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BigQuery Clustering and Partition

Introduction​

BigQuery serves as the backbone of our app's data warehouse and analytics features, providing a powerful platform for managing and analyzing large datasets. However, querying data from BigQuery can become costly if optimizations are not in place. One of the key strategies to reduce query costs and enhance performance is the use of clustering and partitioning. These techniques ensure your data warehouse is structured in a way that minimizes the need to scan entire datasets, making your queries more efficient and cost-effective.

What are Clustering and Partitioning?​

To grasp the concept of clustering and partitioning, imagine a police department storing all its documentation, files, and case records in a warehouse. If everything is randomly piled together without grouping by categories like case type or filing date, retrieving a specific document would be time-consuming and frustrating.

Similarly, in a data warehouse, clustering and partitioning help organize your data for easier access:

  • Clustering groups data by specific attributes, such as type, status, or severity, making it quicker to query related data.
  • Partitioning organizes data based on time intervals, like year, month, or day, optimizing queries that rely on temporal data.

By structuring your data warehouse in this way, you ensure faster query performance, reduced costs, and greater operational efficiency.

Benefits of Clustering and Partitioning​

Here is the actual implementation of clustering and partitioning in BigQuery,in which cost of running Big Query queries reduced by almost 10 times compared to no clustering and partitioning.

![https://i.imgur.com/of5tnQI.png]

How to​

If you already have a table in BigQuery without clustering and partitioning, you will need to create a new table from the existing one by:


CREATE TABLE `your_project_id.your_dataset.new_table_name`
PARTITION BY DATE(your_partition_column)  -- Specify the partition column
CLUSTER BY column1, column2               -- Specify up to 4 clustering columns
AS
SELECT *
FROM `your_project_id.your_dataset.existing_table_name`;

Then, you will need to stream new data to the new table and keep the old table if you want. It is best to keep the table.