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Jeff M. Nested materialized views incur the space overhead of materializing the join and having a materialized view log. In contrast, materialized aggregate views do not have demanding space requirements for the materialized join view and its log, but they have relatively long refresh times due to multiple computations of the same join.
It is not uncommon in a data warehouse to have already created summary or aggregation tables, and you might not wish to repeat this work by building a new materialized view. In this case, the table that already exists in the database can be registered as a prebuilt materialized view. This technique is described in "Registering Existing Materialized Views". Once you have selected the materialized views you want to create, follow the steps below for each materialized view.
However, if the materialized view is based on a user-defined prebuilt table, then the name of the materialized view must exactly match that table name. If you already have a naming convention for tables and indexes, you might consider extending this naming scheme to the materialized views so that they are easily identifiable.
Unless the materialized view is based on a user-defined prebuilt table, it requires and occupies storage space inside the database. Therefore, the storage needs for the materialized view should be specified in terms of the tablespace where it is to reside and the size of the extents. This information can then assist the design team in determining the tablespace in which the materialized view should reside.
Depending on the size of the tables to be scanned, this build process can take a considerable amount of time. Even though a materialized view is defined, it will not automatically be used by the query rewrite facility.
Query rewrite is not possible with all materialized views. If query rewrite is not occurring when expected, check to see if your materialized view satisfies all of the following conditions. If the materialized view contains the same table more than once, it is now possible to do a general rewrite, provided the query has the same aliases for the duplicate tables as the materialized view. Query Rewrite Restrictions If a query has both local and remote tables, only local tables will be considered for potential rewrite.
Neither the detail tables nor the materialized view can be owned by SYS. Aggregate functions must occur only as the outermost part of the expression. Refresh Options When you define a materialized view, you can specify two refresh options: how to refresh and what type of refresh. Depending on the materialized view you create, some of the options may not be available.
This can be specified as long as the materialized view is fast refreshable in other words, not complex. Therefore this method may not be suitable if many users are concurrently changing the tables upon which the materialized view is based.
If you anticipate performing insert, update or delete operations on tables referenced by a materialized view concurrently with the refresh of that materialized view, and that materialized view includes joins and aggregation, Oracle recommends you use ON COMMIT fast refresh rather than ON DEMAND fast refresh. If you think the materialized view did not refresh, check the alert log or trace file. Until this is done, the view will no longer be refreshed automatically at commit time.
Whether the fast refresh option is available depends upon the type of materialized view. Fast refresh is available for both general classes of materialized views:. Materialized views with aggregates General Restrictions on Fast Refresh The defining query of the materialized view is restricted as follows:. Defining queries for materialized views with joins only and no aggregates have these restrictions on fast refresh:.
If the WHERE clause of the query contains outer joins, then unique constraints must exist on the join columns of the inner join table. Materialized view logs must exist with rowids for all the base tables in the FROM list of the query. Restrictions on Fast Refresh on Materialized Views with Aggregates Defining queries for materialized views with joins and aggregates have these restrictions on fast refresh:.
See Table for further details. If the materialized view has one of the following, then fast refresh is supported on conventional DML inserts or direct loads or a combination of both but not not on deletes or updates. Note that all other requirements for fast refresh specified above must also be satisfied.
Materialized views with named views or subqueries in the FROM clause can be fast refreshed provided the views can be completely merged. For information on which views will merge, refer to the Oracle9i Database Performance Guide and Reference. Materialized aggregate views with self joins that is, multiple instances of the same table in the defining query are fast refreshable after conventional DML and direct loads.
The two tables will be treated as if they were separate tables. Materialized aggregate views with outer joins are fast refreshable after conventional DML and direct loads, provided only the outer table has been modified. Also, unique constraints must exist on the join columns of the inner join table. It is used only during the initial creation of the materialized view. It is not used during a full refresh or a fast refresh. To improve the performance of queries against large materialized views, store the rows in the materialized view in the order specified in the ORDER BY clause.
This initial ordering provides physical clustering of the data. As a result, there is no difference in the manner in which Oracle detects the various types of materialized views for example, materialized join views with no aggregates. Materialized view logs are required if you want to use fast refresh. They are not created on the materialized view. For fast refresh of materialized views, the definition of the materialized view logs must specify the ROWID clause.
An example of a materialized view log is shown below where one is created on the table sales. There is no difference in the information required if this approach is used. However, you must complete three property sheets and you must ensure that the option Enable Query Rewrite on the General sheet is selected. When using certain materialized views, you must ensure that your NLS parameters are the same as when you created the materialized view.
Materialized views with this restriction are:. Equijoins where one side of the join is character data. The result of this equijoin depends on collation and this can change on a session basis, giving an incorrect result in the case of query rewrite or an inconsistent materialized view after a refresh operation. Expressions that generate internal conversion to character data in the SELECT list of a materialized view, or inside an aggregate of a materialized aggregate view.
Registering Existing Materialized Views Some data warehouses have implemented materialized views in ordinary user tables. Although this solution provides the performance benefits of materialized views, it does not:. Enable materialized views defined in one application to be transparently accessed in another application Generally support fast parallel or fast materialized view refresh Because of these limitations, and because existing materialized views can be extremely large and expensive to rebuild, you should register your existing materialized view tables with Oracle whenever possible.
Once registered, the materialized view can be used for query rewrites or maintained by one of the refresh methods, or both. The contents of the table must reflect the materialization of the defining query at the time you register it as a materialized view, and each column in the defining query must correspond to a column in the table that has a matching datatype. The table and the materialized view must have the same name, but the table retains its identity as a table and can contain columns that are not referenced in the defining query of the materialized view.
These extra columns are known as unmanaged columns. If rows are inserted during a refresh operation, each unmanaged column of the row is set to its default value. Unmanaged columns are not supported by single-table aggregate materialized views or materialized views containing joins only. See Also: Chapter 22, "Query Rewrite" , for details about integrity levels When you drop a materialized view that was created on a prebuilt table, the table still exists--only the materialized view is dropped.
Therefore, only stale integrity modes can be used. The following example illustrates the two steps required to register a user-defined table. First, the table is created, then the materialized view is defined using exactly the same name as the table. In some cases, user-defined materialized views are refreshed on a schedule that is longer than the update cycle.
For example, a monthly materialized view might be updated only at the end of each month, and the materialized view values always refer to complete time periods. Reports written directly against these materialized views implicitly select only data that is not in the current incomplete time period. If a user-defined materialized view already contains a time dimension:.
You can create a view that selects the complete time period of interest. The reports should be modified to refer to the view instead of referring directly to the user-defined materialized view.
If the user-defined materialized view does not contain a time dimension, then:. The view should aggregate over the time column in the new materialized view.
Partitioning and Materialized Views Because of the large volume of data held in a data warehouse, partitioning is an extremely useful option when designing a database. Partitioning the fact tables improves scalability, simplifies system administration, and makes it possible to define local indexes that can be efficiently rebuilt.
Partitioning the fact tables provides greater opportunities for fast refresh of the materialized view, when the partition maintenance operation occurs. Partitioning the fact tables also improves the opportunity of fast refreshing the materialized view when the partition maintenance operation occurs.
Partitioning a materialized view also has benefits for refresh, because the refresh procedure can use parallel DML to maintain the materialized view. See Also: Chapter 5, "Parallelism and Partitioning in Data Warehouses" for further details about partitioning Partition Change Tracking It is possible and advantageous to track freshness to a finer grain than the entire materialized view. The ability to identify which rows in a materialized view are affected by a certain detail table partition, is known as Partition Change Tracking PCT.
When one or more of the detail tables are partitioned, it may be possible to identify the specific rows in the materialized view that correspond to a modified detail partition s ; those rows become stale when a partition is modified while all other rows remain fresh.
Partition Change Tracking can be used to identify which materialized view rows correspond to a particular detail table partition is used to support fast refresh after partition maintenance operations on detail tables. For instance, if a detail table partition is truncated or dropped, the affected rows in the materialized view are identified and deleted.
Partitioned tables must use either range or composite partitioning. The partition key must consist of only a single column. The materialized view must contain either the partition key column or a partition marker of the detail table. Data modifications can only occur on the partitioned table. Partition change tracking is not supported for a materialized view that refers to views, remote tables, or outer joins.
Partition change tracking requires sufficient information in the materialized view to be able to correlate each materialized view row back to its corresponding detail row in the source partitioned detail table. Depending on the desired level of aggregation and the distinct cardinalities of the partition key columns, this has the unfortunate effect of significantly increasing the cardinality of the materialized view.
For example, say a popular metric is the revenue generated by a product during a given year. If there were different products sold each day, it would substantially increase the number of rows in the materialized view.
In many cases, the advantages of PCT will be offset by this restriction for highly aggregated materialized views. The function returns a partition identifier that uniquely identifies the partition for a specified row within a specified partition table. The following example uses the Sales History Schema and the three detail tables sales , products , and times to create two materialized views. The second materialized view is for monthly customer sales. As customers tend to purchase in bulk, sales average just two orders per customer per month.
However, most orders are large and contain many different products. Therefore, PCT is not enabled on table products for this materialized view. However, PCT fast refresh is not possible after any kind of modification to the products table.
Following a partition maintenance operation, such as a drop partition, it is recommended a PCT fast refresh be performed on any materialized view that is referencing the table upon which the partition operations are undertaken. This enables partition change tracking on both the sales table and the products table with significantly less cardinality impact than grouping by the respective partition key columns.
PMARKER function, the materialized view cardinality is increased only by a factor of the number of partitions in the sales table times, the number of partitions in the products table. This would generally be significantly less than the cardinality impact of including the respective partition key columns. Partitioning a materialized view involves defining the materialized view with the standard Oracle partitioning clauses, as illustrated in the example below. Alternatively, a materialized view can be registered to a partitioned prebuilt table as illustrated in the following example:.
When the data warehouse or data mart contains a time dimension, it is often desirable to archive the oldest information and then reuse the storage for new information. This is called the rolling window scenario. If the fact tables or materialized views include a time dimension and are horizontally partitioned by the time attribute, then management of rolling materialized views can be reduced to a few fast partition maintenance operations provided the unit of data that is rolled out equals, or is at least aligned with, the range partitions.
If you plan to have rolling materialized views in your warehouse, you should determine how frequently you plan to perform partition maintenance operations, and you should plan to partition fact tables and materialized views to reduce the amount of system administration overhead required when old data is aged out. You are not restricted to using range partitions. For example, a composite partition using both a time value and a key value could result in a good partition solution for your data.
Query execution might need to access any subset of the materialized view key columns, and might need to join and aggregate over a subset of those columns. Consequently, query execution usually performs best if a single-column bitmap index is defined on each materialized view key column. In the case of materialized views containing only joins using fast refresh, Oracle recommends that indexes be created on the columns that contain the rowids to improve the performance of the refresh operation.
See Also: Chapter 21, "Using Parallel Execution" , for further details Invalidating Materialized Views Dependencies related to materialized views are automatically maintained to ensure correct operation. When a materialized view is created, the materialized view depends on the detail tables referenced in its definition.
A materialized view is automatically revalidated when it is referenced. In many cases, the materialized view will be successfully and transparently revalidated.
The decision to use the aggregate table is done by OBI, based on our definition of the data sources. With this function, the incompatibilities of objects must be defined. But if the query contains an incompatible object, BusinessObjects will revert to the second source. Typically, aggregate tables are created at the end of the ETL process and may not be ready when users start working with the system.
You would need to create a logic in order to ignore the aggregates if they have not been refreshed. This logic can be complicated to create for aggregate tables. However, it is easy to set Materialized Views online or offline by enabling or disabling them for query rewrite. With Materialized Views, all aggregate navigation logic resides in an Oracle database. The database will automatically rewrite the query if Materialized Views exist.
If there are no Materialized Views, the detail tables are used. When the Oracle database detects that a user query would benefit from using aggregates, it automatically rewrites the query and targets the aggregates.
You only need to define one source, the detail table. A single logical table source pointing to the detail table is now sufficient for the Oracle BI repository. Simplified OBI repository when using Oracle materialized views.
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