Spark的Standalone和On Yarn的环境搭建

Standalone（独立模式）

注意：伪分布式集群环境搭建

准备工作

• 准备物理虚拟机一台

  版本：CentOS7

• 配置网络

  # 如果需要配置双网卡，需要添加网卡硬件支持
  vi /etc/sysconfig/network-scripts/ifcfg-ens33
  
  1. 将ip的分配方式改为static
  2. 添加 IPADDR=静态IP地址
  3. 添加 NETMASK=255.255.255.0
  4. 网卡开机自启动 ONBOOT=yes
  

• 关闭防火墙

  [root@bogon ~]# systemctl stop firewalld
  [root@bogon ~]# systemctl disable  firewalld
  Removed symlink /etc/systemd/system/multi-user.target.wants/firewalld.service.
  Removed symlink /etc/systemd/system/dbus-org.fedoraproject.FirewallD1.service.
  

• 修改主机名

  [root@bogon ~]# vi /etc/hostname
  Spark
  

• 配置hosts映射

  [root@Spark ~]# vi /etc/hosts
  127.0.0.1   localhost localhost.localdomain localhost4 localhost4.localdomain4
  ::1         localhost localhost.localdomain localhost6 localhost6.localdomain6
  192.168.11.100  Spark
  [root@Spark ~]# ping Spark
  

• 安装vim编辑器

  [root@Spark ~]# yum install -y vim
  

安装Hadoop

• 配置SSH免密登陆

  [root@Spark ~]# ssh-keygen -t rsa
  Generating public/private rsa key pair.
  Enter file in which to save the key (/root/.ssh/id_rsa):
  Created directory '/root/.ssh'.
  Enter passphrase (empty for no passphrase):
  Enter same passphrase again:
  Your identification has been saved in /root/.ssh/id_rsa.
  Your public key has been saved in /root/.ssh/id_rsa.pub.
  The key fingerprint is:
  SHA256:KSQFcIduz5Fp5gS+C8FGJMq6v6GS+3ouvjcu4yT0qM4 root@Spark
  The key's randomart image is:
  +---[RSA 2048]----+
  |..o.ooo          |
  |o...oo           |
  |.+ o...o         |
  |. + +oB  .       |
  |.o o O..S        |
  |..+ . +.         |
  |o+.o .           |
  |O=.+.            |
  |XE@o.            |
  +----[SHA256]-----+
  
  [root@Spark ~]# ssh-copy-id Spark
  /usr/bin/ssh-copy-id: INFO: Source of key(s) to be installed: "/root/.ssh/id_rsa.pub"
  The authenticity of host 'spark (192.168.11.100)' can't be established.
  ECDSA key fingerprint is SHA256:nLUImdu8ZPpknaVGhMQLRROdZ8ZfJCRC+lhqCv6QuF0.
  ECDSA key fingerprint is MD5:7e:a3:1f:b0:e3:c7:51:7c:24:70:e3:24:b9:ac:45:27.
  Are you sure you want to continue connecting (yes/no)? yes
  /usr/bin/ssh-copy-id: INFO: attempting to log in with the new key(s), to filter out any that are already installed
  /usr/bin/ssh-copy-id: INFO: 1 key(s) remain to be installed -- if you are prompted now it is to install the new keys
  root@spark's password:
  
  Number of key(s) added: 1
  
  Now try logging into the machine, with:   "ssh 'Spark'"
  and check to make sure that only the key(s) you wanted were added.
  
  
  [root@Spark ~]# ssh Spark
  Last login: Tue Oct 29 11:32:59 2019 from 192.168.11.1
  [root@Spark ~]# exit
  登出
  Connection to spark closed.
  

• 安装JDK

  [root@Spark ~]# rpm -ivh jdk-8u171-linux-x64.rpm
  准备中...                          ################################# [100%]
  正在升级/安装...
     1:jdk1.8-2000:1.8.0_171-fcs        ################################# [100%]
  Unpacking JAR files...
          tools.jar...
          plugin.jar...
          javaws.jar...
          deploy.jar...
          rt.jar...
          jsse.jar...
          charsets.jar...
          localedata.jar...
  

• 安装Hadoop

  [root@Spark ~]# tar -zxf hadoop-2.9.2.tar.gz -C /usr
  [root@Spark ~]# cd /usr/hadoop-2.9.2/
  [root@Spark hadoop-2.9.2]# ll
  总用量 128
  drwxr-xr-x. 2 501 dialout    194 11月 13 2018 bin
  drwxr-xr-x. 3 501 dialout     20 11月 13 2018 etc
  drwxr-xr-x. 2 501 dialout    106 11月 13 2018 include
  drwxr-xr-x. 3 501 dialout     20 11月 13 2018 lib
  drwxr-xr-x. 2 501 dialout    239 11月 13 2018 libexec
  -rw-r--r--. 1 501 dialout 106210 11月 13 2018 LICENSE.txt
  -rw-r--r--. 1 501 dialout  15917 11月 13 2018 NOTICE.txt
  -rw-r--r--. 1 501 dialout   1366 11月 13 2018 README.txt
  drwxr-xr-x. 3 501 dialout   4096 11月 13 2018 sbin
  drwxr-xr-x. 4 501 dialout     31 11月 13 2018 share
  

• 修改HDFS的配置文件

  [root@Spark hadoop-2.9.2]# vim /usr/hadoop-2.9.2/etc/hadoop/core-site.xml
  
  <property>
      <name>fs.defaultFSname>
      <value>hdfs://Spark:9000value>
  property>
  
  <property>
      <name>hadoop.tmp.dirname>
      <value>/usr/hadoop-2.9.2/hadoop-${user.name}value>
  property>
  
  
  [root@Spark hadoop-2.9.2]# vim /usr/hadoop-2.9.2/etc/hadoop/hdfs-site.xml
  
  
  <property>
      <name>dfs.replicationname>
      <value>1value>
  property>
  
  <property>
      <name>dfs.namenode.secondary.http-addressname>
      <value>Spark:50090value>
  property>
  
  <property>
          <name>dfs.datanode.max.xcieversname>
          <value>4096value>
  property>
  
  <property>
          <name>dfs.datanode.handler.countname>
          <value>6value>
  property>
  
  [root@Spark hadoop-2.9.2]# vim /usr/hadoop-2.9.2/etc/hadoop/slaves
  Spark
  

• 配置JDK和Hadoop的环境变量

  [root@Spark hadoop-2.9.2]# vim /root/.bashrc
  
  JAVA_HOME=/usr/java/latest
  PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
  CLASSPATH=.
  export HADOOP_HOME
  export JAVA_HOME
  export PATH
  export CLASSPATH
  [root@Spark hadoop-2.9.2]# source /root/.bashrc
  

• 启动HDFS分布式文件系统，Yarn暂时不用配置

  [root@Spark hadoop-2.9.2]# hdfs namenode -format
  
  [root@Spark hadoop-2.9.2]# start-dfs.sh
  Starting namenodes on [Spark]
  Spark: starting namenode, logging to /usr/hadoop-2.9.2/logs/hadoop-root-namenode-Spark.out
  Spark: starting datanode, logging to /usr/hadoop-2.9.2/logs/hadoop-root-datanode-Spark.out
  Starting secondary namenodes [Spark]
  Spark: starting secondarynamenode, logging to /usr/hadoop-2.9.2/logs/hadoop-root-secondarynamenode-Spark.out
  [root@Spark hadoop-2.9.2]# jps
  10400 Jps
  9874 NameNode
  10197 SecondaryNameNode
  10029 DataNode
  

• 访问HDFS WebUI：http://192.168.11.100:50070

安装Spark

• 安装

  [root@Spark ~]# tar -zxf spark-2.4.4-bin-without-hadoop.tgz -C /usr
  [root@Spark ~]# mv /usr/spark-2.4.4-bin-without-hadoop/ /usr/spark-2.4.4
  [root@Spark ~]# cd /usr/spark-2.4.4/
  [root@Spark spark-2.4.4]# ll
  总用量 100
  drwxr-xr-x. 2 1000 1000  4096 8月  28 05:52 bin  # spark操作指令目录
  drwxr-xr-x. 2 1000 1000   230 8月  28 05:52 conf  # 配置文件
  drwxr-xr-x. 5 1000 1000    50 8月  28 05:52 data  # 测试样例数据
  drwxr-xr-x. 4 1000 1000    29 8月  28 05:52 examples  # 示例Demo
  drwxr-xr-x. 2 1000 1000  8192 8月  28 05:52 jars  # 运行依赖类库
  drwxr-xr-x. 4 1000 1000    38 8月  28 05:52 kubernetes  # spark 容器支持
  -rw-r--r--. 1 1000 1000 21316 8月  28 05:52 LICENSE
  drwxr-xr-x. 2 1000 1000  4096 8月  28 05:52 licenses
  -rw-r--r--. 1 1000 1000 42919 8月  28 05:52 NOTICE
  drwxr-xr-x. 7 1000 1000   275 8月  28 05:52 python
  drwxr-xr-x. 3 1000 1000    17 8月  28 05:52 R
  -rw-r--r--. 1 1000 1000  3952 8月  28 05:52 README.md
  -rw-r--r--. 1 1000 1000   142 8月  28 05:52 RELEASE
  drwxr-xr-x. 2 1000 1000  4096 8月  28 05:52 sbin  # spark系统服务相关指令
  drwxr-xr-x. 2 1000 1000    42 8月  28 05:52 yarn  # spark yarn集群支持
  

• Spark简单配置

  [root@Spark spark-2.4.4]# cp conf/spark-env.sh.template  conf/spark-env.sh
  [root@Spark spark-2.4.4]# cp conf/slaves.template conf/slaves
  [root@Spark spark-2.4.4]# vim conf/spark-env.sh
  SPARK_WORKER_INSTANCES=2
  SPARK_MASTER_HOST=Spark
  SPARK_MASTER_PORT=7077
  SPARK_WORKER_CORES=4
  SPARK_WORKER_MEMORY=2g
  LD_LIBRARY_PATH=/usr/hadoop-2.9.2/lib/native
  SPARK_DIST_CLASSPATH=$(hadoop classpath)
  export SPARK_MASTER_HOST
  export SPARK_MASTER_PORT
  export SPARK_WORKER_CORES
  export SPARK_WORKER_MEMORY
  export LD_LIBRARY_PATH
  export SPARK_DIST_CLASSPATH
  export SPARK_WORKER_INSTANCES
  
  [root@Spark spark-2.4.4]# vim conf/slaves
  Spark
  

• 启动Spark Standalone集群

  [root@Spark spark-2.4.4]# sbin/start-all.sh
  starting org.apache.spark.deploy.master.Master, logging to /usr/spark-2.4.4/logs/spark-root-org.apache.spark.deploy.master.Master-1-Spark.out
  Spark: starting org.apache.spark.deploy.worker.Worker, logging to /usr/spark-2.4.4/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-Spark.out
  Spark: starting org.apache.spark.deploy.worker.Worker, logging to /usr/spark-2.4.4/logs/spark-root-org.apache.spark.deploy.worker.Worker-2-Spark.out
  [root@Spark spark-2.4.4]#
  [root@Spark spark-2.4.4]#
  [root@Spark spark-2.4.4]# jps
  16433 Jps
  9874 NameNode
  16258 Worker
  10197 SecondaryNameNode
  16136 Master
  16328 Worker
  10029 DataNode
  

• 访问Spark WebUI：http://192.168.11.100:8080/

• Spark shell 命令窗口

  –master 远程连接的spark应用的运行环境 spark://host:port, mesos://host:port, yarn, k8s://https://host:port, or local (Default: local[*]).

  –total-executor-cores NUM Executor 计算JVM进程 cores： 线程

  [root@Spark spark-2.4.4]# bin/spark-shell --master spark://Spark:7077   --total-executor-cores 2
  Setting default log level to "WARN".
  To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
  Spark context Web UI available at http://Spark:4040
  Spark context available as 'sc' (master = spark://Spark:7077, app id = app-20191029144958-0000).
  Spark session available as 'spark'.
  Welcome to
        ____              __
       / __/__  ___ _____/ /__
      _\ \/ _ \/ _ `/ __/  '_/
     /___/ .__/\_,_/_/ /_/\_\   version 2.4.4
        /_/
  
  Using Scala version 2.11.12 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_171)
  Type in expressions to have them evaluated.
  Type :help for more information.
  

• Spark 版的WordCount

  # 1. 准备测试数据 并上传到HDFS中
  root@Spark ~]# vim test.txt
  [root@Spark ~]# hdfs dfs -put test.txt /
  
  # 2. 开发Spark 批处理应用
  scala> sc.textFile("hdfs://Spark:9000/test.txt").flatMap(_.split(" ")).map((_,1)).groupBy(_._1).map(t=>(t._1,t._2.size)).saveAsTextFile("hdfs://Spark:9000/result")
  
  # 3. 计算的结果需要保存到HDFS
  [root@Spark ~]# more test.txt
  Hello World
  Hello Spark
  Hello Scala
  Hello Kafka
  Kafka Good
  [root@Spark ~]# hdfs dfs -cat /result/*
  (Kafka,2)
  (Hello,4)
  (World,1)
  (Scala,1)
  (Spark,1)
  (Good,1)
  

Spark On Yarn

将spark应用发布运行在Hadoop Yarn集群中

YARN：分布式资源管理和调度系统

准备工作

• 克隆机

• 修改ens33网卡的ip地址： 192.168.11.101

• 修改hostname： SparkOnYarn

• 修改hosts文件： 192.168.11.101 SparkOnYarn

• 配置SSH免密登陆

  [root@SparkOnYarn ~]# ssh-keygen -t rsa
  Generating public/private rsa key pair.
  Enter file in which to save the key (/root/.ssh/id_rsa):
  Created directory '/root/.ssh'.
  Enter passphrase (empty for no passphrase):
  Enter same passphrase again:
  Your identification has been saved in /root/.ssh/id_rsa.
  Your public key has been saved in /root/.ssh/id_rsa.pub.
  The key fingerprint is:
  SHA256:YfN4vpOtE5dk3+cGQ1LsnnmfPH8zRb7HXnFt+xcruKE root@SparkOnYarn
  The key's randomart image is:
  +---[RSA 2048]----+
  |             .   |
  |              o  |
  |        +    o   |
  |       . =  + o o|
  |        S oo * B+|
  |         o. o BoX|
  |          .*. .BX|
  |          =oo. O@|
  |         Eo=. .o%|
  +----[SHA256]-----+
  [root@SparkOnYarn ~]# ssh-copy-id SparkOnYarn
  /usr/bin/ssh-copy-id: INFO: Source of key(s) to be installed: "/root/.ssh/id_rsa.pub"
  /usr/bin/ssh-copy-id: INFO: attempting to log in with the new key(s), to filter out any that are already installed
  /usr/bin/ssh-copy-id: INFO: 1 key(s) remain to be installed -- if you are prompted now it is to install the new keys
  root@sparkonyarn's password:
  
  Number of key(s) added: 1
  
  Now try logging into the machine, with:   "ssh 'SparkOnYarn'"
  and check to make sure that only the key(s) you wanted were added.
  

• 安装JDK

  [root@SparkOnYarn ~]# rpm -ivh jdk-8u171-linux-x64.rpm
  准备中...                          ################################# [100%]
  正在升级/安装...
     1:jdk1.8-2000:1.8.0_171-fcs        ################################# [100%]
  Unpacking JAR files...
          tools.jar...
          plugin.jar...
          javaws.jar...
          deploy.jar...
          rt.jar...
          jsse.jar...
          charsets.jar...
          localedata.jar...
  

• 安装Hadoop（HDFS&YARN）

  [root@SparkOnYarn ~]# tar -zxf hadoop-2.9.2.tar.gz -C /usr
  [root@SparkOnYarn ~]# cd /usr/hadoop-2.9.2/
  

• 修改HDFS配置文件

  [root@SparkOnYarn ~]# vi /usr/hadoop-2.9.2/etc/hadoop/core-site.xml
  
  <!--nn访问入口-->
  <property>
      <name>fs.defaultFS</name>
      <value>hdfs://SparkOnYarn:9000</value>
  </property>
  <!--hdfs工作基础目录-->
  <property>
      <name>hadoop.tmp.dir</name>
      <value>/usr/hadoop-2.9.2/hadoop-${user.name}</value>
  </property>
  
  [root@SparkOnYarn ~]# vi /usr/hadoop-2.9.2/etc/hadoop/hdfs-site.xml
  <!--block副本因子-->
  <property>
      <name>dfs.replication</name>
      <value>1</value>
  </property>
  <!--配置Sencondary namenode所在物理主机-->
  <property>
      <name>dfs.namenode.secondary.http-address</name>
      <value>SparkOnYarn:50090</value>
  </property>
  <!--设置datanode最大文件操作数-->
  <property>
          <name>dfs.datanode.max.xcievers</name>
          <value>4096</value>
  </property>
  <!--设置datanode并行处理能力-->
  <property>
          <name>dfs.datanode.handler.count</name>
          <value>6</value>
  </property>
  
  [root@SparkOnYarn ~]# vi /usr/hadoop-2.9.2/etc/hadoop/slaves
  SparkOnYarn
  
  #-----------------------------------------------------------------------
  
  [root@SparkOnYarn ~]# vi /usr/hadoop-2.9.2/etc/hadoop/yarn-site.xml
  <!-- Site specific YARN configuration properties -->
  
  <!--配置MapReduce计算框架的核心实现Shuffle-洗牌-->
  <property>
      <name>yarn.nodemanager.aux-services</name>
      <value>mapreduce_shuffle</value>
  </property>
  <!--配置资源管理器所在的目标主机-->
  <property>
      <name>yarn.resourcemanager.hostname</name>
      <value>SparkOnYarn</value>
  </property>
  <!--关闭物理内存检查-->
  <property>
          <name>yarn.nodemanager.pmem-check-enabled</name>
          <value>false</value>
  </property>
  <!--关闭虚拟内存检查-->
  <property>
          <name>yarn.nodemanager.vmem-check-enabled</name>
          <value>false</value>
  </property>
  
  [root@SparkOnYarn ~]# cp /usr/hadoop-2.9.2/etc/hadoop/mapred-site.xml.template /usr/hadoop-2.9.2/etc/hadoop/mapred-site.xml
  <property>
      <name>mapreduce.framework.name</name>
      <value>yarn</value>
  </property>
  
  [root@SparkOnYarn ~]# vim .bashrc
  HADOOP_HOME=/usr/hadoop-2.9.2
  JAVA_HOME=/usr/java/latest
  CLASSPATH=.
  PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
  export JAVA_HOME
  export CLASSPATH
  export PATH
  export HADOOP_HOME
  [root@SparkOnYarn ~]# source .bashrc
  

• 启动Hadoop相关服务

  [root@SparkOnYarn ~]# hdfs namenode -format
  [root@SparkOnYarn ~]# start-dfs.sh
  Starting namenodes on [SparkOnYarn]
  SparkOnYarn: starting namenode, logging to /usr/hadoop-2.9.2/logs/hadoop-root-namenode-SparkOnYarn.out
  SparkOnYarn: starting datanode, logging to /usr/hadoop-2.9.2/logs/hadoop-root-datanode-SparkOnYarn.out
  Starting secondary namenodes [SparkOnYarn]
  SparkOnYarn: starting secondarynamenode, logging to /usr/hadoop-2.9.2/logs/hadoop-root-secondarynamenode-SparkOnYarn.out
  [root@SparkOnYarn ~]# jps
  9634 Jps
  9270 DataNode
  9115 NameNode
  9469 SecondaryNameNode
  [root@SparkOnYarn ~]#
  [root@SparkOnYarn ~]# start-yarn.sh
  starting yarn daemons
  starting resourcemanager, logging to /usr/hadoop-2.9.2/logs/yarn-root-resourcemanager-SparkOnYarn.out
  SparkOnYarn: starting nodemanager, logging to /usr/hadoop-2.9.2/logs/yarn-root-nodemanager-SparkOnYarn.out
  [root@SparkOnYarn ~]# jps
  9844 NodeManager
  9270 DataNode
  9115 NameNode
  9723 ResourceManager
  10156 Jps
  9469 SecondaryNameNode
  

• 配置安装Spark

  [root@SparkOnYarn ~]# tar -zxf spark-2.4.4-bin-without-hadoop.tgz -C /usr
  [root@SparkOnYarn ~]# mv /usr/spark-2.4.4-bin-without-hadoop/ /usr/spark-2.4.4
  [root@SparkOnYarn ~]# cd /usr/spark-2.4.4/
  [root@SparkOnYarn spark-2.4.4]# cp conf/spark-env.sh.template conf/spark-env.sh
  [root@SparkOnYarn spark-2.4.4]# cp conf/slaves.template conf/slaves
  [root@SparkOnYarn spark-2.4.4]# vim conf/spark-env.sh
  
  HADOOP_CONF_DIR=/usr/hadoop-2.9.2/etc/hadoop
  YARN_CONF_DIR=/usr/hadoop-2.9.2/etc/hadoop
  SPARK_EXECUTOR_CORES=4
  SPARK_EXECUTOR_MEMORY=1g
  SPARK_DRIVER_MEMORY=1g
  LD_LIBRARY_PATH=/usr/hadoop-2.9.2/lib/native
  SPARK_DIST_CLASSPATH=$(hadoop classpath)
  SPARK_HISTORY_OPTS="-Dspark.history.fs.logDirectory=hdfs:///spark-logs"
  export HADOOP_CONF_DIR
  export YARN_CONF_DIR
  export SPARK_EXECUTOR_CORES
  export SPARK_DRIVER_MEMORY
  export SPARK_EXECUTOR_MEMORY
  export LD_LIBRARY_PATH
  export SPARK_DIST_CLASSPATH
  # 开启historyserver optional
  export SPARK_HISTORY_OPTS
  
  [root@SparkOnYarn spark-2.4.4]# vim conf/spark-defaults.conf
  # 开启spark history server日志记录功能
  spark.eventLog.enabled=true
  spark.eventLog.dir=hdfs:///spark-logs
  
  [root@SparkOnYarn spark-2.4.4]# hdfs dfs -mkdir /spark-logs
  [root@SparkOnYarn spark-2.4.4]# sbin/start-history-server.sh
  starting org.apache.spark.deploy.history.HistoryServer, logging to /usr/spark-2.4.4/logs/spark-root-org.apache.spark.deploy.history.HistoryServer-1-SparkOnYarn.out
  [root@SparkOnYarn spark-2.4.4]# jps
  13968 Jps
  9844 NodeManager
  9270 DataNode
  13880 HistoryServer
  9115 NameNode
  9723 ResourceManager
  9469 SecondaryNameNode
  

• 测试SparkOnYarn

  Spark Shell

  –executor-cores 每一个Executors JVM进程中的核心（线程）数量

  –num-executors 总共 有多少个Executors JVM,进程

  – master yarn

  [root@SparkOnYarn spark-2.4.4]# bin/spark-shell --master yarn --executor-cores 2 --num-executors 2
  Setting default log level to "WARN".
  To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
  19/10/29 16:41:31 WARN yarn.Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
  Spark context Web UI available at http://SparkOnYarn:4040
  Spark context available as 'sc' (master = yarn, app id = application_1572337552150_0001).
  Spark session available as 'spark'.
  Welcome to
        ____              __
       / __/__  ___ _____/ /__
      _\ \/ _ \/ _ `/ __/  '_/
     /___/ .__/\_,_/_/ /_/\_\   version 2.4.4
        /_/
  
  Using Scala version 2.11.12 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_171)
  Type in expressions to have them evaluated.
  Type :help for more information.
  
  scala> sc.textFile("hdfs://SparkOnYarn:9000/test.txt").flatMap(_.split(" ")).map((_,1)).groupBy(_._1).map(t=>(t._1,t._2.size)).saveAsTextFile("hdfs://SparkOnYarn:9000/result")
  
  

  Spark Submit

  [root@SparkOnYarn spark-2.4.4]# bin/spark-submit --master yarn --class com.baizhi.WordCountApplication3 --executor-cores 2 --num-executors 1 /root/spark-day1-1.0-SNAPSHOT.jar
  [root@SparkOnYarn hadoop-2.9.2]# hdfs dfs -cat /result2/*
  (are,4)
  (you,4)
  (how,2)
  (bj,1)
  (welcome,1)
  (from,1)
  (ok,1)
  (old,1)
  (to,1)
  (where,1)