PreparedStatement.addBatch and thousands of rows from a file and a confusion

Question

Hi I am trying to write to Sybase IQ using JDBC from a file which contains thousands of rows. People say that I should use batchUpdate. So I am reading file by NIO and adding it to PreparedStatement batches. But I dont see any advantage here for all the rows I need to do the following

PreparedStatement prepStmt = con.prepareStatement(    
    "UPDATE DEPT SET MGRNO=? WHERE DEPTNO=?");
  prepStmt.setString(1,mgrnum1);            
  prepStmt.setString(2,deptnum1);
  prepStmt.addBatch();

I dont understand what is the advantage of batches. I have to anyhow execute addBatch for thousands of time for all the records of file. Or Should I even be using addBatch() to write records from a file to sybase iq. Please guide. Thanks a lot.

Answer 1

With batch updates, basically, you're cutting down on your Network I/O overhead. It's providing the benefits analogous to what a BufferedWriter provides you while writing to the disk. That's basically what this is: buffering of database updates.

Any kind of I/O has a cost; be it disk I/O or network. By buffering your inserts or updates in a batch and doing a bulk update you're minimizing the performance hit incurred every time you hit the database and come back.

The performance hit becomes even more obvious in case of a real world application where the database server is almost always under some load serving other clients as opposed to development where you're the only one.

When paired with a PreparedStatement the bulk updates are even more efficient because the Statement is pre-compiled and the execution plan is cached as well throughout the execution of the batch. So, the binding of variables happen as per your chosen batch size and then a single batchUpdate() call persists all the values in one go.

Answer 2

The advantage of addBatch is that it allows the jdbc driver to write chunks of data instead of sending single insert statements to the database. This can be faster in certain situations, but real life performance may vary. It should also be noticed that it's recommended to use batches of 50-100 rows, instead of adding all the data into a single batch.