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Statistics --------------------------------------------------------- 0 db block gets 7 consistent gets 0 physical reads 14 rows processed The following insert_emp_sql procedure implements our requirements using a multitable insert statement and our first SQL solution: benchmark@ORA10G> create or replace procedure insert_emp_sql 2 as 3 begin 4 insert 5 when above_avg_flag = 'Y' then 6 into above_avg_emp( deptno, ename, sal) values( deptno, ename, sal) 7 when above_avg_flag = 'N' then 8 into below_avg_emp( deptno, ename, sal) values( deptno, ename, sal) 9 select deptno, ename, sal, 'Y' as above_avg_flag 10 from emp e1 11 where e1.sal >= (select avg(sal) 12 from emp e2 13 where e1.deptno = e2.deptno) 14 union all 15 select deptno, ename, sal, 'N' 16 from emp e1 17 where e1.sal < (select avg(sal) 18 from emp e2 19 where e1.deptno = e2.deptno); 20 end; 21 / Procedure created.
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The following procedure, insert_emp_sql_analytics, encapsulates our SQL analytic function based solution in a procedure using multitable insert again: benchmark@ORA10G> create or replace procedure insert_emp_sql_analytics 2 as 3 begin 4 insert 5 when sal >= avg_sal then 6 into above_avg_emp( deptno, ename, sal) values( deptno, ename, sal) 7 when sal < avg_sal then 8 into below_avg_emp( deptno, ename, sal) values( deptno, ename, sal) 9 select deptno, ename, sal, avg_sal 10 from 11 ( 12 select deptno, ename, sal, avg( sal ) over( partition by deptno ) avg_sal 13 from emp 14 ); 15 end; 16 / Procedure created. I compared the elapsed times and latches of all three solutions (one PL/SQL-based and two SQL-based) that we discussed on the schema that we created in the benchmark schema. Table 2-1 shows the results. Table 2-1. Comparing the PL/SQL Solution with Two SQL Solutions (with and Without the Use of SQL Analytic Functions)
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PL/SQL solution in procedure
Average Elapsed Time (Seconds)
1.87 0.23 0.17
Relative Number of Latches
7 times that of insert_emp_sql 1.5 times that of insert_emp_sql
insert_emp_plsql
SQL solution in procedure
insert_emp_sql_analytics
SQL solution based on analytic function in procedure insert_emp_sql_analytics
Once again, we see that SQL-based solutions outperformed the PL/SQL solution. Within the SQL-based solutions, we were able to further improve performance and scalability by using analytic function based techniques. Overall, our best SQL-based solution ran in less than 10% of the time and consumed around 10% of the latches our PL/SQL solution took. These performance improvements will, of course, directly translate to performance improvements in JDBC programs that invoke them. The examples we went through in this and the previous section have hopefully convinced you that that you need to have a solid grasp of performance improvement techniques and approaches in PL/SQL and SQL to write high-performance and scalable JDBC applications. In the next section, we will look at the importance of getting your database schema design right.
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Get Your Database Schema Design Right
Like everything else, your database schema should also be designed according to your application s business needs. From a developer s point of view, the database schema typically includes tables, indexes, views, and various constraints, among other things. As a developer, you will write SQL that works against database tables. Hence, you need to understand how these tables are designed, since schema design has a direct impact on your SQL. One of the central themes of table design is that you should design tables with prioritized performance requirements in mind from day one. Various aspects of table design include choosing the appropriate normalization level, table organization type (heap, index-organized, etc.), indexing strategy, integrity checks, constraints, and column data types for your tables. In this section, we will consider just one aspect of table design: choosing the appropriate table type based on your requirements. We will see how this single factor can significantly affect the performance of an application.
I strongly urge you to read 7 of the book Effective Oracle by Design by Tom Kyte (Osborne Tip McGraw-Hill, ISBN: 0-07-223065-7) for a very interesting discussion on this topic. Similarly, I suggest you read s 6 and 7 of Expert One-on-One Oracle (Apress, ISBN: 1-59059-243-3), also by Tom Kyte, to understand how to use indexes and constraints as part of your database schema design.
Say the requirement is that of a table that will be loaded with a large amount of data once during night, but queried lots of times during the day. Furthermore, the most frequently used queries select based on the table s primary key. We have the following facts: Our queries (based on primary keys) should be fast (since they are used frequently). Somewhat slower inserts are acceptable (since data is loaded once during off-peak hours). It would be a bonus if we can save space on the large amount of data. This is an ideal design scenario to try out an index-organized table structure. Normally when you create a table in Oracle, it is organized as a heap by default. In a heap-organized table, data is managed as a heap that is, the inserts fill up the first available free space with no particular maintained order. The primary key of a heap table uses a separate index structure. By contrast, in an index-organized table (IOT for short), the table data is stored in the primary key index structure itself in a sorted order according to the primary key values. This has the following implications: In an IOT, since the data is stored sorted by the primary key, we need fewer logical I/Os compared to a heap table to get the same data for primary key based lookups. This is because, in a heap table, typical data access by index occurs in two steps: 1. Index access to get the ROWID. 2. Table data access by the ROWID obtained in step 1.
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