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CHAPTER 10 DATABASE TABLES
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and emp.empno = x.empno ) loop null; end loop; end loop; end; /
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PL/SQL procedure successfully completed. Here, we are just emulating a busy period and running the query some 45,000 times, once for each EMPNO. If we run that for the HEAP_ADRESSES and IOT_ADDRESSES tables, TKPROF shows us the following: SELECT EMP.ENAME, A.STREET, A.CITY, A.STATE, A.ZIP FROM EMP, HEAP_ADDRESSES A WHERE EMP.EMPNO = A.EMPNO AND EMP.EMPNO = :B1 call count ------- -----Parse 1 Execute 72073 Fetch 72073 ------- -----total 144147 cpu elapsed disk query current -------- ---------- ---------- ---------- ---------0.00 0.00 0 0 0 11.53 11.79 0 0 0 6.55 6.58 3641 722160 0 -------- ---------- ---------- ---------- ---------18.09 18.38 3641 722160 0 rows ---------0 0 288292 ---------288292
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Misses in library cache during parse: 0 Optimizer mode: ALL_ROWS Parsing user id: 286 (recursive depth: 1) Rows Row Source Operation ------- --------------------------------------------------4 NESTED LOOPS (cr=10 pr=0 pw=0 time=60 us cost=8 size=280 card=4) 1 TABLE ACCESS BY INDEX ROWID EMP (cr=3 pr=0 pw=0 time=0 us cost=2 siz ) 1 INDEX UNIQUE SCAN EMP_PK (cr=2 pr=0 pw=0 time=0 us cost=1 size=0 ) 4 TABLE ACCESS BY INDEX ROWID HEAP_ADDRESSES (cr=7 pr=0 pw=0 time=54 us ) 4 INDEX RANGE SCAN SYS_C0019080 (cr=3 pr=0 pw=0 time=3 us cost=2 size= ) ******************************************************************************** SELECT EMP.ENAME, A.STREET, A.CITY, A.STATE, A.ZIP FROM EMP, IOT_ADDRESSES A WHERE EMP.EMPNO = A.EMPNO AND EMP.EMPNO = :B1 call count ------- -----Parse 1 Execute 72073 Fetch 72073 ------- -----total 144147 cpu elapsed disk query current -------- ---------- ---------- ---------- ---------0.00 0.00 0 0 0 11.61 11.74 0 0 0 4.70 4.78 4863 437132 0 -------- ---------- ---------- ---------- ---------16.32 16.52 4863 437132 0 rows ---------0 0 288292 ---------288292
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Misses in library cache during parse: 0 Optimizer mode: ALL_ROWS Parsing user id: 286 (recursive depth: 1)
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CHAPTER 10 DATABASE TABLES
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Rows ------4 1 1 4
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Row Source Operation --------------------------------------------------NESTED LOOPS (cr=7 pr=3 pw=0 time=9 us cost=4 size=280 card=4) TABLE ACCESS BY INDEX ROWID EMP (cr=3 pr=0 pw=0 time=0 us cost=2 size=30 ) INDEX UNIQUE SCAN EMP_PK (cr=2 pr=0 pw=0 time=0 us cost=1 size=0 ) INDEX RANGE SCAN SYS_IOT_TOP_93124 (cr=4 pr=3 pw=0 time=3 us cost=2 )
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Both queries fetched exactly the same number of rows, but the HEAP table performed considerably more logical I/O. As the degree of concurrency on the system goes up, we would likewise expect the CPU used by the HEAP table to go up more rapidly as well, while the query possibly waits for latches into the buffer cache. Using runstats (a utility of my own design; refer to the introductory section Setting Up Your Environment for This Book for details), we can measure the difference in latching. On my system, I observed the following STAT...consistent gets STAT...consistent gets from ca STAT...no work - consistent re STAT...session logical reads STAT...consistent gets from ca STAT...table fetch by rowid STAT...buffer is not pinned co LATCH.cache buffers chains STAT...physical read total byt STAT...physical read bytes STAT...cell physical IO interc 723,073 723,073 362,683 723,131 359,225 360,385 648,695 1,090,022 30,703,616 30,703,616 30,703,616 438,048 438,048 77,651 438,086 72,982 72,085 288,324 523,161 40,747,008 40,747,008 40,747,008 -285,025 -285,025 -285,032 -285,045 -286,243 -288,300 -360,371 -566,861 10,043,392 10,043,392 10,043,392
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Run1 latches total versus runs -- difference and pct Run1 Run2 Diff Pct 1,204,593 624,834 -579,759 192.79% where Run1 was the HEAP_ADDRESSES table and Run2 was the IOT_ADDRESSES table. As you can see, there was a dramatic and repeatable decrease in the latching taking place, mostly due to the cache buffers chains latch (the one that protects the buffer cache). The IOT in this case would provide the following benefits: Increased buffer cache efficiency, as any given query needs to have fewer blocks in the cache. Decreased buffer cache access, which increases scalability. Less overall work to retrieve our data, as it is faster. Less physical I/O per query possibly, as fewer distinct blocks are needed for any given query and a single physical I/O of the addresses most likely retrieves all of them (not just one of them, as the heap table implementation does).
The same would be true if you frequently use BETWEEN queries on a primary or unique key. Having the data stored physically sorted will increase the performance of those queries as well. For example, I maintain a table of stock quotes in my database. Every day, for hundreds of stocks, I gather together the stock ticker, date, closing price, days high, days low, volume, and other related information. The table looks like this: ops$tkyte@ORA11GR2> create table stocks 2 ( ticker varchar2(10), 3 day date,
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