使用休眠

Question

我有一个csv文件，大约有100万条记录。每行代表一个学生对象。

在服务层中，我正在读取csv文件，并将其更改为实体，如下所示：

 File file = new File("test.csv");
    try
    {
        String csvFile = "test.csv";
        BufferedReader br = null;
        String line = "";
        String cvsSplitBy = ",";
        try
        {
            br = new BufferedReader(new FileReader(csvFile));
            while ((line = br.readLine()) != null)
            {
                // use comma as separator
                String[] student = line.split(cvsSplitBy);
                System.out
                    .println("Country [code= " + student[0] + " , name=" + student[1] + "]");
                StudentTable st = new StudentTable();
                st.setEmplyoee(student[0]);
                st.setName(student[1]);
                springbootDao.saveStudent(st);
            }
        }
        catch (FileNotFoundException e)
        {
            e.printStackTrace();
        }
        catch (IOException e)
        {
            e.printStackTrace();
        }
        finally
        {
            if (br != null)
            {
                try
                {
                    br.close();
                }
                catch (IOException e)
                {
                    e.printStackTrace();
                }
            }
        }
    }
    catch (Exception e)
    {
        e.printStackTrace();
    }

道层

void saveStudent(StudentTable st)
    {
        getSession();
        session.save(st);
        session.beginTransaction().commit();
    }


private Session getSession()
    {
        if (session == null)
        {
            session = entity.unwrap(SessionFactory.class).openSession();
        }
        return session;
    }

当我尝试保存它时，它可以节省一百万次时间，比如说保存每一行，这应该符合逻辑。

显然，这不是一个好方法。无论如何，我是否可以使用多个线程来优化它以提高速度，因为单线程显然会花费更多时间，或者休眠时可以使用任何方法转储数据以提高速度？

更多更新：

我将其更改为

while ((line = br.readLine()) != null)
{
// use comma as separator
String[] student = line.split(cvsSplitBy); //java.lang.OutOfMemoryError: Java heap space
StudentTable st = new StudentTable();
st.setEmplyoee(student[0]);
st.setName(student[1]);
stList.add(st);
}
springbootDao.saveStudent(stList);

现在我正面临内存不足错误

Answer 1

以这种方式完成

服务层：

String csvFile = "test.csv";
    BufferedReader br = null;
    String line = "";
    String cvsSplitBy = ",";
    try
    {
        List<StudentTable> stList = new ArrayList<StudentTable>();
        br = new BufferedReader(new FileReader(csvFile));
        StudentTable st = new StudentTable();
        while ((line = br.readLine()) != null)
        {
            // use comma as separator
            String[] student = line.split(cvsSplitBy);
            st.setEmplyoee(student[0]);
            st.setName(student[1]);
            stList.add(st);
        }
        springbootDao.saveStudent(stList);
    }
    catch (FileNotFoundException e)
    {
        e.printStackTrace();
    }
    catch (IOException e)
    {
        e.printStackTrace();
    }
    finally
    {
        if (br != null)
        {
            try
            {
                br.close();
            }
            catch (IOException e)
            {
                e.printStackTrace();
            }
        }
    }
dao layer:




// System.out.println("dao layer" + st);
    getSession();
    Transaction tx = session.beginTransaction();
    session.doWork(new Work()
    {
        @Override
        public void execute(Connection conn)
            throws SQLException
        {
            PreparedStatement pstmt = null;
            try
            {
                String sqlInsert = "insert into student (name,emplyoee) values (?,?) ";
                pstmt = conn.prepareStatement(sqlInsert);
                int i = 0;
                for (StudentTable name : st)
                {
                    pstmt.setString(1, name.getName());
                    pstmt.setString(2, name.getEmplyoee());
                    pstmt.addBatch();
                    // 20 : JDBC batch size
                    if (i % 200000 == 0)
                    {
                        System.out.println(name.getEmplyoee() + "-------------------");
                        pstmt.executeBatch();
                    }
                    i++;
                }
                pstmt.executeBatch();
            }
            finally
            {
                pstmt.close();
            }
        }
    });
    tx.commit();
    session.close();
    // session.save(st);
    // session.beginTransaction().commit();