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1 Chapter 5 Index and Clustering. 2 Overview of Indexes and Clustering B-tree indexes Bitmap indexes...

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1 Chapter 5 Index and Clustering
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Page 1: 1 Chapter 5 Index and Clustering. 2 Overview of Indexes and Clustering B-tree indexes Bitmap indexes (and bitmap join indexes) Index-organized tables.

1

Chapter 5Index and Clustering

Page 2: 1 Chapter 5 Index and Clustering. 2 Overview of Indexes and Clustering B-tree indexes Bitmap indexes (and bitmap join indexes) Index-organized tables.

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Overview of Indexes and Clustering

B-tree indexes Bitmap indexes (and bitmap join indexes) Index-organized tables (IOT) Hash Clusters Index Cluster Nested Tables

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B-tree Indexes

“Balanced tree” – has hierarchical tree structure– Header block

Contains pointers to “Branch blocks” for given value sets

– Branch blocks Contains pointers to other branch blocks, or Contains pointers to “Leaf Blocks”

– Leaf Blocks Contains list of key values and pointers (ROWIDS) to

actual table row data

See figures 5-1 (p. 112), 5-2 (p. 113)

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B-tree Indexes (cont.)

Can provide efficient query performance– Header, branch blocks often in memory– Reading leafs blocks to retrieve data requires often

few I/O’s– Goal is to keep “balanced” tree

Maintenance can be expensive Index splits can occur

– Reduces performances and increases I/O– Requires rebuild to repair

Page 5: 1 Chapter 5 Index and Clustering. 2 Overview of Indexes and Clustering B-tree indexes Bitmap indexes (and bitmap join indexes) Index-organized tables.

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B-tree Indexes (cont.)

Index selectivity– Is a measure of usefulness of an index– Selective for columns with large number of unique

values– More efficient than non-selective indexes because

they point to more specific values

Unique Indexes– No duplicate values allowed– Very selective by nature

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B-tree Indexes (cont.)

Implicit Indexes– Created automatically by oracle– For unique and primary key constraints– For some object type tables

Concatenated Indexes– More than one column makes up the index– Usually more selective than single column index– Effective if leading column is used in WHERE clause

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B-tree Indexes (cont.)

Concatenated Indexes (cont.)– Create index statement example

CREATE INDEX emp_name_ix on employees(Last_name, first_name)

– Query example that uses index

SELECT cust_id FROM sh.customers c WHERE first_name = ‘Connor’ AND last_name = ‘Bishop’ (leading column of index) AND cust_year_of_birth = 1976;

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B-tree Indexes (cont.)

Concatenated Indexes (cont.)– “Covering Index”

Query that only uses indexed columns Means table data need not be read Only Index needs to be read

– Index skip-scans Means using index when leading column not used Works best when leading column is less selective

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B-tree Indexes (cont.)

Guidelines for use of concatenated indexes– Create if WHERE clause needs these columns together– Analyze which column is best suited to be the leading column– The more selective a column is, the better it is as the leading

column Isn’t true if wanting to use only non-leading column Keep in mind Index skip-scan performs less than a normal range

scan (i.e. try to use leading column)

– Supports queries that doesn’t use all the columns of the index in the WHERE clause (e.g. if only using the leading column)

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B-tree Indexes (cont.)

Index merges– Performed by Oracle if more than one column in the

WHERE clause– Is an alternative to a concatenated index if individual

indexes exist on columns in WHERE clause– Merges values for the values of each column– Generally less efficient than concatenated index– If seen in EXPLAIN PLAN consider creating

concatenated index

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B-tree Indexes (cont.)

Null values in indexes– No value represented in B-tree index for NULLS– Therefore, index can’t find NULL values – So, use NOT NULL for indexed columns where

possible– Conditions exist where may be acceptable

Column is almost always null You never want to find rows where the column in NULL Want to minimized space required for index

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B-tree Indexes (cont.)

Reverse key indexes– Can create with REVERSE keyword– For example stores ‘Smith’ as ‘htimS’– Can reduce contention for the leading edge of an index– New entries spread more evenly across index– However, range scans no longer possible– Consider if the count is high for:

Buffer busy waits Cache buffer chains latch waits

– Also beneficial in RAC implementations (Chapter 23)

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B-tree Indexes (cont.)

Index compression– Oracle allows leaf block compression– Works best on concatenated indexes where leading

part is repeated (e.g. Last Name of Smith would be likely repeated)

– Saves storage– Reduces I/O operations– Can reduce index “height”

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B-tree Indexes (cont.)

Functional Indexes– Means creating an index on an expression

CREATE INDEX cust_uppr_name_ix ON customers

(UPPER(cust_last_name),UPPER(cust_first_time));

– Use with care, can produce incorrect results See the use of the DETERMINISTIC keyword

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B-tree Indexes (cont.)

Foreign Keys and Locking– Indexing foreign key columns can prevent table-level

locking and reduce lock contention– These indexes reduce lock contention more than

improve query performance– These indexes help optimize DELETE CASCADE

operations– Locks especially occur if parent table is subject to

primary key updates or deletions

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B-tree Indexes (cont.)

Indexes and Partitioning– Local index means index partitioned in same manner as data

There is a 1-1 relationship between data partition and index partition values

– Global index means index partitioned differently than table– Key goal is to achieve partition elimination for queries (read

only a portion of the table or index)– Maintenance on global indexes higher than local indexes– Global indexes more often used in OLTP applications

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Bitmap Indexes

Completely different structure than B-tree Oracle creates bitmap for each unique value of a single

column Each bitmap contains a single bit (0 or 1) for each row

– ‘1’ means row matches value in bitmap– ‘0’ means row does not match value in bitmap

See Figure 5-7 (p. 125) Efficient for non-selective columns (e.g. gender) Very compact, fast to create Often used with Star Schema implementations Not recommended if many updates occur on column(s)

used for bitmap index

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Bitmap Indexes (cont.)

Index merge operations more efficient than B-tree Bitmap join indexes

– Identifies rows in one table that have matching value in 2nd table– Can prevent join of two tables– Example of bitmap join index:

CREATE BITMAP INDEX sales_bm_join_i ON sales

(c.cust_email)

FROM sales s, customers c

WHERE s.cust_id = c.cust_id;

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Index overhead

Indexes reduce performance of DML operations Columns with high update activity will have more

significant DML overhead Batch deletes incur very high overhead,

especially with non-unique indexes Ensure indexes are used in user queries in order

to reduce DML overhead where possible– Use MONITORING USAGE clause to validate if index

is being used, or– Monitor V$SQL_PLAN view

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Index Organized Tables (IOT)

Stored as B-tree index Entire table is stored as index Avoids duplicating storage of data and index Key lookups are fast as data is in leaf block of

index Can in turn mean more leaf blocks needed Can optionally store some of the data in an

“overflow segment”

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Index Organized Tables (IOT)

The overflow segment– You have some control over which columns live here– Can specify different tablespace for overflow– Generally a good idea to have– Can reduce depth of an index– May require more frequent rebuilds– See Figure 5-13, 5-14, 5-15 (pp. 135-137)

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Clustering

Two basic types– Index cluster

Storing rows from multiple tables with same key values in the same block

Speeds up joins Usually only used in specific circumstances Disadvantages include

– Full table scans against one of the clustered tables is slower– Inserts are slower– Join performance increase may be nominal– May require frequent rebuilds

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Clustering (cont.)

Two basic types (cont.)– Hash cluster

Stores rows in a location deduced algorithmically Reduces number of I/O operations needed Can reduce contention of oft-used blocks Consider using when

– High selectivity (high cardinality)– Optimization of primary key lookups is desired

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Nested Tables

Is an object type with relational characteristics Can define column in table of that object type One table appears to be nested within a column

of another table See code snippet example on p. 149

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Choosing an Index Strategy

Need to weigh the use of– B-tree– Bitmap– Hash Clusters

See Table 5-1 (pp. 150-151) for comparisons


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