Explore the Materialized View of Snowflake in detail. Read the blog to learn it in indepth.
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The primary purpose of Materialized View (MV) is to tweak up the query performance.
Materialized View is a pre-computed data set cumulated of query results, information and stored for later use. Querying a materialized view is quicker than the query against the base table of the view because the data is pre-computed. This exhibition distinction can be huge, when a question is run habitually or is adequately intricate. Consequently materialized views can accelerate expensive aggregation, projection, and selection activities, particularly that run frequently on enormous data sets.
Materialized view would rather avoid the ordinary view in the manner that it has its own physical information. While the standard view alludes to its fundamental information. You can consider a materialized view as a different object similar to a table., However, it consistently syncs with the data of its base table.
Here is the demonstration of the design for MV- The below diagram exhibits how MV is designed to query and link MV with the base table:
(a showcase of the below diagram)
(a showcase of the below diagram)
As you probably are aware the physical files of MV are independent of the base table, almost like a new table. The metadata stored in the MV links the files to the source table to represent how far MV is from its base table.
The above diagram shows MV data in the red bar and base table data in the green bar as different versions of its object data. A single number below the green bar represents the table version, and 2 numbers below the red bar represent the MV version- here the first number indicates the MV version and the 2nd number in parentheses is the base table version. For instance, 1 (1) the first red MV version bar signifies that version 1 of MV is based on version 1 of its base table. 2 (4), the second red MV version bar signifies that version 2 of MV is based on version 4 of its base table.
Observantly, querying this MV currently, it cannot be done directly. As seen, there are more updates to be made on the MV version ( including version 5 and version 6), this is not an updated MV. And, if we query on MV, it will bring wrong results as neither the MV is refreshed nor the updated MV data on the base table. Aiming to solve the problem, we inaugurated the files as delta+ (triangle shape with a plus sign as illustrated in the above diagram) and the eliminated files as delta- (triangle shape with a minus sign as illustrated in the above diagram). The delta+ showcases the added files since the last MV refresh whereas the delta- stands for the deleted files since the last MV refresh.
In order to get the right results while querying for MV, we need to focus following basic options at the highest level:
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A simplified query profile appears like the following ( inclusive of the 3 components as explained above):
Respectively, there can be two DMLs allowed to be used against an MV alongside the a CREATE statement of DDL.
Refresh: operation as a Snowflake service performed in the background.
Auto clustering: Add a clustering key to an MV and enable auto clustering on the MV ( another Snowflake service performed in the background).
The other two operations supposedly suspend or resume an MV.
Stats File compaction triggers the MV refresh on the base table.
1- No. of files changed on the base table since the last MV refresh.
2- No. of DML operations on the base table since the last refresh.
3- Stats File defragmentation or compaction service in Snowflake background.
The first two include the changes performed by either the end-user or done by the Snowflake services.
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3 Ways to monitor : MV refresh in general
Commonly misinterpreted elements
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