Partitioning and Clustering

BigQuery is a robust data warehouse that enables fast and interactive analysis of large datasets. However, it’s important to be as efficient as possible with your data. By leveraging partitioning and clustering, you can help control your costs and increase the effectiveness of your work. Both of these methods help improve performance by letting BigQuery reduce the amount of data retrieved during a query.

What is partitioning and what are the benefits?

Partitioning in BigQuery refers to dividing a table into smaller, more manageable segments called partitions. Each partition can be queried independently, improving query performance and reducing costs. Partitioning essentially ‘slices’ your table into segments allowing BigQuery to skip over slices that do not meet your query criteria.

CTA regularly leverages partitioning for sync tables to make it faster and cheaper to deliver data for our partners every day!

When partitioning, you benefit from faster query performances, help control costs and usage, and facilitate more efficient data management. 

For more information on partitioning, please refer to Google’s documentation here.

Types of partitioning

• Date Range: Data is partitioned based on a DATE, TIMESTAMP, or DATETIME column.
  
  

• Integer Range: Partition the data based on an INTEGER column.
  
  

• Ingestion Time: Data is partitioned in BigQuery based on the time it is loaded
  
  

What is clustering and what are the benefits?

Clustering in BigQuery is a method for organizing data within a table by grouping it based on the values of one or more specified columns. This helps improve query efficiency because related data is stored together, reducing the amount of data scanned during queries. Similarly to partitioning, clustering allows BigQuery to skip chunks of rows that do not meet your query criteria based on the specified columns. 

For example, if you have a table containing nationwide data but almost always query it at the state level, it would make sense to cluster your table on the state column. This would let BigQuery skip over the rows of data from other states rather than checking every row in the table to see if it meets the query criteria.

With clustering, you benefit from faster query performances, reduced data scanning, and automatic data maintenance. 

For more information on clustering, please refer to Google’s documentation here.

Process of Partitioning and Clustering Tables

The steps below highlight the benefit of leveraging both partitioned and clustered tables. However, you can partition or cluster as needed; they don’t need to be used simultaneously.

Step 1: Creating a Partitioned Table

CREATE TABLE `project.dataset.table`
PARTITION BY DATE(column_name)
AS SELECT * FROM `source_table`;

Step 2: Adding Clustering to a Table

CREATE TABLE `project.dataset.table`
PARTITION BY DATE(column_name)
CLUSTER BY column1, column2
AS SELECT * FROM `source_table`;

Step 3: Querying Partitioned and Clustered Tables

SELECT * FROM `project.dataset.table`
WHERE column_name = '2024-01-01' 
AND column1 = 'value1';

Best Practices

1. Selecting the Right Partitioning: Your partitioning strategy should align the data you query most often and prioritize commonly accessed timeframes where possible.
   
   

2. Limit the Number of Partitions: Aim for a partition count that balances query performance with ease of data management; too many partitions can lead to overhead in your code.
   
   

3. Cluster on Frequently Filtered Columns: Choose columns commonly used in WHERE clauses for clustering to maximize performance benefits.
   
   

4. Monitor Performance: Before you run your query on partitioned or clustered tables, check the BigQuery UI to see how much data will be scanned. This can help give you an idea of the performance and allow you to adjust as needed.

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