How to Create and Manage Azure Materialized Views Effectively

Creating an Azure materialized view involves defining the view using a SQL query, which can be done using the Azure portal, Azure Data Studio, or the Azure SQL Database Management Studio. This query defines the data that will be materialized in the view.

Materialized views can be managed by refreshing them periodically to ensure the data is up-to-date, which can be done using the REFRESH MATERIALIZED VIEW statement. This statement can be run manually or scheduled to run automatically at regular intervals.

To manage storage for materialized views, consider the size of the data being materialized and the storage options available in Azure, such as Azure Blob Storage or Azure Data Lake Storage. This will help ensure that the materialized view does not consume too much storage space.

Materialized views can also be used to improve query performance by pre-aggregating data, which can be especially useful for complex queries that require a lot of processing power.

A unique perspective: Azure Data Studio Connect to Azure Sql

Design and implementation of Azure Materialized View involves evaluating how it can be used in workload design to address performance efficiency goals. The Materialized View pattern can help reduce overall resource consumption by storing the results of complex computations or queries without requiring the database engine or client to recompute for every request.

To achieve performance efficiency, consider the tradeoffs against the goals of other pillars that might be introduced with this pattern. Data consistency is a key consideration, as the summary information in a materialized view has to be maintained so that it reflects the underlying data values.

In designing your materialized view, keep in mind that it might not be practical to update the summary data in real time, and instead you'll have to adopt an eventually consistent approach. This means finding a balance between consistency and performance efficiency.

Here are some key considerations to keep in mind when implementing Azure Materialized View:

Implementation for Different Partition Keys is a crucial aspect of any database design.

For example, in the case of a high-traffic e-commerce website, a partition key based on the customer's location can be used to distribute data across multiple servers, reducing latency and improving performance.

When dealing with time-series data, a partition key based on a time interval, such as a day or a month, can be used to store and retrieve data efficiently.

In the case of a social media platform, a partition key based on the user's ID can be used to store and retrieve user data quickly.

Partitioning by user ID can also be used to store and retrieve user preferences and behavior data.

Using a partition key based on a specific attribute, such as a product category, can be beneficial for e-commerce websites with a large product catalog.

In the case of a location-based service, a partition key based on the location can be used to store and retrieve data related to specific geographic areas.

Partitioning by location can also be used to store and retrieve data related to weather, traffic, or other location-based information.

Consider reading: The Azure Key

When designing your workload, it's essential to consider how the Materialized View pattern can help you achieve your goals. This pattern can be a game-changer for performance efficiency.

The Materialized View pattern stores the results of complex computations or queries without requiring the database engine or client to recompute for every request, which reduces overall resource consumption.

To get the most out of this pattern, you should evaluate how it fits into your workload's design, taking into account the goals and principles of the Azure Well-Architected Framework pillars.

Here's how the Materialized View pattern supports the Performance Efficiency pillar:

As with any design decision, consider any tradeoffs against the goals of the other pillars that might be introduced with this pattern. For instance, you might have to adopt an eventually consistent approach to maintain the summary information in a materialized view.

Querying a materialized view can be done in two ways: querying the entire view or querying the materialized part only. Querying the entire view combines the materialized part with the records in the source table that haven't been materialized yet.

There's a significant performance difference between querying the entire view and querying the materialized part only. Queries over the materialized part only always perform better, so it's recommended to use the materialized_view() function when applicable.

To optimize materialized view queries, you can use client request properties. For example, you can disable summarize/join optimizations by setting the materialized_view_query_optimization_costbased_enabled property to false. This forces the query optimizer to use default strategies.

Here are some client request properties that can be used to control optimizations in materialized view queries:

Querying the entire view can perform better if the query includes filters on the group by keys of the materialized view query. This is because the query optimizer can take advantage of the filtered data to improve performance.

The query optimizer chooses summarize/join strategies that are expected to improve query performance. For example, it may decide to shuffle the query based on the number of records in the delta part.

You can control some of these optimizations by using client request properties. The `materialized_view_query_optimization_costbased_enabled` property, for instance, can be set to `false` to disable summarize/join optimizations and use default strategies.

Here's a list of client request properties that can be used to control query optimizer behavior:

By using these properties, you can fine-tune the query optimizer's behavior and improve the performance of your queries.

Backfill by extents is an alternative to backfilling a materialized view using the backfill property. This method involves moving extents from an existing table into the underlying materialized view table.

The data in the specified table should have the same schema as the materialized view schema. Records in the specified table are moved to the view as is, and they're assumed to be deduped based on the definition of the materialized view.

For example, if the materialized view has an aggregation on EventID, the records in the source table for the move extents operation should already be deduped by EventID.

Backfill by move extents is not supported for all aggregation functions, including avg(), dcount(), and in which the underlying data stored in the view is different than the aggregation itself.

If the source table of the materialized view is continuously ingesting data, creating the view by using move extents might result in some data loss. This is because records ingested into the source table, in the short time between the time of preparing the table to backfill from and the time that the view is created, won't be included in the materialized view.

You can set the source_ingestion_time_from property to the start time of the materialized view over the source table to handle this scenario.

To summarize, here are the key differences between backfill and backfill by move extents:

When defining a materialized view, it's essential to reference a single fact table as the source of the view. This fact table should be the only one included in the query.

The query should include a single summarize operator, and one or more aggregation functions aggregated by one or more groups by expressions. This operator must always be the last one in the query.

Materialized views that include only arg_max/arg_min/take_any aggregations might perform better than those with mixed aggregations. This is because some optimizations are specific to these kinds of views.

Queries should not include any operators that depend on now(). Instead, use the retention policy on the materialized view to limit the period of time it covers.

The following operators are not supported in the materialized view query: sort, top-nested, top, partition, and serialize.

Materialized views can't include composite aggregations in their definition. For instance, instead of using a single query to calculate a composite aggregation, define the view as separate queries for each aggregation.

Here are some unsupported features in materialized view queries:

Azure materialized views can impact your cluster's performance, so it's essential to monitor your cluster's health using cluster health metrics.

Materialized views consume cluster resources, including CPU and memory, so an overloaded cluster may experience performance degradation. Optimized autoscale currently doesn't take materialized view health into consideration as part of autoscale rules.

The intersection between new records and already materialized records can significantly impact performance. A materialized view works best when the number of records being updated is a small subset of the source table.

Higher ingestion rates may still perform well, but the recommended ingestion rate for materialized views is no more than 1-2GB/sec. Performance depends on database size, available resources, and the amount of intersection with existing data.

Each materialized view consumes its own resources, and many views compete with each other on available resources. While there are no hard-coded limits to the number of materialized views in a cluster, the cluster may not be able to handle all materialized views when there are many defined.

Here are some key performance considerations to keep in mind:

Materialized views are a powerful tool in Azure, and understanding their use cases and best practices can help you get the most out of them. They're particularly useful when creating materialized views over data that's difficult to query directly, or where queries must be very complex to extract data that's stored in a normalized, semi-structured, or unstructured way.

Materialized views can also be used to create temporary views that can dramatically improve query performance, or act directly as source views or data transfer objects for the UI, for reporting, or for display. They're a great way to simplify queries and expose data for experimentation in a way that doesn't require knowledge of the source data format.

One key scenario where materialized views shine is when you need to improve the performance of complex analytical queries against large data in size. By creating materialized views for the data returned from common computations of queries, you can avoid recomputation and reduce compute costs.

Here are some common scenarios where materialized views are typically used:

In terms of backfilling, materialized views can be useful in two main scenarios:

To orchestrate the backfill process, you can use ingest from query commands, or one of the recommended orchestration tools. Here are some examples of how to use the "move extents from" option:

Remember, materialized views are not useful in the following situations:

The syntax for creating a materialized view in Azure is quite straightforward. If the `ifnotexists` flag is specified, the command is ignored, and no change is applied, even if the new definition doesn't match the existing definition.

To create a materialized view, you'll need to specify the `create` keyword, followed by the `async` and `ifnotexists` flags if needed, and then the `materialized-view` keyword. The `with` keyword is also used to specify properties in the form of name and value pairs.

Here are the required parameters for creating a materialized view:

Syntax is a crucial aspect of working with materialized views. If the ifnotexists flag is specified, the command is ignored, and no change is applied, even if the new definition doesn't match the existing definition.

This can be a bit counterintuitive, but it's essential to understand the behavior of the command. If the ifnotexists flag isn't specified, an error is returned.

Here are the key implications of using the ifnotexists flag:

The Parameters section is where things get interesting. You have to provide a list of properties in the form of name and value pairs, from the list of supported properties.

The MaterializedViewName is required, and it's the name of the materialized view. It can't conflict with table or function names in the same database and must adhere to the identifier naming rules.

The SourceTableName is also required, and it's the name of the source table on which the view is defined.

The Query is another required parameter, and it's the query definition of the materialized view. For more information and limitations, see the Query parameter section.

Here are the required parameters in a concise table:

These parameters are the foundation of creating a materialized view, and understanding them is key to success.