Partitioning and Clustering in BigQuery

In the world of big data, performance optimization is crucial. Google BigQuery, a serverless and highly scalable data warehouse, offers two essential features to improve query performance and cost-efficiency: Partitioning and Clustering. This blog explains both in depth, with examples and a real-time use case including queries.

What is Partitioning

Partitioning in BigQuery is a technique to divide a large table into smaller, manageable parts based on a column, which improves performance and reduces cost by scanning only necessary partitions.

Types of Partitioning:

• Ingestion Time Partitioning
• Column-based Partitioning (Date/Datetime/Timestamp)
• Integer Range Partitioning

1) Ingestion Time Partitioning

Automatically partitions data based on the _PARTITIONTIME pseudo column.

Best for streaming data.

   Example:

In detail we can see the type of partition

2) Column-based Partitioning (Date/Datetime/Timestamp)

Partitions based on a specific date/timestamp column.

   Example:

3) Integer Range Partitioning Partitions table by specifying integer ranges.

Example:

What is Clustering

Clustering organizes data in the table based on the values of one or more columns. Clustering works within each partition or across the table if not partitioned. It reduces data scanned during queries.

How Clustering Works:

When you cluster a table, BigQuery organizes the data based on specified columns.

Queries with filters or group by on those columns perform better.

Example of Clustering:

Real-Time Use Case: How partition and clustering works on tables as a performance tuning technique

Now I would like to create two tables with and without partition and see when query the tables how much cost and time BigQuery will take likewise cluster also

STEP 1: Create Unpartitioned, Unclustered Table

This creates 1 million rows without partitioning or clustering.

 STEP 2: Create Partitioned + Clustered Table

 STEP 3: Run Query on Both Tables and Compare

When I’m querying the optimize table it will scan only 168 kb only and consuming very less slots and time for execution

But when I’m querying the plain table, it will scan almost 61mb data and consume very high amount of slot and time to execute

Latest Enhancements in Partitioning and Clustering

 Increased Maximum Partitions per Table: As of May 2024, BigQuery increased the maximum number of partitions per table from 4,000 to 10,000. This is a significant update, allowing for more granular partitioning strategies, especially for hourly or daily partitions in very large, long-running tables.

 Partitioning and Clustering Recommender: Introduced in 2023 and actively refined in 2024, BigQuery offers a recommender system that analyzes your workloads and tables to identify potential cost optimization opportunities through partitioning and clustering. It uses machine learning to estimate potential savings and provides recommendations via the BigQuery UI, Recommendation Hub, or Recommender API. This helps users identify tables that would benefit most from these optimizations.

 Enhanced JSON Functions: While not directly partitioning/clustering feature, improved JSON functions in BigQuery can indirectly impact how data is prepared for partitioning and clustering, especially if your data involves complex JSON structures that need to be extracted into partitionable or cluster able columns.

 Query Execution Graph: This visual tool helps users understand how queries are executed, which can be crucial for identifying bottlenecks and understanding how partitioning and clustering are impacting query performance. This helps in validating the effectiveness of your chosen strategy.

Conclusion

Partitioning and clustering remain cornerstone techniques in BigQuery for performance and cost optimization. With the latest updates like automatic clustering, nested field support, and smarter pruning, engineers can achieve even better query efficiencies with minimal manual configuration.