All you want to know about Snowflake Materialized Views

Materialized views in Snowflake are similar to regular views in that they are defined by SQL queries, but they differ in that their query results are cached like tables. Essentially, a materialized view is a pre-computed dataset derived from an underlying SQL query specified in the view definition, and this dataset is cached for future use. When users query a materialized view, they are provided with the results from the cached output, resulting in faster query execution compared to running the underlying query without a materialized view.

In Snowflake, materialized views are managed by users using the CREATE MATERIALIZED VIEW AS … statement. However, it's important to note that Snowflake charges additional credits for materialized views. One of the key benefits of materialized views in Snowflake is that the platform automatically keeps the cache of materialized views refreshed, ensuring that users always have access to the most current data.

Materialized views are particularly beneficial when the same query is expected to run frequently or when the query is resource-intensive due to its complexity. Moreover, accessing data through materialized views guarantees that the data is always current, unlike in conventional data warehouses where the output of materialized views may become stale if the underlying tables change after the last refresh.

Snowflake handles the maintenance of materialized views through background processes that update them every time changes occur in the underlying tables. Unlike materialized views in traditional data warehouses, there is no need for any manual process to refresh materialized views in Snowflake.

Additionally, Snowflake can utilize Query Rewrite to automatically use materialized views if doing so improves performance, even if users do not explicitly mention the materialized view in their query. Users can monitor the usage of materialized views by examining Query Profiles or EXPLAIN Plans to see if a materialized view is being utilized in the query execution plan. This provides transparency and insight into the optimization of query performance with materialized views in Snowflake.

Advantages

Snowflake Materialized views offer several advantages. Firstly, querying a materialized view is notably faster compared to executing the underlying query without utilizing materialized views. This enhanced speed results from the precomputed nature of the materialized view, which reduces the need for complex calculations during query execution. Additionally, data accessed through materialized views is always current, ensuring that users retrieve the most up-to-date information from the underlying tables. Unlike conventional data warehouses where the output of materialized views may become stale if the underlying data changes after the last refresh, Snowflake automatically maintains materialized views. This maintenance process occurs through background processes that update materialized views whenever changes occur in the underlying tables.

One significant advantage of Snowflake's approach is the elimination of manual processes for refreshing materialized views. This automation streamlines data management tasks and reduces the potential for human error. Furthermore, Snowflake utilizes Query Rewrite functionality to leverage materialized views for query optimization. This means that even if users do not explicitly mention materialized views in their queries, Snowflake may still utilize them if doing so enhances performance.

Use Cases

To ensure transparency and optimization, Snowflake provides tools such as Query Profiles and EXPLAIN Plans. These tools allow users to inspect query execution plans and determine whether materialized views are being utilized.

Snowflake Materialized views are particularly beneficial in certain use cases. For example, they are ideal for complex queries that consume significant compute resources. Additionally, materialized views are well-suited for scenarios where query results do not change frequently, and the results are accessed repeatedly by users. By leveraging materialized views in such situations, Snowflake users can enhance query performance, reduce computational overhead, and improve overall system efficiency.