View is a virtual table , created using Create View command. This virtual table contains the data retrieved from a query expression , in Create View command. View can be created from one or more than one base tables or views. A view can be queried like you query the original base tables. It is not that the View is precomputed and stored on the disk instead, a View is computed each time it is used or accessed.
Whenever a view is used the query expression in Create View command is executed at that particular moment. Hence, you always get the updated data in a View. If you update any content in View, it is reflected in the original table, and if any changes had been done to the original base table, it would reflect in its View.
But this makes the performance of a View slower. For example, a view is created from the join of two or more tables. In that case, you have to pay time to resolve Joins each time a View is used. But it has some advantages like it do not require storage space. You can create a customized view of a complex database. Anonymous October 12, at PM.
Unknown December 30, at PM. Ravi May 18, at AM. Anonymous October 26, at AM. Unknown May 22, at PM. Unknown July 27, at AM.
Unknown June 1, at AM. Unknown January 20, at AM. Unknown April 30, at AM. Dayakar February 6, at PM. Improve this question. I would change the "Tables" with "Rollup Tables" in the question title. Like here: citusdata. Add a comment.
Active Oldest Votes. Improve this answer. Donnie Donnie Dave Costa Dave Costa Materialized views can be refreshed - they are snapshots of data taken at regular intervals. Roopesh Shenoy Roopesh Shenoy 3, 1 1 gold badge 31 31 silver badges 49 49 bronze badges. The materialized view will stay synchronized with the base relations on which it depends. Rose Perrone Rose Perrone Are they really sync? In additition to the already mentionned advantages: dynamic query rewriting in short, the DB optimizer knows how the MV is created, so it can reuse it to optimize other queries , optional, automatic, possibly incremental refresh, I'd like to mention: some materialized views can be written to, which updates the source table for instance joins with primary keys can be written to, on the opposite if the materialized view is the result of a group by it can't be written to the DB server retains the query that created the data and can rerun it.
I work for a company developing a tool that does that and much more. Sriwantha Attanayake Sriwantha Attanayake 7, 4 4 gold badges 37 37 silver badges 44 44 bronze badges. The first point doesnt sound like an advantage. Also it seems like it is copied from here without any reference itknowledgeexchange. To inquire about upgrading, please contact Snowflake Support. 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.
This performance difference can be significant when a query is run frequently or is sufficiently complex. As a result, materialized views can speed up expensive aggregation, projection, and selection operations, especially those that run frequently and that run on large data sets.
Materialized views are designed to improve query performance for workloads composed of common, repeated query patterns. However, materializing intermediate results incurs additional costs. As such, before creating any materialized views, you should consider whether the costs are offset by the savings from re-using these results frequently enough. Deciding When to Create a Materialized View. Advantages of Materialized Views. About Materialized Views in Snowflake. Access Control Privileges. Privileges on a Materialized View.
Secure Materialized Views. Creating and Working With Materialized Views. Planning to Create a Materialized View. Creating a Materialized View.
Limitations on Creating Materialized Views. Basic Example: Creating a Materialized View. Working With a Materialized View. Limitations on Working With Materialized Views. Materialized Views in Cloned Schemas and Databases.
Maintenance Costs for Materialized Views. Estimating and Controlling Costs. Materialized Views and Clustering. Best Practices for Materialized Views. Best Practices for Creating Materialized Views. Best Practices for Maintaining Materialized Views. Simple Materialized View. Joining a Materialized View. Suspending Updates to a Materialized View.
Clustering a Materialized View. Creating a Materialized View on Shared Data. Sharing a Materialized View. The query is on an external table i. Materialized views can improve the performance of queries that use the same subquery results repeatedly. Materialized views are automatically and transparently maintained by Snowflake.
A background service updates the materialized view after changes are made to the base table. This is more efficient and less error-prone than manually maintaining the equivalent of a materialized view at the application level.
Data accessed through materialized views is always current, regardless of the amount of DML that has been performed on the base table. If a query is run before the materialized view is up-to-date, Snowflake either updates the materialized view or uses the up-to-date portions of the materialized view and retrieves any required newer data from the base table.
The automatic maintenance of materialized views consumes credits. For more details, see Maintenance Costs for Materialized Views in this topic. In general, when deciding whether to create a materialized view or a regular view, use the following criteria:.
The results of the view are used often typically significantly more often than the query results change. The query consumes a lot of resources. Typically, this means that the query consumes a lot of processing time or credits, but it could also mean that the query consumes a lot of storage space for intermediate results. These criteria are just guidelines. A materialized view might provide benefits even if it is not used often — especially if the results change less frequently than the usage of the view.
Also, there are other factors to consider when deciding whether to use a regular view or a materialized view. For example, the cost of storing the materialized view is a factor; if the results are not used very often even if they are used more often than they change , then the additional storage costs might not be worth the performance gain.
Materialized views are similar to tables in some ways and similar to regular i. In addition, materialized views have some similarities with cached results, particularly because both enable storing query results for future re-use. This section describes some of the similarities and differences between these objects in specific areas, including:. Snowflake caches query results for a short period of time after a query has been run.
In some situations, if the same query is re-run and if nothing has changed in the table s that the query accesses, then Snowflake can simply return the same results without re-running the query.
This is the fastest and most efficient form of re-use, but also the least flexible. For more details, see Using Persisted Query Results.
Regular views do not cache data, and therefore cannot improve performance by caching. However, in some cases, views help Snowflake generate a more efficient query plan. Also, both materialized views and regular views enhance data security by allowing data to be exposed or hidden at the row level or column level. The following table shows key similarities and differences between tables, regular views, cached query results, and materialized views:. Used only if data has not changed and if query only uses deterministic functions e.
Storage and maintenance requirements typically result in increased costs. Data is maintained outside Snowflake and, therefore, does not incur any storage charges within Snowflake. This section describes some general usage scenarios that also provide a conceptual overview of materialized views:.
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