Spark3.1.2 on TDH622_大数据系统

一、在linux搭建spark环境

1.下载spark

spark官方下载地址：http://spark.apache.org/downloads.html 。这里选择spark-3.1.2-bin-hadoop2.7版本。

2.上传spark，下载TDH客户端

上传 spark-3.1.2-bin-hadoop2.7.tgz 至linux的/opt目录下
在manager下载TDH客户端，上传至/opt目录下
解压spark。tar -zxvf spark-3.1.2-bin-hadoop2.7.tgz
解压客户端。tar -xvf tdh-client.tar

3.配置spark环境变量

cd ${spark_home}/conf （注：${spark_home}即/opt/spark-3.1.2-bin-hadoop2.7目录）
cp spark-env.sh.template spark-env.sh
vim spark-env.sh
在末尾加上如下配置：

export SPARK_DIST_CLASSPATH=$(hadoop classpath)
export HADOOP_CONF_DIR=/opt/TDH-Client/conf/hdfs1
export YARN_CONF_DIR=/opt/TDH-Client/conf/yarn1
export HIVE_CONF_DIR=/opt/TDH-Client/conf/inceptor1

4.修改spark默认配置

cd ${spark_home}/conf
cp spark-defaults.conf.template spark-defaults.conf
cp /etc/inceptor1/conf/inceptor.keytab ./
klist -kt inceptor.keytab ,记录下Principal
vim spark-defaults.conf
在末尾加上如下配置：（注：若集群未开安全，则不需要设置spark.kerberos.keytab和spark.kerberos.principal这两个参数）

spark.yarn.historyServer.address=tdh01:18080
spark.yarn.historyServer.allowTracking=true
spark.kerberos.keytab           /opt/spark-3.1.2-bin-hadoop2.7/conf/inceptor.keytab
spark.kerberos.principal               hive/tdh01@TDH
spark.executorEnv.JAVA_HOME         /usr/java/jdk1.8.0_25
spark.yarn.appMasterEnv.JAVA_HOME   /usr/java/jdk1.8.0_25

5.拷贝hive-site.xml文件

cp /opt/TDH-Client/conf/inceptor1/hive-site.xml ${spark_home}/conf/

6.配置log4j.properties

cd ${spark_home}/conf
cp log4j.properties.template log4j.properties

7.更换spark中的开源jar包

共需更换15个hadoop相关jar包，更换1个zookeeper相关jar包，新增一个guardian相关jar包
旧jar包：

hadoop-annotations-2.7.4.jar
hadoop-auth-2.7.4.jar
hadoop-client-2.7.4.jar
hadoop-common-2.7.4.jar
hadoop-hdfs-2.7.4.jar
hadoop-mapreduce-client-app-2.7.4.jar
hadoop-mapreduce-client-common-2.7.4.jar
hadoop-mapreduce-client-core-2.7.4.jar
hadoop-mapreduce-client-jobclient-2.7.4.jar
hadoop-mapreduce-client-shuffle-2.7.4.jar
hadoop-yarn-api-2.7.4.jar
hadoop-yarn-client-2.7.4.jar
hadoop-yarn-common-2.7.4.jar
hadoop-yarn-server-common-2.7.4.jar
hadoop-yarn-server-web-proxy-2.7.4.jar
zookeeper-3.4.14.jar

新jar包：

hadoop-annotations-2.7.2-transwarp-6.2.2.jar
hadoop-auth-2.7.2-transwarp-6.2.2.jar
hadoop-client-2.7.2-transwarp-6.2.2.jar
hadoop-common-2.7.2-transwarp-6.2.2.jar
hadoop-hdfs-2.7.2-transwarp-6.2.2.jar
hadoop-mapreduce-client-app-2.7.2-transwarp-6.2.2.jar
hadoop-mapreduce-client-common-2.7.2-transwarp-6.2.2.jar
hadoop-mapreduce-client-core-2.7.2-transwarp-6.2.2.jar
hadoop-mapreduce-client-jobclient-2.7.2-transwarp-6.2.2.jar
hadoop-mapreduce-client-shuffle-2.7.2-transwarp-6.2.2.jar
hadoop-yarn-api-2.7.2-transwarp-6.2.2.jar
hadoop-yarn-client-2.7.2-transwarp-6.2.2.jar
hadoop-yarn-common-2.7.2-transwarp-6.2.2.jar
hadoop-yarn-server-common-2.7.2-transwarp-6.2.2.jar
hadoop-yarn-server-web-proxy-2.7.2-transwarp-6.2.2.jar
zookeeper-3.4.5-transwarp-6.2.2.jar
federation-utils-guardian-3.1.3.jar

jar包位置

/opt/TDH-Client/hyperbase/lib ---- 14个hadoop jar包
/opt/TDH-Client/hadoop/hadoop-yarn ---- 1个hadoop jar包
/opt/TDH-Client/hadoop/hadoop/lib ---- 1个zookeeper jar包
/opt/TDH-Client/inceptor/lib ---- 1个guardian jar包

二、idea开发spark程序 1.新建maven项目 2.加入如下maven依赖

spark版本要一致，即3.1.2

3.编写代码 4.打包

Project Structure – Artifacts
Build – Build Artifacts – Build
在out目录下可以看到打包好的jar

5.将打包好的jar上传到{spark_home}下三、在TDH集群上运行spark任务 1.local模式

/opt/spark-3.1.2-bin-hadoop2.7/bin/spark-submit --master local[3] --class io.transwarp.demo.MySpark /opt/spark-3.1.2-bin-hadoop2.7/spark-demo.jar

2.yarn-client模式

/opt/spark-3.1.2-bin-hadoop2.7/bin/spark-submit --master yarn --deploy-mode client --class io.transwarp.demo.MySpark /opt/spark-3.1.2-bin-hadoop2.7/spark-demo.jar

3.yarn-cluster模式

/opt/spark-3.1.2-bin-hadoop2.7/bin/spark-submit --master yarn --deploy-mode cluster --class io.transwarp.demo.MySpark /opt/spark-3.1.2-bin-hadoop2.7/spark-demo.jar

四、pycharm搭建pyspark开发环境 1.下载spark并解压

官网下载spark相应版本，解压缩到某个目录。例：C:sparkspark-3.1.2-bin-hadoop2.7

2.新建python工程

新建python工程，选择python版本。我选择的是python3.7版本

3.pycharm上下载相关的依赖，比如numpy等

4.新建demo.py 5.打开Run-Edit Configurations.如图点击：

6.配置环境变量

7.add content root

在Settings-perferences中的project structure中点击右边的“add content root”，添加py4j-some-version.zip和pyspark.zip。（这两个文件都在C:sparkspark-3.1.2-bin-hadoop2.7pythonlib下）。

8.开发程序，本地run测试五、搭建pyspark运行环境 1.参考第一章节 2.需补充jar包

拷贝TDH-Client/hadoop/hadoop/lib/jsch-0.1.54.jar 到{spark_home}/jars目录

六、linux创建python虚拟环境 1.下载安装anaconda

a.官方下载地址：https://www.anaconda.com/download/#linux
b.上传到linux服务器的目录下
c.sh Anaconda3-2021.05-Linux-x86_64.sh
d.一路回车和yes

2.给anaconda配置环境变量

在/etc/profile增加如下内容并source:
export PATH=$PATH:/root/anaconda3/bin

3.增加清华的conda源

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --set show_channel_urls yes

4.创建虚拟环境

这里选择python3.7和1.16.4版本的numpy
conda create --name py3env --quiet --copy --yes python=3.7 numpy=1.16.4

5.查看新创建的虚拟环境

/root/anaconda3/envs目录下，可以看到建好的py3env文件夹，即python虚拟环境。将依赖程序mymath.py拷贝到/root/anaconda3/envs/py3env/lib/python3.7目录下。

6.压缩虚拟环境，拷贝到{spark_home}目录下

zip py3env.zip py3env/
cp py3env.zip /opt/spark-3.1.2-bin-hadoop2.7/

七、在TDH集群上运行pyspark任务 1.local模式

export PYSPARK_DRIVER_PYTHON=/root/anaconda3/envs/py3env/bin/python
/opt/spark-3.1.2-bin-hadoop2.7/bin/spark-submit --master local[2]   /opt/spark-3.1.2-bin-hadoop2.7/demo.py

2.yarn-client模式

export PYSPARK_DRIVER_PYTHON=/root/anaconda3/envs/py3env/bin/python
export PYSPARK_PYTHON=py3env/py3env/bin/python
/opt/spark-3.1.2-bin-hadoop2.7/bin/spark-submit --master yarn --deploy-mode client --archives /opt/spark-3.1.2-bin-hadoop2.7/py3env.zip#py3env /opt/spark-3.1.2-bin-hadoop2.7/demo.py

3.yarn-cluster模式

export PYSPARK_DRIVER_PYTHON=py3env/py3env/bin/python
export PYSPARK_PYTHON=py3env/py3env/bin/python
/opt/spark-3.1.2-bin-hadoop2.7/bin/spark-submit --master yarn --deploy-mode cluster --archives /opt/spark-3.1.2-bin-hadoop2.7/py3env.zip#py3env /opt/spark-3.1.2-bin-hadoop2.7/demo.py

Spark3.1.2 on TDH622

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