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Java一次性查询几百万数据的高效方法

        【IT168 技术】java一次性查询几十万,几百万数据解决办法。很早的时候写工具用的一个办法。

  当时是用来把百万数据打包 成rar文件。所以用了个笨办法。 希望高手指导一下,有什么好方法没有啊

  1、先批量查出所有数据,例子中是一万条一批。

  2、在查出数据之后把每次的数据按一定规则存入本地文件。

  3、获取数据时,通过批次读取,获得大批量数据。此方法参见:http://yijianfengvip.blog.163.com/blog/static/175273432201191354043148/

  以下是查询数据库。按批次查询

public static void getMonthDataList() {

  ResultSet rs
= null;

  Statement stat
= null;

  Connection conn
= null;

  List list
= new ArrayList();

  
try {

  conn
= createConnection();

  
if(conn!=null){

  SimpleDateFormat sdf
= new SimpleDateFormat("yyyy-MM-dd");

  SimpleDateFormat timesdf
= new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");

  String nowDate
= sdf.format(new Date());

  Config.lasttimetext
= timesdf.format(new Date());

  String lastDate
= sdf.format(CreateData.addDaysForDate(new Date(), 30));

  stat
= conn.createStatement(ResultSet.TYPE_SCROLL_SENSITIVE,ResultSet.CONCUR_UPDATABLE);

  
int lastrow = 0;

  
int datanum = 0;

  String countsql
= "SELECT count(a.id) FROM trip_special_flight a" +

  
" where a.dpt_date >= to_date('"+nowDate+"','yyyy-mm-dd') " +

  
"and a.dpt_date <= to_date('"+lastDate+"','yyyy-mm-dd') and rownum>"+lastrow+" order by a.get_time desc";

  rs
= stat.executeQuery(countsql);

  
while (rs.next()) {

  datanum
= rs.getInt(1);

  }

  
int onerun = 10000;

  
int runnum = datanum%onerun==0?(datanum/onerun):(datanum/onerun)+1;

  
for(int r =0;r

  System.out.println(
"getMonthDataList--"+datanum+" 开始查询第"+(r+1)+"批数据");

  String sql
= "SELECT * FROM (SELECT rownum rn, a.dpt_code, a.arr_code,a.dpt_date,a.airways,a.flight," +

  
"a.cabin,a.price FROM trip_special_flight a" +

  
" where a.dpt_date >= to_date('"+nowDate+"','yyyy-mm-dd') " +

  
"and a.dpt_date <= to_date('"+lastDate+"','yyyy-mm-dd') order by rownum asc) WHERE rn > "+lastrow;

  stat.setMaxRows(onerun);

  stat.setFetchSize(
1000);

  rs
= stat.executeQuery(sql);

  String text
= "";

  
int i = 1;

  
while (rs.next()) {

  text
+= rs.getString(2)+"|"+rs.getString(3)+"|"+rs.getDate(4)+"|"+rs.getString(5)+"|"+rs.getString(6)+"|"+rs.getString(7)+"|"+rs.getString(8)+"||";

  
if(i%1000==0){

  FileUtil.appendToFile(Config.tempdatafile, text);

  text
= "";

  }

  i
++;

  }

  
if(text.length()>10){

  FileUtil.appendToFile(Config.tempdatafile, text);

  }

  lastrow
+=onerun;

  }

  }

  }
catch (Exception e) {

  e.printStackTrace();

  }
finally {

  closeAll(rs, stat, conn);

  }

  }

  -----java一次性查询几十万,几百万数据解决办法

  存入临时文件之后,再用读取大量数据文件方法。

  设置缓存大小BUFFER_SIZE ,Config.tempdatafile是文件地址

  来源博客http://yijianfengvip.blog.163.com/blog/static/175273432201191354043148/

package com.yjf.util;

  
import java.io.File;

  
import java.io.RandomAccessFile;

  
import java.nio.MappedByteBuffer;

  
import java.nio.channels.FileChannel;

  
public class Test {

  
public static void main(String[] args) throws Exception {

  
final int BUFFER_SIZE = 0x300000; // 缓冲区为3M

  File f
= new File(Config.tempdatafile);

  
// 来源博客http://yijianfengvip.blog.163.com/blog/static/175273432201191354043148/

  
int len = 0;

  Long start
= System.currentTimeMillis();

  
for (int z = 8; z >0; z--) {

  MappedByteBuffer inputBuffer
= new RandomAccessFile(f, "r")

  .getChannel().map(FileChannel.MapMode.READ_ONLY,

  f.length()
* (z-1) / 8, f.length() * 1 / 8);

  
byte[] dst = new byte[BUFFER_SIZE];// 每次读出3M的内容

  
for (int offset = 0; offset < inputBuffer.capacity(); offset += BUFFER_SIZE) {

  
if (inputBuffer.capacity() - offset >= BUFFER_SIZE) {

  
for (int i = 0; i < BUFFER_SIZE; i++)

  dst[i]
= inputBuffer.get(offset + i);

  }
else {

  
for (int i = 0; i < inputBuffer.capacity() - offset; i++)

  dst[i]
= inputBuffer.get(offset + i);

  }

  
int length = (inputBuffer.capacity() % BUFFER_SIZE == 0) ? BUFFER_SIZE

  : inputBuffer.capacity()
% BUFFER_SIZE;

  len
+= new String(dst, 0, length).length();

  System.out.println(
new String(dst, 0, length).length()+"-"+(z-1)+"-"+(8-z+1));

  }

  }

  System.out.println(len);

  
long end = System.currentTimeMillis();

  System.out.println(
"读取文件文件花费:" + (end - start) + "毫秒");

  }

  }

  读取大量数据文件方法

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