Java Programming with Oracle JDBC

Java Programming with Oracle JDBC

by Donald Bales
     
 

JDBC is the key Java technology for relational database access. Oracle is arguably the most widely used relational database platform in the world. In this book, Donald Bales brings these two technologies together, and shows you how to leverage the full power of Oracle's implementation of JDBC.See more details below

Overview

JDBC is the key Java technology for relational database access. Oracle is arguably the most widely used relational database platform in the world. In this book, Donald Bales brings these two technologies together, and shows you how to leverage the full power of Oracle's implementation of JDBC.

Product Details

ISBN-13:
9780596000882
Publisher:
O'Reilly Media, Incorporated
Publication date:
12/28/2001
Pages:
498
Sales rank:
1,191,563
Product dimensions:
7.04(w) x 9.22(h) x 1.14(d)

Read an Excerpt

Chapter 19: Performance

Performance is usually considered an issue at the end of a development cycle when it should really be considered from the start. Often, a task called "performance tuning" is done after the coding is complete, and the end user of a program complains about how long it takes the program to complete a particular task. The net result of waiting until the end of the development cycle to consider performance includes the expense of the additional time required to recode a program to improve its performance. It's my opinion that performance is something that is best considered at the start of a project.

When it comes to performance issues concerning JDBC programming there are two major factors to consider. The first is the performance of the database structure and the SQL statements used against it. The second is the relative efficiency of the different ways you can use the JDBC interfaces to manipulate a database.

In terms of the database's efficiency, you can use the EXPLAIN PLAN facility to explain how the database's optimizer plans to execute your SQL statements. Armed with this knowledge, you may determine that additional indexes are needed, or that you require an alternative means of selecting the data you desire.

On the other hand, when it comes to using JDBC, you need to know ahead of time the relative strengths and weaknesses of using auto-commit, SQL92 syntax, and a Statement versus a PreparedStatement versus a CallableStatement object. In this chapter, we'll examine the relative performance of various JDBC objects using example programs that report the amount of time it takes to accomplish a given task. We'll first look at auto-commit. Next, we'll look at the impact of the SQL92 syntax parser. Then we'll start a series of comparisons of the Statement object versus the PreparedStatement object versus the CallableStatement object. At the same time we'll also examine the performance of the OCI versus the Thin driver in each situation to see if, as Oracle's claims, there is a significant enough performance gain with the OCI driver that you should use it instead of the Thin driver. For the most part, our discussions will be based on timing data for 1,000 inserts into the test performance table TESTXXXPERF. There are separate programs for performing these 1,000 inserts using the OCI driver and the Thin driver.

The performance test programs themselves are very simple and are available online with the rest of the examples in this book. However, for brevity, I'll not show the code for the examples in this chapter. I'll only talk about them. Although the actual timing values change from system to system, their relative values, or ratios from one system to another, remain consistent. The timings used in this chapter were gathered using Windows 2000. Using objective data from these programs allows us to come to factual conclusions on which factors improve performance, rather than relying on hearsay.

I'm sure you'll be surprised at the reality of performance for these objects, and I hope you'll use this knowledge to your advantage. Let's get started with a look at the testing framework used in this chapter.

A Testing Framework

For the most part, the test programs in this chapter report the timings for inserting data into a table. I picked an INSERT statement because it eliminates the performance gain of the database block buffers that may skew timings for an UPDATE, DELETE, or SELECT statement.

The test table used in the example programs in this chapter is a simple relational table. I wanted it to have a NUMBER, a small VARCHAR2, a large VARCHAR2, and a DATE column. Table TESTXXXPERF is defined as:

create table TestXXXPerf (
id          number,
code        varchar2(30),
descr       varchar2(80),
insert_user varchar2(30),
insert_date date )
tablespace users pctfree 20
storage( initial 1 M next 1 M pctincrease 0 );
 
alter table TestXXXPerf
add constraint TestXXXPerf_Pk
primary key ( id )
using index
tablespace users pctfree 20
storage( initial 1 M next 1 M pctincrease 0 );

The initial extent size used for the table makes it unlikely that the database will need to take the time to allocate another extent during the execution of one of the test programs. Therefore, extent allocation will not impact the timings. Given this background, you should have a context to understand what is done in each section by each test program.

Auto-Commit

By default, JDBC's auto-commit feature is on, which means that each SQL statement is committed as it is executed. If more than one SQL statement is executed by your program, then a small performance increase can be achieved by turning off auto-commit.

Let's take a look at some numbers. Table 19-1 shows the average time, in milliseconds, needed to insert 1,000 rows into the TESTXXXPERF table using a Statement object. The timings represent the average from three runs of the program. Both drivers experience approximately a one-second loss as overhead for committing between each SQL statement. When you divide that one second by 1,000 inserts, you can see that turning off auto-commit saves approximately 0.001 seconds (1 millisecond) per SQL statement. While that's not interesting enough to write home about, it does demonstrate how auto-commit can impact performance.

Table 19-1: Auto-commit timings (in milliseconds)

Auto-commit

OCI

Thin

On

3,712

3,675

Off

2,613

2,594

Clearly, it's more important to turn off auto-commit for managing multistep transactions than for gaining performance. But on a heavily loaded system where many users are committing transactions, the amount of time it takes to perform commits can become quite significant. So my recommendation is to turn off auto-commit and manage your transactions manually. The rest of the tests in this chapter are performed with auto-commit turned off....

SQL92 Token Parsing

Like auto-commit, SQL92 escape syntax token parsing is on by default. In case you don't recall, SQL92 token parsing allows you to embed SQL92 escape syntax in your SQL statements (see "Oracle and SQL92 Escape Syntax" in Chapter 9). These standards-based snippets of syntax are parsed by a JDBC driver transforming the SQL statement into its native syntax for the target database. SQL92 escape syntax allows you to make your code more portable--but does this portability come with a cost in terms of performance?

Table 19-2 shows the number of milliseconds needed to insert 1,000 rows into the TESTXXXPERF table. Timings are shown with the SQL92 escape syntax parser on and off for both the OCI and Thin drivers. As before, these timings represent the result of three program runs averaged together.

Table 19-2: SQL92 token parser timings (in milliseconds)

SQL92 parser

OCI

Thin

On

2,567

2,514

Off

2,744

2,550

Notice from Table 19-2 that with the OCI driver we lose 177 milliseconds when escape syntax parsing is turned off, and we lose only 37 milliseconds when the parser is turned off with the Thin driver. These results are the opposite of what you might intuitively expect. It appears that both drivers have been optimized for SQL92 parsing, so you should leave it on for best performance.

Now that you know you never have to worry about turning the SQL92 parser off, let's move on to something that has some potential for providing a substantial performance improvement.

Statement Versus PreparedStatement

There's a popular belief that using a PreparedStatement object is faster than using a Statement object. After all, a prepared statement has to verify its metadata against the database only once, while a statement has to do it every time. So how could it be any other way? Well, the truth of the matter is that it takes about 65 iterations of a prepared statement before its total time for execution catches up with a statement. This has performance implications for your application, and exploring these issues is what this section is all about.

When it comes to which SQL statement object performs better under typical use, a Statement or a PreparedStatement, the truth is that the Statement object yields the best performance. When you consider how SQL statements are typically used in an application--1 or 2 here, maybe 10-20 (rarely more) per transaction--you realize that a Statement object will perform them in less time than a PreparedStatement object. In the next two sections, we'll look at this performance issue with respect to both the OCI driver and the Thin driver.

The OCI Driver

Table 19-3 shows the timings in milliseconds for 1 insert and 1,000 inserts in the TESTXXXPERF table. The inserts are done first using a Statement object and then a PreparedStatement object. If you look at the results for 1,000 inserts, you may think that a prepared statement performs better. After all, at 1,000 inserts, the PreparedStatement object is almost twice as fast as the Statement object, but if you examine Figure 19-1, you'll see a different story.

Table 19-3: OCI driver timings (in milliseconds)

Inserts

Statement

PreparedStatement

1

10

113

1,000

2,804

1,412

Figure 19-1 is a graph of the timings needed to insert varying numbers of rows using both a Statement object and a PreparedStatement object. The number of inserts begins at 1 and climbs in intervals of 10 up to a maximum of 150 inserts. For this graph and for those that follow, the lines themselves are polynomial trend lines with a factor of 2. I chose polynomial lines instead of straight trend lines so you can better see a change in the performance as the number of inserts increases. I chose a factor of 2 so the lines have only one curve in them. The important thing to notice about the graph is that it's not until about 65 inserts that the PreparedStatement object outperforms the Statement object. 65 inserts! Clearly, the Statement object is more efficient under typical use when using the OCI driver.

The Thin Driver

If you examine Table 19-4 (which shows the same timings as for Table 19-3, but for the Thin driver) and Figure 19-2 (which shows the data incrementally), you'll see that the Thin driver follows the same behavior as the OCI driver. However, since the Statement object starts out performing better than the PreparedStatement object, it takes about 125 inserts for the PreparedStatement to outperform Statement....

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