Indexes

22

22

IndexTable

KeyRow

pointer

… WHERE key = 22

Types of Indexes

These are several types of index structures available to you, depending on the need:– A B-tree index is in the form of a binary tree and is the

default index type.– A bitmap index has a bitmap for each distinct value

indexed, and each bit position represents a row that may or may not contain the indexed value. This is best for low-cardinality columns.

B-Tree Index

Index entry headerKey column lengthKey column valueROWID

Root

Branch

Leaf

Index entry

B-Tree Index

Structure of a B-tree indexAt the top of the index is the root, which contains entries that point to the next level in the index. At the next level are branch blocks, which in turn point to blocks at the next level in the index. At the lowest level are the leaf nodes, which contain the index entries that point to rows in the table. The leaf blocks are doubly linked to facilitate the scanning of the index in an ascending as well as descending order of key values.

Format of index leaf entriesAn index entry is made up of the following components:An entry header, which stores the number of columns and locking

informationKey column length-value pairs, which define the size of a column in the

key followed by the value for the column (The number of such pairs is a maximum of the number of columns in the index.)

ROWID of a row that contains the key values

Bitmap Indexes

<Blue, 10.0.3, 12.8.3, 1000100100010010100><Green, 10.0.3, 12.8.3, 0001010000100100000>

<Red, 10.0.3, 12.8.3, 0100000011000001001><Yellow, 10.0.3, 12.8.3, 0010001000001000010>

KeyStartROWID

EndROWID Bitmap

Table

Index

Block 10

Block 11

Block 12

File 3

Bitmap Indexes Structure of a bitmap indexA bitmap index is also organized as a B-tree, but the leaf node stores a bitmap for each key value instead of a list of ROWIDs. Each bit in the bitmap corresponds to a possible ROWID, and if the bit is set, it means that the row with the corresponding ROWID contains the key value.As shown in the diagram, the leaf node of a bitmap index contains the following:An entry header that contains the number of columns and lock infoKey values consisting of length and value pairs for each key columnStart ROWIDEnd ROWIDA bitmap segment consisting of a string of bits. (The bit is set when the

corresponding row contains the key value and is unset when the row does not contain the key value. The Oracle server uses a patented compression technique to store bitmap segments.)

Index Options– A unique index ensures that every indexed value is unique.CREATE UNIQUE INDEX emp1 ON EMP (ename);DBA_INDEXES. UNIQUENESS ‘UNIQUE’

– Bitmap index CREATE BITMAP INDEX emp2 ON EMP (deptno); DBA_INDEXES.INDEX_TYPE ‘BITMAP’

An index can have its key values stored in ascending or descending order.

CREATE INDEX emp3 ON emp (sal DESC);DBA_IND_COLUMNS.DESCEND ‘DESC’

Index Options– A composite index is one that is based on more than one column.CREATE INDEX emp4 ON emp (empno, sal);DBA_IND_COLUMNS.COLUMN_POSITION 1,2 …

– A function-based index is an index based on a function’s return value.

CREATE INDEX emp5 ON emp (SUBSTR(ename, 3, 4)); DBA_IND_EXPRESSIONS.COLUMN_EXPRESSION ‘SUBSTR …’

– A compressed index has repeated key values removed.CREATE INDEX emp6 ON emp (empno, ename, sal) COMPRESS 2; DBA_INDEXES.COMPRESSION ‘ENABLED’DBA_INDEXES.PREFIX_LENGTH 2

Creating Indexes

CREATE INDEX my_index ONemployees(last_name, first_name);

Table Types

•Related data from more than one table are stored together.

•Clustered table

•Data (including non-key values) is sorted and stored in a B-tree index structure.

•Index-organized table (IOT)

•Data is divided into smaller, more manageable pieces.

•Partitioned table

•Data is stored as an unordered collection (heap).

•Ordinary (heap-organized) table

•Description•Type

Heap

IOTPartitioned

Clustered

What Is a Partition and Why Use It?

•A partition is:– A piece of a “very large” table or index– Stored in its own segment– Used for improved performance and

manageability

RANGE PARTITION

CREATE TABLE eladasok ( szla_szam NUMBER(5), szla_nev CHAR(30), mennyiseg NUMBER(6), het INTEGER ) PARTITION BY RANGE ( het ) (PARTITION negyedev1 VALUES LESS THAN ( 13 )

TABLESPACE users, PARTITION negyedev2 VALUES LESS THAN ( 26 )

TABLESPACE example, PARTITION negyedev3 VALUES LESS THAN ( 39 )

TABLESPACE users )

DBA_PART_TABLESDBA_TAB_PARTITIONSDBA_TAB_SUBPARTITIONS

HASH PARTITION, LIST PARTITION

CREATE TABLE eladasok2 (szla_szam NUMBER(5), szla_nev CHAR(30), mennyiseg NUMBER(6), het INTEGER ) PARTITION BY HASH ( het ) (PARTITION part1 TABLESPACE users, PARTITION part2 TABLESPACE example, PARTITION part3 TABLESPACE users );

CREATE TABLE eladasok3 (szla_szam NUMBER(5), szla_nev CHAR(30), mennyiseg NUMBER(6), het INTEGER ) PARTITION BY LIST ( het ) (PARTITION part1 VALUES(1,2,3,4,5) TABLESPACE users, PARTITION part2 VALUES(6,7,8,9) TABLESPACE example, PARTITION part3 VALUES(10,11,12,13) TABLESPACE users ) ;

SUBPARTITIONS (RANGE-HASH)

CREATE TABLE eladasok4 (szla_szam NUMBER(5), szla_nev CHAR(30), mennyiseg NUMBER(6), het INTEGER ) PARTITION BY RANGE ( het )SUBPARTITION BY HASH (mennyiseg)SUBPARTITIONS 3 (PARTITION negyedev1 VALUES LESS THAN ( 13 )

TABLESPACE users, PARTITION negyedev2 VALUES LESS THAN ( 26 )

TABLESPACE example, PARTITION negyedev3 VALUES LESS THAN ( 39 )

TABLESPACE users );

Table accessby ROWID

Index-Organized Tables

•Regular table access IOT access

Non-key columnsKey column

Row header

Index-Organized Tablesand Heap Tables

– Compared to heap tables, IOTs:• Have faster key-based access to table data• Do not duplicate the storage of primary key values• Require less storage • Use secondary indexes and logical row IDs• Have higher availability because table reorganization does

not invalidate secondary indexes

Index-Organized Tables

CREATE TABLE cikk_iot ( ckod integer, cnev varchar2(20), szin varchar2(15), suly float, CONSTRAINT cikk_iot_pk PRIMARY KEY (ckod) )ORGANIZATION INDEXPCTTHRESHOLD 20 INCLUDING cnevOVERFLOW TABLESPACE users;

DBA_INDEXES index_type ‘IOT-TOP’ table_name ‘CIKK_IOT’DBA_TABLES.IOT_TYPE ‘IOT’ or ‘IOT_OVERFLOW’ DBA_TABLES.IOT_NAME ‘CIKK_IOT’ for overflow segment

Clusters

Clustered orders and order_item tables

Cluster Key(ORD_NO) 101 ORD_DT CUST_CD 05-JAN-97 R01

PROD QTYA4102 20A5675 19 W0824 10

102 ORD_DT CUST_CD 07-JAN-97 N45

PROD QTYA2091 11G7830 20 N9587 26

Unclustered orders and order_item tables

ORD_NO ORD_DT CUST_CD------ ------ ------

101 05-JAN-97 R01102 07-JAN-97 N45

ORD_NO PROD QTY ...----- ------ ------

101 A4102 20102 A2091 11102 G7830 20

102 N9587 26101 A5675 19101 W0824 10

Cluster TypesIndex Index

clusterclusterHash Hash

clustercluster

Hash functionHash function

Situations Where Clusters Are Useful

Criterion

Uniform key distribution

Evenly spread key values

Rarely updated key

Often joined master-detail tables

Predictable number of key values

Queries using equality predicate on key

Hash

X

X

X

X

X

Index

X

X

X

INDEX CLUSTER

CREATE CLUSTER personnel ( department_number NUMBER(2) ) SIZE 512;

CREATE TABLE emp_cl ( empno NUMBER PRIMARY KEY,ename VARCHAR2(30), job VARCHAR2(27), mgr NUMBER(4), hiredate DATE, sal NUMBER(7,2), comm NUMBER(7,2), deptno NUMBER(2) NOT NULL) CLUSTER personnel (deptno);CREATE TABLE dept_cl ( deptno NUMBER(2), dname VARCHAR2(9), loc VARCHAR2(9))CLUSTER personnel (deptno);CREATE INDEX idx_personnel ON CLUSTER personnel;

DBA_CLUSTERSDBA_CLU_COLUMNSDBA_TABLES.CLUSTER_NAME ‘PERSONNEL’

HASH CLUSTER

CREATE CLUSTER personnel1( department_number NUMBER ) SIZE 512 HASHKEYS 500 STORAGE (INITIAL 100K NEXT 50K);

CREATE CLUSTER personnel2 ( home_area_code NUMBER, home_prefix NUMBER ) HASHKEYS 20 HASH IS MOD(home_area_code + home_prefix, 101); CREATE CLUSTER personnel3 (deptno NUMBER) SIZE 512 SINGLE TABLE HASHKEYS 500;

DBA_CLUSTERSDBA_CLU_COLUMNSDBA_CLUSTER_HASH_EXPRESSIONS