From 7a6e1de845f029d07e2cfb4ca9f688a09074d59e Mon Sep 17 00:00:00 2001
From: luoxiang <2806718453@qq.com>
Date: Sat, 1 Jun 2019 19:48:00 +0800
Subject: [PATCH] modify
---
README.md | 25 ++-
notes/installation/HBase集群环境搭建.md | 183 +++++++++++++++
...搭建.md => Hadoop单机环境搭建.md} | 0
.../installation/Hadoop集群环境搭建.md | 212 ++++++++++++++++++
notes/installation/Spark集群环境搭建.md | 172 ++++++++++++++
pictures/hadoop集群规划.png | Bin 0 -> 22060 bytes
pictures/hbase集群规划.png | Bin 0 -> 21851 bytes
pictures/spark集群规划.png | Bin 0 -> 21966 bytes
8 files changed, 581 insertions(+), 11 deletions(-)
create mode 100644 notes/installation/HBase集群环境搭建.md
rename notes/installation/{hadoop单机版本环境搭建.md => Hadoop单机环境搭建.md} (100%)
create mode 100644 notes/installation/Hadoop集群环境搭建.md
create mode 100644 notes/installation/Spark集群环境搭建.md
create mode 100644 pictures/hadoop集群规划.png
create mode 100644 pictures/hbase集群规划.png
create mode 100644 pictures/spark集群规划.png
diff --git a/README.md b/README.md
index d11355b..37dbd20 100644
--- a/README.md
+++ b/README.md
@@ -52,9 +52,10 @@
1. [分布式文件存储系统——HDFS](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hadoop-HDFS.md)
2. [分布式计算框架——MapReduce](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hadoop-MapReduce.md)
3. [集群资源管理器——YARN](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hadoop-YARN.md)
-4. [Hadoop单机伪集群环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/hadoop单机版本环境搭建.md)
-5. [HDFS常用Shell命令](https://github.com/heibaiying/BigData-Notes/blob/master/notes/HDFS常用Shell命令.md)
-6. [HDFS Java API的使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/HDFS-Java-API.md)
+4. [Hadoop单机伪集群环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Hadoop单机环境搭建.md)
+5. [Hadoop集群环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Hadoop集群环境搭建.md)
+6. [HDFS常用Shell命令](https://github.com/heibaiying/BigData-Notes/blob/master/notes/HDFS常用Shell命令.md)
+7. [HDFS Java API的使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/HDFS-Java-API.md)
## 二、Hive
@@ -77,6 +78,7 @@
5. [RDD常用算子详解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Spark_Transformation和Action算子.md)
5. [Spark运行模式与作业提交](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Spark部署模式与作业提交.md)
6. [Spark累加器与广播变量](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Spark累加器与广播变量.md)
+7. [基于Zookeeper搭建Spark高可用集群](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Spark集群环境搭建.md)
**Spark SQL :**
@@ -113,14 +115,15 @@ TODO
1. [Hbase 简介](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase简介.md)
2. [HBase系统架构及数据结构](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase系统架构及数据结构.md)
-3. [HBase基本环境搭建（Standalone /pseudo-distributed mode）](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Hbase基本环境搭建.md)
-4. [HBase常用Shell命令](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase_Shell.md)
-5. [HBase Java API](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase_Java_API.md)
-6. [Hbase 过滤器详解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase过滤器详解.md)
-7. [HBase 协处理器详解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase协处理器详解.md)
-8. [HBase 容灾与备份](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase容灾与备份.md)
-9. [HBase的SQL中间层——Phoenix](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase的SQL中间层_Phoenix.md)
-10. [Spring/Spring Boot 整合 Mybatis + Phoenix](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Spring+Mybtais+Phoenix整合.md)
+3. [HBase基本环境搭建(Standalone /pseudo-distributed mode)](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/HBase基本环境搭建.md)
+4. [HBase集群环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/HBase集群环境搭建.md)
+5. [HBase常用Shell命令](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase_Shell.md)
+6. [HBase Java API](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase_Java_API.md)
+7. [Hbase 过滤器详解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase过滤器详解.md)
+8. [HBase 协处理器详解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase协处理器详解.md)
+9. [HBase 容灾与备份](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase容灾与备份.md)
+10. [HBase的SQL中间层——Phoenix](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase的SQL中间层_Phoenix.md)
+11. [Spring/Spring Boot 整合 Mybatis + Phoenix](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Spring+Mybtais+Phoenix整合.md)
## 七、Kafka
diff --git a/notes/installation/HBase集群环境搭建.md b/notes/installation/HBase集群环境搭建.md
new file mode 100644
index 0000000..cc76b96
--- /dev/null
+++ b/notes/installation/HBase集群环境搭建.md
@@ -0,0 +1,183 @@
+# HBase集群环境配置
+
+## 一、集群规划
+
+这里搭建一个3节点的HBase集群，其中三台主机上均为`Regin Server`。同时为了保证高可用，除了在hadoop001上部署主`Master`服务外，还在hadoop002上署备用的`Master`服务，Master服务由Zookeeper集群进行协调管理，如果主`Master`不可用，则备用`Master`会成为新的主`Master`。
+
+
+
+## 二、前置条件
+
+HBase的运行需要依赖JDK和Hadoop，HBase 2.0+需要安装JDK 1.8+ 。同时为了保证高可用，这里我们不采用HBase内置的Zookeeper，而采用外置的Zookeeper集群。相关搭建步骤可以参阅：
+
+- [Linux环境下JDK安装](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Linux下JDK安装.md)
+- [Zookeeper单机环境和集群环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Zookeeper单机环境和集群环境搭建.md)
+- [Hadoop集群环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Hadoop集群环境搭建.md)
+
+
+
+## 三、集群搭建
+
+### 3.1 下载并解压
+
+下载并解压，官方下载地址：https://hbase.apache.org/downloads.html
+
+```shell
+# tar -zxvf hbase-2.1.4-bin.tar.gz
+```
+
+### 3.2 配置环境变量
+
+```shell
+# vim /etc/profile
+```
+
+添加环境变量：
+
+```shell
+export HBASE_HOME=/usr/app/hbase-2.1.4
+export PATH=$HBASE_HOME/bin:$PATH
+```
+
+使得配置的环境变量立即生效：
+
+```shell
+# source /etc/profile
+```
+
+### 3.3 集群配置
+
+进入`${HBASE_HOME}/conf`目录下，修改配置：
+
+#### 1. hbase-env.sh
+
+```shell
+# 配置JDK安装位置
+export JAVA_HOME=/usr/java/jdk1.8.0_201
+# 不使用内置的zookeeper服务
+export HBASE_MANAGES_ZK=false
+```
+
+#### 2. hbase-site.xml
+
+```xml
+
+
+
+ hbase.cluster.distributed
+ true
+
+
+
+ hbase.rootdir
+ hdfs://hadoop001:8020/hbase
+
+
+
+ hbase.zookeeper.quorum
+ hadoop001:2181,hadoop002:2181,hadoop003:2181
+
+
+```
+
+#### 3. regionservers
+
+```
+hadoop001
+hadoop002
+hadoop003
+```
+
+#### 4. backup-masters
+
+```
+hadoop002
+```
+
+` backup-masters`这个文件是不存在的，需要新建，主要用来指明备用的master节点，可以是多个，这里我们以1个为例。
+
+### 3.4 HDFS客户端配置
+
+这里有一个可选的配置：如果您在Hadoop集群上进行了HDFS客户端配置的更改，比如将副本系数`dfs.replication`设置成5，则必须使用以下方法之一来使HBase知道，否则HBase将依旧使用默认的副本系数3来创建文件：
+
+> 1. Add a pointer to your `HADOOP_CONF_DIR` to the `HBASE_CLASSPATH` environment variable in *hbase-env.sh*.
+> 2. Add a copy of *hdfs-site.xml* (or *hadoop-site.xml*) or, better, symlinks, under *${HBASE_HOME}/conf*, or
+> 3. if only a small set of HDFS client configurations, add them to *hbase-site.xml*.
+
+以上是官方文档的说明，这里解释一下：
+
+**第一种** ：将Hadoop配置文件的位置信息添加到`hbase-env.sh`的`HBASE_CLASSPATH` 属性，示例如下：
+
+```shell
+export HBASE_CLASSPATH=usr/app/hadoop-2.6.0-cdh5.15.2/etc/hadoop
+```
+
+**第二种** ：将Hadoop的` hdfs-site.xml`或`hadoop-site.xml` 拷贝到 `${HBASE_HOME}/conf `目录下，或者通过符号链接的方式。如果采用这种方式的话，建议将两者都拷贝或建立符号链接，示例如下：
+
+```shell
+# 拷贝
+cp core-site.xml hdfs-site.xml /usr/app/hbase-1.2.0-cdh5.15.2/conf/
+# 使用符号链接
+ln -s /usr/app/hadoop-2.6.0-cdh5.15.2/etc/hadoop/core-site.xml
+ln -s /usr/app/hadoop-2.6.0-cdh5.15.2/etc/hadoop/hdfs-site.xml
+```
+
+> 注：`hadoop-site.xml`这个配置文件现在叫做`core-site.xml`
+
+**第三种** ：如果你只有少量更改，那么直接配置到`hbase-site.xml`中即可。
+
+
+
+### 3.5 安装包分发
+
+将HBase的安装包分发到其他服务器，分发后建议在这两台服务器上也配置一下HBase的环境变量。
+
+```shell
+scp -r /usr/app/hbase-1.2.0-cdh5.15.2/ hadoop002:usr/app/
+scp -r /usr/app/hbase-1.2.0-cdh5.15.2/ hadoop003:usr/app/
+```
+
+
+
+## 四、启动集群
+
+### 4.1 启动ZooKeeper集群
+
+分别到三台服务器上启动ZooKeeper服务：
+
+```shell
+ zkServer.sh start
+```
+
+### 4.2 启动Hadoop集群
+
+```shell
+# 启动dfs服务
+start-dfs.sh
+# 启动yarn服务
+start-yarn.sh
+```
+
+### 4.3 启动HBase集群
+
+进入hadoop001的`${HBASE_HOME}/bin`，使用以下命令启动HBase集群。执行此命令后，会在hadoop001上启动`Master`服务，在hadoop002上启动备用`Master`服务，在`regionservers`文件中配置的所有节点启动`region server`服务。
+
+```shell
+start-hbase.sh
+```
+
+
+
+### 4.5 查看服务
+
+访问HBase的Web-UI界面，这里我安装的HBase版本为1.2，访问端口为`60010`，如果你安装的是2.0以上的版本，则访问端口号为`16010`。可以看到`Master`在hadoop001上，三个`Regin Servers`分别在hadoop001，hadoop002，和hadoop003上，并且还有一个`Backup Matser` 服务在 hadoop002上。
+
+
+
+
+
+hadoop002 上的 HBase出于备用状态：
+
+
+
+
\ No newline at end of file
diff --git a/notes/installation/hadoop单机版本环境搭建.md b/notes/installation/Hadoop单机环境搭建.md
similarity index 100%
rename from notes/installation/hadoop单机版本环境搭建.md
rename to notes/installation/Hadoop单机环境搭建.md
diff --git a/notes/installation/Hadoop集群环境搭建.md b/notes/installation/Hadoop集群环境搭建.md
new file mode 100644
index 0000000..c25c5e5
--- /dev/null
+++ b/notes/installation/Hadoop集群环境搭建.md
@@ -0,0 +1,212 @@
+# Hadoop集群环境搭建
+
+## 一、集群规划
+
+这里搭建一个3节点的Hadoop集群，其中三台主机均部署`DataNode`和`NodeManager`服务，但只有hadoop001上部署`NameNode`和`ResourceManager`服务。
+
+
+
+## 二、前置条件
+
+Hadoop的运行依赖JDK，需要预先安装。其安装步骤单独整理至：
+
++ [Linux下JDK的安装](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/JDK%E5%AE%89%E8%A3%85.md)
+
+
+
+## 三、配置免密登录
+
+### 3.1 生成密匙
+
+在每台主机上使用ssh-keygen产生公钥私钥对：
+
+```shell
+ssh-keygen
+```
+
+### 3.2 免密登录
+
+将`hadoop001`的公钥写到本机和远程机器的` ~/ .ssh/authorized_key`文件中：
+
+```shell
+ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop001
+ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop002
+ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop003
+```
+
+### 3.3 验证免密登录
+
+```she
+ssh hadoop002
+ssh hadoop003
+```
+
+
+
+## 四、集群搭建
+
+### 3.1 下载并解压
+
+下载Hadoop。这里我下载的是CDH版本Hadoop，下载地址为：http://archive.cloudera.com/cdh5/cdh/5/
+
+```shell
+# tar -zvxf hadoop-2.6.0-cdh5.15.2.tar.gz
+```
+
+### 3.2 配置环境变量
+
+编辑`profile`文件：
+
+```shell
+# vim /etc/profile
+```
+
+增加如下配置：
+
+```
+export HADOOP_HOME=/usr/app/hadoop-2.6.0-cdh5.15.2
+export PATH=${HADOOP_HOME}/bin:$PATH
+```
+
+执行`source`命令，使得配置立即生效：
+
+```shell
+# source /etc/profile
+```
+
+### 3.3 修改配置
+
+进入`${HADOOP_HOME}/etc/hadoop`目录下，修改配置文件。各个配置文件内容如下：
+
+#### 1. hadoop-env.sh
+
+```shell
+# 指定JDK的安装位置
+export JAVA_HOME=/usr/java/jdk1.8.0_201/
+```
+
+#### 2. core-site.xml
+
+```xml
+
+
+
+ fs.defaultFS
+ hdfs://hadoop001:8020
+
+
+
+ hadoop.tmp.dir
+ /home/hadoop/tmp
+
+
+```
+
+#### 3. hdfs-site.xml
+
+```xml
+
+
+ dfs.namenode.name.dir
+ /home/hadoop/namenode/data
+
+
+
+ dfs.datanode.data.dir
+ /home/hadoop/datanode/data
+
+```
+
+#### 4. yarn-site.xml
+
+```xml
+
+
+ yarn.nodemanager.aux-services
+ mapreduce_shuffle
+
+
+
+ yarn.resourcemanager.hostname
+ hadoop001
+
+
+
+```
+
+#### 5. mapred-site.xml
+
+```xml
+
+
+
+ mapreduce.framework.name
+ yarn
+
+
+```
+
+#### 5. slaves
+
+配置所有从属节点的主机名或IP地址，每行一个。
+
+```properties
+hadoop001
+hadoop002
+hadoop003
+```
+
+### 3.4 分发程序
+
+将Hadoop安装包分发到其他两台服务器，分发后建议在这两台服务器上也配置一下Hadoop的环境变量。
+
+```shell
+# 将安装包分发到hadoop002
+scp -r /usr/app/hadoop-2.6.0-cdh5.15.2/ hadoop002:/usr/app/
+# 将安装包分发到hadoop003
+scp -r /usr/app/hadoop-2.6.0-cdh5.15.2/ hadoop003:/usr/app/
+```
+
+### 3.5 初始化
+
+在`Hadoop001`上执行namenode初始化命令：
+
+```
+hadoop namenode -format
+```
+
+### 3.6 启动集群
+
+进入到`Hadoop001`的`${HADOOP_HOME}/sbin`目录下，启动Hadoop。此时`hadoop002`和`hadoop003`上的相关服务也会被启动。
+
+```shell
+# 启动dfs服务
+start-dfs.sh
+# 启动yarn服务
+start-yarn.sh
+```
+
+### 3.7 查看集群
+
+在每台服务器上使用`jps`命令查看服务进程，或直接进入Web-UI界面进行查看，端口为`50070`。可以看到此时有三个可用的`Datanode`：
+
+
+
+点击`Live Nodes`进入，可以看到每个`DataNode`的详细情况：
+
+
+
+接着可以查看Yarn集群的情况，端口号为`8088` ：
+
+
+
+
+
+## 五、提交服务到集群
+
+提交作业到集群的方式和单机环境完全一致，这里以提交Hadoop内置的计算Pi的示例程序为例，在任何一个节点上执行都可以，命令如下：
+
+```shell
+hadoop jar /usr/app/hadoop-2.6.0-cdh5.15.2/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0-cdh5.15.2.jar pi 3 3
+```
+
diff --git a/notes/installation/Spark集群环境搭建.md b/notes/installation/Spark集群环境搭建.md
new file mode 100644
index 0000000..a95758c
--- /dev/null
+++ b/notes/installation/Spark集群环境搭建.md
@@ -0,0 +1,172 @@
+# 基于ZooKeeper搭建Spark高可用集群
+
+## 一、集群规划
+
+这里搭建一个3节点的Spark集群，其中三台主机上均部署`Worker`服务。同时为了保证高可用，除了在hadoop001上部署主`Master`服务外，还在hadoop002和hadoop003上分别部署备用的`Master`服务，Master服务由Zookeeper集群进行协调管理，如果主`Master`不可用，则备用`Master`会成为新的主`Master`。
+
+
+
+## 二、前置条件
+
+搭建Spark集群前，需要保证JDK环境、Zookeeper集群和Hadoop集群已经搭建，相关步骤可以参阅：
+
+- [Linux环境下JDK安装](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Linux下JDK安装.md)
+- [Zookeeper单机环境和集群环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Zookeeper单机环境和集群环境搭建.md)
+- [Hadoop集群环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Hadoop集群环境搭建.md)
+
+## 三、Spark集群搭建
+
+### 3.1 下载解压
+
+下载所需版本的Spark，官网下载地址：http://spark.apache.org/downloads.html
+
+ 
+
+
+
+下载后进行解压：
+
+```shell
+# tar -zxvf spark-2.2.3-bin-hadoop2.6.tgz
+```
+
+
+
+### 3.2 配置环境变量
+
+```shell
+# vim /etc/profile
+```
+
+添加环境变量：
+
+```shell
+export SPARK_HOME=/usr/app/spark-2.2.3-bin-hadoop2.6
+export PATH=${SPARK_HOME}/bin:$PATH
+```
+
+使得配置的环境变量立即生效：
+
+```shell
+# source /etc/profile
+```
+
+### 3.3 集群配置
+
+进入`${SPARK_HOME}/conf`目录，拷贝配置样本进行修改：
+
+#### 1. spark-env.sh
+
+```she
+ cp spark-env.sh.template spark-env.sh
+```
+
+```shell
+# 配置JDK安装位置
+JAVA_HOME=/usr/java/jdk1.8.0_201
+# 配置hadoop配置文件的位置
+HADOOP_CONF_DIR=/usr/app/hadoop-2.6.0-cdh5.15.2/etc/hadoop
+# 配置zookeeper地址
+SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=hadoop001:2181,hadoop002:2181,hadoop003:2181 -Dspark.deploy.zookeeper.dir=/spark"
+```
+
+#### 2. slaves
+
+```
+cp slaves.template slaves
+```
+
+配置所有Woker节点的位置：
+
+```properties
+hadoop001
+hadoop002
+hadoop003
+```
+
+### 3.4 安装包分发
+
+将Spark的安装包分发到其他服务器，分发后建议在这两台服务器上也配置一下Spark的环境变量。
+
+```shell
+scp -r /usr/app/spark-2.4.0-bin-hadoop2.6/ hadoop002:usr/app/
+scp -r /usr/app/spark-2.4.0-bin-hadoop2.6/ hadoop003:usr/app/
+```
+
+
+
+## 四、启动集群
+
+### 4.1 启动ZooKeeper集群
+
+分别到三台服务器上启动ZooKeeper服务：
+
+```shell
+ zkServer.sh start
+```
+
+### 4.2 启动Hadoop集群
+
+```shell
+# 启动dfs服务
+start-dfs.sh
+# 启动yarn服务
+start-yarn.sh
+```
+
+### 4.3 启动Spark集群
+
+进入hadoop001的` ${SPARK_HOME}/sbin`目录下，执行下面命令启动集群。执行命令后，会在hadoop001上启动`Maser`服务，会在`slaves`配置文件中配置的所有节点上启动`Worker`服务。
+
+```shell
+start-all.sh
+```
+
+分别在hadoop002和hadoop003上执行下面的命令，启动备用的`Master`服务：
+
+```shell
+# ${SPARK_HOME}/sbin 下执行
+start-master.sh
+```
+
+### 4.4 查看服务
+
+查看Spark的Web-UI页面，端口为`8080`。此时可以看到hadoop001上的Master节点处于`ALIVE`状态，并有3个可用的`Worker`节点。
+
+
+
+而hadoop002和hadoop003上的Master节点均处于`STANDBY`状态，没有可用的`Worker`节点。
+
+
+
+
+
+
+
+## 五、验证集群高可用
+
+此时可以使用`kill`命令杀死hadoop001上的`Master`进程，此时`备用Master`会中会有一个再次成为`主Master`，我这里是hadoop002，可以看到hadoop2上的`Master`经过`RECOVERING`后成为了新的`主Master`，并且获得了全部可以用的`Workers`。
+
+此时如果你再在hadoop001上使用`start-master.sh`启动Master，那么其会作为`备用Master`存在。
+
+
+
+Hadoop002上的`Master`成为`主Master`，并获得了全部可以用的`Workers`。
+
+
+
+## 六、提交作业
+
+和单机环境下的提交到Yarn上的命令完全一致，这里以Spark内置的计算Pi的样例程序为例，提交命令如下：
+
+```shell
+spark-submit \
+--class org.apache.spark.examples.SparkPi \
+--master yarn \
+--deploy-mode client \
+--executor-memory 1G \
+--num-executors 10 \
+/usr/app/spark-2.4.0-bin-hadoop2.6/examples/jars/spark-examples_2.11-2.4.0.jar \
+100
+```
+
diff --git a/pictures/hadoop集群规划.png b/pictures/hadoop集群规划.png
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