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@sasaki 2016-05-25T10:55:46.000000Z 字数 16133 阅读 5238

大数据学习知识疏理及问题解决

BigData Hadoop Spark

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  1. @Title          大数据学习知识疏理及问题解决
  2. @Version        v1.0
  3. @Timestamp      2015-12-23 17:48
  4. @Author         Nicholas
  5. @Mail           redskirt@outlook.com

一、

二、问题解决

  1. 在HDFS中添加或修改文件时的root用户无权限问题：

    2. # HDFS会使用和name node 相同的用户名，获取到所有文件的访问权限
    4. [root@master ~]# hadoop fs -mkdir /temp 
    5. mkdir: Permission denied: user=root, access=WRITE, inode="/":hdfs:supergroup:drwxr-xr-x
    8. # 查看HDFS目录权限，可知root用户仅相当于访客
    10. [root@master ~]# hadoop fs -ls /
    11. Found 2 items
    12. drwxrwxrwx   - hdfs supergroup          0 2015-12-22 15:48 /tmp
    13. drwxr-xr-x   - hdfs supergroup          0 2015-12-23 17:41 /user
    16. # 使用超级用户hdfs在/user目录新建与系统用户名对应的文件夹，即新增一个用户
    18. [root@master ~]# sudo -uhdfs hadoop fs -mkdir /user/root
    21. # 更改root用户的权限
    23. [root@master ~]# sudo -uhdfs hadoop fs -chown root:root /user/root
    26. # 现在可在/user/root目录下进行文件操作
    28. [root@master ~]# hadoop fs -mkdir /user/root/temp

  2. 在Hadoop环境中运行MapReduce pi测试程序

    1. [root@master ~]# hadoop jar /usr/application/tmp/hadoop-2.6.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar pi 100 100

  3. 测试Spark自带的计算Pi程序
    在YARN上运行spark测试程序

    1. [root@master /]# cd /opt/cloudera/parcels/CDH-5.3.8-1.cdh5.3.8.p0.5/lib/spark/bin 
    2. [root@master bin]# ./spark-class org.apache.spark.deploy.yarn.Client --jar /usr/application/tmp/spark-1.3.1-bin-hadoop2.6/lib/spark-assembly-1.3.1-hadoop2.6.0.jar --class org.apache.spark.examples.SparkPi --args yarn-standalone --num-workers 3 --master-memory 512m --worker-memory 512m

    经查Log是如下异常，苦思冥想好长时间未解决：

    1. Application application_1452070738190_0018 failed 2 times due to AM Container for appattempt_1452070738190_0018_000002 exited with exitCode: -1000 due to: java.io.IOException: Resource file:/usr/spark-1.3.1-bin-hadoop2.6/lib/spark-assembly-1.3.1-hadoop2.6.0.jar changed on src filesystem (expected 1428730376000, was 1448531854000
    2. .Failing this attempt.. Failing the application.

    Log界面：
    QQ截图20160107110333.png-120.6kB

    该问题经查应该是导入环境变量有误引起，查看变量如下：

    1. [root@master lib]# cd /etc/spark/conf/
    2. [root@master conf]# ls
    3. __cloudera_generation__  log4j.properties  spark-defaults.conf  spark-env.sh
    4. [root@master conf]# cat spark-env.sh |grep -v '^#'|grep -v '^$'
    5. export SPARK_HOME=/opt/cloudera/parcels/CDH-5.3.8-1.cdh5.3.8.p0.5/lib/spark
    6. export DEFAULT_HADOOP_HOME=/opt/cloudera/parcels/CDH-5.3.8-1.cdh5.3.8.p0.5/lib/hadoop
    7. export SPARK_JAR_HDFS_PATH=${SPARK_JAR_HDFS_PATH:-/user/spark/share/lib/spark-assembly.jar}
    8. export SPARK_LAUNCH_WITH_SCALA=0
    9. export SPARK_LIBRARY_PATH=${SPARK_HOME}/lib
    10. export SCALA_LIBRARY_PATH=${SPARK_HOME}/lib
    11. export HADOOP_HOME=${HADOOP_HOME:-$DEFAULT_HADOOP_HOME}
    12. if [ -n "$HADOOP_HOME" ]; then
    13.     export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${HADOOP_HOME}/lib/native
    14. fi
    15. export HADOOP_CONF_DIR=${HADOOP_CONF_DIR:-/etc/hadoop/conf}

    发现最后一行是所设环境变量

    1. [root@master hadoop]# pwd
    2. /etc/hadoop
    3. [root@master hadoop]# ll
    4. total 12
    5. lrwxrwxrwx. 1 root root   29 Jan  5 23:35 conf -> /etc/alternatives/hadoop-conf
    6. drwxr-xr-x. 2 root root 4096 Dec 26 12:02 conf.cloudera.hdfs
    7. drwxr-xr-x. 2 root root 4096 Dec 26 12:03 conf.cloudera.mapreduce
    8. drwxr-xr-x. 2 root root 4096 Jan  6 16:46 conf.cloudera.yarn

    据教程中老师说的该目录实际是指向配置文件的一个软链，查看/etc/hadoop发现conf的确是指到一个为/etc/alternatives/hadoop-conf的其他目录，此处开始怀疑自己此前的人生Q_Q
    于是又进入目录，查看文件顿时傻眼，/etc/alternatives目录下有200个文件全是软链。此刻终于发现我需要的hadoof-conf目录，然而该软链又链接到/etc/hadoop/conf.cloudera.yarn。

    1. [root@master alternatives]# pwd
    2. /etc/alternatives
    3. [root@master alternatives]# ll #文件过多就不完全列举
    4. total 200
    5. lrwxrwxrwx. 1 root root 30 Jan  5 23:35 hadoop-conf -> /etc/hadoop/conf.cloudera.yarn
    6. [root@master alternatives]# ll |grep hadoop-conf
    7. lrwxrwxrwx. 1 root root 30 Jan  5 23:35 hadoop-conf -> /etc/hadoop/conf.cloudera.yarn

    最后进入/etc/hadoop/conf.cloudera.yarn目录，终于发现里边“深藏的配置文件”，然而这里我并没有轻易尝试该目录。我想找到最原生的CDH部署的配置文件存放目录。

    1. [root@master conf.cloudera.yarn]# pwd
    2. /etc/hadoop/conf.cloudera.yarn
    3. [root@master conf.cloudera.yarn]# ls
    4. __cloudera_generation__  hadoop-env.sh  log4j.properties  ssl-client.xml  topology.py
    5. core-site.xml            hdfs-site.xml  mapred-site.xml   topology.map    yarn-site.xml

    想到上述步骤中用[root@master conf]# cat spark-env.sh |grep -v '^#'|grep -v '^$'命令查看到export DEFAULT_HADOOP_HOME=/opt/cloudera/parcels/CDH-5.3.8-1.cdh5.3.8.p0.5/lib/hadoop，猜测原生的配置文件应该也存放在这里。

    1. [root@master hadoop]# pwd
    2. /opt/cloudera/parcels/CDH-5.3.8-1.cdh5.3.8.p0.5/lib/hadoop/etc/hadoop
    3. [root@master hadoop]# ls
    4. __cloudera_generation__  hadoop-env.sh  log4j.properties  ssl-client.xml  topology.py
    5. core-site.xml            hdfs-site.xml  mapred-site.xml   topology.map    yarn-site.xml

    之后经尝试如下导入，运行测试程序终于没有再遇到同样异常

    1. [root@master hadoop]# export HADOOP_CONF_DIR=/opt/cloudera/parcels/CDH-5.3.8-1.cdh5.3.8.p0.5/lib/hadoop/etc/hadoop

    处理后运行成功但是异常出缺org.apache.spark.examples.SparkPi这个类，找到原因是之前的提交代码给的 --jar错了，给的是spark-assembly-1.2.0-cdh5.3.8-hadoop2.5.0-cdh5.3.8.jar而不是spark-examples-1.2.0-cdh5.3.8-hadoop2.5.0-cdh5.3.8.jar，里边当然没有测试的类。

    QQ截图20160106224928.png-133.8kB

    更换之，为了保险起见，在运行测试前不忘所有节点设置SPARK_JAR变量，为要提交至YARN上的Spark环境，此处因为跑的是CDH环境中的example，为避免不兼容我仍然导入CDH目录中的spark-assembly-1.2.0-cdh5.3.8-hadoop2.5.0-cdh5.3.8.jar。

    1. [root@master hadoop]# SPARK_JAR=/opt/cloudera/parcels/CDH-5.3.8-1.cdh5.3.8.p0.5/jars/spark-assembly-1.2.0-cdh5.3.8-hadoop2.5.0-cdh5.3.8.jar
    2. [root@master hadoop]# ./spark-class org.apache.spark.deploy.yarn.Client \
    3. --jar /opt/cloudera/parcels/CDH-5.3.8-1.cdh5.3.8.p0.5/lib/spark/lib/spark-examples-1.2.0-cdh5.3.8-hadoop2.5.0-cdh5.3.8.jar \
    4. --class org.apache.spark.examples.SparkPi \
    5. --args yarn-standalone --num-workers 3 \
    6. --master-memory 512m --worker-memory 512m

    终于可以顺利执行了，然而又爆出一个更奇葩的问题，竟然不识别yarn-standalone，提交的命令写法应该是对 的，经查也找不出这个问题所在。索性就把--args这个参数删掉再尝试。
    QQ截图20160107144334.png-105.9kB

    QQ截图20160107144315.png-80.2kB

    1. [root@master hadoop]# ./spark-class org.apache.spark.deploy.yarn.Client \
    2. --jar /opt/cloudera/parcels/CDH-5.3.8-1.cdh5.3.8.p0.5/lib/spark/lib/spark-examples-1.2.0-cdh5.3.8-hadoop2.5.0-cdh5.3.8.jar \ 
    3. --class org.apache.spark.examples.SparkPi \
    4. --num-workers 3 \
    5. --master-memory 512m \
    6. --worker-memory 512m

    这里一路畅通，看到最终结果，结果在Log最后一行：
    QQ截图20160107105250.png-202.5kB

    测试程序

    1. [root@master ~]# spark-submit --class com.spark.test.NyTime --master \ yarn-cluster --executor-memory 512m  --executor-cores 10 /usr/application/tmp/spark-yarn.jar hdfs://localhost:8020/user/root/wordcount/input/ hdfs://localhost:8020/user/root/wordcount/output

  4. 部署Kafka测试代码，Maven环境
    因Nexus中下载Maven依赖有缺失，特更换OpenSource China的Maven仓库。

    1. <mirror>
    2.     <id>oschina</id>
    3.     <mirrorOf>*</mirrorOf>
    4.     <name>Human Readable Name for this Mirror.</name>
    5.     <url>http://maven.oschina.net/content/groups/public/</url>
    6. </mirror>

    后发现还有一个jdk.tools-*.jar的包缺失，本地repo目录中也不存在目录。
    QQ截图20160120170950.jpg-505.5kB

    QQ截图20160120171010.jpg-518.8kB

    经查jdk.tools:jdk.tools是与JDK一起分发的一个JAR文件，可以如下方式加入到Maven项目中：

    1. <dependency>
    2.     <groupId>jdk.tools</groupId>
    3.     <artifactId>jdk.tools</artifactId>
    4.     <version>1.7</version>
    5.     <scope>system</scope>
    6.     <systemPath>${JAVA_HOME}/lib/tools.jar</systemPath>
    7. </dependency>

  5. hive shell启动问题
    CM中Hive服务启动完成，但是CLI中使用hive命令，时一直没有回应，也不报错，发现CM中Hive服务Hive Metastore Server有异常大概为不能创建测试的meterstore，经看Log怀疑是Hive没有mysql驱动jar文件，但是在初次安装过程中已经放入lib中了，可能是之后删除hive服务又会初始化Hive的lib，再次将jar文件放入/opt/cloudera/parcels/CDH-5.3.8-1.cdh5.3.8.p0.5/lib/hive/lib/中，顺利进入hive CLI。

    1. [root@master hive]# pwd
    2. /var/log/hive
    3. [root@master hive]# cat hadoop-cmf-hive-HIVEMETASTORE-master.log.out
    4. Caused by: org.datanucleus.exceptions.NucleusException: Attempt to invoke the "BONECP" plugin to create a ConnectionPool gave an error : The specified datastore driver ("com.mysql.jdbc.Driver") was not found in the CLASSPATH. Please check your CLASSPATH specification, and the name of the driver.
    5. at org.datanucleus.store.rdbms.ConnectionFactoryImpl.generateDataSources(ConnectionFactoryImpl.java:259)
    6. at org.datanucleus.store.rdbms.ConnectionFactoryImpl.initialiseDataSources(ConnectionFactoryImpl.java:131)
    7. at org.datanucleus.store.rdbms.ConnectionFactoryImpl.<init>(ConnectionFactoryImpl.java:85)
    8. ... 53 more
    9. Caused by: org.datanucleus.store.rdbms.connectionpool.DatastoreDriverNotFoundException: The specified datastore driver ("com.mysql.jdbc.Driver") was not found in the CLASSPATH. Please check your CLASSPATH specification, and the name of the driver.
    10. at org.datanucleus.store.rdbms.connectionpool.AbstractConnectionPoolFactory.loadDriver(AbstractConnectionPoolFactory.java:58)
    11. at org.datanucleus.store.rdbms.connectionpool.BoneCPConnectionPoolFactory.createConnectionPool(BoneCPConnectionPoolFactory.java:54)
    12. at org.datanucleus.store.rdbms.ConnectionFactoryImpl.generateDataSources(ConnectionFactoryImpl.java:238)
    13. ... 55 more

  6. hive创建外部表问题
    Hive创建外部表时

    1. hive> create external table test_sogo(time varchar(8), userid varchar(30), query string, pagerank int, clickrank int, site string) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' LOCATION 'hdfs://master:8020/user/root/sogo/SogouQ.reduced';

    报错如下

    1. FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.DDLTask. MetaException(message:hdfs://master:8020/user/root/sogo/SogouQ.reduced is not a directory or unable to create one)gouQ.reduced';

    可是确定hdfs://master:8020/user/root/sogo/SogouQ.reduced文件确实存在。

    经查：location后面跟的是目录，不是文件，hive会把整个目录下的文件都加载到表中，否则就会找不到文件。

  7. hbase启动时Master启动失败

    QQ截图20160122115541.jpg-845.3kB

    1. 2016-01-22 11:55:10,496 ERROR org.apache.hadoop.hbase.master.HMasterCommandLine: Master exiting
    2. java.lang.RuntimeException: HMaster Aborted
    3.     at org.apache.hadoop.hbase.master.HMasterCommandLine.startMaster(HMasterCommandLine.java:194)
    4.     at org.apache.hadoop.hbase.master.HMasterCommandLine.run(HMasterCommandLine.java:135)
    5.     at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:70)
    6.     at org.apache.hadoop.hbase.util.ServerCommandLine.doMain(ServerCommandLine.java:126)
    7.     at org.apache.hadoop.hbase.master.HMaster.main(HMaster.java:2829)

    经查详细日志有以下异常：
    QQ截图20160122140215.jpg-559.4kB

    1. Unhandled exception. Starting shutdown.
    2. org.apache.hadoop.hbase.TableExistsException: hbase:namespace
    3. at org.apache.hadoop.hbase.master.handler.CreateTableHandler.prepare(CreateTableHandler.java:133)
    4. at org.apache.hadoop.hbase.master.TableNamespaceManager.createNamespaceTable(TableNamespaceManager.java:232)
    5. at org.apache.hadoop.hbase.master.TableNamespaceManager.start(TableNamespaceManager.java:86)
    6. at org.apache.hadoop.hbase.master.HMaster.initNamespace(HMaster.java:1076)
    7. at org.apache.hadoop.hbase.master.HMaster.finishInitialization(HMaster.java:943)
    8. at org.apache.hadoop.hbase.master.HMaster.run(HMaster.java:614)
    9. at java.lang.Thread.run(Thread.java:745)

    进入zookeeper查看目录，zookeeper还保留着上一次的Hbase设置，所以造成了冲突，删除之。

    1. [root@master ~]# zookeeper-client
    2. [zk: localhost:2181(CONNECTED) 0] ls /
    3. [hbase, zookeeper]
    4. [zk: localhost:2181(CONNECTED) 1] rmr /hbase

    再次尝试，顺利启动Hbase服务。
    QQ截图20160122140654.jpg-221.9kB

  8. Linux root用户不能删除文件
    rm: cannot remove 'd': Operation not permitted

    1. [root@ntp etc]# lsattr ntp.conf
    2. ----i--------e- ntp.conf
    4. # 该文件带有一个"i"的属性，所以才不可以删除
    6. [root@ntp etc]# chattr -i ntp.conf

    这个属性专门用来保护重要的文件不被删除。

  9. Flume收集日志导入Kafka时遇到的问题

    1. 16/02/02 17:25:24 INFO instrumentation.MonitoredCounterGroup: Component type: SOURCE, name: s started
    2. 16/02/02 17:25:24 ERROR source.SpoolDirectorySource: FATAL: Spool Directory source s: { spoolDir: /usr/git-repo/bootcamp/practise/sogouquery/data }: Uncaught exception in SpoolDirectorySource thread. Restart or reconfigure Flume to continue processing.
    3. java.nio.charset.MalformedInputException: Input length = 1
    4.     at java.nio.charset.CoderResult.throwException(CoderResult.java:277)
    5.     at org.apache.flume.serialization.ResettableFileInputStream.readChar(ResettableFileInputStream.java:282)
    6.     at org.apache.flume.serialization.LineDeserializer.readLine(LineDeserializer.java:133)
    7.     at org.apache.flume.serialization.LineDeserializer.readEvent(LineDeserializer.java:71)
    8.     at org.apache.flume.serialization.LineDeserializer.readEvents(LineDeserializer.java:90)
    9.     at org.apache.flume.client.avro.ReliableSpoolingFileEventReader.readEvents(ReliableSpoolingFileEventReader.java:252)
    10.     at org.apache.flume.source.SpoolDirectorySource$SpoolDirectoryRunnable.run(SpoolDirectorySource.java:228)
    11.     	at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
    12.     at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:304)
    13.     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:178)
    14.     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
    15.     	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    16.     	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    17.     at java.lang.Thread.run(Thread.java:745)

    初步判断为不能识别输入的字符。

    这个字节是双字节汉字的一部分，这样我们解码时就不要包含这个字节，而是把这个字节放进下次解码之前的Bytebuffer中。这样做，系统就不会抛出“无法正确解码”这类的异常了。

    原因：日志文件创建时的字符编码与读取时不同，在Windows平台创建的文件放入Linux中可能会出现乱码问题。
    解决：在Linux中创建日志文件，在终端Copy原日志内容进入该文件中保存。得到日志文件如下，再尝试Flume启动导入不会出现该异常。
    QQ截图20160218163251.jpg-523.1kB

    1. java.lang.ClassCastException: [B cannot be cast to java.lang.String
    2.     at kafka.serializer.StringEncoder.toBytes(Encoder.scala:46)
    3.     at kafka.producer.async.DefaultEventHandler$$anonfun$serialize$1.apply(DefaultEventHandler.scala:130)
    4.     	at kafka.producer.async.DefaultEventHandler$$anonfun$serialize$1.apply(DefaultEventHandler.scala:125)
    5.     at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
    6.     	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
    7.     at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    8.     	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    9.     	at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
    10.     at scala.collection.AbstractTraversable.map(Traversable.scala:105)
    11.     at kafka.producer.async.DefaultEventHandler.serialize(DefaultEventHandler.scala:125)
    12.     at kafka.producer.async.DefaultEventHandler.handle(DefaultEventHandler.scala:52)
    13.     at kafka.producer.async.ProducerSendThread.tryToHandle(ProducerSendThread.scala:104)
    14.     at kafka.producer.async.ProducerSendThread$$anonfun$processEvents$3.apply(ProducerSendThread.scala:87)
    15.     	at kafka.producer.async.ProducerSendThread$$anonfun$processEvents$3.apply(ProducerSendThread.scala:67)
    16.     at scala.collection.immutable.Stream.foreach(Stream.scala:547)
    17.     at kafka.producer.async.ProducerSendThread.processEvents(ProducerSendThread.scala:66)
    18.     at kafka.producer.async.ProducerSendThread.run(ProducerSendThread.scala:44)

    配置文件中的Producer输入类型须原源码中保持一致。
    解决：看源码中Producer的value类型为byte[]，则在配置文件中更改producer.sinks.r.serializer.class=kafka.serializer.DefaultEncoder

    key的类型需要和serializer保持一致，如果key是String，则需要配置为kafka.serializer.StringEncoder，如果不配置，默认为kafka.serializer.DefaultEncoder，即二进制格式

  10. Kafka Consumer消费时的问题

    1. [root@master ~]# /usr/kafka_2.10-0.8.2.0/bin/kafka-console-consumer.sh --zookeeper master:2181 --from-beginning --topic topic
    2. [2016-02-19 10:55:47,066] WARN [console-consumer-29985_master-1455850545951-88b63a80-leader-finder-thread], Failed to add leader for partitions [topic,0]; will retry (kafka.consumer.ConsumerFetcherManager$LeaderFinderThread)
    3. kafka.common.NotLeaderForPartitionException
    4. 	at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
    5. 	at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:57)
    6. 	at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
    7. 	at java.lang.reflect.Constructor.newInstance(Constructor.java:526)
    8. 	at java.lang.Class.newInstance(Class.java:379)
    9. 	at kafka.common.ErrorMapping$.exceptionFor(ErrorMapping.scala:86)
    10. at kafka.consumer.SimpleConsumer.earliestOrLatestOffset(SimpleConsumer.scala:169)
    11. at kafka.consumer.ConsumerFetcherThread.handleOffsetOutOfRange(ConsumerFetcherThread.scala:60)
    12. at kafka.server.AbstractFetcherThread$$anonfun$addPartitions$2.apply(AbstractFetcherThread.scala:177)
    13. 	at kafka.server.AbstractFetcherThread$$anonfun$addPartitions$2.apply(AbstractFetcherThread.scala:172)
    14. at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
    15. 	at scala.collection.immutable.Map$Map1.foreach(Map.scala:109)
    16. at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
    17. 	at kafka.server.AbstractFetcherThread.addPartitions(AbstractFetcherThread.scala:172)
    18. 	at kafka.server.AbstractFetcherManager$$anonfun$addFetcherForPartitions$2.apply(AbstractFetcherManager.scala:87)
    19. 	at kafka.server.AbstractFetcherManager$$anonfun$addFetcherForPartitions$2.apply(AbstractFetcherManager.scala:77)
    20. 	at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
    21. at scala.collection.immutable.Map$Map1.foreach(Map.scala:109)
    22. at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
    23. at kafka.server.AbstractFetcherManager.addFetcherForPartitions(AbstractFetcherManager.scala:77)
    24. at kafka.consumer.ConsumerFetcherManager$LeaderFinderThread.doWork(ConsumerFetcherManager.scala:95)
    25. at kafka.utils.ShutdownableThread.run(ShutdownableThread.scala:60)

    每个分区都由一系列有序的、不可变的消息组成，这些消息被连续的追加到分区中。分区中的每个消息都有一个连续的序列号叫做offset,用来在分区中唯一的标识这个消息。

    解决：猜测可能是某个Broker Down掉未切换Leader导致，可是查看Kafka进程发现所有节点都运行正常，在所有节点尝试重启Kafka后异常消失。

  11. Mysql相关

    配置远程访问

    1. MariaDB [(none)]> GRANT ALL ON *.* TO root@'%' IDENTIFIED BY 'root' WITH GRANT OPTION;
    2. Query OK, 0 rows affected (0.05 sec)
    3. MariaDB [(none)]> flush privileges;
    4. Query OK, 0 rows affected (0.01 sec)
    7. #这句话的意思 ，允许任何IP地址（上面的 % 就是这个意思）的电脑 用admin帐户  和密码（admin）来访问这个MySQL Server   
    10. #必须加类似这样的帐户，才可以远程登陆。 root帐户是无法远程登陆的，只可以本地登陆

    Mysql修改密码

    1. [root@iZ28vbkq2xxZ ~]# mysqladmin -u root -p password 'Redskirt0608_'
    2. Enter password:
    3. [root@iZ28vbkq2xxZ ~]# mysql -u root -p

    远程复制数据库

    1. mysqldump --user=YOURNAME --password=YOUR_PASSWORD --host=YOUR_HOST --opt YOUR_DATABASE|mysql --user=YOUR_NAME --password=YOUR_PASSWORD -C YOUR_DATABASE
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