标签：创建文件 pil -- 而不是 tip 导致 fbx meta format
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软件版本:
CentOS release 6.6 (Final) Hdk-8u131-linux-x64 Hadoop-2.7.3 Hive-2.1.1 Apache-flume-1.7.0-bin
下载JDK、Hadoop、Hive、Flume:
[root@Hadoop-Data01 soft]# wget --no-check-certificate --no-cookies --header "Cookie: oraclelicense=accept-securebackup-cookie" http://download.oracle.com/otn-pub/java/jdk/8u131-b11/d54c1d3a095b4ff2b6607d096fa80163/jdk-8u131-linux-x64.tar.gz [root@Hadoop-Data01 soft]# wget http://apache.fayea.com/hadoop/common/hadoop-2.7.3/hadoop-2.7.3-src.tar.gz [root@Hadoop-Data01 soft]# wget http://apache.fayea.com/hive/hive-2.1.1/apache-hive-2.1.1-bin.tar.gz
修改主机名，/etc/hosts文件，确保各主机DNS解析正确：
[root@Hadoop-Data01 ~]# cat /etc/hosts 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 ::1 localhost localhost.localdomain localhost6 localhost6.localdomain6 192.168.0.194 Hadoop-Data01 192.168.0.195 Hadoop-Data02 192.168.0.196 Hadoop-Data03 注：Slave服务器内容同上.
配置Hadoop-Master、Hadoop-Slave主机间的免key登录：
[root@Hadoop-Data01 ~]# vim /etc/ssh/sshd_config RSAAuthentication yes PubkeyAuthentication yes 注：这里可以通过sed：sed -i ‘47,48s/^#//g‘ /etc/ssh/sshd_config [root@Hadoop-Data01 ~]# ssh-keygen -t rsa [root@Hadoop-Data01 .ssh]# cat authorized_keys ssh-rsa AAAAB3NzaC1yc2EAAAABIwAAAQEA2JGjCEwc+H3/5Y939DHSkhHYAO7qPjO86gyaqvlN2j1ZMUhdKhXUmTH0pBBwXIqp9jooTXxtIu55cuBvOeBD6eUKN5mH9rydRIXm8HEvb9nQzOvVghP1E9lBTGsGXkUWDo0KPkFYOhb2NguYibzVUgpUpAt0NY5iqdenXNqvDOWGhWqDsg/C6VnUzsxskiT9x2EROhddWQnYsObXxjOasgdGPngzZsJZPchRboS+HfvVF0uSyUjljtKsQqYOX2Nt0plO4t6VlcnZXvjDXKezJCNwGToFvvoiIHnjVu/akgtv/bpd8HZp1dZEj7cYnSFkqN5xdodg7TmtjAjobutU5Q== root@Hadoop-Data01 ssh-rsa AAAAB3NzaC1yc2EAAAABIwAAAQEAvQ3JZOtdfFvrsM/m6YwQQuGkOCpNt0+tw87tS4p1gB98ZAn+zaUnFMw5Gvo0i1KvHVaxmb0s1gqDjGDNVLQM5MB60emyVFHLs6DZBI5f4c0BiA17KfDRzlsfuTmuLdymmoj54OhPbEcH+mwo/N1UK9V0gqxAB9abC6UFT00MXXXJN1+qBkV9mUuFbXhn4m5/DCoEbIxvMlWghAsSrDtMaMtJYRumRvd7MLwwefdCYyQd8dZASE1Z8VP0K/BDRntWXCeKGCVMb4uJAnSdhN6ZcRme/Qlx0YCkPpQir3jgcblVW5RODNUyaIc+vUMp9UYagvK7nKKfWAGa/MPdyfu2nw== root@Hadoop-Data02 ssh-rsa AAAAB3NzaC1yc2EAAAABIwAAAQEA1pC5Py1aqbojVetakak3WmxJf4DgmTe1ci60tn9Hyq84kdAhw7z1lAQN544uPDDvl4XPki36Y13Hjl0P+S3g11iOi42FRugkBDmokqADZrUfp5tqWX8K9QvYMePoyiuQlnrGAyCpOiMmEAykBR6lVkNHgPAWThjU9eggt6dalMPiy/dDKZNemlWGHy8wdS5PyjVsIuDGgTtNLADn6OOaYcO/UWq78gqc1Nkq4mNxKSTYorh7taki9SKw4cq0NeggDFz7cZEewtgJdRla0W2ZKz8bgfuUSSntbN55/uCVUSgK+kurqRmklQ3sA3c9687BH1Lse5luDFJRaYo2wa5nlQ== root@Hadoop-Data03 注：合并三台服务器/root/.ssh/id_rsa.pub文件到authorized_keys [root@Hadoop-Data01 .ssh]# scp authorized_keys root@192.168.0.195:/root/.ssh/ [root@Hadoop-Data01 .ssh]# scp authorized_keys root@192.168.0.196:/root/.ssh/
在各个主机上安装JDK
[root@Hadoop-Data01 soft]# tar -xf jdk-8u131-linux-x64.tar.gz [root@Hadoop-Data01 soft]# \cp -r jdk1.8.0_131 /usr/local/ [root@Hadoop-Data01 soft]# cd /usr/local/ [root@Hadoop-Data01 local]# ln -s jdk1.8.0_131 jdk [root@Hadoop-Data01 ~]# vim /etc/profile >>>>> ulimit -n 10240 export JAVA_HOME=/usr/local/jdk export CLASSPATH=$CLASSPATH:$JAVA_HOME/lib:$JAVA_HOME/jre/lib export PATH=$JAVA_HOME/bin:$JAVA_HOME/jre/bin:$PATH [root@Hadoop-Data01 ~]# source /etc/profile [root@Hadoop-Data03 ~]# java -version java version "1.8.0_131" Java(TM) SE Runtime Environment (build 1.8.0_131-b11) Java HotSpot(TM) 64-Bit Server VM (build 25.131-b11, mixed mode)
安装hadoop
/usr/local/hadoop/etc/hadoop/core-site.xml配置文件： [root@Hadoop-Data01 soft]# tar -xf hadoop-2.7.3.tar.gz [root@Hadoop-Data01 soft]# mv hadoop-2.7.3 /usr/local/ [root@Hadoop-Data01 soft]# cd /usr/local/ [root@Hadoop-Data01 local]# ln -s hadoop-2.7.3 hadoop [root@Hadoop-Data01 hadoop]# vim core-site.xml >>>>> <configuration> <property> <name>fs.defaultFS</name> <value>hdfs://192.168.0.194:9000</value> </property> <property> <name>hadoop.tmp.dir</name> <value>file:/usr/local/hadoop/tmp</value> </property> <property> <name>io.file.buffer.size</name> <value>1024</value> </property> </configuration> 注： <fs.defaultFS>:默认文件系统的名称，URI形式，uri的scheme需要由(fs.SCHEME.impl)指定文件系统实现类；uri的authority部分用来指定host、port等；默认是本地文件系统。HA方式，这里设置服务名，例如：hdfs:// 192.168.0.194:9000，HDFS的客户端访问HDFS需要此参数； <hadoop.tmp.dir>:Hadoop的临时目录，其它目录会基于此路径，本地目录。只可以设置一个值；建议设置到一个足够空间的地方，而不是默认的/tmp下，服务端参数，修改需重启； <io.file.buffer.size>:在读写文件时使用的缓存大小，这个大小应该是内存Page的倍数，建议1M。 ---------- /usr/local/hadoop/etc/hadoop/hdfs-site.xml配置文件： [root@Hadoop-Data01 hadoop]# vim hdfs-site.xml >>>>> <configuration> <property> <name>dfs.namenode.name.dir</name> <value>file:/usr/local/hadoop/dfs/name</value> </property> <property> <name>dfs.datanode.data.dir</name> <value>file:/usr/local/hadoop/dfs/data</value> </property> <property> <name>dfs.replication</name> <value>2</value> </property> <property> <name>dfs.namenode.secondary.http-address</name> <value>192.168.0.194:9001</value> </property> <property> <name>dfs.webhdfs.enabled</name> <value>true</value> </property> </configuration> 注： <dfs.namenode.name.dir>:本地磁盘目录，NN存储fsimage文件的地方;可以是按逗号分隔的目录列表，fsimage文件会存储在全部目录，冗余安全;这里多个目录设定，最好在多个磁盘，另外，如果其中一个磁盘故障，不会导致系统故障，会跳过坏磁盘。由于使用了HA，建议仅设置一个,如果特别在意安全，可以设置2个; <dfs.datanode.data.dir>:本地磁盘目录，HDFS数据应该存储Block的地方。可以是逗号分隔的目录列表（典型的，每个目录在不同的磁盘），这些目录被轮流使用，一个块存储在这个目录，下一个块存储在下一个目录，依次循环；每个块在同一个机器上仅存储一份，不存在的目录被忽略；必须创建文件夹，否则被视为不存在； <dfs.replication>:数据块副本数，此值可以在创建文件是设定，客户端可以只有设定，也可以在命令行修改；不同文件可以有不同的副本数，默认值用于未指定时。 <dfs.namenode.secondary.http-address>:SNN的http服务地址，如果是0，服务将随机选择一个空闲端口，使用了HA后，就不再使用SNN； <dfs.webhdfs.enabled>:在NN和DN上开启WebHDFS (REST API)功能。 ---------- /usr/local/hadoop/etc/hadoop/mapred-site.xml配置文件： [root@Hadoop-Data01 hadoop]# cp mapred-site.xml.template mapred-site.xml [root@Hadoop-Data01 hadoop]# vim mapred-site.xml >>>>> <configuration> <property> <name>mapreduce.framework.name</name> <value>yarn</value> </property> <property> <name>mapreduce.jobhistory.address</name> <value>192.168.0.194:10020</value> </property> <property> <name>mapreduce.jobhistory.webapp.address</name> <value>192.168.0.194:19888</value> </property> </configuration> /usr/local/hadoop/etc/hadoop/yarn-site.xml配置文件： [root@Hadoop-Data01 hadoop]# vim yarn-site.xml >>>>> <configuration> <property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> </property> <property> <name>yarn.nodemanager.auxservices.mapreduce.shuffle.class</name> <value>org.apache.hadoop.mapred.ShuffleHandler</value> </property> <property> <name>yarn.resourcemanager.address</name> <value>192.168.0.194:8032</value> </property> <property> <name>yarn.resourcemanager.scheduler.address</name> <value>192.168.0.194:8030</value> </property> <property> <name>yarn.resourcemanager.resource-tracker.address</name> <value>192.168.0.194:8031</value> </property> <property> <name>yarn.resourcemanager.admin.address</name> <value>192.168.0.194:8033</value> </property> <property> <name>yarn.resourcemanager.webapp.address</name> <value>192.168.0.194:8088</value> </property> <property> <name>yarn.nodemanager.resource.memory-mb</name> <value>8192</value> </property> </configuration> 注： <mapreduce.framework.name>:MapReduce按照任务大小和设置的不同，提供了两种任务模式：①本地模式（LocalJobRunner实现）mapreduce.framework.name设置为local，则不会使用YARN集群来分配资源，在本地节点执行。在本地模式运行的任务，无法发挥集群的优势。在web UI是查看不到本地模式运行的任务。②Yarn模式（YARNRunner实现）mapreduce.framework.name设置为yarn，当客户端配置mapreduce.framework.name为yarn时, 客户端会使用YARNRunner与服务端通信, 而YARNRunner真正的实现是通过ClientRMProtocol与RM交互, 包括提交Application, 查询状态等功能。 <mapreduce.jobhistory.address>和<mapreduce.jobhistory.webapp.address>:Hadoop自带了一个历史服务器，可以通过历史服务器查看已经运行完的Mapreduce作业记录，比如用了多少个Map、用了多少个Reduce、作业提交时间、作业启动时间、作业完成时间等信息。 ---------- 配置hadoop环境变量： [root@Hadoop-Data01 hadoop]# vim /etc/profile >>>>> export HADOOP_HOME=/usr/local/hadoop export PATH=$HADOOP_HOME/bin:$PATH [root@Hadoop-Data01 hadoop]# vim hadoop-env.sh >>>>> export JAVA_HOME=/usr/local/jdk 添加从节点IP到Slave文件： [root@Hadoop-Data01 hadoop]# echo > slave && echo 192.168.0.195 > slave 拷贝hadoop服务目录到从主机： [root@Hadoop-Data01 local]# \scp -r hadoop-2.7.3 root@192.168.0.195:/usr/local/ 进入Hadoop目录，启动Hadoop-Master主机上的服务： ①初始化： [root@Hadoop-Data01 bin]# sh /usr/local/hadoop/bin/hdfs namenode -format ②启动服务： [root@Hadoop-Data01 sbin]# sh /usr/local/hadoop/sbin/start-all.sh ③关闭服务： [root@Hadoop-Data01 sbin]# sh /usr/local/hadoop/sbin/stop-all.sh ④查看组件： [root@Hadoop-Data01 sbin]# jps 6517 SecondaryNameNode 6326 NameNode 6682 ResourceManager 6958 Jps
测试访问OK
浏览器访问：http://192.168.0.194:8088/
浏览器访问：http://192.168.0.194:50070/
解压部署、配置环境变量:
[root@Hadoop-Data01 soft]# tar -xf apache-hive-2.1.1-bin.tar.gz [root@Hadoop-Data01 soft]# mv apache-hive-2.1.1-bin /usr/local/ [root@Hadoop-Data01 soft]# cd /usr/local/ [root@Hadoop-Data01 local]# ln -s apache-hive-2.1.1-bin hive [root@Hadoop-Data01 conf]# cp hive-env.sh.template hive-env.sh [root@Hadoop-Data01 conf]# vim hive-env.sh >>>>> HADOOP_HOME=/usr/local/hadoop export HIVE_CONF_DIR=/usr/loca/hive/conf export HIVE_AUX_JARS_PATH=/usr/loca/hive/lib
安装部署mysql环境
[root@Hadoop-Data01 conf]# yum install httpd php mysql mysql-server php-mysql -y [root@Hadoop-Data01 conf]# /usr/bin/mysqladmin -u root password ‘hadoopmysql‘ [root@Hadoop-Data01 conf]# /usr/bin/mysqladmin -u root -h192.168.0.194 password ‘hadoopmysql‘ [root@Hadoop-Data01 conf]# mysql -uroot -phadoopmysql mysql> create user ‘hive‘ identified by ‘hive‘; mysql> grant all privileges on *.* to ‘hive‘@‘localhost‘ identified by ‘hive‘; Query OK, 0 rows affected (0.00 sec) mysql> grant all privileges on *.* to ‘hive‘@‘%‘ identified by ‘hiveycfw‘; Query OK, 0 rows affected (0.00 sec) mysql> flush privileges; Query OK, 0 rows affected (0.00 sec) mysql> create database hive; Query OK, 1 row affected (0.00 sec)
修改HIVE配置文件:
[root@Hadoop-Data01 conf]# vim hive-site.xml 44行：>>>>> <name>hive.exec.local.scratchdir</name> <value>/usr/local/hive/iotmp</value> 批量替换：:%s/${system:java.io.tmpdir}/\/usr\/local\/hive\/iotmp/g 486行：>>>>> <name>javax.jdo.option.ConnectionPassword</name> <value>hive</value> 501行：>>>>> <name>javax.jdo.option.ConnectionURL</name> <value>jdbc:mysql://localhost:3306/hive?createDatabaseIfNotExist=true</value> 686行：>>>>> <name>hive.metastore.schema.verification</name> <value>false</value> 933行：>>>>> <name>javax.jdo.option.ConnectionDriverName</name> <value>com.mysql.jdbc.Driver</value> 957行：>>>>> <name>javax.jdo.option.ConnectionUserName</name> <value>hive</value> 拷贝JDBC驱动到lib目录下： [root@Hadoop-Data01 mysql-connector-java-5.1.42]# cp mysql-connector-java-5.1.42-bin.jar /usr/local/hive/lib/ 精简版hive-site.xml： <?xml version="1.0" encoding="UTF-8" standalone="no"?> <?xml-stylesheet type="text/xsl" href="configuration.xsl"?> <configuration> <property> <name>javax.jdo.option.ConnectionURL</name> #数据库连接串 <value>jdbc:mysql://localhost:3306/hive?createDatabaseIfNotExist=true</value> </property> <property> <name>javax.jdo.option.ConnectionDriverName</name> #JDBC驱动 <value>com.mysql.jdbc.Driver</value> </property> <property> <name>javax.jdo.option.ConnectionUserName</name> #数据库账号 <value>hive</value> </property> <property> <name>javax.jdo.option.ConnectionPassword</name> #数据库密码 <value>hive</value> </property> <property> <name>hive.metastore.warehouse.dir</name> #该参数指定了 Hive 的数据存储目录，默认位置在 HDFS 上面的 /user/hive/warehouse 路径下 <value>/user/hive/warehouse</value> </property> <property> <name>hive.exec.scratchdir</name> #该参数指定了 Hive 的数据临时文件目录，默认位置为 HDFS 上面的 /tmp/hive 路径下 <value>/tmp/hive</value> </property> </configuration>
初始化Mysql
[root@Hadoop-Data01 bin]# schematool -initSchema -dbType mysql #初始化完成后，mysql数据库中会增加hive库 which: no hbase in (/usr/local/hive/bin:/usr/local/hive/conf:/usr/local/hadoop/bin:/usr/local/jdk//bin:/usr/local/jdk//jre/bin:/usr/local/sbin:/usr/local/bin:/sbin:/bin:/usr/sbin:/usr/bin:/root/bin) SLF4J: Class path contains multiple SLF4J bindings. SLF4J: Found binding in [jar:file:/usr/local/apache-hive-2.1.1-bin/lib/log4j-slf4j-impl-2.4.1.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: Found binding in [jar:file:/usr/local/hadoop-2.7.3/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation. SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory] Metastore connection URL: jdbc:mysql://localhost:3306/hive?createDatabaseIfNotExist=true Metastore Connection Driver : com.mysql.jdbc.Driver Metastore connection User: hive Starting metastore schema initialization to 2.1.0 Initialization script hive-schema-2.1.0.mysql.sql Initialization script completed schemaTool completed
启动Hive
[root@Hadoop-Data01 bin]# ./hive Logging initialized using configuration in jar:file:/usr/local/apache-hive-1.2.2-bin/lib/hive-common-1.2.2.jar!/hive-log4j.properties hive> hive> show functions; #查看hive函数； hive> desc function day; #查看day函数详细信息； OK day(param) - Returns the day of the month of date/timestamp, or day component of interval Time taken: 0.039 seconds, Fetched: 1 row(s)
解压flume文件，传输到/usr/local/下(安装到hadoop服务器)：
[root@Hadoop-Data01 soft]# \cp -r apache-flume-1.7.0-bin /usr/local/ [root@Hadoop-Data01 soft]# cd /usr/local/ [root@Hadoop-Data01 local]# ln -s apache-flume-1.7.0-bin flume [root@Hadoop-Data01 conf]# cp flume-env.sh.template flume-env.sh [root@Hadoop-Data01 conf]# vim flume-env.sh >>>>： export JAVA_HOME=/usr/local/jdk [root@Hadoop-Data01 bin]# ./flume-ng version Flume 1.7.0 Source code repository: https://git-wip-us.apache.org/repos/asf/flume.git Revision: 511d868555dd4d16e6ce4fedc72c2d1454546707 Compiled by bessbd on Wed Oct 12 20:51:10 CEST 2016 From source with checksum 0d21b3ffdc55a07e1d08875872c00523
下载flume服务到需要采集的服务器，这里是windows，然后配置/conf/flume-conf.properties:
a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = spooldir a1.sources.r1.channels = c1 a1.sources.r1.spoolDir = D:\\flume\\log #收集这个目录下的文件 a1.sources.r1.fileHeader = true a1.sources.r1.basenameHeader = true a1.sources.r1.basenameHeaderKey = fileName a1.sources.r1.ignorePattern = ^(.)*\\.tmp$ a1.sources.r1.interceptors = i1 a1.sources.r1.interceptors.i1.type = timestamp a1.sinks.k1.type = avro a1.sinks.k1.hostname = 192.168.0.194 #接受agent端地址 a1.sinks.k1.port = 19949 # Use a channel which buffers events in memory a1.channels.c1.type=memory a1.channels.c1.capacity=10000 a1.channels.c1.transactionCapacity=1000 a1.channels.c1.keep-alive=30 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1
启动采集端服务，windows端：
D:\apache-flume-1.7.0-bin\bin>flume-ng.cmd agent --conf ..\conf --conf-file ..\conf\flume-conf.properties --name a1
配置Linux端，agent配置/conf/flume-conf.properties:
tier1.sources=source1 tier1.channels=channel1 tier1.sinks=sink1 tier1.sources.source1.type=avro tier1.sources.source1.bind=192.168.0.194 #flume接收端地址 tier1.sources.source1.port=19949 tier1.sources.source1.channels=channel1 tier1.channels.channel1.type=memory tier1.channels.channel1.capacity=10000 tier1.channels.channel1.transactionCapacity=1000 tier1.channels.channel1.keep-alive=30 tier1.sinks.sink1.channel=channel1 tier1.sources.source1.interceptors=e1 e2 tier1.sources.source1.interceptors.e1.type=com.huawei.flume.InterceptorsCommons$Builder tier1.sources.source1.interceptors.e2.type=com.huawei.flume.InterceptorsFlows$Builder tier1.sinks.sink1.type = hdfs tier1.sinks.sink1.hdfs.path=hdfs://192.168.0.194:9000/user/hive/warehouse/%{table_name}/inputdate=%Y-%m-%d #flume接受端agent，hive表名 tier1.sinks.sink1.hdfs.writeFormat = Text tier1.sinks.sink1.hdfs.fileType = DataStream tier1.sinks.sink1.hdfs.fileSuffix = .log tier1.sinks.sink1.hdfs.rollInterval = 0 tier1.sinks.sink1.hdfs.rollSize = 0 tier1.sinks.sink1.hdfs.rollCount = 0 tier1.sinks.sink1.hdfs.useLocalTimeStamp = true tier1.sinks.sink1.hdfs.idleTimeout = 60 tier1.sinks.sink1.hdfs.rollSize = 125829120 tier1.sinks.sink1.hdfs.minBlockReplicas = 1
启动Linux端，agent服务：
[root@Hadoop-Data01 conf]# flume-ng agent -c /usr/local/flume/conf/ -f /usr/local/flume/conf/flume-conf.properties -n tier1 -Dflume.root.logger=DEBUG,console
Linux系统运维之Hadoop、Hive、Flume数据处理
标签：创建文件 pil -- 而不是 tip 导致 fbx meta format
原文地址：http://www.cnblogs.com/zzlain/p/6895346.html
