Chapter 5 Scheduled queries, materialised views and partitioned tables

5.1 Scheduled Queries

Scheduled queries are a powerful feature in BigQuery that allow you to automate the execution of SQL queries on a schedule. With scheduled queries, you can specify the frequency, start time, and other settings for running queries on a regular basis. This can be useful for a wide range of use cases, from data ingestion to data processing and analysis.

To create a scheduled query in BigQuery, you can use the Cloud Console.

In this example, we’re creating a scheduled query that runs every day at 5:00 AM. The query itself simply counts the number of rows in a table and stores the result in a destination table called mydataset.mytable.

Scheduled queries can be useful for a variety of use cases, such as:

  • Data ingestion: If you’re ingesting data from external sources on a regular basis, you can use scheduled queries to automate the process of moving data from external sources into BigQuery.

  • Data processing: If you’re working with large datasets, you may need to run complex queries that take a long time to execute. By scheduling these queries to run during off-peak hours, you can avoid impacting the performance of other queries and workloads.

  • Data analysis: If you’re running complex analysis on your data, you may need to run multiple queries to get the insights you need. By scheduling these queries to run on a regular basis, you can automate the process of generating reports and insights.

Overall, scheduled queries can be a powerful tool for automating the execution of SQL queries in BigQuery. By specifying the frequency, start time, and other settings for your queries, you can save time and improve the efficiency of your data processing and analysis workflows.

5.2 Materialized Views

A materialized view is a precomputed table that stores the result of a query. Unlike a regular view, a materialized view is not just a virtual table based on a query. Instead, it is a physical table that is created and populated with data when the view is created. The data in a materialized view is updated automatically based on a specified refresh schedule, or manually triggered by the user.

In BigQuery, you can create materialized views using the CREATE MATERIALIZED VIEW statement:

CREATE MATERIALIZED VIEW view_name
AS SELECT column1, column2, ...
FROM table_name
WHERE condition
WITH [NO] DATA

The view_name is the name of the materialized view you want to create, and the SELECT statement defines the SQL query that the view is based on. The WITH DATA option specifies that the materialized view should be populated with data when it is created, while the WITH NO DATA option creates an empty materialized view that you can populate later.

Materialized views can be useful in a number of situations. Here are a few examples:

  • Reducing query costs: If you have a query that is computationally expensive or that takes a long time to run, you can create a materialized view to store the result of the query. This can reduce query costs by eliminating the need to recompute the result every time the query is run.

  • Improving query performance: Materialized views can improve query performance by precomputing the results of frequently executed queries. This can speed up your queries and reduce the amount of time it takes to get results.

  • Improving data freshness: If you have a query that requires real-time data, you can use a materialized view to store the result of the query and refresh it at regular intervals. This can help to ensure that the data in the materialized view is always up-to-date.

  • Enabling complex queries: Materialized views can also enable complex queries that would be difficult or impossible to run otherwise. By precomputing the results of complex queries, you can simplify your code and make it easier to work with your data.

  • Overall, materialized views can be a powerful tool for reducing query costs, improving query performance, and enabling complex queries. By precomputing the results of frequently executed queries, you can save time and improve data freshness, making it easier to work with your data in BigQuery.

5.3 Partitioned tables

Partitioning tables in Google BigQuery involves dividing a table into smaller, more manageable partitions based on a specified column or columns. Partitioning tables can help improve query performance and reduce costs by limiting the amount of data that needs to be scanned.

To partition a table in BigQuery, you can use the CREATE TABLE statement with a partitioning clause. For example, the following statement creates a table partitioned by date:

CREATE TABLE my_partitioned_table
PARTITION BY DATE(timestamp)
OPTIONS(
  partition_expiration_days=365,
  description="A partitioned table"
) AS
SELECT *
FROM my_source_table;

n this example, the PARTITION BY clause specifies that the my_partitioned_table should be partitioned by the timestamp column. The OPTIONS clause sets the partition expiration to 365 days and provides a description for the table.

Once the partitioned table is created, queries can be run against individual partitions, rather than the entire table. This can help improve query performance and reduce costs by limiting the amount of data that needs to be scanned. Partitions are currently limited to 4000 partitions and can be done on integer or date columns.

There are several scenarios in which partitioning tables can be particularly useful. For example:

  • When working with large tables that contain many rows, partitioning can help improve query performance by limiting the amount of data that needs to be scanned.
  • When working with time-series data, partitioning by date or time can be particularly useful, as it allows you to quickly and easily query data for specific time periods.
  • When working with tables that are frequently updated, partitioning can help reduce query costs by limiting the amount of data that needs to be scanned.