Hadoop分析日志实例的详细步骤及出现的问题分析和解决

yzc164

　　1). 日志格式分析
首先分析 Hadoop 的日志格式, 日志是一行一条, 日志格式可以依次描述为:日期、时间、级别、相关类和提示信息。如下所示:
2014-01-07 00:31:25,393 INFO org.apache.hadoop.mapred.JobTracker: SHUTDOWN_MSG: 
/************************************************************
SHUTDOWN_MSG: Shutting down JobTracker at hadoop1/192.168.91.101
************************************************************/
2014-01-07 00:33:42,425 INFO org.apache.hadoop.mapred.JobTracker: STARTUP_MSG: 
/************************************************************
STARTUP_MSG: Starting JobTracker
STARTUP_MSG:   host = hadoop1/192.168.91.101
STARTUP_MSG:   args = []
STARTUP_MSG:   version = 1.1.2
STARTUP_MSG:   build = https://svn.apache.org/repos/asf/hadoop/common/branches/branch-1.1 -r 1440782; compiled by 'hortonfo' on Thu Jan 31 02:03:24 UTC 2013
************************************************************/
2014-01-07 00:33:43,305 INFO org.apache.hadoop.metrics2.impl.MetricsConfig: loaded properties from hadoop-metrics2.properties
2014-01-07 00:33:43,358 INFO org.apache.hadoop.metrics2.impl.MetricsSourceAdapter: MBean for source MetricsSystem,sub=Stats registered.
2014-01-07 00:33:43,359 INFO org.apache.hadoop.metrics2.impl.MetricsSystemImpl: Scheduled snapshot period at 10 second(s).
2014-01-07 00:33:43,359 INFO org.apache.hadoop.metrics2.impl.MetricsSystemImpl: JobTracker metrics system started
2014-01-07 00:33:43,562 INFO org.apache.hadoop.metrics2.impl.MetricsSourceAdapter: MBean for source QueueMetrics,q=default registered.
2014-01-07 00:33:44,118 INFO org.apache.hadoop.metrics2.impl.MetricsSourceAdapter: MBean for source ugi registered.
2014-01-07 00:33:44,118 INFO org.apache.hadoop.security.token.delegation.AbstractDelegationTokenSecretManager: Updating the current master key for generating delegation tokens
2014-01-07 00:33:44,119 INFO org.apache.hadoop.mapred.JobTracker: Scheduler configured with (memSizeForMapSlotOnJT, memSizeForReduceSlotOnJT, limitMaxMemForMapTasks, limitMaxMemForReduceTasks) (-1, -1, -1, -1)
2014-01-07 00:33:44,120 INFO org.apache.hadoop.util.HostsFileReader: Refreshing hosts (include/exclude) list
2014-01-07 00:33:44,125 INFO org.apache.hadoop.security.token.delegation.AbstractDelegationTokenSecretManager: Starting expired delegation token remover thread, tokenRemoverScanInterval=60 min(s)
2014-01-07 00:33:44,125 INFO org.apache.hadoop.security.token.delegation.AbstractDelegationTokenSecretManager: Updating the current master key for generating delegation tokens
2014-01-07 00:33:44,126 INFO org.apache.hadoop.mapred.JobTracker: Starting jobtracker with owner as root
2014-01-07 00:33:44,187 INFO org.apache.hadoop.metrics2.impl.MetricsSourceAdapter: MBean for source RpcDetailedActivityForPort9001 registered.
2014-01-07 00:33:44,187 INFO org.apache.hadoop.metrics2.impl.MetricsSourceAdapter: MBean for source RpcActivityForPort9001 registered.
2014-01-07 00:33:44,188 INFO org.apache.hadoop.ipc.Server: Starting SocketReader
2014-01-07 00:33:44,490 INFO org.mortbay.log: Logging to org.slf4j.impl.Log4jLoggerAdapter(org.mortbay.log) via org.mortbay.log.Slf4jLog
2014-01-07 00:33:44,805 INFO org.apache.hadoop.http.HttpServer: Added global filtersafety (class=org.apache.hadoop.http.HttpServer$QuotingInputFilter)
2014-01-07 00:33:44,825 INFO org.apache.hadoop.http.HttpServer: Port returned by webServer.getConnectors()[0].getLocalPort() before open() is -1. Opening the listener 
这只是部分日志。

2). 程序设计
本程序是在个人机器用 Eclipse 开发,该程序连接 Hadoop 集群,处理完的结果存储在MySQL 服务器上。下面是程序开发示例图。

MySQL 数据库的存储信息的表“hadooplog”的 SQL 语句如下:
 

drop table if exists  hadooplog;
create table hadooplog(
id int(11) not null auto_increment,
rdate varchar(50)  null,
time varchar(50) default null,
type varchar(50) default null,
relateclass tinytext default null,
information longtext default null,
primary key (id)
) engine=innodb default charset=utf8;
　　
操作如下：进入mysql 直接执行sql语句就行，创建一个hadooplog表
 
 3). 程序代码

 

package com.wzl.hive;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.SQLException;
/**
* 该类的主要功能是负责建立与 Hive 和 MySQL 的连接, 由于每个连接的开销比较大, 所以此类的设计采用设计模式中的单例模式。
*/
class DBHelper {
private static Connection connToHive = null;
private static Connection connToMySQL = null;
private DBHelper() {
}
// 获得与 Hive 连接,如果连接已经初始化,则直接返回
public static Connection getHiveConn() throws SQLException {
if (connToHive == null) {
try {
Class.forName("org.apache.hadoop.hive.jdbc.HiveDriver");
} catch (ClassNotFoundException err) {
err.printStackTrace();
System.exit(1);
}
connToHive = DriverManager.getConnection("jdbc:hive://192.168.91.101:10000/default", "hive", "");
}
return connToHive;
}
// 获得与 MySQL 连接
public static Connection getMySQLConn() throws SQLException {
if (connToMySQL == null) {
try {
Class.forName("com.mysql.jdbc.Driver");
} catch (ClassNotFoundException err) {
err.printStackTrace();
System.exit(1);
}
connToMySQL = DriverManager.getConnection("jdbc:mysql://192.168.91.101:3306/hive?useUnicode=true&characterEncoding=UTF8",
"root", "root"); //编码不要写成UTF-8
}
return connToMySQL;
}
public static void closeHiveConn() throws SQLException {
if (connToHive != null) {
connToHive.close();
}
}
public static void closeMySQLConn() throws SQLException {
if (connToMySQL != null) {
connToMySQL.close();
}
}
public static void main(String[] args) throws SQLException {
System.out.println(getMySQLConn());
closeMySQLConn();
}
}

package com.wzl.hive;
import java.sql.Connection;
import java.sql.ResultSet;
import java.sql.SQLException;
import java.sql.Statement;
/**
*
* 针对 Hive 的工具类
*/
class HiveUtil {
// 创建表
public static void createTable(String sql) throws SQLException {
Connection conn = DBHelper.getHiveConn();
Statement stmt = conn.createStatement();
ResultSet res = stmt.executeQuery(sql);
}
// 依据条件查询数据
public static ResultSet queryData(String sql) throws SQLException {
Connection conn = DBHelper.getHiveConn();
Statement stmt = conn.createStatement();
ResultSet res = stmt.executeQuery(sql);
return res;
}
// 加载数据
public static void loadData(String sql) throws SQLException {
Connection conn = DBHelper.getHiveConn();
Statement stmt = conn.createStatement();
ResultSet res = stmt.executeQuery(sql);
}
// 把数据存储到 MySQL 中
public static void hiveToMySQL(ResultSet res) throws SQLException {
Connection conn = DBHelper.getMySQLConn();
Statement stmt = conn.createStatement();
while (res.next()) {
String rdate = res.getString(1);
String time = res.getString(2);
String type = res.getString(3);
String relateclass = res.getString(4);
String information = res.getString(5) + res.getString(6) + res.getString(7);
StringBuffer sql = new StringBuffer();
sql.append("insert into hadooplog values(0,'");
sql.append(rdate + "','");
sql.append(time + "','");
sql.append(type + "','");
sql.append(relateclass + "','");
sql.append(information + "')");
System.out.println(sql.toString());
int i = stmt.executeUpdate(sql.toString());
}
}
}

package com.wzl.hive;
import java.sql.ResultSet;
import java.sql.SQLException;
public class AnalyszeHadoopLog {
public static void main(String[] args) throws SQLException {
StringBuffer sql = new StringBuffer();
// 第一步:在 Hive 中创建表
sql.append("create table if not exists loginfo( ");
sql.append("rdate string,  ");
sql.append("time array<string>, ");
sql.append("type string, ");
sql.append("relateclass string, ");
sql.append("information1 string, ");
sql.append("information2 string, ");
sql.append("information3 string)  ");
sql.append("row format delimited fields terminated by ' '  ");
sql.append("collection items terminated by ','   ");
sql.append("map keys terminated by  ':'");
System.out.println(sql);
HiveUtil.createTable(sql.toString());
// 第二步:加载 Hadoop 日志文件
sql.delete(0, sql.length());
sql.append("load data local inpath ");
sql.append("'/usr/local/hadoop/logs/hadoop-root-jobtracker-hadoop1.log'");
sql.append(" overwrite into table loginfo");
System.out.println(sql);
HiveUtil.loadData(sql.toString());
// 第三步:查询有用信息
sql.delete(0, sql.length());
sql.append("select rdate,time[0],type,relateclass,");
sql.append("information1,information2,information3 ");
sql.append("from loginfo where type='INFO'");
System.out.println(sql);
ResultSet res = HiveUtil.queryData(sql.toString());
// 第四步:查出的信息经过变换后保存到 MySQL 中
HiveUtil.hiveToMySQL(res);
// 第五步:关闭 Hive 连接
DBHelper.closeHiveConn();
// 第六步:关闭 MySQL 连接
DBHelper.closeMySQLConn();
}
}
　　
4). 运行结果
在执行之前要注意的问题：
 

 

• 在运行前必须保证hive远端服务端口是开的  执行命令：nohup hive --service hiveserver  &  如果没有执行这句命令常出现这个错误：Could not establish connection to 192.168.91.101:10000/default: java.net.ConnectException: Connection refused: connect
  
• mysql已经建立了hadooplog表
  
• mysql数据库允许本机连接数据库执行命令：grant all privileges on *.* to root@'%' identified by 'root'; 这句意思是允许任何的ip都能访问mysql数据库。如果如果没有执行这句命令常出现这个错误：java连接linux中mysql出现：Access denied for user 'root'@'192.168.91.1' (using password: YES)
  

 

 

mysql> use hive;
mysql> show tables;
mysql> select * from hadooplog;
 
5). 经验总结
在示例中同时对 Hive 的数据仓库库和 MySQL 数据库进行操作,虽然都是使用了 JDBC接口,但是一些地方还是有差异的,这个实战示例能比较好地体现 Hive 与关系型数据库的异同。
如果我们直接采用 MapReduce 来做,效率会比使用 Hive 高,因为 Hive 的底层就是调用了 MapReduce,但是程序的复杂度和编码量都会大大增加,特别是对于不熟悉 MapReduce编程的开发人员,这是一个棘手问题。Hive 在这两种方案中找到了平衡,不仅处理效率较高,而且实现起来也相对简单,给传统关系型数据库编码人员带来了便利,这就是目前 Hive被许多商业组织所采用的原因。