Is it possible to create index on view oracle

Click the Details tab to view additional information. Description of the illustration a3 The Details tab shows additional information about the index definition. Right-click the Employees table. Select Index in the menu and then select Create Index. Click OK. List the date and time the record was last refreshed.

Click the Save button. On saving the page, the Run button gets enabled. Click the Run button. Click the OK button to initiate the process. For more information on Process Scheduler Request page see, Maintaining Materialized Views documented under the Oracle platform section.

You can see on the page, a list of the views delivered from applications. This example illustrates the Change Properties page. The columns on the page are explained below:. The page provides option to enable or disable indexed views and summary tables from the PeopleSoft Internet Architecture. Select to convert the view as an indexed view or summary table. Select to convert the indexed view or summary table to a normal SQL view.

Displays Enabled or Disabled depending on the current status of the view. A row containing all nulls must not appear in the query result set. For this result to be guaranteed, at least one column in the index must have either:. If the last name and salary are a composite key in an index, then a fast full index scan can read the index entries to obtain the requested information:. An index range scan is an ordered scan of an index that has the following characteristics:.

One or more leading columns of an index are specified in conditions. The database commonly uses an index range scan to access selective data. The selectivity is the percentage of rows in the table that the query selects, with 0 meaning no rows and 1 meaning all rows.

A predicate becomes more selective as the value approaches 0 and less selective or more unselective as the value approaches 1. For example, a user queries employees whose last names begin with A. For example, two employees are named Austin, so two rowids are associated with the key Austin. An index range scan can be bounded on both sides, as in a query for departments with IDs between 10 and 40, or bounded on only one side, as in a query for IDs over To scan the index, the database moves backward or forward through the leaf blocks.

For example, a scan for IDs between 10 and 40 locates the first index leaf block that contains the lowest key value that is 10 or greater. The scan then proceeds horizontally through the linked list of leaf nodes until it locates a value greater than In contrast to an index range scan, an index unique scan must have either 0 or 1 rowid associated with an index key. The database performs a unique scan when a predicate references all of the columns in a UNIQUE index key using an equality operator.

An index unique scan stops processing as soon as it finds the first record because no second record is possible. In this case, the database can use an index unique scan to locate the rowid for the employee whose ID is 5.

An index skip scan uses logical subindexes of a composite index. The database "skips" through a single index as if it were searching separate indexes. Skip scanning is beneficial if there are few distinct values in the leading column of a composite index and many distinct values in the nonleading key of the index. The database may choose an index skip scan when the leading column of the composite index is not specified in a query predicate.

For example, assume that you run the following query for a customer in the sh. Example shows a portion of the index entries. In a skip scan, the number of logical subindexes is determined by the number of distinct values in the leading column. In Example , the leading column has two possible values.

The database logically splits the index into one subindex with the key F and a second subindex with the key M. When searching for the record for the customer whose email is Abbey company. Conceptually, the database processes the query as follows:.

The index clustering factor measures row order in relation to an indexed value such as employee last name. The more order that exists in row storage for this value, the lower the clustering factor.

The index entries point to random table blocks, so the database may have to read and reread the same blocks over and over again to retrieve the data pointed to by the index. The index keys in a range tend to point to the same data block, so the database does not have to read and reread the same blocks over and over. Whether you should consider using an index-organized table, partitioning, or table cluster if rows must be ordered by the index key. For example, assume that the employees table fits into two data blocks.

Table depicts the rows in the two data blocks the ellipses indicate data that is not shown. Rows are stored in the blocks in order of last name shown in bold. For example, the bottom row in data block 1 describes Abel, the next row up describes Ande, and so on alphabetically until the top row in block 1 for Steven King.

The bottom row in block 2 describes Kochar, the next row up describes Kumar, and so on alphabetically until the last row in the block for Zlotkey. Assume that an index exists on the last name column. Each name entry corresponds to a rowid.

Conceptually, the index entries would look as follows:. Assume that a separate index exists on the employee ID column.

Conceptually, the index entries might look as follows, with employee IDs distributed in almost random locations throughout the two blocks:.

A reverse key index is a type of B-tree index that physically reverses the bytes of each index key while keeping the column order. For example, if the index key is 20 , and if the two bytes stored for this key in hexadecimal are C1,15 in a standard B-tree index, then a reverse key index stores the bytes as 15,C1. Reversing the key solves the problem of contention for leaf blocks in the right side of a B-tree index.

This problem can be especially acute in an Oracle Real Application Clusters Oracle RAC database in which multiple instances repeatedly modify the same block.

For example, in an orders table the primary keys for orders are sequential. One instance in the cluster adds order 20, while another adds 21, with each instance writing its key to the same leaf block on the right-hand side of the index. In a reverse key index, the reversal of the byte order distributes inserts across all leaf keys in the index.

For example, keys such as 20 and 21 that would have been adjacent in a standard key index are now stored far apart in separate blocks. Because the data in the index is not sorted by column key when it is stored, the reverse key arrangement eliminates the ability to run an index range scanning query in some cases.

For example, if a user issues a query for order IDs greater than 20, then the database cannot start with the block containing this ID and proceed horizontally through the leaf blocks. In an ascending index , Oracle Database stores data in ascending order. By default, character data is ordered by the binary values contained in each byte of the value, numeric data from smallest to largest number, and date from earliest to latest value.

For an example of an ascending index, consider the following SQL statement:. Oracle Database sorts the hr.

In this case, the index stores data on a specified column or columns in descending order. If the index in Figure on the employees. The default search through a descending index is from highest to lowest value. Descending indexes are useful when a query sorts some columns ascending and others descending.

If a user queries hr. Oracle Database Performance Tuning Guide to learn more about ascending and descending index searches. Oracle Database can use key compression to compress portions of the primary key column values in a B-tree index or an index-organized table. Key compression can greatly reduce the space consumed by the index.

In general, index keys have two pieces, a grouping piece and a unique piece. Key compression breaks the index key into a prefix entry , which is the grouping piece, and a suffix entry , which is the unique or nearly unique piece. The database achieves compression by sharing the prefix entries among the suffix entries in an index block. By default, the prefix of a unique index consists of all key columns excluding the last one, whereas the prefix of a nonunique index consists of all key columns.

For example, suppose that you create a composite index on the oe. An index block may have entries as shown in Example If this index were created with default key compression, then duplicate key prefixes such as online , 0 and online , 2 would be compressed.

Conceptually, the database achieves compression as shown in the following example:. Suffix entries form the compressed version of index rows. Each suffix entry references a prefix entry, which is stored in the same index block as the suffix entry. Alternatively, you could specify a prefix length when creating a compressed index. For the values in Example , the index would factor out duplicate occurrences of online as follows:.

The index stores a specific prefix once per leaf block at most. Only keys in the leaf blocks of a B-tree index are compressed. In the branch blocks the key suffix can be truncated, but the key is not compressed. Oracle Database Administrator's Guide to learn how to use compressed indexes. In a bitmap index , the database stores a bitmap for each index key. In a conventional B-tree index, one index entry points to a single row. In a bitmap index, each index key stores pointers to multiple rows.

Bitmap indexes are primarily designed for data warehousing or environments in which queries reference many columns in an ad hoc fashion. Situations that may call for a bitmap index include:. The indexed columns have low cardinality , that is, the number of distinct values is small compared to the number of table rows.

The indexed table is either read-only or not subject to significant modification by DML statements. For a data warehouse example, the sh. Suppose that queries for the number of customers of a particular gender are common. In this case, the customers. Each bit in the bitmap corresponds to a possible rowid. If the bit is set, then the row with the corresponding rowid contains the key value.

A mapping function converts the bit position to an actual rowid, so the bitmap index provides the same functionality as a B-tree index although it uses a different internal representation.

If the indexed column in a single row is updated, then the database locks the index key entry for example, M or F and not the individual bit mapped to the updated row. Because a key points to many rows, DML on indexed data typically locks all of these rows. For this reason, bitmap indexes are not appropriate for many OLTP applications.

Oracle Database Performance Tuning Guide to learn how to use bitmap indexes for performance. Oracle Database Data Warehousing Guide to learn how to use bitmap indexes in a data warehouse. Example shows a query of the sh. Some columns in this table are candidates for a bitmap index.

A bitmap index is probably not useful for the other columns. Instead, a unique B-tree index on these columns would likely provide the most efficient representation and retrieval. It consists of two separate bitmaps, one for each gender. A mapping function converts each bit in the bitmap to a rowid of the customers table.

Each bit value depends on the values of the corresponding row in the table. A function-based index increases the variety of ways in which you can access data. Function-based indexes also support linguistic sorts based on collation keys, efficient linguistic collation of SQL statements, and case-insensitive sorts. Like other indexes, function-based indexes improve query performance.

For example, if you need to access a computationally complex expression often, then you can store it in an index. Then when you need to access the expression, it is already computed. You can find a detailed description of the advantages of function-based indexes in "Advantages of Function-Based Indexes". Function-based indexes have all of the same properties as indexes on columns. However, unlike indexes on columns which can be used by both cost-based and rule-based optimization, function-based indexes can be used by only by cost-based optimization.

Other restrictions on function-based indexes are described in "Restrictions for Function-Based Indexes". The optimizer can estimate how many rows are selected by expressions more accurately if the expressions are materialized in a function-based index.

Such indexes are treated as function-based indexes. Descending indexes cannot be bitmapped or reverse, and cannot be used in bitmapped optimizations. A query could use this index to quickly find cities that are more than miles from the equator:.

Another index stores the temperature delta and the maximum temperature. The result of the delta is sorted in descending order. A query could use this index to quickly find table rows where the temperature delta is less than 20 and the maximum temperature is greater than The following command computes a value for each row using columns A, B, and C, and stores the results in the index.

The SELECT statement can either use index range scan since the expression is a prefix of index IDX or index fast full scan which may be preferable if the index has specified a high parallel degree.