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you got exactly the same values for all numbers, especially the latching numbers. You should, however, see similar numbers, assuming you are using Oracle9i Release 2 on Linux, as I was here. In all releases, I would expect the number of latches used to hard parse to be higher than those for soft parsing each insert, or parsing the insert once and executing it over and over. Running the preceding test in Oracle 10g Release 1 on the same machine produced results such that the elapsed time of the bind variable approach was onetenth of the non bind variable approach, and the amount of latches taken was 17 percent. This was due to two factors, one being that 10g is a new release and some internal algorithms changed. The other was due to an improved way dynamic SQL is processed in PL/SQL in 10g.

You can see that there were only four hard parses with the bind variable approach, but over 10,000 hard parses without bind variables (once for each of the inserts). But that is just the tip of the iceberg. You can see here that twice as many latches were used in the non bind variable approach than when using bind variables. This is because in order to modify this shared structure, Oracle 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 device, to serialize access. Don t be fooled by the word lightweight these are serialization devices, allowing one-at-a-time, short duration access to a data structure. The latches overused by the hard-parsing implementation are among the most used latches out there. The latch into the shared pool and the latch for the library cache are big-time latches; they re the ones that people compete for frequently. What that means is that as we increase the number of users attempting to hard-parse statements simultaneously, our performance problem will get progressively worse over time. The more people parsing, the more people fighting for the right to latch the shared pool, the longer the queues, the longer the wait.

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FieldPtr; RID Type: 03; Token Type: None; MD Stream: #-

There is also a Perl function for performing the same task. You can access it like this:

transactions or Data Guard in maximum availability mode. Since there are two participants, PL/SQL must wait for the commit to actually be complete before continuing on.

To demonstrate that a COMMIT is a flat response time operation, we ll generate varying amounts of redo and time the INSERTs and COMMITs. To do this, we ll again use AUTOTRACE in SQL*Plus. We ll start with a big table of test data we ll insert into another table and an empty table:

Field; RID Type: 04; Token Type: 0x04000000; MD Streams: #~, #-

ops$tkyte@ORA10G> @big_table 100000 ops$tkyte@ORA10G> create table t as select * from big_table where 1=0; Table created. And then in SQL*Plus we ll run the following: ops$tkyte@ORA10G> ops$tkyte@ORA10G> ops$tkyte@ORA10G> ops$tkyte@ORA10G> set timing on set autotrace on statistics; insert into t select * from big_table where rownum <= 10; commit;

Next we determine the number of days that have passed since 1/1/1970. Passwordaging information in the /etc/shadow file is held in the third field of each account entry. This field contains an integer that expresses the date on which the password was last changed as a number of days since 1/1/1970. Subtracting this number from the current number of days elapsed since that date gives us the age of the password.

We monitor the redo size statistic presented by AUTOTRACE and the timing information presented by set timing on. I performed this test and varied the number of rows inserted from 10 to 100,000 in multiples of 10. Table 9-1 shows my observations. Table 9-1. Time to COMMIT by Transaction Size*

MethodPtr; RID Type: 05; Token Type: None; MD Stream: #-

10 100 1,000 10,000 100,000

seconds_since_epoch=$((`date +%s`)) seconds_per_day=86400 days_since_epoch=$(($seconds_since_epoch/$seconds_per_day))

Method; RID Type: 06; Token Type: 0x06000000; MD Streams: #~, #-

Time to Insert (Seconds)

0.05 0.08 0.07 0.25 1.94

We now back up the shadow file for safety. We want to be able to return to the original file to start over if necessary. Using cp -p will also preserve the original modification time and permissions on the new copy of the file.

Must be 0 or point at read-only section Validity mask: 0x10BF Validity mask: 0xFDF7 No longer than 1,023 bytes Cannot be 0

Redo Size (Bytes)

116 3,594 372,924 3,744,620 37,843,108

ParamPtr; RID Type: 07; Token Type: None; MD Stream: #-

The following code lets you clean the archive directory by removing old reports and backup shadow files more than seven days old.

Commit Time (Seconds)

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