92课程作业，hive自定义IMFRecordReader，切分^^尖尖头及| 分割符

Ne0inhk

25 Dec 2024 — 13 min read

代码写好了，还没有运行，到时候在hive中测试一把

IMFInputFormat

package com.dt.spark.hive;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileSplit;
import org.apache.hadoop.mapred.InputSplit;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.JobConfigurable;
import org.apache.hadoop.mapred.RecordReader;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;

public class IMFInputFormat extends TextInputFormat implements
JobConfigurable
{
public RecordReader<LongWritable, Text> getRecordReader(
InputSplit genericSplit, JobConf job, Reporter reporter)
throws IOException {

reporter.setStatus(genericSplit.toString());
return new IMFRecordReader((FileSplit) genericSplit,job);
}

}

IMFRecordReader

package com.dt.spark.hive;

import java.io.IOException;
import java.io.InputStream;
import java.util.regex.Matcher;
import java.util.regex.Pattern;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.compress.CompressionCodec;
import org.apache.hadoop.io.compress.CompressionCodecFactory;
import org.apache.hadoop.mapred.FileSplit;
import org.apache.hadoop.util.LineReader;
import org.apache.hadoop.mapred.RecordReader;

public class IMFRecordReader implements
RecordReader<LongWritable, Text> {

private CompressionCodecFactory compressionCodecs = null;
private long start;
private long pos;
private long end;
private LineReader lineReader;
int maxLineLength;

public IMFRecordReader(FileSplit inputSplit, Configuration job)
throws IOException {
maxLineLength = job.getInt("mapred.IMFRecordReader.maxlength",
Integer.MAX_VALUE);
start = inputSplit.getStart();
end = start + inputSplit.getLength();
final Path file = inputSplit.getPath();
compressionCodecs = new CompressionCodecFactory(job);
final CompressionCodec codec = compressionCodecs.getCodec(file);

// Open file and seek to the start of the split
FileSystem fs = file.getFileSystem(job);
FSDataInputStream fileIn = fs.open(file);
boolean skipFirstLine = false;
if (codec != null) {
lineReader = new LineReader(codec.createInputStream(fileIn), job);
end = Long.MAX_VALUE;
} else {
if (start != 0) {
skipFirstLine = true;
--start;
fileIn.seek(start);
}
lineReader = new LineReader(fileIn, job);
}
if (skipFirstLine) {
start += lineReader.readLine(new Text(), 0,
(int) Math.min((long) Integer.MAX_VALUE, end - start));
}
this.pos = start;
}

public IMFRecordReader(InputStream in, long offset, long endOffset,
int maxLineLength) {
this.maxLineLength = maxLineLength;
this.lineReader = new LineReader(in);
this.start = offset;
this.pos = offset;
this.end = endOffset;
}

public IMFRecordReader(InputStream in, long offset, long endOffset,
Configuration job) throws IOException {
this.maxLineLength = job.getInt(
"mapred.IMFRecordReader.maxlength", Integer.MAX_VALUE);
this.lineReader = new LineReader(in, job);
this.start = offset;
this.pos = offset;
this.end = endOffset;
}

public LongWritable createKey() {
return new LongWritable();
}

public Text createValue() {
return new Text();
}

/**
* Reads the next record in the split. get usefull fields from the raw nginx
* log.
*
* @param key
* key of the record which will map to the byte offset of the
* record's line
* @param value
* the record in text format
* @return true if a record existed, false otherwise
* @throws IOException
*/

public synchronized boolean next(LongWritable key, Text value)
throws IOException {
// Stay within the split
while (pos < end) {
key.set(pos);
int newSize = lineReader.readLine(value, maxLineLength,
Math.max((int) Math.min(Integer.MAX_VALUE, end - pos),
maxLineLength));

if (newSize == 0)
return false;

// String str = value.toString().toLowerCase()
// .replaceAll("\\@\\_\\@", "\001");
// String str = value.toString().toLowerCase()
// .replaceAll("\\^\\^", "\001");

String patternhive ="^(.*)\\^\\^(.*)\\^\\^(.*)\\^\\^(.*)\\|(.*)\\|(.*)\\|(.*)\\^\\^(.*)";

Pattern phive = Pattern.compile(patternhive);
String strhive ="0^^Hadoop^^America^^5000|8000|12000|level8^^male";
System.out.println(strhive);
Matcher mhive = phive.matcher(strhive);
String resultstr ="defaultisblank";
while(mhive.find()){
resultstr = mhive.group(1)+"\t"+mhive.group(2)+"\t"+mhive.group(3)+"\t"+
mhive.group(4)+"\t"+mhive.group(5)+"\t"+mhive.group(6)+"\t"+
mhive.group(7)+"\t"+mhive.group(8);

System.out.println(resultstr);
} ;

value.set(resultstr);
pos += newSize;

if (newSize < maxLineLength)
return true;
}

return false;
}

public float getProgress() {
if (start == end) {
return 0.0f;
} else {
return Math.min(1.0f, (pos - start) / (float) (end - start));
}
}

public synchronized long getPos() throws IOException {
return pos;
}

public synchronized void close() throws IOException {
if (lineReader != null)
lineReader.close();
}

// 测试输出
public static void main(String ags[]){
//http://blog.csdn.net/iquicksandi/article/details/8533699
String str1 ="123@_@abcd@_@fk".replaceAll("\\@\\_\\@", "\001");

System.out.println(str1);
System.out.println("\001");

String str2 ="0^^Hadoop^^America^^5000|8000|12000|level8^^male".replaceAll("\\^\\^", "\001");
System.out.println(str2);

String str3 ="0^^Hadoop^^America^^5000|8000|12000|level8^^male".replaceAll("\\^\\^", "\001");
System.out.println(str3);

String patternhive ="^(.*)\\^\\^(.*)\\^\\^(.*)\\^\\^(.*)\\|(.*)\\|(.*)\\|(.*)\\^\\^(.*)";

Pattern phive = Pattern.compile(patternhive);
String strhive ="0^^Hadoop^^America^^5000|8000|12000|level8^^male";
System.out.println(strhive);
Matcher mhive = phive.matcher(strhive);
while(mhive.find()){
String resultstr = mhive.group(1)+"\t"+mhive.group(2)+"\t"+mhive.group(3)+"\t"+
mhive.group(4)+"\t"+mhive.group(5)+"\t"+mhive.group(6)+"\t"+
mhive.group(7)+"\t"+mhive.group(8);

System.out.println(resultstr);
}

System.out.println();
System.out.println("=========================");
System.out.println();
while(mhive.find())

{
System.out.println("m.group():"+mhive.group()); //打印一个大组

System.out.println("m.group(1):"+mhive.group(1)); //打印组1

System.out.println("m.group(2):"+mhive.group(2)); //打印组2
System.out.println("m.group(3):"+mhive.group(3));
System.out.println("m.group(4):"+mhive.group(4));
System.out.println("m.group(5):"+mhive.group(5));
System.out.println("m.group(6):"+mhive.group(6));

System.out.println("m.group(7):"+mhive.group(7));
System.out.println("m.group(8):"+mhive.group(8));
// System.out.println("m.group(9):"+mhive.group(9));
// System.out.println("m.group(10):"+mhive.group(10));
//System.out.println("m.group(11):"+mhive.group(11));
System.out.println();

}

System.out.println();
System.out.println("=========================");
System.out.println();
/*String pattern ="^(\\S+) (\\S+) (\\S+) \\[([\\w/]+)([\\w:/]+)\\s([+\\-]\\d{4})\\] \"(\\S+) (\\S+) (\\S+)\" (\\d{3}) (\\d+)";

Pattern p = Pattern.compile(pattern);

String s = "1.1.11.1- - [1 /oct/2016:00:00:05 +0800] \"POST /Pay HTTP/1.1\" 200 1285";

Matcher m = p.matcher(s);
while(m.find())

{

System.out.println("m.group():"+m.group()); //打印一个大组

System.out.println("m.group(1):"+m.group(1)); //打印组1

System.out.println("m.group(2):"+m.group(2)); //打印组2
System.out.println("m.group(3):"+m.group(3));
System.out.println("m.group(4):"+m.group(4));
System.out.println("m.group(5):"+m.group(5));
System.out.println("m.group(6):"+m.group(6));

System.out.println("m.group(7):"+m.group(7));
System.out.println("m.group(8):"+m.group(8));
System.out.println("m.group(9):"+m.group(9));
System.out.println("m.group(10):"+m.group(10));
System.out.println("m.group(11):"+m.group(11));
System.out.println();

}
System.out.println("捕获个数:groupCount()="+m.groupCount());

*/

}
}

1,/usr/local/IMF_testdata/hivestudy/employeesinputformat.txt
0^^Hadoop^^America^^5000|8000|12000|level8^^male
1^^Spark^^America^^8000|10000|15000|level9^^famale
2^^Flink^^America^^7000|8000|13000|level10^^male
3^^Hadoop^^America^^9000|11000|12000|level10^^famale
4^^Spark^^America^^10000|11000|12000|level12^^male
5^^Flink^^America^^11000|12000|18000|level18^^famale
6^^Hadoop^^America^^15000|16000|19000|level16^^male
7^^Spark^^America^^18000|19000|20000|level20^^male
8^^Flink^^America^^15000|16000|19000|level19^^male

二：导出jar包IMFInputFormat.jar

三，加载jar包
hive> add jar /usr/local/IMF_testdata/hivestudy/IMFInputFormat.jar;

四,建立表

hive> CREATE TABLE employee_InputFormat(userid INT,name String,address String, salarys1 int ,salarys2 int ,salarys3 int ,salarys4 string , gendre string) stored as INPUTFORMAT 'com.dt.spark.hive.IMFInputFormat' OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat';
OK
Time taken: 0.399 seconds
hive>
>

五,导入数据

LOAD DATA LOCAL INPATH '/usr/local/IMF_testdata/hivestudy/employeesinputformat.txt' INTO TABLE employee_InputFormat;

六,查询表描述
hive> desc formatted employee_inputformat;
OK
# col_name data_type comment

userid int
name string
address string
salarys1 int
salarys2 int
salarys3 int
salarys4 string
gendre string

# Detailed Table Information
Database: default
Owner: root
CreateTime: Sun Dec 11 18:14:08 CST 2016
LastAccessTime: UNKNOWN
Protect Mode: None
Retention: 0
Location: hdfs://master:9000/user/hive/warehouse/employee_inputformat
Table Type: MANAGED_TABLE
Table Parameters:
COLUMN_STATS_ACCURATE true
numFiles 1
totalSize 467
transient_lastDdlTime 1481451523

# Storage Information
SerDe Library: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat: com.dt.spark.hive.IMFInputFormat
OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
Compressed: No
Num Buckets: -1
Bucket Columns: []
Sort Columns: []
Storage Desc Params:
serialization.format 1
Time taken: 0.165 seconds, Fetched: 36 row(s)

7,查询表数据

> select * from employee_inputformat;
OK
0^^Hadoop^^America^^5000|8000|12000|level8^^male
0 Hadoop America 5000 8000 12000 level8 male
0^^Hadoop^^America^^5000|8000|12000|level8^^male
0 Hadoop America 5000 8000 12000 level8 male
0^^Hadoop^^America^^5000|8000|12000|level8^^male
0 Hadoop America 5000 8000 12000 level8 male
0^^Hadoop^^America^^5000|8000|12000|level8^^male
0 Hadoop America 5000 8000 12000 level8 male
0^^Hadoop^^America^^5000|8000|12000|level8^^male
0 Hadoop America 5000 8000 12000 level8 male
0^^Hadoop^^America^^5000|8000|12000|level8^^male
0 Hadoop America 5000 8000 12000 level8 male
0^^Hadoop^^America^^5000|8000|12000|level8^^male
0 Hadoop America 5000 8000 12000 level8 male
0^^Hadoop^^America^^5000|8000|12000|level8^^male
0 Hadoop America 5000 8000 12000 level8 male
0^^Hadoop^^America^^5000|8000|12000|level8^^male
0 Hadoop America 5000 8000 12000 level8 male
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
Time taken: 0.563 seconds, Fetched: 9 row(s)

8,里面重复了数据,重新来,原因是代码里测试写的
String strhive ="0^^Hadoop^^America^^5000|8000|12000|level8^^male";
源文件的数据一部分搞乱了,重新清洗了源数据.

删除表,重新来一次
drop table employee_inputformat;

运行结果

rties
hive>
>
>
>
>
>
> drop table employee_inputformat;
OK
Time taken: 1.733 seconds
hive> add jar /usr/local/IMF_testdata/hivestudy/IMFInputFormat2.jar;
Added [/usr/local/IMF_testdata/hivestudy/IMFInputFormat2.jar] to class path
Added resources: [/usr/local/IMF_testdata/hivestudy/IMFInputFormat2.jar]
hive> CREATE TABLE employee_InputFormat(userid INT,name String,address String, salarys1 int ,salarys2 int ,salarys3 int ,salarys4 string , gendre string) stored as INPUTFORMAT 'com.dt.spark.hive.IMFInputFormat' OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat';
OK
Time taken: 0.477 seconds
hive> LOAD DATA LOCAL INPATH '/usr/local/IMF_testdata/hivestudy/employeesinputformat.txt' INTO TABLE employee_InputFormat;
Loading data to table default.employee_inputformat
Table default.employee_inputformat stats: [numFiles=1, totalSize=467]
OK
Time taken: 0.708 seconds
hive> select * from employee_InputFormat;
OK
resultstr============0 Hadoop America 5000 8000 12000 IMFlevel8 male
resultstr============1 Spark America 8000 10000 15000 IMFlevel9 famale
resultstr============2 Flink America 7000 8000 13000 IMFlevel10 male
resultstr============3 Hadoop America 9000 11000 12000 IMFlevel10 famale
resultstr============4 Spark America 10000 11000 12000 IMFlevel12 male
resultstr============5 Flink America 11000 12000 18000 IMFlevel18 famale
resultstr============6 Hadoop America 15000 16000 19000 IMFlevel16 male
resultstr============7 Spark America 18000 19000 20000 IMFlevel20 male
resultstr============8 Flink America 15000 16000 19000 IMFlevel19 male
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
NULL NULL NULL NULL NULL NULL NULL NULL
Time taken: 0.412 seconds, Fetched: 9 row(s)
hive> select count(*) from employee_InputFormat;
Query ID = root_20161211194323_fb9af8cb-dda4-45ac-98a3-cb4222e27652
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1481447173435_0001, Tracking URL = http://master:8088/proxy/application_1481447173435_0001/
Kill Command = /usr/local/hadoop-2.6.0/bin/hadoop job -kill job_1481447173435_0001
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2016-12-11 19:43:42,363 Stage-1 map = 0%, reduce = 0%
2016-12-11 19:43:58,094 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.67 sec
2016-12-11 19:44:16,900 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 4.67 sec
MapReduce Total cumulative CPU time: 4 seconds 670 msec
Ended Job = job_1481447173435_0001
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 4.67 sec HDFS Read: 7714 HDFS Write: 2 SUCCESS
Total MapReduce CPU Time Spent: 4 seconds 670 msec
OK
9
Time taken: 55.879 seconds, Fetched: 1 row(s)
hive> select address from employee_InputFormat;
OK
resultstr============0 Hadoop America 5000 8000 12000 IMFlevel8 male
resultstr============1 Spark America 8000 10000 15000 IMFlevel9 famale
resultstr============2 Flink America 7000 8000 13000 IMFlevel10 male
resultstr============3 Hadoop America 9000 11000 12000 IMFlevel10 famale
resultstr============4 Spark America 10000 11000 12000 IMFlevel12 male
resultstr============5 Flink America 11000 12000 18000 IMFlevel18 famale
resultstr============6 Hadoop America 15000 16000 19000 IMFlevel16 male
resultstr============7 Spark America 18000 19000 20000 IMFlevel20 male
resultstr============8 Flink America 15000 16000 19000 IMFlevel19 male
NULL
NULL
NULL
NULL
NULL
NULL
NULL
NULL
NULL
Time taken: 0.135 seconds, Fetched: 9 row(s)
hive>

drop table employee_inputformat;
add jar /usr/local/IMF_testdata/hivestudy/IMFInputFormat2.jar;
CREATE TABLE employee_InputFormat(userid INT,name String,address String, salarys1 int ,salarys2 int ,salarys3 int ,salarys4 string , gendre string) stored as INPUTFORMAT 'com.dt.spark.hive.IMFInputFormat' OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat';
LOAD DATA LOCAL INPATH '/usr/local/IMF_testdata/hivestudy/employeesinputformat.txt' INTO TABLE employee_InputFormat;
select * from employee_InputFormat;

修改以后的代码

package com.dt.spark.hive;

import java.io.IOException;
import java.io.InputStream;
import java.util.regex.Matcher;
import java.util.regex.Pattern;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.compress.CompressionCodec;
import org.apache.hadoop.io.compress.CompressionCodecFactory;
import org.apache.hadoop.mapred.FileSplit;
import org.apache.hadoop.util.LineReader;
import org.apache.hadoop.mapred.RecordReader;

public class IMFRecordReader implements
RecordReader<LongWritable, Text> {

private CompressionCodecFactory compressionCodecs = null;
private long start;
private long pos;
private long end;
private LineReader lineReader;
int maxLineLength;

public IMFRecordReader(FileSplit inputSplit, Configuration job)
throws IOException {
maxLineLength = job.getInt("mapred.IMFRecordReader.maxlength",
Integer.MAX_VALUE);
start = inputSplit.getStart();
end = start + inputSplit.getLength();
final Path file = inputSplit.getPath();
compressionCodecs = new CompressionCodecFactory(job);
final CompressionCodec codec = compressionCodecs.getCodec(file);

// Open file and seek to the start of the split
FileSystem fs = file.getFileSystem(job);
FSDataInputStream fileIn = fs.open(file);
boolean skipFirstLine = false;
if (codec != null) {
lineReader = new LineReader(codec.createInputStream(fileIn), job);
end = Long.MAX_VALUE;
} else {
if (start != 0) {
skipFirstLine = true;
--start;
fileIn.seek(start);
}
lineReader = new LineReader(fileIn, job);
}
if (skipFirstLine) {
start += lineReader.readLine(new Text(), 0,
(int) Math.min((long) Integer.MAX_VALUE, end - start));
}
this.pos = start;
}

public IMFRecordReader(InputStream in, long offset, long endOffset,
int maxLineLength) {
this.maxLineLength = maxLineLength;
this.lineReader = new LineReader(in);
this.start = offset;
this.pos = offset;
this.end = endOffset;
}

public IMFRecordReader(InputStream in, long offset, long endOffset,
Configuration job) throws IOException {
this.maxLineLength = job.getInt(
"mapred.IMFRecordReader.maxlength", Integer.MAX_VALUE);
this.lineReader = new LineReader(in, job);
this.start = offset;
this.pos = offset;
this.end = endOffset;
}

public LongWritable createKey() {
return new LongWritable();
}

public Text createValue() {
return new Text();
}

/**
* Reads the next record in the split. get usefull fields from the raw nginx
* log.
*
* @param key
* key of the record which will map to the byte offset of the
* record's line
* @param value
* the record in text format
* @return true if a record existed, false otherwise
* @throws IOException
*/

public synchronized boolean next(LongWritable key, Text value)
throws IOException {
// Stay within the split
while (pos < end) {
key.set(pos);
int newSize = lineReader.readLine(value, maxLineLength,
Math.max((int) Math.min(Integer.MAX_VALUE, end - pos),
maxLineLength));

if (newSize == 0)
return false;

// String str = value.toString().toLowerCase()
// .replaceAll("\\@\\_\\@", "\001");
// String str = value.toString().toLowerCase()
// .replaceAll("\\^\\^", "\001");

String patternhive ="^(.*)\\^\\^(.*)\\^\\^(.*)\\^\\^(.*)\\|(.*)\\|(.*)\\|(.*)\\^\\^(.*)";

Pattern phive = Pattern.compile(patternhive);
// String strhive ="0^^Hadoop^^America^^5000|8000|12000|level8^^male";

String strhive =value.toString();
// System.out.println("value.toString()============"+strhive);
//System.out.println("===imf=====");
Matcher mhive = phive.matcher(strhive);
String resultstr ="defaultisblank";
while(mhive.find()){
resultstr = mhive.group(1)+"\t"+mhive.group(2)+"\t"+mhive.group(3)+"\t"+
mhive.group(4)+"\t"+mhive.group(5)+"\t"+mhive.group(6)+"\t"+
"IMF"+mhive.group(7)+"\t"+mhive.group(8);

//System.out.println(resultstr);
} ;
if (resultstr == null || resultstr =="defaultisblank" ) { }
else {
System.out.println("resultstr============"+resultstr);

value.set(resultstr);
pos += newSize;

if (newSize < maxLineLength)
return true;

}
}

return false;
}

public float getProgress() {
if (start == end) {
return 0.0f;
} else {
return Math.min(1.0f, (pos - start) / (float) (end - start));
}
}

public synchronized long getPos() throws IOException {
return pos;
}

public synchronized void close() throws IOException {
if (lineReader != null)
lineReader.close();
}

// 测试输出

/* public static void main(String ags[]){
//http://blog.csdn.net/iquicksandi/article/details/8533699
String str1 ="123@_@abcd@_@fk".replaceAll("\\@\\_\\@", "\001");

System.out.println(str1);
System.out.println("\001");

String str2 ="0^^Hadoop^^America^^5000|8000|12000|level8^^male".replaceAll("\\^\\^", "\001");
System.out.println(str2);

String str3 ="0^^Hadoop^^America^^5000|8000|12000|level8^^male".replaceAll("\\^\\^", "\001");
System.out.println(str3);

String patternhive ="^(.*)\\^\\^(.*)\\^\\^(.*)\\^\\^(.*)\\|(.*)\\|(.*)\\|(.*)\\^\\^(.*)";

Pattern phive = Pattern.compile(patternhive);
String strhive ="0^^Hadoop^^America^^5000|8000|12000|level8^^male";
System.out.println(strhive);
Matcher mhive = phive.matcher(strhive);
while(mhive.find()){
String resultstr = mhive.group(1)+"\t"+mhive.group(2)+"\t"+mhive.group(3)+"\t"+
mhive.group(4)+"\t"+mhive.group(5)+"\t"+mhive.group(6)+"\t"+
mhive.group(7)+"\t"+mhive.group(8);

System.out.println(resultstr);
}

System.out.println();
System.out.println("=========================");
System.out.println();
while(mhive.find())

{
System.out.println("m.group():"+mhive.group()); //打印一个大组

System.out.println("m.group(1):"+mhive.group(1)); //打印组1

System.out.println("m.group(2):"+mhive.group(2)); //打印组2
System.out.println("m.group(3):"+mhive.group(3));
System.out.println("m.group(4):"+mhive.group(4));
System.out.println("m.group(5):"+mhive.group(5));
System.out.println("m.group(6):"+mhive.group(6));

System.out.println("m.group(7):"+mhive.group(7));
System.out.println("m.group(8):"+mhive.group(8));
// System.out.println("m.group(9):"+mhive.group(9));
// System.out.println("m.group(10):"+mhive.group(10));
//System.out.println("m.group(11):"+mhive.group(11));
System.out.println();

}

System.out.println();
System.out.println("=========================");
System.out.println();*/
/*String pattern ="^(\\S+) (\\S+) (\\S+) \\[([\\w/]+)([\\w:/]+)\\s([+\\-]\\d{4})\\] \"(\\S+) (\\S+) (\\S+)\" (\\d{3}) (\\d+)";

Pattern p = Pattern.compile(pattern);

String s = "110.75.141.3 - - [18/Feb/2016:00:00:55 +0800] \"POST /alipayBillPay/InvoicePay HTTP/1.1\" 200 1285";

Matcher m = p.matcher(s);
while(m.find())

{

System.out.println("m.group():"+m.group()); //打印一个大组

System.out.println("m.group(1):"+m.group(1)); //打印组1

System.out.println("m.group(2):"+m.group(2)); //打印组2
System.out.println("m.group(3):"+m.group(3));
System.out.println("m.group(4):"+m.group(4));
System.out.println("m.group(5):"+m.group(5));
System.out.println("m.group(6):"+m.group(6));

System.out.println("m.group(7):"+m.group(7));
System.out.println("m.group(8):"+m.group(8));
System.out.println("m.group(9):"+m.group(9));
System.out.println("m.group(10):"+m.group(10));
System.out.println("m.group(11):"+m.group(11));
System.out.println();

}
System.out.println("捕获个数:groupCount()="+m.groupCount());

*/

// }
}