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

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CHAPTER 11 ■ INDEXES 487 SELECT /*+ INDEX( t t_idx_1 ) */ COUNT(*) FROM T WHERE OBJECT_NAME = :B3 AND OBJECT_TYPE = :B2 AND OWNER = :B1 call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------- Parse 1 0.00 0.00 0 0 0 0 Execute 48243 10.63 10.78 0 0 0 0 Fetch 48243 1.90 1.77 0 145133 0 48243 ------- ------ -------- ---------- ---------- ---------- ---------- ---------- total 96487 12.53 12.55 0 145133 0 48243 Rows Row Source Operation ------- --------------------------------------------------- 48243 SORT AGGREGATE (cr=145133 pr=0 pw=0 time=2334197 us) 57879 INDEX RANGE SCAN T_IDX_1 (cr=145133 pr=0 pw=0 time=1440672 us)(object... ******************************************************************************** SELECT /*+ INDEX( t t_idx_2 ) */ COUNT(*) FROM T WHERE OBJECT_NAME = :B3 AND OBJECT_TYPE = :B2 AND OWNER = :B1 call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------- Parse 1 0.00 0.00 0 0 0 0 Execute 48243 11.00 10.78 0 0 0 0 Fetch 48243 1.87 2.10 0 145168 0 48243 ------- ------ -------- ---------- ---------- ---------- ---------- ---------- total 96487 12.87 12.88 0 145168 0 48243 Rows Row Source Operation ------- --------------------------------------------------- 48243 SORT AGGREGATE (cr=145168 pr=0 pw=0 time=2251857 us) 57879 INDEX RANGE SCAN T_IDX_2 (cr=145168 pr=0 pw=0 time=1382547 us)(object... They processed the same exact number of rows and very similar numbers of blocks (minor variations coming from accidental ordering of rows in the table and consequential optimizations made by Oracle), used equivalent amounts of CPU time, and ran in about the same elapsed time (run this same test again and the CPU and ELAPSED numbers will be a little different, but on average they will be the same). There are no inherent efficiencies to be gained by placing the columns in order of how discriminating they are, and as stated previously, with index key compression there is an argument for putting the least selective first. If you run the preceding example with COMPRESS 2 on the indexes, you’ll find that the first index will perform about two-thirds the I/O of the second, given the nature of the query in this case. However, the fact is that the decision to put column C1 before C2 must be driven by how the index is used. If you have lots of queries like the following: select * from t where c1 = :x and c2 = :y; select * from t where c2 = :y;
488 CHAPTER 11 ■ INDEXES it makes more sense to place the index on T(C2,C1). This single index could be used by either of the queries. Additionally, using index key compression (which we looked at with regard to IOTs and will examine further later), we can build a smaller index if C2 is first. This is because each value of C2 repeats itself on average four times in the index. If C1 and C2 are both, on average, 10 bytes in length, the index entries for this index would nominally be 2,000,000 bytes (100,000 ✕ 20). Using index key compression on (C2, C1), we could shrink this index to 1,250,000 (100,000 ✕ 12.5), since three out of four repetitions of C2 could be suppressed. In Oracle 5 (yes, version 5!), there was an argument for placing the most selective columns first in an index. It had to do with the way version 5 implemented index compression (not the same as index key compression). This feature was removed in version 6 with the addition of row-level locking. Since then, it is not true that putting the most discriminating entries first in the index will make the index smaller or more efficient. It seems like it will, but it will not. With index key compression, there is a compelling argument to go the other way since it can make the index smaller. However, it should be driven by how you use the index, as previously stated. Summary In this chapter, we covered the different types of indexes Oracle has to offer. We started with the basic B*Tree index and looked at various subtypes of this index, such as the reverse key index (designed for Oracle RAC) and descending indexes for retrieving data sorted in a mix of descending and ascending order. We spent some time looking at when you should use an index and why an index may not be useful in various circumstances. We then looked at bitmap indexes, an excellent method for indexing low to medium cardinality data in a data warehouse (read-intensive, non-OLTP) environment. We covered the times it would be appropriate to use a bitmapped index and why you would never consider one for use in an OLTP environment—or any environment where multiple users must concurrently update the same column. We moved on to cover function-based indexes, which are actually special cases of B*Tree and bitmapped indexes. A function-based index allows us to create an index on a function of a column (or columns), which means that we can precompute and store the results of complex calculations and user-written functions for blazing-fast index retrieval later. We looked at some important implementation details surrounding function-based indexes, such as the necessary system- and session-level settings that must be in place for them to be used. We followed that with examples of function-based indexes both on built-in Oracle functions and user-written ones. Lastly, we looked at a few caveats with regard to function-based indexes. We then examined a very specialized index type called the application domain index. Rather than go into how to build one of those from scratch (which involves a long, complex sequence of events), we looked at an example that had already been implemented: the text index. We closed with some of the most frequently asked questions on indexes that I receive as well as some myths about indexes. This section covered topics ranging from the simple question “Do indexes work with views?” to the more complex and sublime myth “Space is never reused in an index.” We answered these questions and debunked the myths mostly through example, demonstrating the concepts as we went along.
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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 xlii
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CHAPTER 1 ■ ■ ■ Developing Su
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CHAPTER 1 ■ DEVELOPING SUCCESSFUL
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CHAPTER 3 ■ FILES 89 A Brief Revi
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CHAPTER 4 ■ ■ ■ Memory Struct
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CHAPTER 4 ■ MEMORY STRUCTURES 117
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CHAPTER 5 ■ ORACLE PROCESSES 157
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CHAPTER 6 ■ LOCKING AND LATCHING
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CHAPTER 8 ■ ■ ■ Transactions
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284 CHAPTER 9 ■ REDO AND UNDO cri
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338 CHAPTER 10 ■ DATABASE TABLES
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CHAPTER 11 ■ ■ ■ Indexes Inde
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CHAPTER 11 ■ INDEXES 431 The data
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CHAPTER 13 ■ ■ ■ Partitioning
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Apress.Expert.Oracle.Database.Architecture.9i.and.10g.Programming.Techniques.and.Solutions.Sep.2005

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