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Setting the UNDO_RETENTION parameter to zero or just leaving it out of your SPFILE will instruct Oracle to perform proactive automatic undo retention tuning, thus reducing the occurrence of the wellknown snapshot too old errors that lead to the failure of many an overnight production batch job. Under automatic undo retention tuning, Oracle will figure out the ideal retention period for undo data, based on the length of the transactions and other related factors. I discuss automatic undo retention tuning in 8.

Table created. ops$tkyte@ORA11GR2> insert /*+ APPEND */ 2 select rownum, rownum, rownum 3 from all_objects 4 / 72089 rows created. ops$tkyte@ORA11GR2> commit; Commit complete. we then execute the same query against each type of column, using a complex mathematical function such as LN (natural log). We observe in a TKPROF report radically different CPU utilization: select sum(ln(num_type)) from t call count cpu elapsed ------- ------ -------- ---------total 4 4.45 4.66 select sum(ln(float_type)) from t call count ------- -----total 4 cpu elapsed -------- ---------0.07 0.08 into t

Oracle Database 11g a automatically gathers statistics for the cost-based optimizer through a regularly scheduled job. The job gathers statistics on all objects in the database that have missing or stale statistics. Oracle creates this job automatically at database creation time, and the Scheduler automatically manages it. 19 discusses the automatic collection of optimizer statistics.

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This issue is compounded by the fact that our transaction is much larger than just outlined. The UPDATE and SELECT in the example are only two statements of potentially many other statements that make up our transaction. We have yet to insert the row into the table with this key we just generated and do whatever other work it takes to complete this transaction. This serialization will be a huge limiting factor in scaling. Think of the ramifications if this technique were used on web sites that process orders, and this was how we generated order numbers. There would be no multiuser concurrency, so we would be forced to do everything sequentially. The correct approach to this problem is to use the best code for each database. In Oracle, this would be (assuming the table that needs the generated primary key is T) as follows: create table t ( pk number primary key, ... ); create sequence t_seq; create trigger t_trigger before insert on t for each row begin select t_seq.nextval into :new.pk from dual; end; This will have the effect of automatically and transparently assigning a unique key to each row inserted. A more performance driven approach would be simply Insert into t ( pk, ... ) values ( t_seq.NEXTVAL, ... ); That is, skip the overhead of the trigger altogether (this is my preferred approach). In the first example, we ve gone out of our way to use each database s feature to generate a non-blocking, highly concurrent unique key, and we ve introduced no real changes to the application code all of the logic is contained in this case in the DDL.

Automatic Storage Management is a way of organizing your storage that integrates your file system with a volume manager that s designed for Oracle files. ASM divides Oracle datafiles into extents, which it distributes evenly across the disk system. ASM automatically redistributes I/O load across all available disks whenever storage configuration changes, avoiding manual disk tuning. ASM also provides mirroring and striping, thus enhancing protection and performance, as in RAID systems. I discuss ASM in detail in 17.

select sum(ln(double_type)) from t call count ------- -----total 4 cpu elapsed -------- ---------0.06 0.06

In Oracle Database 11g, the database runs the SQL Tuning Advisor as part of its nightly maintenance tasks. The Automatic SQL Tuning Advisor makes recommendations to improve poorly performing SQL queries. I discuss automatic SQL tuning in 19.

The Oracle NUMBER type used some 63 times the CPU of the floating-point types in this example. But, you have to remember that we did not receive precisely the same answer from all three queries! ops$tkyte%ORA11GR2> set numformat 999999.9999999999999999 ops$tkyte%ORA11GR2> select sum(ln(num_type)) from t; SUM(LN(NUM_TYPE)) -------------------------734280.3209126472927309 ops$tkyte%ORA11GR2> select sum(ln(double_type)) from t; SUM(LN(DOUBLE_TYPE)) -------------------------734280.3209126447300000 The floating-point numbers were an approximation of the number, with between 6 and 13 digits of precision. The answer from the NUMBER type is much more precise than from the floats. However, when you are performing data mining or complex numerical analysis of scientific data, this loss of precision is typically acceptable, and the performance gain to be had can be dramatic.

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