In BigQuery, materialized views provide a way to precompute and store the results of a query as a table. This allows for faster query performance by reducing the amount of computation required at runtime. Materialized views are particularly useful for queries that involve complex aggregations or joins, as they can significantly speed up the execution of such queries.

Creating a Materialized View #

To create a materialized view in BigQuery, you can use the CREATE MATERIALIZED VIEW statement. Here's an example:

CREATE MATERIALIZED VIEW dataset.materialized_view_name
OPTIONS (
expiration_timestamp = TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)
) AS
SELECT
column1,
column2,
aggregate_function(column3) AS aggregated_value
FROM
dataset.source_table
GROUP BY
column1,
column2;

In this example, dataset.materialized_view_name is the name of the materialized view you want to create. You can specify additional options, such as the expiration_timestamp, which determines how long the materialized view will be retained before it needs to be refreshed.

The SELECT statement defines the query that generates the data for the materialized view. You can perform aggregations, transformations, and joins as needed. The resulting table will have the specified columns and the computed values.

Refreshing a Materialized View #

Materialized views in BigQuery are not automatically refreshed. You need to manually refresh them to update the data. To refresh a materialized view, you can use the REFRESH MATERIALIZED VIEW statement. Here's an example:

REFRESH MATERIALIZED VIEW dataset.materialized_view_name;

This statement updates the data in the materialized view by rerunning the underlying query and recomputing the results. You can schedule the refresh operation at regular intervals using external scheduling tools or by setting up a Cloud Function or Cloud Scheduler job.

Querying a Materialized View #

Once a materialized view is created and refreshed, you can query it just like any other table in BigQuery. Materialized views have the advantage of faster query performance, as the results are precomputed and stored.

SELECT *
FROM dataset.materialized_view_name
WHERE column1 = 'value';

In this example, dataset.materialized_view_name is the name of the materialized view you want to query. You can apply filters, joins, and other operations to retrieve the data you need.

Dropping a Materialized View #

If you no longer need a materialized view, you can drop it using the DROP MATERIALIZED VIEW statement. Here's an example:

DROP MATERIALIZED VIEW dataset.materialized_view_name;

This statement removes the materialized view and frees up the storage space associated with it.

Conclusion #

Materialized views in BigQuery offer a convenient way to improve query performance by precomputing and storing the results of a query. By creating and refreshing materialized views, you can reduce the computational overhead of complex queries, leading to faster and more efficient data analysis.