A Materialized View in general is a Database Object that contains the results of a Previously Computed Query. Materialized Views have been around for a while and are frequently used to support BI and OLAP workloads as part of an I/O Reduction Strategy. In the latest release of Google BigQuery, Google rolled out support for Materialized Views.
Upon a complete walkthrough of this article, you will gain a decent understanding of Google BigQuery Materialized Views and the impact that it can create on your Analytical workload. This article will also provide you with a step-by-step guide on how to create BigQuery Materialized Views and list down 3 of the best practices for creating BigQuery Materialized Views. Read along!
Table of Contents
Prerequisites
- Basic understanding of SQL.
- An active Google BigQuery account.
What is Google BigQuery?
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Google BigQuery is a robust Cloud-based Data Warehouse and Analytics platform. It is a serverless platform that does not require the installation of any software or maintenance and management of large infrastructure. This is a very cost-effective solution for a growing business as it eliminates the need for large server rooms and the investment in hardware that is required in the case of traditional On-Premise Databases. You can query Terabytes and Petabytes of data in a matter of just a few minutes using Google BigQuery’s Scalable and Distributed Analytics Engine.
Google BigQuery is Serverless and built to be highly Scalable. Google leverages its existing Cloud architecture to successfully manage a Serverless design, as well as various Data Models that enable users to store dynamic data. It also supports Machine Learning (ML) operations by allowing users to use the BigQuery ML functionality. BigQuery ML allows users to develop and train various Machine Learning Models by querying data from the desired database using built-in SQL capabilities.
Key Features of Google BigQuery
Some of the key features of Google BigQuery are as follows:
- Scalability: To provide consumers with true Scalability and consistent Performance, Google BigQuery leverages Massively Parallel Processing(MPP) and a Highly Scalable Secure Storage Engine. The entire Infrastructure with over a thousand machines is managed by a complex software stack.
- Serverless: The Google BigQuery Serverless Model automatically distributes processing across a large number of machines running in parallel, so any organization using Google BigQuery can focus on extracting insights from data rather than configuring and maintaining the Infrastructure/Server.
- Storage: Google BigQuery uses a Columnar Architecture to store mammoth scales of datasets. Column-based Storage has several advantages, including better Memory Utilization and the ability to scan data faster than typical Row-based Storage.
- Integrations: Google BigQuery as a part of the Google Cloud Platform (GCP) supports seamless integration with all Google products and services. Google also offers a variety of Integrations with numerous third-party services, as well as the functionality to integrate with application APIs that are not directly supported by Google.
For further information on Google BigQuery, you can click here to check out their official website.
What is a BigQuery Materialized View?
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Materialized Views in Google BigQuery are precomputed views that cache the results of a query on a regular basis to improve performance and efficiency. Google BigQuery uses pre-computed results from Materialized Views and reads only delta changes from the Base Table to compute up-to-date results whenever possible. Materialized Views can be queried directly or by the BigQuery Optimizer, which uses them to process queries to the Base Tables.
Queries involving Materialized Views are generally faster and consume fewer resources than queries that only retrieve data from the base table. Materialized Views can significantly improve the performance of workloads with common and repetitive queries.
Advantages of using Google BigQuery Materialized Views
Some of the key advantages that BigQuery Materialized Views offer are:
- Automatic Query Optimization: If a Materialized View is available, the BigQuery Optimizer leverages it to improve the Query Execution plan. This optimization necessitates no changes to the queries.
- Aggregation of Real-time Data: If you need access to the data in real-time to make informed decisions, you can use BigQuery Materialized Views in conjunction with BigQuery Streaming to perform aggregations and provide up-to-date information.
- Smart Tuning: If a query or a part of the query against the Source Table can be resolved by querying the Materialized View, BigQuery rewrites (reroutes) the query to use the Materialized View for improved performance and/or efficiency.
- No Maintenance Required: Whenever the Base Table changes, Materialized Views are recomputed in the background. Any incremental data changes from the Base Tables are automatically added to the Materialized Views, requiring no user intervention.
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How to Create a Google BigQuery Materialized View?
DDL(Data Definition Language) statements allow you to create and modify tables and views by using standard SQL Query syntax. You can leverage DDL statements to create BigQuery Materialized Views. Follow the steps given below to do so:
- Step 1: Click here to get redirected to the Google Cloud Platform.
- Step 2: Navigate to the BigQuery section and click on +Compose New Query.
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- Step 3: Now paste the following piece of code into the text editor to create BigQuery Materialized View.
CREATE MATERIALIZED VIEW project-id.my_dataset.my_mv_table
AS SELECT product_id, SUM(clicks) AS sum_clicks
FROM project-id.my_dataset.my_base_table
GROUP BY 1
Where,
- project-id is your project ID.
- my_dataset is the ID of a dataset in your project.
- my_mv_table is the ID of the Materialized View that you’re creating.
- my_base_table is the ID of a table in your dataset that serves as the Base Table for your Materialized View.
- product_id is a column from the base table.
- clicks is a column from the base table.
- sum_clicks is a column in the Materialized View that you are creating.
- Now, click on the Run button to execute the query.
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How to Alter a BigQuery Materialized View?
Follow the steps given below to alter a BigQuery Materialized View by leveraging DDL statements:
- Click on the +Compose New Query button and enter the following piece of code into the text editor area.
ALTER MATERIALIZED VIEW project-id.my_dataset.my_mv_table
SET OPTIONS (enable_refresh=true)
- Once you have successfully written the code, click on the Run button to execute the query.
How to Delete a BigQuery Materialized View?
Follow the steps given below to delete a BigQuery Materialized View by leveraging DDL statements:
- Click on the +Compose New Query button and enter the following piece of code into the text editor area
DROP MATERIALIZED VIEW project-id.my_dataset.my_mv_table
- Once you have successfully written the code, click on the Run button to execute the query.
3 Best Practices for BigQuery Materialized Views
1) Join Considerations
You can reduce the cost and latency of a query that performs aggregation on top of a join by leveraging Materialized Views. If you want to join a large Fact Table with a few small Dimension Tables and then perform aggregation on top of the join. It might be wise to rewrite the query in such a way that it first performs the aggregation on top of the Fact Table using Foreign Keys as grouping keys, then joins the result with the Dimension Table, and finally performs a post-aggregation.
2) Size Considerations
Make sure that your Materialized View definition reflects Query Patterns against the Base Table. You should not create a Materialized View for every permutation of a query because there is a limit of 20 Materialized Views per table. Instead, you should create Materialized Views to target a broader range of queries. If the Base Table is partitioned and its Materialized View is large, the Materialized View should also be partitioned. A Materialized View is considered significant if it is the same size as, or larger than, one partition of the Base Table.
3) Cost Considerations
Monitor the cost of the Refresh Job and, if necessary, adjust the Automatic Refresh Interval by constantly keeping an eye on the total bytes processed. For instance, if the Ingestion Rate in the Base Table is low, it makes sense to refresh the view less frequently. If the underlying data changes frequently, it makes sense to refresh the view more often.
Conclusion
This article introduced you to the steps required to create Google BigQuery Materialized Views. Furthermore, it highlighted the best practices for using the BigQuery Materialized Views. With your Data Warehouse, Google BigQuery live and running, you’ll need to extract data from multiple platforms to carry out your analysis. However, integrating and analyzing your data from a diverse set of data sources can be challenging and this is where Hevo Data comes into the picture.
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