How to Update Materialized Views in Real Time
Learn how to update materialized views in real time with this informative blog. Discover efficient techniques and best practices for synchronizing your materialized views with the underlying data, empowering you to make data-driven decisions with up-to-date insights.
What Are Materialized Views in Databases?
Materialized views are database objects that contain the results of your operations, calculations, or queries. They exist as a locally saved representation of your data that you can use and reuse without recalculating or computing data values. Using materialized views saves processing time and cost and allows you to front-load your frequently used or most time-consuming queries.
Materialized views are similar to views in your SQL databases. However, while views are also virtual copies of your data tables, their query results are not stored or cached locally, and only the query expression is retained. Therefore, a major difference between views and materialized views is that you need to recompute results stored within views every time you execute or query a view.
Views are typically utilized when your data is constantly changing and updating but is only viewed sporadically. On the other hand, materialized views are utilized when data has to be viewed often, but the saved tables are not updated frequently.
This article explores materialized views in modern-day databases, their use cases, why they are important, how they are connected with data analytics, and their limitations.
Why Do You Need Materialized Views?
The process of creating a materialized view, often called materialization, involves caching the outcome of your query operations. It can be a basic SELECT statement, a JOIN operation, an aggregate summary of your data, or any other SQL query. Your materialization creates a concrete table that is stored locally and takes up space just like any other data table.
You can then query this stored table directly instead of executing from your original data tables. With materialized views, your frequently used variables can be precomputed with the needed joins, aggregates, and filters, so they're ready for all subsequent queries. This helps optimize your queries and alleviates performance issues with common and repeated queries.
Materialized views can be read-only, updateable, or writeable. DML statements (such as INSERT and MERGE statements) cannot be executed on read-only materialized views; however, they may be executed on updatable and writeable materialized views. Your materialized view can also become outdated if not updated as values change in the original data tables. Using triggers, you can refresh your materialized views to recompute the values saved, either manually or on a schedule. There are a few databases, such as RisingWave, that automatically update your materialized views with incremental changes as new values appear in the base table.
Use Cases for Materialized Views
As a rule, queries utilizing materialized views are significantly faster and require fewer resources for their operations compared to similar queries that access data directly from your basic tables. This is especially true for modern databases, where every query execution has an associated cost, and the use of your resources and compute power needs to be optimized.
Materialized views can codify the common logic that's important to your business or data processes and then precompute this logic, making it available and more efficient for your downstream processes such as data reporting or analytics.
Materialized views improve and optimize the execution time of your most important or resource-intensive queries, enabling better and more efficient access to immediately relevant data at the cost of some extra storage. While materialized views need to be refreshed to combat outdated data, and you can utilize triggered updates or databases with auto-refresh features combined with stream processing to get real-time data precomputed and available for your queries and operations.
Why Are Materialized Views Useful in Modern Data Warehouses?
As your organization continues to generate data, the amount of data you need to process for operations such as analytics, monthly data reporting, or dashboarding increases. Certain operations may require data contained across several tables saved on distributed servers. Executing queries on these data tables, in turn, requires an increasing level of compute resources as the data grows larger and more spread out.
Materialized views offer significant benefits to organizations with OLAP operations, which require substantial computation with expected and recurring queries, such as those in extract, transform, load (ETL) or business intelligence (BI) pipelines. A data warehouse with enhanced materialized view capability allows you to improve your query performance, especially when your queries are frequently used and require aggregated, filtered, clustered, or joined data across several data tables.
Modern data warehouses, especially those utilizing cloud computing, offer per-query pricing or cost over time for the use of available compute power. Materialized views help you reduce the execution time and compute power spent on running the same queries.
Cost-Efficient Queries and Reduced Execution Time
Precomputing results on relevant variables for your business logic and processes is a cost-efficient way to streamline the amount of work done when querying, especially considering how much of this work is repeated as queries are used frequently.
Materialized views are low-maintenance and cost-efficient compared to the other options. Though there is a storage cost to cache the data and compute power will be used on the creation. During updates, significantly fewer resources are used to rerun queries or utilize regular views. You can reduce maintenance costs by ensuring your materialized view is filtered and aggregated to provide only the necessary data. Additionally, compute costs can be reduced if you maintain best practices and use materialized views for data that doesn't require regular recomputation.
Streamlined Query Data across Departments
For example, an e-commerce organization will find it useful to know their top-performing customers and where they come from, which is valuable information across the marketing, operations, and merchandising departments. The information could be collated from different tables (such as KYC tables and orders) and aggregated in different buckets and categories (such as monthly, by region, and all-time best). The query for this information could be used regularly across departments.
Your operations team could use it for reporting purposes, and your marketing team could use the information to reward customers with discounts and market products differently. Lastly, merchandising can optimize products using your customer data. Rather than querying your database every time this information is needed, you can create a materialized view that precomputes this query and makes it available to the various departments faster with fewer resources used.
Secure Data with Segmented Data Privileges
With materialized views, you can limit data privileges within your organization, giving departments or individuals access to only the data subsets they need without full access to data tables that might contain sensitive data and customer information. This creates a more secure environment for your database within your organization.
Limitations in Modern Capabilities
As a concept, materialized views are still being improved and optimized across modern databases. Some databases only offer limited operations in materialized views, and the refresh and update capabilities of materialized views also vary.
Only a few systems support automatically maintained materialized views, which is an important feature for maintaining data freshness and avoiding outdated data within your materialized views. Additionally, incremental updates in materialized views are not available in all systems. Therefore, refreshing or updating your materialized view requires recomputation of your query result and rebuilding the view from scratch every time you require an update.
This can be a critical limitation for you and your organization, as the ability to update your materialized views with incremental changes automatically is integral to building real-time materialized views. Real-time materialized views are immediately updated with only the new data from their base tables, avoiding outdated data and extra compute costs.
You can overcome these limitations with a solution that offers real-time materialized views that can be used for cost-efficient, real-time data analytics, ensuring your most relevant data values are available, precomputed, and up-to-date.
RisingWave's mission is to empower real-time data analytics, enabling stream processing for everyone by making it simple, affordable, and accessible. RisingWave uses stream processing inside databases to refresh materialized views in real time, allowing organizations to benefit from cost-efficient and performant queries on real-time data. This reduces the complexity and cost of running data operations and downstream processes in real time.
In this article, you learned about materialized views, how they differ from regular views in SQL databases, their use cases, and how they are useful to your modern data architecture. You also learned about the limitations of materialized views and how real-time materialized views are a more efficient option.
RisingWave Labs is an early-stage startup developing stream processing applications that offer real-time materialized views. Its flagship product, RisingWave, is a cloud-native streaming database that employs SQL as its interface to decrease the difficulty and expense of developing real-time data solutions. RisingWave consumes streaming data from various sources, such as Kafka and Pulsar, and utilizes real-time materialized views to enable continuous queries and dynamic updates.
Check out this latest tutorial to learn how to how to use RisingWave as a metrics store to monitor its runtime metrics and visualize them using Grafana.