Clustering and Partitioning in SQL BigQuery

Optimizing Query Performance and Reducing Costs in SQL

Partitioning and clustering are powerful techniques in Google BigQuery that help optimize query performance and reduce costs.

1. Partitioning

Partitioning is the process of dividing a large table into smaller, more manageable pieces called partitions. Each partition contains a subset of the data

Reducing Cost: Partitioning reduces the cost of bytes processed by improving the efficiency of data retrieval.

Use Case: Partitioning is particularly useful for large datasets where queries often filter on specific date ranges such as a partition by day in the example below :

2. Clustering

Clustering is the process of organising data within a table based on the values of one or more columns.

Reducing Cost: Clustering reduces the cost of bytes processed by improving the efficiency of data retrieval.

Use Case: Clustering is beneficial when queries frequently filter or aggregate data based on specific columns such as Country in the example below:

Query example to create a partitioned and clustered table in BigQuery:

CREATE TABLE Orders

PARTITION BY DATE(Order_date)

CLUSTER BY Country AS

SELECT Order_date, Country, Status FROM example_table;