To gain an in-depth understanding of materialized views, consider reading the first article in this series. Today, we will discuss the specific implementations and features of materialized views in BigQuery, Google Cloud's fully managed, petabyte-scale analytics data warehouse.
What is materialized views?
In BigQuery, materialized views serve as a powerful tool for optimizing query performance. These precomputed views automatically cache query results, enabling faster data retrieval and reducing computational load. By intelligently managing updates, BigQuery ensures materialized views reflect the latest changes in base tables, seamlessly integrating new data for up-to-date results.
Using materialized views can dramatically accelerate query execution, especially for workloads characterized by frequent and similar queries. This efficiency gains stem from BigQuery's ability to leverage cached results, avoiding the need to reprocess entire datasets contained within base tables.
Key benefits of materialized views include:
- Automated Updates: Materialized views automatically refresh, incorporating incremental changes from base tables with zero manual intervention.
- Current Data: They guarantee the delivery of fresh data. BigQuery dynamically decides whether to source data directly from base tables or the materialized view based on the most recent changes, ensuring accuracy.
- Optimized Performance: BigQuery's optimizer smartly utilizes materialized views when possible, rerouting portions of queries to these precomputed results for enhanced speed and resource efficiency.
These features make materialized views an essential strategy for optimizing query performance in BigQuery, simplifying data management while ensuring rapid access to critical insights.
When to use materialized views?
In the fast-paced world of data management, quick and efficient data access and processing are key. Materialized Views shine in this aspect. So, when should you opt for them? The materialized view is useful in the below scenario:
- The underlying base table of the Materialized View does not change frequently.
- The query is on an external table (i.e. data sets stored in files in an external stage), which might have slower performance compared to querying native database tables.
- Query results contain results that require significant processing, including: analysis of semi-structured data and aggregates that take a long time to calculate.
- If a query is based on a large table, but you frequently only need a few rows or columns based on certain criteria, an MV can be beneficial.
Materialized view replicas in BigQuery
BigQuery enables the creation of materialized view replicas for tables that are enhanced by the BigLake metadata cache over data stored in Amazon Simple Storage Service (Amazon S3). These replicas facilitate query executions by replicating Amazon S3 data into a dataset within a BigQuery-supported region, thereby localizing data access and circumventing data egress charges, which in turn enhances query performance.
Once established, the replication mechanism periodically checks for updates in the source materialized view and copies any changes to the replica according to a predetermined schedule set by the replication_interval_seconds parameter during creation. The freshness of the replicated data not only hinges on this replication frequency but also on the refresh rates of both the original materialized view and the metadata cache of the Amazon S3 table involved.
The Google Cloud console provides tools to monitor the freshness of the materialized view replica and its underlying resources. The "Last modified" detail within the replica's information panel indicates the most recent update, while the "Last modified" and "Max staleness" fields in the original materialized view's panel reflect the update frequency of the source view and the metadata cache of the Amazon S3 table, respectively.
Interaction with BigQuery's other key features
BigQuery integrates materialized views with several of its key features to enhance query performance and manage costs effectively:
- Query Plan Insights: The query execution plan offers detailed visibility into the utilization of materialized views. It specifies whether materialized views are accessed during a query and outlines the combined data consumption from both the materialized views and the underlying base tables.
- Query Caching Efficiency: Queries optimized through materialized views are eligible for BigQuery's caching mechanisms, adhering to standard criteria such as the use of deterministic functions and the absence of recent streaming data insertions into the base tables.
- Cost Management: BigQuery respects the set limits for maximum bytes billed. If executing a query—whether directly on base tables or through materialized views—would exceed this budget, it halts without generating costs.
- Accurate Cost Predictions with Dry Runs: Leveraging dry runs for queries provides cost estimations by simulating the query rewrite process with existing materialized views. This functionality serves as an effective tool to verify the application of materialized views to specific queries, aiding in strategic planning and optimization efforts.
Use cases
Materialized views optimize high-cost queries with small result sets, benefitting processes like OLAP, ETL, and BI analytics that demand substantial processing and repeat queries. They're valuable for pre-aggregating data, including streaming data aggregation, filtering specific data subsets for efficient query execution, pre-joining tables for queries across varied table sizes, and reclustering data for queries that perform better with different clustering than base tables.
- Business Intelligence (BI) and Reporting:
For a company needing to generate daily, weekly, and monthly sales reports from a large transactional database, materialized views can precompute and store aggregated data. This approach speeds up report generation and gives users quick access to the latest figures.
- Geospatial Data Analysis:
A mapping application providing near real-time traffic updates and route recommendations can use materialized views to store geospatial data indexed for quick retrieval. This ensures updates and recommendations are generated quickly, even with vast amounts of dynamic location data.
- Financial Analysis and Risk Management:
Financial institutions can use materialized views to store and aggregate trading data, enabling rapid risk assessment and financial analysis. This is especially crucial in fast-paced financial markets.
- Inventory Management and Supply Chain Optimization:
Companies managing large inventories can use materialized views to store inventory data and demand forecasting results. This enables quick decision-making in inventory management and supply chain optimization.
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In this blog post, we introduce materialized views in BigQuery, starting with an exploration of what materialized views entail. Google BigQuery's materialized views feature smart tuning, a mechanism that automatically rewrites queries to utilize materialized views. Additionally, we cover some BigQuery features that seamlessly interact with materialized views. > >
