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CHAPTER 1 DEVELOPING SUCCESSFUL ORACLE APPLICATIONS
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The second procedure constructs a unique SQL statement for each row to be inserted: ops$tkyte%ORA11GR2> create or replace procedure proc2 2 as 3 begin 4 for i in 1 .. 10000 5 loop 6 execute immediate 7 'insert into t values ( '||i||')'; 8 end loop; 9 end; 10 / Procedure created. Now, the only difference between the two is that one uses a bind variable and the other does not. Both are using dynamic SQL and the logic is identical. The only difference is the use of a bind variable in the first. We are ready to evaluate the two approaches and we ll use runstats, a simple tool I ve developed, to compare the two in detail:
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Note For details on runstats and other utilities, please see the appendix on Tools & Scripts used in this book.
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ops$tkyte%ORA11GR2> exec runstats_pkg.rs_start PL/SQL procedure successfully completed. ops$tkyte%ORA11GR2> exec proc1 PL/SQL procedure successfully completed. ops$tkyte%ORA11GR2> exec runstats_pkg.rs_middle PL/SQL procedure successfully completed. ops$tkyte%ORA11GR2> exec proc2 PL/SQL procedure successfully completed. ops$tkyte%ORA11GR2> exec runstats_pkg.rs_stop(10000) Run1 ran in 65 cpu hsecs Run2 ran in 1224 cpu hsecs run 1 ran in 5.31% of the time
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Note You may not observe exactly the same values for cpu, nor in fact for any metric, as I report here.
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Differences will be caused by different Oracle versions, different operating systems, or different hardware platforms. The idea will be the same, but the exact numbers will undoubtedly be marginally different.
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Now, the preceding result clearly shows that based on CPU time, it took significantly longer and significantly more resources to insert 10,000 rows without bind variables than it did with them. In fact, it
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CHAPTER 1 DEVELOPING SUCCESSFUL ORACLE APPLICATIONS
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took almost 20 times as much CPU time to insert the rows without bind variables. For every insert without bind variables, we spent the vast preponderance of the time to execute the statement simply parsing the statement! But it gets worse. When we look at other information, we can see a significant difference in the resources utilized by each approach: Name STAT...parse count (hard) STAT...parse count (total) STAT...consistent gets from ca STAT...consistent gets STAT...consistent gets from ca LATCH.simulator hash latch STAT...db block gets from cach STAT...db block gets STAT...db block gets from cach LATCH.enqueues LATCH.enqueue hash chains STAT...session logical reads STAT...recursive calls LATCH.kks stats STAT...session uga memory STAT...session pga memory LATCH.cache buffers chains LATCH.shared pool simulator LATCH.row cache objects LATCH.shared pool LATCH.JS slv state obj latch Run1 5 34 78 135 135 83 10,440 10,440 79 40 74 10,575 10,326 23 0 0 51,532 42 294 20,302 1 Run2 10,010 10,055 10,120 10,290 10,290 10,990 30,364 30,364 20,041 20,372 20,414 40,654 40,960 65,141 65,512 65,536 120,773 104,558 184,697 446,397 435,890 Diff 10,005 10,021 10,042 10,155 10,155 10,907 19,924 19,924 19,962 20,332 20,340 30,079 30,634 65,118 65,512 65,536 69,241 104,516 184,403 426,095 435,889
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Run1 latches total versus runs -- difference and pct Run1 Run2 Diff Pct 73,082 1,411,695 1,338,613 5.18% PL/SQL procedure successfully completed. The runstats utility produces a report that shows differences in latch utilization as well as differences in statistics. Here I asked runstats to print out anything with a difference greater than 10,000. You can see that we hard parsed 4 times in the first approach using bind variables, and that we hard parsed over 10,000 times without bind variables (once for each of the inserts). But that difference in hard parsing is just the tip of the iceberg. You can see here that we used almost 20 times as many latches in the non-bind variable approach as we did with bind variables. That difference might beg the question What is a latch Let s answer that question. A latch is a type of lock that is used to serialize access to shared data structures used by Oracle. The shared pool is an example; it s a big shared data structure found in the System Global Area (SGA), and this is where Oracle stores parsed, compiled SQL. When you modify this shared structure, you must take care to allow only one process in at a time. (It is very bad if two processes or threads attempt to update the same in-memory data structure simultaneously corruption would abound). So, Oracle employs a latching mechanism, a lightweight locking method to serialize access. Don t be fooled by the word lightweight. Latches are serialization devices, allowing access one process at a time. The latches used by the hard-parsing implementation are some of the most used latches out there. These include the latches for the shared pool and for the library cache. Those are big time latches that people compete for frequently. What all this means is that as we increase the number of users attempting to hard parse statements simultaneously, our performance gets progressively worse
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