Materialized Views in Oracle (2023)

FAQs

What is the use of materialized view in Oracle? ›

Oracle uses materialized views (also known as snapshots in prior releases) to replicate data to non-master sites in a replication environment and to cache expensive queries in a data warehouse environment.

Views are used when data is to be accessed infrequently and the data in a table gets updated on a frequent basis. In contrast, Materialized Views are used when data is to be accessed frequently and data in table not get updated on frequent basis.

A materialized view is a pre-computed data set derived from a query specification (the SELECT in the view definition) and stored for later use. Because the data is pre-computed, querying a materialized view is faster than executing a query against the base table of the view.

Unlike an ordinary Oracle Database View which does not contain any data, a materialized view is an Oracle Database object that contains the results of a query. Materialized views can be stored in the same database as their base tables or views, or in a separate database.

Faster response times – A materialized view responds faster in comparison to a view. This is because the materialized view is precomputed and therefore doesn't waste time resolving the query or joins in the query that create the materialized view.

Unlike indexes, materialized views are not automatically updated with every data change. They must explicitly be refreshed, either on every commit, on a periodically time schedule or – typically in data warehouses – at the end of an ETL job.

You can query your materialized views directly, the same way you query a regular table or standard view. Queries against materialized views are always consistent with queries against the view's base tables, even if those tables have changed since the last time the materialized view was refreshed.

To update the data in a materialized view, you can use the REFRESH MATERIALIZED VIEW statement at any time. When you use this statement, Amazon Redshift identifies changes that have taken place in the base table or tables, and then applies those changes to the materialized view.

Advantages: local copy of the data in the view sql structure, Can be indexed and partitioned (tuned) Queries don't impact source tables (great for OLTP) Disadvantages: Takes space Can only be based on a simple Select if you require realtime data. maintaining the MV Logs has an overhead on the master system.

What is an example of a materialized view? ›

For example, let's say you have a database with two tables: one contains the number of employees in your business, and the other contains the number of departments in your business. Using a materialized view, you could query the database to retrieve all the employees who are associated with a particular department.

You cannot delete rows from a read-only materialized view. If you delete rows from a writable materialized view, then the database removes the rows from the underlying container table. However, the deletions are overwritten at the next refresh operation.

Usage Notes. Dropping a materialized view does not update references to that view. For example, if you create a view named “V1” on top of a materialized view, and then you drop the materialized view, the definition of view “V1” will become out of date.

Users cannot perform data manipulation language (DML) statements on read-only materialized views, but they can perform DML on updatable and writeable materialized views.

Materialized views find use especially in data warehousing scenarios, where frequent queries of the actual base tables can be expensive. In a materialized view, indexes can be built on any column.

Materialized views are physical structures that improve data access time by pre- computing intermediary results. The ETL process bridging the online transaction processing (OLTP) system and the online analytical processing (OLAP) system is often modeled as a separate and independent process.

A materialized view is built on top of existing tables, whilst a table is the original data storage object that is used. Another major difference is that a materialized view is self updating whenever the underlying tables change, whilst to update a table a job has to be run to append or replace the data.

Like SELECT statements, materialized views can join on several tables.

You can't insert data into a materialized view as you can with a table. To update the contents of a materialized view, you can execute a query to refresh it. This will re-execute the query used to create it.

As such, we do not generally recommend truncating a materialized view. Although each query on the view will still show up-to-date results, the query might run more slowly as Snowflake updates the materialized view or looks up data in the base table.

Are materialized views read only? ›

In a basic configuration, materialized views can provide read-only access to the table data that originates from a master site or master materialized view site. Applications can query data from read-only materialized views to avoid network access to the master site, regardless of network availability.

You can use a materialized view in any SQL query by referencing the materialized view name as the data source, like a table or standard view. When a query accesses a materialized view, it sees only the data that is stored in the materialized view as of its most recent refresh.

When you choose a name for the materialized view, note that a schema cannot contain a table and view with the same name. CREATE [ MATERIALIZED ] VIEW produces an error if a table with the same name already exists in the schema.

You can select data from a materialized view as you would from a table or view. In replication environments, the materialized views commonly created are primary key, rowid, object, and subquery materialized views.

To refresh the Oracle materialized view, call one of the procedures in DBMS_MVIEW. The DBMS_MVIEW package provides three types of refresh operations: DBMS_MVIEW. REFRESH: Refreshes one or more Oracle materialized views.

An update of a row in a materialized view might be done as a DELETE+INSERT. A refresh of a materialized view might involve a delete + insert of every row. A refresh might involve a truncate plus direct path load of ever row.

In particular, Materialized Views have a few drawbacks to consider: They take up additional storage space. Data storage costs money. Materialized Views will often contain orders of magnitude less rows than the original data tables, but this is still something to keep in mind.

Physically the materialized view is a table. The materialized view objects contains the metadata about the refresh mode, type, method and other properties. If you check the ALL_OBJECTS you see that MATERIALIZED VIEW object doesn't have its segment.

Automatic refresh. By default, materialized views are automatically refreshed within 5 minutes of a change to the base tables, but no more frequently than every 30 minutes.

Where is materialized view stored in Oracle? ›

A materialized view can be stored in the same database as its base tables or in a different database. Materialized views are often used to improve performance, especially when storing data locally that is sourced from tables or views using a database link.

Views are the virtual projection of an output query or the dynamic view of the data in a database that is presented to the user whenever requested. Materialized views, on the other hand, are a non-virtual schema. It is a common part of database warehousing.

The columns that you specify in the selection list must exist in the base table. You must specify at least one column in the selection list. You can select up to 64 columns for your materialized view. The columns in the ORDER BY list must be specified in the selection list.

So if you want to refresh mview daily, you need to keep it refresh on demand and set the next refresh time as sysdate + 1 . You can set any interval although. Once you do this the materialized view is created and a job is set in Oracle that will refresh mview every 24 hrs (sysdate + 1) .

PostgreSQL will lock the materialized view table while refreshing. You won't be able to execute any queries until it gets all the data updated, and that takes time.

Materialized view refresh can be performed in incremental or a complete refresh. Incremental refresh contains two methods known as log-based refresh and partition change tracking (PCT) refresh. Incremental refresh also known as FAST refresh because it usually performs faster than complete refresh.

While a job is running, a SELECT * FROM V$SESSION_LONGOPS statement will tell you the progress of each materialized view being refreshed. To look at the progress of which jobs are on which queue, use a SELECT * FROM DBA_JOBS_RUNNING statement.

You can set autorefresh for materialized views using CREATE MATERIALIZED VIEW. You can also use the AUTO REFRESH clause to refresh materialized views automatically.

You can use materialized views to increase the speed of queries on very large databases. Queries to large databases often involve joins between tables, aggregations such as SUM, or both. These operations are expensive in terms of time and processing power.

In certain situations, views can provide multiple advantages over regular tables: Views can contain (and usually do) joins between multiple tables, producing a single result set, thus reducing the complexity.

How does materialized view improve performance in Oracle? ›

Materialized views improve query performance by precalculating expensive join and aggregation operations on the database prior to execution and storing the results in the database. The query optimizer automatically recognizes when an existing materialized view can and should be used to satisfy a request.

You can't insert data into a materialized view as you can with a table. To update the contents of a materialized view, you can execute a query to refresh it. This will re-execute the query used to create it.

Materialized views use a restricted SQL syntax and a limited set of aggregation functions. For more information, see Supported materialized views. Materialized views cannot be nested on other materialized views. Materialized views cannot query external tables.

To update the data in a materialized view, you can use the REFRESH MATERIALIZED VIEW statement at any time. When you use this statement, Amazon Redshift identifies changes that have taken place in the base table or tables, and then applies those changes to the materialized view.

A materialized view can be either read-only, updatable, or writeable. Users cannot perform data manipulation language (DML) statements on read-only materialized views, but they can perform DML on updatable and writeable materialized views.

According to Delete Duplicate Rows in SQL, you can also use the SQL RANK feature to get rid of the duplicate rows. Regardless of duplicate rows, the SQL RANK function returns a unique row ID for each row. You need to use aggregate functions like Max, Min, and AVG to perform calculations on data.

Although there are many advantages to views, the main disadvantage to using views rather than real tables is performance degradation. Because views only create the appearance of a table, not a real table, the query processor must translate queries against the view into queries against the underlying source tables.

Because you store data in a table on the database, it can be quicker to access. Once you open the application, you can quickly access the information you seek. Data in a view can take longer to access because you have to run a query first. If you want results for data from multiple tables, this can take even longer.

Use the ALTER MATERIALIZED VIEW statement to modify an existing materialized view in one or more of the following ways: To change its storage characteristics. To change its refresh method, mode, or time.