Apress.Expert.Oracle.Database.Architecture.9i.and.10g.Programming.Techniques.and.Solutions.Sep.2005

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CHAPTER 6 ■ LOCKING AND LATCHING 225 Now, if we were to run two of these programs simultaneously, we might expect the hard parsing to jump to about 1,600 per second (we have two CPUs available, after all) and the CPU time to double to perhaps 52 CPU seconds. Let’s take a look: Elapsed: 0.78 (mins) Load Profile ~~~~~~~~~~~~ Per Second Per Transaction --------------- --------------- Parses: 1,066.62 16,710.33 Hard parses: 1,064.28 16,673.67 Top 5 Timed Events ~~~~~~~~~~~~~~~~~~ % Total Event Waits Time (s) Call Time -------------------------------------------- ------------ ----------- --------- CPU time 74 97.53 log file parallel write 53 1 1.27 latch: shared pool 406 1 .66 control file parallel write 21 0 .45 log file sync 6 0 .04 What we discover is that the hard parsing goes up a little bit, but the CPU time triples rather than doubles! How could that be? The answer lies in Oracle’s implementation of latching. On this multi-CPU machine, when we could not immediately get a latch, we “spun.” The act of spinning itself consumes CPU. Process 1 attempted many times to get a latch onto the Shared pool only to discover that process 2 held that latch, so process 1 had to spin and wait for it (consuming CPU). The converse would be true for process 2—many times it would find that process 1 was holding the latch to the resource it needed. So, much of our processing time was spent not doing real work, but waiting for a resource to become available. If we page down through the Statspack report to the “Latch Sleep Breakdown” report, we discover the following: Latch Name Requests Misses Sleeps Sleeps 1->3+ ---------------- ------------- ----------- -------- ------------ shared pool 1,126,006 229,537 406 229135/398/4/0 library cache 1,108,039 45,582 7 45575/7/0/0 Note how the number 406 appears in the SLEEPS column here? That 406 corresponds to the number of waits reported in the preceding “Top 5 Timed Events” report. This report shows us the number of times we tried to get a latch and failed in the spin loop. That means the “Top 5” report is showing us only the tip of the iceberg with regard to latching issues—the 229,537 misses (which means we spun trying to get the latch) are not revealed in the “Top 5” report for us. After examination of the “Top 5” report, we might not be inclined to think “We have a hard parse problem here,” even though we have a very serious one. To perform 2 units of work, we needed to use 3 units of CPU. This was due entirely to the fact that we need that shared resource, the Shared pool—such is the nature of latching. However, it can be very hard to diagnose a latching-related issue, unless we understand the mechanics of how they are implemented. A quick glance at a Statspack report, using the “Top 5” section, might cause us
226 CHAPTER 6 ■ LOCKING AND LATCHING to miss the fact that we have a fairly bad scaling issue on our hands. Only by deeper investigation in the latching section of the Statspack report will we see the problem at hand. Additionally, it is not normally possible to determine how much of the CPU time used by the system is due to this spinning—all we know in looking at the two-user test is that we used 74 seconds of CPU time and that we missed getting a latch on the Shared pool 229,537 times. We don’t know how many times we spun trying to get the latch each time we missed, so we have no real way of gauging how much of the CPU time was spent spinning and how much was spent processing. We need multiple data points to derive that information. In our tests, because we have the single-user example for comparison, we can conclude that about 22 CPU seconds was spent spinning on the latch, waiting for that resource. With Bind Variables Now I’d like to look at the same situation as presented in the previous section, but this time using a program that uses significantly less latches during its processing. We’ll take that Java program and code it using bind variables. To accomplish this, we’ll change the Statement into a PreparedStatement, parse a single INSERT statement, and then bind and execute that PreparedStatement repeatedly in the loop: import java.sql.*; public class instest { static public void main(String args[]) throws Exception { DriverManager.registerDriver(new oracle.jdbc.driver.OracleDriver()); Connection conn = DriverManager.getConnection ("jdbc:oracle:thin:@dellpe:1521:ora10gr1", "scott","tiger"); conn.setAutoCommit( false ); PreparedStatement pstmt = conn.prepareStatement ("insert into "+ args[0] + " (x) values(?)" ); for( int i = 0; i < 25000; i++ ) { pstmt.setInt( 1, i ); pstmt.executeUpdate(); } conn.commit(); conn.close(); } } Let’s look at the single and dual user Statspack reports, as we did for the “no bind variable” example. We’ll see dramatic differences here. Here is the single-user report:
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Expert Oracle Database Architecture
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Contents Foreword . . . . . . . . .
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■CONTENTS v Block Buffer Cache .
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Foreword “THINK.” In 1914, Thom
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About the Technical Reviewers ■JO
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Introduction The inspiration for th
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