druid集群，并打算首次以集群模式部署运行Druid，则本指南提供了一个包含预先配置的集群部署示例。
-
-##### Master服务
-
-Coordinator进程和Overlord进程负责处理集群的元数据和协调需求，它们可以运行在同一台服务器上。
-
-在本示例中，我们将在等效于AWS[m5.2xlarge](https://aws.amazon.com/ec2/instance-types/m5/)实例的硬件环境上部署。
-
-硬件规格为：
-
-* 8核CPU
-* 31GB内存
-
-可以在`conf/druid/cluster/master`下找到适用于此硬件规格的Master示例服务配置。
-
-##### Data服务
-
-Historical和MiddleManager可以分配在同一台服务器上运行，以处理集群中的实际数据，这两个服务受益于CPU、内存和固态硬盘。
-
-在本示例中，我们将在等效于AWS[i3.4xlarge](https://aws.amazon.com/cn/ec2/instance-types/i3/)实例的硬件环境上部署。
-
-硬件规格为：
-* 16核CPU
-* 122GB内存
-* 2 * 1.9TB 固态硬盘
-
-可以在`conf/druid/cluster/data`下找到适用于此硬件规格的Data示例服务配置。
 
 ##### Query服务
 
diff --git a/tutorials/tutorial-rollup.md b/tutorials/tutorial-rollup.md
index 1fdd76c..1604021 100644
--- a/tutorials/tutorial-rollup.md
+++ b/tutorials/tutorial-rollup.md
@@ -130,7 +130,7 @@ dsql>
 {"timestamp":"2018-01-01T01:01:59Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":11,"bytes":5780}
 ```
 
-上面的 3 调原始数据使用 "rolled up" 后将会合并成下面 1 条数据进行导入：
+上面的 3 条原始数据使用 "rolled up" 后将会合并成下面 1 条数据进行导入：
 
 ```bash
 ┌──────────────────────────┬────────┬───────┬─────────┬─────────┬─────────┐