1. What is a materialized view in Amazon Redshift?
Answer: A materialized view in Amazon Redshift is a precomputed result set based on an SQL query over one or more base tables that can be queried like other tables or views in the database. It returns the precomputed results from the materialized view, without having to access the base tables at all.
2. What are the benefits of using materialized views in Amazon Redshift?
Answer: Materialized views in Amazon Redshift are especially useful for speeding up queries that are predictable and repeated. Instead of performing resource-intensive queries against large tables (such as aggregates or multiple joins), applications can query a materialized view and retrieve a precomputed result set. This results in faster query results compared to retrieving the same data from the base tables.
3. What kind of SQL statements can initiate and create an automated materialized view in Amazon Redshift?
Answer: An automated materialized view can be initiated and created by a query or subquery, provided it contains a GROUP BY clause or one of the following aggregate functions: SUM, COUNT, MIN, MAX or AVG. But it cannot contain any of the following: Left, right, or full outer joins, aggregate functions other than SUM, COUNT, MIN, MAX, and AVG, any aggregate function that includes DISTINCT, any window functions, SELECT DISTINCT or HAVING clauses, external tables, such as datashares and federated tables, or other materialized views.
4. Can you define a materialized view in terms of other materialized views in Amazon Redshift?
Answer: Yes, you can define a materialized view in terms of other materialized views in Amazon Redshift. You can use materialized views on materialized views to expand the capability of materialized views. In this approach, an existing materialized view plays the same role as a base table for the query to retrieve data.
5. What is the STV_MV_DEPS table in Amazon Redshift?
Answer: The STV_MV_DEPS table in Amazon Redshift shows the dependencies of a materialized view on other materialized views. This table is especially useful for reusing precomputed joins for different aggregate or GROUP BY options.