What you Didn’t Know About JDBC Batch

In our previous blog post “10 Common Mistakes Java Developers Make When Writing SQL“, we have made a point about batching being important when inserting large data sets. In most databases and with most JDBC drivers, you can get a significant performance improvement when running a single prepared statement in batch mode as such:

PreparedStatement s = connection.prepareStatement(
    "INSERT INTO author(id, first_name, last_name)"
  + "  VALUES (?, ?, ?)");

s.setInt(1, 1);
s.setString(2, "Erich");
s.setString(3, "Gamma");
s.addBatch();

s.setInt(1, 2);
s.setString(2, "Richard");
s.setString(3, "Helm");
s.addBatch();

s.setInt(1, 3);
s.setString(2, "Ralph");
s.setString(3, "Johnson");
s.addBatch();

s.setInt(1, 4);
s.setString(2, "John");
s.setString(3, "Vlissides");
s.addBatch();

int[] result = s.executeBatch();

Or with jOOQ:

create.batch(
        insertInto(AUTHOR, ID, FIRST_NAME, LAST_NAME)
       .values((Integer) null, null, null))
      .bind(1, "Erich", "Gamma")
      .bind(2, "Richard", "Helm")
      .bind(3, "Ralph", "Johnson")
      .bind(4, "John", "Vlissides")
      .execute();

What you probably didn’t know, however, is how dramatic the improvement really is and that JDBC drivers like that of MySQL don’t really support batching, whereas Derby, H2, and HSQLDB don’t really seem to benefit from batching. James Sutherland has assembled this very interesting benchmark on his Java Persistence Performance blog, which can be summarised as such:

The above table shows the improvement when comparing each database against itself for INSERT, not databases against each other. Regardless of the actual results, it can be said that batching is never worse than not batching for the data set sizes used in the benchmark.

See the full article here to see a more detailed interpretation of the above benchmark results, as well as results for UPDATE statements:

http://java-persistence-performance.blogspot.ch/2013/05/batch-writing-and-dynamic-vs.htm