Super CSV是一个用于处理CSV文件的Java开源项目。它完全围绕面向对象的思想进行设计，因此可以利用你的面向对象代码来使得处理CSV文件变得更加简易。它支持输入/输出类型转换、数据完整性校验，支持从任何地方以任何编码读写数据，只要提供相应的Reader与Writer对象。可配置分割符，空格符号和行结束符等。

一、下面先来看简单数据处理 引入依赖包：

<dependency>
    <groupId>net.sf.supercsv</groupId>
    <artifactId>super-csv</artifactId>
    <version>2.4.0</version>
</dependency>
下面来看一下官方文档中的代码示例。
根据头来读取CSV文件
把文件中的每行记录读取出来转化为java对象，假设你有一个UserBean类，代码如下：
import java.util.Date;
public class UserBean {
    int id;
    Date date;
    String username, password, town;
    int zip;
    public Date getDate() {return date;}
    public void setDate(Date date) {this.date = date;}
    public int getId() { return id;}
    public String getPassword() { return password; }
    public String getTown() { return town; }
    public String getUsername() { return username; }
    public int getZip() { return zip; }
    public void setId(int id) { this.id = id; }
    public void setPassword(String password) { this.password = password; }
    public void setTown(String town) { this.town = town; }
    public void setUsername(String username) { this.username = username; }
    public void setZip(int zip) { this.zip = zip; }
并且有一个CSV文件，包含一个文件头，假设文件内容如下：
id,username,password,date,zip,town
1,Klaus,qwexyKiks,17/1/2007,1111,New York
2,Oufud,bobilop213,10/10/2007,4555,New York
3,Oufud1,bobilop213,10/10/2007,4555,New York
4,Oufud2,bobilop213,10/10/2007,4555,New York
5,Oufud3,bobilop213,10/10/2007,4555,New York
6,Oufud4,bobilop213,10/10/2007,4555,New York
7,Oufud5,bobilop213,10/10/2007,4555,New York
8,Oufud6,bobilop213,10/10/2007,4555,New York
9,Oufud7,bobilop213,10/10/2007,4555,New York
10,Oufud8,bobilop213,10/10/2007,4555,New York
11,Oufud9,bobilop213,10/10/2007,4555,New York
12,Oufud10,bobilop213,10/10/2007,4555,New York
13,Oufud11,bobilop213,10/10/2007,4555,New York
14,Oufud12,bobilop213,10/10/2007,4555,New York
15,Oufud13,bobilop213,10/10/2007,4555,New York
然后你可以使用一下代码来创建UserBean的实例对象，并打印出对象的属性值：
class ReadingObjects {  
        public static void main(String[] args) throws Exception{  
            ICsvBeanReader inFile = new CsvBeanReader(new FileReader("foo.csv"), CsvPreference.STANDARD_PREFERENCE);  
            try {  
                final String[] header = inFile.getCSVHeader(true);  
                UserBean user;  
                while( (user = inFile.read(UserBean.class, header, processors)) != null) {  
                    System.out.println(user.getZip());  
            } finally {  
                inFile.close();  
我们还剩下processors没有定义，通过名字我们可以看出是解析器，用来处理每列的数据，当然你也可以传入null，表示该列不做特殊处理，每个解析器可以被另外一个包含在内部，new Unique(new StrMinMax(5，20))，这个代码该列的值为唯一的，并且长度为8到20，具体处理细节我们先不讲，来看一下我们所需要的processors是如何定义的：
final CellProcessor[] processors = new CellProcessor[] {  
        new Unique(new ParseInt()),
        new Unique(new StrMinMax(5, 20)),  
        new StrMinMax(8, 35),  
        new ParseDate("dd/MM/yyyy"),  
        new Optional(new ParseInt()),  
上面的代码的具体意思为：
第一列是一个字符串，并且值是唯一的，长度为5到20
第二列是一个字符串，长度是8到35
第三列为一个日期类型，格式为天/月/年（day/month/year）
第四列是一个整型数字，但只有这列有值的时候ParseInt处理器才会去处理这个值（其实就是该列可以为空）
第五列为一个字符串（默认），不使用处理器
如果你的CSV文件没有头，你也可以定义个数组来替代：
final String[] header = new String[] { "id",”"username", "password", "date", "zip", "town"}; 
如果你想忽略某一列，和定义处理器类似，直接在头数组中使用null。
全部代码如下：
import Java.io.FileReader;  
import Java.io.IOException;  
import org.supercsv.cellprocessor.Optional;  
import org.supercsv.cellprocessor.ParseDate;  
import org.supercsv.cellprocessor.ParseInt;  
import org.supercsv.cellprocessor.constraint.StrMinMax;  
import org.supercsv.cellprocessor.constraint.Unique;  
import org.supercsv.cellprocessor.ift.CellProcessor;  
import org.supercsv.io.CsvBeanReader;  
import org.supercsv.io.ICsvBeanReader;  
import org.supercsv.prefs.CsvPreference;  
class ReadingObjects {  
    static final CellProcessor[] userProcessors = new CellProcessor[] {  
		    new Unique(new ParseInt()),
        new Unique(new StrMinMax(5, 20)),  
        new StrMinMax(8, 35),  
        new ParseDate("dd/MM/yyyy"),  
        new Optional(new ParseInt()),  
    public static void main(String[] args) throws Exception {  
        ICsvBeanReader inFile = new CsvBeanReader(new FileReader("D:\\foo.csv"), CsvPreference.STANDARD_PREFERENCE);  
        try {  
          final String[] header = inFile.getHeader(true);  
          UserBean user;  
          while( (user = inFile.read(UserBean.class, header, userProcessors)) != null) {  
            System.out.println(user.getZip());  
        } finally {  
          inFile.close();  
public class UserBean {  
    String username, password, town;  
    Date date;  
    int zip;  
    public Date getDate() {  
        return date;  
    public String getPassword() {  
        return password;  
    public String getTown() {  
        return town;  
    public String getUsername() {  
        return username;  
    public int getZip() {  
        return zip;  
    public void setDate(final Date date) {  
        this.date = date;  
    public void setPassword(final String password) {  
        this.password = password;  
    public void setTown(final String town) {  
        this.town = town;  
    public void setUsername(final String username) {  
        this.username = username;  
    public void setZip(final int zip) {  
        this.zip = zip;  
如果你在读取文件之前根本不知道文件的具体格式，你可以选择CsvListReader.read()方法，把每行读出出来的数据放在一个List里面。
读取文件的代码我们看到了，下面来看一下写的操作，也很简单。
import Java.util.HashMap;  
import org.supercsv.io.*;  
import org.supercsv.prefs.CsvPreference;  
class WritingMaps {  
  main(String[] args) throws Exception {  
    ICsvMapWriter writer = new CsvMapWriter(new FileWriter(...), CsvPreference.STANDARD_PREFERENCE);  
    try {  
      final String[] header = new String[] { "name", "city", "zip" };  
      // set up some data to write  
      final HashMap<String, ? super Object> data1 = new HashMap<String, Object>();  
      data1.put(header[0], "Karl");  
      data1.put(header[1], "Tent city");  
      data1.put(header[2], 5565);  
      final HashMap<String, ? super Object> data2 = new HashMap<String, Object>();  
      data2.put(header[0], "Banjo");  
      data2.put(header[1], "River side");  
      data2.put(header[2], 5551);  
      // the actual writing  
      writer.writeHeader(header);  
      writer.write(data1, header);  
      writer.write(data2, header);  
    } finally {  
      writer.close();  
利用MapReader方式解析的代码：
csv文件:
ustomerNo,firstName,lastName,birthDate,mailingAddress,married,numberOfKids,favouriteQuote,email,loyaltyPoints
1,John,Dunbar,13/06/1945,"1600 Amphitheatre Parkway Mountain View, CA 94043 United States",,,"""May the Force be with you."" - Star Wars",jdunbar@gmail.com,0
2,Bob,Down,25/02/1919,"1601 Willow Rd. Menlo Park, CA 94025 United States",Y,0,"""Frankly, my dear, I don't give a damn."" - Gone With The Wind",bobdown@hotmail.com,123456
3,Alice,Wunderland,08/08/1985,"One Microsoft Way Redmond, WA 98052-6399 United States",Y,0,"""Play it, Sam. Play ""As Time Goes By."""" - Casablanca",throughthelookingglass@yahoo.com,2255887799
4,Bill,Jobs,10/07/1973,"2701 San Tomas Expressway Santa Clara, CA 95050 United States",Y,3,"""You've got to ask yourself one question: ""Do I feel lucky?"" Well, do ya, punk?"" - Dirty Harry",billy34@hotmail.com,36
示例代码：
import org.supercsv.cellprocessor.Optional;
import org.supercsv.cellprocessor.ParseBool;
import org.supercsv.cellprocessor.ParseDate;
import org.supercsv.cellprocessor.ParseInt;
import org.supercsv.cellprocessor.constraint.LMinMax;
import org.supercsv.cellprocessor.constraint.NotNull;
import org.supercsv.cellprocessor.constraint.StrRegEx;
import org.supercsv.cellprocessor.constraint.UniqueHashCode;
import org.supercsv.cellprocessor.ift.CellProcessor;
import org.supercsv.io.CsvMapReader;
import org.supercsv.io.ICsvMapReader;
import org.supercsv.prefs.CsvPreference;
import java.io.FileReader;
import java.util.Map;
public class ReadingObjects {
    public static void main(String[] args) throws Exception{
        ICsvMapReader mapReader = null;
        try {
            mapReader = new CsvMapReader(new FileReader("D:\\foo.csv"), CsvPreference.STANDARD_PREFERENCE);
            // the header columns are used as the keys to the Map
            final String[] header = mapReader.getHeader(true);
            final CellProcessor[] processors = getProcessors();
            Map<String,Object> customerMap;
            while( (customerMap = mapReader.read(header, processors)) != null ) {
                System.out.println(String.format("lineNo=%s, rowNo=%s, customerMap=%s", mapReader.getLineNumber(),
                        mapReader.getRowNumber(), customerMap));
        finally {
            if( mapReader != null ) {
                mapReader.close();
    private static CellProcessor[] getProcessors() {
        final String emailRegex = "[a-z0-9._] @[a-z0-9.] "; // just an example, not very robust!
        StrRegEx.registerMessage(emailRegex, "must be a valid email address");
        final CellProcessor[] processors = new CellProcessor[] {
                new UniqueHashCode(), // customerNo (must be unique)
                new NotNull(), // firstName
                new NotNull(), // lastName
                new ParseDate("dd/MM/yyyy"), // birthDate
                new NotNull(), // mailingAddress
                new Optional(new ParseBool()), // married
                new Optional(new ParseInt()), // numberOfKids
                new NotNull(), // favouriteQuote
                new StrRegEx(emailRegex), // email
                new LMinMax(0L, LMinMax.MAX_LONG) // loyaltyPoints
        return processors;}
二、并发分批处理大数据量的数据更新
import org.supercsv.cellprocessor.Optional;
import org.supercsv.cellprocessor.ParseDate;
import org.supercsv.cellprocessor.ParseInt;
import org.supercsv.cellprocessor.constraint.StrMinMax;
import org.supercsv.cellprocessor.constraint.Unique;
import org.supercsv.cellprocessor.ift.CellProcessor;
import org.supercsv.io.CsvBeanReader;
import org.supercsv.io.ICsvBeanReader;
import org.supercsv.prefs.CsvPreference;
import java.io.FileReader;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;
class ThreadReadingObjects {
    static final CellProcessor[] userProcessors = new CellProcessor[] {
            new Unique(new ParseInt()),//唯一的，int id
            new Unique(new StrMinMax(5, 20)),//唯一的，长度为5到20
            new StrMinMax(8, 35), //长度是8到35
            new ParseDate("dd/MM/yyyy"), //格式为天/月/年（day/month/year）
            new Optional(new ParseInt()), //整型数字，但只有这列有值的时候ParseInt处理器才会去处理这个值（其实就是该列可以为空）
            null //不使用处理器
    public static void main(String[] args) throws Exception {
        // InputStreamReader freader = new InputStreamReader(inputStream,"UTF-8");
        // ICsvBeanReader inFile = new CsvBeanReader(freader, CsvPreference.STANDARD_PREFERENCE);
        ICsvBeanReader inFile = new CsvBeanReader(new FileReader("D:\\foo.csv"), CsvPreference.STANDARD_PREFERENCE);
        ExecutorService executorService = null;
        try {
            //如果你的CSV文件没有头，你也可以定义个数组来替代：
            // final String[] header = new String[] { "id","username", "password", "date", "zip", "town"};
            final String[] header = inFile.getHeader(true);
            //创建线程池
            //注意： 线程数不宜过多,jdbc操作时会占用连接数,过多会超出数据库连接
            List<Future<String>> futureList = new ArrayList<Future<String>>();
            executorService = Executors.newFixedThreadPool(5);
            //分页读取数据后,加入线程池处理
            while (getPageUserList(executorService,futureList,inFile, header)) {}
            //获取线程处理结果
            for (Future<String> future : futureList) {
                while (true) {
                    if (future.isDone() && !future.isCancelled()) {
                        System.out.println("future result: "+future.get());
                        break;
                    } else {
                        Thread.sleep(1000);
        } finally {
            inFile.close();
            executorService.shutdown();
    private static boolean getPageUserList(ExecutorService executorService, List<Future<String>> futureList, ICsvBeanReader inFile, String[] header) throws IOException {
        int index = 0;
        boolean status = false;
        List<UserBean> userBeans = new ArrayList<UserBean>();
        UserBean user;
        while ((user = inFile.read(UserBean.class, header, userProcessors)) != null) {// 这里从第一行开始取数据
            userBeans.add(user);
            index++;
            //每次读取的行数，每个线程处理的记录数,根据实际情况修改
            if (index == 10) {
                status = true;
                break;
        //添加到线程集合
        if(!userBeans.isEmpty()){
            Future<String> future = executorService.submit(getUpdateDbJob(futureList.size(),userBeans));
            futureList.add(future);
        return status;
    private static Callable<String> getUpdateDbJob(int threadNo,List<UserBean> userBeans) {
        return new Callable<String>() {
            @Override
            public String call() throws Exception {
                int count = userBeans.size();
                //第一种： 数组List函数分批量处理方法
                batchPageInsertDataOne(threadNo,userBeans);
                //第二种：取% 分批处理方法
//                batchPageInsertDataTwo(threadNo,userBeans);
                return String.valueOf(count);
    private static void batchPageInsertDataOne(int threadNo,List<UserBean> userBeans){
        int perCount = 4, index = 0;
        int times = userBeans.size() / perCount;
        long stime=System.currentTimeMillis();
        try {
                // 休眠50ms
                Thread.sleep(50);
                List<UserBean> listTemp= null;
                if (userBeans.size() >= perCount) {
                    listTemp = userBeans.subList(0, perCount);// listTemp是分段处理逻辑的参数
                    System.out.println("线程"+threadNo+"更新用户："+listTemp.size()+" 个");
                }else{
                    listTemp = userBeans.subList(0, userBeans.size());// listTemp是分段处理逻辑的参数
                    System.out.println("线程"+threadNo+"更新用户："+listTemp.size()+" 个");
                // 事务单元执行个数==尽量在事务里面处理少一点(事务尽量小一点)
                //注意: 每次分批事务提交时数量不宜过多,太多会造成行锁；
                jdbcPerBatchInsert(listTemp);
                userBeans.removeAll(listTemp);
                index++;
            }while(index<= times);
            // 计算时间
            long etime=System.currentTimeMillis();
            System.out.println("线程"+threadNo+"批量事务插入总共耗时-----------------------:"+(etime-stime)+"ms!");
        }catch(Exception e) {
            e.printStackTrace();
            System.out.println("JDBC批量执行插入异常:>>" + userBeans.size());
            throw new RuntimeException();
    private static void batchPageInsertDataTwo(int threadNo,List<UserBean> userBeans){
         long stime=System.currentTimeMillis();
         try {
            //分批量写入数据库
            int perCount = 4;
            List<UserBean> userList = new ArrayList<UserBean>();
            for(int i=0;i<userBeans.size();i++){
                userList.add(userBeans.get(i));
                //如果数据量比较大再次事务分批commit,提交 perCount 条记录
                //取 % 条数根据实际情况修改
                if (i > 0 && i % perCount == 0) {
                    System.out.println("线程"+threadNo+"更新用户："+userList.size()+" 个成功");
                    //采用jdbcTemplate 批量写入数据库
                    jdbcPerBatchInsert(userBeans);
                    userList.clear();
                } else if (i == userBeans.size() - 1) {
                    //处理最后一批数据提交
                    System.out.println("线程"+threadNo+"更新用户："+userList.size()+" 个成功");
                    //采用jdbcTemplate 批量写入数据库
                    jdbcPerBatchInsert(userBeans);
                    userList.clear();
             // 计算时间
             long etime=System.currentTimeMillis();
             System.out.println("线程"+threadNo+"批量事务插入总共耗时-----------------------:"+(etime-stime)+"ms!");
         }catch(Exception e) {
             e.printStackTrace();
             System.out.println("JDBC批量执行插入异常:>>" + userBeans.size());
             throw new RuntimeException();
     * 采用jdbcTemplate 批量写入数据库
     * @param listTemp
    private static void jdbcPerBatchInsert(List<UserBean> listTemp){
运行后返回结果:
线程0更新用户：4 个
线程1更新用户：4 个
线程0更新用户：4 个
线程1更新用户：1 个
线程1批量事务插入总共耗时-----------------------:100ms!
线程0更新用户：2 个
线程0批量事务插入总共耗时-----------------------:151ms!
future result: 10
future result: 5
实际工作中遇到的坑,一起分享给大家,本人实际操作几百万数据处理遇到的问题
org.springframework.jdbc.CannotGetJdbcConnectionException: Could not get JDBC Connection; nested exception is com.alibaba.druid.pool.GetConnectionTimeoutException: wait millis 100000, active 8, maxActive 8, runningSqlCount 7 :
线程数过多,造成数据库连接数不够，调整maxActive最大连接数参数;
Caused by: java.sql.BatchUpdateException: Deadlock found when trying to get lock; try restarting transaction
每次jdbc事务comiit的数量过大，造成锁表问题,尽量在事务里面处理少一点；

好的，记录完毕,觉得看了有帮助的点个赞！O(∩_∩)O哈！