10 Oracle Materialized View Best Practices

Oracle Materialized Views are an important tool for improving the performance of database queries. By pre-computing the results of a query, Materialized Views can dramatically reduce the amount of time it takes to execute a query.

However, Materialized Views can also be a source of confusion and frustration if they are not implemented correctly. In this article, we will discuss 10 best practices for using Materialized Views in Oracle databases. By following these best practices, you can ensure that your Materialized Views are optimized for maximum performance and reliability.

1. Use materialized views to improve query performance

Materialized views are pre-computed, stored results of a query. When the underlying data changes, the materialized view can be refreshed to reflect those changes. This means that when a user runs a query against the materialized view, they don’t have to wait for the database to compute the result set from scratch; instead, they get an immediate response from the already computed and stored result set.

Oracle Materialized Views also offer additional performance benefits such as parallelism, which allows multiple processes to work on different parts of the same query simultaneously. Additionally, Oracle’s Query Rewrite feature can automatically rewrite queries to use materialized views if it determines that doing so will improve performance. This helps ensure that users always get the best possible performance out of their queries.

2. Use the DBMS_MVIEW package to manage materialized views

The DBMS_MVIEW package provides a set of procedures and functions that allow users to manage materialized views. This includes creating, refreshing, dropping, and analyzing the performance of materialized views.

Using this package is beneficial because it allows for more control over the refresh process. For example, users can specify when and how often they want their materialized view to be refreshed. Additionally, the package also offers options such as fast refreshes, which are much faster than complete refreshes.

Furthermore, the DBMS_MVIEW package also helps with managing the storage associated with materialized views. It allows users to store materialized views in different tablespaces, which can help optimize disk space usage.

3. Define a refresh group for multiple related materialized views

A refresh group is a collection of materialized views that can be refreshed together. This allows for the efficient use of resources, as all related materialized views are refreshed at once instead of individually. It also ensures data consistency between related materialized views, since they will always have the same timestamp and thus the same data.

To create a refresh group, you must first define it in the database using the DBMS_REFRESH package. Then, add each materialized view to the group by calling the ADD procedure with the name of the refresh group and the name of the materialized view. You can also specify how often the refresh group should be refreshed, such as daily or weekly. Once the refresh group has been created, you can call the REFRESH procedure to refresh all the materialized views in the group.

4. Create indexes on materialized views to speed up query performance

Creating indexes on materialized views can improve query performance by reducing the amount of data that needs to be scanned when a query is executed. Indexes are used to quickly locate rows in a table, and they can also be used to speed up queries against materialized views. By creating an index on a materialized view, Oracle can use the index to quickly locate the relevant data instead of having to scan all of the underlying tables. This reduces the amount of time it takes for the query to execute, resulting in improved query performance.

When creating an index on a materialized view, it’s important to consider which columns should be indexed. Generally speaking, columns that are frequently used in WHERE clauses or JOIN conditions should be indexed. Additionally, if there are any columns with high cardinality (i.e., many distinct values), those should also be considered for indexing. Once the appropriate columns have been identified, the index can be created using the CREATE INDEX statement.

5. Consider using incremental refreshes for large data sets

Incremental refreshes are more efficient than full refreshes, as they only update the data that has changed since the last refresh. This is especially beneficial for large data sets because it reduces the amount of time and resources needed to complete a refresh. To use incremental refreshes with Oracle Materialized Views, you must first create an index on the materialized view’s primary key column(s). Then, when refreshing the materialized view, specify the INCREMENTAL option in the REFRESH clause. The database will then compare the values in the indexed columns between the base table and the materialized view, and only those rows that have been modified or added since the last refresh will be updated. This can significantly reduce the amount of time and resources required to keep the materialized view up-to-date.

6. Monitor and tune materialized view logs

Materialized view logs are used to track changes in the master table, and they must be created before a materialized view can be refreshed. Without them, Oracle cannot determine which rows have been changed since the last refresh. Monitoring and tuning these logs is important because it helps ensure that refreshes occur quickly and efficiently.

To monitor and tune materialized view logs, you should first check for any errors or warnings in the alert log. If there are any issues, you should investigate further and take corrective action as needed. You should also periodically review the performance of your materialized views by looking at the query execution plans and statistics. This will help identify any potential bottlenecks or areas where performance could be improved. Additionally, you should regularly analyze the size of the materialized view logs and adjust their settings accordingly. For example, if the logs are growing too large, you may need to increase the retention period or reduce the frequency of refreshes.

7. Utilize advanced Oracle features such as Query Rewrite, Result Cache, and In-Memory Column Store

Query Rewrite is a feature that allows Oracle to rewrite queries against materialized views, which can improve query performance. This works by rewriting the original query into one or more equivalent queries that use the materialized view instead of the base tables. The rewritten query will then be executed and the results returned to the user.

Result Cache is another advanced Oracle feature that can help improve query performance when using materialized views. Result Cache stores the result set of a query in memory so that subsequent executions of the same query do not need to access the underlying data sources. This can significantly reduce response times for frequently used queries.

The In-Memory Column Store (IMCS) is an Oracle Database 12c feature that enables faster analytics on large datasets. IMCS stores columnar data in memory, allowing for faster retrieval and analysis of data. When combined with materialized views, this can provide significant performance improvements for analytical queries.

8. Leverage Oracle’s Automatic Data Optimization (ADO) feature

ADO is a feature that allows Oracle to automatically refresh materialized views based on the data usage patterns of the underlying tables. This helps ensure that the most up-to-date data is available for queries, while also reducing the amount of time and resources needed to manually refresh the view.

ADO works by monitoring the access frequency of the underlying table data and then refreshing the materialized view when it detects changes in the data usage pattern. For example, if there are frequent updates to the underlying table data, ADO will detect this and trigger an automatic refresh of the materialized view. This ensures that the latest version of the data is always available for querying. Additionally, ADO can be configured to use different refresh strategies depending on the type of query being executed. This allows for more efficient refreshes, as only the necessary data is refreshed instead of the entire view.

9. Take advantage of Oracle’s Database Vault security options

Database Vault is a security feature that provides an extra layer of protection for the data stored in Oracle databases. It helps to prevent unauthorized access and manipulation of sensitive data by enforcing fine-grained access control policies, as well as providing real-time monitoring and auditing capabilities. Database Vault also offers additional features such as encryption, masking, and redaction of sensitive data.

When using Oracle Materialized Views, it is important to take advantage of these security options provided by Database Vault. This will help ensure that only authorized users can access the materialized view data, and that any changes made to the data are tracked and monitored. Additionally, Database Vault’s encryption, masking, and redaction features can be used to protect sensitive data from being exposed or manipulated. By taking advantage of these security options, organizations can ensure that their data remains secure and protected.

10. Implement partitioning strategies to optimize maintenance and access times

Partitioning is a technique used to divide large tables into smaller, more manageable pieces. This can be done by splitting the table into multiple physical partitions based on certain criteria such as date range or geographic region. By partitioning the data in this way, it becomes easier to manage and query the data since only the relevant partitions need to be accessed. Additionally, when performing maintenance operations such as index rebuilds or refreshes, only the affected partitions need to be processed instead of the entire table. This reduces the amount of time required for these operations and improves overall performance. Furthermore, Oracle Materialized Views are able to take advantage of partition pruning which further optimizes access times by eliminating unnecessary partitions from the query plan.