Apress.Expert.Oracle.Database.Architecture.9i.and.10g.Programming.Techniques.and.Solutions.Sep.2005

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CHAPTER 10 ■ DATABASE TABLES 383 ops$tkyte@ORA10GR1> exec runstats_pkg.rs_stop(10000); Run1 ran in 263 hsecs Run2 ran in 268 hsecs run 1 ran in 98.13% of the time Name Run1 Run2 Diff LATCH.cache buffers chains 99,891 148,031 48,140 STAT...Cached Commit SCN refer 48,144 0 -48,144 STAT...no work - consistent re 48,176 0 -48,176 STAT...cluster key scans 48,176 0 -48,176 STAT...cluster key scan block 48,176 0 -48,176 STAT...table fetch by rowid 0 48,176 48,176 STAT...rows fetched via callba 0 48,176 48,176 STAT...buffer is not pinned co 48,176 96,352 48,176 STAT...index fetch by key 0 48,176 48,176 STAT...session logical reads 48,901 145,239 96,338 STAT...consistent gets 48,178 144,530 96,352 STAT...consistent gets from ca 48,178 144,530 96,352 STAT...consistent gets - exami 1 144,529 144,528 Run1 latches total versus runs -- difference and pct Run1 Run2 Diff Pct 347,515 401,961 54,446 86.45% Now, these two simulations ran in about the same amount of time by the wall clock. As I had a buffer cache larger than large enough to cache these results, I was expecting that. The material difference to note, however, is the large reduction in cache buffers chains latches. The first implementation (hashed) used significantly fewer, meaning the hashed implementation should scale better in a read-intensive environment, since it needs fewer resources that require some level of serialization. This was due entirely to the fact that the I/O needed by the hashed implementation was significantly reduced over the HEAP table—you can see the statistic consistent gets in that report bears this out. The TKPROF shows it even more clearly: SELECT * FROM T_HASHED WHERE OBJECT_ID = :B1 call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------- Parse 1 0.00 0.00 0 0 0 0 Execute 48174 4.77 4.83 0 2 0 0 Fetch 48174 1.50 1.46 0 48174 0 48174 ------- ------ -------- ---------- ---------- ---------- ---------- ---------- total 96349 6.27 6.30 0 48176 0 48174 Rows Row Source Operation ------- --------------------------------------------------- 48174 TABLE ACCESS HASH T_HASHED (cr=48174 pr=0 pw=0 time=899962 us) ******************************************************************************** SELECT * FROM T_HEAP WHERE OBJECT_ID = :B1
384 CHAPTER 10 ■ DATABASE TABLES call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------- Parse 1 0.00 0.00 0 0 0 0 Execute 48174 5.37 5.02 0 0 0 0 Fetch 48174 1.36 1.32 0 144522 0 48174 ------- ------ -------- ---------- ---------- ---------- ---------- ---------- total 96349 6.73 6.34 0 144522 0 48174 Rows Row Source Operation ------- --------------------------------------------------- 48174 TABLE ACCESS BY INDEX ROWID T_HEAP (cr=144522 pr=0 pw=0 time=1266695 us) 48174 INDEX UNIQUE SCAN T_HEAP_PK (cr=96348 pr=0 pw=0 time=700017 us)(object ... The HASHED implementation simply converted the OBJECT_ID passed into the query into a FILE/BLOCK to be read and read it—no index. The HEAP table, however, had to do two I/Os on the index for each row. The cr=96348 in the TKPROF Row Source Operation line shows us exactly how many consistent reads were done against the index. Each time I looked up OBJECT_ID = :B1, Oracle had to get the root block of the index and then find the leaf block containing the location of that row. Then, I had to take the leaf block information, which included the ROWID of that row, and access that row in the table for a third I/O. The HEAP table did three times the I/O of the HASHED implementation. The points of interest here are as follows: • The hash cluster did significantly less I/O (query column). This is what we anticipated. The query simply took the random OBJECT_IDs, performed the hash on them, and went to the block. The hash cluster has to do at least one I/O to get the data. The conventional table with an index had to perform index scans followed by a table access by rowid to get the same answer. The indexed table has to do at least three I/Os in this case to get the data. • The hash cluster query took the same amount of CPU for all intents and purposes, even though it went to the buffer cache one-third as many times. This, too, could be anticipated. The act of performing a hash is very CPU intensive. The act of performing an index lookup is I/O intensive. It was a tradeoff here. However, as we scale up users, we would expect the hash cluster query to scale better, as it has to get in line to access the buffer cache less frequently. This last point is the important one. When working with computers, it is all about resources and their utilization. If we are I/O bound and perform queries that do lots of keyed reads like I just did, a hash cluster may improve performance. If we are already CPU bound, a hash cluster may possibly decrease performance since it needs more CPU horsepower to hash, but if the extra CPU we are burning is due to spinning on cache buffers chains latches, the hash cluster could significantly reduce the CPU needed. This is one of the main reasons why rules of thumb do not work on real-world systems: what works for you might not work for others in similar but different conditions. There is a special case of a hash cluster called a single table hash cluster. This is an optimized version of the general hash cluster we’ve already looked at. It supports only one table in
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Expert Oracle Database Architecture
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Contents Foreword . . . . . . . . .
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■CONTENTS v Block Buffer Cache .
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■CONTENTS vii ■CHAPTER 9 Redo a
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■CONTENTS ix ■CHAPTER 12 Dataty
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Foreword “THINK.” In 1914, Thom
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■FOREWORD xiii Tom is an aficiona
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About the Technical Reviewers ■JO
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Introduction The inspiration for th
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■INTRODUCTION xix • Exposure to
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■INTRODUCTION xxi Chapter 3: File
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■INTRODUCTION xxiii Next up are t
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Setting Up Your Environment In this
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■SETTING UP YOUR ENVIRONMENT xxvi
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■SETTING UP YOUR ENVIRONMENT xxxv
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■SETTING UP YOUR ENVIRONMENT xlii
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CHAPTER 1 ■ ■ ■ Developing Su
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CHAPTER 1 ■ DEVELOPING SUCCESSFUL
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CHAPTER 3 ■ ■ ■ Files In this
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CHAPTER 3 ■ FILES 67 Without a pa
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CHAPTER 3 ■ FILES 69 and even dat
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CHAPTER 3 ■ FILES 71 all operatio
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CHAPTER 3 ■ FILES 73 *.cluster_da
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CHAPTER 3 ■ FILES 75 ops$tkyte@OR
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CHAPTER 3 ■ FILES 77 • To maint
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CHAPTER 3 ■ FILES 79 • Resource
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CHAPTER 3 ■ FILES 81 3 l_dummy nu
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CHAPTER 3 ■ FILES 83 Trace Files
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CHAPTER 3 ■ FILES 85 _qerixAlloca
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CHAPTER 3 ■ FILES 87 6 ( 7 TYPE O
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CHAPTER 3 ■ FILES 89 A Brief Revi
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CHAPTER 3 ■ FILES 91 Redundant Ar
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CHAPTER 3 ■ FILES 93 (data from m
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CHAPTER 3 ■ FILES 95 dictionary t
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CHAPTER 3 ■ FILES 99 “accidenta
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CHAPTER 3 ■ FILES 101 So, at the
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CHAPTER 3 ■ FILES 103 Password Fi
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CHAPTER 3 ■ FILES 105 USER is "SY
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CHAPTER 3 ■ FILES 107 Flashback L
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CHAPTER 3 ■ FILES 109 of the requ
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CHAPTER 3 ■ FILES 111 IMPDP, howe
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CHAPTER 3 ■ FILES 113 tkyte@ORA10
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CHAPTER 4 ■ ■ ■ Memory Struct
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CHAPTER 4 ■ MEMORY STRUCTURES 117
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CHAPTER 5 ■ ■ ■ Oracle Proces
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CHAPTER 5 ■ ORACLE PROCESSES 157
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CHAPTER 6 ■ ■ ■ Locking and L
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CHAPTER 6 ■ LOCKING AND LATCHING
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CHAPTER 8 ■ ■ ■ Transactions
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CHAPTER 8 ■ TRANSACTIONS 257 •
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CHAPTER 8 ■ TRANSACTIONS 259 So,
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CHAPTER 8 ■ TRANSACTIONS 261 X --
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CHAPTER 8 ■ TRANSACTIONS 263 “s
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CHAPTER 8 ■ TRANSACTIONS 265 busi
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CHAPTER 8 ■ TRANSACTIONS 267 Many
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CHAPTER 8 ■ TRANSACTIONS 269 ops$
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CHAPTER 8 ■ TRANSACTIONS 271 last
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CHAPTER 8 ■ TRANSACTIONS 275 Auto
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284 CHAPTER 9 ■ REDO AND UNDO cri
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286 CHAPTER 9 ■ REDO AND UNDO Fir
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288 CHAPTER 9 ■ REDO AND UNDO The
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290 CHAPTER 9 ■ REDO AND UNDO We
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292 CHAPTER 9 ■ REDO AND UNDO Wha
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294 CHAPTER 9 ■ REDO AND UNDO row
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296 CHAPTER 9 ■ REDO AND UNDO If
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298 CHAPTER 9 ■ REDO AND UNDO ops
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300 CHAPTER 9 ■ REDO AND UNDO Inv
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302 CHAPTER 9 ■ REDO AND UNDO The
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306 CHAPTER 9 ■ REDO AND UNDO ins
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308 CHAPTER 9 ■ REDO AND UNDO So,
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314 CHAPTER 9 ■ REDO AND UNDO •
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316 CHAPTER 9 ■ REDO AND UNDO ...
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318 CHAPTER 9 ■ REDO AND UNDO •
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320 CHAPTER 9 ■ REDO AND UNDO bac
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326 CHAPTER 9 ■ REDO AND UNDO wil
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328 CHAPTER 9 ■ REDO AND UNDO Thi
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CHAPTER 11 ■ INDEXES 433 if ( (++
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CHAPTER 11 ■ INDEXES 435 Table 11
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CHAPTER 11 ■ INDEXES 437 When Sho
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CHAPTER 11 ■ INDEXES 439 an 8KB b
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CHAPTER 11 ■ INDEXES 441 select *
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CHAPTER 11 ■ INDEXES 445 Indicate
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CHAPTER 11 ■ INDEXES 447 an index
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CHAPTER 11 ■ INDEXES 449 Table 11
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CHAPTER 11 ■ INDEXES 453 column w
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CHAPTER 11 ■ INDEXES 455 Bitmap j
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CHAPTER 11 ■ INDEXES 457 INSERT a
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CHAPTER 11 ■ INDEXES 471 Frequent
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CHAPTER 11 ■ INDEXES 479 Predicat
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CHAPTER 12 ■ ■ ■ Datatypes Ch
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CHAPTER 12 ■ DATATYPES 491 • TI
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CHAPTER 12 ■ DATATYPES 493 (in th
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CHAPTER 12 ■ DATATYPES 499 Table
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CHAPTER 13 ■ ■ ■ Partitioning
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CHAPTER 13 ■ PARTITIONING 561 els
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CHAPTER 13 ■ PARTITIONING 563 BIG
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CHAPTER 13 ■ PARTITIONING 565 Enh
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CHAPTER 13 ■ PARTITIONING 567 Tab
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CHAPTER 14 ■ ■ ■ Parallel Exe
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CHAPTER 14 ■ PARALLEL EXECUTION 6
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706 ■INDEX autonomous transaction
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Apress.Expert.Oracle.Database.Architecture.9i.and.10g.Programming.Techniques.and.Solutions.Sep.2005

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